Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm ISBN 978-951-40-2031-5 (PDF) ISBN 978-951-40-2032-2 (paperback) ISSN 1795-150X www.metla.fi Forest Condition Monitoring in Finland National Report 2002–2005 Edited by Päivi Merilä, Tuire Kilponen and John Derome Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 2 Working Papers of the Finnish Forest Research Institute publishes preliminary research results and conference proceedings. The papers published in the series are not peer-reviewed. http://www.metla.fi/julkaisut/workingpapers/ ISSN 1795-150X Office Unioninkatu 40 A FI-00170 Helsinki tel. +358 10 2111 fax +358 10 211 2101 e-mail julkaisutoimitus@metla.fi Publisher Finnish Forest Research Institute Unioninkatu 40 A FI-00170 Helsinki tel. +358 10 2111 fax +358 10 211 2101 e-mail info@metla.fi http://www.metla.fi/ Printed in: Vammalan Kirjapaino Oy Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Authors Merilä, Päivi, Kilponen, Tuire & Derome, John (eds.) Title Forest condition monitoring in Finland – National report 2002–2005 Year Pages ISBN ISBN: 978-951-40-2031-5 (PDF) ISSN 2007 166 1795-150X ISBN: 978-951-40-2032-2 (paperback) Unit / Research programme / Projects Parkano Research Unit / Forest Health Monitoring / Project 3153 Long-term monitoring of forest ecosystem Accepted by Pasi Puttonen, Director of Research, 27 February 2007 Abstract Since 1985 Finland has been participating in the Pan-European forest condition monitoring programme – the International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP forests) – which is based on international agreements on the long-range transportation of air pollutants (LRTAP). In member countries of the European Union, forest condition monitoring is based on regulations enacted in 1986, 1994 and 2003. In Finland, the Finnish Forest Research Institute (Metla) is responsible for carrying out annual forest vitality and health surveys on a 610 permanent plot network (Level I, extensive monitoring), and for studying the relationships between forest condition and air pollution and other stress factors on a network of 31 stands located throughout the country (Level II, intensive monitoring). This report presents the results of monitoring carried out under the Finnish Forest Focus/ICP Forests programmes during 2002 to 2004/5 as well as the results of other studies of forest condition in Finland. Suomi on vuodesta 1985 lähtien osallistunut yleiseurooppalaiseen metsien terveydentilan seurantaohjel- maan (ICP metsäohjelma), joka perustuu kansainväliseen ilman epäpuhtauksien kaukokulkeutumista koskevaan sopimukseen (CLRTAP). Euroopan Unionin jäsenmaissa metsien terveydentilan seuranta pohjautuu vuosina 1986, 1994 ja 2003 vahvistettuihin säädöksiin. Metsäntutkimuslaitos (Metla) inventoi puiden kunnon vuosittain kansainvälisesti sovituin menetelmin 610:llä pysyvällä havaintoalalla (taso I, laaja-alainen seuranta). Metsien kunnon, ilman epäpuhtauksien sekä muiden stressitekijöiden välisiä vuorosuhteita tutkitaan 31 metsikössä eri puolilla Suomea (taso II, intensiivinen seuranta). Tässä rapor- tissa esitetään ICP metsäohjelman Suomea koskevia tuloksia vuosilta 2002–2004/5 sekä muiden Suomen metsien terveydentilaa käsittelevien tutkimusten tuloksia. Keywords acidification, air pollution, biodiversity, biotic and abiotic forest damage, boreal forests, coverage, crown condition, defoliation, deposition, discolouration, forest health monitoring, forest pests, fungal diseases, forest soil, grasses, heavy metals, insect damage, litterfall, mass balance budgets, meteorology, monitoring, moose damage, national forest inventory, needle chemistry, nitrogen, Norway spruce, ozone, phenology, Scots pine, soil solution, sulphur, throughfall, understorey vegetation, windthrows abioottiset ja bioottiset tuhot, ainetaseet, aluskasvillisuus, boreaaliset metsät, fenologia, happamoituminen, harsuuntuminen, hirvituhot, hyönteistuhot, ilman saasteet, karike, kuusi, laskeuma, latvuskunto, maavesi, meteorologia, metsien terveydentilan seuranta, metsikkösadanta, metsämaa, monimuotoisuus, monitorointi, myrskytuhot, mänty, neulaskemia, otsoni, peittävyys, raskasmetallit, rikki, sienitaudit, typpi, valtakunnan metsien inventointi, värioireet Available at http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Contact information Päivi Merilä Finnish Forest Research Institute, Parkano Research Unit, Kaironiementie 54, FI-39700 Parkano, Finland. E-mail: paivi.merila @ metla.fi Bibliographical information Merilä, P., Kilponen, T. & Derome, J. (eds.). 2007. Forest Condition Monitoring in Finland – National report 2002–2005. Working Papers of the Finnish Forest Research Institute 45. 166 p. ISBN 978-951-40-2031-5 (PDF), ISBN 978-951-40-2032-2 (paperback). Available at: http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm. Other information http://www.metla.fi/hanke/3153/index.htm http://www.metla.fi/hanke/3153/index-en.htm 3 Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Contents Preface – Alkusanat 6 Summary 7 Yhteenveto 9 1 Forest condition monitoring under the UN/ECE and EC programmes in Finland 11 Yleiseurooppalainen metsien terveydentilan seuranta (YK-ECE/EU) Suomessa John Derome, Martti Lindgren, Päivi Merilä, Egbert Beuker & Pekka Nöjd 2 Forest condition in national systematic network (Forest Focus/ICP Forests, Level I) in 2002–2005 21 Metsien terveydentila systemaattisen havaintoalaverkoston aloilla vuosina 2002–2005 (Forest Focus/ICP metsäohjelma, taso I) 2.1 Results of the national crown condition survey 21 Valtakunnallisen latvuskunnon seurannan tulokset Martti Lindgren, Seppo Nevalainen & Antti Pouttu 2.2 Biotic and abiotic damage on the Level I network 32 Bioottiset ja abioottiset tuhot tason I havaintoaloilla Seppo Nevalainen, Martti Lindgren & Antti Pouttu 3 Results of the intensive monitoring of forest ecosystems (Forest Focus/ICP Forests, Level II) 41 Metsien intensiiviseurannan tuloksia (Forest Focus/ICP metsäohjelma, taso II) 3.1 Crown condition on the Level II network 2001–2004 41 Puiden latvuskunto tason II havaintoaloilla vuosina 2001–2004 Martti Lindgren, Seppo Nevalainen & Antti Pouttu 3.2 Needle chemistry on the intensive monitoring plots 1995–2003 46 Neulasten kemiallinen koostumus intensiviseurannan havaintoaloilla vuosina 1995–2003 Päivi Merilä 3.3 Litterfall production on 14 Level II plots during 1996–2003 63 Karikesato 14 havaintoalalla (taso II) vuosina 1996–2003 Liisa Ukonmaanaho 3.4 Understorey vegetation on the Level II plots during 1998–2004 69 Aluskasvillisuus tason II havaintoaloilla vuosina 1998–2004 Maija Salemaa & Leena Hamberg 3.5 Open area bulk deposition and stand throughfall in Finland during 2001–2004 81 Avoimen paikan ja metsikkösadannan laskeuma Suomessa vuosina 2001–2004 Antti-Jussi Lindroos, John Derome & Kirsti Derome 5Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 3.6 Soil percolation water quality during 2001–2004 on 11 Level II plots 93 Vajoveden kemiallinen koostumus 11 havaintoalalla (taso II) vuosina 2001–2004 John Derome, Antti-Jussi Lindroos & Kirsti Derome 3.7 Phenological assessments on the intensive monitoring plots 99 Fenologinen seuranta intensiiviseurannan havaintoaloilla Boy Possen & Egbert Beuker 3.8 Assessment of air quality on Level II plots 112 Ilman laadun seuranta tason II havaintoaloilla Kirsti Derome  Results of other studies related to forest damages and long-term monitoring 120 Muiden ympäristö- ja metsätuhoseurantojen ja -tutkimusten tuloksia 4.1 The use of light microscopy to assess impact of ozone stress on Norway spruce needles in the field 120 Otsonivaurioiden havainnointi kuusen neulasista valomikroskooppisesti Sirkka Sutinen & Minna Kivimäenpää 4.2 Pest and disease situation during 2002–2005 according to the Forest Damage Advisory Service 130 Metsätuhot vuosina 2002–2005 metsätuhotietopalvelun saamien tietojen perusteella Antti Pouttu, Katriina Lipponen, Seppo Nevalainen, Martti Lindgren, Arja Lilja, Marja Poteri, Seppo Neuvonen, Heikki Henttonen & Jarkko Hantula 4.3 Forest damage observed in the 10th National Forest Inventory of Finland during 2004–2005 136 Valtakunnan metsien 10. inventoinnissa vuosina 2004–2005 havaitut tuhot Kari T. Korhonen & Seppo Nevalainen 4.4 Hietajärvi – long-term results from a Finnish ICP Integrated Monitoring (IM) catchment 147 Hietajärvi – pitkäaikaisen seurannan tuloksia ICP Yhdennetyn ympäristön seurannan (YYS) valuma-alueelta Mike Starr, Martin Forsius, Tarja Hatakka, Riitta Niinioja, Tuija Ruoho-Airola, Liisa Ukonmaanaho & Jussi Vuorenmaa Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Preface Since 1985 Finland has been participating in the Pan-European forest condition monitoring programme – the International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) – which is based on international agreements on the long-range transportation of air pollutants (LRTAP). In member countries of the European Union, forest condition monitoring is based on regulations enacted in 1986 and 1994, and on modifications subsequently made to these regulations. Since 2003 the monitoring programme has been carried out under the EU Forest Focus regulation. In Finland, the Finnish Forest Research Institute (Metla) is responsible for carrying out annual forest vitality and health surveys on a 610 permanent plot network (Level I, extensive monitoring), and for studying the relationships between forest condition and air pollution and other stress factors on a network of 31 stands located throughout the country (Level II, intensive monitoring). This report presents the results of monitoring carried out under the Finnish Forest Focus/ICP Forests programmes during 2002 to 2004/5 as well as the results of other studies of forest condition in Finland. All the researchers involved in Metla’s Forest Monitoring programme have participated to a varying extent in writing this report. However, the work would not have been possible without the skilful and highly motivated support provided by the field, laboratory and office personnel at Metla. Alkusanat Suomi on vuodesta 1985 lähtien osallistunut yleiseurooppalaiseen metsien terveydentilan seurantaohjelmaan (ICP metsäohjelma), joka perustuu kansainväliseen ilman epäpuhtauksien kaukokulkeutumista koskevaan sopimukseen (CLRTAP). Euroopan Unionin jäsenmaissa metsien terveydentilan seuranta pohjautuu vuosina 1986, 1994 vahvistetuihin säädöksiin ja niihin myöhemmin tehtyihin täydennyksiin. Vuodesta 2003 seurantaohjelma on toteutettu EU:n Forest Focus -säädöksen alaisuudessa. Metsäntutkimuslaitos (Metla) inventoi puiden kunnon vuosittain kansainvälisesti sovituin menetelmin 610 pysyvällä havaintoalalla (taso I, laaja-alainen seuranta). Metsien kunnon, ilman epäpuhtauksien sekä muiden stressitekijöiden välisiä vuorosuhteita tutki- taan 31 metsikössä eri puolilla Suomea (taso II, intensiivinen seuranta). Tässä raportissa esitetään ICP metsäohjelman Suomea koskevia tuloksia vuosilta 2002–2004/5 sekä muiden Suomen metsien terveydentilaa käsittelevien tutkimusten tuloksia. Kaikki metsien terveydentilan seurantaohjelmassa työskentelevät tutkijat ovat osallistuneet tämän raportin laadintaan. Haluamme osoittaa kiitoksemme myös Metlan maasto-, laboratorio- ja toimistohenkilöstölle, jonka ammattitaitoinen työpanos on seurantaohjelman menestykselliselle toteuttamiselle korvaamattoman tärkeää. 7Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Summary There were no notable changes in the average defoliation level of the tree species (Norway spruce, Scots pine, broadleaves, mainly birch) on the Level I extensive monitoring network during 2002–2005. The average tree-specific degree of defoliation for the period 2002–2005 on mineral soil sites was 9.4% in pine, 18.3% in spruce and 11.7% in broadleaves. In 2004, the plots located on peatlands were included in the survey for the first time and the average defoliation was 8.2% in pine, 17.0% in spruce and 9.3% in broadleaves. The relatively high stand age, weather and climatic factors, and fungal and insect damage were the main factors affecting defoliation. No correlation was found between the defoliation pattern of conifers or broadleaves and the modelled sulphur or nitrogen deposition at the national level in 2002–2005. During the period 2002–2005, 33.4% of the pines, 36.5% of the spruces and 40.1% of the broadleaves on the Level I network showed signs of biotic or abiotic damage. Most of the observed damage was slight, i.e. it had no effect on the vitality of the trees. There was considerable variation in the occurrence of individual damaging agents between the years: e.g. the increase in insect (Tomicus sp.) damage in 2003, the increase in damage caused by Gremmeniella abietina in pine in 2004, and needle rust Chrysomyxa ledi in 2005 in spruce. Birch rust and leaf anthracnose were common on birches in 2004. The air quality (SO2, NO2, O3, NH3) at a number of Level II plots was monitored using passive samplers during 2000–2001 and in 2004. The SO2 and NO2 concentrations were clearly higher in wintertime, and the O3 concentration reached a maximum in early spring. The seasonal patterns were similar in different parts of the country. There was no clear difference between the locations in the O3 concentrations, but the SO2 and NO2 concentrations were higher at the sites in south- eastern Finland close to the Russian border, and lower at the site in Lapland. The annual nitrogen and sulphur deposition in southern Finland was clearly higher than in northern Finland. Sulphate deposition in the open and in stand throughfall (8 pine plots, 8 spruce plots) during 2001–2004 was clearly lower than that measured in earlier years (monitoring started in 1996), especially on the plots in southern Finland. There was no corresponding decrease in the deposition of nitrogen compounds in either bulk deposition or in stand throughfall. The reduction in sulphur deposition was reflected as a slight decrease in foliar sulphur concentrations in spruce and pine during the same period. Stand litterfall was monitored on 8 spruce plots and 6 pine Level II plots during 1996–2003. The annual litterfall production varied considerably between the years and plots. The annual amount of litterfall varied strongly between the years and between the plots. The mean annual litterfall on the spruce plots ranged from 61 to 503 g m-2, and on the pine plots from 123 to 342 g m-2. Needle litterfall accounted for 29% to 87% of the total litterfall on the spruce plots and 52% to 69% on the pine plots. There was a clear peak in litterfall on the pine plots in the autumn, while on the spruce plots it was more evenly distributed throughout the year. A complete vegetation survey of the 31 Level II plots was carried out in 2003. The vegetation on the mineral soil plots was primarily determined by the site fertility gradient, combined with the variation in soil moisture and location along the south-north axis. The number of vascular plant species decreased towards the north on both the pine and spruce plots. In contrast, the number of bryophyte and lichen species increased from south to north on the pine plots, but not on the spruce plots. The cover percentages of the understorey plant species remained relatively constant on six Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm of the Level II plots that were surveyed every year during 1998–2003. The largest annual changes in the coverage of vascular plants and bryophytes were 10–15%-units. The coverage of dwarf shrubs increased on two of the southern plots, and that of bryophytes correspondingly decreased. Between-year variation in the amount of precipitation and needle/leaf litter appeared to regulate the coverage of the bryophyte layer. Phenological monitoring has been carried out on four Level II plots since 2000. The trees on the northern plots flushed later than those on the southern plots, but did so at a lower effective temperature sum. Spruce flushed earlier than pine. No relationship was found between growth onset and any of the weather or climatic parameters, indicating that a 5-year time series is too short to predict shifts in growth onset. A study was carried out on the use of light microscopy in the diagnosis of ozone-induced symptoms in spruce needles. Samples were collected from the three youngest needle age classes in two stands growing on sites of different soil fertility. Light microscopy revealed ozone-specific symptoms: decreased chloroplast size with electron dense stroma advancing gradually from the outer to inner cell layers. The symptoms were expressed as ozone syndrome indexes at the needle age class, tree and stand levels. The index value was the highest on the less fertile site. The study showed that light microscopy can be used for quantitative diagnosis of the impact of ozone stress on spruce in the field. The report includes two summaries of biotic and abiotic forest damage based on the annual forest damage reports compiled for the Ministry of Agriculture and Forestry (available in Finnish in the Internet) and on the results of stand level damage assessments made on the 23611 sample plots in the 10th National Forest Inventory (NFI) during 2004–2005. The results of NFI show that the total area of all types of damage was 5.314 mill. ha, or 26.3% of the total forest land area. Abiotic factors and fungi were the most common groups of causal agents in the10th NFI data. The most frequently identified causes of damage in all stands were snow and moose. Resin-top disease and Scleroderris canker caused by Gremmeniella abietina are other commonly identified causal agents in pine-dominated forests. Rot fungi are the most frequent causes of damage in spruce- dominated forests. Annosum root rot (Heterobasidion sp.) was found in almost 100 000 ha of spruce forests. Other decay fungi were the most frequent causes of damage in deciduous stands. Compared to the previous inventory (9th NFI), the area of forest showing damage symptoms appears to have increased by 1.8%-units. The damage caused by moose has increased the most, especially in pine stands. The results of integrated monitoring (ICP IM) activities at the Hietajärvi catchment in eastern Finland are presented at the end of the report. One of the Level II plots is also located in the catchment. The long-term monitoring data are used to evaluate the effectiveness of international agreements on the reduction of sulphur, nitrogen and heavy metals emissions. The data have also been used in numerous dynamic modelling studies on the impact of air pollution abatement policy and the future recovery of forest ecosystems. The data are being used to an increasing extent to assess the impacts of climate change on carbon cycling in catchments located in the boreal zone. The results clearly demonstrate the importance of long-term, multidisciplinary monitoring programmes. Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Yhteenveto Metsien terveydentilan laaja-alaisen seurannan (taso I) mukaan kaikkien puulajien keskimääräinen harsuuntumisaste on viime vuosina pysynyt melko vakaana. Kivennäismailla kasvavien mäntyjen keskimääräinen harsuuntumisaste jaksolla 2002–2005 oli 9,4 %, kuusien 18,3 % ja lehtipuiden (pääasiassa koivuja) 11,7 %. Vuonna 2004 otettiin seurantaan mukaan myös turvemaiden näytealoja ja niillä mäntyjen keskimääräinen harsuuntumisaste oli 8,2 %, kuusien 17 % ja lehtipuiden 9,3 %. Harsuuntuminen johtuu Suomessa pääasiassa puuston ikääntymisestä, erilaisista epäedullisista ilmasto- ja säätekijöistä sekä sieni- ja hyönteistuhoista. Koko maata tarkasteltaessa ei havaittu yhteyttä ilman epäpuhtauksien ja neulaskadon välillä vuosina 2002–2005. Tutkimusjakson (2002–2005) aikana metsien terveydentilan laaja-alaisessa seurannassa (taso I) havaittiin bioottisia tai abioottisia tuhoja 33,4 %:ssa mäntyhavaintopuita, 36,5 %:ssa kuusia ja 40,1 %:ssa lehtipuita. Suurin osa havaituista tuhoista oli lieviä eli ei vähentänyt puiden elin- voimaisuutta. Vuosien välillä oli kuitenkin suuria eroja eri tuhonaiheuttajien esiintymisessä, esim. männyllä hyönteistuhot (ytimennävertäjätuhot) lisääntyivät vuonna 2003 ja versosurmatuhot 2004. Kuusella suopursuruoste yleistyi vuonna 2005. Koivuilla koivunruoste ja erilaiset lehtilaikut olivat yleisiä vuonna 2004. Raportissa esitetään passiivikeräimillä saatuja tuloksia ilman laadusta muutamilla Metsien intensiiviseurannan (taso II) aloilla vuosilta 2000–2001 ja 2004. Rikkidioksidi- (SO2) ja typpidioksidi- (NO2) pitoisuudet olivat korkeimmat talviaikana, kun taas otsonipitoisuudet olivat korkeimmillaan aikaisin keväällä. Vuodenaikaisvaihtelu oli samansuuntaista kaikilla mittauspaikoilla. Otsonipitoisuuksissa ei havaittu selviä eroja eri mittauspaikkojen välillä, sen sijaan SO2- ja NO2-pitoisuudet olivat korkeimpia Kaakkois-Suomessa, lähellä Venäjän rajaa olevilla mittauspaikoilla ja matalimpia Lapissa sijaitsevilla mittauspaikoilla. Avoimen paikan ja metsikkösadannan (8 mänty- ja 8 kuusialaa) laskeumamittausten mukaan koko- naistypen ja sulfaattirikin (SO4-S) keskiarvolaskeumat olivat selvästi suurempia Etelä-Suomessa verrattuna Pohjois-Suomeen vuosina 2001–2004. Verrattaessa vuosien 2001–2004 tuloksia aikaisempiin vuosiin (seuranta alkoi 1996) havaittiin, että avoimen paikan ja metsikkösadannan rikkilaskeuma on alentunut etenkin Etelä-Suomen havaintoaloilla. Vastaavaa laskeuman vähen- tymistä ei ollut havaittavissa typen yhdisteille avoimella paikalla tai metsikkösadannassa. Rikkilaskeumassa tapahtunut lasku näkyy lievästi laskevana trendinä myös neulasten rikki- pitoisuudessa. Muutoin puiden ravinnetilassa ei ole havaintojakson aikana tapahtunut jyrkkiä muutoksia. Karikesatoa on seurattu intensiiviseurannan kahdeksalla kuusi- ja kuudella mäntyalalla vuosina 1996–2003. Karikesato vaihteli runsaasti sekä vuosien että näytealojen välillä. Kuusikoissa keski- määräinen vuosittainen karikesato vaihteli 61–503 g m-2, vastaavasti männiköissä 123–342 g m-2. Neulaskarikkeen osuus kokonaiskarikesadosta oli kuusialoilla 29–87 % ja mäntyaloilla 52–69 %. Männiköissä karikesadossa esiintyi selkeä vuodenaikaisvaihtelu määrän ollessa suurimmillaan syksyisin, sen sijaan kuusikoissa karikesato oli tasaisemmin jakautunut ympäri vuoden. Raportissa esitetään yhteenveto intensiiviseuranta-alojen toisesta aluskasvillisuusinventoinnista (v. 2003). Kivennäismailla sijaitsevien havaintoalojen aineistossa tärkein kasvillisuuden raken- netta kuvaava vaihtelusuunta ilmensi kasvupaikan ravinteisuutta, maaperän kosteutta ja koe- alan sijaintia etelä-pohjoissuunnassa. Putkilokasvilajien lukumäärä vähentyi pohjoiseen päin sekä männiköissä että kuusikoissa. Toisaalta sammal- ja jäkälälajien lukumäärä lisääntyi männi- 10 Working Papers of the Finnish Forest Research Institute 5 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm köissä pohjoiseen päin, mutta vastaavaa vaihtelua ei havaittu kuusikoissa. Kasvilajien peittävyys- prosentit ovat pysyneet suhteellisen vakaina kuudella vuosittain tutkitulla taso II:n havaintoalalla seurantajakson 1998–2003 aikana; suurimmillaan muutokset ovat olleet 10–15 %-yksikköä. Varpujen peittävyydet lisääntyivät kahdella eteläisellä havaintoalalla, mutta samanaikaisesti sammalten peittävyys pieneni. Vuosien väliset erot sade- ja neulas/lehtikarikkeen määrissä näyttivät säätelevän sammalkerroksen peittävyyttä. Fenologista havainnointia on tehty neljällä intensiiviseurannan havaintoalalla vuodesta 2000 lähtien. Kullakin havaintoalalla on seurattu kasvuunlähtöä, ts. silmujen puhkeamista. Pohjoi- silla havaintoaloilla silmut puhkeavat selvästi myöhemmin kuin etelässä, mutta silmujen puh- jetessa lämpösummakertymä on pohjoisessa alhaisempi kuin etelässä. Kasvuunlähtö tapah- tuu aikaisemmin kuusella kuin männyllä. Kasvuunlähdön ja säätekijöiden välillä ei havaittu merkitsevää yhteisvaihtelua, mikä osoittaa, että viiden vuoden aikasarja on liian lyhyt kasvuun- lähdon ennustamiseksi. Raportissa julkaistaan tulokset erillistutkimuksesta, jossa tutkittiin otsonin aiheuttamiksi tun- nettuja oireita valomikroskooppisesti kuusen neulasista. Neulasnäytteet kerättiin kahdesta ravinnetasoltaan erilaisesta metsiköstä, kuusten kolmesta nuorimmasta neulasvuosikerrasta. Valomikroskooppisesti voitiin todeta otsonille tyypilliset oireet: kloroplastin koon pieneneminen ja samanaikainen strooman tummuminen sekä oireiston eteneminen asteittain uloimmista solu- kerroksista sisempiin kerroksiin. Oireet esitettiin otsonioireindeksinä neulasvuosikerta-, puu- ja metsikkötasoille. Indeksi oli korkein ravinteisuudeltaan