20040622_oceans_web.psd BFW-Dokumentation 23/2016 Bundesforschungszentrum für Wald Seckendorff-Gudent-Weg 8 1131 Wien, Österreich http://bfw.ac.at ISSN 1811-3044 ISBN 978-3-902762-65-8 C Press law responsibility: DI Dr Peter Mayer Austrian Research and Training Centre for Forests, Natural Hazards and Landscape (BFW) Seckendorff-Gudent-Weg 8 1131 Vienna, Austria Phone: +43-1-878380 Cover photo: Level II long-term forest monitoring plot in Finland by Erkki Oksanen Contact: Alexa Michel, Walter Seidling (Eds.) Programme Co-ordinating Centre (PCC) of ICP Forests Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42 16225 Eberswalde, Germany http://icp-forests.net Reproduction is authorised provided the source is acknowledged. Chlorine-free and climate-neutral - For the benefit of the environment opyright 2016 by BFW Impressum Forest Condition in Europe 2016 Technical Report of ICP Forests Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) ALEXA MICHEL & WALTER SEIDLING (Eds.) Forest Condition in Europe 2016 Technical Report of ICP Forests Report under the UNECE Convention on Long-range Transboundary Air Pollution (CLRTAP) Alexa Michel and Walter Seidling (editors) Acknowledgements We wish to thank the Federal Ministry of Food and Agriculture (BMEL) and all participating countries for the continued financial support of the ICP Forests, and the United Nations Economic Commission for Europe (UNECE) and the Thünen Institute for the partial funding of the ICP Forests Programme Co- ordinating Centre. We would like to express our gratitude to Zafran Janez and Miha Marenče from the Slovenian Ministry of Agriculture, Forestry and Food, Primož Simončič, the director of the Slovenian Forestry Institute, and Daniel Žlindra for the hosting and organisation of the 4th Scientific Conference and 31st Task Force Meeting of ICP Forests in Ljubljana in May 2015. We especially wish to thank all participating countries in ICP Forests that have provided their national data and written report. For a complete list of all countries that are participating in ICP Forests with their responsible Ministries and National Focal Centres (NFC), please refer to Annex IV. We also wish to thank the ICP Forests community for their valuable comments on draft versions of this report and Mr Ferdinand Kristöfel and the Austrian Research Centre for Forests (BFW) for its publication. Contact Programme Co-ordinating Centre of ICP Forests Walter Seidling, Alexa Michel Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42 16225 Eberswalde, Germany Recommended citation Michel A, Seidling W, editors (2016) Forest Condition in Europe: 2016 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW- Dokumentation 23/2016. Vienna: BFW Austrian Research Centre for Forests. 206 p. BFW-Dokumentation 23/2016 ISBN 978-3-902762-65-8 ISSN 1811-3044 FDK 181.45:531:97:(4) United Nations Economic Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution (CLRTAP) International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) http://icp-forests.net 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SUMMARY SUMMARY ICP Forests is one of the most diverse programmes within the Working Group on Effects (WGE) under the Convention on Long-range Transboundary Air Pollution (CLRTAP). To provide a regular overview of the major results of the programme, the Programme Co-ordinating Centre (PCC) of ICP Forests yearly invites all ICP Forests Expert Panels, Working Groups, and Committees to publish a comprehensive chapter on their most recent results in the annual ICP Forests Technical Report. This 2016 Technical Report presents results of the ICP Forests 2015 large-scale (Level I) and 2014 intensive (Level II) forest monitoring from up to 32 of the 42 countries participating in ICP Forests. It focuses on: − a description of the monitoring and research infrastructure of ICP Forests; − tree crown condition and damage causes in 2015 including trend analyses; − the spatial variation of atmospheric throughfall deposition in forests in Europe in 2014; − the spatial and temporal distribution of ozone symptoms across Europe from 2002 to 2014; − the water, soil, and foliage ring tests within the quality assurance and control programme to guarantee the comparability of the analytical results between different laboratories; − a description of the ICP Forests Level I biodiversity data on plant species and structural diversity in European forest ecosystems. This year all ICP Forests Expert Panels, Working Groups, and Committees have additionally provided a concise description of their latest activities and outcomes in their specific field of study. The report also includes numerical results and national reports of the 2015 national crown condition survey in the participating countries. It contains information on the 4th ICP Forests Scientific Conference in Ljubljana in May 2015 and lists all 48 ICP Forests projects ongoing for at least one month between June 2015 and May 2016 and 28 scientific publications between January 2015 and May 2016 for which ICP Forests data and/or the ICP Forests infrastructure were used. For additional maps, tables, figures, and contact information of persons responsible, please refer to the extensive annex at the end of this report. The assessment of crown condition has been a core feature of the ICP Forests monitoring for over 30 years. It is based on the concept that tree crowns are reflecting overall tree condition and may therefore provide an early warning signal of tree deterioration. In 2015, the crown condition of 88 052 trees on 4 818 transnational Level I plots in 25 participating countries was assessed. The overall mean defoliation of all trees was 20.7%; means ranging between 19.6% and 29.3% for the major species and species groups. Broadleaved trees showed a slightly higher mean defoliation than coniferous trees (21.3% vs. 20.2%). Correspondingly, conifers had a higher frequency of trees in the defoliation classes ‘none’ or ‘slight’ (78.0%) than broadleaves (75.0%). Among the main tree species and tree species groups, evergreen oaks and deciduous temperate oaks displayed the highest mean defoliation (29.3% and 23.4%, respectively). Evergreen oaks had also by far the highest proportion of severely defoliated trees (4.9%), while deciduous (sub-) Mediterranean oaks and Austrian pine had the highest mortality rates (1.7% and 1.6%, respectively). Austrian pine and common beech had the lowest mean defoliation (19.5% and 19.6%, respectively). Evergreen oaks had the lowest percentage (54.2%) of not or only slightly defoliated trees (≤ 25% defoliation) while Mediterranean lowland pines had the highest (81.2%). Most species or species groups showed an improvement in defoliation in 2015 compared to 2014, especially the broadleaved species. An exception was the group of evergreen oaks with a strong increase in defoliation in 2015. However, this increase can largely be attributed to a much smaller sample in comparison with 2014. Due to Spanish data missing, the sample of evergreen oaks was reduced from 4 500 trees in 2014 to only 1 000 trees in 2015, located mostly in France. The causes of tree damage were assessed on 88 052 trees on 4 818 plots in 25 countries and 42.3% of the trees showed symptoms of damage of at least one defined agent group. The predominant cause of 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SUMMARY damage, causing almost one quarter of all recorded damage symptoms (22.5%), were insects. Almost half of these insect-caused symptoms were attributed to defoliators (44.0%), which also represented the most frequent of all damage causes. Leaf-mining insects were responsible for damage on nearly 19.0% and wood-boring insects on 9.6% of the trees with insect-caused symptoms. Fungi were the second major causal agent group affecting 10.9% of all assessed trees. Of those 30% showed signs of canker, followed by needle cast and needle rust fungi (20.1%) and decay and root rot fungi (12.8%). The third major identified cause of tree damage was abiotic agents (10.1% of all damage symptoms). Within this agent group, 24.5% of the symptoms were attributed to drought, 11.4% to wind, and 7.9% to frost. The measurement of atmospheric deposition is one of the core activities of ICP Forests, and it aims to quantify and qualify the acidifying, buffering, and eutrophying compounds deposited to forests. It is thus an important source of knowledge about the amount and type of anthropogenic and naturally emitted substances relevant for plants after they have been transported over more or less long distances by air. In this report, the spatial variation of atmospheric throughfall deposition in forests in Europe in 2014 is described for N-NH4, N-NO3, S-SO4, Ca, and Mg. Maps for the input of calcium and magnesium are depicted with and without sea salt corrections. − High throughfall deposition of N-NO3 was mainly found in central Europe, while the lower values (below 1 kg ha-1 y-1) were found in Finland, Bulgaria and on the Alps. − The central European area of high throughfall deposition (> 8 kg ha-1 y-1) of N-NH4 is larger, covering parts of Belgium, the Netherlands, Germany, the Czech Republic, Austria, Slovenia and Serbia. Other plots with high N-NH4 deposition are also found in Poland, Italy, France and Spain. Low values, below 1 kg ha-1 y-1, were found again in Finland and Bulgaria, but also in parts of Switzerland and France. − High throughfall deposition of S-SO4 is spread over all of Europe, partly due to the contribution of marine aerosol. After sea salt correction, the area with higher S-SO4 deposition in central Europe is smaller than for N-NO3 and N-NH4 deposition, but high values are also found in southern and eastern Europe, partly due to the input of Saharan dust. The lowest values of S-SO4 deposition are found in the Swiss Alps. − High values of Ca throughfall deposition are recorded in almost all plots in southern Europe, from Spain to Romania, probably due to the relevant contribution of Saharan dust. Isolated plots with high Ca deposition are also found in Belgium, Germany, Denmark, the Czech Republic, Poland and Lithuania, probably related to local mineral sources. Low values of Ca deposition (below 2 kg ha-1 y-1) were mainly found in northern Europe. The correction for the marine contribution does not affect the spatial pattern of Ca deposition. − On the contrary, Mg throughfall deposition is mainly related to the marine aerosol. After sea salt correction, values below 1.5 kg ha-1 y-1 are found in most of Europe, while the highest values are reported in eastern Europe and on isolated plots in Italy, Germany and the Czech Republic. Ozone-induced visible foliar injury has been assessed during 2002-2014 according to ICP Forests standardized methods on 285 woody plant species on 169 plots in 19 countries. Data were evaluated for the entire period 2002-2014 as well as for 2009 only, when spatial coverage was the greatest. First results reveal that 55.0% of the assessed plots were symptomatic, and 26.0% of species developed ozone visible injury. Beech (Fagus sylvatica) was the species with the highest frequency of symptomatic observations in both 2002-2014 (40.1%) and in 2009 (42.9%). The frequency of symptom reports occurred without a clear spatial pattern. Higher frequency of symptom occurrence seemed more common from northern Italy to north-western Germany, and towards East Europe. At country level, temporal trend analysis indicates a downward trend of mean frequency of symptomatic species for five 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SUMMARY out of six countries. Overall, there is a slightly decreasing trend, which is consistent with the decreasing trend observed for ambient ozone concentrations. These first results demonstrate the potential of the survey on visible foliar injury to detect the potential impact of ozone on European vegetation. Further, enhanced quality control procedures are underway to aggregate the datasets and promote a more in- depth exploitation of cause-effect relationships, considering ozone symptoms, ozone concentration and measurements on forest health, growth, nutrition, biodiversity and climate undertaken at the ICP Forests plots. To guarantee the comparability of the analytical results between different laboratories that analyse water, soil and foliage samples from Level I and Level II plots in almost 30 countries and over time, a quality assurance (QA) programme is necessary. The main part of the external quality control (QC) programme is the implementation of interlaboratory comparisons (ring tests) between all labs. The results of all water, soil and foliage ring tests within the last 20 years shows the development of the quality of the labs, but also the limitations due to different analytical methods can be seen. The participation in the regularly organised meetings of the heads of the labs, where many analytical problems are being discussed, has improved the laboratory quality and has led to better results in the ring tests during the last 10 years. The best results were achieved for the foliage ring tests. Since 2004 only 5 to 10% of the results for the main parameters have been non-tolerable. In soil ring tests the ratio of non-tolerable results started with 20 to 60% in 1993 and decreased to 10 to 20% for most of the parameters in 2015. For water samples the percentage of non-tolerable results decreased from 20 to 60% in 2002 to 5 to 15% in 2015. An explanation could be found in the growing (or increasing) experience of the laboratories over time, especially for foliar analyses. Also the use of better equipment in many laboratories has led to better results. One reason for the higher number of non-tolerable results for soil compared to other matrices is the inhomogeneity of sieved soil samples which have to be used for some of the extracts. A second reason could be found in the two steps analysis (extraction/digestion and measurement), which can bring a higher variation than the one step analysis used for water samples. Structural and compositional biodiversity surveys on the ICP Forests extensive monitoring plots (Level I) have been incorporated into the ICP Forests database as LI-BioDiv dataset. Data were collected in the period 2005-2008 and delivered by 27 partners according to harmonized methods. During the integration process data was validated based on a complex system of checkroutines that had been defined before. Conflicts were solved in collaboration with the experts from National Focal Centres and the Expert Panels on Biodiversity and Ground Vegetation, and on Forest Growth. The LI-BioDiv dataset is structured in six forms: GPL (general plot location and information, 3340 plots), DBH (tree diameter, status, and composition, 3201 plots), THT (tree top and crown base height, 3083 plots), CAN (canopy closure, layers, number of trees, 3210 plots), DWD (deadwood, 2950 plots), and GVG (ground vegetation composition, 3124 plots). A transnational internal evaluation process was established and a set of items approved by the related Expert Panels and the ICP Forests Programme Co-ordinating Centre (PCC). Four working groups are producing the first results in terms of scientific papers; the other evaluation projects and the related groups of experts and scientists are described. Recommendations and lessons learned from this experience are shortly provided. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTENTS 1 INTRODUCTION 9 2 THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS 11 3 TREE CROWN CONDITION AND DAMAGE CAUSES 20 4 SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 59 5 SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 73 6 RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA 83 7 THE ICP FORESTS LEVEL I BIODIVERSITY DATA 89 8 ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 106 9 REVIEW OF THE 4TH ICP FORESTS SCIENTIFIC CONFERENCE, LJUBLJANA, 19-20 MAY 2015 121 10 ONGOING ICP FORESTS PROJECTS 123 11 SCIENTIFIC ICP FORESTS PUBLICATIONS IN 2015/16 127 12 NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 129 ANNEX 149 TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS (CHAPTER 3) 150 RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS (CHAPTER 12) 168 LIST OF WOODY SPECIES (CHAPTER 5) 189 CONTACTS 192 W. SEIDLING V. TIMMERMANN, N. POTOČIĆ, T. SANDERS, S. TROTZER, W. SEIDLING A. MARCHETTO, P. WALDNER E. GOTTARDINI, V. CALATAYUD, M. FERRETTI, M. HAENI, M. SCHAUB N. KÖNIG, N. COOLS, K. DEROME, A. FÜRST, T. JAKOVLJEVIČ, A. MARCHETTO R. CANULLO A. MICHEL, W. SEIDLING REPRESENTATIVES OF THE NATIONAL FOCAL CENTRES OF ICP FORESTS 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S INTRODUCTION | 9 1 INTRODUCTION The International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) was established in 1985. Its main aim is to collect and compile data on the condition of forest ecosystems across the UNECE region and monitor their condition and performance over time. ICP Forests is led by Germany, and its Programme Co-ordinating Centre is based at the Thünen Institute of Forest Ecosystems in Eberswalde. It is one of eight subsidiary bodies (six ICPs, a Task Force, and a Joint Expert Group) that report to the Working Group on Effects (WGE) of the Convention on Long-range Transboundary Air Pollution (CLRTAP) on the effects of air pollution on a wide range of ecosystems, on materials, and on human health. After more than 30 years ICP Forests is still constantly moving forward. The most important recent activities of ICP Forests include further developments in the domain of the ICP Forests Strategy, cooperation actvities, the ICP Forests Manual, and the data unit. − The new Strategy of ICP Forests (2016–2023) was adopted at the ICP Forests Task Force Meeting (TFM) in Luxembourg in May 2016. It defines the mission of ICP Forests, its aims, current features, vision for the future, and actions to be taken. − A Letter of Intent for future co-operation between the Wood Buffalo Environmental Association (WBEA, Canada) and ICP Forests was adopted at the last ICP Forests TFM in May 2016. − The ICP Forests Manual is currently being updated. The manual ensures a standard approach for data collection among the participating countries. A new version will be available in 2016. − The data unit at the Programme Co-ordinating Centre (PCC) of ICP Forests is constantly improving the data management, data availability and usability, and information flow within the programme and to the scientific community and the public. Recent developments of the data unit include the creation of a new online data documentation1. With the Strategy and the Manual, ICP Forests defines its aims and ways of implementation. As subsidiary body under the WGE, however, ICP Forests is first and foremost obliged and indebted to contribute to the biannual workplan of the LRTAP Convention which sets the objectives and deliverables of all bodies under the Convention. The joint 2016-2017 workplan (WP) for the further implementation of the Convention for EMEP (European Monitoring and Evaluation Programme), the WGE and the other subsidiary bodies of CLRTAP was adopted by the Executive Body (EB) at its 34th meeting on 18 December 2015 (ECE/EB.AIR/133/Add12). Following is a list with the respective tasks and deliverables expected of ICP Forests in 2016-2017: WP item Description Actions to be taken by ICP Forests 1.1.1.1 Set priorities for monitoring and other collection of data on effects by Parties in view of policy needs and given financial constraints. Prioritize calls for data and data collection for ICPs in view of the policy needs and given financial constraints. This is meant as a general guideline to consolidate the Convention work under decreasing financial support. The expected outcome/deliverable is an updated list of monitoring and inventory priorities and recommendation to the Executive Body in 2016. This will be organised by the WGE. 1.1.1.7 Set up a contact group between EMEP and WGE to compare WGE exposure measurements and modelled and monitored exposure by EMEP. The expected outcome/deliverable is the imple- mentation of joint meetings. The Task Force on Measurement and Modelling (TFMM), the Task Force on Health, and all ICPs are requested to 1 http://www.icp-forests.org/documentation/ 2 http://www.unece.org/fileadmin/DAM/env/documents/2015/AIR/EB/ece.eb.air.133_add1_E.pdf 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S INTRODUCTION 10 | WP item Description Actions to be taken by ICP Forests contribute. 1.1.1.10 Further investigate the influence of N deposition on the more sensitive parts of forest ecosystems (e.g., mycorrhiza, foliage N content of trees, N in soil solution). Data analyses by ICP Forests and its partners. 1.1.1.10 Evaluate ozone impacts on forest trees (injury of leaves/needles, defoliation, and/or discoloura- tion of tree crowns) and responses of sensitive plant species at forest edges. Joint activity of ICP Forests and ICP Vegetation. 1.1.1.24 Further evaluate ecosystem responses, in particular air pollution-induced changes in biodiversity, for setting critical loads, based on long-term monitoring within ICPs, including the interactions between pollutants, climate change, land use and nutrients (including phosphorus). It is expected that an annual report on the progress in dynamic modelling (2016) and a scientific paper (2017) is to be delivered. This activity will be carried out by all ecosystem- related ICPs and the Joint Expert Group on Dynamic Modelling (JEG). 1.1.4.2 Assess implications of air pollution mitigation strategies in the northern hemisphere for health, ecosystem and climate impact. This is a global scale issue and aims at a workshop on impact assessment methods of regional and transported air pollution in cooperation between WGE, EMEP bodies (TFHTAP, CIAM, TFIAM) and similar expert groups from south and east Asia. This activity will be funded by the USA, the EU, and in-kind contributions from national experts. 1.4.1 Develop common standards of all ICPs and a web portal approach to enable access to data/information. This is to improve the WGE/EMEP functioning incl. its subsidiary bodies and will result in an im- provement of data access via the web, the devel- opment of a common web-based portal, and a formal set of agreed common standards. Here, EMEP, the WGE including the ICPs, and other subsidiary bodies are expected to work together. 1.4.2 – Explore ways to combine/merge the activities of some ICPs (e.g. Integrated Monitoring, ICP Forests, ICP Waters) – Improve integrated working and reporting – Organise joint meetings These measures aim at a more effective overall organisation of the work carried out by the ICPs. 1.5.1 Assess the long-term trends in air pollution and its adverse effects. To improve the transition domain between sci- ence and policy, two activities are planned: (1) This one will lead to another Trends Report issued by the WGE. These activities will be funded by mandatory EMEP contributions and France. 1.5.2 Assess scientific and policy outcomes within the Convention over the past few decades, including scientific understanding, trends and achieve- ments under the Gothenburg Protocol, and outline future challenges (2) The outcome will be a second comprehensive assessment report and an executive summary for policymakers (both in 2016). We would like to hereby express again our sincere gratitude to everyone involved in ICP Forests and especially to the participating countries for their commitment. This co-operative programme depends on the help and support and constant extra input of many dedicated individuals given the limited resources available for ecosystem monitoring these days. The 2016 Technical Report of ICP Forests can be downloaded from the ICP Forests website3. Please send comments and suggestions to pcc-icpforests@thuenen.de; we highly appreciate your feedback. 3 http://icp-forests.net/page/icp-forests-technical-report 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS | 11 2 THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS Walter Seidling4 2.1 Background For the last 30 years the aim of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) has been to collect and compile data on the condition of forest ecosystems across the UNECE region and monitor its condition and performance over time (see Seidling & Michel 2015 for more explicit information). ICP Forests is not only addressing the scientific information needs of CLRTAP, thereby underpinning the advancement of air pollution abatement measures in Europe, but provides quantitative policy-relevant information on monitored and modelled air pollution effects on forests to a variety of other national and international forest and environmental bodies and programmes, such as Forest Europe (Ferretti et al. 2015 a,b) and the FAO Global Forest Survey (GFS). According to the strategy of ICP Forests (Anonymus 2016), its mission is “to carry out multifunctional long-term monitoring of forests within the UNECE region and beyond and provide scientific knowledge on the effects of air pollution, climate change and other stressors on forest ecosystems”. More explicit the main aims are to: − provide a continuing overview on forest health, forest vitality, forest soil condition and the biodiversity status in relation to anthropogenic (air pollution, atmospheric deposition) and natural stressors; − contribute to a better understanding of cause-effect relationships between anthropogenic as well as natural stressors and forest condition and processes; − provide high quality and open access data managed in one central database for the risk assessment of forests across Europe, the large-scale and long-term trend analyses as well as model validation and calibration, serving also as a reference for global assessments; − develop and maintain highly equipped forest measurement stations as central data hubs and standardized forest monitoring and research infrastructures across Europe. An outstanding feature of both levels of the ICP Forests monitoring is the implementation of standardized methods and additional measures for quality control and quality assurance. The transnational standardisation of methods has led to consistent sampling practices across Europe. All methods are described in the extensive ICP Forests Manual (ICP Forests 2010), which has been developed over the years and is presented together with the respective scientific background of each of the surveys by Ferretti & Fischer (2013). Within ICP Forests the experience and expertise of eight Expert Panels is essential to further develop the monitoring methods of each survey (cf. Table 2-1). At present, 42 countries are co-operating in ICP Forests. Of those, 27 are EU-Member States hence all EU countries but Malta are participating in the Programme. Of the 15 non-EU countries, nine are countries from Southeast Europe (SEE) or from Eastern Europe, the Caucasus, and Central Asia (EECCA). 4 For contact information, please refer to Annex IV-4. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS 12 | ICP Forests is further actively promoting membership across the wider UNECE region which is one of the aims of the CLRTAP Working Group on Effects (UNECE 2012). Table 2-1. Surveys performed at ICP Forests monitoring sites. Survey Level I Level II, standard Level II, core Crown condition annually annually annually Foliar chemistry project every 2 years every 2 years Tree growth every 5 years annually Tree phenology several times / year Ozone induced injury continuously continuously Litterfall continuously Ground vegetation diversity every 5 years every 5 years Soil condition project every 10 years every 10 years Soil solution chemistry continuously Soil water continuously Deposition continuously continuously Air quality continuously continuously Meteorology continuously continuously 2.2 The large-scale forest monitoring (Level I) The large-scale monitoring (Level I) is an annual, transnational survey to study the spatial and temporal variations in forest condition. The network consists of more than 7,500 plots on a 16 x 16 km transnational grid giving an overall density of one plot per 256 km² forested area (see Figure 2-1 for an overview on Level I plots active in 2015). In the early 1990s annual assessments of crown defoliation were complemented by data on soil condition (Vanmechelen et al. 1997) and the nutritional status of foliage (Stefan et al. 1997). Since then a second survey on Level I plots on soil condition (De Vos & Cools 2011) and a survey on ground vegetation have been performed within the BioSoil project under the EC Forest Focus Regulation No. 2152/2003. After the end of the FutMon project in 2011 some participating countries and subnational territorial units have moved their Level I plots from their original positions to sites co-located with plots of the respective National Forest Inventories (NFIs) (Kovač et al. 2014). This shift of plots causes constraints for comprehensive longitudinal and time series analyses, due to disruptions of the plot-specific continuity of the crown condition assessment (cf. Chapter 3). However, the information drawn from the NFI surveys may foster biomass-oriented approaches in the future. 2.3 The intensive forest ecosystem monitoring (Level II) Complementing the large-scale Level I monitoring, the intensive and continuous monitoring of forest ecosystems (Level II) was established in 1994 to study ecosystem related processes and their relationships with environmental influences in forest ecosystems and their compartments on permanent observation plots (De Vries et al. 2003a). The overall aim of the Level II monitoring is to better understand cause-effect relationships in forest ecosystems (cf. De Vries et al. 2000), including the assessment of crown and soil condition, carbon stocks and fluxes, climate change effects, and biodiversity-related issues. The selection and maintenance of the plots lies in the responsibility of each participating country (for details see Ferretti et al. 2010). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS | 13 Figure 2-1. Location of Level I plots surveyed in 2015 underlaid by European forest type information. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS 14 | The number of Level II plots varies over time for different reasons (e.g. windthrow, vandalism, or ceased funding). Therefore we find a number of 1041 ever registered Level II plots in the ICP Forests database, with 791 active around the year 2000 (De Vries et al. 2003b: 11). Later, Lorenz et al. (2005: 46) counted more than 860 active Level II plots; with for example deposition measurements carried out on 513 plots or meteorological data on 206 plots (De Vries et al. 2003b, Lorenz et al. 2005). Today from a total of 618 active Level II plots, we have 207 continuous deposition measurements from 2009 to 2013 and 164 with continuous meteorological measurements. A 60% reduction in deposition measurements against only 20% reduction of meteorological measurements reflects probably a shift in the perception of the grand societal challenges during the last decade towards climate change. Many publications based on ICP Forests data (cf. Michel & Seidling 2015: 84 ff.) are derived from data collected at Level II sites. However, one problem faced while evaluating the data is the number of plots featuring respective measurements continuously. Combining, for example, meteorological and deposition data mentioned above, the final number of plots will be 128 (Figure 2-2). If species-specific evaluations are performed, this kind of reduction can even be stronger. For instance Ferretti et al. (2015c) could base their evaluations on a total of 71 plots, however, for the species-specific models 33 plots could be used for spruce, 20 for beech and 18 for Scots pine. Therefore, future reductions within the network should be properly planned, minimising consequences for statistical and other scientific evaluations. Figure 2-2. Level II plots with data submitted to the central data base of ICP Forests between 2009 and 2013 (as of October 2015) with continuous measurement of deposition between 2009 and 2013 or continuous recording of meteorological data or continuous measurements of both surveys; plots with data on any other survey are shown as well. It is not only the mere number of plots limiting statistical evaluations, but also the geographical distributions may cause bias in statistical models. Even if the found sample concept introduced by Overton et al. (1993) may cover more general concerns about the applicability of statistical models 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS | 15 performed with deliberately distributed sampling sites, biases caused by specific geographical distributions of plots – similar to the nonresponse bias in polls – have rarely been investigated in monitoring networks up to now. Resampling techniques might among other approaches be an appropriate means to investigate such effects, eventually based on relationships probably varying in geographic space. This rather general issue cannot be solved within a country-based plot selection approach as contextual constrains are unequally distributed across the countries within the UNECE region. Both, Figure 2-2 and Figure 2-3, reveal certain geographic imbalances in the distribution of intensive monitoring plots across Europe generally and for certain combinations of surveys in particular. Therefore, both gap closure and complementing the network at the edges – especially in the eastern parts of the UNECE region – should be aspired and is a major goal for bringing ICP Forests into the future. Figure 2-3. Level II plots with data submitted to the central database of ICP Forests between 2009 and 2013 (as of October 2015) with continuous biannual measurements of foliar element concentrations between 2009 and 2013 or continuous recording of soil solution element concentrations or measurements of both surveys together; plots with data on any other survey are shown as well. Co-location of monitoring plots is one important mean to foster co-operations with other networks in- and outside the UNECE Working Group on Effects like ICP Integrated Monitoring (IM), the European Long-term Ecosystem Research network (LTER), the European Critical Zone Observatory Community, or the Integrated Carbon Observation System (ICOS). Also for promoting bilateral co-operations, like those with the mycorrhiza working group at the Imperial College London (Suz et al. 2015), well-structured documentations about the geographic extent of different aspects of the ICP Forests network is indispensable. One important toehold for fostering co-operations with mutual benefits for all sides contributing to such systems is the knowledge about already existing collaboration at the plot level. Therefore, a 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS 16 | questionnaire was sent out in September 2015 to all National Focal Centres (NFCs) of ICP Forests. It contained – besides the questions itself (Block 2-1) – country-wise lists of all 1041 Level II plots ever registered. In terms of numbers of plots the response rate was 57%; the results will be summarized in the following. Since there is empirically no clear relationship between return rates of polls and the accuracy/precision of the results achieved, the respective shares have to be rated as best estimates available. Block 2-1. Questions sent out to all NFCs of ICP Forests in September 2015 together with country-specific lists of all 1041 plots ever registered as Level II plots. 1) Plot is situated within an area protected by local (L), regional (R) or national (N) nature protection legislation stricto sensu. 2) Plot is part of an area protected by EU legislation: Natura 2000, Special Protection Areas of the Bird Directive (SPA), Site of Community Importance (SCI), Special Area of Conservation (SAC) 3) Plot is part of an area investigated by ICP Integrated Monitoring (IM) 4) At the plot samples of the ICP Vegetation moss survey are collected 5) At the plot activities of another sister ICP (please specify) take place 6) Plot belongs to a national (N) or the European (E) LTER (Long-term Ecosystem Research) programme 7) Plot belongs to another national monitoring programme (please specify) 8) Plot is part of another national research programme (please specify) 9) Plot is part of another international monitoring programme (please specify) 10) Plot is part of another international research programme (please specify) 11) Does the plot contain additional research infrastructures beyond the ICP Forests programme (Eddy flux tower etc.)? 12) Any additional remarks Table 2-2 informs about the general plot status. While 59% are still active, a total of 232 plots have been declared as closed down, which is 39% of all registered plots. Six plots have been newly established in recent years. One question to follow up in this context is whether the ecologically more redundant plots have been closed down or those which cover important parts of natural or anthropogenic environmental gradients or those diminishing the geographic extent of the whole network. The latter two cases have to be seen much more critical and should be avoided (see also workplan item 1.1.1.1 in Table 1-1). Table 2-2. Status of 593 Level II plots according to returns of a questionnaire sent out in September 2015 to all NFCs of ICP Forests; total number of plots ever registered: 1041. Plot status Active old Active new Unknown status Closed down Number (percentage) 352 (59.4%) 6 (1.0%) 3 (0.5%) 232 (39.1%) The ability of the ICP Forests network to collaborate with different international programmes in- or outside CLRTAP depends largely on co-location of monitoring infrastructures. On the basis of all active Level II plots, a spatial integration or co-location with ICP Integrated Monitoring sites was indicated for 42 plots, which is ca. 12% of all active Level II sites. These sites may deliver a certain potential to directly compare estimates and relationships gained independently in both programmes (cf. De Vries et al. 2002). For altogether 69 Level II plots, samples for the ICP Vegetation moss survey were gained. Data from such sites have already been used in both programmes (e.g. Skudnik et al. 2014, Harmens et al. 2014) and the potentials for further respective collaborations have to be fathomed. For about 9% of all ICP Forests plots, collaboration with ICP Modelling and Mapping was indicated. This means that monitoring data are also used for calculations or calibration of estimates of Critical Loads and Levels (e.g. Bonanni et al. 2012, cf. Posch et al. 2015). For around 10% of the plots also a co-location or integration with sites of the ICP Waters network is declared giving the opportunity to connect sources and sinks of certain substances like DOC at the landscape scale (e.g. de Wit et al. 2015). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS | 17 Table 2-3 also contains co-locations or integration between ICP Forests plots and sites registered within national (LTER) or European (E-LTER) Long-term Ecological Research networks. Here a total of 81 plots were indicated as being also part of a national or the European LTER network. Collaborations between both networks are highly recommended. Table 2-3. Co-location or co-operation with infrastructure of other ICPs at 358 active Level II plots. ICP IM ICP Vegetation, moss survey ICP M&M ICP Waters LTER and E-LTER LTER and E-LTER proposed 42 (11.7%) 69 (19.2%) 33 (9.2%) 37 (10.3%) 81 (22.6%) 9 (2.5%) The NFCs of ICP Forests were also asked about the involvement of plots and their infrastructure in research activities (Table 2-4). Four plots are directly involved in international research programmes and another 20 are involved in international and national research programmes. This means that almost 7% of all plots are part of an international research programme, while almost 44% are part of a national research programme. Table 2-4. Research activities complementing ICP Forests monitoring at 358 active Level II plots. No additional research activities International research programmes National and international research programmes National research programmes 4 (1.1%) 20 (5.6%) 156 (43.6%) 178 (49.7%) 180 (50.3%] Nature protection is another important societal issue at both, national and international level. The question how many Level II plots of ICP Forests are overlaid by any national or international nature protection regulation was also part of the poll. It turned out that 38% of all plots are subject to any area- related nature protection regulation (Table 2-5). 27% of all sites are part of one or more categories of Natura 2000 areas. Even if monitoring has not been established to serve directly any nature protection aims, there is a considerable potential to contribute to research related to nature protection in forests. Apart from the ground vegetation survey, which is directly related to questions concerning biodiversity and bio-indication, investigations in other domains of nature protection like plot-related bird censuses should be taken into consideration in the future. Table 2-5. Nature protection status of 358 active Level II plots. No nature protection Any kind of national nature protection only Both, national and Natura 2000 status Any Natura 2000 status 40 (11.2%) 65 (18.2%) 31 (8.7%) 222 (62.0%) 136 (38.0%) The ICP Forests database informs about the MCPFE (Forest Europe) management status (MCPFE 2006) of in total 95 Level II plots from five countries (Germany, Spain, Sweden, Slovenia and Latvia), however, even for those countries the datasets cannot be considered as complete (Table 2-6). Therefore no country-specific evaluation will be presented here. However, the overall figures give a higher percentage of non-protected sites than the evaluation above. As with 95 captured cases the statistical population is even smaller than those of the poll, no further conclusions can be drawn. What is interesting is the fact that almost all of the more management oriented MCPFE (Forest Europe) categories are covered by ICP Forests plots, even if the percentage is quite preliminary in sight of the low number of plots with this information available. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS 18 | Table 2-6. Assignment of 95 Level II plots to MCPFE (Forest Europe) management classes according to ICP Forests database. ICP F Code MCPFE class Main management objective N of cases Percen- tage Sum protected [%] 1 1.1 "Biodiversity"- "No Active Intervention" 5 5.3 27.4 2 1.2 "Biodiversity"- "Minimum Intervention" 13 13.7 3 1.3 "Biodiversity"- "Conservation Through Active Management" 6 6.3 4 2 “Protection of Landscapes and Specific Natural Elements” 0 0.0 5 3 “Protective Functions” 2 2.1 9 - No protection status 69 72.6 Sum 95 100 This overview highlights the importance of continuity within both, the plot locations and the methods, but also shows the immense potential for integrating evaluations and collaborations a long-standing programme like ICP Forests offers. 2.4 References Anonymus (2016) Strategy of ICP Forests 2016–2023, 4 p [http://www.icp-forests.org/Manual.htm] Bonanni P, Fornasier FM, de Marco A, Casaccia CR, Vitale M (2012) Part 3: NFC Reports: Italy. In: Posch M, Slootweg J, Hettelingh J-P (eds) Modelling and mapping of atmospherically-induced ecosystem impacts in Europe, CCE Status Report 2012. Coordination Centre for Effects, RIVM, Bilthoven, The Netherlands, pp 93-96 De Vos B, Cools N (2011) Second European forest soil condition report. Vol. I: Results of the BioSoil Survey. INBO.R.2011.35 (Research Institute for Nature and Forests), Brussels, 359 p De Vries W, Forsius M, Lorenz M, Lundin L, Haußmann T, Augustin S, Ferretti M, Kleemola S, Vel E (2002) Cause- effect relationships of forest ecosystems. Joint Report by ICP Forests and ICP Integrated Monitoring, UNECE CLRTAP, Geneva, 46 p De Vries W, Klap JM, Erisman JW (2000) Effects of environmental stess on forest crown condition in Europe, Part I: Hypotheses and approach to the study. Water Air Soil Poll 119:317-333 De Vries W, Reinds GJ, Posch M, Sanz MJ, Krause GHM, Calatayud V, Renaud JP, Dupouey JL, Sterba H, Vel EM, Dobbertin M, Gundersen P, Voogd JCH (2003b) Intensive monitoring of forest ecosystems in Europe. Technical Report 2003, EC, UNECE, Brussels, Geneva, 163 p De Vries W, Vel E, Reinds GJ, Deelstra H, Klap J, Leeters EEJM, Hendriks CMA, Kerkvoorden M, Landmann G, Herkendell J, Haussmann T, Erisman JW (2003a) Intensive monitoring of forest ecosystems in Europe. I. Objectives, set-up and evaluation strategy. For Ecol Manag 174:77-95 De Wit H, Wright R, Garmo Ø, Fjellheim A (2015) Trends in water chemistry and biology (ICP Waters). NIVA Report No 6946-2015:33-36. Ferretti M, Fischer R, Mues V, Granke O, Lorenz M (2010) Part II: Basic design principles for the ICP Forests networks. In: ICP Forests (ed): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests, Hamburg, 22 p [http://www.icp- forests.org/Manual.htm] Ferretti M, Fischer R (eds) (2013) Forest monitoring: methods for terrestrial investigations in Europe with an overview of North America and Asia. Elsevier, Amsterdam, 507 p Ferretti M, Fischer U, Hansen K, Michel A, Sanders T, Seidling W (2015a): Indicator 2.1 Deposition of air pollutants. In: Köhl M, San-Miguel Ayanz J, Domíngues Torres G (coordinating authors) Criterion 2: Maintenance of Forest Ecosystem Health and Vitality, Forest Europe Liason Unit Mardrid (ed): State of Europe’s forests 2015 report, pp 90-95 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE MONITORING AND RESEARCH INFRASTRUCTURE OF ICP FORESTS | 19 Ferretti M, Michel A, Seidling W (2015b): Indicator 2.3 Defoliation. In: Köhl M, San-Miguel Ayanz J, Domíngues Torres G (coordinating authors): Criterion 2: Maintenance of forest ecosystem health and vitality, Forest Europe Liason Unit Mardrid (ed.): State of Europe’s forests 2015 report, pp 98-100 Ferretti M, Calderisi M, Marchetto A, Waldner P, Thimonier A, Jonard M, Cools N, Rautio P, Clarke N, Hansen K, Merilä P, Potočić N (2015c) Variables related to nitrogen deposition improve defoliation models for European forests. Ann For Sci 72:897-906 Harmens H, Schnyder E, Thöni L, Cooper DM, Mills G, Leblond S, Mohr K, Poikolainen J, Santamaria J, Skudnik M, Zechmeister HG, Lindroos A-J, Hanus-Illnar A (2014) Relationship between site-specific nitrogen concentrations in mosses and measured wet bulk atmospheric nitrogen deposition across Europe. Environ Poll 194:50-59 ICP Forests (ed) (2010) Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests, Hamburg [http://www.icp- forests.org/Manual.htm, as of 10-02-2016] Kovač M, Bauer A, Ståhl G (2014) Merging national forest and national forest health inventories to obtain an integrated forest resource inventory - experiences from Bavaria, Slovenia and Sweden. PLOS ONE 9(6): e100157, 13 p. doi: 10.1371/journal.pone.0100157 Lorenz M, Becher G, Mues V, Fischer R, Becker R, Calatayud V, Diese N, Krause GHM, Sanz M, Ulrich E (2005) Forest condition in Europe. 