alhaisemmalla kasvupaikalla. Tutkimus osoitti, että valomikroskopia soveltuu kvantitatiiviseen otsonioireiden havainnointiin havupuilla kenttäolosuhteissa. Lisäksi esitetään kaksi metsien abioottisia ja bioottisia tuhoja koskevaa katsausta, joista toinen perustuu maa- ja metsätalousministeriölle toimitettuihin metsätuhoraportteihin vuosilta 2002–2005 (http://www.metla.fi/metinfo/metsienterveys) ja toinen Valtakunnan metsien 10. inventoinnin (10. VMI) kuviokohtaisiin tuhotuloksiin 23 611 koealalta vuosilta 2004–2005. VMI-aineistossa tuhoja esiintyi kaikkiaan 5,314 milj. hehtaarilla tai 26,3 % metsämaan pinta- alasta. Abioottiset tekijät ja sienet ovat tärkeimpiä tuhonaiheuttajaryhmiä, ja lumi- ja hirvituhot ovat yleisimpiä tuhonaiheuttajia koko aineistossa. Tervasroso ja versosurma ovat mäntyvaltaisten metsien yleisimmät tunnistetut tuhonaiheuttajat. Lahottajasienet ovat puolestaan yleisimpiä kuusi- valtaisissa metsissä. Juurikääpien aiheuttamaa lahoa tavattiin lähes 100 000 ha:lla kuusikoissa. Lahottajasienet ovat yleisimpiä tuhonaiheuttajia myös lehtipuuvaltaisissa metsissä. Edelliseen inventointiin (9. VMI) verrattuna sellaisten metsiköiden pinta-ala, joissa tuhoja esiintyy, näyttää lisääntyneen 1,8 %-yksiköllä. Erityisesti hirvituhot ovat lisääntyneet männiköissä. Lopuksi raportissa esitetään tuloksia Itä-Suomessa sijaitsevalta Hietajärven valuma-alueelta, joka kuuluu Ympäristön yhdennetyn seurannan (YYS) havaintoaloihin. Myös Metsien inten- siiviseurannan (taso II) Lieksan havaintoala sijaitsee kyseisellä valuma-alueella. Alueelta on kerätty seuranta-aineistoa, jonka avulla voidaan arvioida kansainvälisten rikki-, typpi- ja raskas- metallipäästöjen rajoittamista koskevien sopimusten toteutumista. Tämän lisäksi aineistoa on hyödynnetty lukuisissa mallinnustehtävissä, joiden tarkoituksena on ollut kuvata päästöjen vähentämistoimien vaikutuksia ja ekosysteemin toipumiskehitystä. Aineistoa käytetään yhä enemmän myös arvioitaessa ilmastomuutoksen vaikutuksia valuma-alueiden hiilen kiertoon bore- aalisessa vyöhykkeessä. Tulokset osoittavat pitkäaikaisen ja monitieteisen yhdennetyn seurannan tärkeyden. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 11 1 Forest condition monitoring under the UN/ECE and EU programmes in Finland Yleiseurooppalainen metsien terveydentilan seuranta (YK-ECE/EU) Suomessa John Derome1, Martti Lindgren2, Päivi Merilä3, Egbert Beuker 4 & Pekka Nöjd2 Finnish Forest Research Institute; 1) Rovaniemi Research Unit, 2) Vantaa Research Unit, 3) Parkano Research Unit, 4) Punkaharju Research Unit Introduction The International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) was established in 1985 under the UN/ECE Convention on Long- Range Transboundary Air Pollution (CLRTAP). In 1986 the European Union adopted the Scheme on the Protection of Forests against Atmospheric Pollution, and a legal basis for co-financing of the assessments in EU member states was provided through Council Regulation (EEC) No. 3528/86. Since then, co-financing has also been available under a number of regulations such as the Forest Focus regulation (2003–2006). At the present time, the monitoring of forest condition and the effect of stress factors on ecosystem functioning is being carried out in 38 participating countries within these ICP Forests and EU programmes. Large-scale, extensive monitoring takes place on a network of ca. 6,000 plots arranged on a systematic grid (16 x 16 km) covering the whole of Europe. This Level I network provides an annual picture of large-scale trends in crown condition (defoliation, discoloration, abiotic and biotic damage) at the European level. It also offers the possibility to investigate relationships between stress factors and forest condition. Finland has been participating since 1985 in the Level I monitoring of forest condition. In order to gain a better understanding of the effects of air pollution and other stress factors on forests, the Pan-European Programme for Intensive and Continuous Monitoring of Forest Ecosystems (Level II) was implemented, in 1995, and EU co-funding was extended to cover these activities. Approximately 800 intensive monitoring plots have been established in the participating countries. Investigations are carried out on these plots on site and stress factors, as well as on the biological and chemical status of the forest ecosystems. When Finland joined the European Union in 1995, some modifications were made to the national forest condition monitoring programme (Level I), and the intensive monitoring of forest ecosystems (Level II) was started at the same time. By the end of 1997, 31 intensive monitoring plots had been established in different parts of Finland. The Finnish Forest Research Institute (Metla) is responsible for forest condition monitoring under the ICP Forests and EU programmes in Finland. The Parkano Research Unit of the Finnish Forest Research Institute is responsible for the tasks of the National Focal Centre, and Dr. John Derome has acted as the national coordinator since 2004. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 12 Extensive monitoring of forest condition – Level I The Finnish Forest Research Institute annually inventories tree condition, using internationally standardised methods, on a representative sample of tree stands. The inventory is carried out on about 500 mineral soil and 100 peatland plots selected from the permanent sample plot network of the 8th National Forest Inventory, established in 1985 (Fig. 1). The systematic network used in the annual crown condition survey has been designed to provide information at the national level about crown condition and its variation in background areas. A number of parameters are measured on the trees. The most important variables used to describe crown condition in Finland are relative leaf- and needle-loss (i.e. defoliation), discoloration and abiotic and biotic damage of the crown. The distribution of tree species assessed in the 2005 inventory was ca. 56% Scots pine (Pinus sylvestris), ca. 27% Norway spruce (Picea abies), and ca. 17% birch (Betula spp.). In addition, a soil survey was carried out on 338 plots in 1986–1987, and an additional 104 plots in 1995. Needle samples were collected for elemental analysis on 160 plots (98 pine plots, 62 spruce plots) during 1987–1989 (Raitio 1994), and on ca. 30 plots (16–18 pine, 12–14 spruce) annually since 1992 (Luyssaert et al. 2005). Figure 1. The network of the annual, largescale crown condition survey (Level I) in Finland. Kuva 1. Laajamittainen metsien tilan seuranta (taso I), näytealaverkko Suomessa. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 13 Intensive and continuous monitoring of forest ecosystems – Level II Monitoring plot network By 1997, 31 intensive monitoring plots had been established in different parts of the country (Fig. 2, Table 1): 27 of the plots on mineral soil sites and 4 on peatlands. 17 of the plots are located in Scots pine stands and 14 in Norway spruce stands. All the plots, except for the four Integrated Figure 2. The intensive monitoring network of forest ecosystems in Finland. Kuva 2. Metsäekosysteemien intensiiviseurannan havaintoalat Suomessa. Table 1. Overview of the intensive monitoring network of forest ecosystems in Finland. Taulukko 1. Havaintoalojen numero, nimi ja pää- puulaji. Plot Plot Tree number name species Havaintoalan Havaintoalan Pääpuulaji numero nimi 1 Sevettijärvi_P Scots pine 2 Pallasjärvi_P Scots pine 3 Pallasjärvi_S Norway spruce 4 Sodankylä_P Scots pine 5 Kivalo_S Norway spruce 6 Kivalo_P Scots pine 7 Oulanka_S Norway spruce 8 Oulanka_P Scots pine 9 Ylikiiminki_P Scots pine 10 Juupajoki_P Scots pine 11 Juupajoki_S Norway spruce 12 Tammela_S Norway spruce 13 Tammela_P Scots pine 14 Lapinjärvi_P Scots pine 15 Lapinjärvi_S Norway spruce 16 Punkaharju_P Scots pine 17 Punkaharju_S Norway spruce 18 Miehikkälä_P Scots pine 19 Evo_Sim Norway spruce 20 Lieksa_Pim Scots pine 21 Oulanka_Sim Norway spruce 22 Kevo_Pim Scots pine 23 Uusikaarlepyy_S Norway spruce 24 Närpiö_S Norway spruce 25 Vilppula_Spro Norway spruce 26 Ikaalinen_P Scots pine 27 Ikaalinen_Pfer Scots pine 28 Solböle_Spro Norway spruce 29 Pyhäntä_P Scots pine 30 Pyhäntä_Pfer Scots pine 31 Kivalo_Spro Norway spruce P = Scots pine – Mänty S = Norway spruce – Kuusi pro = Provenance – Alkuperäkoe Pim = Scots pine, Integrated Monitoring Mänty, ympäristön yhdennetty seuranta Sim = Norway spruce, Integrated Monitoring Kuusi, ympäristön yhdennetty seuranta fer = Fertilization – Lannoitettu Scots pine - Mänty Norway spruce - Kuusi Meterological station - Sääasema Deposition + soil water - Laskeuma + maavesi Integrated monitoring Ympäristön yhdennetty seuranta 1 22 2 3 4 5 31 6 21 8 7 9 29 30 20 23 24 26 27 25 17 16 19 14 15 18 12 13 28 11 10 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 14 Monitoring (ICP-IM) plots, are located in commercially managed forest. The IM plots represent natural stands in catchment areas. A number of the plots are located close to background, air quality monitoring stations primarily run by the Finnish Meteorological Institute. Four of the intensive monitoring plots were established on drained peatland. The sites were originally wet, sparsely stocked pine mires that represent the most typical drained peatland site types in Finland. The peat in these site types has a low mineral nutrient status, but usually relatively high nitrogen reserves. As this may result in an unbalanced nutrient status in the tree stand, two of the four plots have been fertilized. The four plots are located at two locations in Finland, with a pair of unfertilized and fertilized plots at each location. Three of the plots were established in long-term spruce provenience trials. The design of the observation plot and location of the sub-plots The observation plots proper consist of three sub-plots and a surrounding mantle (sub-plot 4) (Fig. 3). The sub-plots are square in shape (30 x 30 m). A 5–10 m wide strip has been left between the sub-plots for possible future use in special studies and for additional sampling. Sampling methods that may have a detrimental, long-term effect on the soil or stand, e.g. soil sampling, deposition and soil water collection, needle and litter sampling etc., are concentrated on one sub-plot. One of the other two sub-plots is reserved for vegetation studies, and the other for tree growth measurements. The centre point of the observation plot, the corners of the sub-plots and the outer edge of the mantle area have been marked with wooden posts. The mantle is surrounded by a buffer zone. The width of the mantle and buffer zones varies from 10–30 m. Basic stand measurements and mapping All the trees on the observation plot have been numbered at a height of 1.3 m on the side of the tree facing the centre point. The following parameters have been recorded or measured on each tree: tree species, canopy layer, diameter at 1.3 m, tree height, and length of the living crown. The measurements have been performed on the trees on sub-plots 1–3 and those located in the mantle area (sub-plot 4). Twenty additional trees representing different diameter classes have been selected and numbered on the buffer zone (sub-plot 5). In addition to the above measurements, bark thickness has been measured and increment cores taken at 1.3 m height for determining earlier growth and tree age. The forest site type has also been determined. The location and elevation of all the trees on the observation plots have been mapped using a tachymeter. The exposition and gradient of each sub-plot have also been determined. Care has been taken during the field work to avoid causing unnecessary trampling of the ground vegetation or other forms of damage. Wooden walkways have been laid on the sub-plot used for collecting deposition and soil water. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 15 Figure 3. The design of the observation plot and location of the sub-plots. Kuva 3. Kaavio metsäekosysteemien intensiivisen seurannan havaintoaloista. x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x xxx xx x x x xx x xx xx x xx x x x x x x x x x x x xx x x x x x x x x x x x xxxx x xx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x 30 m x x x x x x x xx x x x x xx x x x x xx x x x x x x x x xx x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x xx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x xx x x x xx x x x x x x x x x x x x x x x x x x xx x x x x x x x xxx x x xx x xx x x x x x x x x x xxx x x x x x x x x x x x x x x x x x x x x x xx xx x x x x xxx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x x xx x x xx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x N Sub-plot 4 Osaruutu 4 Sub-plot 2 Osaruutu 2 Sub-plot 3 Osaruutu 3 Buffer zone 5 Puskurivyöhyke 5 Sub-plot 1 Osaruutu 1 x x x x Boundary of the sub-plot - Osaruudun raja Boundary of the buffer zone - Puskurivyöhykkeen raja Tree - Puu Sample tree for age determination Puu puuston iän määrittämiseksi Sample tree for assessment of crown condition Puu harsuuntumisarviointia varten Sample tree for needle chemistry Puu neulasnäytteiden keruuta varten Stand throughfall sampler Laskeumakeräin Litterfall sampler - Karikekeräin Gravity lysimeter - Vajolysimetri Suction-cup lysimeter Alipainelysimetri Meteorological station Sääasema Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 16 Monitoring activities Survey Nr. of plots Frequency of assessments Crown condition 31 Annual Soil condition 31 Every 10 years Needle chemistry 31 Every 2 years Tree growth 31 Every 5 years Stem diameter growth 12 Continuous* Deposition 16 Continuous (Sampling every 4 weeks, but every 2 weeks during the snowfree period) Soil solution – gravity lysimeter 16 Continuous (Sampling every 4 weeks during the snowfree period) – suction-cup lysimeter 16 Continuous (Sampling every 2 weeks during the snowfree period) Meteorology 12 – air temperature Continuous* – relative humidity Continuous* – soil temperature (-10, -20, -30, ..... -100 cm) Continuous* – precipitation Continuous* – wind speed Continuous* – wind direction Continuous* – photosynthetically active radiation (PAR) Continuous* – solar radiation Continuous* Ground vegetation 31 Every 5 years 6 Every year Litterfall 14 Every 2 weeks during the snow-free period, once at the end of the winter Phenology 7 Three times/week during the critical period * = Hourly measurements Database and data evaluation A database has been set up for handling and archiving the Level I and Level II data, access to which is restricted to persons participating in the programme. The Level II database is maintained by Jarmo Mäkinen at the Parkano Research Unit (Metla) and Olavi Kurttio at the Vantaa Research Unit (Metla). The main database, containing the data forwarded annually to the data centres in Hamburg (Level I data, ICP Forests) and at the Join Research Centre in Ispra, Italy (Level II data), is located at the Parkano Research Unit. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 17 Seurantatoiminnot Toiminto Havaintoalojen Arviointi- tai mittausjakso lukumäärä Latvuskunto 31 Vuosittain Maaperä 31 Joka kymmenes vuosi Neulaskemia 31 Joka toinen vuosi Puuston kasvu 31 Joka viides vuosi Läpimitan kasvu 12 Jatkuva* Laskeuma 16 Jatkuva (Näytteenotto joka neljäs viikko, mutta lumettomana aikana joka toinen viikko) Maavesi – vajovesilysimetrit 16 Jatkuva (Näytteenotto joka neljäs viikko lumettomana aikana) – imulysimetrit 16 Jatkuva (Näytteenotto joka toinen viikko lumettomana aikana) Meteorologia 12 – ilman lämpötila Jatkuva* – suhteellinen kosteus Jatkuva* – maan lämpötila (-10, -20, -30, ..... -100 cm) Jatkuva* – sademäärä Jatkuva* – tuulen nopeus Jatkuva* – tuulen suunta Jatkuva* – fotosynteettisesti aktiivinen säteily (PAR) Jatkuva* – kokonaissäteily Jatkuva* Aluskasvillisuus 31 Joka viides vuosi 6 Joka vuosi Karike 14 Joka toinen viikko lumettomana aikana, kerran talven lopussa Fenologia 7 Kolme kertaa viiikossa kriittisenä aikana * = Mittaukset tunneittain Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 18 Ta bl e 2. T he b as ic s ta nd c ha ra ct er is tic s of IC P Le ve l I I o bs er va tio n pl ot s (m ea su re d du rin g th e w in te r 2 00 4– 20 05 ). Ta ul uk ko 2 . I C P -h av ai nt oa lo je n (ta so II ) k es ke is im m ät p uu st ot un nu ks et (m ita ttu ta lv ik au de lla 2 00 4– 20 05 ). P lo t n r. an d na m e B as al a re a S te m M ea n M ea n he ig ht S te m v ol um e S ta nd Fo re st ty pe S oi l t yp e H av ai nt oa la n no . w ith b ar k nu m be r di am et er , ar ith m et ic al w ith b ar k ag e M et sä ty yp pi *: m is si ng ja n im i P PA , R un ko - w ei gh te d w ith K es ki pi tu us R un ko til av uu s M et si kö n M aa nn os ku or el lin en , lu ku , ba sa l a re a (a rit m ee t- (k uo re lli ne n) , ik ä *: p uu ttu u m 2 / ha kp l/h a K es ki lä pi m itt a tin en ), m 3 / ha P PA :ll a m pa in ot et tu , cm 1 S ev et tij är vi _P 13 .3 35 0 24 .7 11 .5 76 .4 20 5 U lig in os um -V ac ci ni um -E m pe tru m T yp e Fe rr ic p od zo l 2 P al la sj är vi _P 14 .6 73 3 19 .0 10 .7 80 .4 95 E m pe tru m -M yr til lu s Ty pe * 3 P al la sj är vi _S 13 .9 11 04 17 .6 11 .0 72 .8 14 5 H yl oc om iu m -M yr til lu s Ty pe Fe rr ic p od zo l 4 S od an ky lä _P 19 .8 11 33 17 .0 13 .9 13 7. 1 85 E m pe tru m -M yr til lu s Ty pe * 5 K iv al o_ S 23 .2 16 63 15 .3 11 .6 13 3. 3 75 H yl oc om iu m -M yr til lu s Ty pe Fe rr ic p od zo l 6 K iv al o_ P 24 .8 17 55 14 .4 13 .3 16 7. 3 60 E m pe tru m -M yr til lu s Ty pe C ar bi c po dz ol 7 O ul an ka _S 27 .1 11 96 23 .1 15 .4 19 2. 7 19 5 H yl oc om iu m -M yr til lu s Ty pe * 8 O ul an ka _P 21 .4 68 9 21 .7 16 .8 17 4. 1 85 H yl oc om iu m -M yr til lu s Ty pe * 9 Y lik iim in ki _P 14 .1 54 8 19 .3 14 .3 10 0. 1 95 E m pe tru m -C al lu na T yp e Fe rr ic p od zo l 10 Ju up aj ok i_ P 20 .1 37 8 27 .2 22 .4 21 0. 6 85 Va cc in iu m T yp e Fe rr ic p od zo l 11 Ju up aj ok i_ S 35 .8 85 2 25 .2 21 .9 37 5. 5 85 O xa lis -M yr til lu s Ty pe D ys tri c ca m bo so l 12 Ta m m el a_ S 30 .1 66 3 25 .2 21 .6 30 9. 4 65 M yr til lu s Ty pe H ap lic p od zo l 13 Ta m m el a_ P 25 .6 60 4 24 .0 21 .1 25 4. 5 65 Va cc in iu m T yp e H ap lic p od zo l 14 La pi nj är vi _P 29 .3 11 74 19 .1 17 .9 25 5. 6 55 Va cc in iu m T yp e * 15 La pi nj är vi _S 30 .1 64 4 25 .6 22 .8 32 7. 7 70 O xa lis -M yr til lu s Ty pe * 16 P un ka ha rju _P 33 .2 95 9 22 .1 22 .8 35 8. 6 85 Va cc in iu m T yp e Fe rr ic p od zo l 17 P un ka ha rju _S 31 .1 37 4 33 .1 27 .1 38 6. 7 75 O xa lis -M yr til lu s Ty pe C am bi c ar en os ol 18 M ie hi kk äl ä_ P 18 .6 41 5 24 .9 20 .2 17 7. 8 12 5 C al lu na T yp e Fe rr ic p od zo l 19 E vo _S im 55 .2 12 54 30 .9 26 .3 65 8. 1 17 5 O xa lis -M yr til lu s Ty pe C am bi c po dz ol 20 Li ek sa _P im 28 .7 58 8 31 .9 22 .8 29 8. 1 13 5 E m pe tru m -V ac ci ni um T yp e H ap lic p od zo l 21 O ul an ka _S im 26 .8 17 38 23 .4 14 .3 18 2. 0 17 5 H yl oc om iu m -M yr til lu s Ty pe H ap lic p od zo l 22 K ev o_ P im 12 .1 68 8 28 .5 11 .5 68 .6 18 5 U lig in os um -E m pe tru m -M yr til lu s Ty pe * 23 U us ik aa rle py y_ S 38 .8 96 3 24 .0 20 .7 38 7. 2 60 O xa lis -M yr til lu s Ty pe C am bi c po dz ol 24 N är pi ö_ S 27 .8 64 1 28 .2 19 .5 24 4. 4 60 M yr til lu s Ty pe * 25 Vi lp pu la _S pr o 30 .8 44 8 30 .8 27 .2 39 2. 3 80 O xa lis -M yr til lu s Ty pe * 26 Ik aa lin en _P 12 .1 71 9 17 .6 12 .8 77 .7 95 O lig ot ro ph ic p in e m ire (d ra in ed ) * 27 Ik aa lin en _P fe r 12 .0 66 3 17 .9 13 .2 80 .8 10 5 O lig ot ro ph ic p in e m ire (d ra in ed ) * 28 S ol bö le _S pr o 28 .5 44 8 29 .2 24 .3 32 6. 8 80 O xa lis -M yr til lu s Ty pe * 29 P yh än tä _P 15 .2 13 26 13 .9 10 .6 84 .0 11 5 O lig ot ro ph ic p in e m ire (d ra in ed ) * 30 P yh än tä _P fe r 13 .9 12 52 13 .2 10 .3 75 .0 12 5 O lig ot ro ph ic p in e m ire (d ra in ed ) * 31 K iv al o_ S pr o 22 .7 12 19 17 .5 12 .7 14 0. 7 80 H yl oc om iu m -M yr til lu s Ty pe * Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 19 Table 3. Growing season and its length 2001 – 2004 and temperature sum and June – September precipitation for the periods 2001 – 2004 and 1971–2000 on Level II plots with meteorological measurements. Taulukko 3. Vuosien 2001 – 2004 kasvukausi ja sen pituus, lämpösumma ja kesä – syyskuun sademäärä sekä vertailujakson 1971 – 2000 keskimääräinen lämpösumma ja kesä–syyskuun sademäärä metsien inten- siiviseurannan säähavaintoasemilla (taso II). Plot Period and length (days) of the growing season1 – Kasvukausi ja sen pituus1 Havaintoala 2001 2002 2003 2004 3 Pallasjärvi_S 30.5. – 22.9. (114) 21.4. – 13.09. (146) 11.5 .– 29.08. (111) 28.4. – 11.09. (137) 5 Kivalo_S 13.5. – 22.9. (121) 22.4. – 14.09. (146) 11.5. – 29.08. (111) 27.4. – 12.09. (139) 9 Ylikiiminki_P 23.4. – 22.9. (144) 22.4. – 15.09. (147) 10.5. – 10.10. (154) 10 Juupajoki_P 23.4. – 18.10. (169) 20.4. – 18.09. (152) 05.5. – 12.10. (161) 15.4. – 08.10. (177) 11 Juupajoki_S 22.4. – 18.10. (169) 20.4. – 18.09. (152) 04.5. – 06.10. (156) 15.4. – 08.10. (177) 12 Tammela_S 22.4. – 19.10. (175) 10.4. – 19.09. (163) 05.5. – 12.10. (161) 15.4. – 08.10. (177) 17 Punkaharju_S 22.4. – 18.10. (173) 10.4. – 18.09. (162) 05.5. – 13.10. (162) 17.4. – 08.10. (175) 18 Miehikkälä_P 22.4. – 18.10. (173) 10.4. – 19.09. (163) 05.5. – 14.10. (163) 15.4. – 08.10. (177) 23 Uusikaarlepyy_S 23.4. – 28.10. (174) 21.4. – 02.10. (165) 10.5. – 17.10. (161) 16.4. – 08.10. (176) 24 Närpiö_S 23.4. – 19.10. (170) 22.4. – 19.09. (167) 04.5. – 17.10. (167) 25 Vilppula_Spro 22.4. – 19.10. (172) 20.4. – 18.09. (152) 04.5. – 12.10. (162) 28 Solböle_Spro 22.4. – 04.11. (192) 10.4. – 03.10. (177) 03.5. – 17.10. (168) Plot Rainfall (mm) for the period 1.6. – 30.9. Long term mean2 Havaintoala Sademäärä (mm) 1.6. – 30.9. Pitkän ajan keskiarvo2 2001 2002 2003 2004 1971 – 2000 3 Pallasjärvi_S 570 254 52 230 5 Kivalo_S 244 268 264 358 247 9 Ylikiiminki_P 433 207 131 239 10 Juupajoki_P 682 228 166 218 288 12 Tammela_S 301 247 134 281 283 17 Punkaharju_S 161 129 164 234 269 18 Miehikkälä_P 212 104 101 376 278 23 Uusikaarlepyy_S 167 53 80 65 232 24 Närpiö_S 354 246 141 258 25 Vilppula_Spro 496 206 104 293 28 Solböle_Spro 661 131 146 278 1) The growing season is defined as the period during which the temperature sum accumulates. Terminen kasvukausi on se osa vuodesta, jolloin lämpösumma kertyy. 2) Long term means were calculated according to Ojansuu and Henttonen (1983). Pitkän ajan keskiarvot on laskettu Ojansuun ja Henttosen (1983) mukaisesti. Gaps in the data set were supplemented by modelling the missing observations using the data from the nearest weather station of the Finnish Meteorological Institute. Puuttuvat havainnot saatiin mallittamalla lähimmän Ilmatieteen laitoksen säähavaintoaseman havaintojen perusteella. Plot Temperature sum (5oC threshold) – Lämpösumma (>5oC) Long term mean2 Havaintoala Pitkän ajan keskiarvo2 2001 2002 2003 2004 1971 – 2000 3 Pallasjärvi_S 785 886 753 718 687 5 Kivalo_S 928 1073 873 852 832 9 Ylikiiminki_P 1193 1165 1177 1033 10 Juupajoki_P 1359 1507 1300 1232 1166 11 Juupajoki_S 1314 1510 1232 1184 1142 12 Tammela_S 1415 1562 1346 1252 1262 17 Punkaharju_S 1524 1591 1435 1360 1304 18 Miehikkälä_P 1504 1600 1415 1395 1361 23 Uusikaarlepyy_S 1249 1457 1267 1233 1142 24 Närpiö_S 1257 1429 1281 1187 25 Vilppula_Spro 1330 1652 1351 1178 28 Solböle_Spro 1614 1680 1462 1375 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 20 References Luyssaert, S., Sulkava, M., Raitio, H. & Hollmén, J. 2005. Are N and S deposition altering the mineral composition of Norway spruce and Scots pine needles in Finland? Environmental Pollution 138: 5–17. Ojansuu, R. & Henttonen, H. 1983. Kuukauden keskilämpötilat, lampösumman ja sademäärän paikalisten arvojen johtaminen ilmatieteen laitoksen mittaustiedoista. Summary: Estimation of local values of monthly mean temperature, effective temperature sum and precipitation sum from the measurements made by the Finnish meteorological Office. Silva Fennica 17(2): 142–160. Raitio H. 1994. Kangasmetsien ravinnetila neulasanalyysin valossa. In: Mälkönen, E. & Sivula, H. (eds.). Suomen metsien kunto. Metsäntutkimuslaitoksen tiedonantoja 527: 25–34. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 21 2 Forest condition in national systematic network (Forest Focus/ICP Forests, Level I) in 2002 – 2005 Metsien terveydentila systemaattisen havaintoala­ verkoston aloilla vuosina 2002 – 2005 (Forest Focus/ ICP metsäohjelma, taso I) 2.1 Results of the national crown condition survey Valtakunnallisen latvuskunnon seurannan tulokset Martti Lindgren1, Seppo Nevalainen2 & Antti Pouttu1 Finnish Forest Research Institute; 1) Vantaa Research Unit, 2) Joensuu Research Unit The forest condition survey was conducted on 457 sample plots in 2002, on 453 sample plots in 2003, on 594 sample plots in 2004 and on 609 sample plots in 2005. The degree of defoliation and foliage discoloration and the occurrence of abiotic and biotic damage on Scots pine, Norway spruce and broadleaves were recorded. There were no notable changes in the average defoliation level of any tree species between the years 2002 and 2005. The average tree-specific degree of defoliation for the period 2002–2005 on mineral soil sites was 9.4% in pine, 18.3% in spruce and 11.7% in broadleaves. In 2004, the plots on peatland were included in the survey for the first time and the average defoliation was 8.2% in pine, 17.0% in spruce and 9.3% in broadleaves, and in 2005 8.2%, 16.8% and 9.4%, respectively. The proportion of dead trees was 0.4% during 2001–2002, 0.1% during 2002–2003, 0.14% in 2003–2004 and 0.1% during 2004–2005. No correlation was found between the defoliation pattern of conifers or broadleaves and the modelled sulphur or nitrogen deposition at the national level in 2002, 2003, 2004 or 2005. High stand age and weather and climatic factors, as well as abiotic and biotic damage, have a considerable effect on defoliation in Finland. Metsien vuosittaisessa terveydentilan seurannassa arvioitiin puiden latvuskunto 457 näytealal­ la vuonna 2002, 453:lla 2003, 594:llä 2004 ja 609:llä 2005. Puiden kunnon mittareina käytetään latvuksen harsuuntumisastetta, värioireiden määrää sekä abioottisia että bioottisia tuhoja. Viime vuosina kaikkien puulajien keskimääräinen harsuuntumisaste on pysynyt melko vakaana. Kiven­ näismailla kasvavien mäntyjen keskimääräinen harsuuntumisaste jaksolla 2002–2005 oli 9,4 %, kuusien 18,3 % ja lehtipuiden (pääasiassa koivuja) 11,7 %. Vuonna 2004 otettiin seurantaan mukaan myös turvemaiden näytealoja. Mäntyjen keskimääräinen harsuuntumisaste turvemailla oli vuon­ na 2004 8,2 %, kuusien 17 % ja lehtipuiden 9,3 %. Vuonna 2005 vastaavat luvut olivat 8,2 %, 16,8 % ja 9,4 %. Vuosina 2001/2002 kuoli puista 0,4 % ja seuraavina vuosina kuolleisuus oli noin 0,1 %. Harsuuntuminen johtuu Suomessa pääasiassa puuston ikääntymisestä, erilaisista epäedulli­ sista ilmasto­ ja säätekijöistä sekä sieni­ ja hyönteistuhoista. Koko maata tarkasteltaessa ei havaittu yhteyttä ilman epäpuhtauksien ja neulaskadon välillä vuosina 2002–2005. Introduction Concern about large­scale decline in forest vitality in central Europe in the late 1970’s and early 1980’s led Finland to initiate an extensive national survey of forest condition. The Finnish Forest Research Institute has surveyed crown condition annually since 1986. The surveys have been carried out in accordance with the methodology of the UN/ECE Convention on Long­Range Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 22 Transboundary Air Pollution of the International Co­operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (current edition: Manual on methods… 2006) and, since 1995, also in accordance with Commission regulations (EEC) Nos. 3528/86 and 1398/95, and since 2003 Regulation 2152/2003. This report presents 1) the regional distribution of forest condition in Finland, 2) the year­to­year variation in forest condition, and 3) the factors which may explain, based on correlation analysis, the regional pattern and changes in forest condition. Materials and methods The large­scale crown condition survey (Level I) was carried out in Finland on a systematic network of permanent sample plots established during 1985–1986 in connection with the 8th National Forest Inventory (NFI) (Jukola­Sulonen et al. 1990). The country was divided into a southern and a northern region (demarcation line 66º N). The network in the southern region is based on a 16 x 16 km grid, and that in the northern region on a 24 x 32 km grid. The total forest area represented by the plots is approximately 15 million ha. According to the Commission regulation (EC no: 1398/95), the minimum number of sample trees per plot must be 20 in southern Finland and 10 in northern Finland. Because a fixed plot size was used in Finland during 1986–1994, the number of sample trees on many of the plots was insufficient to fulfil the minimum criteria for tree number. During summer 1995 over 4000 new trees and 82 new sample plots were added to the network (Table 1). The new trees were added systematically to the network by increasing the radius of the plot. During summer 2004, 150 new plots were added to the monitoring network, and in 2005 15 plots. The present network includes 499 sample plots on mineral soil and 110 on peatland (Table 1). The forest condition survey was conducted on 457 sample plots in 2002, on 453 sample plots in 2003, on 594 sample plots in 2004, and on 609 sample plots in 2005 (Table 1). The degree of defoliation and foliage discoloration and occurrence of abiotic and biotic damage on pine, spruce and broadleaves were recorded. Defoliation and discoloration of Scots pine and broad-leaved trees are estimated on the upper 2/3 of the living crown, and on Norway spruce on the upper half of the living crown, in 5% classes (Lindgren et al. 2005). A tree is classified as damaged when its leaf or needle loss is more than 25%, and as discoloured when 10% of its leaf or needle mass has abnormal coloration (e.g. needle yellowing). The degree of recognizable damage is also assessed and grouped into three categories: 1) slight, 2) moderate, and 3) severe. Since 2004 the new damage assessment method (UN/ECE/ICP manual update 6/2004) has been applied on the large­ scale assessment of crown condition in Finland. For more comprehensive information about the abiotic and biotic damage in Finland, see Chapters 2.2, 4.2 and 4.3 in this volume. Results There were no notable changes in the average defoliation level of any tree species between the years 2002 and 2005. On all tree species the average tree-specific degree of defoliation varied by less than 1%­unit on mineral soil plots during 2002–2005 (Fig. 1). In 2005 the average defoliation degree was 9.5% in pine, 17.9% in spruce and 11.4% in broadleaves (Fig. 1). On the peatland plots the corresponding average defoliation degree was 8.2% (8.2% in 2004) in pine, 16.8% Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 23 (17%) in spruce and 9.4% (9.3%) in broadleaves. Of the trees assessed on mineral soil plots in 2002, 97% of the pines, 73% of the spruces and 91% of broadleaves (mainly Betula spp.) were not or slight defoliated (leaf or needle loss less than 25%) (Fig. 2). In 2003 the proportions were 96% in pine, 75% in spruce and 92% in broadleaves, and in 2004 96%, 76% and 91% and 2005 97%, 78% and 92%, respectively (Fig. 2). The proportion of moderate or severely defoliated trees remained relatively constant in pine (ca. 3%) and in broadleaves (ca. 8–9%) during the period 2002–2005. In spruce the proportion of moderate or severely defoliated trees was 27% in 2002, 26% in 2003, 24% in 2004, and 22% in 2005 (Fig. 2). In 2004, peatland stands were included in the survey for the first time. The proportion of less than 25% defoliated pines was 99%, spruces 77% and broadleaves 96% (Fig. 2) in 2004, and 99%, 80% and 96% in 2005, respectively. The proportion of dead trees on all plots was 0.4% during 2001–2002, 0.1% during 2002–2003, 0.14% during 2003–2004 and 0.1% during 2004–2005. On the mineral soil plots the proportion of over­25% defoliated pines was 1.2% in 2002 (Fig. 3), 2.1% in 2003 (Fig. 4), 2.5% in 2004 (Fig. 5), and 2.8% in 2005 (Fig. 6). For spruce the proportions were 32.8%, 34.5%, 28.6% and 26.5%, and in broadleaves 5.1%, 5.8%, 6.0% and 6.8%, respectively (Figs. 3, 4, 5 and 6). On the peatland plots the proportion of over­25 defoliated pines were 1.3% in 2004 and 2005, and in broadleaves 4.9% in 2004, 2.5% in 2005, and in spruce 26.9% in 2004 and 2005 (Figs. 5 and 6). Table 1. The number of assessed trees, sample plots and observers during 1986–2005. The number of plots includes 97 peatland plots in 2004 and 110 in 2005. Number of trees in 2004 includes 1200 Scots pines, 313 Norway spruces and 379 broadleaves and for 2005 1361 pines, 347 spruce and 446 broadleaves growing on peatland plots. Taulukko 1. Seurantajakson 1986–2005 aikana arvioitujen puiden, näytealojen sekä arvioijien lukumää­ rät. Vuoden 2004 näytealojen lukumäärä sisältää 97 turvemaiden alaa ja puiden lukumäärä 1200 mäntyä, 313 kuusta ja 379 lehtipuuta. Vuonna 2005 turvemaiden alojen lukumäärä oli 110 ja näillä aloilla kasvoi yhteensä 1361 mäntyä, 347 kuusta ja 446 lehtipuuta. Year Number of Scots pine Norway spruce Broadleaves Number of Number of trees plots observers Vuosi Puiden lkm Mänty Kuusi Lehtipuut Näytealojen lkm Arvioijien lkm 1986 3982 2233 1445 304 378 4 1987 3971 2171 1432 368 376 4 1988 3870 2129 1391 347 370 4 1989 3807 2032 1355 500 360 4 1990 3746 2002 1329 415 358 4 1991 3764 2004 1272 488 356 4 1992 4391 2377 1367 647 409 4 1993 4276 2347 1307 622 399 4 1994 4180 2301 1265 614 392 4 1995 8754 4520 2838 1396 455 7 1996 8732 4522 2851 1359 455 7 1997 8779 4582 2814 1383 460 7 1998 8758 4584 2829 1345 459 8 1999 8662 4538 2816 1308 457 8 2000 8576 4560 2706 1310 453 8 2001 8579 4608 2693 1278 454 8 2002 8593 4648 2691 1254 457 9 2003 8482 4610 2622 1250 453 10 2004 11210 6174 3123 1913 594 11 2005 11535 6450 3089 1996 609 11 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 24 Between 2002 and 2005, there was a more than 5%-unit increase in the plot-specific defoliation degree on 5.4% of the pine, 14.2% of the broadleaved, and 8.1% of the spruce plots (Fig. 7). A recovery of more than 5 %-units (i.e. decrease) in the plot-specific defoliation degree occurred on 2.5% of the pine, 12.7% of the broadleaved, and 7.5% of the spruce plots between 2002 and 2005 (Fig. 7). The proportion of needle discoloration (extent of discoloured needle/leaf mass more than 10 %) on pine remained at the same level (under 1%) in 2003, as in 2002, and that of spruce decreased from 4.5% to 3.8%. However, the proportion of slightly discoloured (extent of discoloured needle mass 1–10 %) conifers was higher in 2003 than that in the previous year. The proportion of discoloured trees in 2004 was under 1% in pine, 6.4% in spruce and 6.6% in broadleaves. In 2005, the proportion of needle discoloration on pine remained at the same level (under 1%) as in 2004, and that of spruce increased from 7.5% to 10.2%. However, most of these discoloured spruces belonged to the 10 to 25% discoloration class, and the incidence of moderate or severe discolouration was rare. Leaf discoloration on broadleaves decreased from 8.5% to 2.1%. During the study period, the most frequent discoloration symptoms in conifers were needle tip yellowing and needle yellowing, and the symptoms were mainly concentrated on needles older than two years. On broadleaved trees the most frequent symptoms were yellowing and browning of the leaves. The results of abiotic and biotic damage are presented in chapter 2.2 in this volume. No correlation was found between the defoliation pattern of conifers or broadleaves and the modelled sulphur or nitrogen deposition at the national level in 2002, 2003, 2004 (deposition data for 1993 are based on the HILATAR model, Hongisto 1998) or 2005 (updated data from the Finnish Meteorological Institute). Figure 1. The average defoliation level of Scots pine, Norway spruce and broadleaves on mineral soil plots during 1986–2005. The average defoliation degree was calculated using the same defoliation-class mid- point values as in 1986 (0–10% defoliation = 5%, 11–20% def. = 15%, 21–30% def. = 25% etc.). Kuva 1. Männyn, kuusen ja lehtipuiden keskimääräinen harsuuntumisaste kivennäismailla vuosina 1986–2005. Keskiarvoja laskettaessa on käytetty samaa harsuuntumisluokan keskilukua kuin vuonna 1986, esim. (0–10 % lehti­ tai neulaskato eli harsuuntumisaste = 5 %, 11–20 % harsuuntumisaste = 15 %, 21–30 % harsuuntumisaste = 25 % jne.) Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 25 Figure 2. Defoliation frequency distribution for Scots pine, Norway spruce and broadleaves on mineral soil and peatland (P) plots during 1986–2005. Kuva 2. Männyn, kuusen ja lehtipuiden harsuuntumisjakaumat kangasmailla ja turvemailla (P) 1986–2005. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 26 Figure 3. Plot-wise defoliation degrees for Scots pine, Norway spruce and broadleaves on mineral soil plots in 2002. Kuva 3. Männyn, kuusen ja lehtipuiden näytealakohtaiset keskiarvot kivennäismailla vuonna 2002. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 27 Figure 4. Plot-wise defoliation degrees for Scots pine, Norway spruce and broadleaves on mineral soil plots in 2003. Kuva 4. Männyn, kuusen ja lehtipuiden näytealakohtaiset keskiarvot kivennäismailla vuonna 2003. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 28 Figure 5. Plot-wise defoliation degrees for Scots pine, Norway spruce and broadleaves on mineral soil and peatland plots in 2004. Kuva 5. Männyn, kuusen ja lehtipuiden näytealakohtaiset keskiarvot kivennäis­ ja turvemailla vuonna 2004. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 29 Figure 6. Plot-wise defoliation degrees for Scots pine, Norway spruce and broadleaves on mineral soil and peatland plots in 2005. Kuva 6. Männyn, kuusen ja lehtipuiden näytealakohtaiset keskiarvot kivennäis­ ja turvemailla vuonna 2005. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 30 Figure 7. Change in plot-wise defoliation degree of Scots pine, Norway spruce and broadleaves on mineral soil plots between 2002 and 2005. Kuva 7. Vuosien 2002 ja 2005 välinen, näytealakohtainen harsuuntumisasteen muutos männyllä, kuusella ja lehtipuilla kivennäismailla. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 31 Conclusions A large number of natural factors, the most important of which are connected with stand age, climate and weather, and abiotic or biotic damage, affect forest condition in Finland (Jukola­ Sulonen et al. 1990, Salemaa et al. 1991, Lindgren et al. 2000, Nevalainen and Heinonen 2000). In the northern parts of the country especially, the harsh climate has a strong effect on forest development. At the beginning of the monitoring period, the increase in defoliation coincided with the extremely cold winter of 1987, and defoliation increased in all tree species during 1986 to 1989. Since then the tree crowns have recovered (Salemaa et al. 1991). Defoliation in broadleaves again increased in the years 1992–1993. A slight increase in pine defoliation was also observed in 1993 and 1997. Although the proportion of non­defoliated and slightly defoliated trees has varied in recent years, there have been no essential changes in the proportion of moderately or severely defoliated trees, or average defoliation degree, of any of the tree species during the previous years. The average defoliation level was slightly lower on the peatland plots than on mineral soil plots in 2004 and 2005 when peatland sites were included for the first time in the survey. No correlation was found between the defoliation pattern of conifers or broadleaves and the modelled sulphur or nitrogen deposition at the national level in 2002, 2003, 2004 or 2005. References Council Regulation (EEC) No. 3528/86 on the protection of forest in the Community against atmospheric pollution. Brussels: 1986. Official Journal of the European Communities, No. L362/2 of Nov. 1986, 3 p. Commission Regulation (EC) No. 1398/95 amending Regulation (EEC) No. 1696/87 (inventories, network, reports). Brussels: 1995, Official Journal of European Communities No. L139/4 of 22 June 1995. 2 p. Hongisto, M. 1998. Hilatar, a regional scale grid model for transport of sulphur and nitrogen compounds. Description of the model. Finnish Meteorological Institute Contributions No. 21. 152 p. ISBN 951– 697–479–1. Jukola­Sulonen, E.­L., Mikkola, K. & Salemaa, M. 1990. The vitality of conifers in Finland, 1986–88. In: Kauppi, P., Anttila, P. & Kenttämies, K. (eds.). Acidification in Finland. Springer Verlag, Berlin. p. 523–560. Lindgren, M., Salemaa, M. & Tamminen, P. 2000. Forest condition in relation to environmental factors. In: Mälkönen, E. (ed.). Forest condition in a changing environment – the Finnish case. Forestry Sciences, Vol. 65. Kluwer Academic Publishers. p. 142–155. Lindgren, M., Nevalainen, S., Pouttu, A., Rantanen, H. & Salemaa, M. 2005. Metsäpuiden elinvoimaisuuden arviointi. Forest Focus/ICP­Forests, Level I. Maasto­ohje (Field guide). Metsäntutkimuslaitos. 38 p. Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. 2006. Part II. Visual assessment of crown condition. 69 p. [Internet site]. UN­ECE. Programme coordination centre, Hamburg. Available at: http://www.icp­forests.org/ manual.htm. Nevalainen, S. & Heinonen, J. 2000. Dynamics of defoliation, biotic and abiotic damage during 1986– 1998. In: Mälkönen, E. (ed.). Forest condition in a changing environment – the Finnish case. Forestry Sciences 65. Kluwer Academic Publishers, Dordrecht. p. 133–141. Regulation (EC) No. 2152/2003 of 17 November 2003, Concerning monitoring of forest and environmental interactions in the community (Forest Focus). Brussels: 2003, Official Journal of European Communities No. L324/1 of 11 December 2003. 8 p. Salemaa, M., Jukola­Sulonen, E.­L. & Lindgren, M. 1991. Forest condition in Finland, 1986–1990. Silva Fennica 25(3): 147–175. 32 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 2.2 Biotic and abiotic damage on the Level I network Bioottiset ja abioottiset tuhot tason I havainto-aloilla Seppo Nevalainen1, Martti Lindgren2 & Antti Pouttu2 Finnish Forest Research Institute; 1) Joensuu Research Unit, 2) Vantaa Research Unit During the period 2002–2005, 33.4% of the Scots pine observation trees, 36.5% of the Norway spruces and 40.1% of the broadleaves showed signs of biotic or abiotic damage. Most of the observed damage was slight, i.e. did not decrease the vitality of the trees. Considerable changes were observed in the occurrence of the individual causal agents over the years, for instance the increase of insect (Tomicus sp.) damage in 2003, the increase of damage caused by Gremmeniella abietina in pine in 2004, and needle rust Chrysomyxa ledi in 2005 in spruce. Birch rust and leaf anthracnose were common on birches in 2004. Abiotic damage, due to soil factors (dryness, wetness or nutrient imbalance), was also common in 2003 and 2004. The geographical distribution of the most common causes was also plotted on maps. From the point of view of tree condition (measured as the extent of damage), Gremmeniella abietina, competition and Tomicus sp. were by far the most important of the individual damage causes in pine. The number of damage causes important for spruce and broadleaved trees was much larger than for pine. Competition, needle rust, soil factors (dryness, wetness or nutrient imbalance), unknown causes and non-identified decay fungi were among the causes in spruce trees. Competition, anthracnose, unknown insects and rust fungi were important in broadleaved trees. Spatial and temporal patterns of the most important abiotic or biotic epidemics are clearly visible in this annual survey. The observed causes of damage can act as predisposing, inciting or contributing factors in forest decline. The results should therefore be interpreted against this theoretical background. Tutkimusjakson (2002–2005) aikana havaittiin bioottisia tai abioottisia tuhoja 33,4 %:ssa mänty­ havaintopuita, 36,5 %:ssa kuusia ja 40,1 %:ssa lehtipuita. Suurin osa havaituista tuhoista oli lieviä, eli ei vähentänyt puiden elinvoimaisuutta. Vuosien välillä oli kuitenkin suuria eroja eri tuhonaiheut­ tajien esiintymisessä, esim. männyllä hyönteistuhot (ytimennävertäjätuhot) lisääntyivät vuonna 2003 ja versosurmatuhot 2004. Kuusella suopursuruoste yleistyi vuonna 2005. Koivuilla koivunruoste ja erilaiset lehtilaikut olivat yleisiä vuonna 2004. Maaperätekijöistä (kuivuus, märkyys tai ravinteiden epätasapaino) johtuvia tuhoja tavattiin myös yleisesti vuosina 2003 ja 2004. Yleisimmistä tuhonai­ heuttajista esitetään esiintymiskarttoja. – Puiden kunnon kannalta (kun arviointikriteerinä käytetään tuhon laajuutta yksittäisissä puissa) männyllä selvästi tärkeimpiä tuhonaiheuttajia olivat versosur­ ma, kilpailu ja ytimennävertäjät. Kuusella ja lehtipuilla tuhonaiheuttajien kirjo oli suurempi kuin männyllä: kuusella tärkeimpiä olivat kilpailu, suopursuruoste, maaperätekijät (kuivuus, märkyys, ravinne­epätasapaino) sekä tunnistamattomien tekijöiden ja tunnistamattomien lahottajasienten aiheuttamat tuhot; lehtipuilla puolestaan kilpailu, lehtilaikut ja tunnistamattomat hyönteiset sekä ruosteet. Tärkeimmät tuhoepidemiat paljastuvat hyvin tässä vuotuisessa seurannassa. Havaitut tuhot voivat olla metsävaurioissa altistavina, vaurioita lisäävinä tai puiden lopulliseen kuolemaan myötä­ vaikuttavina tekijöinä, ja tuloksia tulisikin arvioida tätä teoreettista viitekehystä vasten. Introduction In Europe, the overall vitality of forests is mainly monitored on the basis of the relative loss of leaf and needle biomass (defoliation, crown thinning) and discoloration. The defoliation method has several disadvantages, despite its practicality. The leaf biomass of the crown is strongly affected by tree age, climatic and genetic factors, shading and a large number of abiotic or biotic stresses. In some cases, the rapid deterioration in the vitality of forests has been attributed to abiotic or Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 33 biotic damage (Innes et al. 1986, Keane et al. 1989, Innes and Schwyzer 1994). It has even been proposed that the condition of trees strongly reflects the fluctuating effects of biotic or abiotic agents or site conditions (Skelly and Innes 1994). In Finland the variation in annual defoliation caused by abiotic and biotic damage can be so large that it is difficult to identify any long-term trends in defoliation (Nevalainen and Heinonen 2000). The monitoring results have indicated no clear correlation between air pollution and crown condition in Finland (Lindgren 2002). However, as the annual variation is large, it is essential to carry out a thorough nalysis of the causes of variations in the forest health results, especially in areas subjected to low levels of air pollution. The issues of climate change and increased frequency of extreme weather events, as well changes in silvicultural practices, have the potential to increase biotic and abiotic damage. Large­scale, regular monitoring of forest health has therefore become more important than ever before. The aims of this study are to describe the temporal and spatial occurrence of the most important biotic and abiotic damage on the Level I network in Finland during 2002–2005. Material and methods In Finland the occurrence of biotic and abiotic damage has been monitored since the beginning of forest health monitoring in 1985. Damage is recorded at different levels of intensity and spatial extent, ranging from the 75,000 plots of the National Forest Inventory (NFI) to 31 plots at Level II. Currently, the Level I network of the ICP Forests/Forest Focus programme comprises ca. 11,000 trees on 609 permanent plots. The network includes plots on mineral soil (499) and on peatland (110). The material of this study consisted of all the trees monitored at Level I. The number of monitored trees varied between the years, due to cuttings and the selection of new trees and new plots. The number of trees in each year is given in Table 1. For details of the selection of the plots and sample trees, see chapter 2.1 in this volume. A national system for describing the symptom, apparent severity (degree of damage) and the cause, as well as the age of the damage, was used prior to 2004. The degree of damage was recorded as follows: 0) symptoms observed, but the condition of the tree not affected, 1) slight – the damage can slightly reduce the vitality of the tree, 2) moderate – the damage can strongly reduce the viability of the tree, and 3) severe – the injury can kill the tree. An example of the variables and codes used in the national damage survey can be found e.g. in Nevalainen (1999). The ICP Forests manual ‘Assessment of damage causes’ (referred to as Biotic manual), was tested in 2004 and fully adopted in 2005 in Finland. Currently, the European assessment of damage consists of symptom description, determination of the causal factor, and quantification of the symptoms. The age of the damage (new or old) is also recorded. The principles of the national damage survey in Finland have thus always been similar to that in the current Biotic manual, except that the coding of damage symptoms and causes was less detailed, and the quantification was not used prior to 2004. The common codes used during 2002–2005 for the causal agents are shown in Table 2. The coding of causes was more detailed in 2005. For instance, 22 insects and 16 fungi were coded to the species level. 