2005 Technical Report of ICP Forests. Work Report of the Institute for World Forestry, Hamburg Mc Overton JC, Young TC, Overton WS (1993) Using ‘found’ data to augment a probability sample: procedure and case study. Environ Monit Assess 26:65-83 MCPFE (2006) MCPFE information document on data collection and compiling the statistics on protected and protective forest and other wooded land in Europe. Warsaw, 16 p. [http://www.foresteurope.org/ docs/reporting/MCPFE_INFO_DOC_on_data_collection_on_Protected_forests.pdf, as of 31.3.2016] Michel A, Seidling W (eds) (2015) Technical Report of ICP Forests. BFW-Dokumentation 21/2015, Vienna, 182 p Posch M, de Vries W, Sverdrup HU (2015) Mass balance models to derive critical loads of nitrogen and acidity for terrestrial and aquatic ecosystems. In: De Vries W, Hettelingh J-P, Posch M (eds) Critical loads and dynamic risk assessment. Environ Pollut 25:171-205 Seidling W, Michel A (2015) The monitoring system of ICP Forests. In: Michel A, Seidling W (eds) Forest Condition in Europe, 2015 Technical Report of ICP Forests. BFW-Dokumentation 21/2015:7-11 Skudnik M, Jeran Z, Batič F, Simončič P, Lojen S, Kastelec D (2014) Influence of canopy drip on the indicative N, S and δ(15)N content in moss Hypnum cupressiforme. Environ Poll 190:27-35 Stefan K, Fürst A, Hacker R, Bartels U (1997) Forest foliar condition in Europe: Results of large-scale foliar chemistry surveys. UNECE, EC, Brussels, Geneva, 207 p Suz LM, Barsoum N, Benham S, Cheffings C, Cox F, Hackett L,Jones A, Mueller GM, Orme D, Seidling W, Van der Linde S, Bidartondo MI (2015) Monitoring ectomycorrhizal fungi at large scales for science, forest management, fungal conservation and environmental policy. Ann For Sci 72:877-885 UNECE (2012) Revised long-term strategy of the effects-oriented activities. ECE/EB.AIR/2009/17/ Rev.1, [http://www.unece.org/fileadmin/DAM/env/documents/2013/air/wge/ Informal_document_no_18_Revised_Long-term_Strategy_of_the_effects-oriented_activities_clean_text.pdf], 8 p. Vanmechelen L, Groenemans R, Van Ranst E (1997) Forest soil condition in Europe: Results of the large-scale soil survey. UNECE, EC, Brussels, Geneva, 261 p 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 20 | 3 TREE CROWN CONDITION AND DAMAGE CAUSES Volkmar Timmermann, Nenad Potočić, Tanja Sanders, Serina Trotzer, Walter Seidling5 3.1 Introduction and scientific background Tree crown defoliation and occurrence of biotic and abiotic damage are important indicators of forest health, and are considered within the Criterion 2, “Forest health and vitality”, one of the six criteria adopted by Forest Europe (formerly the Ministerial Conference on the Protection of Forests in Europe – MCPFE) to provide information for sustainable forest management in Europe 6 . Defoliation surveys are linked with detailed assessments of biotic and abiotic damage causes. Unlike assessments of tree damage, which can in some instances trace the tree damage to a single cause, defoliation is an unspecific parameter of tree vitality, which can be influenced by a number of anthropogenic and natural factors. By combining visible damage symptoms and their causes with defoliation observations we are allowed to gain a better insight into the condition of trees, and the interpretation of the annual state of European forests and its trends in time and space is made easier. This chapter presents results from the crown condition and tree damage cause assessments on the large-scale, representative, transnational monitoring network (Level I) of ICP Forests carried out in 2015, as well as long-term trends for the main species and species groups. 3.2 Methods of the 2015 survey The assessment of tree condition in the transnational Level I network is conducted according to European-wide, harmonized methods described in the ICP Forests Manual by Eichhorn et al. (2010, see also Eichhorn & Roskams 2013). Regular national calibration trainings of the survey teams and international cross-comparison courses (ICCs) ensure the quality of the data and comparability across the participating countries (e.g. Dobbertin et al. 1997, Eickenscheidt 2015). Defoliation Defoliation is the key parameter of tree condition within forest monitoring describing a loss of needles or leaves in the assessable crown compared to a local reference tree in the field or an absolute, fully foliated reference tree from a photo guide. Defoliation is estimated in 5% steps, ranging from 0% (no defoliation) to 100% (dead tree). Defoliation values are grouped into five classes (Table 3-1). In the maps presenting the mean plot defoliation in the result part of this chapter and in Table 3-7, class 2 is divided (> 25–40% and > 40–60%). 5 For contact information, please refer to Annex IV-4. 6 http://www.foresteurope.org/docs/MC/MC_lisbon_resolution_annex1.pdf 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 21 Table 3-1. Defoliation classes. Defoliation class Needle/leaf loss Degree of defoliation 0 up to 10% None 1 > 10–25% Slight (warning stage) 2 > 25–60% Moderate 3 > 60–< 100% Severe 4 100% Dead Damage cause assessments The damage cause assessment of trees consists of three major parts: − Symptom description The description of damage symptoms indicates which part of a tree is affected and the type of symptom it shows. It focuses on important factors that may influence tree condition and it is important in the diagnosis of the causal agent and for the study of cause-effect mechanisms. Three main categories indicate the affected part of a tree: (a) leaves/needles, (b) branches, shoots, and buds, and (c) stem and collar. For each affected part in the first two categories, also the position within the crown is given. − Determination of the damage cause (causal agents / factors) Causal agents are those thought to be directly responsible for the observed damage symptoms. Therefore, for each symptom description a causal agent should be determined, which is crucial for the study of cause-and-effect mechanisms. Causal agents are grouped into nine categories (Table 3- 2). In each category a more detailed description is possible through a hierarchical coding system. − Quantification of symptoms (damage extent) The extent is the estimated percentage of affected parts caused by the action as specified by causal agents. The extent is classified in eight classes (Table 3-3). In trees with multiple types of damage (and thus multiple extent classes), all extent values are evaluated individually. Table 3-2. Main categories of causal agents. Causal agents Game and grazing Insects Fungi Abiotic agents Direct action of man Fire Atmospheric pollutants (visible symptoms of direct atmospheric pollution impact only) Other factors (Investigated but) unidentified Table 3-3. Classes of damage extent. Class Extent 0 0% 1 1–10% 2 11–20% 3 21–40% 4 41–60% 5 61–80% 6 81–99% 7 100% 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 22 | Additional parameters Besides defoliation and damaging agents, additional parameters are annually assessed providing information for the analysis of the crown condition data (Table 3-4). All data are checked for consistency by the participating countries and submitted online to the Programme Co-ordinating Centre (PCC) of ICP Forests. Table 3-4. Tree, stand, and site parameters provided in the crown condition database. Registry and location Country Country in which the plot is assessed [code] Plot number Identification of each plot Plot coordinates Latitude and longitude [degrees, minutes, seconds] Date Day, month, and year of observation Physiography Altitude [m a.s.l.] Elevation above sea level, in 50 m steps Aspect [°] Aspect at the plot, direction of strongest decrease of altitude in eight classes (N, E, … , NW) and ‘flat’ Soil Water availability Three classes: insufficient, sufficient, excessive water availability to main tree species Humus type Mull, moder, mor, anmor, peat or other Stand related data Forest type 14 forest categories according to EEA (2007) Mean age of dominant storey Classified age, class size 20 years; class 1: <20 years,…., class7: >121 years, class 8: irregular stands Additional tree related data Tree number Tree ID, allows the identification of each particular tree over all observation years Tree age Classified age for single trees, class size 20 years; class 1: <20 years,…., class 9: >160 years Tree species Species of the observed tree [code] Certain criteria were defined prior to data analysis. Only plots with a minimum number of three trees per plot were analysed. For analyses at species level, three trees per species had to be present. These criteria are consistent with earlier evaluations (e.g. Wellbrock et al. 2014, Becher et al. 2014) and explain the discrepancy between the number of trees in Table 3-6 and ANNEX II. Participating countries The annual transnational tree condition survey in 2015 was conducted on 4 962 plots in 24 countries (Table 3-5). In total, 91 741 trees were assessed in the field for crown condition (Table 3-6). Both the number of plots and the number of trees may vary in the course of time between countries due to e.g. mortality or changes in the sampling design. This fact may influence the suitability of the data for joint trend analyses. Spain for instance, is re-organising their Level I network and therefore did not submit crown condition data for 2015. As the sudden discontinuation of plots from a large country like Spain strongly biased the results of the overall aggregates for most Mediterranean tree species or tree species groups, data from Spain could not be considered in the respective time series and trend analyses. Referring to statistical coherent datasets, however, considerably reduced the sample sizes for Mediterranean lowland pines, evergreen oaks, and Austrian pine. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 23 Table 3-5. Number of plots assessed for crown condition from 2005 to 2015 in countries with at least one Level I crown condition survey since 2005 according to the current database. Country 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Andorra 3 3 3 3 3 3 3 11 11 12 Austria 136 135 135 Belarus 403 398 400 400 409 410 416 373 Belgium 29 27 27 26 26 9 9 8 8 8 8 Bulgaria 102 97 104 98 159 159 159 159 159 159 159 Croatia 85 88 83 84 83 83 92 100 105 103 95 Cyprus 15 15 15 15 15 15 15 15 15 15 15 Czech Republic 138 136 132 136 133 132 136 135 138 136 Denmark 22 22 19 19 16 17 18 18 18 18 17 Estonia 92 92 93 92 92 97 98 97 96 96 97 Finland 605 606 593 475 886 931 717 784 France 509 498 504 508 500 532 544 553 550 545 542 Germany 451 423 420 423 412 411 404 415 416 422 424 Greece 87 97 98 57 47 Hungary 73 73 72 72 73 71 72 74 68 68 67 Ireland 18 21 30 31 32 29 20 Italy 238 251 238 236 252 253 253 245 247 244 234 Latvia 92 93 93 92 207 207 203 203 115 116 116 Lithuania 62 62 62 70 72 75 77 77 79 81 81 Luxembourg 4 4 4 4 4 4 4 Montenegro 49 49 49 49 Netherlands 11 11 11 11 Norway 460 463 476 481 487 491 493 496 461 488 411 Poland 432 376 458 453 376 374 367 369 364 365 361 Portugal 125 124 Romania 229 228 218 227 239 242 240 236 240 242 Russian Fed. 365 288 292 Serbia 129 127 125 123 122 121 119 121 121 128 127 Slovakia 108 107 107 108 108 108 109 108 108 106 105 Slovenia 44 45 44 44 44 44 44 44 44 44 44 Spain 620 620 620 620 620 620 620 620 620 620 Sweden 784 790 789 752 571 570 684 842 837 Switzerland 48 48 48 48 48 48 47 47 47 47 47 Turkey 43 396 560 554 563 578 583 531 590 United Kingdom 84 82 32 76 TOTAL 6 235 6 065 5 063 5 057 7 224 7 442 6 732 6 148 5 581 5 496 4 818 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 24 | Table 3-6. Number of sample trees assessed for crown condition from 2005 to 2015 in countries with at least one Level I crown condition survey since 2005 according to the current database. Country 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Andorra 74 72 72 73 72 72 72 264 264 289 Austria 3 528 3 425 3 087 Belarus 9 484 9 373 9 424 9 438 9 615 9 617 9 583 8 503 Belgium 676 618 611 599 599 216 217 206 195 194 187 Bulgaria 3 592 3 510 3 569 3 304 5 560 5 569 5 583 5 608 5 517 5 439 5 513 Croatia 2 046 2 109 2 013 2 015 1 991 1 992 2 208 400 2 520 2 472 2 280 Cyprus 361 360 360 360 362 360 360 360 360 361 360 Czech Republic 3 450 3 425 3 300 3 400 3 325 3 300 3 400 3 375 3 450 3 400 Denmark 528 527 442 452 384 408 411 411 419 409 389 Estonia 2 167 2 191 2 209 2 196 2 202 2 348 2 372 2 348 2 329 2 329 2 397 Finland 11 498 11 489 11 199 8 812 7 182 7 876 4 190 4 637 France 10 129 9 950 10 073 10 138 9 949 10 584 11 111 11 129 11 065 10 959 10 892 Germany 13 630 10 327 10 241 10 347 10 088 10 063 9 635 9 917 9 997 10 142 10 178 Greece 2 054 2 289 2 311 1 345 1 113 Hungary 1 662 1 674 1 650 1 662 1 668 1 626 1 702 1 655 1 519 1 554 1 501 Ireland 382 445 646 679 717 641 486 Italy 6 548 6 936 6 636 6 579 6 794 8 338 8 082 5 082 5 092 4 978 4 761 Latvia 2 263 2 242 2 228 2 183 3 911 3 888 3 797 3 879 1 718 1 743 1 732 Lithuania 1 512 1 505 1 507 1 688 1 734 1 814 1 846 1 847 1 907 1 956 1 956 Luxembourg 97 96 96 96 96 96 96 Montenegro 1 176 1 176 1 176 1 176 Netherlands 232 230 247 227 Norway 5 319 5 525 5 824 6 085 6 014 6 330 6 332 6 397 2 473 2 620 2 207 Poland 8 640 7 520 9 160 9 036 7 520 7 482 7 342 7 404 7 300 7 304 7 151 Portugal 3 748 3 748 Romania 5 496 5 472 5 227 5 448 5 736 5 808 5 760 5 656 5 696 5 808 Russian Fed. 11 016 8 958 9 116 Serbia 2 995 2 902 2 860 2 788 2 751 2 786 2 742 2 782 2 789 2 922 2 898 Slovakia 5 033 4 808 4 904 4 956 4 898 4 753 4 870 4 736 4 626 4 408 4 342 Slovenia 1 056 1 069 1 056 1 056 1 056 1 052 1 057 1 053 1 056 1 055 1 051 Spain 14 880 14 880 14 880 14 880 14 880 14 880 14 880 14 880 14 880 14 880 Sweden 11 422 11 186 2 207 2 301 1 709 1 703 1 834 2 775 2 843 Switzerland 807 812 790 773 800 785 780 852 786 775 1 043 Turkey 941 9 291 13 156 12 974 13 282 13 603 13 553 12 332 13 665 United Kingdom 2 016 1 968 768 1 803 TOTAL 137 251 130 396 112 686 112 885 138 436 145 353 133 663 111 758 107 630 102 458 88 052 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 25 In 2015, 44.9% of the plots were dominated by broadleaved and 55.1% by coniferous trees (Figure 3-1). This distribution illustrates the natural predominance of coniferous species in boreal and mountainous regions as well as the preference of forest management for coniferous species outside their natural distribution range. Figure 3-1. Distribution of Level I plots assessed in 2015 across the ICP Forests region and according to prevailing tree classification (broadleaves vs. conifers). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 26 | Tree species Most Level I plots with crown condition assessments contained one (40.1%) or up to three (45.5%) tree species per plot (Figure 3-2). Only 2.4% of the plots featured more than five tree species per plot, most of those were located in Italy, Slovenia, parts of France, Germany, and Lithuania. On all assessed Level I plots, Pinus sylvestris (18.3%) is the most abundant tree species followed by Picea abies (14.1%), Fagus sylvatica (12%), Quercus petraea (4.7%), Pinus nigra (4.5%), Q. robur (4.4%), Pinus brutia (3.9%) and Q. cerris (3.6%). Some tree species belonging to the Pinus and Quercus genus were combined into species groups before further analysis: − Mediterranean lowland pines (Pinus brutia, P. halepensis, P. pinaster, P. pinea) − Deciduous temperate oaks (Quercus petraea and Q. robur) − Deciduous (sub-) Mediterranean oaks (Quercus cerris, Q. frainetto, Q. pubescens, Q. pyrenaica) − Evergreen oaks (Quercus coccifera, Q. ilex, Q rotundifolia, Q. suber). Statistical analyses Trends in defoliation over time presented in this chapter were calculated according to Sen (1968) and their significance tested by the non-parametric Mann-Kendall test (tau). These methods are appropriate for monotonous, single-direction trends without the need to assume any particular distribution and they are robust against outliers (Sen 1968, Drápela & Drápelová 2011, Curtis & Simpson 2014). Therefore, trends are not influenced by individual outliers into one direction but are rather stable depicting the median of the slopes. The regional Sen’s slopes for Europe were calculated according to Helsel & Frans (2006). For both the calculation of Mann-Kendall’s tau and the plot-related as well as the regional Sen’s slopes, the rkt package (Marchetto 2014) in the R version 3.1.3 (R Core Team 2015) was used. The graphs with the over-all trend and yearly over-all mean defoliation display plot-related Sen’s slopes, each singularly tested by Mann-Kendall’s tau at a significance level of p ≤ 0.05. All Sen’s slope calculations and yearly over-all mean defoliation values were based on consistent plot selections with minimum three trees per species analysed per plot. Plots were included when data were available over the years 1992–2015 with a minimum assessment length of 20 years. For that reason some plots or countries could not be included in the long-term time series analyses presented in the graphs. For maps on the trends in defoliation over the years 2002–2015 with a minimum assessment length of 10 years and 2006–2015 with a minimum assessment length of 5 years, please refer to ANNEX I. Statistical analyses were performed with R version 3.1.3 (R Core Team 2015; Mann-Kendall test and Sen’s slope) and SAS 9.4 (SAS Institute Inc. 2015). National surveys In addition to the transnational surveys, in many countries national surveys are conducted, relying on denser national grids and aiming at the documentation of forest condition and its development in the respective country. Since 1986, densities of national grids between 1x1 km and 32x32 km have been used due to differences in the size of forest area, structure of forests and forest policies. The results of defoliation assessments on national grids are presented in ANNEX I. Comparisons between the national surveys of different countries should be made with great care because of differences in species composition, site conditions and methods applied. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 27 Figure 3-2. Number of tree species assessed on Level I plots in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 28 | 3.3 Results of the transnational tree condition survey Defoliation In 2015, 88 052 trees were assessed for defoliation on 4 818 plots (Table 3-7). The overall mean defoliation for all species was 20.7%; with means ranging between 19.6% and 29.3% for the major species or species groups. Broadleaved trees showed a slightly higher mean defoliation than coniferous trees (21.3% vs. 20.2%). Correspondingly, conifers had a higher frequency of trees in the defoliation classes ‘none’ or ‘slight’ (78.0%) than broadleaves (75.0%). Among the main tree species and tree species groups, evergreen oaks and deciduous temperate oaks displayed the highest mean defoliation (31.5% and 23.4%, respectively). Evergreen oaks had also by far the highest proportion of severely defoliated trees (5.8%). Of the specified groups deciduous (sub-) Mediterranean oaks and Austrian pine had the highest mortality rates (1.5% and 1.6%, respectively). Austrian pine and common beech had the lowest mean defoliation (19.5% and 19.6%, respectively). Of the specified groups Mediterranean lowland pine had the highest percentage (81.1%) of not or only slightly defoliated trees (≤ 25% defoliation) while evergreen oaks had the lowest (47.2%). Most species or species groups showed an improvement in defoliation in 2015 compared to 2014, especially the broadleaved species (Table 3-7). An exception was the group of evergreen oaks with a strong increase in defoliation in 2015. However, this increase can largely be attributed to a much smaller sample in comparison with 2014. Due to Spanish data missing, the sample of evergreen oaks was reduced from 4 500 trees in 2014 to only 1 000 trees in 2015, located mostly in France. Table 3-7. Percentage of trees in defoliation classes 0-4 in 2015 (cf. Table 3-1, class 2 subdivided), mean defoliation for the main species or species groups (change from year 2014 in parentheses) and the number of trees in each group. Main species or Class 0 Class 1 Class 2 Class 2 Class 3 Class 4 Mean No. of species groups (0-10% defoliation) (>10-25% defoliation) (>25-40% defoliation) (>40-60% defoliation) (>60% defoliation) Dead defoliation trees Common beech (Fagus sylvatica) 38.9 39.6 14.8 4.0 1.6 1.1 19.6 (-1.7) 10 877 Deciduous temperate oaks 24.2 45.2 21.8 5.8 2.0 1.0 23.4 (-1.8) 8 316 Dec. (sub-) Mediterra- nean oaks 28.7 39.6 19.5 7.5 3.2 1.5 24.1 (-0.7) 3 547 Evergreen oaks 14.8 32.3 27.9 19.0 5.8 0.1 31.5 (+4.6) 795 Other broadleaves 39.8 38.8 12.1 4.4 3.2 1.7 20.4 (-1.6) 19 079 Scots pine (Pinus sylvestris) 24.3 54.2 14.8 4.1 1.7 0.9 21.4 (+0.7) 16 716 Norway spruce (Picea abies) 36.6 36.8 19.5 4.8 1.7 0.7 20.2 (-0.8) 12 706 Austrian pine (Pinus nigra) 42.0 38.5 12.2 3.8 2.0 1.6 19.5 (+1.3) 4 102 Mediterranean lowland pines 22.9 58.3 13.0 4.2 1.4 0.2 20.5 (-0.3) 4 720 Other conifers 44.9 36.6 12.2 4.1 1.7 0.4 17.7 (-0.9) 7 194 TOTAL Broadleaves 35.1 40.2 15.6 5.1 2.6 1.4 21.3 (-1.5) 42 614 Conifers 32.4 45.6 15.3 4.3 1.7 0.7 20.2 (-0.1) 45 438 All species 33.7 43.0 15.4 4.7 2.1 1.1 20.7 (-0.8) 88 052 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 29 Mean defoliation of all species at plot level is shown in Figure 3-3. Almost three quarters (73.2%) of all plots had a mean defoliation less than 25%, and only 0.9% of the plots showed severe defoliation (more than 60%). Plots with high mean defoliation (>40%) were primarily found in southern (Mediterranean) France and Corsica, northern Italy, Slovenia, coastal Croatia and the Czech Republic. Plots with low mean defoliation were found across almost all of Europe, but mainly in south-eastern Norway, Romania and Serbia as well as in Turkey. Figure 3-3. Mean plot defoliation of all species in 2015. The following sections describe the species-specific mean plot defoliation in 2015 and the over-all trend and yearly mean plot defoliation from 1992 to 2015. For additional maps of trends in mean plot defoliation for the period 2002–2015 and 2006–2015, please refer to ANNEX I. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 30 | Scots pine Scots pine (Pinus sylvestris) was the most frequently assessed tree species in the Level I network in 2015. It has a wide ecological niche due to its ability to grow on dry and nutrient poor soils and has frequently been used for reforestation. Scots pine is found over large parts of Europe from northern Scandinavia to the Mediterranean region and from Spain to Turkey and is also distributed considerably beyond the UNECE region. More than three-fourths of the Scots pine plots (76.7%) showed no or only slight mean defoliation (≤ 25% defoliation) (Figure 3-4). Defoliation on 22.8% of the plots was classified as moderate (>25-60% defoliation) and on 0.5% of the plots as severe. Trees with no defoliation were primarily found in plots in southern Norway and northern Germany, whereas plots with comparably high defoliation were located in the Czech Republic, Slovakia, southern France and Bulgaria. Figure 3-4. Mean plot defoliation of Scots pine (Pinus sylvestris) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 31 From 1992 to 2015, there was no over-all trend in mean plot defoliation of Scots pine (regional Sen’s slope = 0, p > 0.05; Figure 3-5). The annual over-all mean defoliation hardly fluctuated from year to year although relative to the long-term mean a pronounced below average value was observed in 2000. However, from 2012 to 2015 annual mean defoliation in Scots pine has slightly but continuously been increasing. Figure 3-5. Over-all trend (regional Sen’s slope = 0.0, p > 0.05; minimum length of time span: 20 years, red line) and yearly over-all mean defoliation (black line) of Scots pine at Level I sites; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 32 | Norway spruce Norway spruce (Picea abies) is the second most frequently assessed species on the Level I plots. The area of its distribution ranges from Scandinavia to northern Italy and from north-eastern Spain to Romania. Favouring cold and humid climate, Norway spruce is found at the southern edge of its distribution area only at higher elevations. In 2015, trees on more than two-thirds of the Norway spruce plots (68.5%) were on average not or only slightly defoliated (≤ 25% defoliation; Figure 3-6). Defoliation on 30.8% of the plots was classified as moderate (>25-60% defoliation) and on 0.8% of the plots as severe. Plots with low mean defoliation were found e.g. in Norway, eastern France, and Romania. Clusters of plots with mean defoliation values above 25% were mainly found in Slovakia, in the mountainous regions of the Czech Republic, in the Black Forest and other mountainous regions in Germany, in central and western parts of Slovenia, in the French Alps, and more scattered, in Norway and Sweden. Figure 3-6. Mean plot defoliation of Norway spruce (Picea abies) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 33 From 1992 to 2015, a very slight but statistically significant increasing trend in mean plot defoliation of less than 1 percentage point every 10 years was observed (regional Sen’s slope = 0.08 , p = 0.001; Figure 3-7). Deviations in the yearly mean plot defoliation of more than 2 percentage points from the trend line were observed only for the year 2013 (lower defoliation than average). Figure 3-7. Over-all trend (regional Sen’s slope = 0.076, p <0.001; minimum length of time span: 20 years, red line) and yearly over-all mean defoliation (black line) of Norway spruce at Level I sites; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 34 | Austrian (or black) pine The distribution range of Austrian pine (Pinus nigra) is mostly restricted to southern Europe. It is occurring in many Mediterranean countries and most frequently in Turkey. Scattered occurrences are found as far north as central France, northern Austria and northern Hungary. In 2015, trees in more than two-thirds of the Austrian pine plots (77.5%) were on average not or only slightly defoliated (≤ 25% defoliation; Figure 3-8). Austrian pine showed the largest percentage of plots with less than 10% mean plot defoliation of all the considered tree species and species groups (23.4%), and these plots were primarily located in Turkey. Defoliation on 21.6% of the plots was classified as moderate (>25-60% defoliation) and on 0.9% of the plots as severe. Plots with no or low defoliation were mostly found in Turkey, while plots with high defoliation were scattered throughout Europe. Figure3-8. Mean plot defoliation of Austrian pine (Pinus nigra) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 35 From 1992 to 2015, the over-all trend in mean plot defoliation in Austrian pine has been strongly increasing by five percentage points every 10 years with high statistical significance (regional Sen’s slope = 0.5, p < 0.001; Figure 3-9). There were some deviations from this trend with lower defoliation in the early and late 1990s and from 2011 to 2012, while the years 2003, 2004, 2008 and 2015 were characterized by defoliation well above the mean trend. It is important to notice, however, that this trend analysis (as for the other species) is based on a consistent dataset with minimum of three trees of Austrian pine per plot and data available over the years 1992–2015 and a minimum assessment length of 20 years without Spain. Figure 3-9. Over-all trend (regional Sen’s slope = 0.5, p < 0.001; minimum length of time span: 20 years , red line) and yearly over-all mean defoliation (black line) of Austrian pine at Level I sites in France, Italy, Hungary, Romania, Bulgaria, Croatia and Belgium; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 36 | Mediterranean lowland pines Four pine species are included in the group of Mediterranean lowland pines: Aleppo pine (Pinus halepensis), maritime pine (P. pinaster), stone pine (P. pinea), and Turkish pine (P. brutia). These species occur in the Mediterranean region with warm and dry summers and mild and wet winters. Most plots dominated by Mediterranean lowland pines are located in Spain, some near the Atlantic and Mediterranean coasts in France, very few in Italy, Croatia, and Greece and again more in the lowlands of Turkey and Cyprus. Aleppo and maritime pine are more abundant in the western parts, and Turkish pine in the eastern parts of this area. In 2015, trees in nearly four out of five plots with Mediterranean lowland pines (79.1%) were on average not or only slightly defoliated (Figure 3-10). Plots with moderate to high mean defoliation values (>40% defoliation) were mostly concentrated in south-eastern France, but also in northern Italy and Croatia, while plots with severe defoliation (>60%) were scattered in the distribution range. Figure 3-10. Mean plot defoliation of Mediterranean lowland pines (Pinus halepensis, P. pinaster, P. pinea, P. brutia) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 37 From 1992 to 2015, there was a strong and highly significant increase in the trend in mean plot defoliation of 8 percentage points every 10 years (regional Sen’s slope = 0.8, p < 0.001; Figure 3-11). In the years 1992-1993, 2000-2002 and 2013, the yearly over-all mean plot defoliation was distinctly lower than the long-term trend. In contrast, in 1997 and 1998 values were higher than the trend with a maximum deviation of up to five percentage points from the trend. Concerning the strong increase in the trend line, it is important to notice that this trend analysis is based on a restricted sample of plots in only a few countries that have time series of minimum 20 years of assessments. Furthermore, due to the Spanish data missing in 2015, the sample of Mediterranean lowland pines was reduced from 8 100 trees in 2014 to 4 700 in 2015. Figure 3-11. Over-all trend (regional Sen’s slope = 0.8, p < 0.001; minimum length of time span: 20 years , red line) and yearly over-all mean defoliation (black line) of Mediterranean lowland pines (Pinus halepensis, P. pinaster, P. pinea, P. brutia) at Level I sites in France, Italy and Croatia; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 38 | Common beech Common beech (Fagus sylvatica) is the most frequently assessed deciduous tree species within the ICP Forests monitoring programme. It is found on Level I plots from southern Scandinavia in the north to southernmost Italy, and from the Atlantic coast of northern Spain in the West to the Bulgarian Black Sea coast in the east. In 2015, common beech plots with less than 10% mean plot defoliation were primarily located in Romania (Figure 3-12). On more than half of the monitored plots (52.6%), trees were only slightly defoliated so that on three quarters (75.5%) of all beech plots defoliation was either absent or low (≤ 25% defoliation). Plots with moderate (23.6% of all plots) to severe (0.9% plots) mean defoliation values were predominantly located in Germany, France, northern Italy, Slovenia and Croatia. Figure 3-12. Mean plot defoliation of common beech (Fagus sylvatica) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 39 From 1992 to 2015, the over-all trend in mean plot defoliation in beech has been slightly but significantly increasing by approximately 2 percentage points every 10 years (regional Sen’s slope = 0.17, p < 0.001; Figure 3-13). There were only a few deviations from this trend. In 2004, for example, the annual over-all mean defoliation was more than 4 percentage points higher than the trend, possibly as a result of the drought in the preceding year which had affected large parts of Europe (Ciais et al. 2005, Seidling 2007). In years like 1993, 2010 and 2015 on the other hand, trees have been recovering as indicated by a negative deviation from the over-all trend. Figure 3-13. Over-all trend (regional Sen’s slope = 0.174, p < 0.001; minimum length of time span: 20 years , red line) and yearly over-all mean defoliation (black line) of Fagus sylvatica at Level I sites; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 40 | Deciduous temperate oaks Deciduous temperate oaks include pedunculate and sessile oak (Quercus robur and Q. petraea) and their hybrids. They cover a large geographical area from southern Scandinavia to southern Italy and from the northern coast of Spain to the eastern parts of Turkey. In 2015, deciduous temperate oaks were on average not or only slightly defoliated (≤ 25% defoliation) in more than half of the plots (58.1%), moderately defoliated (>25–60% defoliation) in 40.7% and severely defoliated (i.e. more than 60% defoliation) in 1.2% of the plots (Figure 3-14). Most of the plots with moderate to severe defoliation were located in France, on the other hand many other plots in France showed no or only little defoliation. Figure 3-14. Mean plot defoliation of deciduous temperate oaks (Quercus robur and Q. petraea) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 41 Deciduous temperate oaks showed a rather strong increase of the over-all trend in mean plot defoliation from 1992 to 2015 with a statistically significant increase of 3.3 percentage points every 10 years (regional Sen’s slope = 0.333, p < 0.001; Figure 3-15). The annual plot mean development has not been linear. Between 1992 and 1997 there was a steeper than average increase in defoliation and from 2005 onwards a stagnation at a comparatively high level took place. Apart from these long-term dynamics, short-term developments can also be identified with a peak around 1997 and a second peak between 2003 and 2005. The latter can be connected with the drought year 2003 and its medium-term consequences for trees (delayed recovery). With the exception of 2012, defoliation seems to have stabilized since 2009, and although still at a high level, the annual mean has been well below the over-all trend in this period. Figure 3-15. Over-all trend (regional Sen’s slope = 0.333, p < 0.001; minimum length of time span: 20 years , red line) and yearly over-all mean defoliation (black line) of deciduous temperate oaks (Quercus robur and Q. petraea) at Level I sites; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 42 | Deciduous (sub-) Mediterranean oaks The group of deciduous (sub-) Mediterranean oaks includes Turkey oak (Quercus cerris), Hungarian or Italian oak (Q. frainetto), downy oak (Q. pubescens) and Pyrenean oak (Q. pyrenaica). The range of distribution of these oaks is confined to southern Europe. In 2015, trees in more than two thirds (69.8%) of the plots dominated by deciduous (sub-) Mediterranean oaks were on average not or only slightly defoliated (Figure 3-16). These plots were spread all over the area of these oaks’ distributions, although most of them were found in eastern countries like Hungary, Romania, Serbia and Turkey. 8% of the plots had a mean defoliation between 40% and 60%, and 1.4% of the plots had defoliation more than 60%. Most of those plots were located in southern France and scattered throughout Italy. Figure 3-16. Mean plot defoliation of deciduous (sub-) Mediterranean oaks (Quercus cerris, Q. frainetto, Q. pubescens, Q. pyrenaica) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 43 From 1992 to 2015, the over-all trend in mean plot defoliation of deciduous (sub-) Mediterranean oaks showed the same statistically significant increase of 1 percentage point every 3 years (i.e. 3.3 percentage points every 10 years) as the deciduous temperate oaks (regional Sen’s slope = 0.33, p < 0.001; Figure 3-17). Mean plot defoliation strongly increased from 1992 to 1996 before levelling off in the consecutive years. Figure 3-17. Over-all trend (regional Sen’s slope = 0.333, p < 0.001; minimum length of time span: 20 years , red line) and yearly over-all mean defoliation (black line) of deciduous (sub-) Mediterranean oaks (Quercus cerris, Q. frainetto, Q. pubescens, Q. pyrenaica) at Level I sites; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 44 | Evergreen oaks The group of evergreen oaks consists of kermes oak (Quercus coccifera), holm oak (Q. ilex), Ballota oak (Q. rotundifolia) and cork oak (Q. suber). The occurrence of this species group as a typical element of the sclerophyllous woodlands is confined to the Mediterranean basin. In 2015, evergreen oaks in roughly half of the plots (53.3%) were on average not or only slightly defoliated (Figure 3-18). The other half of the plots was either moderately defoliated (45%) or severely defoliated (1.7%). Trees in southern (Mediterranean) France (including Corsica) and one plot in northern Italy showed particularly high defoliation. Figure 3-18. Mean plot defoliation of evergreen oaks (Quercus coccifera, Q. ilex, Q. rotundifolia, Q. suber) in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 45 From 1992 to 2015, evergreen oak plots showed a dramatically increasing and statistically significant over-all trend in mean defoliation with an increase of 1 percentage point every year (regional Sen’s slope of 1.0, p < 0.001; Figure 3-19). Several comparably large deviations from the linearly increasing trend were observed in both directions. As already mentioned in the subchapters on Austrian pine and Mediterranean lowland pines, it is important to notice that this trend analysis is based on a restricted sample of plots from a few countries. Furthermore, the sample of evergreen oak trees was reduced drastically from 4 600 in 2014 to only 950 in 2015 due to the Spanish data missing in 2015. Figure 3-19. Over-all trend (regional Sen’s slope = 1.0, p < 0.001; minimum length of time span: 20 years , red line) and yearly over-all mean defoliation (black line) of evergreen oaks (Quercus coccifera, Q. ilex, Q. rotundifolia, Q. suber) at Level I sites France, Italy and Croatia; points represent annual plot means, for clarity these are not interconnected from year to year. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 46 | Damage causes In 2015, damage cause assessments were carried out on 88 052 trees on 4 818 plots in 25 countries (Figure 3-20). In total, 37 211 trees (42.3%) showed symptoms of damage of at least one defined agent group, of those 1 221 trees were deemed dead. In total 47 829 observations of damage were recorded. 1 539 plots showed no symptoms of damage on any tree. The number of damage on any individual tree can be more than one and the causes can also be multiple within one location. Therefore the number of cases analysed varies depending on the parameter. Figure 3-20. Plots with damage cause assessment in 2015. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 47 Symptom description and damage extent In total 47 829 damage symptoms were recorded, with some trees showing more than one symptom. For specification the affected parts of the tree or the location in the crown were recorded during the damage assessments (Figure 3-21). Most of the symptoms were observed on leaves (33.6%), followed by twigs and branches (23.7%), and the stem (20.6%). Needles were also often affected (13.8%), while roots & collar and shoots & buds were less frequently affected (3.2% and 0.1%, respectively). Figure 3-21. Damage symptoms (%) according to specifications of the affected part of a tree (n=47 829). Trees could have more than one affected part. More than half (55.9%) of all recorded damage symptoms had an extent of up to 10% (extent classes 0 and 1, Figure 3-22; cf. Table 3-3), approximately one third (35.9%) had an extent of >11–40% (extent classes 2 and 3), and only 8.2% of the symptoms covered more than 40% of the affected part of a tree (extent classes 4 to 7). Figure 3-22. Damage symptoms according to their extent class in 2015 (n=47 095). In trees with multiple types of damage symptoms of different extents, all extent values were evaluated. 0.3 55.6 22.6 13.2 3.8 1.8 0.8 1.9 0 10 20 30 40 50 60 0 1 2 3 4 5 6 7 Fr eq u en cy ( % ) Extent class 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 48 | Causal agents and factors responsible for the observed damage symptoms Insects were the predominant identified cause of damage, causing almost one quarter of all recorded damage symptoms (22.5%; Figure 3-23). Almost half of these insect-caused symptoms were attributed to defoliators (44%), which also represented the most frequent of all damage causes. Leaf-mining insects were responsible for damage on nearly 19% and wood-boring insects on 9.6% of the trees with insect-caused symptoms Fungi were the second major causal agent group affecting 10.9% of all assessed trees. Of those 30% showed signs of canker, followed by needle cast and needle rust fungi (20.1%) and decay and root rot fungi (12.8%). The third major identified cause of tree damage was abiotic agents (10.1% of all damage symptoms). Within this agent group, 24.5% of the symptoms were attributed to drought, while wind caused 11.4% and frost 7.9%. The damaging agent group ‘Game and grazing’ was of minor importance (1%) and may mainly be relevant in young tree stands. Direct action of men, including amongst others silvicultural operations and mechanical damage from vehicles, accounted for 4.9% of all recorded damage symptoms. Fire caused only 0.2% of all damage symptoms. The agent group ‘Atmospheric pollutants’ refers to local incidents mainly in connection with factories, power plants, etc. Visible symptoms of direct atmospheric pollution impact, however, were rare (0.1% of all damage symptoms). Apart from these identifiable causes of damage symptoms, a considerable amount of symptoms could not be identified (42.1%) or was caused by other causal agents not explicitly listed here (7.9%). 1 Visible symptoms of direct atmospheric pollution impact only Figure 3-23. Damage symptoms according to agent group and specific agents/factors (n=54 029). Each agent group was only counted once per tree. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 49 The occurrence of damaging agent groups slightly differed between major species or species groups. Of all of the identified damage causes, insects were the most prominent in four of the seven main groups. This holds especially for common beech (43.5%) and the decididous Mediterranean oak species (31.8%; Figure 3-24). Fungi as damaging agents were almost equally important in all species or species groups but for Pinus sylvestris which had more than 15% of fungal damage. Abiotic factors caused most damage in evergreen oak (15.6%) and Pinus nigra (19%). Damage from game and grazing played a minor role in all species and species groups but for Picea abies (6.2%). Figure 3-24. Damage symptoms according to agent group in the main tree species and species groups on Level I plots. 1 Visible symptoms of direct atmospheric pollution impact only. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 50 | Distribution of agent groups The mean number of assessed trees per plot is 10.4, ranging from only one tree assessed to 56. The classification of agent groups on the following maps is based on the extent to which each individual agent group occurred. Values smaller or equal to the 1st Quantile are in class 1 (blue), between the 1st and 3rd Quantile are in class 2 (yellow) and greater than the 3rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. Table 3-8. Quantiles for the specific agent groups. Category 1st Quantile Median 3rd Quantile Game and grazing 1 1 3 Insects 2 6 14 Fungi 1 3 8 Abiotic agents 1 2 4 Direct action of men 1 2 4 Fire 1 3 7 Atmospheric pollutants 2 3 20 Other factors 1 3 7 The agent groups on the following maps will also be discussed with regard to the forest type they had occurred in. Of the assessed plots about 10% are located in broadleaved monocultures (Figure 3-25). The majority is found in broadleaved- or coniferous-mixed stands making up about 30% each. Another 25% are located in broadleaved-coniferous-mix stands and only 4% are coniferous monocultures. Figure 3-25: Location of assessed plots with regard to forest type. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 51 Agent group ‘Game and grazing’ In 2015, damage caused by game and grazing was mainly observed in the Baltic States (Figure 3-26). Futher plots heavily affected by game and grazing were found in the mountainous border regions between the Czech Republic, Germany, and Poland, as well as on some sites in Germany. It is important to note that these results are not representative as they may be biased due to the fact that young trees, the main target trees for game and grazing, are underrepresented in the damage assessments. Figure 3-26. Extent of damaging agent group ‘Game and grazing’ in 2015. Values smaller or equal to the 1 st Quantile are in class 1 (blue), values between the 1 st and 3 rd Quantile are in class 2 (yellow), and values greater than the 3 rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 52 | Agent group ‘Insects’ The most frequently observed agent group was ‘Insects’ with 19% of the plots affected by any damaging agent. Occurences are reported across Europe with very low numbers only found in Norway and Sweden in the north and the Czech Republic (Figure 3-27). The majority of plots affected by insect damage were found in broadleaved-mixed stands, followed by broadleave-coniferous-mixed stands; only a minority below 5% are broadleave monocultures, coniferous-mixed and coniferous monocultures. Figure 3-27. Extent of damaging agent group ‘Insects’ in 2015. Values smaller or equal to the 1 st Quantile are in class 1 (blue), values between the 1 st and 3 rd Quantile are in class 2 (yellow), and values greater than the 3 rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 53 Agent group ‘Fungi’ Plots with a high frequency of fungal occurances were reported from Estonia, the south-eastern border of Poland and Bulgaria (Figure 3-28). However there are also non clustered occurences in Romania, Hungary, Italy, France and Germany. Fungal damage occured mostly in broadleaved-coniferous-mixed stands (55.2%). Figure 3-28. Extent of damaging agent group ‘Fungi’ in 2015. Values smaller or equal to the 1 st Quantile are in class 1 (blue), values between the 1 st and 3 rd Quantile are in class 2 (yellow), and values greater than the 3 rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 54 | Agent group ‘Abiotic agents’ Abiotic agents comprise direct stress by e.g. drought, temperature, wind, or landslides. About a quarter (24.5%) of the recorded damage by abiotic agents was caused by drought in 2015. Plots heavily affected by abiotic agents were found throughout Europe but overall more pronounced in southern Europe. Plots with lower frequency of affected trees were widely distributed across the participating countries (Figure 3-29). Figure 3-29. Extent of damaging agent group ‘Abiotic agents’ in 2015. Values smaller or equal to the 1 st Quantile are in class 1 (blue), values between the 1 st and 3 rd Quantile are in class 2 (yellow), and values greater than the 3 rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 55 Agent group ‘Direct action of man’ The damage agent group ‘Direct action of man’ refers mainly to impacts of silvicultural operations like soil compaction related to the use of heavy machinery, mechanical injuries caused by skidding etc. It was responsible for 4.9% of all damage symptoms in 2015 (Figure 3-30). Clusters of heavily impacted plots were found in Germany, Estonia, Poland, Hungary, Slovenia, Croatia, Bulgaria, and Turkey. Figure 3-30. Extent of damaging agent group ‘Direct action of man’ in 2015. Values smaller or equal to the 1 st Quantile are in class 1 (blue), values between the 1 st and 3 rd Quantile are in class 2 (yellow), and values greater than the 3 rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 56 | Agent ‘Fire’ There were few incidents of fire on Level I plots in Europe in 2015, with the only higher frequencies of affected trees found in Sicily, Croatia, and Hungary (Figure 3-31). Figure 3-31. Extent of damaging agent group ‘Fire’ in 2015. Values smaller or equal to the 1 st Quantile are in class 1 (blue), values between the 1 st and 3 rd Quantile are in class 2 (yellow), and values greater than the 3 rd Quantile are in class 3 (red). The specific ranges are given in Table 3-8. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES | 57 3.4 Conclusions In 2015, crown condition assessments with defoliation being the key parameter were carried out on 88 052 trees on 4 818 plots in 25 countries. The sample trees were also assessed for visible symptoms of damaging agents. In most species or species groups an improvement in defoliation in 2015 compared to 2014 was observed, especially for broadleaved species. An exception was the group of evergreen oaks with a strong increase in defoliation in 2015, but this increase was mainly caused by a reduction in sample size (from 4 500 trees in 2014 to less than 1 000 in 2015). Damage symptoms of different agent groups were recorded on 37 211 trees. As in the year before, insects were the predominant identified cause of damage with more than 12 000 damaged trees reported, followed by fungi (over 5 900 damaged trees) and abiotic agents (more than 5 400 damaged trees). While the proportion of insect and fungal damage and other minor important agent groups in 2015 was comparable to that of 2014, the damage caused by abiotic agents was less than in 2014, mostly related to fewer drought problems in 2015. More than 40% of the observed damage symptoms could not be identified in the field, indicating the need for further training of field crews in symptom identification. Presenting scientifically and statistically sound trends in defoliation is becoming more and more difficult due to interruptions in time series or methodological changes in some participating countries. For instance, the trend analyses in defoliation presented for Austrian pine, Mediterranean lowland pines and evergreen oaks are based on a relatively small sample of plots in a few countries having continuous data series of assessments for at least 20 years. Plots in countries that have changed their assessment (e.g. by changing plot location) or that have not delivered data in one or several years, had to be excluded from the trend analysis due to statistical reasons. Therefore, the trends presented for these three species/species groups are not representative for the whole Mediterranean region, but rather for the country/countries having the largest sample of plots with consecutive, long time series. The trend analyses for other species and species groups presented in this chapter are based on much larger samples of plots and countries and thus representative for Europe as a whole. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES 58 | 3.5 References Becher G, Lorenz M, Haelbich H, Mues V (2014) Tree crown condition and damage causes. In: Michel A, Seidling W, Lorenz M, Becher G (eds) Forest Condition in Europe: 2013 Technical Report of ICP Forests, Thünen Working Paper 19:10-54 Ciais P, Reichstein M, Viovy N, Granier A, Oge´e J, Allard V, Aubinet M, Buchmann N, Bernhofer C, Carrara A, Chevallier F, De Noblet N, Friend AD, Friedlingstein P, Grünwald T, Heinesch B, Keronen P, Knohl A, Krinner G, Loustau D, Manca G, Matteucci G, Miglietta F, Ourcival JM, Papale D, Pilegaard K, Rambal S, Seufert G, Soussana JF, Sanz MJ, Schulze ED, Vesalla T, Valentini R (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437:529-533 Curtis CJ, Simpson GL (2014) Trends in bulk deposition of acidity in the UK, 1988-2007, assessed using additive models. Ecol Indic 37:274-286 Dobbertin M, Landmann G, Pierrat JC, Müller-Edzards C (1997) Quality of crown condition data. In: Müller-Edzards C, De Vries W, Erisman JW (eds) Ten years of monitoring forest condition in Europe. UNECE, EU, Brussels, Geneva, 7-22 Drápela K, Drápelová I (2011) Application of Mann-Kendall test and the Sen’s slope estimate for trend detection in deposition data from Bílý Kříž (Beskydy Mts., the Czech Republic) 1997-2010. Beskydy (Brno) 4(2):133-146 EEA (European Environmental Agency) (2007) European forest types: Categories and types for sustainable forest management reporting and policy. European Environment Agency (EEA) Technical Report 9/2006, 2nd ed, Copenhagen, 111 p Eichhorn J, Roskams P (2013) Assessment of Tree Condition. In: Ferretti M, Fischer R (eds) Forest Monitoring – Methods for terrestrial investigations in Europe with an overview of North America and Asia. Elsevier, Amsterdam, 139-167 Eichhorn J, Roskams P, Ferretti M, Mues V, Szepesi A, Durrant D (2010) Visual assessment of crown condition and damaging agents. In: UNECE (ed) Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE ICP Forests, Hamburg, 49 p [http://www.icp-forests.org/Manual.htm] Eickenscheidt N (2015) Results of the International Cross-Comparison Course in Witzenhausen, Germany, 11-13 June 2014 Helsel DR, Frans LM (2006) Regional Kendall test for trend. Environ Sci Technol 40(13):4066-4073 Marchetto A (2014) rkt: Mann-Kendall test, Seasonal and Regional Kendall Tests. R packages version 1.3. [http://CRAN.R-project.org/package=rkt] R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna [http://www.R-project.org/] SAS Institute Inc. (2015) The SAS System for Windows Release 9.4. Cary, NC, USA. [http://www.sas.com] Seidling W (2007) Signals of summer drought in crown condition data from the German Level I network. Eur J For Res 126:529-544 Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379-1389 Wellbrock N, Eickenscheidt N, Haelbich H (2014) Tree crown condition and damage causes. In: Michel A, Seidling W (eds) Forest Condition in Europe: 2014 Technical Report of ICP Forests, BFW-Dokumentation 18/2014, Vienna, 11- 71 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 59 4 SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 Aldo Marchetto, Peter Waldner7 Summary The evaluation of the atmospheric deposition of major inorganic ions emitted into the atmosphere from natural sources and human activities is needed to quantify ion fluxes within the forest ecosystem. In this report we focus on acidifying, buffering, and eutrophying compounds in deposition collected under forest canopy (throughfall deposition). High deposition of N-NO3 deposition was mainly found in Central Europe, while the lower values (below 1 kg ha-1 y-1) were found in Finland, Bulgaria and on the Alps. The Central European area of high deposition (> 8 kg ha-1 y-1) of N-NH4 is larger, covering parts of Belgium, the Netherlands, Germany, the Czech Republic, Austria, Slovenia and Serbia. Other plots with high N-NH4 deposition are also found in Poland, Italy, France and Spain. Low values, below 1 kg ha -1 y-1, were found again in Finland and Bulgaria, but also in Switzerland and France. High deposition of S-SO4 deposition is spread over all Europe, partly due the contribution of marine aerosol. After sea-salt correction, the area with higher S-SO4 deposition in Central Europe is smaller than for N-NO3 and N-NH4 deposition, but high values are also found in Southern and Eastern Europe, partly due to the input of Saharan dust. The lowest values of S-SO4 deposition are found in the Swiss Alps. High values of Ca deposition are recorded in almost all plots in Southern Europe, from Spain to Romania, probably due to the relevant contribution of Saharan dust. Isolated plots with high Ca deposition are also found in Belgium, Germany, Denmark, the Czech Republic, Poland and Lithuania, probably related to local mineral sources. Low values of Ca deposition (below 2 kg ha-1 y-1) were mainly found in Northern Europe. The correction for the marine contribution does not affect the spatial pattern of Ca deposition. On the contrary, Mg deposition is mainly related to the marine aerosol. After sea-salt correction, values below 1.5 kg ha-1 y-1 are found in most of Europe, while the highest values are reported in Eastern Europe and on isolated plots in Italy, Germany and the Czech Republic. 4.1 Introduction The amount and seasonal pattern of precipitation is one of the main factors controlling the distribution of forest ecosystems. Beside this, precipitation, such as rain and snow, also carries to the forests a number of organic and inorganic substances that can affect, positively or negatively, forest growth and health (Rowe et al. 2014), or sensitive compartments of the forest ecosystem, such as epiphytic lichens (Giordani et al. 2014), or ground vegetation (Dirnböck et al. 2014) or forest soils (Ferretti et al. 2014). Beside this “wet” deposition of substances, aerosol of natural origin or emitted into the atmosphere by human activities can settle directly forming the so-called “dry” deposition. Depending on leaf traits and 7 For contact information, please refer to Annex IV-4. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 60 | humidity, forest canopies can collect significant amounts of aerosol by “filtering” large volumes of air (Mayer and Ulrich 1977). Finally, deposition related to the collection by leaves of fog droplets and atmospheric humidity is called “occult” deposition. In this chapter, we focus on the deposition of nitrogen (N) and sulphur (S) compounds and of base cations, which represent the major inorganic compounds found in wet and dry deposition. Anthropogenic sulphur dioxide (SO2) emission, mainly resulting from combustion, has increased since the 1950s, and resulted in the deposition of sulphate (SO4 --) and in an increase of deposition acidity, which can be partly buffered by the deposition of base cations, mainly calcium (Ca2+), magnesium (Mg2+) and potassium (K+). Sulphate deposition can also be the consequence of natural processes, such as SO2 emission by volcanoes and deposition of marine aerosol. In the last decades, a strong reduction in SO2 emissions in Europe led to a marked negative trend in sulphate deposition and a similar decrease of deposition acidity (Waldner et al., 2014). Atmospheric deposition mainly contains two inorganic N compounds: nitrate (NO3 -) and ammonium (NH4 +). The former originates from the transformation of nitrogen oxides (NOx), which are also released during combustion, while the latter mainly derives from the emission of ammonia (NH3) in agriculture and farming. Since N availability often controls forest productivity (Tamm 1991), N compounds carried out by atmospheric deposition can stimulate forest growth and enhance carbon uptake (e.g. Nair et al. 2016), but it can also cause, for example, forest growth decline (e.g. Silva et al. 2015) and alterations in soil biological activity (Janssen et al. 2010) and vegetation biodiversity (Bobbink et al. 2010), impacting the forest food-web (Meunier et al. 2016). N compounds are important nutrients that can produce ecosystem eutrophication, but they both can also act as acidifying compounds (Bobbink and Hettelingh, 2011). Figure 4-1. Deposition sampler located under forest canopy to collect throughfall in Italy. Figure 4-2. Stemflow sampler on a beech stem in Italy. Gabriele A. Tartari Gabriele A. Tartari 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 61 In the ICP Forests network, atmospheric bulk deposition is collected using bulk collectors (Figure 4-1), i.e. collectors which are always open. Apart from precipitation, they also collect particulate and gaseous deposition during dry periods, and to evaluate this effect, on a small number of plots wet-only samplers are also used, which open automatically during precipitation. A first series of bulk collectors are located in the open-field, to estimate wet deposition, not influenced by the exchange processes within the canopies. A second series of bulk collectors are located under the forest canopy, across the plot (througfall collectors) to collect total deposition (i.e. the sum of wet, dry and occult deposition). In the case of N compounds, throughfall deposition can be markedly affected by leaf uptake and/or canopy leaching. Stemflow collectors (Figure 4-2) are also used to collect precipitation that is intercepted in the canopy and runs off along branches and stems to the soil. In stands with trees suitable for stemflow (such as beech), contributions of stemflow to throughfall fluxes are typically about 15%. In this report we will focus on througfall deposition collected by the bulk collectors below the forest canopy, which represent an estimate of the total amount of deposition reaching the forest plots. This estimate is a good proxy to assess temporal changes. For nitrogen, however, depending on site conditions leaf uptake and stemflow may result in total deposition being a factor 1 to 2 higher than throughfall (Clarke et al. 2010). 4.2 Materials and methods Within ICP Forests, sampling procedures are harmonized according to a specific manual (Clarke et al. 2010), but througfall samplers differ from country to country, by type (including funnels and gutters), by number (3 to 27) and by location (random vs. systematic). However, an accurate intercomparison of the different collection methods (Žlindra et al. 2011) showed good agreement in the amount and the chemical composition of precipitation between all national collectors and a harmonized one. Throughfall data for 2014 were available for 235 plots. Annual deposition of N, distinguishing nitrate N (NO3-N) and ammonium N (NH4-N), S from sulphate (S-SO4), Ca and Mg were obtained by multiplying the volume weighted average concentrations by the annual amount of precipitation. Quality assurance procedures were carried out to assure the quality of the data: plots were discarded when (i) analysed samples covered less than 321 days (90% of the year) or sampling periods were not correctly reported; or (ii) less than 30% of the samples passed the conductivity check (König et al. 2010). As the deposition of marine aerosol represents an important contribution to the total deposition of SO4, Ca and Mg, a sea-salt correction was applied, subtracting from the deposition fluxes the marine contribution, calculated as a fraction of the chloride deposition on the basis of the formulas reported in the manual of the ICP Modelling & Mapping (CLRTAP, 2004). 4.3 Results High deposition of N-NO3 deposition (Figure 4-3) was mainly found in Central Europe (parts of Germany, Denmark and the Czech Republic), while the lower values, below 1 kg ha-1 y-1, were found in Finland, Bulgaria and on the Alps. The Central European area of high deposition (> 8 kg ha-1 y-1) of N-NH4 is larger, covering parts of Belgium, the Netherlands, Germany, the Czech Republic, Austria, Slovenia and Serbia (Figure 4-4). Other 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 62 | plots with high N-NH4 deposition are also found in Poland, Italy, France and Spain. Low values, below 1 kg ha-1 y-1, were found again in Finland and Bulgaria, but also in Switzerland and France. High deposition of S-SO4 deposition is spread over all Europe (Figure 4-5), partly due the contribution of marine aerosol. After sea-salt correction, the area with higher S-SO4 deposition in Central Europe is smaller than for N-NO3 and N-NH4 deposition (Figure 4-6), but high values are also found in Greece, the Balkans and in Southern Italy. In this last plot, volcanic contribution can be relevant. The high values in Southern and Eastern Europe can be partly ascribed to the input of Saharan dust (Loye-Pilot et al. 1986). The lowest values of S-SO4 deposition are found in the Swiss Alps. The spatial pattern of Ca deposition is different: high values of Ca deposition are recorded in almost all plots in Southern Europe, from Spain to Romania (Figure 4-7), and are probably due to the relevant contribution of Saharan dust, transported northward up to the Alps (Rogora et al. 2004). Isolated plots with high Ca deposition are also found in Belgium, Germany, Denmark, the Czech Republic, Poland and Lithuania, probably related to local mineral sources. Low values of Ca deposition (below 2 kg ha-1 y-1) were mainly found in Northern Europe. The correction for the marine contribution (Figure 4-8) does not affect the spatial pattern of Ca deposition. On the contrary, Mg deposition (Figure 4-9) is mainly related to the marine aerosol. After sea-salt correction (Figure 4-10), values below 1.5 kg ha-1 y-1 are found in most of Europe, while the highest values are reported in Eastern Europe and on isolated plots Southeastern Europe, Italy, Germany and the Czech Republic. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 63 Figure 4-3. Throughfall atmospheric deposition of nitrate nitrogen (NO3-N) in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 64 | Figure 4-4. Throughfall atmospheric deposition of ammonium nitrogen (NH4 -N) in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 65 Figure 4-5. Throughfall atmospheric deposition of sulphate sulphur (SO4-S) in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 66 | Figure 4-6. Sea-salt corrected throughfall atmospheric deposition of sulphate sulphur (SO4-S) in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 67 Figure 4-7. Throughfall atmospheric deposition of calcium in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 68 | Figure 4-8. Sea-salt corrected throughfall atmospheric deposition of calcium in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 69 Figure 4-9. Throughfall atmospheric deposition of magnesium in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 70 | Figure 4-10. Sea-salt corrected throughfall atmospheric deposition of magnesium in European forests in 2014. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 | 71 4.4 References Bobbink R, Hettelingh JP (2011) Review and revision of empirical critical loads and dose-response relationships. Coordination Centre for Effects, National Institute for Public Health and the Environment (RIVM), Bilthoven Bobbink R, Hicks K, Galloway J, Spranger T, Alkemade R, Ashmore M, Bustamante M, Cinderby S, Davidson E, Dentener F, Emmett B, Erisman JW, Fenn M, Gilliam F, Nordin A, Pardo L, De Vries W (2010) Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis. Ecol Appl 20:30-59 Clarke N, Zlindra D, Ulrich E, Mosello R, Derome J, Derome K, König N, Lövblad G, Draaijers GPJ, Hansen K, Thimonier A, Waldner P (2010) Sampling and Analysis of Deposition, Manual Part XIV. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE ICP Forests Programme Co-ordinating Centre, Hamburg, 66 pp CLRTAP (2004) Manual on methodologies and criteria for modelling and mapping critical loads and levels and air pollution effects, risks and trends. Convention on Long-Range Transboundary Air Pollution, 251 pp Dirnböck T, Grandin U, Bernhardt-Römermann M, Beudert B, Canullo R, Forsius M, Grabner MT, Holmberg M, Kleemola S, Lundin L, Mirtl M, Neumann M, Pompei E, Salemaa M, Starlinger F, Staszewski T, Uziębło AK (2014) Forest floor vegetation response to nitrogen deposition in Europe. 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Springer, Berlin/Heidelberg, 116 pp 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL VARIATION OF ATMOSPHERIC DEPOSITION IN EUROPE IN 2014 72 | Waldner P, Marchetto A, Thimonier A, Schmitt M, Rogora M, Granke O, Mues V, Hansen K, Pihl Karlsson G, Žlindra D, Clarke N, Verstraeten A, Lazdins A, Schimming C, Iacoban C, Lindroos AJ, Vanguelova E, Benham S, Meesenburg H, Nicolas M, Kowalska A, Apuhtin V, Napa U, Lachmanová Z, Kristoefel F, Bleeker A, Ingerslev M, Vesterdal L, Molina J, Fischer U, Seidling W, Jonard M, O’Dea P, Johnson J, Fischer R, Lorenz M (2014) Detection of temporal trends in atmospheric deposition of inorganic nitrogen and sulphate to forests in Europe. Atmos Environ 95:363- 374 Žlindra D, Eler K, Clarke N, Simončič P (2011) Towards harmonization of forest deposition collectors - case study of comparing collector designs. iForest 4:218-225 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 | 73 5 SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 Elena Gottardini, Vicent Calatayud, Marco Ferretti, Matthias Haeni, Marcus Schaub*,8 Abstract Ozone-induced visible foliar injury has been assessed during 2002-2014 according to ICP Forests standardized methods. This activity provided 29,809 records from 285 woody plant species, 169 plots and 19 countries. Data were evaluated for the entire period 2002-2014 as well as for 2009 only, when spatial coverage was the greatest. First results reveal that 55.0% of the assessed plots were symptomatic, and 26.0% of species developed ozone visible injury. Beech (Fagus sylvatica) was the species with the highest frequency of symptomatic observations (plot and years) in both 2002-2014 (40.1%) and 2009 (42.9%). The frequency of symptom reports occurred without a clear spatial pattern. In case, higher frequency of symptom occurrence seemed more common from northern Italy to North- West Germany, and towards East Europe. At country level, temporal trend analysis indicates a downward trend of mean frequency of symptomatic species for five out of six countries. Overall (all plots together), there is a slightly decreasing trend, which is consistent with the decreasing trend observed for ambient ozone concentrations. These first results demonstrate the potential of the survey on visible foliar injury to detect the potential impact of ozone on European vegetation. Further, enhanced quality control procedures are underway to aggregate the datasets and promote a more in- depth exploitation of cause-effect relationships, considering ozone symptoms, ozone concentration and measurements on forest health, growth, nutrition, biodiversity and climate undertaken at the ICP Forests plots. Keywords: ICP Forests; ozone symptoms; woody species; forest edge; Light Exposed Sampling Site (LESS) 5.1 Introduction Tropospheric ozone (O3) is well known to be an air pollutant causing injury to plants (Innes et al. 2001; Karlsson et al. 2007; Matyssek et al. 2007). Ozone pollution leaves no elemental residue in plant tissues that can be detected by analytical techniques; therefore, visible injury on leaves and needles is the only easily detectable indication in the field. Although visible symptoms do not include all the possible forms of injury to vegetation (i.e. physiological changes, reduction in growth, etc.), observation of typical symptoms on foliage has turned out to be a valuable tool for the assessment of the impact of ambient ozone concentrations on sensitive plant species (Skelly et al. 1987; Schenone 1993; Lorenzini et al. 1995; Bussotti and Ferretti 1998; Inclán et al. 1999; Innes et al. 2001; VanderHeyden et al. 2001; Novak et al. 2003; Benham et al. 2010). The assessment of ozone visible injury serves therefore as a means to estimate the ozone potential risk for European ecosystems, and is very relevant in the context of ICP Forests (Schaub et al. 2010b). Starting in the year 2000, a specific pan-European programme for the assessment, validation, and mapping of ozone visible injury on the vegetation has been launched, based on the ICP Forests intensive monitoring network (Level II plots, see http://icp-forests.net) where also ozone concentration is 8 For contact information, please refer to Annex IV-4. * Corresponding author 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 74 | measured (Schaub et al. 2010a; 2015). The programme considers both the main tree species (MTS) of each plot and the vegetation in Light Exposed Sampling Sites (LESS) at the forest edge. A specific manual has been developed for this purpose (Schaub et al. 2010b). Alongside, Intercalibration Courses on the Assessment of Ozone Injury on European Species among experts from the participating European countries have been implemented to promote quality assurance (QA) and quality control (QC) (Bussotti et al. 2003). QA/QC procedures are essential to ensure spatial and temporal data comparability. Participants in the UN/ECE ICP Forests programme must therefore follow the methods and QA/QC procedures described in the Manual. The main objective of assessing ozone visible injury is to contribute to an ozone risk assessment for European forest ecosystems. In this paper, we aim at providing first, comprehensive results on occurrence of visible foliar injury over space and time in Europe. We will present findings from data collected on woody plant species at the LESS over the period of 2002-2014 across Europe and stored in the central ICP Forests database. Although data have been subjected to routine QA/QC procedures, enhanced QA/QC is yet to be implemented. Therefore, results presented here should be considered as first outcome of the evaluation procedure, and interpreted with care. 5.2 Materials and methods Sampling design and data collection In order to assess ozone visible injury at the very site, a Light Exposed Sampling Site (LESS) has been established close to the off-plot sites of the ICP Forests Level II plots, where meteorological variables, deposition chemistry and ozone concentrations are also recorded. A LESS consists of a number of 2 x 1 m quadrates randomly selected along the forest edge. The number of randomly selected quadrates depends on the size (length) of the forest edge. Identification of ozone visible injury on woody species within the LESS quadrates has been carried out at least once during late summer and before natural leaf discoloration. Details on how to calculate the number of sampling units and conduct the assessment are outlined in Schaub et al. (2010b). QA and QC Field assessments of symptoms were carried out according to the standard QA/QC procedures of the ICP Forests described by Schaub et al. (2010b). Training and intercalibration courses were organised on an annual basis across Europe between 2002 and 2010, with most of the participating countries attending. Although uncertainty and subjectivity are impossible to be eliminated, they can be controlled: early results from 11 European field crews suggest that Data Quality Limits set by Schaub et al. (2010b) were achieved in most cases (Ferretti et al. 2013). Additional symptoms validation procedures, such as microscopical analysis, were implemented on a limited number of cases (872 out of 42,329 records). Data used in this report were extracted from the ICP Forests database on 28 October 2015 for validation purposes within the activity of the Expert Panel on Ambient Air Quality. Data completeness (number of quadrates reported vs. expected) of at least 80% was mandatory for the field survey. Thus, we assume data were complete, and we just considered all the available data. On such a dataset, however, enhanced QA/QC has been (and is still being) implemented. Plot codes, species names and codes, distinction between perennial and annual species, woody and non-woody species have been controlled. These new datasets are now being verified by National Focal Centers of the participating countries. For France, additional data will be submitted and considered for further analysis. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 | 75 Data description and analyses Overall, the database on ozone foliar injuries (OZ_LSS) taken into account for this work consists of 42,329 records of data, collected between 2002 and 2014. For the present evaluation, only woody species have been considered (29,809 records). Results for both, the entire period (2002-2014) and for 2009 only, i.e. the year with the highest number of countries participating in the programme, are reported. Specific analyses focusing on the most frequently recorded species were also carried out. For the spatial pattern representation, the number of assessed years (three classes: 1; 2-5; >5 years) and the frequency of symptomatic years (three classes: 0%; >0-50%; >50%) at plot level was calculated, both considering all woody species and only Fagus sylvatica. A plot was classified as symptomatic if at least one species was found symptomatic in one year. For the ranking of symptomatic species, only species observed on at least 30 plots (2002-2014) or 10 plots (2009) were considered. For the detection of temporal trends, only countries and plots with at least seven years of data were considered. Mean values of symptomatic species percentage at country level were used for the statistical analysis. The MAKESENS application (Version 1.0 Freeware, Copyright Finnish Meteorological Institute 2002, http://en.ilmatieteenlaitos.fi/makesens) was used to perform the non-parametric Mann– Kendall (Hollander and Wolfe 1999) and the Sen’s (Sen 1968) tests in order to verify the null hypothesis (H0) of no temporal trend in the frequency of symptomatic species. 5.3 Results Overview: occurrence of ozone foliar symptoms on woody species in Europe For woody species grown at the Light Exposed Sampling Sites (LESS), we analyzed data from 285 species on 169 plots in 19 countries (Table 5-1; see Annex III for the full account). Nineteen countries have at least one plot observed for at least one year. Longest time series were provided by Spain and Switzerland (9 years), Hungary and Lithuania (8 years), Italy and the Slovak Republic (7 years) respectively. A number of plots was observed for less than 7 years in Belgium, the Czech Republic, Germany, Greece and Serbia (5 years), Romania (3 years), Austria, Cyprus and France (2 years) and Croatia, Latvia, Slovenia and UK (1 year). The largest spatial coverage was found in 2009-2011, which was likely due to the financial contribution by the LIFE project FutMon. Over the entire period of 2002-2014, the majority of countries reported ozone visible injury at least on one single species in one single year and on one single plot. Four out of 19 countries, i.e. Cyprus, Romania, Serbia and the United Kingdom did not observe any ozone-induced symptoms. Over all, from 169 assessed plots, 55.0% plots were found symptomatic and 26.0% of the 285 assessed species developed ozone visible injury (Table 5-2). In 2009 however, the frequency of records was lower when 15 countries assessed a total of 194 species from 109 plots, of which 12.4% species (33.0% plots) were symptomatic. Table 5-3 provides a list based on the 10 most symptomatic species in 2002-2014 (left) and 2009 (right), and their frequency of symptom records (observations at different years and plots). For the 2002-2014 period, only species recorded at least on 30 plots and for 2009, only species recorded on at least 10 plots were considered. Among the 10 most symptomatic species in 2002-2014 and the 10 most symptomatic species in 2009, seven were in common. The ranking of frequency of symptom records shifted between 2002-2014, with the exception of beech (Fagus sylvatica), which was found to be symptomatic with the highest frequency during both periods. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 76 | Table 5-1. Number of assessed plots per country and year. Country code Country Survey year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 1 France 2 14 2 Belgium 1 1 1 4 4 4 Germany 3 18 25 11 11 11 5 Italy 4 8 4 4 2 22 22 4 4 6 United Kingdom 7 9 Greece 2 3 3 3 3 11 Spain 11 10 3 13 13 13 13 13 12 14 Austria 6 6 50 Switzerland 13 8 7 7 7 8 8 7 9 51 Hungary 9 9 9 9 5 5 5 1 2 52 Romania 4 4 3 54 Slovak Republic 1 3 8 8 8 3 3 56 Lithuania 9 9 9 9 9 9 9 9 57 Croatia 1 58 Czech Republic 6 3 4 6 7 60 Slovenia 4 64 Latvia 1 66 Cyprus 2 2 67 Serbia 1 1 1 2 2 Table 5-2. Number of countries, plots and species assessed during the entire period (2002-2014) and in 2009 only. Informative stratum 2002 - 2014 2009 Tot (n) Symptomatic (%) Tot (n) Symptomatic (%) Country 19 68.4 15 60.0 Plot 169 55.0 109 33.0 Species 285 26.0 194 12.4 Table 5-3. Total number of observations and frequency of the ten most symptomatic species assessed at different years and on different plots in 2002-2014 and 2009 only. For the 2002-2014 period, only species recorded at least on 30 plots were considered; for 2009, only species recorded on at least 10 plots were considered. Most symptomatic species 2002 - 2014 2009 Total number of observations (plots and years) (n) Frequency of symptom records (%) Total number of observations (plots and years) (n) Frequency of symptom records (%) Fagus sylvatica 237 40.1 42 42.9 Rubus idaeus 191 33.0 39 17.9 Carpinus betulus 113 24.8 20 10.0 Corylus avellana 160 22.5 21 23.8 Cornus sanguinea 58 22.4 11 27.3 Sambucus racemosa 30 20.0 - - Salix caprea 146 17.8 20 15.0 Viburnum lantana 33 18.2 - - Rubus fruticosus group 70 12.9 - - Fraxinus excelsior 118 16.9 15 13.3 Acer campestre 0 - 15 13.3 Acer pseudoplatanus 0 - 21 19.0 Frangula alnus 0 - 12 8.3 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 | 77 Spatial pattern The spatial pattern of symptoms (all species) over the period 2002-2014 across Europe against the estimated seasonal mean ozone concentrations (see Schaub et al. 2015 for details) is shown in Figure 5- 1. The map distinguishes the plots with respect of the number of available survey years (size of dots), and the frequency of survey years when the plot was found symptomatic (color of dots). A higher number of survey years (>5 years) was available for plots in Spain, Switzerland, Northern Italy, Hungary, the Slovak Republic and Lithuania. The frequency of symptom reports occurred without a clear spatial pattern. Higher frequency of symptom occurrence (red dots; >50%) seemed to be more common from northern Italy to North-West Germany, and towards East Europe. Interestingly, plots in regions with high ozone levels (e.g. central and southern Italy) showed no symptoms; and plots in regions with low ozone concentrations (e.g. South-West Germany, Lithuania and Latvia) showed frequent symptom occurrence. However, the majority of the plots seems to show a frequency of >0-50% symptom occurrence, with seasonal background ozone concentrations around 20-60 ppb. Figure 5-2 shows the same data for beech only. Also in this case, no clear pattern is obvious. Figure 5-1. Spatial distribution of April – September mean ozone concentrations (ppb) from passive samplers on 203 plots and 20 countries during 2000-2013 and ozone symptom occurrence on 169 plots and 19 countries during 2002-2014. For ozone symptoms, dot size represents temporal data coverage (small = only 1 year; medium = 2-5 years; large > 5 years) and color represents frequency of symptom occurrence (green = 0%; orange = 0.1-50%; red = >50% of years measured were symptomatic). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 78 | Figure 5-2. Spatial distribution of April – September mean ozone concentrations (ppb) from passive samplers on 203 plots and 20 countries during 2000-2013 and ozone symptoms occurrence for Fagus sylvatica on 74 plots and 15 countries during 2002-2014. For ozone symptoms, dot size represents temporal data coverage (small = only 1 year; medium = 2-5 years; large > 5 years) and color represents frequency of symptom occurrence (green = 0%; orange = 0.1-50%; red = >50% of years measured were symptomatic). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 | 79 Temporal pattern Mean frequency of symptomatic species per country and year is reported in Figure 5-3. The Mann– Kendall test (Table 5-4) indicates a downward trend of mean frequency of symptomatic species for 5 out of 6 countries, which is significant only for Hungary. When data are processed on the basis of individual plots (Figure 5-4), there is a slightly decreasing trend that is consistent with the decreasing trend of ambient ozone concentrations reported by Schaub et al. (2015). Figure 5-3. Temporal trend of the mean frequency of symptomatic species per country. For each country, only the plots with at least seven years of data have been considered. The legend reports the country codes and the correspondent number of plots. Table 5-4. Makesens statistics (S Mann-Kendall test) to detect and estimate trend in time series 2002-2014 of frequency of symptomatic species in six European countries with at least seven years of data. Plots may vary from year to year. Ns, not significant; * P<0.05; ** P<0.01. Country code Country No of years No of plots Mann-Kendall trend Sen's slope estimate S Significance Q B 5 Italy 7 1 11 ns 7.639 -12.92 11 Spain 9 12 -11 ns -0.335 3.67 50 Switzerland 9 7 -17 ns -1.213 12.13 51 Hungary 8 4 -24 ** -1.352 34.71 54 Slovak Republic 7 1 -5 ns -2.917 37.50 56 Lithuania 8 9 -8 ns -0.302 13.94 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 80 | Figure 5-4. Overall temporal trend of the mean frequency of symptomatic species. Only the plots with at least seven years of data have been considered. 5.4 Discussion and conclusions Ozone is the only air pollutant occurring in remote areas at concentrations that may cause visible injury on plants. Over the period 2002-2014, the assessment of ozone-induced symptoms on woody plant species at Light-Exposed Sampling Sites (LESS) nearby selected ICP Forests Level II plots reveals that visible injury attributed to ozone occurs every season on numerous plots and plant species across Europe. In fact, the 29,809 records from 169 plots in 19 countries, with 285 assessed species provide evidence that ozone still occurs at levels which are harmful to forest vegetation. Moreover, preliminary results demonstrate the complexity of the interactions between ozone exposures and forest ecosystems across Europe. As a matter of fact, symptoms were frequent even on plots with seasonal ozone background concentrations of 20-30 ppb. On the opposite, no or infrequent symptoms were found on plots with seasonal ozone background concentrations exceeding 50 ppb. A range of intermediate situations also occurred. Among the symptomatic species, Fagus sylvatica turned out to be the species with the highest frequency of symptom occurrence, during both periods, 2002-2014 and 2009 only. VanderHeyden et al. (2001) compared 16 common woody species with each other and developed a sensitivity ranking, based on ozone visible injury development under ambient ozone concentrations. They found that among the symptomatic species, Viburnum lantana was the most sensitive one, followed by Fraxinus excelsior, Frangula alnus, and Fagus sylvatica. Novak et al. (2003) also found Viburnum lantana to be the most sensitive species, followed by Fraxinus excelsior and others, which are not included in Table 3. It must be noted, however, that for some species (e.g., Fagus sylvatica, Rubus sp.), identification of ozone symptoms in the field can be confounded by various factors (see Bussoti et al. 2003; 2006). Therefore, further QA/QC checks and training are necessary to gain a better insight. The discrepancy between spatial distribution of seasonal ozone concentrations, frequency of symptom occurrence, and species specific sensitivity may be explained by the influence of internal and external 0 20 40 60 80 100 2001 2003 2005 2007 2009 2011 2013 2015 S y m p to m a ti c s p e c ie s , % 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 | 81 factors affecting the sensitivity of an individual plant to ozone. External phenomena affecting ozone sensitivity include both a range of factors that influence gaseous uptake rates in the leaf and the characteristics of the ozone regime. Nutrition, water availability, temperature, atmospheric and soil humidity, wind speed, and incident light levels are all known to affect ozone uptake (Sandermann et al. 1997). These factors interact in a complex fashion to determine whether or not the leaf will develop symptoms of injury, making the experimental simulation of ozone exposures extremely difficult. Leuzinger et al. (2011) postulated that the larger the spatial perspective of estimating water use under elevated CO2, the smaller the response compared to the control scenario – often being conducted under experimental conditions. Here, we may face a similar phenomenon. Although much is known about the mechanistic understanding of plant-ozone interactions under experimental conditions, the actual effects on forest ecosystems in the real world is less certain (e.g. Bussotti and Ferretti 2009). In combination with the measurement of ozone concentrations at the very forest sites, the assessment of ozone visible injury across Europe and in different forest ecosystems can be valuable to evaluate the risk for vegetation and to document spatial patterns, temporal variability, and trends of ozone effects. The results presented here, originated from the first comprehensive, European-wide evaluation of the data collected by the participants to the ICP Forests, and must be considered with caution due to some pending issues in terms of QA/QC checks. With this limitation in mind, however, results demonstrate that the survey on ozone visible injury can provide important indication for ozone risk assessment. Given the extended spatial and long-term coverage as well as the concurrent measurements of ozone concentrations and several other variables on forest health, growth, nutrition, biodiversity and climate, the potential of the ICP Forests ozone symptom dataset is unique. Enhanced QA/QC are being performed, new perspectives (e.g., survey restricted only to sensitive species or bio-indicators) and follow-up studies are being designed to study spatial and temporal trends and the relationship between ozone, ozone-induced symptoms, tree health and growth. 5.5 References Benham S, Broadmeadow M, Schaub M, Calatayud V, Bussotti F (2010) Using commercial tree nurseries to monitor visible ozone injury - an evaluation. Forest Ecol Manag 260:1824-1831 Bussotti F, Cozzi A, Ferretti M (2006) Field surveys of ozone symptoms on spontaneous vegetation. Limitations and potentialities of the European programme. Environ Monit Assess 115:335-348 Bussotti F, Ferretti M (1998) Air pollution, forest condition and forest decline in southern Europe: an overview. Environ Pollut 101 49-65 Bussotti F, Ferretti M (2009) Visible injury, crown condition, and growth response of selected Italian forests in relation to ozone exposure. Environ Pollut 157:1427-1437 Bussotti F, Schaub M, Cozzi A, Kräuchi N, Ferretti M, Novak N, Skelly JM (2003) Assessment of ozone visible symptoms in the field: perspectives of quality control. Environ Pollut 125:81-89. Ferretti M, Beuker G, Calatayud V, Canullo R, Dobbertin M, Eichhorn J, Neumann M, Roskams P, Schaub M (2013) Data Quality in Field Surveys: Methods and Results for Tree Condition, Phenology, Growth, Plant Diversity and Foliar Injury due to Ozone. In: Ferretti, Fischer (eds) Forest Monitoring, Vol 12, DENS, Elsevier, UK, pp 397-414 Hollander M, Wolfe DA (1999) Nonparametric statistical methods, 2nd ed. Wiley, New York Inclán R, Ribas A, Gimeno BS (1999) The relative sensitivity of different Mediterranean plant species to ozone exposure. Water Air Soil Pollut 116:273-27 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S SPATIAL AND TEMPORAL DISTRIBUTION OF OZONE SYMPTOMS ACROSS EUROPE FROM 2002 TO 2014 82 | Innes JL, Skelly JM, Schaub M (2001) Ozone and braodleaved species. A guide to the identification of ozone- induced foliar injury. Ozon, Laubholz- und Krautpflanzen. Ein Führer zum Bestimmen von Ozonsymptomen. Birmensdorf, Eidgenössische Forschungsanstalt WSL, Birmensdorf. Paul Haupt Verlag, Bern, Stuttgart, Wien, 136 pp Karlsson PE, Braun, S, Broadmeadow M, Elvira S, Emberson L, Gimeno BS, Le Thiec D, Novak K, Oksanen E, Schaub M, Uddling J, Wilkinson M (2007) Risk assessments for forest trees: The performance of the ozone flux versus the AOT concepts. Environ Pollut 146:608-616 Leuzinger S, Luo YQ, Beier C, Dieleman W, Vicca S, Koerner C (2011) Do global change experiments overestimate impacts on terrestrial ecosystems? Trends Ecol Evol 26:236-241 Lorenzini G, Nali C, Biagioni M (1995) An analysis of the distribution of surface ozone in Tuscany (Central Italy) with the use of a new miniaturized bioassay with ozone-sensitive tobacco seedlings. Environ Monit Assess 34:59-72 Matyssek R, Bytnerowicz A, Karlsson PE, Paoletti E, Sanz M, Schaub M, Wieser G (2007) Promoting the O3 flux concept for European forest trees. Environ Pollut 146:587-607 Novak K, Skelly JM, Schaub M, Kräuchi N, Hug C, Landolt W, Bleuler P (2003) Ozone air pollution and foliar injury development on native plants of Switzerland. Environ Pollut 125:41-52 Sandermann H, Wellburn AR, Heath RL (1997) Forest Decline and Ozone. A Comparison of Controlled Chamber and Fieldm Experiments. Springer, Berlin Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ (2010a) Monitoring of Air Quality. Manual Part XV, 13 pp. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests Programme Co-ordinating Centre, Hamburg. [http://www.icp-forests.org/Manual.htm] Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ (2010b) Monitoring of Ozone Injury. Manual Part VIII, 22 pp. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests Programme Co-ordinating Centre, Hamburg. [http://www.icp-forests.org/Manual.htm] Schaub M, Haeni M, Ferretti M, Gottardini E, Calatayud V (2015) Ground level ozone concentrations and exposures from 2000 to 2013. In: Michel A, Seidling W (eds) Forest Condition in Europe: 2015 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW Dokumentation 21/2015. BFW Austrian Research Centre for Forests, Vienna, 182 pp Schenone G (1993) Impact of air pollution on plants in hot, dry climates. In: Jackson MB, Black CR (eds) Interacting Stresses on Plants in a Changing Climate. NATO ASI Series 16. Springer-Verlag, Berlin Heidelberg, pp 139-152 Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379-1389 Skelly JM, Davis DD, Merrill W, Cameron EA, Brown HD, Drummond DB, Dochinger LS (1987) Diagnosing Injury to Eastern Forest Trees. USDA Forest Service and Penn State University, 122 pp VanderHeyden DJ, Skelly JM, Innes JL, Hug C, Zhang J, Landolt W, Bleuler P (2001) Ozone exposure thresholds and foliar injury on forest plants in Switzerland. Environ Pollut 111:321-331 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA | 83 6 RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA WITHIN THE ICP FORESTS MONITORING PROGRAMME Nils König, Nathalie Cools, Kirsti Derome, Alfred Fürst, Tamara Jakovljević, Aldo Marchetto Many laboratories from almost 30 different European countries are producing hundreds of thousands of analytical results each year within the ICP Forests monitoring programme. They are analysing water, soil and foliage samples from Level I and Level Il plots all over Europe (Table 6-1). Table 6-1: Number of laboratories within ICP Forests during the FutMon programme 2009–2011 Kind of laboratories (2009–2011) Number of labs Labs for water analysis (deposition, soil solution) 41 Labs for plant analysis (foliage, litterfall, vegetation) 36 Labs for soil analysis (soil, humus layer) 38 Labs for soil physical analysis 25 Total number of labs (some labs are analysing two or more sample types) 63 To guarantee the comparability of the analytical results between different laboratories in several countries and over time, a quality assurance (QA) programme is necessary with participation of all laboratories. The ICP Forests QA programme is based on three pillars: − the use of harmonized, well-defined and documented analytical methods − an internal quality control (QC) procedure within each lab − an external QC programme coordinated by the monitoring programme organisers To assure comparable results, first of all harmonized, well-defined and documented analytical methods are needed and have to be used by all laboratories. Therefore the expert panels and working groups of ICP Forests have compiled the “ICP Forests Manual on methods and criteria for harmonized sampling assessment, monitoring and analysis of the effect of air pollution on forests”, where all analytical reference methods have been described and published. On the basis of this ICP Forests manual each participating laboratory has developed its own quality control system. Basics are: − the use of the reference methods in the ICP Forests programme − different quality checks like ion balance checks (for water samples), nitrogen balance checks (for water samples), comparison of measured and calculated conductivity (for water samples), sum checks (for soil samples) or plausible range checks (for all types of samples) − repeated measurement of standard material − the use of control charts for continuous controlling of analytical repeatability and instrument stability 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA 84 | Control charts are mandatory within the ICP Forests monitoring programme; the results have to be submitted to the ICP Forests database together with analytical data. The main part of the external QC programme is the implementation of interlaboratory comparisons (ring tests) between all labs. At present the participation is mandatory; ring tests for water (every 2 years), soil (every 3 years) and plant (annualy) samples are organised regularly. So far 8 soil, 7 water and 18 foliar ring tests have been organised within the ICP Forests programme and the FutMon-project. For each parameter the different expert panels have determined tolerable limits (in percentage of the mean) to assess the ring tests. The percentage of non-tolerable results in ring tests can be seen as a degree of quality and comparability of results from participating labs. When the ring test programmes have been started, the tolerable limits were higher than today. For comparing the ring tests over time all ring tests have been evaluated again on the basis of the latest tolerable limits. The results of all water, soil and foliage ring tests within the last 20 years are shown in the following graphs. The development of the quality of the labs, but also the limitations due to different analytical methods can be seen from these results. Figure 6-1a: Percentage of non-tolerable results in soil ring tests from 1993 to 2015 (parameters: OC = Organic Carbon, Total N = total nitrogen, PS Clay = particle size distribution clay, PS Sand = particle size distribution sand, PS Silt = particle size distribution silt, pH CaCl2 = soil pH in 0.01 M CaCl2, pH H2O = soil pH in water, Reactive Fe = acid oxalate extractable iron, Reactive Al = acid oxalate extractable aluminium) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA | 85 Figure 6-1b: Percentage of non-tolerable results in soil ring tests from 1993 to 2015 (parameters: Exchangeable cations and acidity; Ac = acidity, Al = aluminium, Ca = calcium, Fe = iron, K = potassium, Mg = magnesium, Mn = manganese, Na = sodium) Figure 6-1c: Percentage of non-tolerable results in soil ring tests from 1993 to 2015 (parameters: aqua regia extractable elements; Al = aluminium, Ca = calcium, Cd = cadmium, Cu = copper, Fe = iron, K = potassium, Mg = magnesium, Mn = manganese, Na = sodium, P = phosphorus, Pb = lead, S = sulphur, Zn = zinc) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA 86 | Figure 6-2a: Percentage of non-tolerable results in water ring tests from 2002 to 2015 (parameters: Cond = conductivity, pH, Alk = alkalinity, TDN = total dissolved nitrogen, DOC = dissolved organic carbon) Figure 6-2b: Percentage of non-tolerable results in water ring tests from 2002 to 2015 (parameters: cations; Ca = calcium, Mg = magnesium, Na = sodium, K = potassium, NH4-N = ammonium-N) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA | 87 Figure 6-2c: Percentage of non-tolerable results in water ring tests from 2002 to 2015 (parameters: anions; Cl = chloride, SO4-S = sulphate-S, NO3-N = nitrate-N) Figure 6-3: Percentage of non-tolerable results in foliage ring tests from 1997 to 2015 (parameters: S = sulphur, P = phosphorus, Ca = calcium, Mg =magnesium, K = potassium, N = nitrogen) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RING TESTS AS MAIN PARTS OF THE QUALITY ASSURANCE AND CONTROL PROGRAMME FOR THE COMPARABILITY OF ANALYTICAL DATA 88 | The best results have been achieved for the foliage ring tests. Since 2004 only 5 to 10% of the results for the main parameters have been non-tolerable. In soil ring tests the ratio of non-tolerable results started with 20 to 60% in 1993 and decreased to 10 to 20% for most of the parameters in 2015. For water samples the percentage of non-tolerable results decreased from 20 to 60% in 2002 to 5 to 15% in 2015. The explanation could be found in the growing (or increasing) experience of the laboratories over time, especially for foliar analyses. Also the use of better equipment in many laboratories has led to better results. One reason for the higher number of non-tolerable results for soil compared to the other matrices is the inhomogeneity of sieved soil samples which have to be used for some of the extracts. A second reason could be found in the two steps analysis (extraction/digestion and measurement), which can bring a higher variation than one step analysis used for water samples. The participation in the regularly organised meetings of the heads of the labs, where many analytical problems have been discussed, has improved the laboratory quality and has led to better results in the ring tests during the last 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 89 7 THE ICP FORESTS LEVEL I BIODIVERSITY DATA A HARMONIZED DATA SOURCE AND BASELINE FOR PLANT SPECIES AND STRUCTURAL DIVERSITY ON EUROPEAN FOREST ECOSYSTEMS Roberto Canullo Abstract Structural and compositional biodiversity surveys on the ICP Forests extensive monitoring plots (Level I) have been incorporated into the collaborative ICP Forests database as LI-BioDiv dataset. Data were collected in the period 2005-2008 and delivered by 27 partners according to harmonized methods. During the integration process data was validated based on a complex system of checkroutines that had been defined before. Conflicts were solved in collaboration with the experts from National Focal Centres (NFCs) and the Expert Panels (EPs) on Biodiversity and Ground Vegetation, and on Forest Growth. Each Level I plot is georeferenced, commonly related to the soil pit and the crown condition survey. It consists of a circular plot of 2000 m2 which contains a concentric subplot (400 m2), and a second smaller circle (30 m2) designed for different field variables assessments. The LI-BioDiv dataset is structured in six forms: GPL (general plot location and information, 3340 plots), DBH (tree diameter, status, and composition, 3201 plots), THT (tree top and crown base height, 3083 plots), CAN (canopy closure, layers, number of trees, 3210 plots), DWD (deadwood, 2950 plots), and GVG (ground vegetation composition, 3124 plots). A transnational internal evaluation process was established and a set of items approved by the related Expert Panels and the ICP Forests Programme Co-ordinating Centre (PCC). Four working groups are producing the first results in terms of scientific papers; the other evaluation projects and the related groups of experts and scientists are described. Recommendations and lessons learned from this experience are shortly provided. Keywords: ICP Forests, Level I, biodiversity, LI-BioDiv dataset, validation 7.1 Introduction In 1985 ICP Forests established a large-scale monitoring network (Level I), aimed at gaining insights into the geographic patterns and temporal variations in forest condition. The extensive European monitoring network is based on a probabilistic sampling design, assured by around 6000 plots on a representative 16 x 16 km systematic grid (Ferretti et al. 2010). Annual crown condition assessments were performed as well as foliar nutrient and soil surveys under the EC Regulation 2152/03 Forest Focus, addressed to a harmonised, broad-based, comprehensive and long-term monitoring of European forest ecosystems (following EEC Regulation 3528/86). Forest Focus also promoted studies and pilot or demonstration projects to broaden the scope of the monitoring scheme from the protection of forests against atmospheric pollution and forest fires, towards environmental issues such as soils and forest biodiversity. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 90 | A first draft of a demonstration project including information relevant to forest biodiversity at the European scale, based on the Level I network, was prepared along 2005. The proposal was conceived with two modules addressed to a harmonised collection, handling and assessment of soil data and biodiversity indicators, consistent with the scope of European forest research and policy. The “BioSoil-Biodiversity” module, treasuring the achievements of the ForestBIOTA project and the COST ACTION E43 9 , was developed by the “Working Group on Forest Biodiversity” (WGFB) and discussed at the meetings of the ICP Forests Expert Panel on Biodiversity and Ground Vegetation (EPBDGV) and the Expert Panel on Forest Growth (EPFG). The stand structural approach was adopted, assuming that structurally diverse stands have more associated habitats, thus higher potential for biological diversity (WGFB 2007; Olivier 1981). Sampling effort was directed to few, simple and most recognised, robust and operational indicators of forest compositional and structural diversity, to be assessed with common harmonized or standardized methods and techniques. The reference to this respect was taken from existing forest monitoring parameters related to ground vegetation, forest growth and crown condition, adding new surveys on forest deadwood, and forest classification. With respect to the traditional Level I network, BioSoil moved from sampling point to circular sampling plots. A common manual was prepared for field activities (WGFB 2007). This experience was defined as a valuable baseline on forest biodiversity monitoring, in the frame of both the EU biodiversity policy and the EU 2020 biodiversity strategy (Durrant et al. 2011). Unfortunately, the original BioSoil datasets were unavailable for running projects or submitted proposals (e.g. EU Life+ FutMon project; Blust et al. 2013). ICP Forests, after some preliminary discussion in 2012 (Joint Expert Panel Meeting on European Level Data Evaluation, Helsinki, FI; 28th Task Force Meeting, Białowieża, PL) recognised the relevance of this data on forest biodiversity, as supported by the research community (e.g.: Clarke et al. 2011, Mikkelsen et al. 2013; Danielewska 2013). The need of a Level I dataset for species and structural diversity on European forest ecosystems was pinpointed, aimed to: − corroborate the Level I network as European infrastructure for biodiversity assessment, − provide harmonised, representative data to be combined with other information, − built a benchmark against which temporal and spatial patterns should be further monitored, − facilitate the ICP Forests internal evaluation effort, and − improve data access according to internationally accepted rules. The task to get together the defined dataset was undertaken by the PCC and the Chair of the EPBDGV (through Camerino University). The objective was to collect all the datasets from biodiversity surveys realised on the plots of the Level I European network, asking the NFCs to submit the data to the ICP Forests network. This was intended to be the founding action of a new common harmonised dataset on European forest biodiversity (LI BioDiv) based on a representative network of plots. 7.2 Data source All the NFCs participating in ICP Forests received a formal request to voluntarily submit the national datasets, potentially originating in different projects, according to the expected categories: general information about the plot (GPL), tree dbh, status, and composition (DBH), tree height and height of the 9 Details can be found on the web at http://www.forestbiota.org/ and http://www.metla.fi/eu/cost/e43/ 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 91 canopy base (THT), canopy closure and number of tree layers (CAN), lying deadwood (DWD), and ground vegetation (GVG). Validation and integration of national datasets was a complex task which has been discussed at the joint Expert Panels meetings in Wien 2012, Freising 2013, and Eberswalde 2014, before the data could finally be integrated into the collaborative ICP Forests database. The first version of the dataset is at the moment further evaluated within internal projects by the ICP Forests network. The documentation of the above steps and the revised system of checkroutines, will allow further data submissions for comparable repeated surveys. The countries that have acknowledged the new LI-BioDiv dataset, by delivering data, are reported in Table 7-1, with the respective surveys performed in different years (2005-2008). Table 7-1 Submitted datasets by country and survey years. GPL - general plot location and information; CAN - canopy closure and tree density; DBH - tree species, diameter, and status; DWD - deadwood dimensions and status; GVG - ground vegetation vascular species and cover; THT – heights of the largest trees. Codes and Country description and alphanumeric coding refer to LI-Biodiv dataset and ICP Forests identification. Country 10 2005 2006 2007 2008 G P L C A N D B H D W D G V G TH T G P L C A N D B H D W D G V G TH T G P L C A N D B H D W D G V G TH T G P L C A N D B H D W D G V G TH T Austria 14 • • • • • • Belgium FL 102 • • • • • • Cyprus 66 • • • • • • • • • • • • Czech Republic 58 • • • • • • • • • • • • • • Germany BW Germany BY Germany BB Germany NWD Germany MV Germany NW Germany RP Germany SL 280 4 • • • • • • 290 4 • • 270 4 • • • • • • • 300 4 • • • • • • • • • • • • 310 4 • • • • • • • • • • • • 320 4 • • • • • • • • • • 330 4 • • • • • • • 350 4 • • • • • Denmark 08 • • • • • • • Canaries (Spain) 95 • • • • • • Spain 11 • • • • • • • • • • • • Finland 15 • • • • • • • • • • • • France 01 • • • • • • • • • • • • Hungary 51 • • • • • • Ireland 07 • • • • • • • • • • • • Italy 05 • • • • • • • • • • • • • • • • Lithuania 56 • • • • • • Latvia 64 • • • • • • • • • • • • Poland 53 • • • • • • • • Sweden 13 • • • • Slovenia 60 • • • • • • • • Slovak Republic 54 • • • • • • • • • • • • • • • • • • • • • • • • United Kingdom 06 • • • • • • • • • • • • • • • • • Bel ium WL 202 early negotiation Switzerland 50 advanced negotiation Netherlands 03 early negotiation The Level I network is here represented by 19 countries (Germany with eight federal states, Belgium with only Flanders, Spain and the Canaries), accounting to overall 27 partners. Contacts are established to include additional data at a later stage. 10 ICP Forests partners (code) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 92 | 7.3 Materials and methods A common field methodology was adopted as described in the BioSoil-Biodiversity field manual (WGFB 2007), which allows different interpretations when translated in the operational manual at national level. Moreover, the fact that different national projects have been included, introduced some deviation from the standard, which was considered as far as possible by following a conservative principle. All the cases have been discussed with national experts and in dedicated sessions of the EPBDGV and EPFG meetings, in order to harmonise the data of the LI-BioDiv dataset. The location of each Level I plot is commonly related to the soil pit and the crown condition survey plots of the Level I network, from which they are established; geo-referencing is provided by countries. Each plot is consistent with the following scheme: a circular plot with a radius of 25.24 m (2000 m2) contains a first concentric subplot (r = 11.28 m, thus 400 m2), and a second smaller circle with a radius of 3.09 m (30 m2), identified as subplot no. 3, 2, and 1 respectively (Figure 7-1). Each subplot is devoted to particular measurements or assessments (Table 7-2) while the entire plot is used for data assessment of the GPL form. Figure 7-1. Representation of the LI plot and the concentric subplots (Pavlenda and Pajtík 2008). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 93 Table 7-2. Mandatory minimum measurements \ assessments, with optional actions and designs in the Level I plots for forest biodiversity. Variables, subplots and related thresholds are indicated. Category Variables Mandatory\ optional Subplots and thresholds 1 - 30 m 2 2 - 400 m 2 3 - 2000 m 2 GPL Previous land use, origin, age, management, forest type and classification, deadwood removal, tree mixture, slope, orientation, fencing m at plot level DBH Diameter at breast height of all woody plants m h > 130 cm; D > 0 cm h > 130 cm; D ≥ 10 cm h > 130 cm; D ≥ 50 cm Species determination m Status (standing living or dead, lying) m Decay stage m Distance and azimuth from plot centre o THT Top height m At least 3 largest measured trees for DBH Height of canopy base m DWD Coarse woody debris (diameter, length, species type, decay class) m D > 10 cm Optional design: 4 replicates 10x10 m Snags (diameter, height, species type, decay class) m h > 130 cm; D > 10 cm Stumps (diameter, length, species type, decay class) m h < 130 cm; D > 10 cm Fine woody debris (diameter, height, species type) o 5 < D ≤ 10 cm CAN Canopy closure m subplots 1 and 2 No. of tree layers m Number and fraction of trees assessed for DBH m GVG Overall vascular species list m subplots 1 and 2 Specific cover o Tree layers distinction o Mosses and lichens o To complement the tree stand structural parameters, deadwood assessments have been added with a common developed methodology, while the vascular plant communities of the ground vegetation were also assessed according to the Flora Europaea with reference to the ICP Forests manual and eventual amendments in the current updated version (Aamlid et al. 2007, Canullo et al. 2010). Forest classification is considered a strategic issue to account for large variability of forest biodiversity information and to adopt ecologically sound stratification for the interpretation of forest monitoring results and harmonized reporting (Barbati et al. 2007, 2014). Pre-assessed European Forest Type Classification was adopted, consisting of 14 categories (Barbati and Marchetti 2005, EEA 2006), to be validated in the field at the plot level. Tree variables for DBH and THT categories are assessed across the entire BioSoil plot, according to the thresholds shown above. DWD, CAN, and GVG categories are based on surveys referred to a common sampling area of 400 m2 usually achieved by the circular subplot 2; optional design with four replicates 10 x 10 m each, randomly distributed on the overall area (subplot 3) is allowed to account for local heterogeneity. Countries representatives have participated in a Forest Biosoil Field Training at Radovljica (Slovenian Forestry Institute) from 19 to 21 April 2006. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 94 | Structure of the dataset The LI-BioDiv dataset consists of six forms: GPL general plot location and information DBH tree diameter, status, and composition THT tree top and crown base height CAN canopy closure, layers, number of trees DWD deadwood GVG ground vegetation composition Each form contains variables related to specific items, and the common reference to country, Level I plot, subplot, and survey. The definition of the objects of survey, the employed methods and techniques for selection, assessments, and measurements of parameters and variables follows the general statements reported in the BioSoil-Biodiversity manual (WFFB 2007) with additional specifications and integrations linked both to operational and harmonising needs and the optional vs. mandatory specifications (see Materials and Methods). GPL The General Plot Location and information (GPL) describes the geographical location and a number of environmental and management characteristics of each plot. A detailed documentation of the form is available under http://icp-forests.org/documentation/BD/GPL.html DBH and THT Structural biodiversity information on the individual trees are contained in two forms: DBH reports the measured diameters, the species and the biological condition (standing dead or living, lying), and THT contains tree top and crown base heights, as assessed on selected largest trees within the plots (as previously included in the DBH dataset). A detailed documentation of the forms is available under: http://icp-forests.org/documentation/BD/DBH.html http://icp-forests.org/documentation/BD/THT.html DWD Deadwood typology, dimensions and status are contained in the DWD form where each record reports the variables of a single deadwood piece. A detailed documentation of the form is available under http://icp-forests.org/documentation/BD/DWD.html CAN In this form details of the state of canopy closure and the number of layers are reported. The number of trees assessed for DBH within the sampling area and the percentage of the total in case of sampling are also included. A detailed documentation of the form is available under http://icp-forests.org/documentation/BD/CAN/html GVG The form GVG (ground vegetation composition) contains the list of all species and the layers and cover assessments if performed. A detailed documentation of the form is available under http://icp-forests.org/documentation/BD/GVG.html 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 95 Plant species codes are given according to a taxonomic reference table based on Flora Europaea, available through EPBDGV (Canullo et al. 2010). Vegetation layers are reported by codes defining the vertical stratification in the system; cover assessment is submitted as percentage. Results Validation of available data could be finalized and data could be integrated into the collaborative ICP Forests database. The approved ongoing projects for internal evaluation with the general items and research questions are also summarized, with the indication of involved researchers. Data processing and validation issues The creation of the LI-BioDiv dataset, was not yet served by web-based submission tools: the files have been delivered to the working group (PCC and EPBDGV) in different formats. Forms are then affected by different national projects, have been submitted by subject aggregation irrespective of the survey, suffered misinterpretation of the common definition, etc. Thus, the first action to assure a high quality of the dataset was the translation of the received files in correct formats, sequence, and survey year. In order to harmonise the whole dataset, the introduction of ancillary parameters was necessary (as common WGS84 coordinates, creation of UTM zones, etc.), as well as the fine-tuning of definitions, data dictionaries, the improvement of identifier fields (as for deadwood pieces, or tree number), the description of objects, thresholds, and intervals, etc. These operations have been conducted by harmonizing the content of the Bio Soil Biodiversity manual (WGFB 2007, and previous versions), the national field manuals and the descriptions of the experimental designs (when available). The validation process started in strict co-operation with the PCC, the company DigSyLand, and the chair of the EPBDGV, by the early identification of attributes defined as primary keys, mandatory and obligatory fields for the six forms. The overall strategy used in the FutMon project was adopted for validation (Granke et al. 2010; Figure 7-2). Figure 7-2. The sequence of the data checks applied to the LI-BioDiv dataset (Granke, 2013). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 96 | The first validation has been processed according to the given format specifications, reference to codes, and data completeness or duplicates (Compliance checks). The second validation was performed by rules covering plausibility and temporal or spatial consistency of the dataset (conformity checks). In both cases, the automatic control resulted in error flags (data to be changed or deleted as implausible) or warning flags (out of defined ranges, can be changed or confirmed). Data was modified and confirmed only after a series of feedback with the data providers. Uniformity testing is to be verified based on expert-based plausibility checks and interpretation of the data with respect to neighbouring and temporal consistency. This issue will be part of the internal evaluation process, as it includes data aggregation analyses, spatial patterns and time series evaluation. A set of simple elaborations have been preliminarily proposed as a tool to support uniformity checks (Table 7-3). Table 7-3. Description of uniformity checks queries, by proposed tests for selected variables and aggregation levels. Category Test GPL age, forest_type, origin, preuse (descriptive to present plots, distribution) DBH dbh (mean and SD per species, and subplot) trees (count, per subplot, and decay I\0) THT height (mean and SD per subplot, main species, and all species) canopy_height (mean and SD per subplot, main species, and all species) DWD dw_ID (count per decay, and subplot) diameter (count, mean and SD per type, and subplot) CAN n_treelayer (per sublot) canopy (per subplot) GVG species_code (count per plot per layer - by layer, and all layers) species_code (sum) It is worth to note that, in some cases, not all parameters were assessed (e.g., mandatory variables) or correctly reported; in other cases some scores are missing or still unclear. For these cases additional options in the reference tables (data dictionary) had to be defined. Nevertheless, including some late contacts with national experts, files integrity can be considered quite complete. Doubtful cases, as well as the differences in sampling design or field techniques, will be documented precisely. The documentation of the LI-BioDiv dataset could be improved continuously during the validation process. The number of plots, and the overall records of the LI-BioDiv dataset by countries are shown in the Table 7-4 and Table 7-5. In some cases, the data from France and Ireland is not fully validated due to lack of information. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 97 Table 7-4. Number of plots delivered by country\region as incorporated into the LI-BioDiv dataset. Country Code 11 GPL DBH THT CAN DWD GVG Austria 14 136 135 129 133 128 136 Belgium Flanders 102 10 10 10 10 10 10 Cyprus 66 19 19 19 19 19 19 Czech Republic 58 146 139 138 141 142 146 Germany Baden-Württemberg 2804 50 49 49 49 50 50 Germany Bavaria\Bayern 2904 97 96 Germany Brandenburg-Berlin 2704 53 53 53 53 40 53 Germany Hessen 3004 29 29 29 29 29 29 Germany Mecklenburg-Vorpommern 3104 17 17 17 17 16 17 Germany Niedersachsen 3204 42 42 42 42 42 42 Germany Rheinland-Pfalz 3304 26 26 25 26 26 25 Germany Saarland 3504 9 9 9 7 9 Denmark 08 22 22 22 22 5 22 Spain 11 151 145 147 151 92 151 Spain Canaries 95 4 4 4 4 4 4 Finland 15 630 621 617 630 577 629 France 01 548 539 526 538 504 547 Hungary 51 78 77 77 78 74 18 Ireland 07 35 35 35 35 35 29 Italy 05 224 219 220 220 179 201 Lithuania 56 62 62 62 62 58 62 Latvia 64 95 95 95 95 88 95 Poland 53 438 432 431 438 408 438 Sweden 13 100 100 100 85 Slovenia 60 44 40 40 44 40 39 Slovak Republic 54 108 107 107 108 104 108 United Kingdom 06 167 163 161 163 121 157 Sum of plots 3340 3189 3064 3214 2885 3123 11 ICP Forests partners (code) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 98 | Table 7-5. Number of records included in the LI-BioDiv dataset by country\region and category. Country Code 12 GPL DBH THT CAN DWD GVG Austria 14 136 3773 628 241 2176 3280 Belgium Flanders 102 10 223 46 20 173 153 Cyprus 66 19 239 95 57 201 478 Czech Republic 58 146 4874 436 417 3772 5692 Germany Baden-Württemberg 2804 50 1425 149 92 1253 1738 Germany Bavaria\Bayern 2904 97 3048 Germany Brandenburg-Berlin 2704 53 1927 160 82 446 429 Germany Hessen 3004 29 667 246 58 794 773 Germany Mecklenburg-Vorpommern 3104 17 532 103 34 289 820 Germany Niedersachsen 3204 42 1050 358 84 1048 1239 Germany Rheinland-Pfalz 3304 26 780 189 52 666 636 Germany Saarland 3504 9 292 292 18 186 Denmark 08 22 699 80 66 8 274 Spain 11 151 2855 737 299 771 3807 Spain Canaries 95 4 105 20 8 15 58 Finland 15 630 20088 1844 1260 6817 18060 France 01 548 18111 2562 1206 6665 15917 Hungary 51 78 2488 284 159 1312 430 Ireland 07 35 1836 173 105 633 278 Italy 05 224 7933 825 1319 3663 17540 Lithuania 56 62 2369 291 186 646 2000 Latvia 64 95 3483 450 190 1182 2746 Poland 53 438 12929 1425 953 4640 13523 Sweden 13 100 2835 100 805 Slovenia 60 44 1372 243 132 460 2391 Slovak Republic 54 108 2898 440 216 1537 2925 United Kingdom 06 167 5092 755 484 1454 2156 Sum of records 3340 100875 12831 7838 41612 100391 Transnational internal evaluation process The discussion about a possible transnational internal evaluation process started at the Joint Meeting of the ICP Forests Expert Panels on Forest Growth and on Biodiversity and Ground Vegetation (Wien, October 23-25, 2012), when the experts agreed to a list of common evaluation items. Further improvements have been reached during the Combined Meeting of Expert Panels on Biodiversity and Ground Vegetation, Forest Growth and Meteorology, Phenology and LAI (Freising, June 17-19, 2013) and finalised at the Combined Meeting of the Expert Panels on Ambient Air Quality, Biodiversity and Ground Vegetation, Crown Condition and Damage Causes, Forest Growth, and Meteorology, Phenology and Leaf Area Index (Eberswalde, March 3-6, 2014). The correct use of the LI-BioDiv dataset is linked to the aim of producing insights into European forests’ biodiversity, covering continental-, landscape-, and stand-level definition. Biodiversity patterns through scales and their drivers are suggested as key focus, as well as contribution to functional diversity and mechanisms, which can be used to model the development of forest biodiversity, e.g. to face global changes. 12 ICP Forests partners (code) 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 99 The scientific evaluations based on the new LI BioDiv dataset are open to participation by country experts of the EPs and external cooperation by the scientific community is foreseen, provided the needs of clear coordination by the Panels, and following the Intellectual Property Policy as defined in the Annex of Part I of the ICP Forests Manual (Hansen et al 2010). The Internal Evaluation Level I-Biodiversity discussion group was created on the ICP Forests website13 as a showcase to appreciate the state of the art on the internal evaluation process related to the new LI BioDiv dataset. The topics which have been launched are described and periodically updated. Each research topic, led by an internal member of the ICP Forests community, will be afforded within a strict Working Group (private), edited for merely information. Invited members, contributing to the elaboration themes, will share the operative information and discussions. The working groups established for each evaluation item are voluntary based, according to the common objective of publishing sound scientific papers, increasing the visibility and the scientific relevance of the ICP Forests infrastructure. Active internal evaluation projects are listed below, which are expected to be finalized, at least partially, within 2016. UPSPEX, under the responsibility of Gherardo Chirici (University of Florence, WGFB), is dealing with up- scaling and spatially explicit estimation of biophysical variables with remote sensing; data consistency and some presentation at national and international congresses have been produced; a paper on testing a GIS expert-based algorithm for automatic classification of the overall ICP Forests Level I monitoring plots by EFCTs, was recently submitted. The working group is composed of up to 16 members14. Δ-Drivers BIOPART, under the responsibility of Roberto Canullo (University of Camerino, EPBDGV), is focused on the driving factors of beta-diversity in European forests, namely assessing interactive effects of ecology and biogeography in determining the total diversity of European forests. A paper was submitted to an international journal about plant species diversity of Italian forests as a first attempt for large scale analyses. European dataset analyses have been presented at various international congresses (EVS, IBS). At present, seven members have joined the related working group15. DWpools, led by Janusz Czerepko (IBLES, EPBDGV), proposes to analyse deadwood volume, decay, type and their diversity in relation to forest parameters across Europe. Results will be necessary to possibly explain the variation among forest types and to provide preliminary estimates of deadwood, which could be used as a reference for sustainable forest management. Data conformity and first general analyses have been performed, national attempts for deadwood estimates have been presented at the EPBDGV meetings. The working group was recently created on the ICP Forests website16, aggregating interested colleagues. NICHES, by Karl Mellert (LWF, EPBDGV), includes studies on the ecological characterisation of marginal (xeric limits) sites for tree species. Pre-evaluation of data structures is running, subsets of data have been already used within papers on modeling forest sensitivity to climate change, and will be used in running projects like MARGINS, for the specification of thresholds for the cultivation of tree species. A discussion about niche models is launched, based on the PROPS model. 13 To be found at http://icp-forests.net/group/inteval1biodiv 14 Cf. http://icp-forests.net/group/upspex 15 Cf. http://icp-forests.net/group/drivers-biopart 16 Cf. http://icp-forests.net/group/dwpool 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 100 | NICHES being a complex issue, a sub task is guided by Han van Dobben (ALTERRA, EPBDGV) who opened the discussion about the modelling approach. Abiotic model (VSD+) combined with niche model calibration should be expanded by using Level I and Level II ground vegetation together with soil data. Members are listed in the discussion group17. The full list of topics, including items on the early stage of progress, is given in Table 7-6. It is possible, of course, that some task or hypothesis which has been defined under a given item, may be merged while the process is underway, in agreement among the participants, for specific effort. Some items have been acknowledged by EPs, but the leadership remained uncertain and they are likely to be included in some other running project. Namely, some multi-indicator approach to a naturalness description was indicated, as well as the linkage of the LI-BioDiv dataset to Natura 2000 (to inspect the distribution of forest habitat types inside and outside of Natura 2000 sites, inspect the relative incidence and changes of the endangered or alien plant species, etc.). Comparison of the representativeness of performances of the Level II with respect to the Level I network in terms of accuracy and representativeness was also commonly underlined as a possible target. “Country effect” as one of the drivers of distribution patterns of biodiversity variables was also claimed due to previous studies underlying the possible differences in the methodology and socio-economic models (e.g. Ferretti 1998, Klap et al. 2000). Related to that, some evaluation of quality issues data (e.g. biased increase in the number of species, thresholds for significant trends, intercalibration of field surveyors, etc.) have been suggested, and some experts will possibly tackle the task. Vegetation response to nitrification was another interesting subject that was partially addressed by an integrated group with ICP Integrated Monitoring (ICP IM), including time series from the ICP Forests Level II network (Dirnböck et al. 2014); the availability of large scale representative datasets at Level I can be of great help for further gradient simulation analyses. The influence of deadwood diversity on bryophytes and vascular plants diversity was the last proposed item, with the deadwood variables being proposed as a possible indicator of the forest ecosystem status. 17 Cf. http://icp-forests.net/group/niche-model-calibration Table 7-6. Updated topics for the internal evaluation of Level I-biodiversity datasets. An extended version is to be found at http://icp-forests.net/group/inteval1biodiv Participating scientists are listed upon their willingness to contribute to a given project. Short name Resp. persons Title Participation Hypothesis being tested Δ-Drivers BIOPART Roberto Canullo Driving factors of beta- diversity in European forests. Chiarucci UNIBO, Landi & Giorgini UNISI, Wellstein UNIBZ, Campetella & Chelli UNICAM, Klinck NW-FVA, Grandin SLU, Salemaa & Tonteri LUKE, Oksanen UNIOULU, Wohlgemuth WSL, Kutnar GODZIS Weight and assess interactive effects of ecology and biogeography in determining the total diversity of European forests using a spatially representative sample: the effects of ecological factors are less important than biogeographical factors. PHYLOPAT Roberto Canullo Phylogenetic patterns at bio-geographical scale. Mucina UWA, Campetella UNICAM, Wellstein UNIBZ Competitive exclusion principle emphasises the limited coexistence of similar species. There is a similarity limit in the niches of competing species; species niches constrained by their evolutionary history. Hypothesis of limiting similarity at the phylogenetic level. FORGUILD Roberto Canullo Plant Functional Groups and species diversity patterns. Campetella UNICAM, Wellstein UNIBZ, Chiarucci UNIBO, Giorgini UNISI, Bartha MTA, Grandin SLU Is evenness in Plant Functional Groups (guild) distribution associated with a higher species richness? Can this explain plant diversity patterns in European forests? FUTPA Roberto Canullo Plant functional trait patterns in key EU forest types Wellstein UNIBZ, Spada UNIR1, Chelli & Campetella UNICAM, Msalemaa & Tonteri LUKE, Wohlgemuth WSL, Kutnar GODZIS The plant functional composition of forest phytocoenosis can be explained by soil parameters, present day climate and legacy of past climate. NICHES Walter Seidling Main drivers of ground vegetation at local and continental scale Fischer (?) TI Drivers acting at different spatial scales are influencing floristic composition of ground vegetation Maija Salemaa Niche definition prediction Mäkipää & Jöksanen LUKE, vanDobben ALTERRA, Klinck NW-FVA, Dupouey INRA, Walthert WSL Species with narrow niche as bioindicators Jean-Luc Dupouey Soil and species Han van Dobben Calibration of niche models on EU scale (incl. non-forest vegetation) Mellert LWF, Ewald HSWT, Canullo UNICAM, Wamelink ALTERRA Species occurrence can be predicted from abiotic model (VSD+) combined with niche model Karl Mellert Ecological characterisation of tree species marginal (xeric limits) sites Ewald HSWT, Canullo UNICAM, 1) SDMs based on coarse resolution climate data require refinement; 2) Topography & soil conditions modulate tree sp. response to climate; 3) Ground vegetation provides proxies for site properties; 4) Refined site variables allow to identify false absences Han van Dobben Indicator values, functional traits\groups Wellstein UNIBZ, Canullo & Chelli UNICAM, Dupouey INRA DWpools Janusz Czerepko Deadwood estimation through forest ecosystems in Europe Gawryś, Sokołowski & Cieśla IBLES, Herrmann WSL, Neumann BFW, Canullo, Campetella & Chelli UNICAM, Puletti CRA What drives deadwood pools and C stocks? Reference patterns - classes; relations with climate gradient, plant richness, productivity? Short name Resp. persons Title Participation Hypothesis being tested WP-KS- KW Henning Meesenburg Forest Productivity, Carbon Sequestration, Climate Change De Vos & Cools INBO, Canullo UNICAM, Michopoulos FRIA, Graf Pannatier WSL, Ilvesniemi & Lindroos LUKE, Mette LWF, Schmidt-Walter NFV Forest productivity is driven by several climatic and site (soil) specific variables; forest growth models can lead to estimates of the future potential of raw timber stocks and carbon storage of forests and face future climate. UPSPEX Gherardo Chirici Upscaling & spatially explicit estimation of biophysical variables with remote sensing Travaglini & Giannetti UNIFI, Attorre UNIR1, Canullo & Campetella UNICAM, Bastrup-Birk EEA, Puletti CRA, Barbati, Corona & Mancini UNITUS, Galic UNS Nearest neighbors techniques for predicting forest variables from satellite imagery and Level I ground data. Population unit predictions as combinations of sample observations (most similar, or nearest, in a space of ancillary variables, to predicted unit) Small Scale Maija Salemaa Small-scale variation of forest floristic diversity under different environmental conditions Thimonier WSL, Canullo UNICAM, Seidling TI Null-hypotheses: z-values and intercepts may not depend on forest type, climatic or edaphic climatic conditions, or anthropogenic influences 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 103 7.4 Conclusions Some conclusions can be considered in terms of lessons learned from the process of validation and evaluation of the LI-BioDiv dataset and the definition and implementation of the system of checkroutines. A noteworthy remark would be that a harmonized large-scale survey is feasible, and the good cooperation among countries enabled ICP Forests to get valuable insights into biodiversity indicators of the European forest systems. To this respect, the BioSoil-Biodiversity experience should be regarded as a funding milestone, and can be used also to avoid the problems linked to incorrect interpretation and lack of logical univocal descriptions, e.g. between the manual and the data forms. The possibility to include, after validation routines, the Level I dataset on biodiversity within the most developed and experienced infrastructure for forest research and monitoring, was the next important step to this respect. The work behind this is an investment that must be structurally included in further projects, as well as the evaluation process. The improved documentation of the methodology and the implementation of the system of checkroutines enables to consider a standard for next biodiversity surveys on the Level I network. During the process of validation it became evident that also a bottom-up approach can be considered, enabling the inclusion of other comparable datasets. For such kind of international surveys, it seems essential to prepare conveniently in advance a manual implementation with clear background, common definitions and the explanation of admissible values, thresholds and selection criteria, to be tested in the field. The experience of the last update of the ICP Forests manual can be of reference for that issue. Any international manual should be translated into an operational field manual for field crews, and the observer errors, both in the application of the sequence of protocols and the field surveys, is a relevant target to be afforded at this level by means of standard field training and intercalibration workshops. The variables to be considered as mandatory must be fixed, and their number, as used in the BioSoil- Biodiversity project, was probably the best agreement between effort and results. Optional parameters and alternative designs must be well regulated as well. The high number of sites (3340) and the hundreds of thousands of records must be somehow optimized in terms of time spent in the field, simplification of the procedures, and selection of the best representative network, in a way that the feasibility can considerably increase, together with the comparability across Europe. The latter issue is the target of a running Life+ project for the Italian CONECOFOR network (SMART4Action1), the results of which could suggest a similar approach for the European Level I network. As for the BioSoil-Soil module (Blust et al. 2013) here we can highlight the need for clear rules in the ownership and distributed rights, according to internationally accepted rules and standards: data availability and engagement for sharing datasets are relevant issues to ensure continuity and benefit for the community. 1 http://www.corpoforestale.it/smart4action 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA 104 | Acknowledgements ICP Forests National Focal Centres of Austria, Belgium (FL), Cyprus, Czech Republic, Germany with the federal states Bavaria, Baden-Wuerttemberg, Berlin, Brandenburg, Hesse, Mecklenburg-Western Pomerania, Lower Saxony, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt, Schleswig-Holstein; Denmark, Spain (incl. Canaries), Finland, France, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Sweden, Slovenia, Slovakia, and the United Kingdom are acknowledged for delivering the Level I biodiversity data and for making the construction of the first version of LI-BioDiv possible. The staff of the ICP Forests Programme Co-ordinating Centre (PCC) was so kind to support technical and “public relation” issues, making easier each difficult task. Thanks to the members of the Expert Panels on Biodiversity and Ground Vegetation, on Forest Growth, and on Crown Condition and Damage Causes for their suggestions and contributions along the process. Many thanks to all the colleagues who dug out their archives (both electronic and mental) to answer my, sometimes naive, questions and deliver details on “ancient surveys”. Special thanks to Annemarie Bastrup-Birk (EEA), Gherardo Chirici and Davide Travaglini (University of Florence) for explaining their former activity in the construction of the references in both ForestBiota and BioSoil-Biodiversity projects, and to Bruno De Vos and Nathalie Cools (both INBO and Chairs of the Expert Panel on Soil and Soil Solution) for their support on plot localisation problems and the overall encouragement. Walter Seidling and Till Kirchner (both PCC) are fully acknowledged for reviewing draft versions of this chapter. References Aamlid D, Canullo R, Starlinger F (2007) Assessment of Ground Vegetation Part VIII. In: Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests. UNECE ICP Forests Programme Co-ordinating Centre, Hamburg Barbati A, Marchetti M (2005) Forest Types for Biodiversity Assessment (FTBAs) in Europe: the revised classification scheme. In: Marchetti M (ed) Monitoring and indicators of forest biodiversity in Europe — from ideas to operationality. EFI Proceedings 51:105-126 Barbati A, Corona P, Marchetti M (2007) A forest typology for monitoring sustainable forest management: the case of European forest types. Plant Biosyst 141(1):93-103 Barbati A, Marchetti M, Chirici G, Corona P (2014) European forest types and forest Europe SFM indicators: tools for monitoring progress of forest biodiversity conservation. Forest Ecol Manag 321:145-157 Blust G de, Laurijssens G, Calster H van, Verschelde P, Bauwens D, Vos B de, Svensson J, Jongman R (2013) Design of a monitoring system and its cost-effectiveness. Optimization of biodiversity monitoring through close collaboration of users and data providers. FP7 EBONE-Alterra, Wageningen Canullo R, Starlinger F, Granke O, Fischer R, Aamlid D (2010) Assessment of ground vegetation. Manual Part VII, 18 pp. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analyses of the effects of air pollution on forests. UNECE, ICP Forests Programme Co‐ordinating Centre, Hamburg http://www.icp‐forests.org/Manual.htm. Accessed 23 March 2016. Clarke N, Fischer R, de Vries W, Lundin L, Papale D, Vesala T, Merilä P, Matteucci G, Mirtl M, Simpson D, Paoletti E (2011) Availability, accessibility, quality and comparability of monitoring data for European forests for use in air pollution and climate change science. iForest 4:162-166. doi: 10.3832/ifor0582-004 Danielewska A, Clarke N, Olejnik J, Hansen K, de Vries W, Lundin L, Tuovinen J, Fischer R, Urbaniak M, Paoletti E (2013) A meta-database comparison from various European Research and Monitoring Networks dedicated to forest sites. iForest 6:1-9. doi: 10.3832/ifor0751-006 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S THE ICP FORESTS LEVEL I BIODIVERSITY DATA | 105 Dirnböck T, Grandin U, Bernhardt-Römermann M, Beudert B, Canullo R, Forsius M, Grabner MT, Holmberg M, Kleemola S, Lundin L, Mirtl M, Neumann M, Pompei E, Salemaa M, Starlinger F, Staszewski T, Uziębło AK (2014) Forest floor vegetation response to nitrogen deposition in Europe. Glob Change Biol 20:429-440. doi: 10.1111/gcb.12440. Durrant T, San-Miguel-Ayanz J, Schulte E, Suarez Meyer A (2011) Analysis of Biodiversity module. In: Joint Research Centre – Institute for Environment and Sustainability (ed) Evaluation of BioSoil Demonstration Project: Forest Biodiversity. Publications Office of the European Union, Luxembourg, pp 7-79. doi:10.2788/84823 EEA – European Environmental Agency (2006) European Forest Types – Categories and Types for Sustainable Forest Management Reporting and Policy. EEA Report 9. Luxembourg: Office for Official Publications of the Communities. http://www.eea.europa.eu/publications/technical_report_2006_9. Accessed 23 March 2016. Ferretti M (1998) Potential and limitation of visual indices of tree condition. Chemosphere 36:1031-1036 Ferretti M, Fischer R, Mues V, Granke O, Lorenz M (2010) Basic design principles for the ICP Forests Monitoring Networks. Manual Part II, 22 pp. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE ICP Forests Programme Co-ordinating Centre, Hamburg, pp 1-22. http://www.icp-forests.org/Manual.htm. Accessed 23 March 2016 Granke O, Hosenfeld F, Rinker A, Schnack K, Mues V (2010) European Forest Monitoring Information System Data management for EU project FutMon. In: 24 th International Conference on Informatics for Environmental Protection in Cooperation with InterGeo2010, Oct 6-8, 2010. Shaker Verlag, Aachen, pp 446-456 Granke O (2013) Methods for Database Quality Assessment. In: Ferretti M, Fischer R (eds) Forest Monitoring. Methods for terrestrial investigations in Europe with an overview of North America and Asia, Chapter 22. Elsevier, Dordrecht, pp 455-467 Hansen K, Fischer R, De Vos B, Matteucci G, Merilä P, Schaub M, Seidling W, Waldner P (2013) Intellectual Property and Publication Policy. Annex to Lorenz M “Objectives, Strategy and Implementation of ICP Forests”. Manual Part I, 21 pp. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests, Hamburg, pp 14-21. http://www.icp-forests.org/ Manual.htm. Accessed 23 March 2016 Klap J, Oude Voshaar J, De Vries W, Erisman J (2000) Effects of environmental stress on forest crown condition in Europe. Part IV. Statistical analysis of relationships. Water Air Soil Pollut 119:387-420. doi:10.1023/ A:1005157208701. Mikkelsen TN, Clarke N, Danielewska A, Fischer R (2013) Chapter 22 – Towards supersites in forest ecosystem monitoring and research. In: Matyssek R, Clarke N, Cudlin P, Mikkelsen TN, Tuovinen JP, Wieser G, Paoletti E (eds) Climate Change, Air Pollution and Global Challenges – Understanding and Perspectives from Forest Research. Chapter 22. Elsevier, Dordrecht, pp 475-495 Olivier CD (1981) Forest development in North America following major disturbances. For Ecol Manag 3:153-168 Pavlenda P, Pajtík J. (eds.) (2008) Monitoring lesov Slovenska, Správa za Forest Focus a ČMS Lesy za rok 2007. Zvolen, NLC-LVÚ, 2008, pp 121. http://www.nlcsk.sk/files/249.pdf. Accessed 23 March 2016. WGFB (2011) The BioSoil Forest Biodiversity field manual, version 1.0/1.1/1.1a for the field assessment 2006-07. In: Joint Research Centre – Institute for Environment and Sustainability (ed) Evaluation of BioSoil Demonstration Project: Forest Biodiversity. Publications Office of the European Union, Luxembourg, pp 81-102. doi:10.2788/84823 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 106 | 8 ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 8.1 Scientific Evaluation Committee (Chair: Marco Ferretti, Italy) Main activities/developments Over the period 2015/16, the Scientific Evaluation Committe (SEC) was active in promoting scientific initiatives, presentations and publications, networking, and participation in ICP Forests meetings. Scientific initiatives included the organisation of the 5th ICP Forests Scientific Conference, planned back to back with the Task Force in Luxembourg, May 2016. Presentations and publications included: finalization of the Special Issue of Annals of Forest Science (Rautio and Ferretti, Eds. 2015) arising after the 2nd ICP Forests Scientific Conference in Belgrade (Serbia); oral presentation at the IUFRO Conference “Global Challenges of Air Pollution and Climate Change to Forests”; invited talk at the Wood Buffalo Environmental Association (WBEA), Fort McMurray, Canada; oral presentation at the “Epidemiology and Critical Levels Methodology Workshops” (Hindas, Sweden); editorship and contribution to the ICP Forests Executive Report 2014 and Anniversary Report. Networking included cooperation with ICP Vegetation and the promotion of a Memorandum of Understanding with the Wood Buffalo Environmental Association (WBEA), Canada. Participation to ICP Forests meetings included: Programme Co-ordinating Group meeting (Berlin, Germany); Combined Expert Panel meeting (Piteşti, Romania). Major results/highlights − Special Issue of Annals of Forest Science, Rautio P, Ferretti M (2015) Monitoring European forests: results for science, policy, and society. Ann For Sci 72:875-876. doi: 10.1007/s13595-015-0505-6 − Executive Report 2014 − Anniversary Report − 4th ICP Forests Scientific Conference, Ljubljana, Slovenia, 2015 − Contribution to the organisation of the 5th ICP Forests Scientific Conference, Luxembourg, 2016 − MoU with WBEA (to be submitted to the Task Force 2016) Meetings (organised/attended) Date Location Title Role / Function / Activity 01.-05.06.2015 Nice, FRA IUFRO Conference “Global Challenges of Air Pollution and Climate Change to Forests” Oral presentation 07.-08.10.2015 Berlin, DEU ICP Forests Programme Coordinating Group Chair of the Scientific Evaluation Committee 22.-24.10.2015 Fort McMurray, CAN Visit to WBEA Invited talk and visit 23.-25.11.2015 Hindas, SWE ICP Vegetation: Epidemiology and Critical Levels Methodology Workshop Oral presentation 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 107 Date Location Title Role / Function / Activity 18.-22.04.2016 Piteşti, ROU Combined Meeting (EPs Biodiversity and Ground Vegetation; Forest Growth; Meteorology, Phenology and LAI) Proposal for oral presentation 10.-12.05.2016 Luxembourg City, LUX 5 th ICP Forests Scientific Conference Organisation of the Conference, Chair of the Scientific Committee Co-operations − IUFRO, by means of participation to meetings − ICP Vegetation, by means of participation to meetings − WBEA, by means of promotion of an MoU − All other EPs of the ICP Forests. Outlook − Continuation of scientific initiatives and networking within the ICP Forests community − Preparation of a joint, co-operative study within the ICP Forests community − Further development of networking at global level. 8.2 Quality Assurance Committee (Chair: Marco Ferretti, Italy; Co-chair: Nils König, Germany; Co-chair: Anna Kowalska, Poland) Main activities/developments Over the period 2015/16, the Quality Assurance Committee (QAC) was active only in promoting the revision of the ICP Forests Manual. On the operational part, much work has been carried out by the WG on Quality Assurance/Quality Control in Laboratories (see below). Besides, the QAC attended ICP Forests meetings (Programme Co-ordinating Group meeting, Berlin, Germany; Combined Expert Panel meeting, Piteşti, Romania) and contributed to the ICP Forests Anniversary Report. Major results/highlights − Continuation of the process necessary to keep the Manual fully updated, and according to the designated revision programme − Revision of individual chapter of the Manual − Field-related QA/QC activity remains to be fully accounted for. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 108 | Meetings (organised/attended) Date Location Title Role / Function / Activity 07.-08.10.2015 Berlin, DEU Programme Coordinating Group Chair of the Quality Assurance Committee 18.-22.04.2016 Piteşti, ROU Combined Meeting (EPs Biodiversity and Ground Vegetation; Forest Growth; Meteorology, Phenology and LAI) Observer for the QA/QC part Co-operations − WG on Quality Assurance/Quality Control in Laboratories − Other EPs of the ICP Forests − WBEA data and QA managers Outlook Continuation of the activity to control the update of the Manual. 8.3 Working Group on Quality Assurance and Quality Control (QA/QC) in Laboratories (Chair: Nils König, Germany; Co-chair: Anna Kowalska, Poland) Main activities/developments In 2015/16, the Working Group finalized a new method code system for all analytical methods used in the monitoring programme and in the ring tests. The code system now describes three analytical steps: sample preparation, pretreatment and determination. With this new code system the structure of the code is harmonized and simplified over all sample types and some discrepancies between the codes for deposition and soil solution samples have been eliminated. In the framework of the regularly mandatory ring test programme of ICP Forests, this year a soil and a needle/leaf interlaboratory comparison test was organised by Tamara Jakovljević (Croatian Forest Research Institute) and Alfred Fürst (Austrian Federal Research and Training Centre for Forests, Natural Hazards and Landscape). At the 5th meeting of the heads of the labs in Vienna, the participants gave 15 presentations about analytical problems and solutions. Anna Kowalska (Polish Forest Research Institute) took over the organisation of the deposition and soil solution ring tests from Kirsti Derome (Natural Resources Institute Finland) and Aldo Marchetto (Italian Institute for Ecosystem Study). The results of the last four ring tests have been discussed. The percentage of non-tolerable results has decreased again for water parameters and also some soil parameters. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 109 Meetings (organised/attended) Date Location Title Role / Function / Activity 22.04.2015 Göttingen, DEU Meeting of the Working Group QA/QC in Labs Combined meeting of the WG together with the Expert Panels Deposition and Foliage Summarizing of the organisational issues of the ring-tests, preparation of the next meeting of the heads of the labs, presentation of the new codes of analytical methods, discussing the plans for assistance programme and QA forms in the database 17.-18.09.2015 Vienna, AUT 5 th Meeting of the heads of the labs Presentation of the results of the last foliar, soil, deposition and soil solution ring tests; exchange of the knowledge between laboratories by presenting analytical problems and new methods. Outlook In 2016/17, the 19th Needle/Leaf Interlaboratory Comparison Test and the 8th Atmospheric Deposition And Soil Solution Working Ringtest is planned. 8.4 Expert Panel on Ambient Air Quality (Chair: Marcus Schaub, Switzerland; Co-chair: Elena Gottardini, Italy) Main activities/developments The entire 2000-2014 dataset on ozone concentrations was validated and aggregated in 2015. Respective results habe been presented and published at various conferences and in several reports. Continuous data validation and aggregation including enhanced QA/QC for data on ozone-induced injury has been (and is still being) implemented. In close collaboration with the national experts from participating countries, the resubmission of the cleaned datasets for both, ozone concentration and ozone symptoms is anticipated for 2016. Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Air quality 17 Level II 4 12 Assessment of ozone injury 64 Level II 9 8 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 110 | Major results/highlights Calatayud V, Diéguez JJ, Sicard P, Schaub M, De Marco A. Testing approaches for calculating stomatal ozone fluxes from passive samplers. Science of the Total Environment (in review) De Vries W, Solberg S, van Dobben H, Schaub M (2015) Impacts of acid deposition, ozone exposure and weather conditions on forest ecosystems in Europe derived from long-term monitoring. In: Sicard P, Paoletti E, Bytnerowicz A (eds) Challenges of Air Pollution and Climate Change to Forests, Programme and Abstracts, IUFRO Research Group 7.01, 1-5 June 2015, Nice, France, 171 pp De Vries, Etzold S, Posch M, Reinds GJ, Bonten LTC, Solberg S, Waldner P, Schaub M, Simpson D (2015) Assessment of impacts of nitrogen deposition, ozone exposure and climate change on carbon sequestration by monitoring and modeling. In: Sicard P, Paoletti E, Bytnerowicz A (eds) Challenges of Air Pollution and Climate Change to Forests, Programme and Abstracts, IUFRO Research Group 7.01, 1-5 June 2015, Nice, France, 171 pp Ferretti M, Hansen K, Calatayud V, Camino-Serrano M, Cools N, De Vos B, Nieminen TM, Potocic N, Rautio P, Schaub M, Timmermann V, Ukonmaanaho L, Waldner P (2015) Monitoring and modeling the long-term impact of air pollution on forest health and growth in Europe. In: Sicard P, Paoletti E, Bytnerowicz A (eds) Challenges of Air Pollution and Climate Change to Forests, Programme and Abstracts, IUFRO Research Group 7.01, 1-5 June 2015, Nice, France, 171 pp Schaub M, Ferretti M, Gottardini E, Calatayud V, Haeni M (2015) 2000-2013 ozone trends across Europe, p. 38. In: Seidling W (ed) Book of abstracts: Long-term trends and effects of air pollution on forest ecosystems, their services, and sustainability, 4th ICP Forests Scientific Conference, May 2015, Ljubljana, Slovenia, 52 pp Schaub M, Haeni M, Ferretti M, Gottardini E, Simpson D, Calatayud V (2015) Ozone risk assessment for European forests – a ten-year study on permanent monitoring plot. In: Sicard P, Paoletti E, Bytnerowicz A (eds) Challenges of Air Pollution and Climate Change to Forests, Programme and Abstracts, IUFRO Research Group 7.01, 1-5 June 2015, Nice, France, 171 pp Schaub M, Haeni M, Ferretti M, Gottardini E, Calatayud V (2015) Ground level ozone concentrations and exposures (ICP Forests). In: De Wit H, Hettelingh JP, Harmens H (eds) Trends in ecosystem and health responses to long-range transported atmospheric pollutants. ICP Waters report 125/2015, pp 48-50 Meetings (organised/attended) Date Location Title Role / Function / Activity 22.04.2015 Göttingen, DEU Combined meeting of the Expert Panels on Deposition, Soil and Soil Solution, Foliar Analysis and Litterfall, and Ambient Air Quality Latest new findings and new initiatives were presented. Suggested changes for the Manual were discussed. 23.11.2015 Hindas, SWE ICP Vegetation Epidemiology and Critical Levels Methodology Workshops Interaction with ICP Vegetation 18.04.2016 Piteşti, ROU Combined Meeting (EPs Biodiversity and Ground Vegetation; Forest Growth; Meteorology, Phenology and LAI) Chairship and interaction with EPs. Preparation of intercalibration course. 10.05.2016 Luxembourg City, LUX 5 th ICP Forests Scientific Conference Member of Scientific Committee, chairing session with five presentations on ozone. Co-operations ICP Vegetation, EMEP 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 111 8.5 Expert Panel on Biodiversity and Ground Vegetation (Chair: Roberto Canullo, Italy; Co-chair: pending) Main activities/developments The chairman of the EP continued the activity for the full validation of the LI BioDiv dataset with continuous advice from experts from the NFCs and EPs; very last contacts with the colleagues of some country and other NFCs experts have been useful for refining the uploaded files. The procedure was completed for the finalisation in more than 95% of cases; pending interpretations and open questions have been isolated, and possible amendments to compliance and conformity checks discussed with the PCC. Consulting exchanges have been also established in order to allow data submission from different countries, form “new” datasets; this claims for harmonization activities through EP experts. In the case of Switzerland, uploading of data based on the same LI BioDiv dataset protocols seems very feasible. Spain has the possibility to apply for submission of some datasets coming from repetition of surveys, compatible with the Level I dataset on biodiversity. The same willingness was expressed by the Netherlands. Recent contacts with Wallonia (BE) have confirmed the possibility of data submission of ground vegetation surveys (4 repetitions since 1998, ending with 2005) but some format conversion should be verified. The Panel was active in promoting internal evaluation processes of the datasets, namely about the Biodiversity module of the Level I network. Some members and the EP chair attended ICP Forests meetings (TF, Joint EPs meetings, etc.). Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Assessment of ground vegetation 64 Level II 7 15 Ground vegetation biomass 13 Level II 1 8 Biodiversity Levei I 11 Major results/highlights EP Biodiversity and Ground Vegetation members co-operated with other EPs in producing published results or providing national reporting, such as: − Mellert KH, Deffner V, Küchenhoff H, Kölling C (2015) Modeling sensitivity to climate change and estimating the uncertainty of its impact: A probabilistic concept for risk assessment in forestry. Ecol Model 316:211-216 − Chirici G and coll. (including R. Canullo) have submitted a paper about the application to ICP Level I plots of a rule-based expert system for the classification of European Forest Types. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 112 | − Canullo R & coll. have submitted a paper on biogeography influences on plant species diversity of Italian forests by using Level I datasets. − Some preliminary results at national or EU level have been presented at international and national scientific congresses and symposia (e.g. International Biogeography Society 7th Biennial Meeting, 8– 12 January 2015, Bayreuth; 4th ICP Forests Scientific Conference, May 19–20 2015, Ljubljana; 58th Symposium of the IAVS, 19–24 July 2015, Brno; 10th SISEF National Congress, 15–18 September 2015, Firenze; 5th ICP Forests Scientific Conference, 10–12 May 2016, Luxembourg). − Contribution to the 30th Anniversary Report Meetings (organised/attended) Date Location Title Role / Function / Activity 19.-24.07.2015 Brno, CZE 58 th IAVS Symposium Poster presentation about the possible use of ICP Forests LI BioDiv dataset to assess the potential distribution of Nature 2000 forest habitats 15.-18.09.2015 Firenze, ITA 10 th SISEF National Congress Participation to a poster presentation about deadwood availability and stand forest attributes from ICP Forest LI BioDiv datasets 18.-22.04.2016 Piteşti, ROU Combined Meeting (EPs Biodiversity and Ground Vegetation; Forest Growth; Meteorology, Phenology and LAI) Chairship: status of internal evaluation on LI- Biodiversity; Running evaluations and activities (Sue Benham: volume and carbon storage in deadwood in British Woodland; Silvia Guerrero: LI deadwood assessments, repetitions, harmonization with NFI in Spain; Janusz Czerepko, DWpools: amount and quality of deadwood by forest type and stand age) 10.-12.05.2016 Luxembourg city, LUX 5 th ICP Forests Scientific Conference & Task Force Meeting Participation to the conference through EP members and related institutions; poster presentations Co-operations In the frame of running projects aimed at internal evaluations of the Level I biodiversity data, scientific cooperation with EPs and other groups, colleagues and external researchers (from Universities, Scientific Academies, Forest Research Centers) have been pursued under the leadership of some of the EP participants. Related discussion groups have been created in the ICP Forests website. Outlook Surveys of ground vegetation and\or deadwood on Level II national networks are foreseen in the summer 2016, sometimes within parallel projects (as in the case of the LIFE+ SMART4Action project in Italy, Level I resampling for soil and plot information with suggested vegetation surveys in Poland, and NFI vs. Level I comparison for deadwood and EFTC in Spain). Normal repetition of 5-yearly Level II surveys will continue in several countries; Latvia will start the ground vegetation assessments in 2016. The internal evaluation of the Level I biodiversity data will continue, aiming at scientific sound papers and dissemination at international congresses. The dataset on biodiversity in the Level I network will be completely included, with some accompanying notes, and opened to external evaluations. Species diversity is one of the targets of a proposal submitted within the H2020 call INFRAIA (ForAccess), and some of the EP members have been involved it it through their institutions. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 113 8.6 Expert Panel on Crown Condition and Damage Causes (Chair: Nenad Potočić, Croatia; Co-chair: Volkmar Timmermann, Norway) Main activities/developments Update of Manual Part IV Visual Assessment of Crown Condition and Damaging Agents and the corresponding online documentation. Data availability The submitted data from Level I plots was collected in 2015, from Level II plots in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Visual assessment of crown condition 4986 Level I 25 20 Visual assessment of crown condition 491 Level II 22 25 Major results/highlights Updated manual was adopted at the TFM in Luxembourg in May 2016. Co-operations A cooperation with the SEED-C project in data analysis and writing of manuscripts regarding the fruiting of trees on Level I plots, involving a number of EP members. Outlook Expert Panel meeting is foreseen to take place in spring 2017 in Croatia. Two International cross- comparison courses are foreseen in 2017, to be held in the Czech Republic and Turkey. 8.7 Expert Panel on Deposition (Chair: Karin Hansen, Sweden; Co-chair: Daniel Žlindra, Slovenia) Main activities/developments Continuous internal data evaluations are forthgoing in the Expert Panel and many member participants take leading roles in this work. Most evaluations were thoroughly discussed at the combined EP meeting in Göttingen April 2015, but continuous mail contact around these evaluations are taking place and developing it further. Furthermore, the deposition data has been requested and provided several times for external evaluations. No manual updates are needed for the time being. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 114 | Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots / year No. of participating countries No. of ongoing projects (06/2015–05/2016) Deposition 248 Level II 23 23 Major results/highlights EP Deposition has co-operated with other EPs in producing results published in following articles: Ferretti M et al (2015) Variables related to nitrogen deposition improve defoliation models for European forests. Ann For Sci 72(7):897-906. doi: 10.1007/s13595-014-0445-6 Erratum: Ferretti M et al (2015) Erratum to: Variables related to nitrogen deposition improve defoliation models for European forests. Ann For Sci 72(7):907-907. doi:10.1007/s13595-015-0472-y Jonard M et al (2015) Tree mineral nutrition is deteriorating in Europe. Glob Change Biol 21(1):418-430. doi: 10.1111/gcb.12657 Waldner P et al (2015) Exceedance of critical loads and of critical limits impacts trees. Ann For Sci 72(7): 929-939. doi: 10.1007/s13595-015-0489-2 Meetings (organised/attended) Date Location Title Role / Function / Activity 22.04.2015 Göttingen, DEU Combined meeting of the Expert Panels on Deposition, Soil and Soil Solution, Foliar Analysis and Litterfall, and Ambient Air Quality. The meeting summarized the latest results on projects and data evaluations concerning deposition to forests. Discussions on the aggregated depostion data and on future new data evaluations. Co-operations The EP is co-operating with many of the other EPs on joint data evaluations. Also, a co-operation with EMEP has been started where the following comparisons have been initiated: − Comparison of measured ICP Forests bulk and throughfall deposition with modelled EMEP (50 x 50 km grid model and the 7 x 7 km grid) for the year 2013 (Lead: Aldo Marchetto) − Comparison of temporal trend of EMEP model, EMEP measurements and ICP Forests bulk and throughfall deposition measurements (Lead: Hilde Fagerli) − Comparison of total deposition estimates calculated with canopy budget models based on ICP Forests Level II bulk, throughfall and stemflow measurements (Lead: Peter Waldner) Outlook The Expert Panel on Deposition will have its next panel meeting in the spring of 2017. Meanwhile we continue working on evaluations of ICP Forests deposition data. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 115 8.8 Expert Panel on Foliar Analysis and Litterfall (Chair: Pasi Rautio, Finland; Co-chair: Liisa Ukonmaanaho, Finland) Main activities/developments 18th needle/leaf interlaboratory comparison test 2015/2016 (http://bfw.ac.at/rz/bfwcms2.web?dok=6008224) Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Foliage 99 Level II 5 18 Foliage Level I 15 Litterfall 151 Level II 16 15 Major results/highlights EP foliage and litterfall co-operated with other EPs in producing results published in following articles: Ferretti M et al (2015) Variables related to nitrogen deposition improve defoliation models for European forests. Ann For Sci 72:897-906 Jonard M et al (2015) Tree mineral nutrition is deteriorating in Europe. Glob Change Biol 21:418-430 Nussbaumer A et al (2016) Patterns of mast fruiting of common beech, sessile and common oak, Norway spruce and Scots pine in Central and Northern Europe. Forest Ecol Manag 363:237-251 Rautio P., Ferretti M (2015) Monitoring European forests: results for science, policy, and society. Ann For Sci 72: 875-876. Talkner U et al (2015) Phosphorus nutrition of beech is decreasing in Europe. Ann For Sci 72:919-928 Waldner P et al (2015) Exceedance of critical limits for soil solution and its impact on tree nutrition. Ann For Sci 72: 929-939 8.9 Expert Panel on Forest Growth (Chair: Tom Levanič, Slovenia; Co-chair: pending) Main activities/developments Evaluation of the 2014/2015 Level II Growth and Yield inventory data Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Growth and yield 270 Level II 15 28 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 116 | Meetings (organised/attended) Date Location Title Role / Function / Activity 18.-22.04.2016 Piteşti, ROU Combined Meeting (EPs Biodiversity and Ground Vegetation; Forest Growth; Meteorology, Phenology and LAI) Active participation of EP Growth 18.-20.05.2015 Ljubljana, SVN 4 th ICP Forests Scientific Conference Long-term trends and effects of air pollution on forest ecosystems, their services, and sustainability Participation and reporting of the EP Growth chair to TF board 10.-12.05.2016 Luxembourg, LUX 5 th ICP Forests Scientific Conference Participation to the Conference through EP members and related institutions, with poster presentations. Outlook − Evaluation of data colleted in the inventory 2014/15 and removal of errors in the database − Changes to the Manual are to be completed till TF meeting in Luxemburg. 8.10 Expert Panel on Meteorology, Phenology and Leaf Area Index (Chair: Stephan Raspe, Germany; Co-chair: Stefan Fleck, Germany) Main activities/developments Main activities in the period 2015/2016 were the development of gap-filled meteo data for the Level II plots, the comprehensive renewal of the manual chapters on phenological observations and leaf area index (LAI) measurements, the phenological observation course at the Expert Panel meeting 2016, and the development of a common standard for the evaluation of hemispherical photographs in cooperation with the ICOS project. The technological development in the area of indirect optical methods for LAI determination in the years since 2012, when the manual was approved, was quick due to the improvements of digital cameras and LAI-related software, the availability of new efficient methods for mean leaf angle determination in canopies, and the newly invented scattering correction that enables hemispherical measurements with the LAI-2200 under direct sunlight conditions. The standardisation of the evaluation of hemispherical photographs was also urgent due to too many degrees of freedom for the operator in the image acquisition and analysis process. Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Meteorological measurements 163 Level II 18 26 Leaf Area Index (LAI) 45 Level II 7 14 Phenology 158 Level II 13 14 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 117 Major results/highlights Meteorology In cooperation with the Swiss project “NitLeach II” quality and completeness of the whole meteorological dataset of ICP Forests were improved. After asking the member states for additional meteorological data the remaining gaps were filled with data from global reanalysis ERA-Interim dataset (http://www.ecmwf.int/). Era-Interim model data was extracted at plot location using bilinear interpolation method between 4 pixels. Before downscaling and gap filling outliers in the measured data were removed with usage of Mahalanobis (Mahalanobis, 1936) distance and a critical distance driven from chi square distribution at a p of 0.995. In order to prevent bias during the gap filling procedure the data was downscaled to plot level using Kernel Density Distribution Mapping (KDDM; McGinnis et al., 2014). As a result a dataset of quality checked and gapless meteorological data for 355 Level II plots and from 1979 to 2013 were established. For 46 Level II plots water budget modelling was conducted by using the model LWF-Brook90. During the Expert Panel meeting in Piteşti atmospheric pressure was added as a new optional variable within the measurement programme. Properties, measurement requirements, plausibility limits, and a formula for calculations of local data from nearby weather stations are given in the ICP Forests Manual, Part IX Meteorological Measurements. Phenology Among other changes, the improved standardisation of phenological observations in the manual comprised a clearer definition of the assessed tree crown, which now in general excludes epicormic branches and preferentially orients the observation to the upper third(s) of the crown. Experiences from the use of phenological cameras were used to clearer define the selection of at least four trees to be assessed with this method. The phenological observation course showed once again how important the timing of phenological observations is: While abundant flowering of beech trees was observed on a pre- excursion to the observation plot, nearly no flowers were left after a heavy thunderstorm a few days later. LAI An in-depth analysis of the whole measurement procedure for hemispherical photographs resulted in several changes to the accepted methods in the manual: The histograms of grey values produced by recent digital cameras allow an easier and more reproducible image acquisition process (now accepted as second option). In order to reach a better comparability to LAI-2200 data, the inversion method used in image analysis is now updated to the method after Norman & Campbell (1989), after it has been Miller (1967) before. Automated thresholding is set as a standard, preferentially using the Ridler & Clavard (1978) method, which has been shown to be most sensitive to gaps in the canopy. The requirements for the camera and lens used are now more precisely defined and a new geometric calibration protocol for the camera-lens combination has been included. Lens projection functions for the most widespread hemispherical lens brands are now included in the annex to the manual. The measurement grid for photograph acquisition was not changed. While it covers in accordance with ICOS an area of 30m x 30m, the measurement density is still a bit higher in ICP Forests. As a consequence, a distance of at least 5 times the stem diameter has to be held to tree stems, while this number is 5.7 within ICOS. The LAI measurement under direct sunlight conditions with LAI-2200 is accepted in the manual. Needle- to-shoot area and woody-to-total area were selected from literature sources as species-specific correction factors for 23 conifer species. Mean leaf angles to be used for ceptometer measurements were compiled and made available for the 20 most common tree species in Europe. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 118 | Meetings (organised/attended) Date Location Title Role / Function / Activity 04.-05.03.2015 07.-08.10.2015 18.-22.04.2016 Antwerp, BEL Berlin, DEU Piteşti, ROU LAI Expert Meeting PCG Meeting Combined Expert Panel Meeting Consultant of ICP Forests Chair and Co-Chair Chairing three sessions, phenological observation course 10.-13.05.2016 Luxembourg, LUX Scientific Conference and Task Force Meeting Oral presentations Co-operations The collaboration with ICOS has the goal to adapt the standards used in both programs in order to increase comparability of the results. It is based on the common interest of both programs to use hemispherical photographs for LAI determination on long-term monitoring plots, which are partly the same plots in both programs and the development of own measurement protocols in the newly funded ICOS program. A series of meetings of LAI experts was set up starting with two meetings in Antwerp (2013 and 2015). Outlook The improved meteorological dataset allows several new analyses and applications in the future. Thus, deviations of recent weather conditions from the long-term averages could be calculated. This could be used to calculate meteorological stress on forest vitality. Moreover this data could be used for parameterisation of different deterministic models (e.g. water budget, phenology, ozone uptake etc.). After the Expert Panel meeting in Piteşti (2016), the necessity to adapt the used methods to future improvements in LAI measurement technology was acknowledged. A follow-up meeting of LAI experts was planned together with Dr. Francesco Chianucci, who will organise this event in 2017 in Italy. Cooperation with other Expert panels such as on Crow Condition and Damage Causes especially should be intensified. References Mahalanobis PC (1936) On the generalised distance in statistics. In: Proceedings of the National Institute of Science of India. Vol. 2(1), pp 49-55 McGinnis S, Nychka D, Mearns LO (2014) A new distribution mapping technique for climate model bias correction. 4th International Workshop on Climate Informatics, Boulder, CO, University Corporation for Atmospheric Research. Retrieved from http://go.nature.com/IjDyt6 Miller JB (1967) A formula for average foliage density. Aust J Bot 15:141-144 Norman JM, Campbell GS (1989) Canopy Structure. In: Pearcy RW, Ehleringer J, Mooney HA, Rundel P (eds) Plant Physiological Ecology. Chapman and Hall, London, pp 301-325 Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE T Syst Man Cyb 8(8):630- 632 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 | 119 8.11 Expert Panel on Soil and Soil Solution (Chair: Bruno De Vos, Belgium; Co-chair: Nathalie Cools, Belgium; Co-chair: Tiina Nieminen, Finland) Main activities/developments Since June 2015 updates of both the solid soil and soil solution manuals were prepared based on the recommendations of the 19th Soil Expert Panel Meeting (Göttingen, April 2015) in order to be presented and adopted by the Task Force meeting in Luxembourg. Regarding the forest soil condition databases, specific initiatives were taken. A dedicated two-day Technical Meeting of the FSCC and the database manager of PCC (Till Kirchner) was held in Geraarsbergen, Belgium (March 2016) to plan the further harmonisation and combination of the solid soil datasets of Level I and Level II, and their full integration into the ICP Forests database. In this process, old soil data (roughly before 2003) and more recent soil data will be combined with consistent coding and definitions in the new online documentation system. For the Level II aggregated soil database, a data paper (Fleck et al.) submitted to Annals of Forest Science was further revised and is expected to be published in 2016. This AFSCDB.LII.2.2 dataset contains 130 soil variables of 286 Level II plots, including derived data as total carbon and nitrogen stocks, C:N ratios, available water capacity, water retention parameters and many more. Also for the aggregated soil solution database, elaborated by Elisabeth Graf Pannatier (CH), work is now continued by Jim Johnson (IE) and other EP members. New solid Level II soil data (Russia 100 plots, France 100 plots and Wallonia 8 plots) has been submitted and is currently under evaluation. Twenty countries submitted soil solution data, in total over 11 000 soil solution samples. A proposal called SoilBio4CN on functional soil biodiversity was submitted for the BiodivERsA 2015 call. Soil Expert Panel members were active in several studies and data evaluations, COST actions (e.g. EuMIXFOR), and related publications. The EP participated in the 8th Solid Soil ringtest and the 8th Deposition and Soil Solution ringtest. A strategy is further developed for better reporting of State of European Forests SFM 2.2 indicators using soil solution quality indicators (every 4 years) in addition to solid soil indicators (available every 10- 15 years). Hence, the EP investigates the need and willingness of countries to organise a harmonized third pan-European Level I soil survey synchronized between 2020 and 2025. Data availability The submitted Level II data was collected in 2014. Survey Data submission External data usage / data dissemination No. of plots No. of participating countries No. of ongoing projects (06/2015–05/2016) Soil 208 Level II 3 22 Soil Level I 18 Soil solution 178 Level II 20 20 Soil water Level II 9 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ACTIVITIES RELATED TO ICP FORESTS OF THE EXPERT PANELS, WORKING GROUPS, AND COMMITTEES 06/2015 – 05/2016 120 | Major results/highlights − Supporting several ongoing data evaluations using soil and soil solution data − Process was started of combining all solid soil data in Level I and Level II soil datasets and full integration in ICP Forests database and online documentation system − Publication of data paper on Level II aggregated Forest Soil Condition database (AFSCDB.LII.2.2) Meetings (organised/attended) Date Location Title Role / Function / Activity 07.-08.10.2015 Berlin, DEU PCG Meeting Chair Expert Panel on Soil & Soil Solution 16.03.2016 Louvain-la- Neuve, BEL EuMIXFOR meeting COST Action FP1206: European mixed forests. Integrating Scientific Knowledge in Sustainable Forest Management Contributions from Soil Expert Panel members (Nathalie Cools, Mathieu Jonard, Lars Vesterdal) on possible evaluations of ICP Forests soil data 17.-18.03.2016 Geraardsbergen, BEL Technical Meeting FSCC – PCC database manager Planning and preliminary work on combining forest soil condition databases for Level I and Level II 10.-12.05.2016 Luxembourg, LUX 5 th ICP Forests Scientific Conference Member of the Scientific Committee, session chair Co-operations − Co-operation with Alternet for elaboration of the SoilBio4CN proposal − Co-operation in COST Action FP1206 (EuMIXFOR) for joint data analyses Outlook − Presentation of the new combined Level I Forest Soil Condition database and Level II FSCDB − Elaboration of the new Aggregated Level II soil solution database − Organisation of next Soil EP meeting (20th anniversary edition!) combined with other EPs in March- April 2017 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S REVIEW OF THE 4TH ICP FORESTS SCIENTIFIC CONFERENCE, LJUBLJANA, 19-20 MAY 2015 | 121 9 REVIEW OF THE 4TH ICP FORESTS SCIENTIFIC CONFERENCE, LJUBLJANA, 19-20 MAY 2015 The 4th ICP Forests Scientific Conference Long-term trends and effects of air pollution on forest ecosystems, their services, and sustainability was hosted by the Slovenian Forestry Institute and held at the Grand Hotel Union in Ljubljana, Slovenia, on May 19–20, 2015 with 73 participants from 26 countries. The conference was aimed at scientists and experts from ICP Forests, the UNECE ICP community under the Working Group on Effects (WGE), partners and respective stakeholders, as well as all interested scientists and experts from related fields. Researchers engaged in projects, evaluations and modelling exercises based on ICP Forests data, or working in co-operation with ICP Forests were encouraged to present and discuss their work and results. The 4th Scientific Conference of ICP Forests addressed the role of air pollution as primary or secondary stressor and its effects on tree growth, crown condition, biodiversity, ecosystem services, and the sustainability of forests. Main topics were: − The temporal development (possibly with predictions) of air pollution effects on forests, including nitrogen deposition and ozone impacts, on different spatial scales − The temporal and spatial development of forest performance indicators, forest ecosystem services, their sustainability and interactions with climate trends − Integrative analyses and modelling exercises based on the above indicated data The conference provided an overview on the latest research in policy relevant fields, such as air pollution trends, trends of response variables and interactions with climate change, as well as on nutrient and water cycles, biodiversity, and forest condition. A comprehensive platform was offered for scientists to discuss scientific questions and share experiences. The conference provided an annual platform to bring together monitoring experts, researchers, and modellers. Data users benefited from background information related to the datasets. Data providers profited from an advanced insight into the latest statistical applications based on “their” data. Data users were able to take advantage of getting in touch with data experts to discuss data availability and data quality as well as metadata. Both, data and evaluations provide a sound basis for future activities at all levels of integration and differentiation: spatial, temporal, and functional. 9.1 PRESENTATIONS AT 4th ICP FORESTS SCIENTIFIC CONFERENCE The following list includes all presentations given at the 4th ICP Forests Scientific Conference. All conference abstracts are available on the ICP Forests website1. Andreassen K, Aas W. Effects of nitrogen deposition on growth of Norway spruce in Norway. Berger T, Muras A. Predicting recovery from Acid Rain using the micro-spatial heterogeneity of soil columns downhill the infiltration zone of beech stemflow. 1 http://www.icp-forests.net/page/icp-forests-other-publications 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S REVIEW OF THE 4TH ICP FORESTS SCIENTIFIC CONFERENCE, LJUBLJANA, 19-20 MAY 2015 122 | Berki I, Rasztovits E, Móricz N, Kolozs L. Retreating sessile oak forest with improving vitality – including tree mortality in vitality assessment. Canini L, Farina A, Marchetto A, Matteucci G, Fares S, Fabbio G, Salvati L, Cecchini G, Bussotti F, Ferretti M. Making forest monitoring cheaper and closer to society: The LIFE+ Project »SMART4Action«. Čater M. A 20-year overview of Quercus robur L. mortality and crown condition in Slovenia. Chirici G, Barbati A, Giannetti F, Travaglini D, Canullo R. The use of ICP Forests Level I BIOSOIL-BIODIVERSITY plots for pan-European estimation of forest variables. Dolschak K, Berger T.W. Modelling sulphur biogeochemistry of beech (Fagus sylvatica) stands at the Vienna Woods. Ferretti M, Calderisi M, Gottardini E, Nicolas M. Defoliation reconsidered? Finžgar D, Westergren M, Fussi B, Konnert M, Aravanopoulos P, Božič G, Kraigher H. LIFEGENMON - LIFE for European Forest Genetic Monitoring System: Development of a system for forest genetic monitoring. Serdar RG, Stefanović T, Češljar G, Bilibajkić S, Nevenić R, Đorđević I, Poduška Z, Rakonjac L. Monitoring within integrated pest management as essential precondition for sustainable governance of natural resources in Serbia – defoliation comparable analysis on ICP Forests plots during period 2009-2014. Galic Z. Soil properties on the level I plots in lowland forests in Serbia. Johnson J, Cummins T, Aherne J. Contrasting responses of two Sitka spruce forest plots in Ireland to reductions in sulphur emissions: results of 20 years of monitoring. Kattge J, Díaz S, Lavorel S, Prentice C, Leadley P, Bönisch G, Wirth C, and the TRY consortium. TRY – the global database of plant traits. König N, Cools N, Derome K, Fürst A, Marchetto A, Blum U, Schönfelder E. Comparability of analytical data as a basis of possible evaluation of European deposition, soil and foliage data. Kutnar L, Eler K. Use of ICP Forests methodology for assessment of species diversity and invasibility of (peri-) urban forests. Leca S, Popa I, Badea O, Neagu S. Intra-annual dynamics of stand basal area increment in four intensive monitoring plots (Level II) in Romania. Marchetto A, Bacaro G, Amici B, Ferretti M. Geo-statistical modelling of bulk deposition of inorganic nitrogen to Italian forests. Merilä P, Starr M, Stephens B, Lindroos A-J, Nieminen TM, Nöjd P, Derome K, Ukonmaanaho L. Impacts of harvesting practice on base cation budgets of coniferous stands in Finland – a sustainability study. Michopoulos P, Bourletsikas A, Kaoukis K, Karetsos G, Tsagari C, Daskalakou E, Samara C, Lazarou D. Deposition and soil solution chemistry in two adjacent mountainous forest ecosystems in Greece. Mues V, Jochheim H, Olschofsky K, Janott M, Köhl M. Forest Management Scenario Study with BiomeBGC at nine ICP Forests Plots. Neagu S, Barbu I, Iacoban C, Angheluş C, Ionescu M. Impact of weather conditions, atmospheric deposition and foliar nutrients in the Romanian intensive monitoring system. Nevalainen S. A trend analysis of the defoliation in boreal forests of Finland. Nicolas M, Le Roncé I, Boulanger V, Pousse N, Dupouey J-L. Plant bio-indicators do not reflect temporal changes measured in forest soil pH and C/N ratio over 15 years. Novotný R, Šrámek V, Hůnová I, Zapletal M. Chemistry of forest soils and the deposition load in the Czech Republic within the last two decades. Príncipe A, Nunes A, Pinho P, do Rosário L, Correia O, Branquinho C. Microclimate matters for the long-term natural regeneration potential of woodlands in semi-arid regions. Proietti C, Anav A, Vitale M, De Marco A. Ozone impacts on forest’s productivity and health in Europe. Saenger A, Jonard M, Ponette Q, Nicolas M. Changes in nutrient and carbon stocks in French forest soils under decreasing atmospheric deposition. Schaub M, Ferretti M, Gottardini E, Calatayud V, Haeni M. 2000-2013 ozone trends across Europe. Scheuschner T, Flügel I, Schlutow A. Impact of air pollution and climate change on forest ecosystems in the Polish-Saxon border region. Schröder W, Nickel S, Jenssen M, Riediger J. Methodology to assess and map potential developments of forest ecosystems exposed to climate change and atmospheric nitrogen deposition by example of Germany. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ONGOING ICP FORESTS PROJECTS | 123 Schröder W, Nickel S, Meyer M. Heavy metals and nitrogen concentrations in moss collected across Europe from 1990-2010: Meaningful for ICP Forests / Modelling and Mapping? Silaghi D, Popa I, Paoletti E, Badea O. Radial growth response to ozone exposure and uptake of sessile oak (Quercus petraea) in Mihaesti Level II forest monitoring plot, Romania. Skudnik M, Jeran Z, Batič F, Simončič P, Kastelec D. Environmental factors explaining the N and δ15N values in the moss collected inside and outside canopy drip lines. Türtscher S, Berger TW. The change of forest soil conditions in beech stands (Fagus sylvatica) of the Vienna Woods within the last three decades due to declining deposition of atmospheric pollutants. Vanguelova EI, Benham S. Long term trends and effects of air pollution on British forests and soils. Vilhar U, Skudnik M, Ferlan M, Simončič P. Tree phenology in relation to meteorological conditions and crown defoliation on intensive forest monitoring plots in Slovenia. Wattel-Koekkoek EJW, Boumans LJM, van der Swaluw E. Changes over the past 25 years in rainwater and groundwater quality in nature areas in The Netherlands as a result of emission reduction policy. Žlindra D, Levanič T, Rupel M, Skudnik M. Degradation of Fagus sylvatica on Trnovo plateau in southwest Slovenia. 