34 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Results In general, the proportion of trees with damage symptoms changed only slightly on the Level I plots during the period. Scots pine (Pinus sylvestris L.) trees had less abiotic and unidentified damage, but more insect damage, than Norway spruce (Picea abies L.) or hardwood species (mostly Betula sp.). The symptoms caused by fungi were the most common in broadleaved species, however (Table 1). Altogether, 33.4% of the pines, 36.5% of the spruces and 40.1% of the broadleaves showed signs of biotic or abiotic damage. Considerable changes were observed in the occurrence of the agent groups over the years. In pine, the most notable changes were the increase in insect damage in 2003 (mostly caused by Tomicus spp., 471 trees, 10.2% pines) and the increase of damage caused by fungi in 2004, most of which was due to Gremmeniella abietina (Lagerb. Morelet) (645 trees, 10.4% of pines). In 2004, Gremmeniella damage was common throughout the middle and western part of the country, but a cluster was also found in the southeastern part of Finland (Fig. 1). In spruce, symptoms caused Table 1. The occurrence of causal agent groups in the observation trees on the Level I plots during 2002–2005. Taulukko 1. Tuhonaiheuttajaryhmien esiintyminen I tason koealojen havaintopuissa 2002–2005. Tree species Agent group % of trees – % puista Number Puulaji Aiheuttajaryhmä of trees Year – Vuosi Mean Puita, kpl 2002 2003 2004 2005 Ka. Scots No damage – Ei tuhoa 68.5 63.1 67.0 66.6 66.4 14497 pine Game and grazing – Selkärankaiset 0.5 0.4 0.4 0.4 0.4 94 Mänty Insects – Hyönteiset 9.4 11.6 9.5 10.9 10.3 2259 Fungi – Sienet 8.1 9.5 12.1 9.7 10.0 2186 Abiotic – Abioottiset 2.0 3.1 2.6 1.8 2.3 511 Direct action of man – Ihmisen toiminta 2.7 2.8 1.8 2.3 2.4 521 Other – Muut tekijät 8.4 8.6 5.2 7.1 7.1 1556 Unknown – Tunnistamaton 0.5 1.0 1.4 1.1 1.0 224 Number of trees – Puita, kpl 4610 4610 6173 6455 21848 Norway No damage – Ei tuhoa 70.6 61.2 65.2 57.9 63.5 7236 spruce Game and grazing – Selkärankaiset 0.1 0.2 0.1 0.1 0.1 13 Kuusi Insects – Hyönteiset 0.3 0.1 0.2 0.6 0.3 35 Fungi – Sienet 7.7 11.9 13.6 20.0 13.6 1550 Abiotic – Abioottiset 3.0 9.9 9.4 5.6 7.1 805 Direct action of man – Ihmisen toiminta 5.1 5.0 3.7 4.0 4.4 498 Other – Muut tekijät 8.5 7.3 4.5 8.3 7.1 805 Unknown – Tunnistamaton 4.7 4.5 3.4 3.5 4.0 452 Number of trees – Puita, kpl 2560 2621 3121 3092 11394 Other No damage – Ei tuhoa 66.9 61.2 55.3 59.3 59.9 3811 Muut Game and grazing – Selkärankaiset 2.0 1.8 1.0 0.8 1.3 81 Insects – Hyönteiset 3.8 2.4 4.7 10.2 5.8 370 Fungi – Sienet 9.1 9.2 24.5 13.2 15.0 956 Abiotic – Abioottiset 4.7 11.4 5.3 4.9 6.2 397 Direct action of man – Ihmisen toiminta 3.5 3.4 1.6 2.1 2.5 158 Other – Muut tekijät 8.2 7.9 2.3 5.6 5.6 354 Unknown – Tunnistamaton 1.9 2.7 5.3 4.0 3.8 239 Number of trees – Puita, kpl 1201 1251 1916 1998 6366 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 35 by fungi were very frequent (20 % of trees) in 2005. In 14.6% of the spruce trees the damage was caused by the needle rust Chrysomyxa ledi (De Bary). This fungus was most frequent in a belt­like zone running through middle Finland, and in scattered plots in the northern part of the country in 2005 (Fig. 2). Abiotic damage, due to soil factors (dryness, wetness or nutrient imbalance), was also common in 2004 (6.9% of the trees). Soil factors were also important in hardwoods in 2003 (6.6% of the trees). Fungal damage was important in hardwood species in 2004. 13.0% of the trees showed symptoms caused by ‘other fungi’. These symptoms were mostly caused by two groups of fungi: “leaf spot fungi”, i.e. unspecified leaf anthracnoses of birch (238 trees, 12.4%) and birch rust Melampsoridium betulinum (Fr.) Kleb. (147 trees, 7.7%). In 2004, birch rust occurred in Central Finland (Fig. 3). The proportion of unidentified damage has remained satisfactorily low (Table 1). Most of the observed damage was slight. The proportion of moderate and severe damage was 3.0% for pine, 10.4% for spruce and 9.4% for broadleaves. The proportion of at least moderate damage was the greatest in 2002 (0.62%) and the smallest during 2003 (0.39%). During the years 2002 and 2003, this kind of damage was statistically significantly more frequent than during the Table 2. Causes of at least moderate damage in Level I observation trees during 2002–2005. Taulukko 2. Puiden elinvoimaa alentavien tuhojen aiheuttajat I tason havaintopuissa 2002–2005. Cause of damage – Tuhonaiheuttaja % of trees – % puista Tree species – Puulaji All – Kaikki Pine Sruce Other Mänty Kuusi Muut Unidentified – Tunnistamaton 4.7 9.7 12.1 8.9 Wind – Tuuli 3.2 0.8 0.3 1.3 Snow – Lumi 4.7 3.3 7.1 4.6 Frost – Halla, pakkanen 1.2 3.1 1.3 Other abiotic – Muut abioottiset 1.1 2.2 5.5 2.7 Soil factors – Maaperätekijät 7.9 25.9 8.4 16.8 Harvesting – Puutavaran korjuu 4.0 9.7 3.5 6.7 Other man-made – Muut ihmisen aiheuttamat 1.5 3.9 4.6 3.4 Moose, deer, reindeer – Hirvieläimet 1.4 4.1 1.4 Other vertebrates – Muut selkärankaiset 0.2 0.3 0.2 Tomicus sp. – Ytimennävertäjät 5.9 1.6 Diprionidae – Mäntypistiäiset 2.7 0.7 Other defoliating insects – Muut neulastuholaiset 0.3 3.8 1.1 Ips sp. – Kirjanpainajat 0.1 0.0 Other insects – Muut hyönteiset 0.2 4.0 1.1 Unidentified insect – Tunnistamattomat hyönteiset 0.3 0.3 0.2 Heterobasidion sp. – Juurikäävät 2.0 22.1 1.7 11.7 Other decay fungi – Muut lahottajasienet 2.4 1.8 19.8 6.4 Gremmeniella – Versosurma 23.4 6.3 Cronartium sp. – Tervasrosot 15.7 0.2 4.2 Other rust fungi – Muut ruostesienet 0.2 1.5 0.8 Needle cast fungi – Neulaskaristeet 0.2 0.2 0.1 Other fungi – Muut sienet 0.5 0.1 Unidentified fungus – Tunnistamaton sieni 2.3 7.9 3.1 Competition – Kilpailu 18.6 9.2 11.2 12.2 Multiple injuries due ageing – Ikääntymisestä 0.5 5.2 1.5 3.0 johtuva monituho Total – Yhteensä 100.0 100.0 100.0 100.0 Number of trees – Puita, kpl 657 1202 605 2464 36 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 1 . T he o cc ur re nc e of G re m m en ie lla a bi et in a on L ev el I pl ot s in 2 00 4 as a p ro po rti on o f t he to ta l nu m be r o f S co ts p in e ob se rv at io n tre es . K uv a 1. V er so su rm an e si in ty m in en I ta so n ha va in to - al oi lla v uo nn a 20 04 , % m än ty ha va in to pu is ta . Fi gu re 2 . T he o cc ur re nc e of C hr ys om yx a le di o n Le ve l I p lo ts in 2 00 5 as a p ro po rti on o f t he to ta l n um be r o f N or w ay s pr uc e ob se rv at io n tre es . K uv a 2. S uo pu rs ur uo st ee n es iin ty m in en I ta so n ha va in to al oi lla v uo nn a 20 05 , % k uu si ha va in to pu is ta . Fi gu re 3. Th e oc cu rr en ce of M el am ps or id iu m be tu lin um o n Le ve l I p lo ts in 2 00 4 as a p ro po rti on o f th e to ta l n um be r o f b irc h ob se rv at io n tre es . K uv a 3. K oi vu nr uo st ee n es in ty m in en I t as on h av ai nt o- al oi lla v uo nn a 20 04 , % k oi vu ha va in to pu is ta . 0 % 0. 1 - 2 2 % 22 .1 - 36 % 36 .1 - 73 % 73 .1 - 10 0 % B irc h ru st Le ve l I 2 00 4 % o f t re es 0 % 0. 1 - 3 0 % 30 .1 - 55 .6 % 55 .7 - 80 % 80 .1 - 10 0 % C hr ys om yx a le di Le ve l I p lo ts 2 00 5 % o f s pr uc e tr ee s 0 % 0. 01 - 20 % 20 .0 1 - 3 7. 5 % 37 .5 1 - 6 6. 67 % 66 .6 8 - 1 00 % G re m m en ie lla a bi et in a Le ve l I 2 00 4 % o f p in es Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 37 other two years of the period. The proportion of severe damage was 0.3% for pines, 0.6% for spruces and 0.9% for broadleaves. The year­to­year variation in this proportion was small. The yearly mortality values (the proportion of trees that had died after the previous year’s inventory) ranged from 0.1% in 2003 to 0.4% in 2002, i.e. 9–28 trees per year. During 2002–2005, only three of the trees that were cut down had suffered from severe damage in the previous year. The causes for at least moderate damage highlight the factors that were potentially the most important for tree condition (Table 2). In all the tree species, competition (between trees) was very important. In pine, Gremmeniella abietina and Cronartium sp. were the most important other causes of at least moderate damage, while in spruce soil factors (dryness, wetness or nutrient imbalance) and Heterobasidion were the most important. Decay fungi were also important in deciduous species. There were no dramatic changes in the degree of defoliation during the period (see also chapter 2.1 in this volume). The degree of defoliation of healthy trees especially remained very stable (Fig. 4). Fig. 4 shows, schematically, the dynamics of defoliation in pines with different causal agents. It appears that defoliation in trees with damage caused by pine saw flies (Diprionidae) is decreasing. Also note the higher levels of defoliation in damaged trees compared to non­symptomatic trees. Using the same approach we can see, for instance, that the needle rust (Chrysomyxa ledi) did not increase the overall defoliation of spruce. In some individual trees, the previous year’s rust infection had clearly increased the degree of defoliation the next year, but it was very difficult to prove this effect in the whole material. In pines, in contrast, the trees were significantly more defoliated throughout the period if they had had a Gremmeniella infection during the inventory year or the year before. The extent of damage was more than 10% in 36% of the trees in 2004–2005. The frequency of this damage, which potentially can affect the defoliation scores, was higher in spruces than in the other species (Table 3). Trees with abiotic damage had the highest mean value of damage Figure 4. The dynamics of defoliation of Scots pine in 2002–2005 according to some causes of damage on Level I plots. Kuva 4. Männyn harsuuntumisen vaihtelu 2002–2005 joidenkin tekijöiden vaivaamissa puissa I tason havaintoaloilla. 