10 ONGOING ICP FORESTS PROJECTS ICP Forests welcomes scientists from within and outside the ICP Forests community to use ICP Forests data for research purposes. Data applicants must fill out a data request form and send it to the Programme Co-ordinating Centre of ICP Forests thereby consenting to the ICP Forests Data Policy. For more information, please refer to the ICP Forests website1. The following list provides an overview of all the 48 ICP Forests projects that were ongoing for at least one month between June 2015 and May 2016. In this period, 13 new projects have started (s. ID number with *). All past and present ICP Forests data uses are listed on the ICP Forests website 2 . ID Name of Applicant Institution Project Title External/ Internal 14 John Caspersen Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Global Forest Monitoring External 25 Nicole Augustin University of Bath Spatial-temporal modelling of defoliation in European forests External 26 Kirsti Ashworth Institute for Meteorology and Climate Research, Atmospheric Environmental Research LPJ-MLC: In-canopy ozone processes External 30 Volker Mues Institute for World Forestry FORMIT, Grant Agreement No. 311970 under the 7th EU-Framework Programme "FORest management strategies to enhance the MITigation potential of European forests" Internal 43 Sietse van der Linde Imperial College London What are the large-scale diversity, distribution and fate of Europe's forest mycorrhiza? External 47 Martina Roß-Nickoll RWTH Aachen University, Institute for Quantifying the effect of sustainable forest management: A case study in the Eiffel region External 1 http://icp-forests.net 2 http://icp-forests.net/page/project-list 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ONGOING ICP FORESTS PROJECTS 124 | ID Name of Applicant Institution Project Title External/ Internal Environmental Research 48 Susanne Jochner Technische Universität München Atmosphere - biosphere interactions External 51 Christine Rösch Karlsruhe Institute of Technology BioenNW - Delivering Local Bioenergy for North-West Europe External 52 Steffen Taeger, Karl Mellert Bavarian State Institute of Forestry (LWF) MARGINS – Specification of threshold values for cultivation of tree species facing climate change using marginal occurrences External 54 Elke Keup-Thiel, Juliane Otto Climate Service Center 2.0 Calculation of climate changes impacts indicators for tree species distribution External 55 Ivan Janssen University of Antwerp Effects of phosphorus limitations on Life, Earth system and Society (IMBALANCE-P) External 56 Elisabeth Graf Pannatier Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Temporal trends of dissolved organic carbon (DOC) in soil solution in European forests Internal 58 Henning Meesenburg NW-FVA / EP Soil and Soil Solution Forest productivity, carbon sequestration, climate change Internal 59 Gherardo Chirici Università degli Studi di Firenze Upscaling & spatially explicit estimation of biophysical variables with remote sensing (UPSPEX) Internal 60 Sebastiaan Luyssaert, Yuan Yan Commissariat à l’énergie atomique et aux énergies alternatives (CEA) ERC-DOFOCO: Do forests cool the Earth? Reconciling sustained productivity and minimum climate response with portfolios of contrasting forest management strategies External 61 Roberto Canullo Università degli Studi di Camerino School of Biosciences and Veterinary Medicine FUTPA: Plant functional trait patterns in key EU forest types Internal 62 Roberto Canullo Università degli Studi di Camerino School of Biosciences and Veterinary Medicine Δ-Drivers BIOPART: Driving factors of beta- diversity in European Forests Internal 63 Jesus San-Miguel European Commission - Joint Research Centre Distribution maps of forest tree species External 64 Marcos Fernández- Martínez CREAF - Center for Ecological Research and Forestry Applications Reproductive productivity and masting behaviour in multiple tree species from the European forests External 65 Mark R. Theobald Centre for Energy, Environmental and Technological Research (CIEMAT) ÉCLAIRE IP [Effects of Climate Change on Air Pollution and Response Strategies for European Ecosystems] External 66 Mark R. Theobald Centre for Energy, Environmental and Technological Research (CIEMAT) EURODELTA III [Intercomparison of European Air Quality Models] External 67 Stefan Fleck Northwest German Forest Research Institute (NW- FVA) LAI-estimations with allometry, litter collections, and optical measurements in relation to stand properties and microclimate Internal 68 Shengwei Shi College of Forestry, Northwest A & F Modeling dissolved organic carbon in forest soils usig a TRIPLEX-DOC model External 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ONGOING ICP FORESTS PROJECTS | 125 ID Name of Applicant Institution Project Title External/ Internal University, China 69 J. Julio Camarero Instituto Pirenaico de Ecología (IPE) Growth and defoliation across European forests: continental patterns and trends of tree vitality External 70 Stefan Fleck Northwest-German Forest Research Station Preparation of the 2nd version of the aggregated soil database of the Level II second soil survey Internal 71 Elena Moreno Universidad Politénica de Madrid Study marginal populations of Pinus uncinata External 72 Marcus Schaub Swiss Federal Institute for Forest, Snow and Landscape Research 2000 - 2014 ground level ozone concentrations and exposures across Europe Internal 73 Christopher Reyer Potsdam Institute for Climate Impact Research (PIK) COST Action FP 1304 Towards robust projections of European forests under climate change (PROFOUND) External 74 Lisa Pedersen Institut for Geovidenskab og Naturforvaltning, Københavns Universitet How different forest covers influence on deep percolation during 120 years – Modelling of the water balance for the tree species beech, Norway spruce and poplar with the CoupModel External 75* Andres Bravo Oviedo INIA-Forest Research Centre ICP Forests-EuMIXFOR Interaction: Evaluation of soil and foliar nutrient status of mixed vs. pure stands in Europe as categorized by European Forest Types External 76 Karin Hansen IVL Swedish Environmental Research Institute Atmospheric Deposition: EMEP - ICP Forests comparisons of level, trend and canopy exchange Internal 77 Mathieu Decuyper Wageningen University - Laboratory of Geo- information Science and Remote Sensing and the Forest Ecology and Management group Leaf phenology and canopy status with remote sensing in relation to climate External 78* Elisabeth Graf Pannatier Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Temporal trends in soil solution acidity in European forests Internal 79 Peter Waldner Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Nitrate leaching risk mapping (NitLeach) Internal 81* Robert Weigel Ernst-Moritz-Arndt- University (Greifswald) "The ecological and biogeochemical importance of snow cover for temperate forest ecosystems" and "Phenotypic plasticity and local adaptation in beech provenances (Fagus sylvatica)" External 82 Axel Weinreich, Konstantin Straub Unique - forestry and land use GmbH Maximising the yield of biomass from residues of agricultural crops and forestry External 84 Yasmina Loozen Utrecht University, Faculty of Geosciences Taking a remote look at canopy nitrogen to improve global climate models External 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ONGOING ICP FORESTS PROJECTS 126 | ID Name of Applicant Institution Project Title External/ Internal 85* Sietse van der Linde Imperial College London & Royal Botanic Garden, Kew Large-scale diversity, distribution and fate of Europe's forest mycorrhizas Internal 86* Josep Peñuelas | Jordi Sardans CREAF - Global Ecology Unit Plant-soil Stoichiometry relationships with tree growth and health along Environmental gradients External 87 Valerio Avitabile Wageningen University GlobBiomass External 88* Axel Göttlein Technical University Munich Specification of biogeochemical thresholds for the cultivation of important forest tree species in the face of climate change External 89* Janusz Czerepko Instytut Badawczy Leśnictwa DWpool: Deadwood estimation through forest ecosystems in Europe Internal 90* Mathias Neumann University of Natural Resources and Life Sciences FORMIT – Forest management strategies to enhance the mitigation potential of European forests External 91* Peter Waldner Swiss Federal Institute for Forest; Snow and Landscape (WSL) Seed C 2 – Carbon allocation to fruits and seeds in European forests as a function of climate, atmospheric deposition and nutrient supply Internal 92* Ece Aksoy European Topic Center - Urban, Land, Soil (ETC_ ULS) of European Envi- ronment Agency (EEA) Land Resource Efficiency Task of European Environment Agency External 93* Martina Temunović University of Zagreb, Faculty of Forestry Phenotypic and Genetic Diversity of Pedunculate oak (Quercus robur L.) in Europe – FGErobur External 94* Hrvoje Marjanović Croatian Forest Research Institute Estimating and Forecasting Forest Ecosystem Productivity by Integrating Field Measurements, Remote Sensing and Modelling External 96* Myriam Legay Office National des Forêts IKSMaps: Providing precalculated future distribution maps for the main French forestry species through IKS model External 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ICP FORESTS SCIENTIFIC PUBLICATIONS IN 2015/16 | 127 11 ICP FORESTS SCIENTIFIC PUBLICATIONS IN 2015/16 The following list includes all 28 English online and in print publications in scientific journals between January 2015 and May 2016 that contain data that either originate from the ICP Forests database or from ICP Forests plots and that have been reported to the ICP Forests Programme Co-ordinating Centre. For a list of all ICP Forests publications throughout the years, please refer to the ICP Forests website1. Achat DL, Pousse N, Nicolas M,. Brédoire F, Augusto L (2016) Soil properties controlling inorganic phosphorus availability: general results from a national forest network and a global compilation of the literature. Biogeochemistry 127:255-272. doi: 10.1007/s10533-015-0178-0 Cristofori A, Bacaro G, Confalonieri M, Cristofolini F, Frati L, Geri F, Gottardini E, Tonidandel G, Zottele F, Ferretti M (2015) Estimating ozone risks using forest monitoring networks – results for science, policy, and society. Ann For Sci 72:887-896. doi: 10.1007/s13595-014-0440-y De Vos B, Cools N, Ilvesniemi H, Vesterdal L, Vanguelova E, Carnicelli S, Ferretti M (2015) Benchmark values for forest soil carbon stocks in Europe: Results from a large scale forest soil survey. Geoderma 251-252: 33-46. doi: 10.1016/j.geoderma.2015.03.008 Ferretti M, Calderisi M, Marchetto A, Waldner P, Thimonier A, Jonard M, Cools N, Rautio P, Clarke N, Hansen K, Merilä P, Potocic N (2015) Variables related to nitrogen deposition improve defoliation models for European forests. Ann For Sci 72(7):897-906. doi: 10.1007/s13595-014-0445-6. Erratum: Ferretti M, Calderisi M, Marchetto A, Waldner P, Thimonier A, Jonard M, Cools N, Rautio P, Clarke N, Hansen K, Merilä P, Potočić N (2015) Erratum to: Variables related to nitrogen deposition improve defoliation models for European forests. Ann For Sci 72(7):907- 907. doi: 10.1007/s13595-015-0472-y Gaudio N, Belyazid S, Gendre X, Mansat A, Nicolas M, Rizzetto S, Sverdrup H, Probst A (2015) Combined effect of atmospheric nitrogen deposition and climate change on temperate forest soil biogeochemistry: A modeling approach. Ecol Model 306:24-34. doi: 10.1016/j.ecolmodel.2014.10.002 Guillemot J, Martin-St Paul NK, Dufrêne E, François C, Soudani K, Ourcival JM, Delpierre N (2015) The dynamic of the annual carbon allocation to wood in European tree species is consistent with a combined source–sink limitation of growth: implications for modelling. Biogeosciences 12:2773-2790. doi: 10.5194/bg-12-2773-2015 Hůnová I, Kurfürst P, Vlček O, Stráník V, Stoklasová P, Schovánková J, Svbová D (2016) Towards a better spatial quantification of nitrogen deposition: A case study for Czech forests. Environ Pollut 213:1028-1041. doi: 10.1016/j.envpol.2016.01.061 Johnson J, Aherne J, Cummins T (2015) Base cation budgets under residue removal in temperate maritime plantation forests. Forest Ecol Manag 343:144-156. doi: http://dx.doi.org/10.1016/j.foreco.2015.01.022 Jonard M, Fürst A, Verstraeten A, Thimonier A, Timmermann V, Potočić N, Waldner P, Benham S, Hansen K, Merilä P, Ponette Q, de la Cruz A, Roskams P, Nicolas M, Croisé L, Ingerslev M, Matteuci G, Decinti B, Bascietto M, Rautio P, Aherne J, Cummins T (2015) Tree mineral nutrition is deteriorating in Europe. Glob Change Biol 21(1):418-430. doi: 10.1111/gcb.12657 Jorge-Araújo P, Quiquampoix H, Matumoto-Pintro PT, Staunton S (2015) Glomalin-related soil protein in French temperate forest soils: interference in the Bradford assay caused by co-extracted humic substances. Eur J Soil Sci 66:311-319. doi: 10.1111/ejss.12218 Kowalska A, Astel A, Boczoń A, Polkowska Ż (2016) Atmospheric deposition in coniferous and deciduous tree stands in Poland. Atmos Environ 133:145-155. doi: 10.1016/j.atmosenv.2016.03.033 Kowalska A, Boczoń A, Hildebrand R, Polkowska Ż (2016) Spatial variability of throughfall in a stand of Scots pine (Pinus sylvestris L.) with deciduous admixture as influenced by canopy cover and stem distance. J Hydrol 538:231-242. doi: 10.1016/j.jhydrol.2016.04.023 Mellert KH, Deffner V, Küchenhoff H, Kölling C (2015) Modeling sensitivity to climate change and estimating the uncertainty of its impact: A probabilistic concept for risk assessment in forestry. Ecol Model 316:211-216. doi: 10.1016/j.ecolmodel.2015.08.014 1 http://icp-forests.net/page/publications 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S ICP FORESTS SCIENTIFIC PUBLICATIONS IN 2015/16 128 | Mellert KH, Ewald J, Horstein D, Dorado–Liñán I, Jantsch M, Taeger S, Zang C, Menzel A, Kölling C (2016) Climatic marginality: a new metric for the susceptibility of tree species to warming exemplified by Fagus sylvatica L. and Ellenberg’s quotient. Eur J For Res 135:137-152. doi: 10.1007/s10342-015-0924-9 Meyer M, Schröder W, Nickel S, Leblond S, Lindroos A-J, Mohr K, Poikolainen J, Santamaria JM, Skudnik M, Thöni L, Beudert B, Dieffenbach-Fries H, Schulte-Bisping H, Zechmeister HG (2015) Relevance of canopy drip for the accumulation of nitrogen in moss used as biomonitors for atmospheric nitrogen deposition in Europe. Sci Total Environ 538:600-610. doi: 10.1016/j.scitotenv.2015.07.069 Napa Ü, Kabral N, Liiv S, Asi E, Timmusk T, Frey J (2015) Current and historical patterns of heavy metals pollution in Estonia as reflected in natural media of different ages: ICP Vegetation, ICP Forests and ICP Integrated Monitoring. Ecol Indic 52:31-39. doi: 10.1016/j.ecolind.2014.11.028 Nevalainen S, Sirkiä S, Peltoniemi M, Neuvonen S (2015) Vulnerability to pine sawfly damage decreases with with site fertility but the opposite is true with Scleroderris canker damage; results from Finnish ICP Forests and NFI data. Ann For Sci 72:909-917. doi: 10.1007/s13595-014-0435-8 Nussbaumer A, Walder P, Etzold S, Gessler A, Benham S, Thomsen IG, Jørgensen BB, Timmermann V, Verstraeten A, Sioen G, Rautio P, Ukonmaanaho L, Skudnik M, Apuhtin V, Hug C, Burkart A, Braun S, Genau K, Wauer A, Bernhard M, Ebinger T (2016) Patterns of mast fruiting of common beech, sessile and common oak, Norwegian spruce and Scots pine in Central and Northern Europe. Forest Ecol Manag 363:237-251. doi:10.1016/j.foreco. 2015.12.033 Pollastrini M, Feducci M, Bonal D, Fotelli M, Gessler A, Grossiord C, Guyot V, Jactel H, Nguyen D, Radoglou K, Bussotti F (2016) Physiological significance of forest tree defoliation: Results from a survey in a mixed forest in Tuscany (central Italy). Forest Ecol Manag 361:170-178. doi: 10.1016/j.foreco.2015.11.018 Rautio P, Ferretti M (2015) Monitoring European forests: results for science, policy, and society. Ann For Sci 72: 875-876. doi: 10.1007/s13595-015-0505-6 Rizzetto S, Belyazid S, Gégout J-C, Nicolas M, Alard D, Corcket E, Gaudio N, Sverdrup H, Probst A (2016) Modelling the impact of climate change and atmospheric N deposition on French forests biodiversity. Environ Pollut 213:1016-1027. doi: 10.1016/j.envpol.2015.12.048 Sardans J, Alonso R, Carnicer J, Fernández-Martínez M, Vivanco MG, Peñuelas J (2016) Factors influencing the foliar elemental composition and stoichiometry in forest trees in Spain. Perspect Plant Ecol 18:52-69. doi: 10.1016/j.ppees.2016.01.001 Suz LM, Barsoum N, Benham S, Cheffings C, Cox F, Hackett L, Jones AG, Mueller GM, Orme D, Seidling W, Van der Linde S, Bidartondo MI (2015) Monitoring ectomycorrhizal fungi at large scales for science, forest management, fungal conservation and environmental policy. Ann For Sci 72:877-885. doi: 10.1007/s13595-014-0447-4 Talkner U, Meiwes KJ, Potočić N, Seletković I, Cools N, De Vos B, Rautio P, Hůnová I (2015) Phosphorus nutrition of beech (Fagus sylvatica L.) is decreasing in Europe. Ann For Sci 72(7): 919-928. doi: 10.1007/s13595-015-0459-8 Tomlinson G, Buchmann N, Siegwolf R, Weber P, Thimonier A, Graf Pannatier E, Schmitt M, Schaub M, Waldner P (2015) Can tree-ring δ15N be used as a proxy for foliar δ15N in European beech and Norway spruce? Trees – Struct Funct 30:627-638. doi: 10.1007/s00468-015-1305-1 Verstraeten A, Verschelde P, De Vos B, Neirynck J, Cools N, Roskams P, Hens M, Louette G, Sleutel S, De Neve S (2016) Increasing trends of dissolved organic nitrogen (DON) in temperate forests under recovery from acidification in Flanders, Belgium. Sci Total Environ 553:107-119. doi: 10.1016/j.scitotenv.2016.02.060 Vlasáková-Matoušková L, Hůnová I (2015) Stomatal ozone flux and visible leaf injury in native juvenile trees of Fagus sylvatica: A field study from the Jizerske hory Mts., the Czech Republic. Environ Sci Pollut R 22:10034- 10046. doi: 10.1007/s11356-015-4174-7 Waldner P, Thimonier A, Graf Pannatier E, Etzold S, Schmitt M, Marchetto A, Rautio P, Derome K, Nieminen TM, Nevalainen S, Lindroos AJ, Merilä P, Kindermann G, Neumann M, Cools N, de Vos B, Roskams P, Verstraeten A, Hansen K. Pihl Karlsson G, Dietrich HP, Raspe S, Fischer R, Lorenz M, Iost S, Granke O, Sanders TGM, Michel A, Nagel HD., Scheuschner T, Simončič P., von Wilpert K, Meesenburg H, Fleck S, Benham S, Vanguelova E, Clarke N, Ingerslev M, Vesterdal L, Gundersen P, Stupak I, Jonard M, Potočić N, Minaya M (2015) Exceedance of critical loads and of critical limits impacts tree nutrition across Europe. Ann For Sci 72(7):929-939. doi: 10.1007/s13595-015- 0489-2 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 129 12 NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS Twenty-nine countries have submitted numerical results of their 2015 national crown condition surveys and 26 countries an additional written national report. All written reports have been slightly edited primarily for consistency and are presented below; the numerical results are compiled in ANNEX II. The responsibility for the national reports and numerical results remains with the National Focal Centres and not with the ICP Forests Programme Co-ordinating Centre. For contact information of the National Focal Centres, please refer to ANNEX IV-3. Please note that in the national surveys the study design and number of plots can differ from the required 16 x 16 km grid used for the transnational analysis of tree crown condition and damage causes in Chapter 3 (Level I). Direct comparisons between the results of the national surveys of individual countries in this chapter may, therefore, be misleading. Missing values in the tables and figures in ANNEX II may indicate that data for certain years are missing or they indicate substantial differences in the samples, e.g. due to changes in the grid or the participation of a new country, as described in this chapter. For an explanation of the defoliation classes used, please refer to Table 3-1 in Chapter 3. 12.1 Andorra The assessment of crown condition in Andorra in 2015 was conducted on 12 plots of the national 4x4 km grid. In 2015, a new plot completely composed of Abies alba was added. Overall, the assessment included 264 trees, 119 Pinus sylvestris, 137 Pinus uncinata, 5 Betula pendula and 27 Abies alba trees. Results for 2015 showed an improving tendency in forest condition, as registered since 2009, with just a slow decrease in 2012. For all species, most of the trees were classified in defoliation and discoloration classes 0 and 1. Favourable climatic conditions in 2015, including high precipitation during the vegetative period could explain the good condition of the forests in terms of defoliation and discoloration. Related to defoliation, the large majority of trees of all species were in the no defoliation class (value range from 69.8% to 100%). Only Betula pendula presented one dead tree (16.7%) although the significance of this result is low due to the reduced number of individuals of birch surveyed, all in the same plot. Results for discoloration were variable depending on the species. The majority of Pinus sylvestris trees (69.2%) were classified as not discolorated. Individuals of Pinus uncinata were classified mainly in the slight discoloration class (57.6%) and in the no discoloration class (34.5%). The total of Abies alba trees were classified as not discolorated. Finally, the great part of Betula pendula individuals (83.3%) were classified as not discolorated, even this last result is not very significant due to the reduced number of birches surveyed. The assessment of damage causes showed, as in previous years, many causal agents, like wind, snow, falling trees, fungus Cronartium flaccidum, rots and lightning scars, which overall affected 6.9% of the sampled trees. 12.2 Belgium Belgium/Flanders The large-scale survey was conducted on 71 plots of the 4x4 km grid. The main tree species in the survey are Pinus sylvestris, Quercus robur, Pinus nigra subsp. laricio, Fagus sylvatica, Q. rubra, and Populus sp. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 130 | Other species are pooled in subsets with ‘other broadleaves’ or ‘other conifers’. Crown condition assessments were performed on 890 broadleaves and 721 conifers. Mean defoliation was 24.1%, and 21.5% of the trees showed more than 25% defoliation. 7.5% of the sample trees were in defoliation class 0, 71% of the trees were slightly affected. Moderate leaf loss was observed on 18.4%, and 2.1% of the trees showed severe defoliation. The mortality rate was 1%. Q. rubra and F. sylvatica revealed a good condition, with 5.4% and 9.3% of the trees being damaged. Consistent with the last survey, crown condition was worse for Populus sp., Q. robur and the ‘other broadleaves’. 18.5% of the Populus sp. were moderately to severely defoliated. The health status of Q. robur is problematic in several plots and 23.8% of the sample trees were rated as damaged. The highest level of defoliation was observed in the category ‘other broadleaves’, with a share of 35.5% in defoliation classes 2-4. P. nigra showed a distinctly higher rate of trees with moderate to severe defoliation compared to P. sylvestris. 42.7% trees were classified as being damaged compared to 12.8% of the P. sylvestris trees. Several infections caused damage, like Scirrhia pini on Pinus nigra, Hymenoscyphus fraxineus on Fraxinus excelsior and Phytophthora alni on Alnus glutinosa. Insect damage and mildew infections on oak were less severe compared to previous years. Forest condition deteriorated compared to last year. Mean defoliation increased by 1.9 percentage points and the share of trees in defoliation classes 2-4 increased by 1.6 percentage points. Mean defoliation increased both in broadleaves and conifers, the share of trees being damaged only in conifers. The extent of deterioration was highest in P. nigra. Regarding broadleaves, there was only a significant higher defoliation in the ‘other broadleaves’. Declining Alnus glutinosa trees in one plot are responsible for this increase. Seed production in Q. robur was moderate to high in 12.5% of the trees, and these results are comparable to 2009, 2011 and 2013. In F. sylvatica fruiting was less remarkable. On 27 December 2014, snowfall caused broken branches and crown break in pine forests in the northern part of Flanders. As a consequence, at least 10% of the P. sylvestris trees showed broken branches with a minimum diameter of 2 cm. Most of the damaged trees will survive but this event caused a significant increase in defoliation of P. sylvestris. In connection with the recent ash dieback, a survey of the condition of Fraxinus excelsior was started in 2014, as a part of a multidisciplinary project. This survey continued in 2015, making use of the Level I grid and additional plots. A subset of 252 common sample trees in 2014-2015 revealed a remarkable deterioration of common ash. Mean defoliation increased from 28.8% to 34.3% and the proportion of trees with moderate to severe leaf loss increased from 32.1% to 47.6%. Belgium/Wallonia The survey in 2015 concerned 402 trees on 45 plots, on a regional systematic grid that has been adapted since 2010 to fit with the national forest inventory. It is now possible to identify trends for these 5 last years. Since 2010 spruces showed a slight decreasing mean defoliation to reach 35% in 2014. This value remains constant in 2015; however the percentage of severely defoliated had not stopped decreasing. Beeches improved their mean defoliation value to reach 36% in 2015. Beeches up to 140 cm were all at least moderately defoliated. English oaks showed less mean defoliation in 2015 (29%). Sessile oaks kept better value with a mean defoliation of only 18%. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 131 12.3 Bulgaria The health status of forest trees in Bulgaria is systematically monitored by the long-term, large-scale monitoring programme for 30 years. In 2015, crown condition assessments were carried out in 159 sample plots on 5513 sample trees. Observations on defoliation, biotic and other stress factors were carried out in plots with the coniferous tree species Pinus sylvestris L., Pinus nigra Arn., Picea abies (L.) Karst. and Abies alba Mill., as well as the deciduous tree species Fagus sylvatica L., Quercus frainetto Ten., Quercus petraea (Matt.) Liebl., Quercus cerris L., Quercus rubra L., Tilia platyphillos Scop. and Carpinus betulus L. The total number of studied coniferous sample trees was 2386 and the number of deciduous trees was 3127. Approximately 74% of the monitored trees had a degree of defoliation up to 25% which coincides with the results obtained in 2014. The highest percentage of trees with an average degree of defoliation was 17.6%, determined within the interval between second and fourth classes, which is 4.2% less than the respective percentage in 2014. Compared to the study results obtained in 2014, the percentage of healthy trees has increased by 6.5%. The percentage of highly-defoliated and dead trees with third and fourth degrees has increased by 4.3%. The observed deciduous trees were in better condition than the coniferous trees - 84.4% of the studied deciduous trees had a defoliation degree of up to 25%, which represents an increase of 4.6% in comparison with 2014. As for the coniferous tree species - 59.9% had a defoliation degree of up to 25%, which is 5.8% less than the results reported in 2014. The health status of European beech trees (Fagus sylvatica L.) was very good. The variation between the different sample plots is associated with effects of an abiotic and biotic character (Nectria sp., Ascodichaena rugosa, Fomes fomentarius, Orchestes fagi, Mikiola fagi etc.). The increased percentage of trees with third and fourth degree (heavily-damaged and dead trees), 2.0% and 7.0% respectively, was mainly due to ice damage and to anthropogenic impact - legally and illegally cut-down. The health status of the oak trees (Quercus cerris L., Quercus frainetto Ten., Quercus petraea (Matt.) Liebl. and Quercus rubra L.) remains at the level of previous years. The decrease in serious damages within the second and third degree was due to the lack of calamities of the main defoliators Lymantria dispar, Geometridae and Tortricidae. The slight increase in heavy damages in Turkey oak was caused by the main stem pathogens - Hypoxylon mediterraneum and Diplodia mutila, and in the sessile oak and Hungarian oak – by the tracheomycosis disease (Ceratocystis roboris). There were no significant changes in the health status of the species Carpinus betulus L. and Tilia platyphyllos Scop. The best condition of coniferous tree species under 60 years of age was determined in Picea abies, where 86.7% of the observed trees had a defoliation degree of up to 25%, followed by Pinus sylvestris and Pinus nigra. Regarding the observed stands over 60 years of age, the best condition was also determined in Picea abies where 88.5% of the trees had defoliation up to 25%. Compared to the results for 2014, the percentage of healthy trees of the species Picea abies and Abies alba has increased; the percentage of the fourth degree has also increased. A higher percentage of defoliation was determined in Pinus nigra and Pinus sylvestris stands, as well as an increase in trees with fourth degree of defoliation. The worst condition, compared to other tree species, was reported in the Pinus sylvestris stands, where 13.6% had 3+4 defoliation degree. The resulting drought stress in pine plantations, under the dry land conditions in recent years, increased the development of the root rot pathogen Heterobasidion annosum and subsequent attacks by the pine shoot beetle Tomicus piniperda. The health status in most Pinus nigra sample plots was relatively good, although typical crown damages occurred caused by the fungal pathogens Sphaeropsis sapinea, Dothistroma sp. and Lophodermium sp.. Anthropogenic impact (illegally cut-down) was also observed. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 132 | The aforementioned biotic damages and their causes did not lead to significant changes in the condition of the observed trees. The impact of abiotic factors, mostly wet snow that fell in March in some parts of the country, as well as windthrows, windbreaks and snow breakages, was more significant. In recent years there has been a marked increase in areas affected by these factors. In 2010, a total area of 3 776 ha was affected, whereas in 2014 the area of forest damaged by abiotic disturbances had increased almost seven times (26 387 ha). 12.4 Croatia Ninety-five sample plots (2280 trees) on the 16 x 16 km grid network were included in the survey 2015. The percentage of trees of all species within classes 2-4 in 2015 (29.7%) was somewhat smaller than in 2014 (31.5%), and similar to year 2013 (29.1%). The percentage of broadleaves in classes 2-4 (25.3 %) was also smaller, but for conifers it was high at 55.9%, a significant increase from last year (49.7%) and year 2013 (48.3%). There were 327 conifer trees and 1953 broadleaves in the sample. While poor crown condition of black pine is more or less a constant (69.3% this year), the deterioration of crown condition of narrow-leaved ash is very dramatic: the percentage of trees in classes 2-4 increased from 23.6% in 2013, through 49.1% in 2014 to 62.5% this year. Along with dry years, and the presence of Stereonychus fraxini, also the increased presence of Hymenoschyphus fraxineus (Chalara fraxinea) in the last few years seems to be a factor causing increased deterioration of ash health. Also Abies alba with 59.6% trees in classes 2-4 remains one of our most defoliated tree species. The percentage of Quercus robur trees in classes 2-4 in the past ten years has been between 20 and 30%. This year we recorded 21.6% of moderately to severely defoliated oak trees, a reduction from last year's 29.7%. Fagus sylvatica is still one of the tree species with lowest defoliation with 20.5% trees in the defoliation class 2-4. In the last ten years of monitoring, this percentage varied from 5.1% in 2003 to 25.5 % in year 2014. The damage causes were this year for the first time assessed in Croatia. The most affected part of trees are leaves/needles (40.1%), followed by branches and shoots (33.7%) and stem and roots (26.3%). The most prominent agent group is insects (18.3 %, of that defoliators 64.3%), then abiotic agents (8.7%, of that drought 50.5%), fungi (5.9%), and direct action of men (5.2%). 12.5 Cyprus The annual assessment of crown condition was conducted on 15 Level I plots, during the period September – November 2015. The assessment covered the main forest ecosystems of Cyprus and a total of 360 trees (Pinus brutia, Pinus nigra and Cedrus brevifolia) were assessed. Defoliation, discoloration and the damaging agents were recorded. A comparison of the results of the conducted survey with those of the previous year (2015) shows an increase of 10.9% in class 0 (not defoliated). A decrease of 10.1% in class 1 (moderately defoliated) and of 0.8% in class 2 (severely defoliated) has been observed. A slight increase of 0.3% has been observed in class 3 and 0.3% decrease has been observed in class 4. From the total number of trees assessed (360 trees), 29.7% of them were not defoliated, 57.8% were slightly defoliated, 11.4% were moderately defoliated and 1.1% were severely defoliated. In the case of Pinus brutia, 28% of the sample trees showed no defoliation, 57.7% were slightly defoliated, 13.3% were moderately defoliated and 1% were severely defoliated. For Pinus nigra, 41.7% 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 133 of the sample trees showed no defoliation and 58.3% showed slight defoliation. For Cedrus brevifolia, 33.3% of the sample trees showed no defoliation, 58.3% were slightly defoliated, 4.2% were moderately defoliated and 4.2% were severely defoliated. From the total number of trees assessed (360 trees), 100% of them were not discolorated. From the total number of sample trees surveyed, 35.6% showed signs of insect attacks and 12.8% showed signs of attacks by “other agents, T8” (lichens and dead branches). Also, 1.1% showed signs of both factors (insect attacks and other agent). The major abiotic factors causing defoliation in some plots, during 2015, were the combination of climatic with edaphic conditions which resulted to secondary attacks by Leucaspis spp. and defoliator insects, to 1/3 of the trees. 12.6 Czech Republic In coniferous species of the older age category (forest stands of 60 years of age and more) no pronounced changes in the trend of total defoliation were observed in 2015 compared to the preceding year. There was only a moderate increase in the total percentage of defoliation in class 3. Particularly Scots pine (Pinus sylvestris) contributed to this change, in which the defoliation percentage in class 3 increased from 5.7% in 2014 to 8.3% in 2015. On the contrary, in silver fir (Abies alba) the defoliation percentage in class 3 decreased from 2.9% in 2014 to 0.0% in 2015. The trend of defoliation in the younger age category of coniferous species (forest stands less than 59 years old) in 2015 shows an evident change only in fir compared to the preceding year, in which the defoliation percentage in class 0 increased from 22.2% in 2014 to 29.6% in 2015 at a simultaneous decrease in class 2. The trend of total defoliation of broadleaved species in the older age category (forest stands 60-years- old and more) indicates a moderate improvement due to a decrease in the defoliation percentage in class 1 at a simultaneous increase in the percentage in class 0. In oak (Quercus sp.) such an improvement was reflected in a decrease in the percentage in defoliation class 2 from 63.6% in 2014 to 59.8% in 2015 at a simultaneous increase in the class 1 percentage from 34.4% in 2014 to 39.1% in 2015. In European beech (Fagus sylvatica) there was a pronounced increase in the defoliation percentage in class 0 from 26.4% in 2014 to 34.6% in 2015 at a simultaneous decrease in classes 1 and 2. In the category of younger broadleaved species (forest stands less than 59 years old) defoliation was clearly reduced only in beech due to an increase in the defoliation percentage in class 0 from 62.9% in 2014 to 67.1% in 2015 at a simultaneous decrease in classes 1 and 2. On the contrary, defoliation obviously increased in younger stands of silver birch (Betula pendula) as a result of a decrease in the defoliation percentage in class 0 from 38.3% in 2014 to 23.7% in 2015 at a simultaneous increase in class 1. Younger coniferous trees (less than 59 years old) show lower defoliation in the long run than the stands of younger broadleaved trees. In older stands (60-years-old and more) this comparison is reverse because older coniferous trees have considerably higher defoliation than the stands of older broadleaved trees. In both age categories it is the pine that substantially contributes to a higher defoliation percentage for the group of coniferous species. Average monthly temperatures in the period March ‒ September always showed a positive deviation from the long-term normal. The highest deviation was recorded for average temperatures in the month of July (deviation +3.3° C) and August (deviation +4.9° C). In the summer months there were 42 tropical days in total while on 16 days the temperatures rose above 35° C. Monthly precipitation totals in the period April – September amounted to 46-86% of the normal, only in March the precipitation total reached 120% of the normal. The entire growing season can be evaluated as one of the warmest and driest seasons in the long history of recording climate characteristics. The adverse ratio of temperature to precipitation total for a major part of the growing season had negative effects on the health status of 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 134 | forest stands mainly at lower altitudes above sea level. The regular defoliation assessment was mostly carried out before the effects of drought on forest stands were fully manifested, and therefore the defoliation values have not been influenced by this factor significantly. The trend of emissions of the main pollutants (particulate matter, SO2, NOx, CO, VOC, NH3) has not shown any pronounced change in the last ten years while total emissions of the majority of these pollutants have decreased very moderately in the long run in spite of some fluctuations, and the emissions of particulate matter and NH3 have been constant. 12.7 Denmark The Danish forest condition monitoring in 2015 was carried out via the National Forest Inventory (NFI) including the remaining Level I and II plots. Monitoring showed most tree species had satisfactory health status, even though all the main species had an increase in defoliation. As in previous years ash (Fraxinus excelsior) showed extensive dieback due to the invasive pathogen Hymenoscyphus fraxineus. Average defoliation remained at 26% for all monitored ash trees, and 36 % of the trees had at least 30% defoliation, which is a higher percentage than last year. Norway spruce (Picea abies) had an increased, but still low average defoliation of 7% and almost 7% damaged trees. Sitka spruce (Picea sitchensis) also saw a higher defoliation of 13% in spite of the removal of the long-term monitoring plot with highest needle loss. Other conifers such as Pinus, Larix and Abies sp. also had slightly elevated levels of defoliation, but defoliation was only around 10% on average. In general, the health of conifers in Denmark can be considered satisfactory. The average defoliation score of beech (Fagus sylvatica) increased slightly to 10%, but the frequency of damaged trees stayed at 4%. Oak (Quercus robur and Q. petraea) showed an increase in average defoliation from 13% to 17%, and the frequency of damaged trees increased to 20%. This was not unexpected considering the reports of health problems in oak in Denmark in recent years. However, based on the monitored trees there is not yet cause for a general concern over forest health in beech and oak. Based on defoliation assessments on NFI plots and Level I & II, the results of the crown condition survey in 2015 showed that 71% of all coniferous trees and 60% of all deciduous trees were undamaged. 21% of all conifers and 30% of all deciduous trees showed warning signs of damage. The mean defoliation of all conifers was 9% in 2015, and the share of damaged trees was 7%. Mean defoliation of all broadleaves was 13%, and 11% of the trees had more than 30% defoliation. 12.8 Estonia Forest condition in Estonia has been systematically monitored using Level I sample points since 1988. The Level I forest monitoring network was used to assess the health status of 2397 trees. 1464 Scots pines (Pinus sylvestris), 584 Norway spruces (Picea abies) and 349 deciduous species, mainly birches (Betula pendula) were assessed. The observation period lasted from July 13th to November 11th, 2015. The total share of not defoliated trees, 50.7%, was 1.2% higher than in 2014. The share of not defoliated conifers, 49.7%, was lower than the share of not defoliated broadleaves, 57.0%, in 2015. Share of trees in classes 2 to 4, moderately defoliated to dead, was 6.8% in 2015 and 6.7% in 2014. No significant change of defoliation in general was observed. Share of conifers and broadleaves in defoliation classes 2 to 4 was 6.6% and 8.0% accordingly. Scots pine has traditionally been and remained the most defoliated tree species in Estonia. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 135 The share of not defoliated pines (defoliation class 0) was 49.3% in 2015, 3.2 % higher than in 2014. Share of pines in classes 2 to 4, moderately defoliated to dead, was 6.2%, slightly lower than in 2014. However no serious long-term trend of Scots pine defoliation since 2010 could be observed. In 2010, the share of not defoliated pine trees increased from 38 % to 45% and is keeping the similar level until now. Concerning Norway spruce some slight long-term increase of defoliation occurred. The share of not defoliated trees (defoliation class 0) was 64% in 2010 and 54.0% and 50.5% accordingly in 2014 and 2015. The share of not defoliated trees was higher, 74.7% in younger stands with the age up to 60 years and 47.8% in older stands. Compared to 2014 there has been a significant decrease in the condition of broadleaves during 2015. The share of broadleaves in classes 2 to 4, moderately defoliated to dead, was 8.0% in 2014. This is higher than 5.7% in 2014. The defoliation of birches (Betula pendula) increased about 22.3% in 2015, mainly caused by birch rust (pathogen Melampsoridium betulinum). The share of not defoliated silver birches was 53.9% in 2015 and 76.2% in 2014. All trees included in the crown condition assessment on Level I plots are also regularly assessed for damage. Numerous factors determine the condition of forests. Climatic factors, disease and insect damage as well as other natural factors have an impact on tree vitality. In 2015, 7.6% of the trees observed, had some insect damages and 39% of trees had identifiable symptoms of disease. Visible damage symptoms recorded on Scots pine were mainly attributed to pine shoot blight (pathogen Gremmeniella abietina). Symptoms of shoot blight were recorded on 43% of the observed pine trees in 2015. Norway spruces mostly suffered from root rot (pathogen Heterobasidion parviporum) – characteristic symptoms of the disease were observed on 7.7% of sample trees. No substantial storm damages and forest fires occurred in 2015. 12.9 France In 2015, the forest damage monitoring in the French part of the systematic European network comprised 11 722 trees on 560 plots. In 2015, summer was particularly hot and dry, with two heat waves in the beginning and the end of July, nevertheless most species showed little consequences of these harsh conditions: defoliation stayed the same as in 2014 for almost all broadleaved species, Fagus sylvatica’s defoliation even decreased. On the contrary, Fraxinus excelsior’s defoliation skyrocketed due to the fungus Chalara fraxinea which arrived in France seven years ago. For conifers, it is quite the same, except for Pinus pinaster, Picea abies and Pinus sylvestris, whose defoliation slightly increased. Death of sampled trees stayed at a relatively low level. The number of discoloured trees was still low except for poplars, beech, wild cherry and Aleppo pine. Damage was reported on about a quarter of the sampled trees, mainly on broad-leaved species. The most important causes of damage were mistletoe (Viscum album) on Pinus sylvestris, chestnut canker (Cryphonectria parasitica) and the oak buprestid (Coroebus florentinus) on Quercus spp. Abnormally small leaves were observed on different species, specially on Quercus spp. (mainly on evergreen and pubescent oaks). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 136 | 12.10 Germany Crown condition In 2015, the crown condition of European beech considerably improved compared to the previous year. For all other tree species, results of the crown condition assessment 2015 are almost the same as in 2014. Since the surveys were first taken in 1984, the share of damaged broadleaved trees as well as the mean defoliation of broadleaved tree species significantly increased. The crown condition of Norway spruce and Scots pine show no clear trend, whilst other conifers improved. There is no clear trend in the average defoliation rates across all tree species. In summer 2015, 24 % of the forest area was assessed and classed as damaged, i.e. more than 25% crown defoliation was recorded (damage classes 2 to 4), compared to 26% in 2014. In 2014, 43% (2014: 41%) were in the warning stage. In 2015 as well as in 2014, 33% showed no defoliation. The mean crown defoliation decreased from 20.4% to 20.0%. Picea abies: The percentage of damage classes 2 to 4 was 28% and has not changed compared the previous year. 37% (2014: 39%) of the trees were in the warning stage. The share of trees without defoliation was 35% (2014: 33%). However, mean crown defoliation increased from 20.2% to 20.6%. This increase is due to a shift to higher defoliation rates within the damage classes. Pinus sylvestris: The share of damage classes 2 to 4 was 13% (2014: 12%). 51% (2014: 50%) were in the warning stage. 36% (2013: 38%) showed no defoliation. The mean crown defoliation increased from 16.4% to 16.9%. Fagus sylvatica: The crown condition of European beech strongly improved compared to 2014. The share of damage classes 2 to 4 decreased from 48% to 33%, which is similar to the level of defoliation reached in 2012 and 2013. 45% (2014: 38%) of the beech area was classified in the warning stage. The share without defoliation was 22% (2014: 14%). Mean crown defoliation decreased from 27.6% to 23.3%. The crown condition in 2014 was strongly influenced by intense fruiting. In 2015, moderate or strong fruiting occurred only on a few trees and crown condition improved accordingly. Quercus robur & Q. petraea: The share of damaged trees was 36%, unchanged compared to the previous year. The share of trees in the warning stage (40%), as well as the share without defoliation (24%), did not change either. Mean crown defoliation decreased from 24.7% to 24.1%. Spring and summer of 2015 were extremely warm and dry in almost all of Germany, resulting in a negative climatic water balance. In some regions drought was even more severe than in 2003. http://www.dwd.de/EN/climate_environment/climateatlas/climateatlas_node.html This, however, is not reflected within the results of the crown condition assessment, starting in early July (in line with the ICP Forests Manual) whilst drought damage on trees only became apparent in late summer. Furthermore, the experience of the year 2003 shows that summer drought in one year may only result in poor crown condition in the following or even subsequent years. Results of an ozone impact study In the south-western German federal states, Rhineland-Palatinate and Saarland, the impact of tropospheric ozone was assessed using three different approaches: MPOC, AOT40 and PODy. Nine Level II sites were included in this study, of which six with co-located active O3 measurements over the years 1998 to 2014. The critical level for AOT40 was exceeded on all sites in each year. For the beech stand in the Rhineland-Palatinate Forests (Merzalben Hortenkopf, 550 m a.s.l.) POD1 was calculated and compared with AOT40. The critical level (CL POD1=4 mmol O3 m -2 PLA) was already exceeded each year by May/June, and by the end of the vegetation period it was exceeded by a factor 4 up to 7. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 137 Similar results have been recorded for Bavarian sites for the period 2002 to 2005 (Baumgarten et al. 2009). Phytotoxic Ozone Dose accumulated over the vegetation period (POD1 in mmol O3 m -2 PLA) compared to AOT40 (ppm.h) for the beech stand at the site Merzalben; on the bottom the climatic water balance (CWB in l m-2) between April and September of the respective year is depicted (W. Werner, Trier University). https://www.uni-trier.de/fileadmin/fb6/prof/GEB/Lehre/OzonBericht_2015_Langfassung.pdf; http://www.wald-rlp.de/fileadmin/website/fawfseiten/fawf/downloads/WSE/2015/Bericht_klein_30_11_2015.pdf 12.11 Greece The crown assessment survey was carried out for the year 2015 on 47 Level I plots in Greece from 13.07.2015 till 30.10.2015. The total number of trees assessed was 1113, 488 of them were trees of broadleaved species and 625 were trees of coniferous species. Comparing the survey of the year 2015 with the last survey (2014), the Level I plots were 17.5% fewer and the total trees assessed were 17.3% fewer. The percentages of the conifer species for all defoliation classes were very similar to those of last year’s survey, although the number of the assessed plots was not the same. The table below shows the results for the two consecutive years (2104 and 2015). The figures are in %. Year No defoliation (0) Slight defoliation (1) Moderate defoliation (2) Severe defoliation (3) Dead trees (4) 2014 43.9 29.3 18.7 6.6 1.5 2015 45.0 27.8 21.9 4.1 1.2 These figures are considered to represent a healthy tree condition (72.8% are in the No and Slight defoliation classes). The main causes assessed in the conifer species resulting in needle losses were epiphytes, insect attacks, and abiotic reasons. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 138 | The three main conifer species assessed in Greece (that means the species with the highest number of trees assessed) were Abies cephalonica with 213 trees, Pinus nigra with 100 trees and Pinus halepensis with 72 trees of a total of 625 conifer trees. The comparison of the health condition with the results of the previous year survey (2014) could lead to mistakes. This is due to the fact that the plots assessed in the current year survey were different. The defoliation percentages for the five classes (0, 1, 2, 3 and 4) of the Abies cephalonica species were found to be 34.7%, 24.9%, 28.6% 9.4% and 2.3%, respectively. That means a significantly worse tree health condition than the conifers in total. In similar tree health condition was Abies borisii-regis. With regard to the Pinus nigra species, the considerably high percentage in class 0 (63%), combined with 0 dead trees (class 4) shows a very good health condition. Finally, in the Pinus halepensis species the results showed a steady but moderately healthy condition. The defoliation percentages were found to be 6.9%, 50.0% and 43.1% for the 0, 1 and 2 classes respectively. The total number of the assessed broadleaved trees in Greece for the current year (2015) was 488. A comparison with the results of the previous year survey showed a slightly better health condition. The table below shows the results for the two consecutive years 2104 and 2015. The figures are in %. Year No defoliation (0) Slight defoliation (1) Moderate defoliation (2) Severe defoliation (3) Dead trees (4) 2014 49.2 33.9 13.6 2.3 0.8 2015 52.1 36.6 8.0 1.8 1.4 The main broadleaved species assessed in Greece (that means the species with the highest number of trees assessed) were Quercus frainetto with 135 trees, Castanea sativa with 72 trees and Fagus moesiaca with 71 trees. The defoliation percentages of the Quercus frainetto species showed a significant improvement of its health condition. This could be attributed to the fact that insect attacks have not been observed with the same intensity as in previous years. The defoliation percentages for the five classes of Castanea sativa species were similar to last year’s survey. But the tree condition of the Fagus moesiaca species was found to be very healthy with 93% in the No and Slight defoliation classes. The main causes assessed in the broadleaved species resulting in foliage losses were insect attacks and abiotic agents. 12.12 Hungary The forest condition survey – based on the 16x16 km grid – in 2015 included 1841 sample trees on 77 sample plots from the total of 78 permanent plots in Hungary (one of them was inaccessible). The assessments were carried out between 15th July and 15th August. 89.2% of all assessed trees were broadleaves, 10.2% were conifers. The health condition of the Hungarian forests is in a positive state, in the recent years the share of healthy and slightly defoliated trees – despite the annual fluctuations – was near 80%. In 2015 the share of trees without visible damage symptoms was 50.5%. The percentage of slightly defoliated trees was 25.5%, and the percentage of all trees within ICP Forests defoliation classes 2-4 (moderately damaged, severely damaged and dead) was 24%. In Hungary the dead trees remain in the sample while they are standing, but the newly (in the surveyed year) died trees can be separated. The rate of trees having died in 2015 was 0.8% of all trees. The mean defoliation level of all species was 20.5% which is higher than in 2014 (18.6%). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 139 In the defoliation classes 2-4 the tree species suffering the most damage are Pinus nigra (90.9%), Pinus sylvestris (37.6%) and Robinia pseudoacacia (30.9%), (the percentages show the rate of sample trees belonging to category 2-4). Quercus cerris (5.4%) and other hardwoods (10.9%) had the lowest defoliation rates in classes 2-4. Defoliation rates by species generally show considerable year to year variation in these categories. The condition of the rest of the tree species represented an average level. Discoloration can rarely be observed in the Hungarian forests, 88.8% of living sample trees did not show any discoloration. According to the classification defined in the ICP Forests manual on crown condition the damage caused by defoliating insects had one of the highest rate, 20.9% of all damages. This damage occurred particularly on the following species: Pinus sylvestris (49.7%), other softwood (43.9%). The mean damage values of these trees were 14.2% and 6.8% respectively. The rate of assessed damage caused by fungi was also 20.9%. Fungal damage was mostly assessed on stem and root (wet rot causing fungus) at 67.2%, on needle and on leaves at 15.3%. The mean damage value was 19.9%. 16% of the assessed damage was abiotic, this is higher than the previous years’. The general intensity was 17.7%. Within the abiotic damage most important identifiable causes were drought (35.9%), frost (33.5%) and wind (17.9%), while the other causes were unimportant. 12.13 Italy The survey of Level I in 2015 took into consideration the condition of the crown of 4757 selected trees in 235 plots belonging to the EU network (16x16 km grid). The results given below relate to the distribution of frequencies of the indicators used, especially transparency - which in our case we use for the indirect assessment of defoliation, and the presence of agents and known causes attributable to both biotic and abiotic factors. For the latter, we not so much analysed the indicators but the frequencies of affected plants, and the comments made about each plant may have multiple symptoms and agents. Defoliation data are reported according to the usual categorical system (class 0:0-10%; class 1: >10-25%; class 2: >25-60%; class 3: >60%; class 4: tree dead): most trees (71.2%) are included in the classes 1 to 4; 29.8% are included in the classes 2 to 4. From a survey of the frequency distribution of the parameter for transparency species were divided into two age categories (<60 and ≥60 years), among the young conifers (<60 years), Picea abies and Pinus sylvestris have respectively 58.1% and 37.0% of trees in the classes 2 to 4, Pinus pinea has 30.0%, Pinus nigra has 26.6% of trees in the classes 2 to 4, but the best conditions was found on Larix decidua with 15.4%. Among the old conifers (60 years), the species which appears to have the worst quality of foliage was Pinus nigra (20.9%), Picea abies (20.9%), and Abies alba (17.5%); while Larix decidua with 7.7% and Pinus cembra with 7.3% of the trees in the classes 2 to 4, were the conifers is in better condition. Among the young broadleaves (<60 years), Castanea sativa, Quercus pubescens and Ostrya carpinifolia have respectively 80.5%, 38.1% and 32.9% of trees in the classes 2 to 4, while others have a frequency range between 21.2% (Fagus sylvatica) and 24.9% (Quercus cerris) in classes 2 to 4. Among the old broadleaves (60 years) in the classes 2 to 4, Castanea sativa has 83.8%, Quercus pubescens 48.9%, Ostrya carpinifolia 30.0%, Quercus ilex 13.8%, while Fagus sylvatica has the lowest level of defoliation of trees in the classes 2 to 4 (8.4%). Starting from 2005, a new methodology for a deeper assessment of damage factors (biotic and abiotic) was introduced. The main results are summarized below. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 140 | Most of the observed symptoms were attributed to insects (20.5%), subdivided into defoliators (16.4%), galls (2.4%). The following symptoms were attributed to fungi (5.1%), the most significant were attributable to “dieback and canker fungi” (2.3%). Then followed those assigned to abiotic agents, the most significant were attributable to the high temperatures recorded in summer: drought (1.9%) and “heat stroke” (1.1%). 12.14 Latvia The forest condition survey 2015 in Latvia was carried out on 116 NFI plots. The national report of 2015 is based on data from this dataset. In total, defoliation of 1732 trees was assessed, of which 77% were conifers and 23% broadleaves. Of all tree species, 9.1% were not defoliated, 86.5% were slightly defoliated and 4.4% moderately defoliated to dead. Comparing to 2014, the proportion of not defoliated trees has decreased by 1.5%, proportion of moderately defoliated to dead trees has decreased by 0.7% but proportion of slightly defoliated trees has increased by 2.2%. In 2015, the proportion of not defoliated broadleaves was by 2.5% higher than that of not defoliated conifers, the proportion of slightly defoliated conifers was by 2.4% higher than that of slightly defoliated broadleaves but the proportion of trees in defoliation classes 2-4 was nearly the same for broadleaves and conifers. Mean defoliation of Pinus sylvestris was 20.2% (20.2 in 2014). The share of moderately damaged to dead trees constituted 5.0% (5.2% in 2014). Mean defoliation of Picea abies was 20.8% (17.6% in 2014). Share of moderately damaged to dead trees for spruce constituted 3.3% (3.8% in 2014). The mean defoliation level of Betula spp. was 19.5% (19.6% in 2014), showing a slight decrease of the defoliation level. The share of trees in defoliation classes 2-4 was 3.8% (compared to 6.3% in 2014). The mean defoliation level for Populus tremula was 17.0% (15.8% in 2014). The mean defoliation level was distinctly lower for younger trees (19.5% for pine, 17.3% for spruce and 18.1% for birch up to 59 years old; the respective defoliation levels for trees 60 years and older were 20.9%, 24.4% and 20.8% for pine, spruce and birch. Visible damage symptoms were observed on 18.6% of all trees - to a larger extent than in the previous year (17.3%) but to a lesser extent than in 2013 (19.7%). The most frequently recorded damages were caused by direct action of men (34.4% of all cases; 35.1% in 2014), animals (21.4%; same in 2014), fungi (10.4%; same in 2014), abiotic factors (12.4%; 13.7% in 2014) and insects (18.9%; 17.0% in 2014), unknown damage causes were recorded for 2.5% of all cases. Proportion of trees damaged by insects continues to grow due to an increase in the population and damages by European pine sawfly, Neodiprion sertifer; that was reported already last year. The greatest share of trees with damage symptoms was recorded for Picea abies (28.9%) and the smallest for Betula spp. (13.5%). Percentage of damaged Pinus sylvestris was 18.9% from all assessed pines trees. 12.15 Lithuania In 2015, the forest condition survey was carried out on 1060 sample plots from which 81 plots were on the transnational Level I grid and 979 plots on the National Forest Inventory grid. In total 6340 sample trees representing 19 tree species were assessed. The main tree species assessed were Pinus sylvestris, Picea abies, Betula pendula, Betula pubescens, Populus tremula, Alnus glutinosa, Alnus incana, Fraxinus excelsior, and Quercus robur. The mean defoliation of all tree species slightly increased up to 22.9% (22.2% in 2014). 13% of all sample trees were not defoliated (class 0), 63% were slightly defoliated and 24% were assessed as moderately defoliated, severely defoliated and dead (defoliation classes 2-4). 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 141 Mean defoliation of conifers slightly increased up to 23.1% (21.7% in 2014) and slightly decreased for broadleaves up to 22.5% (22.8% in 2014). Pinus sylvestris is a dominant tree species in Lithuanian forests and comprises about 40% of all sample trees annually. Mean defoliation of Pinus sylvestris reached 23.8% (23.1% in 2014) with an increasing tendency since 2008. Populus tremula had the lowest mean defoliation and the lowest share of trees in defoliation classes 2-4 since 2006. Mean defoliation of Populus tremula was 18.3% (18.9% in 2014) and the proportion of trees in defoliation classes 2-4 was 10% compared with 12% in 2014. Fraxinus excelsior condition remained the worst among all observed tree species. This tree species had the highest defoliation since year 2000. Mean defoliation increased to 41.1% (40.9% in 2014). The share of trees in defoliation classes 2-4 increased to 54% (52% in 2014). 27% of all sample trees had some kind of identifiable damage symptom. The most frequent damage was caused by abiotic agents (about 8 %) in the period of 2011 – 2015. It is closely connected with the storm that hit the South-Eastern part of Lithuania on August 8, 2010. The highest share of damage symptoms was assessed for Fraxinus excelsior (63%), Populus tremula (35%) and Alnus incana (34%), the least for Betula sp. (20%) and Alnus glutinosa (21%). In general, the mean defoliation of all tree species has varied inconsiderably from 1997 to 2015 and the growing conditions of Lithuanian forests can be defined as relatively stable. 12.16 Luxembourg In 2015 the national crown condition survey was based on a 4 x 4 km grid, which included 1200 sample trees on 51 permanent plots. On average over all tree species, 30.5% of the forest was showing no defoliation, 32.9% were assessed as damaged (classes 2-4), and 36.6% were in the warning stage. In 2015, 18.8% of conifers were in defoliation classes 2-4, 25.6% were slightly defoliated, and 55.6% were not defoliated. For broadleaves 40.7% were assessed as damaged (classes 2-4), 42.6% were slightly defoliated, and 16.7% showed no signs of defoliation. 12.17 Republic of Moldova In 2015, the assessment of forest health was performed for a total of 14 280 trees (14 239 broadleaved trees and 41 coniferous trees). As a result of the negative effect of biotic and abiotic factors, the trees in the defoliation classes “none” constituted only 33.5%. The drought and adverse climatic conditions during the vegetation period affected the health of the trees in the forests of the Republic of Moldova. In 2015, weak unhealthy trees (defoliation class 1 – “slight”) constituted 40.4%, moderately unhealthy trees (defoliation class 2 – “moderate”) 24.2% and the strong unhealthy and dead trees (defoliation classes 3-4 “severe-dead”) 1.9%. Broadleaved forests were more affected than coniferous forests, the share of broadleaved trees in the defoliation classes “slight” and “dead” (classes 1-4) was 66.5% compared to 39.0% for conifers. All monitored deciduous species (oaks, locust, beech, ash, poplar and others) framed in defoliation class 1-4 ranged from 59.0% to 89.5% and trees in defoliation class 2-4 ranged from 15.7% to 29.7%. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 142 | 12.18 Norway 2015 was the third year in Norway with the new sampling design for Level I with annually one fifth of the NFI plots monitored and five year revision intervals on the plots, following the rotation of the National Forest Inventory (NFI). From 2013 we have crown condition assessments only for Picea abies and Pinus sylvestris, while damage assessments are carried out for all tree species present on the NFI plots including birch. This new design produces good estimates of average national crown condition; however estimates of regional crown condition are probably less accurate. In 2015, the mean defoliation for Picea abies was 15.9%, and 14.2 % for Pinus sylvestris. 2015 was a year with a slight increase in defoliation for both spruce and pine after four years in 2011-2014 with decreasing defoliation. Of all the coniferous trees, 45.1 % were rated as not defoliated in 2015, which is a decrease of about 3%- points compared to the year before. 42.4% of the Pinus sylvestris trees were rated as not defoliated which is a decrease of about 5%-points. 47.4% of all Norway spruce trees were not defoliated, a decrease of about 1%-points compared to the year before. With respect to crown discolouration, we observed 7% discoloured trees for Picea abies, a decrease of about 1%-point from 2015. For Pinus sylvestris, 2.8% of the assessed trees were discoloured, a decrease of about 2%-points from the year before. The mean mortality rate for all species was 0.2% in 2015. The mortality rate was 0.2% and 0.1% for spruce and pine, respectively. In general, the observed crown condition values result from interactions between climate, pests, pathogens, and general stress. According to the Norwegian Meteorological Institute the first half of the summer (June and July) of 2015 was cold with a temperature about 1° C lower than normal as an average for the country. The precipitation was slightly higher than normal. In sum, a cold and wet first half of the summer is good for the drought sensitive Norway spruce at dry sites, especially in the lowlands of Southeast Norway. The last half of the summer (August and September) was warm with about 2° C higher temperature than normal and about normal precipitation. The last half of the summer is normally not so crucial for growth and mortality for conifers in Norway. There are of course large climatic variations between regions in Norway, ranging from 58 to 71°N. 12.19 Poland In 2015 the forest condition survey was carried out on 2018 plots (grid 8 km x 8 km). Forest condition (all species total) slightly improved as compared to the previous year because of especially the broadleaved species. 11.9% of all sample trees were without any symptoms of defoliation, indicating an increase by 0.4 percent points compared to 2014. The proportion of defoliated trees (classes 2-4) decreased by 2.2 percent points to an actual level 16.7% of all trees. The health condition of broadleaved species was slightly better than that of the coniferous species. Broadleaved species were characterized by a significantly higher proportion of healthy trees (16.2%) and a slightly higher proportion of damaged trees (18.4%) than coniferous species (9.6% and 15.8% respectively). The share of trees defoliated by more than 25% decreased by 1.4 percent points for conifers and by 3.5 percent points for broadleaves compared to 2014. In 2015, mean defoliation for all species total amounts to 21.5%, with 21.6% for conifers and 21.4% for broadleaved trees. With regard to the three main coniferous species Abies alba remained the species with the lowest defoliation (19.5% trees in class 0, 15.3% trees in classes 2-4, mean defoliation amounting to 20.0%). Pinus sylvestris was characterized by a lower share of trees in class 0 (8.8%), little lower share of trees in classes 2-4 (15.0%) and a little higher mean defoliation (21.6%) than Abies alba. Otherwise Picea abies 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 143 was characterized by a medium share of trees in class 0 (12.2%), a higher share of trees in classes 2-4 (25.1%) and higher mean defoliation (23.0%) compared to Pinus sylvestris and Abies alba. 16.2% of the assessed broadleaved trees were not defoliated. The proportion of trees with more than 25% defoliation (classes 2-4) amounted to 18.4%. As in the previous survey the highest defoliation amongst broadleaved trees was observed in Quercus spp. In 2015 a share of 5.2% of oak trees was without any symptoms of defoliation and 28.1% was in defoliation classes 2-4, mean defoliation amounting to 24.5%. A slightly better condition was observed for Betula spp. (8.9% trees without defoliation, 20.7% damage trees and mean defoliation amounting to 22.5%). Fagus sylvatica remained the broadleaves species with the lowest defoliation. In 2015 a share of 38.3% of beech trees was without any symptoms of defoliation, only 5.2% was in defoliation classes 2-4, mean defoliation amounting to 15.7%. Alnus spp. was in quite good health, but was more defoliated (18.5% trees without defoliation, 11.2% trees in classes 2-4, mean defoliation amounting to 19.7%) than Fagus sylvatica, but less than Quercus spp. and Betula spp. Pinus sylvestris, Picea abies, Abies alba and Alnus sp. were almost in the same health condition compared to the previous year. Damage of Fagus sylvatica, Quercus spp. and Betula spp. slightly decreased. In 2015, discolouration (classes 1-4) was observed on 0.6% of the conifers and on 0.7% of the broadleaves. 12.20 Romania In 2015, the forest condition survey in Romania was carried out on the 16 x 16 km transnational Level I grid net, during 15th of July and 15th of September. The total number of sample trees was 5808, assessed on 242 permanent plots. From the total number of trees, 1092 were conifers (19%) and 4716 broadleaves (81%). The mean defoliation percentage of all tree species was 15.2%. From the total number of the assessed trees, 54.2% were rated as healthy, 32.7% as slightly defoliated, 11.3% as moderately defoliated, 1.4% as severely defoliated and 0.4% were dead. The share of damaged trees (defoliation classes 2-4) was 13.1%. For conifers a percentage of 9.5% of the assessed trees were classified as damaged (classes 2-4). Picea abies was the least affected coniferous species with a share of damaged trees of 7.8% (defoliation classes 2-4), whereas Abies alba had 15.5%. For broadleaves, 13.9% of the trees were recorded as damaged (classes 2-4). Among the main broadleave species, Fagus sylvatica and Robinia pseudoacacia had the lowest share of damaged trees (9.8% and 11.3% respectively). For all Quercus spp. (Q. petraea, Q. cerris, Q. robur, and Q. frainetto) a share of 16.6% were damaged from the total number of the assessed trees. The least affected species was Q. frainetto (9.1%) and the most affected was Q. petraea (17.2%). Q. robur recorded the highest percent (39.0%) of damaged trees (classes 2-4), although this species is very low represented (only 77 trees were assessed). The overall share of damaged trees (classes 2-4) decreased by 0.4 percentage points. The relative increased values of the precipitation regime registered in the south-west of Romania during 2015 led to a significant improvement of the health status of xerophyte oaks from 15.3% in 2014 to 8.8% (Quercus frainetto), and 12.8% (Quercus cerris) in 2015 respectively. Damage symptoms were reported for 23.0% of the conifers and 33.4% of the broadleaves respectively. The most important causes of damages were attributed to defoliator and xylophage insects (49.8%) and 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 144 | fungi (21.3%). In general, the intensity of the visible damage symptoms for the conifers was higher than for broadleaves. 12.21 Serbia In the region of the Republic of Serbia, ICP Forests consists of a 16 x 16 km grid with 103 sampling plots and an additional 4 x 4 grid, with 27 new plots, altogether the number of plots is 130 (not including in the assessment are AP Kosovo and Metohija). Observations at Level I were performed according to the ICP Forests Manual of Methods. During 2015, the researchers of the NFC Serbia - Institute of Forestry with collaborators from other institutions in Serbia, have worked on all sampling points and made visual assessment of the crown condition and collected the other necessary field data. The total number of trees assessed on all sampling points was 2910 trees, of which were 338 conifer trees and a considerably higher number, i.e. 2572, were broadleaf trees. The conifer tree species are: Abies alba, number of trees and percentage of individual tree species 69 (20.4%), Picea abies 146 (43.2%), Pinus nigra 67 (19.8%), Pinus sylvestris 56 (16.6%). The most represented broadleaf tree species are: Carpinus betulus, number of trees and percentage of individual tree species 114 (4.4%) , Fagus moesiaca 847 (32.9%), Quercus cerris 503 (19.6%), Quercus frainetto 380 (14.8%), Quercus petraea 184 (7.2%) and other species 544 (21.2%). The results of the available data processing and the assessment of the degree of defoliation of individual conifer and broadleaf species (%) are: Abies alba (None 85.5, Slight 5.8, Moderate 0.0, Severe 7.2 and Dead 1.5); Picea abies (None 84.3, Slight 9.6, Moderate 3.4, Severe 0.0, Dead 2.7); Pinus nigra (None 34.3, Slight 19.4, Moderate 32.8, Severe 11.9, Dead 1.5); Pinus sylvestris (None 89.3, Slight 5.4, Moderate 0.0, Severe 3.6, Dead 1.8). The degree of defoliation calculated for all conifer trees is as follows: no defoliation 75.4% trees, slight defoliation 10.1% trees, moderate 8.0% trees, severe defoliation 4.4% trees and dead 2.1% trees. Individual tree species’ defoliation (%) is: Carpinus betulus (None 87.7, Slight 5.3, Moderate 3.5, Severe 3.5, Dead 0.0); Fagus moesiaca (None 84.4, Slight 8.6, Moderate 3.9, Severe 2.8, Dead 0.2); Quercus cerris (None 67.8, Slight 22.1, Moderate 8.0, Severe 2.2, Dead 0.0); Quercus frainetto (None 83.2, Slight 12.9, Moderate 1.8, Severe 1.8, Dead 0.3); Quercus petraea (None 54.9, Slight 37.5, Moderate 6.0, Severe 1.1, Dead 0.5) and the rest (None 61.8, Slight 17.1, Moderate 12.9, Severe 6.4, Dead 1.8). Degree of defoliation calculated for all broadleaf species is as follows: no defoliation 74.3% of trees, slight defoliation 15.6% of trees, moderate 6.4%, severe defoliation 3.2% trees and dead 0.5% of trees. The data above show the presence of sample trees with moderate and severe degrees of defoliation, but this does not always signify the reduction of the vitality score caused by the effect of adverse agents (climate stress, insect pests, pathogenic fungi). This can only be a temporary phase of natural variability of crown density. 12.22 Slovakia The 2015 national crown condition survey was carried out on 106 Level I plots on the 16x16 km grid. The assessments covered 4354 trees, 3630 of which were being assessed as dominant or co-dominant trees according to Kraft. Of the 3630 assessed trees, 34.5% were damaged (defoliation classes 2-4). The respective figures were 49.4% for conifers and 24.3% for broadleaves. Compared to the year 2014, the share of trees defoliated more than 25% increased by 0.4%. Mean defoliation for all tree species together was 24.2%, with 28.3% for conifers and 21.4% for broadleaved trees. Results show that crown 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 145 condition in the Slovak Republic is worse than the European average. This is due to the worse condition of coniferous species. Compared to the 2014 survey, improvement of crown condition (average defoliation) was observed in all broadleaves species. The mean defoliation of the main broadleaved tree species (Fagus sylvatica, Quercus sp., Carpinus betulus) in the years 2011-2014 was increased. In 2013 the mean defoliation of broadleaved trees was even as high as the mean defoliation of conifers, which was for the first time in the history of forest monitoring. In 2015 the mean defoliation decreased back to the level that was common before 2009. The most severe damage has been observed in conifers (Pinus sylvestris and Picea abies). The lowest level of defoliation shows hornbeam (Carpinus betulus). From the beginning of the forest condition monitoring in 1987 until 1996 results show significant decrease of defoliation and visible forest damage. Since 1996, the share of damaged trees (25-32%) and average defoliation (22-26%) has been relatively stable (except for the above mentioned situation in the years 2011-2014 for broadleaved tree species). The recorded fluctuation of defoliation depends mostly on meteorological conditions. As a part of crown condition survey, damage types were assessed. 24.7% of all sampling trees (4354) had some kind of damage symptoms. The most damaged tree species according to visual symptoms were oak (32%) and hornbeam (40%). The most frequent damage was caused by harvesting and logging (9.5% of all trees), fungi (8.9%) and insects (5.5%). The most important effect on defoliation have epiphytes. 75% of trees damaged by epiphytes revealed defoliation above 25%. 12.23 Slovenia In 2015 the Slovenian national forest health inventory was carried out on 44 systematically arranged sample plots (grid 16 x 16 km). The assessment encompassed 1051 trees, 388 coniferous and 663 broadleaved trees. The sampling scheme and the assessment method was the same as in the previous years (at each location four M6 (six-tree) plots). Report for the year 2015 includes only 1051 instead of 1056 trees. The reason is the strong sleet damage of Slovenian forests in 2014 and in two plots there wre no trees with dbh bigger than 10 cm for the replacement of the felled trees. The mean defoliation of all tree species was estimated to be 28.1%. Compared to the 2014 survey, the situation improved for 0.1% (mean defoliation in 2014 was 28.2%). In the year 2015 mean defoliation for coniferous trees was 29.4% (in the year 2014 it was 27.6%) and for broadleaves 27.3% (year before 28.6%). In 2015 the share of trees with more than 25% of defoliation (damaged trees) reached 37.8%. In comparison to the results of 2014, when the share of trees with more than 25% of unexplained defoliation was 38.3%, the value decreased for 0.5%. Damaged broadleaves trees decreased from 38.4% in 2014 to 35.9% in 2015. Especially significant is the change of damaged trees for coniferous where the share of damaged trees increased from 38.8% in 2014 to 41.0% in 2015. In the year 2014 the share of damaged coniferous was just slightly greater than the share of damaged broadleaves trees. But in the year 2015 the share of damaged coniferous is significantly higher than the share of damaged broadleaves. In general, the mean defoliation of all tree species has slightly increased since 1991. In comparison to the year 2010 the mean defoliation deteriorated in year 2011, improved in 2012 and again deteriorated 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 146 | in 2013. The biggest change in the mean defoliation can be seen in the year 2014 due to the sleet damage in February 2014. In 2015 the defoliation of broadleaves decreased, but the defoliation of coniferous is even higher. The main reason is probably the bark beetle outbreak in summer of 2015. 12.24 Sweden An annual monitoring of the most important sources of forest damage is carried out by the Swedish National Forest Inventory (NFI). Although the Swedish NFI is an objective and uniform inventory including data about forest damage in Swedish forests at national and regional scales, less common or less widespread occurrences of forests pests and pathogens are difficult to survey solely through large- scale monitoring programmes. Complementary target tailored forest damage inventories (TFDI) have therefor been introduced. TDFIs are developed to give a rapid response to requested information on specific damage outbreaks. The TDFIs are carried out in limited and concentrated samples, with flexible but robust methods and design. The national results are based on assessment of the main tree species Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) in the National Forest Inventory (NFI), and concern, as previously, only forest of thinning age or older. In total, 8032 trees on 4097 sample plots were assessed. The Swedish NFI is carried out on permanent as well as on temporary sample plots. The permanent sample plots, which represent about 60 percent of the total sample, are remeasured every 5th year. The proportion of trees with more than 25 % defoliation is for Norway spruce 25.4% and for Scots pine 14.7%. A minor increase in defoliation for Norway spruce in central and southern Sweden is seen during the last ten years. While a slight improvement is seen in Norway spruce in northern Sweden during recent years. In all of Sweden, defoliation in Scots pine has increased during the last seven years. There are some large temporal changes seen in defoliation levels at regional level however the majority of changes during recent years are minor. A few minor storms affected southern Sweden in 2015. In total about 5–6 million m3 forest were wind felled. There are still wind-felled trees in small groups found spread over a large area in central Sweden. In October 2015 an estimated volume of more than 0.5 million m3 of wind-felled spruce trees were still available for breeding by bark beetles. Also 0.75 million of wind-felled spruce trees were found utilized in 2015 by bark beetles, mainly Ips typographus and Polygraphus sp. An increased damage to the growing forest is also seen. Approximately 0.4 million m3 of spruce trees were killed by bark beetles. The bark beetle populations have increased and it is likely that this will lead to a further increase in damage to the growing forest. The decline in Ash (Fraxinus excelsior) is continuing in southern Sweden. Severe problems remain with Dutch elm disease (Ophiostoma novo-ulmi). In northern Sweden problems with resin top disease (Cronartium flaccidum) still occur in young pine stands. In the same area during the last years damage by pine twisting rust (Melampsora pinitorqua) has also increased. Overall however the most important biotic damage problems are, as previously, due to pine weevil (Hylobius abietis) (in young forest plantations), browsing by ungulates - mainly elk (in young forest), and root rot caused by Heterobasidion annosum. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS | 147 12.25 Switzerland In 2015, the defoliation decreased again after it had been increasing from 2013 to 2014. The proportion of "significantly damaged trees 1 " between 30 % and 100% (class 2-4), decreased from 30.5% in 2014 to 24.7% in 2015 thus being also lower than the values from 2012 to 2013. The basis for this data is the crown assessment for a total of 1051 trees in 2015. The percentage observed in 2015 is still a bit higher than the most recent period with rather low defoliation (2005 to 2010), where the average of significantly damaged trees amounted to 21% of all trees assessed. The value for 2015 is, however, approaching the long-term average of the last twenty years, which is 23.3%. Whilst the proportion of slightly defoliated trees (class 1) did not change clearly between 2014 and 2015, the moderately defoliated ones (class 2) dropped from 19.5% to 13.2%. Moreover, the proportion of not defoliated trees increased between 2014 (18.2%) and 2015 (22.6%). The trends in 2015 fit into previous observations that there are in general strong high-frequency variations that can be seen since the end of the 90s. Thus, after the significant increase in defoliation observed until the mid ‘90s, no clear long-term trend is visible since about 2000. The heavy increases in defoliation and the subsequent recovery coincide often with climatic events. The storm Lothar was responsible for the maximum in 2000 and the dry and hot summer of 2003 for the second peak. However, increases in defoliation from 2009 to 2012 cannot be explained completely by climatic events and also the 2014 increase and the 2015 decrease is not directly attributable to meteorological extremes. We, however, observed a tendency for insect damage to more strongly contributing to defoliation. This relationship is mainly visible in deciduous trees, where the beech leaf miner (Rhynchaenus fagi) is likely to have the greatest influence. Still the defoliation trend for deciduous trees followed that of all trees species in 2015 and decreased from 2014 (28% class 2-4) to 2015 (26.2%). The increased frequency of mast years might contribute to the strong year-to-year variations. After a short relief in 2012, the ash dieback that started in Switzerland in 2008, caused another increase in defoliation in 2015 being comparable to 2013. A third of the ash trees are severely affected but there is also a tendency that new replacement sprouts allow trees to produce relatively dense crowns. 12.26 Turkey Monitoring studies have been conducted on a grid of 16x16 km and crown condition of 13 665 trees in 591 Level I sample plots have been evaluated in 2015. Average needle/leaf loss ratio of all evaluated trees is 15.6%. The ratio of healthy trees (class 0-1) is 95.6% and the remaining 9.5% had a loss ratio of greater than 25 percent. Annual average needle/leaf loss had slightly increased in comparison to last year. The average defoliation ratio of broadleaved species is 16.0% percent. Common tree species with highest defoliation ratios are Quercus pubescens (22.6%), Alnus glutinosa (24.3%), Castanea sativa (20.0%) and Quercus petraea (19.5%). The same species had the greatest needle/leaf loss in the last two years. Among the less common broadleaved species (each of which are presented by less than 20 individuals), Fraxinus ornus, Ceratonia siliqua, Juglans regia, Ostrya carpinifolia, Pistacia lentiscus ve Prunus avium have a 25% or greater defoliation ratio. While 89.2% of all broadleaved trees showed no or slight defoliation (class 0-1), 10.8% of them were defoliated by more than 25% (class 2-4). 1 Trees showing unexplained defoliation subtracting the percentage of defoliation due to known causes such as insect or frost damage. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S NATIONAL REPORTS ON THE 2015 NATIONAL CROWN CONDITION SURVEYS 148 | The average defoliation ratio of coniferous species is 15.4%. 91.4% of all evaluated coniferous trees have a needle loss of less than 25% (class 0-1), and the remaining 8.6% of them have over 25% needle loss (class 2-4). Pinus pinaster, Pinus brutia, Abies cilicica, Junipers (Juniperus foetidissima, J. excelsa, J. oxycedrus, J. communis) have the highest needle loss among common conifers with defoliation ratios between 18.4% and 16.2%. As for pine species, defoliation ratios of P. brutia, P. sylvestris and P. nigra are 17.6%, 15.0% and 12.5%, respectively. In addition, the greatest needle loss was observed in P. pinaster (24.6%), which is a less common species and represented by only 14 sample trees in this monitoring study. Among the biotic causes of damage, Rhynchaenus fagi, Lophodermium pinastri, Cinara cedri, Cryphonectria parasitica and Tomicus spp are the most pronounced. The number of trees affected by Thaumetopoea spp. declined by 7.5% in comparison to last year. As in previous years, mistletoe (Viscum alba) is also among the leading damaging agents. 12.27 Ukraine The field survey on Level I forest monitoring plots was carried out by specialists of the State Forest Management Enterprises (SFME’s) under the methodological guidance experts from the Ukrainian Research Institute of Forestry and Forest Melioration (URIFFM) and experts from Regional Forest Administrations (RFA). Responsibility for QA/QC of the forest monitoring data is placed to RFA and URIFFM, experts from URIFFM are responsible for maintaining the national forest monitoring database. In 2015, 31 978 sample trees were assessed on 1 341 permanent forest monitoring plots in 24 administrative regions of Ukraine (observations were not carried out in Crimea, and partly in the Donetsk and Lugansk regions). The average defoliation of conifers was 11.7 % and of broadleaved trees it was 12.0 %. Generally the tree crown condition is satisfactory: the part of healthy (not defoliated) trees amounts to 62.5%. Compared to the previous results there is some worsening of crown condition in 2015: for the total sample the percentage of healthy trees slightly decreased (62.5 against 65.1%), and respectively the part of slightly defoliated tress increased (from 28.3 to 30.4%). The part of “damaged trees” (with defoliation over 25%) also increased from 6% to 7.1%. For broadleaved the part of healthy trees is 60.9%, and respectively the part of defoliated trees is 39.1%, from those the part of damaged trees (with defoliation over 25%) is 6.3%. For conifers the part of healthy trees is 64.6% and the part of damaged trees (with defoliation of more then 25%) amounts to 7.9%. For the sample of common sample trees (CSTs) (31 678 trees) average defoliation slightly increased – from 11.2% to 11.8% compared to the previous year. In the current year the lowest average defoliation have Pinus sylvestris trees (10.5%), middle values – Quercus robur (12.3%), Fraxinus excelsior (11.1%) and the highest average defoliation have trees of Fagus sylvatica (13.2%), Abies alba (13.3%), and Picea abies (14.9%). ANNEX Annex I Tree crown condition and damage causes – additional maps Annex II Results of the national crown condition surveys Annex III List of woody species (Chapter 5) Annex IV Contacts 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 150 | ANNEX I TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS Annex I-1. Trends in mean plot defoliation (Mann-Kendall test) of all species between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 151 Annex I-2. Trends in mean plot defoliation (Mann-Kendall test) of all species between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 152 | Annex I-3. Trends in mean plot defoliation (Mann-Kendall test) of Scots pine between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 153 Annex I-4. Trends in mean plot defoliation (Mann-Kendall test) of Scots pine between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 154 | Annex I-5. Trends in mean plot defoliation (Mann-Kendall test) of Norway spruce between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 155 Annex I-6. Trends in mean plot defoliation (Mann-Kendall test) of Norway spruce between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 156 | Annex I-7. Trends in mean plot defoliation (Mann-Kendall test) of Austrian pine between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 157 Annex I-8. Trends in mean plot defoliation (Mann-Kendall test) of Austrian pine between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 158 | Annex I-9. Trends in mean plot defoliation (Mann-Kendall test) of Mediterranean lowland pines (Pinus brutia, P. halepensis, P. pinaster, P. pinea) between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 159 Annex I-10. Trends in mean plot defoliation (Mann-Kendall test) of Mediterranean lowland pines (Pinus brutia, P. halepensis, P. pinaster, P. pinea) between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 160 | Annex I-11. Trends in mean plot defoliation (Mann-Kendall test) of common beech between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 161 Annex I-12. Trends in mean plot defoliation (Mann-Kendall test) of common beech between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 162 | Annex I-13. Trends in mean plot defoliation (Mann-Kendall test) of deciduous temperate oaks (Quercus robur and Q. petraea) between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 163 Annex I-14. Trends in mean plot defoliation (Mann-Kendall test) of deciduous temperate oaks (Quercus robur and Q. petraea) between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 164 | Annex I-15. Trends in mean plot defoliation (Mann-Kendall test) of deciduous (sub-) Mediterranean oaks (Quercus cerris, Q. frainetto, Q. pubescens, Q. pyrenaica) between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 165 Annex I-16. Trends in mean plot defoliation (Mann-Kendall test) of deciduous (sub-) Mediterranean oaks (Quercus cerris, Q. frainetto, Q. pubescens, Q. pyrenaica) between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS 166 | Annex I-17. Trends in mean plot defoliation (Mann-Kendall test) of evergreen oaks (Quercus coccifera, Q ilex, Q. rotundifolia, Q. suber) between 2002 and 2015 with a minimum assessment length of 10 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S TREE CROWN CONDITION AND DAMAGE CAUSES – ADDITIONAL MAPS | 167 Annex I-18. Trends in mean plot defoliation (Mann-Kendall test) of evergreen oaks (Quercus coccifera, Q. ilex, Q. rotundifolia, Q. suber) between 2006 and 2015 with a minimum assessment length of 5 years. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 168 | ANNEX II RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS Annex II-1 | Information on the monitoring design in the countries participating in ICP Forests Participating countries Total area (1000 ha) Forest area (1000 ha) Coniferous forest (1000 ha) Broadleaf forest (1000 ha) Area surveyed (1000 ha) Grid size (km x km) No. of sample plots No. of sample trees Albania No data available for 2015 Andorra 46 17 15 2 17 4 x 4 12 289 Austria No data available for 2015 Belarus No data available for 2015 Belgium-Flanders 1 351 146 N/A N/A 146 4 x 4 71 1 611 Belgium-Wallonia 1 684 554 224 260 N/A N/A 45 402 Bulgaria 11 100 4 202 1 261 2 917 4 202 varying 159 5 513 Croatia 5 654 2 061 321 1 740 N/A 16 x 16 95 2 280 Cyprus 925 297 171 0 137 16 x 16 15 361 Czech Republic 7 887 2 666 1 956 710 2 666 N/A 136 5 218 Denmark 4 310 586 289 263 N/A N/A 379 2 003 Estonia 4 510 2 274 1 139 1 135 2 274 16 x 16 97 2 397 Finland No data available for 2015 France 55 150 15 549 3 080 9 769 N/A 16 x 16 567 8 871 Germany 35 721 11 419 5 900 4 728 10 628 16 x 16 424 10 209 Greece 13 196 6 513 1 430 1 930 1 459 16 x 16 47 1 113 Hungary 9 300 1 939 209 1 730 1 939 16 x 16 77 1 841 Ireland No data available for 2015 Italy 30 128 8 675 1 735 6 940 N/A 16 x 16 235 4 757 Latvia 6 459 3 162 1 454 1 711 3 162 16 x 16 116 1 732 Lithuania 6 529 2 180 1 150 906 2 056 4x4/16x16 1 060 6 340 Luxembourg 259 91 27 59 86 4 x 4 51 1 200 FYR of Macedonia No data available for 2015 Rep. of Moldova 3 384 N/A 8 367 375 N/A N/A 14 239 Montenegro 1 381 827 207 620 827 16 x 16 49 1 176 Netherlands No data available for 2015 Norway 32 376 12 000 6 800 5 200 12 000 N/A 1 664 9 153 Poland 31 268 9 177 6 350 2 827 9 177 8 x 8 2 018 40 360 Portugal No data available for 2015 Romania 23 839 6 233 1 873 4 360 6 233 16 x 16 242 5 808 Russian Fed. No data available for 2015 Serbia 8 836 2 360 179 2 181 1 868 16x16/4x4 130 2 910 Slovakia 4 904 2 014 768 1 246 2 014 16 x 16 106 3 630 Slovenia 2 027 1 248 N/A N/A 1 248 16 x 16 44 1 051 Spain No data available in 2015 Sweden 47 496 28 064 14 762 1 265 17 357 varying 4 097 8 032 Switzerland 4 129 1 279 778 501 N/A N/A 47 1 051 Turkey 77 846 21 537 13 158 8 379 9 057 16 x 16 591 13 665 Ukraine 60 350 9 400 2 756 3 285 5 790 16 x 16 1 341 31 978 United Kingdom No data available for 2015 TOTAL 492 045 156 470 66 127 65 031 13915 189 190 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 169 Annex II-2 | Tree defoliation of all species in 2015 Participating countries Area surveyed (1000 ha) No. of sample trees 0 none (%) 1 slight (%) 2 moderate (%) 3+4 severe and dead (%) 2+3+4 moderate to dead (%) Albania No data available for 2015 Andorra 17 289 77.9 17.6 3.8 0.7 4.5 Austria No data available for 2015 Belarus No data available for 2015 Belgium-Flanders 146 1 611 7.5 71.0 18.4 3.1 21.5 Belgium-Wallonia N/A 402 11.7 42.7 40.4 5.7 46.1 Bulgaria 4 202 5 513 33.7 40.1 17.6 8.6 26.2 Croatia N/A 2 280 32.0 38.3 24.6 5.2 29.7 Cyprus 137 361 29.7 57.8 11.4 1.1 12.5 Czech Republic 2 666 5 281 15.8 32.2 48.9 3.1 52.0 Denmark N/A 2 003 66.9 24.4 7.3 1.4 8.7 Estonia 2 274 2 397 50.8 42.5 5.5 1.2 6.7 Finland No data available for 2015 France N/A 8 871 21.0 35.0 39.8 3.6 43.4 Germany 10 628 10 209 33.2 43.1 22.1 1.7 23.8 Greece 1 459 1 841 48.1 31.7 15.8 4.4 20.2 Hungary 1 939 50.5 25.5 16.2 7.8 24.0 Ireland No data available for 2015 Italy N/A 4 757 28.8 41.4 24.6 5.2 29.8 Latvia 3 162 1 732 9.1 86.5 4.3 0.1 4.4 Lithuania 2 056 6 340 13.4 62.8 22.0 1.8 23.8 Luxembourg 86 1200 29.9 37.4 30.3 2.3 32.6 FYR of Macedonia No data available for 2015 Rep. of Moldova 375 14 239 33.6 40.3 24.2 1.9 26.1 Montenegro 827 1 176 31.9 42.7 21.1 4.3 25.4 Netherlands No data available for 2015 Norway 12 000 9 153 45.1 38.4 14.1 2.4 16.5 Poland 9 177 40 360 12.0 71.4 15.4 1.3 16.7 Portugal No data available for 2015 Romania 6 233 5 808 54.2 32.7 11.3 1.8 13.1 Russian Federation No data available for 2015 Serbia 1 868 2 910 74.4 14.9 6.6 4.1 10.7 Slovakia 2 014 3 630 15.0 50.5 33.6 0.9 34.5 Slovenia 1 248 1 051 17.5 44.7 30.8 6.9 37.8 Spain No data available for 2015 Sweden 17 357 8 032 47.4 32.8 17.3 2.5 19.8 Switzerland N/A 1 051 22.6 52.7 13.2 11.6 24.8 Turkey 9 057 13 665 44.1 44.7 8.1 1.3 9.5 Ukraine 5 790 31 978 62.5 30.4 6.6 0.5 7.1 United Kingdom No data available for 2015 Cyprus, Norway, Sweden: only conifers assessed. Note that some differences in the level of defoliation between participating countries may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 170 | Annex II-3 | Tree defoliation of conifers in 2015 Participating countries Coniferous Forest (1000 ha) No. of sample trees 0 None (%) 1 Slight (%) 2 Moderate (%) 3+4 severe and dead (%) 2+3+4 moderate to dead (%) Albania No data available for 2015 Andorra 15 283 77.7 18.0 3.9 0.4 4.3 Austria No data available for 2015 Belarus No data available for 2015 Belgium-Flanders N/A 721 5.3 74.9 19.3 0.5 19.8 Belgium-Wallonia 224 194 7.0 36.0 57.0 1.0 58.0 Bulgaria 1 261 2 386 21.0 38.9 30.5 9.6 40.1 Croatia 321 327 19.9 24.2 45.3 10.7 56.0 Cyprus 171 360 29.7 57.8 11.4 1.1 12.5 Czech Republic 1 956 3 995 13.8 28.4 54.4 3.4 57.8 Denmark 289 1 083 71.3 21.3 6.4 1.0 7.4 Estonia 1 139 2 048 49.7 43.8 5.2 1.3 6.5 Finland No data available for 2015 France 3 080 3 515 30.0 32.0 35.0 3.0 38.0 Germany 5 900 6 157 36.2 43.6 18.8 1.4 20.3 Greece 1 430 625 45.0 27.8 21.9 5.3 27.2 Hungary 209 33.3 20.2 27.8 18.7 46.5 Ireland No data available for 2015 Italy 1 735 1 184 38.5 38.9 19.3 3.3 22.6 Latvia 1 454 1 333 8.6 87.1 4.3 0.1 4.4 Lithuania 1 150 3 795 11.1 63.9 23.9 1.1 25.0 Luxembourg 27 426 55.4 25.7 17.0 1.7 18.7 FYR of Macedonia No data available for 2015 Rep. of Moldova Only broadleaves assessed Montenegro 207 288 36.8 37.2 16.0 10.1 26.1 Netherlands No data available for 2015 Norway 6 800 9 153 45.1 38.4 14.1 2.4 16.5 Poland 6 350 26 057 9.6 74.7 14.6 1.2 15.7 Portugal No data available for 2015 Romania 1 873 1 092 65.2 8.4 6.9 1.1 8.0 Russian Fed. No data available for 2015 Serbia 179 338 75.4 10.1 8.0 6.5 14.5 Slovakia 768 1 467 6.3 44.3 47.7 1.7 49.4 Slovenia N/A 388 18.0 41.0 33.3 7.7 41.0 Spain No data available in 2015 Sweden 14 762 8032 47.4 32.8 17.3 2.5 19.8 Switzerland 778 748 23.8 52.3 15.7 8.3 24.0 Turkey 13 158 8 457 42.7 48.7 7.8 0.9 8.6 Ukraine 2 756 13 816 64.6 27.5 7.5 0.4 7.9 United Kingdom No data available for 2015 Note that some differences in the level of defoliation between participating countries may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 171 Annex II-4 | Tree defoliation of broadleaves in 2015 Participating countries Broadleaf forest (1000 ha) No. of sample trees 0 None (%) 1 Slight (%) 2 Moderate (%) 3+4 severe and dead (%) 2+3+4 moderate to dead (%) Albania No data available for 2015 Andorra 2 5 83.3 0.0 0.0 16.7 16.7 Austria No data available for 2015 Belarus No data available for 2015 Belgium-Flanders N/A 890 9.3 67.9 17.6 5.2 22.8 Belgium-Wallonia 260 208 16.0 49.0 25.0 10.0 35.0 Bulgaria 2 917 3 127 43.4 41.0 7.8 7.9 15.6 Croatia 1 740 1 953 34.0 40.7 21.1 4.4 25.3 Cyprus Only conifers assessed Czech Republic 710 1 223 22.7 44.6 30.7 2.0 32.7 Denmark 263 908 60.1 29.1 8.9 1.9 10.8 Estonia 1 135 349 57.1 35.0 7.2 0.8 8.0 Finland No data available for 2015 France 9 769 5 266 15.0 37.0 43.0 4.0 47.0 Germany 4 728 4 052 28.7 42.2 26.9 2.1 29.0 Greece 1 930 488 52.1 36.6 8.0 3.3 11.3 Hungary 1 730 1 643 52.5 26.1 14.8 6.6 21.4 Ireland No data available for 2015 Italy 6 940 3 573 25.6 42.3 26.3 5.8 32.1 Latvia 1 711 399 11.1 84.7 4.2 0.0 4.2 Lithuania 906 2 545 17.0 61.1 19.1 2.8 21.9 Luxembourg 59 774 15.9 43.8 37.6 2.7 40.3 FYR of Macedonia No data available for 2015 Rep. of Moldova 367 14 201 33.5 40.4 24.2 1.9 26.1 Montenegro 620 888 30.3 44.5 22.8 2.5 25.2 Netherlands No data available for 2015 Norway Only conifers assessed Poland 2 827 14 303 16.2 65.5 16.8 1.6 18.4 Portugal No data available for 2015 Romania 4 360 4 716 51.7 34.4 12.0 1.9 13.9 Russian Fed. No data available for 2015 Serbia 2 181 2 572 74.3 15.6 6.4 3.7 10.1 Slovakia 1 246 2 163 20.9 54.8 23.9 0.4 24.3 Slovenia N/A 663 17.2 46.9 29.4 6.5 35.9 Spain No data available for 2015 Sweden Only conifers assessed Switzerland 501 303 20.0 53.6 8.0 18.4 26.4 Turkey 8 379 5 208 46.3 43.0 8.8 2.0 10.8 Ukraine 3 285 18 162 60.9 32.8 5.8 0.5 6.3 United Kingdom No data available for 2015 Note that some differences in the level of defoliation between participating countries may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 172 | Annex II-5 | Percentage of moderately to severely defoliated trees between 2005 and 2015 – All species Participating countries All species Defoliation classes 2–4 Change % points 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2014/15 Albania 11.1 21.0 N/A Andorra 23.0 47.2 15.3 6.8 15.3 8.3 5.6 3.4 5.3 4.5 -0.8 Austria 14.8 15.0 14.2 N/A Belarus 9.0 7.9 8.1 8.0 8.4 7.4 6.1 N/A Belgium 19.9 17.9 16.4 14.5 20.2 22.1 23.5 28.2 27.6 27.5 26.4 -1.1 Bulgaria 35.0 37.4 29.7 31.9 21.1 23.8 21.6 32.3 33.5 26.0 26.2 +0.2 Croatia 27.1 24.9 25.1 23.9 26.3 27.9 25.2 28.5 29.1 31.5 29.7 -1.8 Cyprus 10.8 20.8 16.7 47.0 36.2 19.2 16.4 10.6 8.9 13.3 12.5 -0.8 Czech Republic 57.1 56.2 57.1 56.7 56.8 54.2 52.7 50.3 51.7 52.0 N/A Denmark 9.4 7.6 6.1 9.1 5.5 9.3 10.0 7.3 4.9 7.0 8.7 +1.7 Estonia 5.4 6.2 6.8 9.0 7.2 8.1 8.1 7.8 8.0 6.7 6.7 0.0 Finland 8.8 9.7 10.5 10.2 9.1 10.5 10.6 14.3 N/A France 34.2 35.6 35.4 32.4 33.5 34.6 39.9 41.4 40.1 42.8 43.4 +0.6 Germany 28.5 27.9 24.8 25.7 26.5 23.2 28.0 24.6 22.7 26.2 23.8 -2.4 Greece 16.3 24.3 23.8 24.8 20.2 -4.6 Hungary 21.0 19.2 20.7 18.4 21.8 18.9 20.2 22.4 24.0 N/A Ireland 16.2 7.4 6.0 10.0 12.5 17.5 1.0 N/A Italy 32.9 30.5 35.7 32.8 35.8 29.8 31.3 35.7 33.7 30.8 29.8 -1.0 Latvia 13.1 13.4 15.0 15.3 13.8 13.4 14.0 9.2 6.4 5.1 4.4 -0.7 Lithuania 11.0 12.0 12.3 19.6 17.7 21.3 15.4 24.5 19.7 21.7 23.8 +2.1 Luxembourg 33.2 32.6 N/A FYR of Macedonia 23.0 N/A Rep. of Moldova 26.5 27.6 32.5 33.6 25.2 22.5 18.4 25.6 19.9 26.1 +6.2 Montenegro 22.7 25.4 N/A Netherlands 30.2 19.5 18.2 21.6 N/A Norway 21.6 23.3 26.2 22.7 21.0 18.9 20.9 18.8 17.7 15.9 16.5 +0.6 Poland 30.7 20.1 20.2 18.0 17.7 20.7 24.0 23.4 18.8 18.9 16.7 -2.2 Portugal 24.3 N/A Romania 8.1 8.6 23.2 18.9 17.8 13.9 13.9 13.6 13.5 13.1 -0.4 Russian Fed. 6.2 4.4 8.3 N/A Serbia 16.4 11.3 15.4 11.5 10.3 10.8 7.6 10.3 14.7 12.4 10.7 -1.7 Slovakia 22.9 28.1 25.6 29.3 32.1 38.6 34.7 37.9 43.4 34.5 N/A Slovenia 30.6 29.4 35.8 36.9 35.5 31.8 31.4 29.1 30.9 38.3 37.8 -0.5 Spain 21.3 21.5 17.6 15.6 17.7 14.6 11.8 17.5 16.6 14.9 N/A Sweden 18.4 19.4 17.9 17.3 15.1 19.2 18.9 15.9 19.9 19.8 N/A Switzerland 28.1 22.6 22.4 19.0 18.3 22.2 30.9 31.3 26.0 30.6 24.8 -5.8 Turkey 24.6 18.7 16.8 13.6 12.4 10.2 11.0 9.5 -1.5 Ukraine 8.7 6.6 7.1 8.2 6.8 5.8 6.8 7.5 7.1 6.0 7.1 +1.1 United Kingdom 24.8 25.9 26.0 48.5 N/A Note that some differences in the level of defoliation between participating countries may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time. Austria: from 2003 on results are based on the 16 x 16 km transnational grid net and must not be compared with previous years. Poland, Belgium-Wallonia: change of grid net since 2006 and 2010, resp. Russian Federation: north-western and Central European parts only. Ukraine: change of grid net in 2005. Hungary, Romania: comparisons not possible due to changing survey designs. Norway: new sampling design since 2013. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 173 Annex II-6 | Percentage of moderately to severely defoliated trees between 2005 and 2015 – Conifers Participating countries Conifers Defoliation classes 2–4 Change % points 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2014/15 Albania 13.6 21.0 N/A Andorra 23.0 47.2 15.3 6.8 15.3 8.3 5.6 3.1 5.4 4.3 -1.1 Austria 15.1 14.5 14.5 N/A Belarus 8.4 7.5 8.1 8.1 8.3 7.7 5.8 N/A Belgium 16.8 15.8 13.9 13.2 13.6 16.2 15.2 20.3 19.7 22.8 27.9 +5.1 Bulgaria 45.4 47.6 37.4 45.6 33.0 31.1 33.3 35.1 40.8 34.1 40.1 +6.0 Croatia 79.5 71.7 61.1 59.1 66.5 56.9 45.1 54.7 48.3 49.7 56.0 +6.3 Cyprus 10.8 20.8 16.7 46.9 36.2 19.2 16.4 10.6 8.9 13.3 12.5 -0.8 Czech Republic 62.7 62.3 62.9 62.8 63.1 60.1 58.9 56.9 59.2 57.8 N/A Denmark 5.5 1.7 3.1 9.9 1.0 5.4 5.7 4.6 2.8 5.3 7.4 +2.1 Estonia 5.6 6.0 6.7 9.3 7.5 9.0 8.7 6.6 8.5 6.9 6.5 -0.4 Finland 9.2 9.6 10.4 10.1 9.9 10.6 11.7 14.6 N/A France 20.8 23.6 24.1 25.1 26.8 27.4 31.9 32.2 33.7 36.6 38.0 +1.4 Germany 24.9 22.7 20.2 24.1 20.3 19.2 20.3 19.3 18.1 19.7 20.3 +0.6 Greece 15.0 26.3 23.7 26.7 27.2 +0.5 Hungary 22.0 20.8 22.3 27.1 35.1 28.7 23.1 23.5 46.5 N/A Ireland 16.2 7.4 6.2 10.0 12.5 17.5 1.0 N/A Italy 22.8 19.5 22.7 24.0 31.6 29.1 32.2 31.8 24.2 24.0 22.6 -1.4 Latvia 13.2 15.2 16.2 16.7 14.8 15.0 16.0 7.9 6.9 4.8 4.4 -0.4 Lithuania 9.3 9.5 10.2 19.1 17.4 19.8 16.3 26.9 23.1 21.1 25.0 +3.9 Luxembourg 17.5 93.3* 18.7 -74.6* FYR of Macedonia N/A Rep. of Moldova 38.0 38.6 34.3 33.3 32.1 44.3 29.4 N/A Montenegro 22.6 26.1 N/A Netherlands 17.9 15.3 14.1 18.9 N/A Norway 19.7 20.2 23.0 19.2 17.9 16.4 17.3 16.1 17.7 15.9 16.5 +0.6 Poland 29.6 21.1 20.9 17.5 17.2 20.3 24.2 22.3 17.8 17.2 15.7 -1.5 Portugal 17.1 N/A Romania 4.7 5.2 21.8 21.7 16.1 15.9 14.9 13.9 13.7 8.0 -5.7 Russian Fed. 7.3 5.1 10.6 N/A Serbia 21.3 12.6 13.3 13.0 12.6 12.0 11.1 11.0 13.0 14.6 14.5 -0.1 Slovakia 35.3 42.4 37.5 41.1 42.7 46.8 46.6 43.5 43.3 49.4 N/A Slovenia 33.8 32.1 36.0 40.7 38.8 37.8 33.6 31.3 31.3 38.1 41.0 +2.9 Spain 19.4 18.7 15.8 12.9 14.9 13.1 10.4 11.4 12.6 11.4 N/A Sweden 19.6 20.1 17.9 17.3 15.1 19.2 18.9 15.9 19.9 18.8 19.8 +1.0 Switzerland 28.2 22.5 20.7 18.7 18.8 20.9 31.5 30.6 23.3 31.7 24.0 -7.7 Turkey 8.1 16.2 16.0 14.5 11.6 9.9 6.9 7.2 8.6 +1.4 Ukraine 8.1 6.9 7.1 7.1 6.3 5.6 6.8 7.5 7.5 6.8 7.9 +1.1 United Kingdom 22.2 23.3 16.1 38.6 N/A * In Luxembourg only 3.5% of the conifers assessed in 2015 were assessed in 2014. Note that some differences in the level of defoliation between participating countries may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time. Austria: from 2003 on results are based on the 16 x 16 km transnational grid net and must not be compared with previous years. Poland, Belgium-Wallonia: change of grid net since 2006 and 2010, resp. Russian Federation: north-western and Central European parts only. Ukraine: change of grid net in 2005. Hungary, Romania: comparisons not possible due to changing survey designs. Norway: new sampling design since 2013. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 174 | Annex II-7 | Percentage of moderately to severely defoliated trees between 2005 and 2015 – Broadleaves Participating countries Broadleaves Defoliation classes 2–4 Change % points 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2013/14 Albania 8.5 19.0 N/A Andorra 20.0 20.0 16.7 -3.3 Austria 12.9 20.1 10.5 N/A Belarus 10.6 8.9 8.2 7.6 8.7 6.9 6.4 N/A Belgium 21.4 18.8 17.5 15.3 23.4 24.6 26.7 32.9 29.4 31.4 25.1 -6.3 Bulgaria 23.1 36.4 21.1 17.8 12.2 18.2 12.8 29.8 28.0 20.0 15.6 -4.4 Croatia 19.2 18.2 20.0 19.1 20.7 21.9 21.5 23.7 25.7 28.1 25.3 -2.8 Cyprus N/A Czech Republic 32.0 31.2 33.5 32.2 32.9 32.2 31.2 28.4 25.7 32.7 N/A Denmark 14.4 14.8 10.3 8.0 10.0 12.1 12.8 10.9 7.9 9.0 10.8 +1.8 Estonia 3.4 8.6 7.6 3.4 3.5 2.5 3.0 14.9 5.3 5.7 8.0 +2.3 Finland 7.2 10.3 10.9 10.6 4.7 9.2 6.0 12.8 N/A France 41.3 42.0 41.6 36.5 37.1 38.7 44.3 45.9 43.6 46.1 47.0 +0.9 Germany 35.8 37.2 32.8 28.4 36.1 29.4 38.0 32.5 29.8 36.1 29.0 -7.1 Greece 17.9 5.2 23.9 16.7 11.3 -5.4 Hungary 20.9 19.0 20.6 17.1 19.7 17.3 19.9 22.3 21.4 N/A Ireland N/A Italy 36.5 35.2 40.4 35.8 36.8 30.1 32.7 37.2 37.1 33.4 32.1 -1.3 Latvia 12.9 8.5 11.8 11.5 11.6 9.4 8.8 12.9 4.4 6.1 4.2 -1.9 Lithuania 15.4 16.6 17.7 20.3 18.4 23.7 13.8 21.0 14.7 22.5 21.9 -0.6 Luxembourg 42.4 *34.6 40.3 *+5.7 FYR of Macedonia N/A Rep. of Moldova 26.4 27.6 32.5 33.6 25.2 22.4 18.4 25.6 19.9 26.1 +6.2 Montenegro 22.8 25.2 N/A Netherlands 53.1 26.2 25.6 26.6 N/A Norway 27.6 33.2 36.3 33.8 31.0 26.8 32.3 27.3 N/A Poland 34.1 18.0 18.9 19.1 18.5 21.5 23.5 25.5 20.7 21.9 18.4 -3.5 Portugal 27.0 N/A Romania 9.3 9.9 23.5 18.3 18.0 13.4 13.6 13.6 13.0 13.9 +0.9 Russian Fed. 4.4 3.2 4.3 N/A Serbia 15.7 11.0 15.7 11.3 9.9 10.7 7.2 10.2 14.9 12.1 10.1 -2.0 Slovakia 13.6 17.0 16.6 20.8 24.5 32.9 26.4 33.9 43.5 43.5 24.3 -19.2 Slovenia 28.5 27.6 35.7 34.6 33.3 28.1 30.0 27.7 30.6 38.4 35.9 -2.5 Spain 23.3 24.4 19.5 18.4 20.7 16.1 13.2 23.6 20.7 18.4 N/A Sweden 9.2 10.8 N/A Switzerland 27.9 22.6 26.1 19.6 17.4 25.2 29.6 33.3 31.5 28.0 26.4 -1.6 Turkey 38.3 23.4 21.2 17.2 16.8 15.7 17.2 10.8 -6.4 Ukraine 9.2 6.2 7.1 9.1 7.2 6.4 6.7 7.5 7.0 5.5 6.3 +0.8 United Kingdom 28.2 29.2 35.3 56.1 N/A * In Luxembourg only 10.1% of the broadleaves assessed in 2015 were assessed in 2014. Note that some differences in the level of damage between participating countries may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time. Austria: from 2003 on results are based on the 16 x 16 km transnational grid net and must not be compared with previous years. Poland, Belgium-Wallonia: change of grid net since 2006 and 2010, resp. Russian Federation: north-western and Central European parts only. Ukraine: change of grid net in 2005. Hungary, Romania: comparisons not possible due to changing survey designs. Norway: new sampling design since 2013. 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 175 Annex II-8 | Change of tree defoliation over time (1991–2015) per country ALBANIA ANDORRA AUSTRIA 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 176 | BELARUS BELGIUM BULGARIA 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 177 CROATIA CYPRUS CZECH REPUBLIC 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 178 | DENMARK ESTONIA FINLAND 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 179 FRANCE GERMANY GREECE 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 180 | HUNGARY IRELAND ITALY 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 181 LATVIA LIECHTENSTEIN LITHUANIA 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 182 | LUXEMBOURG FYR OF MACEDONIA REPUBLIC OF MOLDOVA 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 183 MONTENEGRO THE NETHERLANDS NORWAY 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 184 | POLAND PORTUGAL ROMANIA 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 185 RUSSIAN FEDERATION SERBIA SLOVAKIA 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 186 | SLOVENIA SPAIN SWEDEN 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS | 187 SWITZERLAND TURKEY UKRAINE 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S RESULTS OF THE NATIONAL CROWN CONDITION SURVEYS 188 | UNITED KINGDOM 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S LIST OF WOODY SPECIES (CHAPTER 5) | 189 ANNEX III LIST OF WOODY SPECIES (CHAPTER 5) Taxon N. of records Taxon N. of records Picea abies 2069 Quercus pyrenaica 142 Fagus sylvatica 1812 Euonymus europaeus 134 Rubus idaeus 1310 Alnus glutinosa 130 Betula pendula 982 Lavandula stoechas 130 Fraxinus excelsior 920 Phillyrea latifolia 130 Corylus avellana 919 Genista scorpius 124 Rubus sp. 885 Prunus serotina 121 Carpinus betulus 784 Pinus pinaster 112 Pinus sylvestris 679 Quercus rubra 110 Vaccinium myrtillus 626 Quercus coccifera 108 Salix caprea 580 Viburnum lantana 107 Quercus robur 566 Pinus nigra 105 Prunus spinosa 560 Cytisus scoparius 104 Rubus fruticosus group 504 Lithodora diffusa 103 Crataegus monogyna 500 Fraxinus ornus 99 Populus tremula 473 Pinus pinea 94 Sorbus aucuparia 455 Smilax aspera 92 Acer campestre 444 Robinia pseudacacia 91 Frangula alnus 430 Cistus albidus 90 Acer pseudoplatanus 426 Spartium junceum 90 Hedera helix 377 Castanea sativa 89 Quercus ilex 376 Quercus suber 89 Rubus caesius 373 Lonicera xylosteum 88 Rubus ulmifolius 349 Anthyllis cytisoides 84 Rosa canina 328 Ilex aquifolium 84 Cistus incanus 296 Ulex gallii 83 Quercus petraea 278 Quercus sp. 82 Quercus cerris 273 Pinus cembra 81 Thymus vulgaris 265 Crataegus sp. 80 Rosmarinus officinalis 250 Cistus ladanifer 79 Rubus fruticosus 246 Sorbus aria 79 Prunus avium 242 Ligustrum vulgare 76 Cornus sanguinea 227 Rubus hirtus 76 Pinus halepensis 216 Lavandula latifolia 75 Ulex parviflorus 212 Erica arborea 74 Larix decidua 209 Quercus frainetto 73 Juniperus communis 203 Alnus incana 72 Abies alba 197 Halimium lasianthum 71 Acer platanoides 183 Helianthemum apenninum 71 Helianthemum marifolium 182 Myrtus communis 71 Clematis vitalba 180 Lonicera periclymenum 70 Sambucus nigra 177 Arctostaphylos uva-ursi 68 Cistus salvifolius 174 Cornus mas 68 Tilia cordata 165 Genista hispanica 66 Rosa sp. 159 Pinus radiata 66 Calluna vulgaris 158 Rhamnus lycioides 66 Vaccinium vitis-idaea 150 Ulmus glabra 62 Pistacia lentiscus 146 Rhamnus alaternus 61 Juniperus oxycedrus 144 Rubus nessensis 56 Thymus sp. 55 Cistus crispus 19 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S LIST OF WOODY SPECIES (CHAPTER 5) 190 | Taxon N. of records Taxon N. of records Erica herbacea 51 Helianthemum lavandulifolium 19 Salix myrsinifolia 49 Populus sp. 19 Sambucus racemosa 49 Rhamnus alpinus 19 Quercus alnifolia 48 Sambucus ebulus 19 Salix alba 48 Coronilla emerus 18 Thymus mastichina 48 Fraxinus pennsylvanica 18 Viburnum opulus 48 Pseudotsuga menziesii 18 Abies sp. 47 Pyrus communis 18 Salix cinerea 47 Acer opalus 17 Rosa elliptica 46 Anthyllis hermanniae 17 Phillyrea angustifolia 45 Dorycnium pentaphyllum 17 Fumana ericoides 44 Erica cinerea 17 Ribes rubrum 44 Juniperus phoenicea 17 Cistus clusii 42 Pistacia terebinthus 17 Ostrya carpinifolia 42 Chamaerops humilis 16 Humulus lupulus 40 Cytisus striatus 16 Ononis minutissima 39 Salix purpurea 16 Sorbus torminalis 39 Thymus longicaulis 16 Salix atrocinerea 38 Cistus laurifolius 15 Tilia platyphyllos 38 Cytisus sessilifolius 15 Genista tinctoria 37 Clematis flammula 14 Salix sp. 37 Cotoneaster sp. 14 Arbutus unedo 36 Malus sylvestris 14 Jasminum nudiflorum 35 Tilia sp. 14 Picea pungens 35 Crataegus laevigata 13 Helianthemum nummularium 32 Euonymus verrucosus 13 Helianthemum sp. 32 Genista germanica 13 Ulmus minor 32 Morus alba 12 Prunus padus 30 Prunus sp. 12 Quercus pubescens 30 Quercus dalechampii 12 Alnus viridis 29 Rhamnus catharticus 12 Ulmus laevis 29 Daphne mezereum 11 Erica vagans 27 Pinus uncinata 11 Ulex sp. 27 Scutellaria cypria 11 Malus sp. 26 Acer sp. 10 Populus alba 26 Mespilus germanica 10 Halimium halimifolium 25 Populus x canadensis 10 Pyrus pyraster 25 Thymus serpyllum 10 Salix aurita 25 Vitis vinifera 10 Berberis cretica 24 Amelanchier sp. 9 Rhododendron ferrugineum 23 Cotoneaster integerrimus 9 Acer negundo 22 Daboecia cantabrica 9 Lonicera implexa 22 Erica multiflora 9 Salix fragilis 22 Juglans regia 9 Vaccinium uliginosum 22 Polygala chamaebuxus 9 Berberis vulgaris 21 Sorbus mougeotii 9 Betula pubescens 21 Eucalyptus camaldulensis 8 Coronilla juncea 21 Laburnum alpinum 8 Genista sp. 20 Malus domestica 8 Helianthemum cinereum 20 Ulmus procera 8 Olea europaea 20 Ailanthus altissima 7 Ruscus aculeatus 20 Amorpha fruticosa 7 Aesculus hippocastanum 19 Cytisus sp. 7 Mahonia aquifolium 7 Taxus baccata 2 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S LIST OF WOODY SPECIES (CHAPTER 5) | 191 Taxon N. of records Taxon N. of records Pyrus syriaca 7 Ulex minor 2 Ribes uva-crispa 7 Alyssum bertolonii 1 Spiraea x vanhouttei 7 Arthrocnemum macrostachyum 1 Cotoneaster nebrodensis 6 Calluna sp. 1 Genista hirsuta 6 Celtis australis 1 Genista pilosa 6 Cornus sp. 1 Globularia alypum 6 Coronilla valentina 1 Lithodora fruticosa 6 Cytisus emeriflorus 1 Philadelphus sp. 6 Cytisus patens 1 Prunus mahaleb 6 Dorycnium hirsutum 1 Quercus faginea 6 Fumana sp. 1 Rosa sempervirens 6 Laburnum anagyroides 1 Solanum dulcamara 6 Lavandula angustifolia 1 Ulex europaeus 6 Lonicera nigra 1 Acer tataricum 5 Lonicera sp. 1 Buxus sempervirens 5 Ononis natrix 1 Cytisus villosus 5 Phillyrea sp. 1 Erica sp. 5 Pyrus amygdaliformis 1 Juniperus sabina 5 Ruta chalepensis 1 Juniperus thurifera 5 Salix elaeagnos 1 Osyris quadripartita 5 Salix viminalis 1 Populus nigra 5 Sophora japonica 1 Prunus domestica 5 Sorbus domestica 1 Acer hyrcanum 4 Symphoricarpos albus 1 Buddleja davidii 4 Tilia tomentosa 1 Crataegus macrocarpa 4 Vaccinium sp. 1 Daphne gnidium 4 Larix sp. 4 Lithodora sp. 4 Lonicera alpigena 4 Populus x canescens 4 Ruta graveolens 4 Acacia dealbata 3 Alnus sp. 3 Amelanchier ovalis 3 Daphne laureola 3 Genista anglica 3 Juniperus sp. 3 Polygala sp. 3 Ribes nigrum 3 Amelanchier spicata 2 Chamaecytisus austriacus 2 Ephedra distachya 2 Ononis fruticosa 2 Pinus mugo 2 Ribes petraeum 2 Rosa rugosa 2 Rubus corylifolius group 2 Salix reticulata 2 Satureja montana 2 Sorbus sp. 2 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 192 | ANNEX IV CONTACTS Annex IV-1 | UNECE and ICP Forests UNECE – LRTAP Convention United Nations Economic Commission for Europe LRTAP Convention Secretariat Palais des Nations, 8-14, avenue de la Paix 1211 Geneva 10, SWITZERLAND Phone: +41 22 917 23 58/Fax: +41 22 917 06 21 Email: krzysztof.olendrzynski@unece.org Mr Krzysztof Olendrzynski ICP Forests Lead Country Federal Ministry of Food and Agriculture - Ref. 535 Postfach 14 02 70 53107 Bonn, GERMANY Phone: +49 228 99 529-41 30/Fax: +49 228-99 529 42 62 Email: sigrid.strich@bmel.bund.de, 535@bmel.bund.de Ms Sigrid Strich ICP Forests Chairperson Universität Hamburg, Zentrum Holzwirtschaft Leuschnerstr. 91 21031 Hamburg, GERMANY Phone: +49 40 739 62 101/Fax: +49 40 739 62 199 Email: michael.koehl@uni-hamburg.de Mr Michael Köhl, Chairman of ICP Forests ICP Forests Programme Co-ordinating Centre (PCC) Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42 16225 Eberswalde, GERMANY Phone: +49 3334 3820-338 /Fax: +49 3334 3820-354 Email: walter.seidling@thuenen.de http://icp-forests.net Mr Walter Seidling, Head of PCC Annex IV-2 | Expert panels, working groups, and other coordinating institutions Expert Panel on Soil and Soil Solution Mr Bruno De Vos, Chair Research Institute for Nature and Forest (INBO) Environment & Climate Unit Gaverstraat 4 9500 Geraardsbergen, BELGIUM Phone: +32 54 43 71 20/Fax: +32 54 43 61 60 Email: bruno.devos@inbo.be Ms Nathalie Cools, Co-chair Research Institute for Nature and Forest (INBO) Gaverstraat 4 9500 Geraardsbergen, BELGIUM Phone: + 32 54 43 61 75/Fax: +32 54 43 61 60 Email: nathalie.cools@inbo.be 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 193 Ms Tiina Nieminen, Co-chair Natural Resources Institute Finland (LUKE) Jokiniemenkuja 1 01370 Vantaa, FINLAND Phone: +358 10 211 5457/Fax: +358 10 211 2103 Email: tiina.m.nieminen@luke.fi Expert Panel on Foliar Analysis and Litterfall Mr Pasi Rautio, Chair Natural Resources Institute Finland (LUKE) PO Box 16, Eteläranta 55 96301, Rovaniemi, FINLAND Phone: +358 50 391 4045/Fax: +358 10 211 4401 Email: pasi.rautio@luke.fi Ms Liisa Ukonmaanaho, Co-chair Litterfall Natural Resources Institute Finland (LUKE) Jokiniemenkuja 1 01370 Vantaa, FINLAND Phone: +358 10 211 5115/Fax: +358 10 211 2103 Email: liisa.Ukonmaanaho@luke.fi Expert Panel on Forest Growth Mr Tom Levanič, Chair Slovenian Forestry Institute (SFI) Večna pot 2 1000 Ljubljana, SLOVENIA Phone: +386 1200 78 44 Email: tom.levanic@gozdis.si Mr Vivian Kvist Johannsen, Co-chair University of Copenhagen Department of Geosciences and Natural Resource Management Rolighedsvej 23 1958 Frederiksberg C, DENMARK Phone: +453 53 316 99 Email: vkj@ign.ku.dk Expert Panel on Deposition Ms Karin Hansen, Chair Swedish Environmental Research Institute (IVL) Natural Resources & Environmental Research Effects Box 210 60 100 31 Stockholm, SWEDEN Phone: +46 859 85 64 25(direct) and +46 859 85 63 00 Fax: +46 859 85 63 90 Email: karin.hansen@ivl.se Mr Daniel Žlindra, Co-chair Slovenian Forestry Institute (SFI) Gozdarski Inštitut Slovenije GIS Večna pot 2 1000 Ljubljana, SLOVENIA Phone: +38 6 12 00 78 00/Fax: +38 6 12 57 35 89 Email: daniel.zlindra@gozdis.si 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 194 | Expert Panel on Ambient Air Quality Mr Marcus Schaub, Chair Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Zürcherstr. 111 8903 Birmensdorf, SWITZERLAND Phone: +41 44 739 25 64/Fax: +41 44 739 22 15 Email: marcus.schaub@wsl.ch Ms Elena Gottardini, Co-chair Fondazione Edmund Mach Via Mach 1 38010 San Michele all'Adige, ITALY Phone: +39 0461 615 362 Email: elena.gottardini@fmach.it Expert Panel on Crown Condition and Damage Causes Mr Nenad Potočić, Chair Croatian Forest Research Institute (CFRI) Cvjetno naselje 41 10450 Jastrebarsko, CROATIA Phone: +385 162 73 027/Fax: +385 162 73 035 Email: nenadp@sumins.hr Mr Volkmar Timmermann, Co-chair Norwegian Institute of Bioeconomy Research (NIBIO) P.O. Box 115 1431 Ås, NORWAY Phone: +47 971 59 901 Email: volkmar.timmermann@nibio.no Expert Panel on Biodiversity and Ground Vegetation Assessment Mr Roberto Canullo, Chair Camerino University Dept. of Environmental Sciences Via Pontoni, 5 62032 Camerino, ITALY Phone: +39 0737 404 503/5 /Fax: +39 0737 404 508 Email: roberto.canullo@unicam.it Expert Panel on Meteorology, Phenology and Leaf Area Index Mr Stephan Raspe, Chair Bayerische Landesanstalt für Wald und Forstwirtschaft (LWF) Hans-Carl-von-Carlowitz-Platz 1 85354 Freising, GERMANY Phone: +49 81 61 71 49 21/Fax: +49 81 61 71-49 71 Email: Stephan.Raspe@lwf.bayern.de Mr Stefan Fleck, Co-chair (LAI) Nordwestdeutsche Forstliche Versuchsanstalt (NW-FVA) Grätzelstr. 2 37079 Göttingen, GERMANY Phone: +49 55 16 94 01 107/Fax: +49 55 16 94 01 160 Email: Stefan.Fleck@NW-FVA.de Forest Soil Coordinating Centre (FSCC) Ms Nathalie Cools, Chair Research Institute for Nature and Forest (INBO) Gaverstraat 4 9500 Geraardsbergen, BELGIUM Phone: + 32 54 43 61 75/Fax: +32 54 43 61 60 Email: nathalie.cools@inbo.be 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 195 Forest Foliar Coordinating Centre (FFCC) Mr Alfred Fürst, Chair Austrian Research Centre for Forests (BFW) Seckendorff-Gudent-Weg 8 1131 Wien, AUSTRIA Phone: +43 1878 38-11 14/Fax: +43 1878 38-12 50 Email: alfred.fuerst@bfw.gv.at Quality Assurance Committee Mr Marco Ferretti, Chair TerraData environmetrics Via L. Bardelloni 19 58025 Monterotondo Marittimo, ITALY Phone/Fax: +39 056 691 66 81 Email: ferretti@terradata.it Mr Nils König, Co-chair Nordwestdeutsche Forstliche Versuchsanstalt (NW-FVA) Grätzelstraße 2 37079 Göttingen, GERMANY Phone: +49 551 69 40 11 41/Fax: +49 551 69 40 11 60 Email: Nils.Koenig@NW-FVA.de Ms Anna Kowalska, Co-chair Forest Research Institute (FRI) Sekocin Stary ul. Braci Lesnej 3 05090 Raszyn, POLAND Phone: +48 22 71 50 657/Fax: +48 22 72 00 397 Email: A.Kowalska@ibles.waw.pl WG on Quality Assurance and Quality Control in Laboratories Mr Nils König, Chair Nordwestdeutsche Forstliche Versuchsanstalt (NW-FVA) Grätzelstraße 2 37079 Göttingen, GERMANY Phone: +49 551 69 40 11 41/Fax. +49 551 69 40 11 60 Email: Nils.Koenig@NW-FVA.de Ms Anna Kowalska, Co-chair Forest Research Institute Sękocin Stary, 3 Braci Leśnej Street 05-090 Raszyn, POLAND Phone: +48 22 71 50 300/Fax: +48 22 72 00 397 Email: a.kowalska@ibles.waw.pl Scientific Evaluation Committee Mr Marco Ferretti, Chair TerraData environmetrics Via L. Bardelloni 19 58025 Monterotondo Marittimo, ITALY Phone/Fax: +39 056 691 66 81 Email: ferretti@terradata.it 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 196 | Annex IV-3 | Ministries (Min) and National Focal Centres (NFC) Albania (Min) Ministry of the Environment, Forests and Water Administration (MEFWA) Dep. of Biodiversity and Natural Resources Management Rruga e Durrësit, Nr. 27, Tirana, ALBANIA Phone: +355 42 70 621, +355 42 70 6390/Fax: +355 42 70 627 Email: info@moe.gov.al (NFC) National Environment Agency Bulevardi "Bajram Curri", Tirana, ALBANIA Phone: +355 42 64 903 and +355 42 65 299/646 32 Email: jbeqiri@gmail.com, kostandin.dano@akm.gov.al Mr Julian Beqiri (Head of Agency), Mr Kostandin Dano (Head of Forestry Department) Andorra (Min, NFC) Ministeri de Turisme I Medi Ambient Departament de Medi Ambient C. Prat de la Creu, 62-64, 500 Andorra la Vella, Principat d'Andorra, ANDORRA Phone: +376 87 57 07/Fax: +376 86 98 33 Email: silvia_ferrer_lopez@govern.ad, Anna_Moles@govern.ad Ms Silvia Ferrer, Ms Anna Moles Austria (Min) Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, Abt. IV/2 Stubenring 1, 1010 Wien, AUSTRIA Phone: +43 1 71 100 72 14/Fax: +43 1 71 10 0 0 Email: vladimir.camba@lebensministerium.at Mr Vladimir Camba (NFC) Austrian Research Centre for Forests (BFW) Seckendorff-Gudent-Weg 8, 1131 Wien, AUSTRIA Phone: +43 1 878 38 13 30/Fax: +43 1 878 38 12 50 Email: ferdinand.kristoefel@bfw.gv.at, markus.neumann@bfw.gv.at Mr Ferdinand Kristöfel, Mr Markus Neumann Belarus (Min) Ministry of Forestry of the Republic of Belarus Myasnikova st. 39, 220048 Minsk, BELARUS Phone +375 17 200 46 01/Fax: +375 17 200 4497 Email: mlh@mlh.by Mr Petr Semashko (NFC) Forest inventory republican unitary company "Belgosles" Zheleznodorozhnaja St. 27 220089 Minsk, BELARUS Phone: +375 17 22 63 053/Fax: +375 17 226 30 92 Email: belgosles@open.minsk.by, mlh@mlh.by Mr Valentin Krasouski 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 197 Belgium Wallonia (Min) Service public de Wallonie (SPW), Direction générale opérationnelle Agriculture, Ressources naturelles et Environnement (DGARNE) Département de la Nature et des Forêts - Direction des Ressources Forestières Avenue Prince de Liège 15, 5100 Jambes, BELGIUM Phone: +32 81 33 58 42 and +32 81 33 58 34 Fax: +32 81 33 58 11 Email: christian.laurent@spw.wallonie.be, etienne.gerard@spw.wallonie.be Mr Christian Laurent, Mr Etienne Gérard Wallonia (NFC for Level I) Environment and Agriculture Department/ Public Service of Wallonia Avenue Maréchal Juin, 23, 5030 Gembloux, BELGIUM PHONE: +32 81 626 452/Fax: +32 81 615 727 and Email: elodie.bay@spw.wallonie.be Ms Elodie Bay Wallonia (NFC for Level II) Earth and Life Institute / Environmental Sciences (ELI-e) Université catholique de Louvain Croix du Sud, 2 - L7.05.09, 1348 Louvain-La-Neuve, BELGIUM Phone: +32 10 47 25 48 Fax: +32 10 47 36 97 Email: hugues.titeux@uclouvain.be Mr Hugues Titeux Flanders (Min) Vlaamse Overheid (Flemish Authorities) Agency for Nature and Forest (ANB) Koning Albert II-laan 20, 1000 Brussels, BELGIUM Phone: +32 2 553 81 22/Fax: +32 2 553 81 05 Email: carl.deschepper@lne.vlaanderen.be Mr Carl De Schepper Flanders (NFC) Research Institute for Nature and Forest (INBO) Gaverstraat 4, 9500 Geraardsbergen, BELGIUM Phone: +32 54 43 71 15/Fax: +32 54 43 61 60 Email: peter.roskams@inbo.be Mr Peter Roskams Bulgaria (Min) Ministry of Environment and Water National Nature Protection Service 22, Maria Luiza Blvd., 1000 Sofia, BULGARIA Phone: + 359 2 940 61 12/Fax: +359 2 940 61 27 Email: p.stoichkova@moew.government.bg Ms Penka Stoichkova (NFC) Executive Environment Agency at the Ministry of Environment and Water Monitoring of Lands, Biodiversity and Protected Areas Department 136 Tzar Boris III Blvd., P.O. Box 251, 1618 Sofia, BULGARIA Phone: +359 2 940 64 86/Fax:+359 2 955 90 15 Email: forest@eea.government.bg Ms Genoveva Popova Canada (Min, NFC) Natural Resources Canada 580 Booth Str., 12th Floor, Ottawa, Ontario K1A 0E4, CANADA Phone: +1613 947 90 60/Fax: +1613 947 90 35 Email: Pal.Bhogal@nrcan.gc.ca Mr Pal Bhogal 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 198 | Québec (Min, NFC) Ministère des Ressources naturelles Direction de la recherche forestière 2700, rue Einstein, bureau BRC. 102, Ste. Foy Quebec G1P 3W8, CANADA Phone: +1 418 643 79 94 Ext. 65 33/Fax: +1 418 643 21 65 Email: rock.ouimet@mrnf.gouv.qc.ca Mr Rock Ouimet Croatia (Min, NFC) Croatian Forest Research Institute Cvjetno naselje 41, 10450 Jastrebarsko, CROATIA Phone: +385 1 62 73 027/Fax: + 385 1 62 73 035 Email: nenadp@sumins.hr Mr Nenad Potočić Cyprus (Min, NFC) Ministry of Agriculture Natural Resources and Environment Research Section - Department of Forests Louki Akrita 26, 1414-Nicosia, CYPRUS Phone: +357 22 81 94 90/Fax: +357 22 30 39 35 Email: achristou@fd.moa.gov.cy Mr Andreas Christou Czech Republic (Min) Ministry of Agriculture of the Czech Republic Forest Management Tešnov 17, 117 05 Prague 1, CZECH REPUBLIC Phone: +420 221 81 2677/Fax: +420 221 81 29 88 Email: tomas.krejzar@mze.cz Mr Tomáš Krejzar (NFC) Forestry and Game Management Research Institute (FGMRI) Strnady 136, 252 02 Jíloviště, CZECH REPUBLIC Phone: +420 257 89 22 21/Fax: +420 257 92 14 44 Email: lomsky@vulhm.cz Mr Bohumír Lomský Denmark (Min) Danish Ministry of the Environment; Danish Nature Agency Haraldsgade 53, 2100 Copenhagen, DENMARK Phone: +45 72 54 30 00 Email: nst@nst.dk Ms Gertrud Knudsen (NFC) University of Copenhagen Department of Geosciences and Natural Resource Management Rolighedsvej 23, 1958 Frederiksberg C, DENMARK Phone: +45 35 33 18 97/Fax: +45 35 33 15 08 Email: moi@life.ku.dk Mr Morten Ingerslev Estonia (Min) Ministry of the Environment Forest Department Narva mnt 7a, 15172 Tallinn, ESTONIA Phone: +27 26 26 0726/Fax: +27 26 26 28 01 Email: maret.parv@envir.ee Ms Maret Parv, Head of Forest Department 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 199 (NFC) Estonian Environment Agency (EEIC) Rõõmu tee 2, 51013 Tartu, ESTONIA Phone:+372 733 93 97/Fax: +372 733 94 64 Email: endla.asi@envir.ee Ms Endla Asi Finland (Min) Ministry of Agriculture and Forestry Forest Department Hallituskatu 3 A, P.O.Box 30, 00023 Government, FINLAND Phone: +358 9 160 523 19/Fax +358 9 160 52 400 Email: teemu.seppa@mmm.fi Mr Teemu Seppä (NFC) Natural Resources Institute Finland (LUKE) Oulu Unit PO Box 413, 90014 Oulun yliopisto, FINLAND Phone: +358 29 532 4061 Email: paivi.merila@luke.fi Ms Päivi Merilä France (Min) (NFC for Level I) Ministère de l‘Agriculture, de l’Agroalimentaire et de la Forêt Direction générale de l'alimentation Département de la santé des forêts 251, rue de Vaugirard, 75732 Paris cedex 15, FRANCE Phone: +33 1 49 55 51 03/Fax: +33 1 49 55 59 49 Email: Frederic.delport@agriculture.gouv.fr, fabien.caroulle@agriculture.gouv.fr Mr Frédéric Delport, Mr Fabien Caroulle (crown data) (NFC for Level II) Office National des Forêts Direction technique et commerciale bois Département recherche - Bâtiment B Boulevard de Constance, 77300 Fontainebleau, FRANCE Phone: +33 1 60 74 92-28/Fax: +33 1 64 22 49 73 Email: manuel.nicolas@onf.fr Mr Manuel Nicolas (Level II) Germany (Min, NFC) Bundesministerium für Ernährung und Landwirtschaft (BMEL) - Ref. 535 Rochusstr. 1, 53123 Bonn, GERMANY Phone: +49 228 99 529-41 30/Fax: +49 228 99 529-42 62 Email: sigrid.strich@bmel.bund.de Ms Sigrid Strich Greece (Min) Hellenic Republic – Ministry of Environment, Energy and Climate Change (MEECC) – General Secretariat MEEC General Directorate for the Development & Protection of Forest and Rural Environment – Directorate for the Planning and Forest Policy Development of Forest Resources Section for the Planning and Evaluation of Forest Policy and Development 31 Chalkokondyli, 10164 Athens, GREECE Phone: +30 210 212 45 97, +30 210 212 45 75/Fax: +30 210 52 40 122 Email: p.drougas@prv.ypeka.gr, mipa@fria.gr Mr Konstantinos Dimopoulos, Director General, Mr Panagiotis Drougas 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 200 | (NFC) Hellenic Agricultural Organization “DEMETER” Institute of Mediterranean Forest Ecosystems and Forest Products Technology Terma Alkmanos, 11528 Ilissia, Athens, GREECE Phone: +30 210 77 84 850, +30 210 77 84 240 Fax: +30 210 77 84 602 Email: mipa@fria.gr Mr Panagiotis Michopoulos Hungary (Min) Ministry of Agriculture and Rural Development Department of Natural Resources Kossuth Lajos tér 11, 1055 Budapest, HUNGARY Phone: +36 1 301 40 25/Fax: +36 1 301 46 78 Email: andras.szepesi@fvm.gov.hu Mr András Szepesi (NFC) National Food Chain Safety Office, Forestry Directorate Frankel Leó út 42-44, 1023 Budapest, HUNGARY Phone: +36 1 37 43 220/Fax: +36 1 37 43 206 Email: kolozsl@nebih.gov.hu Mr László Kolozs Ireland (Min) Department of Agriculture, Food and the Marine, Forest Service Mayo West, Michael Davitt House, Castlebar, Co. Mayo, IRELAND Phone: +353 94 904 29 25/Fax: +353 94 902 36 33 Email: Orla.Fahy@agriculture.gov.ie Ms Orla Fahy (NFC) University College Dublin (UCD) School of Agriculture and Food Science Agriculture and Food Science Building Belfield, Dublin 4, IRELAND Email: jim.johnson@ucd.ie Mr Jim Johnson Italy (Min, NFC) Ministry for Agriculture and Forestry Policies Corpo Forestale dello Stato, National Forest Service, Headquarters, Division 6 (NFI, CONECOFOR Service and Forest Monitoring) Via Giosuè Carducci 5, 00187 Roma, ITALY Phone: +39 06 466 556 021 or +39 06 466 561 88 / Fax: +39 06 4281 5632 Email: a.farina@corpoforestale.it, l.canini@corpoforestale.it, Ms Angela Farina, Ms Laura Canini Latvia (Min) Ministry of Agriculture Forest Department Republikas laukums 2, Riga 1981, LATVIA Phone: +371 670 27 285/Fax: +371 670 27 094 Email: lasma.abolina@zm.gov.lv Ms Lasma Abolina (NFC) Latvian State Forest Research Institute „Silava” 111, Rigas str, Salaspils, 2169, LATVIA Phone: +371 67 94 25 55/Fax: +371 67 90 13 59 Email: urdis.zvirbulis@silava.lv Mr Urdis Zvirbulis 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 201 Liechtenstein (Min, NFC) Amt für Umwelt (AU) Dr. Grass-Str. 12, Postfach 684, 9490 Vaduz, FÜRSTENTUM LIECHTENSTEIN Phone: +423 236 64 02/Fax: +423 756 64 02 Email: olivier.naegele@llv.li Mr Olivier Nägele Lithuania (Min) Ministry of Environment Dep. of Forests and Protected Areas A. Juozapaviciaus g. 9, 2600 Vilnius, LITHUANIA Phone: +370 2 72 36 48/Fax: +370 2 72 20 29 Email: v.vaiciunas@am.lt Mr Valdas Vaiciunas (NFC) State Forest Survey Service Pramones ave. 11a, 51327 Kaunas, LITHUANIA Phone: +370 37 49 02 90/Fax: +370 37 49 02 51 Email: alber_k@lvmi.lt Mr Albertas Kasperavicius Luxembourg (Min, NFC) Administration de la nature et des forêts Service des forêts 16, rue Eugène Ruppert, 2453 Luxembourg, LUXEMBOURG Phone: +352 402 20 12 09/Fax: +352 402 20 12 50 Email: elisabeth.freymann@anf.etat.lu Ms Elisabeth Freymann Former Yugoslav Republic of Macedonia (FYROM) (Min) Ministry of Agriculture, Forestry and Water Economy Dep. for Forestry and Hunting 2 Leninova Str. 1000 Skopje, FORMER YUGOSLAV REP. OF MACEDONIA Phone/Fax: +398 2 312 42 98 Email: vojo.gogovski@mzsv.gov.mk Mr Vojo Gogovski (NFC) Ss. Cyril and Methodius University Faculty of Forestry Department of Forest and Wood Protection Blvd. Goce Delcev 9 1000 Skopje, FORMER YUGOSLAV REP. OF MACEDONIA Phone: +389 2 313 50 03 150/Fax: +389 2 316 45 60 Email: nnikolov@sf.ukim.edu.mk, irpc@sumers.org Mr Nikola Nikolov, Mr Srdjan Kasic Republic of Moldova (Min, NFC) Agency Moldsilva 124 bd. Stefan cel Mare, 2001 Chisinau, REPUBLIC OF MOLDOVA Phone: +373 22 27 23 06/Fax: +373 22 27 73 45 Email: icaspiu@starnet.md Mr Stefan Chitoroaga Montenegro (Min, NFC) Ministry of Agriculture, Forestry and Water Management Rimski trg 46, PC "Vektra" 81000 Podgorica, MONTENEGRO Phone: +382 (20) 482 109/Fax: +382 (20) 234 306 Email: ranko.kankaras@mpr.gov.me, milosav.andjelic@mpr.gov.me Mr Ranko Kankaras, Mr Milosavom Anđelićem 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 202 | The Netherlands (Min, NFC) Ministry for Health, Welfare and Sport The National Institute for Public Health and the Environment (RIVM) Antonie van Leeuwenhoeklaan 9 3721 MA Bilthoven, THE NETHERLANDS Phone: + 31 (0)30 274 2520 Email: esther.wattel@rivm.nl Ms Esther J.W. Wattel-Koekkoek Norway (Min) Norwegian Environment Agency P.O. Box 5672 Sluppen, 7485 Trondheim, NORWAY Phone: +47 73 58 05 00 Email: tor.johannessen@miljodir.no Mr Tor Johannessen (NFC) Norwegian Institute of Bioeconomy Research (NIBIO) P.O.Box 115, 1431 ÅS, NORWAY Phone: +47 971 59 901 Email: volkmar.timmermann@nibio.no Mr Volkmar Timmermann Poland (Min) Ministry of the Environment Department of Forestry Wawelska Str. 52/54, 00-922 Warsaw, POLAND Phone: +48 22 579 25 50/Fax: +48 22 579 22 90 Email: Departament.Lesnictwa@mos.gov.pl Mr Edward Lenart (NFC) Forest Research Institute Sękocin Stary, 3 Braci Leśnej Street, 05-090 Raszyn, POLAND Phone: +48 22 715 06 57/Fax: +48 22 720 03 97 Email: j.wawrzoniak@ibles.waw.pl, p.lech@ibles.waw.pl Mr Jerzy Wawrzoniak, Mr Pawel Lech Portugal (Min, NFC) Instituto da Conservação de Natureza e das Florestas (ICNF) Avenida da República, 16 a 16B, 1050-191 Lisboa, PORTUGAL Phone: +351 213 507 900/Fax.: +351 213 507 984 Email: conceicao.barros@icnf.pt Ms Maria da Conceição Osório de Barros Romania (Min) Ministry of Environment, Waters and Forests Waters, Forests and Pisciculture Dept. Bd. Magheru 31, Sect. 1, 010325, Bucharest, ROMANIA Phone: +40 213 160 215/ Fax: +40 213 194 609 Email: claudiu.zaharescu@mmediu.ro Mr Claudiu Zaharescu (NFC) National Institute for Research and Development in Forestry (INCDS) Bd. Eroilor 128 077190 Voluntari, Judetul Ilfov, ROMANIA Phone: +40 21 350 32 38/Fax: +40 21 350 32 45 Email: biometrie@icas.ro, obadea@icas.ro Mr Ovidiu Badea, Mr Romica Tomescu 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 203 Russian Federation (Min) Ministry of Natural Resources of the Russian Federation 4/6, Bolshaya Gruzinskaya Str. Moscow D-242, GSP-5, 123995, RUSSIAN FEDERATION Phone: +7 495 254 48 00/Fax: +7 495 254 43 10 and +7 495 254 66 10 Email: korolev@mnr.gov.ru Mr Igor A. Korolev (NFC) Centre for Forest Ecology and Productivity of the Russian Academy of Sciences Profsouznaya str., 84/32, 117997 Moscow, RUSSIAN FEDERATION Phone: +7 495 332 29 17/Fax: +7 495 332 26 17 Email: lukina@cepl.rssi.ru Ms Natalia Lukina Serbia (Min) Ministry of Agriculture and Environment Protection Directorate of Forests SIV 3, Omladinskih brigada 1, 11070 Belgrade, SERBIA Phone: +381 11 311 76 37/Fax: +381 11 26 034 73 Email: sas.stamatovic@minpolj.gov.rs Mr Sasa Stamatovic (NFC) Institute of Forestry str. Kneza Viseslava 3, 11000 Belgrade, SERBIA Phone: +381 11 3 55 34 54/Fax: + 381 11 2 54 59 69 Email: nevenic@eunet.rs Mr Radovan Nevenic Slovak Republic (Min) Ministry of Agriculture of the Slovak Republic Dobrovičova 12, 81266 Bratislava, SLOVAKIA Phone: +421 2 59 26 63 08/Fax: +421 2 59 26 63 11 Email: henrich.klescht@land.gov.sk Mr Henrich Klescht (NFC) National Forest Centre - Forest Research Institute ul. T.G. Masaryka 22, 962 92 Zvolen, SLOVAKIA Phone: +421 45 531 42 02/ Fax: +421 45 531 41 92 Email: pavlenda@nlcsk.org Mr Pavel Pavlenda Slovenia (Min) Ministry of Agriculture, Forestry and Food (MKGP) Dunajska 56-58, 1000 Ljubljana, SLOVENIA Phone: +386 1 478 90 38/Fax: +386 1 478 90 89 Email: Janez.Zafran@gov.si, robert.rezonja@gov.si Mr Janez Zafran, Mr Robert Režonja (NFC) Slovenian Forestry Institute (SFI) Večna pot 2, 1000 Ljubljana, SLOVENIA Phone: +386 1 200 78 00/Fax: +386 1 257 35 89 Email: marko.kovac@gozdis.si, primoz.simoncic@gozdis.si Mr Marko Kovač, Mr Primož Simončič Spain (Min) Dirección General de Desarrollo Rural y Política Forestal Ministerio de Agricultura, Alimentación y Medio Ambiente Gran Vía de San Francisco, 4-6, 6ª pl., 28005 Madrid, SPAIN Phone: +34 913471503 or +34 913475891 Email: bnieto@magrama.es, jmjaquotot@magrama.es Mr Da Begoña Nieto Gilarte, Mr José Manuel Jaquotot Saenz de Miera 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 204 | (NFC) Área de Inventario y Estadísticas Forestales (AIEF), Dirección General de Desarrollo Rural y Política Forestal, (Ministerio de Agricultura, Alimentación y Medio Ambiente) Gran Vía de San Francisco, 4-6, 5ª pl., 28005 Madrid, SPAIN Phone: +34 91 347 5835 or +34 91 347 5831 Email: rvallejo@magrama.es, btorres@magrama.es, aigonzalez@magrama.es Mr Roberto Vallejo, Ms Belén Torres Martinez, Ms Ana Isabel González Abadías Sweden (Min, NFC) Swedish Forest Agency Vallgatan 6, 551 83 Jönköping, SWEDEN Phone: +46 36 35 93 85/Fax: +46 36 16 61 70 Email: sture.wijk@skogsstyrelsen.se Mr Sture Wijk Switzerland (Min) Department of the Environment, Transport, Energy and Communications (DETEC), Federal Office for the Environment (FOEN), Forest Division Worblentalstr. 68, 3003 Bern, SWITZERLAND Phone: +41 58 462 05 18 Email: sabine.augustin@bafu.admin.ch Ms Sabine Augustin (NFC) Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Zürcherstr. 111, 8903 Birmensdorf, SWITZERLAND Phone: +41 44 739 25 02/Fax: +41 44 739 22 15 Email: peter.waldner@wsl.ch Mr Peter Waldner Turkey (Min) General Directorate of Forestry Foreign Relations, Training and Research Department Beştepe Mahallesi Söğütözü Caddesi No: 8/1 06560 Yenimahalle-Ankara, TURKEY Phone: +90 312 296 17 03 Fax: +90 312 296 17 12 Email: ahmetkarakasadana@ogm.gov.tr Mr Ahmet Karakaş (NFC) General Directorate of Forestry Department of Forest Pests Fighting Beştepe Mahallesi Söğütözü Caddesi No: 8/1 06560 Yenimahalle-Ankara, TURKEY Phone: +90 312 296 3030 Fax: +90 312 296 3004 Email: sitkiozturk@ogm.gov.tr, uomturkiye@ogm.gov.tr Mr Sıtkı Öztürk Ukraine (Min) State Committee of Forestry of the Ukrainian Republic 9a Shota Rustaveli, 01601, KIEV, UKRAINE Phone: +380 44 235 55 63/Fax: +380 44 234 26 35 Email: viktor_kornienko@dklg.gov.ua Mr Viktor P. Kornienko (NFC) Ukrainian Research Institute of Forestry and Forest Melioration (URIFFM) Laboratory of Forest Monitoring and Certification Pushkinska Str. 86, 61024 Kharkiv, UKRAINE Phone: +380 57 707 80 57/Fax: +380 57 707 80 Email: buksha@uriffm.org.ua Mr Igor F. Buksha 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS | 205 United Kingdom (Min, NFC) Forest Research Station, Alice Holt Lodge Gravehill Road, Wrecclesham Farnham Surrey GU10 4LH, UNITED KINGDOM Phone: +44 1 420 52 62 09/Fax: +44 1 420 520 180 Email: sue.benham@forestry.gsi.gov.uk Ms Sue Benham United States of America (Min) USDA Forest Service Environmental Science Research Staff Rosslyn Plaza, Building C 1601 North Kent Street, 4 th Fl. Arlington, VA 22209, UNITED STATES OF AMERICA Phone: +1 703 605 52 86/Fax: +1 703 605 02 79 Email: rpouyat@fs.fed.us Mr Richard V. Pouyat (NFC) USDA Forest Service Pacific Southwest Research Station 4955 Canyon Crest Drive Riverside, CA 92507, UNITED STATES OF AMERICA Phone: +1 951 680 15 62/Fax: +1 951 680 15 01 Email: abytnerowicz@fs.fed.us Mr Andrzej Bytnerowicz 2 0 1 6 T E CH N I CA L R E P O R T O F I CP F O R E S T S CONTACTS 206 | Annex IV-4 | Authors Vicent Calatayud Fundacion Centro de Estudios Ambientales del Mediterraneo (CEAM), Parque Tecnologico, Paterna, SPAIN Roberto Canullo School of Biosciences and Veterinary Medicine - Plant Diversity and Ecosystems Management unit V. Pontoni, 5 - 62032 Camerino, ITALY Nathalie Cools Research Institute for Nature and Forest (INBO) Gaverstraat 4, 9500 Geraardsbergen, BELGIUM Kirsti Derome The Finnish Forest Research Institute (LUKE) PO Box 413, 90014 Oulun yliopisto, FINLAND Marco Ferretti TerraData environmetrics Via L. Bardelloni 19, 58025 Monterotondo M.mo (GR), ITALY Alfred Fürst Federal Research and Training Centre for Forests, Natural Hazards and Landscape – Seckendorff-Gudent-Weg 8, 1131 Vienna, AUSTRIA Elena Gottardini Research and Innovation Centre, Fondazione Edmund Mach (FEM) Via E. Mach 1, 38010 San Michele all'Adige, ITALY Matthias Haeni Swiss Federal Research Institute WSL Zuercherstrasse 111, 8908 Birmensdorf, SWITZERLAND Tamara Jakovljevic Croatian Forest Research Institute Cvetno naselje 41, 10450 Jastrebarsko, CROATIA Nils König Northwest German Forest Research Institute (NW-FVA) Grätzelstr. 2, 37079 Göttingen, GERMANY Aldo Marchetto National Research Council (CNR), Institute of Ecosystem Study (ISE) Largo Tonolli 50, 28922 Verbania (VB), ITALY Alexa Michel Programme Co-ordinating Centre (PCC) of ICP Forests Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42, 16225 Eberswalde, GERMANY Nenad Potočić Croatian Forest Research Institute Cvetno naselje 41, 10450 Jastrebarsko, CROATIA Tanja Sanders Programme Co-ordinating Centre (PCC) of ICP Forests Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42, 16225 Eberswalde, GERMANY Marcus Schaub Swiss Federal Research Institute WSL Zuercherstrasse 111, 8908 Birmensdorf, SWITZERLAND Walter Seidling Programme Co-ordinating Centre (PCC) of ICP Forests Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42, 16225 Eberswalde, GERMANY Volkmar Timmermann Norwegian Institute of Bioeconomy Research (NIBIO) P.O. Box 115, 1431 Ås, NORWAY Serina Trotzer Programme Co-ordinating Centre (PCC) of ICP Forests Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42, 16225 Eberswalde, GERMANY Peter Waldner Swiss Federal Research Institute WSL Zuercherstrasse 111, 8908 Birmensdorf, SWITZERLAND 20040622_oceans_web.psd BFW-Dokumentation 23/2016 Bundesforschungszentrum für Wald Seckendorff-Gudent-Weg 8 1131 Wien, Österreich http://bfw.ac.at ISSN 1811-3044 ISBN 978-3-902762-65-8 C Press law responsibility: DI Dr Peter Mayer Austrian Research and Training Centre for Forests, Natural Hazards and Landscape (BFW) Seckendorff-Gudent-Weg 8 1131 Vienna, Austria Phone: +43-1-878380 Cover photo: Level II long-term forest monitoring plot in Finland by Erkki Oksanen Contact: Alexa Michel, Walter Seidling (Eds.) Programme Co-ordinating Centre (PCC) of ICP Forests Thünen Institute of Forest Ecosystems Alfred-Möller-Str. 1, Haus 41/42 16225 Eberswalde, Germany http://icp-forests.net Reproduction is authorised provided the source is acknowledged. Chlorine-free and climate-neutral - For the benefit of the environment opyright 2016 by BFW Impressum Forest Condition in Europe 2016 Technical Report of ICP Forests Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) ALEXA MICHEL & WALTER SEIDLING (Eds.)