38 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm extent. The agent group ‘other’, which mainly comprises damage due to competition, was also important in this respect. Gremmeniella abietina, competition and Tomicus sp. were by far the most important of the individual damage causes in pine, when both the mean value of the extent and the proportion of injured trees were taken into account. For spruce and broadleaved trees, many more causes were important than for pine trees. Competition, soil factors, needle rust, nutrient imbalance or deficiency, unknown causes and non-identified decay fungi were among the causes in spruce. In addition to competition, anthracnoses, unknown insects and rust fungi were important in broadleaved trees. Table 3. The mean extent of damage and proportion of trees with damage extent greater than 10% by tree species and agent groups. Data: Level I observation trees 2004–2005. Taulukko 3. Tuhon laajuus keskimäärin ja niiden puiden osuus, joissa tuhon laajuus oli yli 10 %, puulajeittain ja aiheuttajaryhmittäin. Aineisto: I tason havaintopuut 2004–2005. Tree species Agent group Mean of damage % of trees Number of Puulaji Aiheuttajaryhmä extent, % with extent trees Tuhon laajuus 11>100 % Puita, kpl keskimäärin, % Niiden puiden osuus, joissa tuhon laajuus oli 11–100 %, % Scots pine Game and grazing – Selkärankaiset 4.6 9.1 11 Mänty Insects – Hyönteiset 4.5 12.4 923 Fungi – Sienet 9.3 29.5 708 Abiotic – Abioottiset 11.4 50.0 186 Direct action of man – Ihmisen toiminta 2.9 6.9 29 Other – Muut tekijät 10.6 66.2 468 Unknown – Tunnistamaton 18.1 42.6 47 Total – Yhteensä 12.1 31.6 2372 Norway spruce Insects – Hyönteiset 12.0 36.4 22 Kuusi Fungi – Sienet 9.9 20.6 863 Abiotic – Abioottiset 26.7 69.1 366 Direct action of man – Ihmisen toiminta 7.5 25.0 8 Other – Muut tekijät 20.7 83.2 291 Unknown – Tunnistamaton 22.8 66.1 56 Total – Yhteensä 16.6 44.8 1606 Decidiuous Game and grazing – Selkärankaiset 65.0 0.0 1 Lehtipuut Insects – Hyönteiset 9.6 16.2 308 Fungi – Sienet 11.5 27.8 565 Abiotic – Abioottiset 21.8 60.2 98 Direct action of man – Ihmisen toiminta 11.9 25.0 8 Other – Muut tekijät 20.3 81.7 104 Unknown – Tunnistamaton 13.3 37.5 32 Total – Yhteensä 16.0 32.8 1116 All Game and grazing – Selkärankaiset 11.7 16.7 12 Kaikki Insects – Hyönteiset 8.47 13.7 1253 Fungi – Sienet 10.8 25.5 2136 Abiotic – Abioottiset 11.7 62.3 650 Direct action of man – Ihmisen toiminta 7.47 13.3 45 Other – Muut tekijät 19.5 73.8 863 Unknown – Tunnistamaton 18.0 51.1 135 Total – Yhteensä 13.4 36.0 5094 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 39 Discussion The results of this study confirm several earlier findings. Abiotic/biotic damage and defoliation have a significant co-occurrence in individual trees. Different causes of damage cause defoliation in different years and in different areas (Nevalainen and Yli­Kojola 1994, Nevalainen and Heinonen 2000). The effects of biotic damage can last for several years, as shown by retrospective analysis (Kurkela et al. 2005). Very few identified agents were common and important even in the specialist surveys. The Level I network is not a representative sample of the forests in Finland, due to the rather sparse network and to the fact that the sample is restricted to dominant and co­dominant trees. Therefore some important damaging agents, such as moose damage, and some locally very severe injuries (storms, insect damage) will not be detected in the monitoring. Level I data cannot be considered suitable for detailed regional analyses nor for estimates of the occurrence of damage per area, for instance. The systematic network used in the annual forest condition monitoring has been designed to give information at the national level about crown condition and its variation principally in background areas. The network is too sparse for surveys of smaller geographical areas. Moreover, the results are only indicative especially in Northern Finland because of the small number of spruce and broad­leaves stands in the sample in that region. Due to the low density of the network, changes in forest vitality in small areas do not appear clearly in the survey results carried out in other countries either. However, identifying the factors that may affect the vitality of forests is the key task in this work. Spatial and temporal patterns of the most important abiotic or biotic epidemics are clearly evident in the Level I data, primarily because it is an annual survey. This kind of data is very suitable for studying the temporal co­occurrence of defoliation and the most important biotic/abiotic forms of damage. The monitoring at Level I can produce excellent time series of some of the most common epidemics. The field teams have shown high motivation and professional skill also in the identification of damage causes. Although some of the causes always remain unknown, the (detailed) symptom codes help in identifying some of the causes of epidemics. Manion (1981) classified stress factors into three groups. Predisposing factors are long-term factors that alter the tree’s ability to defend against or repair damage. Inciting factors are short­term stresses or injuries that reduce carbohydrate storage, often resulting in branch dieback. Contributing factors usually include biotic stresses or disease agents, that invade the tree leading to mortality, and many of these invade trees which have been subject to prolonged stress. This conceptual framework is very relevant and should be utilized when the results of forest health studies are being analysed. In our conditions, tree age and unfavourable soil conditions (including the long­term effects of air pollution) can act as predisposing factors. Insect defoliation, extreme drought, mechanical injury or frost damage may be the most common inciting factors in Finland, while bark­beetles or root­decay fungi may act as contributing factors. Including biotic/abiotic damage in the analysis therefore greatly assists the interpretation of national and regional patterns of forest damage. 40 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm References Assessment of damage causes. Submanual for Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UN/ECE, Convention on long­range transboundery air pollution, International co­operative programme on Assessment and monitoring of air pollution effects on forests. [Internet site]. Updated 06/2004. Available at: http:// www.icp­forests.org/bioticdocs/manual­index.pdf. Keane, M., McCarthy, R. & Hogan, J. 1989. Forest health surveys in Ireland: 1987 and 1988 results. Irish Forestry 46 (1): 59–62. Kurkela, T., Aalto, T., Varama, M. & Jalkanen, R. 2005. Defoliation by the common pine sawfly (Diprion pini) and subsequent growth reduction in Scots Pine: A retrospective approach. Silva Fennica 39(4): 467–480. Innes, J.L. & Schwyzer, A. 1994. Stem damage in Swiss Forests: Incidence, causes and relations to crown transparency. European Journal of Forest Pathology 24: 20–31. Innes, J.L., Boswell, R., Binns, W.O. & Redfern, D.B. 1986. Forest health and air pollution. 1986 survey. Great Britain Forestry Commission, Research and Development Paper 150. 5 p. Lindgren, M. 2002. Results of the 2001 national crown condition survey (ICP­Forests/Level I). In: Rautjärvi, H., Ukonmaanaho, L. & Raitio, H. (eds.). Forest condition monitoring in Finland. National report 2001. The Finnish Forest Research Institute, Research Papers 879: 42–50. Manion, P.D. 1981. Tree disease concepts. Prentice Hall, Inc. Englewood Cliffs, NJ. 399 p. Nevalainen, S. 1999. Nationwide forest damage surveys in Finland. In: Forster, B., Knizek, M. & Grodzki, W. (eds.). Methodology of Forest Insect and Disease Survey in Central Europe. Proceedings of the Second Workshop of the IUFRO Working Party 7.03.10, April 20–23, 1999, Sion­Chateauneuf, Switzerland. Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf. p. 24–29. Nevalainen, S. & Heinonen, J. 2000. Dynamics of defoliation, biotic and abiotic damage during 1986– 1998. In: Mälkönen, E. (ed.). Forest Condition in a Changing Environment – the Finnish case. Forestry Sciences 65. Kluwer Academic Publishers, Dordrecht. p. 133–141. Nevalainen, S. & Yli­Kojola, H. 1994. Abioottisten ja bioottisten tuhojen vaikutus metsien elinvoimaan. In: Mälkönen, E. & Sivula, H. (eds.). Suomen metsien kunto. Metsien terveydentilan tutkimusohjelman väliraportti. Metsäntutkimuslaitoksen tiedonantoja 527: 35–53. Skelly, J.M. & Innes, J.L. 1994. Waldsterben in the forests of Central Europe and eastern North America: fantasy or reality? Plant Disease 78: 1021–1031. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 41 3 Results of the intensive monitoring of forest ecosystems (Forest Focus/ICP Forests, Level II) Metsien intensiiviseurannan tuloksia (Forest Focus/ICP metsäohjelma, taso II) 3.1 Crown condition on the Level II network 2001–2004 Puiden latvuskunto tason II havaintoaloilla vuosina 2001–2004 Martti Lindgren1, Seppo Nevalainen2 & Antti Pouttu1 Finnish Forest Research Institute; 1) Vantaa Research Unit, 2) Joensuu Research Unit The degree of defoliation, foliage discoloration and abiotic and biotic damage were recorded on the 14 Norway spruce mineral soil plots, 13 Scots pine mineral soil plots and 4 Scots pine peatland plots of the Level II network during 2001–2004. During this period, more than 85% of the spruces and more than 95% of the pines were classified as none or slight defoliated. On the spruce and pine mineral soil plots there was an increase in the proportion of slightly or moderately defoliated trees, as well as a slight increase in the average defoliation degree, from 2001 to 2004, but not on the pine peatland plots. In 2004 the average defoliation level of spruce was 19% and of pine was 10.7% on the mineral soil plots and 13.3% on the peatland plots. The proportion of discoloured spruces was lowest in 2003 (3.7%) and highest in 2004 (8.5%). On the mineral soil sites the highest proportion of discoloured pines occurred in 2003 (1.1%) and on peatland plots in 2004 (1.3%). During the period 2001–2004, the most common biotic damage agents were fungal pathogens in both tree species and insects on pine. In addition, abiotic damage on spruce increased especially in 2003. Vuosina 2001–2004 metsäekosysteemien intensiivisessä seurannassa (ICP metsäohjelma/taso II) oli mukana 14 kuusi- ja 13 mäntyalaa ja 4 turvemailla sijaitsevaa mäntyalaa. Latvuskunnon arvioin- nin yhteydessä puista arvioidaan niiden harsuuntumisaste, värioireiden määrä sekä erilaiset tu- hot. Jaksolla 2001–2004 >85 % kuusista ja >95 % männyistä luokiteltiin lievästi tai ei lainkaan harsuuntuneiksi. Seurantajakson aikana alle 25 % harsuuntuneiden puiden osuus samoin kuin keski- määräinen harsuuntumisaste lisääntyivät jonkin verran molemmilla puulajeilla kivennäismaiden aloilla, mutteivät turvemaiden aloilla. Kuusien keskimääräinen harsuuntumisaste oli 19 % vuonna 2004. Mäntyjen keskimääräinen harsuuntumisaste oli kivennäismaiden aloilla 10,7 % ja turvemailla 13,3 % vuonna 2004. Värioireellisten kuusten osuus oli alin vuonna 2003 (3,7 %) ja korkein vuon- na 2004 (8,5 %). Värioireellisia mäntyjä oli seurantajaksolla selvästi vähemmän. Seurantajaksol- la 2001–2004 havupuiden yleisimpiä tuhonaiheuttajia olivat sienet ja männyllä myös hyönteiset. Lisäksi abioottiset tuhot lisääntyivät kuusella vuonna 2003. Introduction The annual crown condition assessment was carried out on the 13 Scots pine (Pinus sylvestris) and 14 Norway spruce (Picea abies) plots situated on mineral soil plots, and on the four Scots pine plots on peatland. Defoliation, needle discoloration, and abiotic and biotic damage were assessed on 20 trees on each sub-plot. The results for 2001 and 2004 are therefore based on 721 pines and 792 spruces growing on the Level II mineral soil plots, and on 240 pines on the peatland plots. The assessment of sample trees was carried out according to the ICP-Forests manual (current Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 42 edition: Manual on methods... 2006). In Finland, however, defoliation and discoloration of spruce were estimated on the upper half of the living crown, and of pine on the upper 2/3 of the living crown in 5% classes in the same way as in Level I. During summer 2004 the new method (manual update 06/2004) for damage assessment was applied. Results In general, more than 85% of the spruces and more than 95% of the pines were classified as none or slight defoliated during 2001–2004 (Table 1). However, on the spruce and pine mineral soil sites there was an increase in the proportion of slightly or moderately defoliated trees, as well as a slight increase in the average defoliation, from 2001 to 2004, but not on the pine peatland plots (Table 1, Fig. 1). The average defoliation degree of spruce and pine on each of the sample plots during 2001–2004, is shown in Figures 2 and 3. On spruce, there was a more than 3%-units increase in the average defoliation degree from 2001 to 2004 on the Juupajoki, Tammela, Uusikaarlepyy and Solböle plots, and on pine on the Juupajoki and Miehikkälä plots. However, the average defoliation degree on these plots was less than 20% for both species (Fig. 2). The maximum site-specific increase from 2001 to 2004 for spruce was 4%-units on the Uusikaarlepyy plot, and for pine 3.7%-units on the Miehikkälä plot. The highest average degree of defoliation on spruce during 2001–2004 occurred on the Evo (ca. 29%, average for 2001–2004) and Oulanka (ca. 27%) plots, and the lowest average degree of defoliation (ca. 10%) on the Uusikarlepyy plot (Fig. 2). The highest average defoliation degree on pine occurred on the Lieksa plot (ca. 25%), and the lowest (ca. 5%) on the Kivalo, Punkaharju and Tammela plots. The proportion of discoloured Scots pines (proportion of discoloured needles mass more than 10%) was very low during the period 2001–2004 (Table 2). However, the proportion of slightly discoloured Norway spruce was higher than in pine and increased during 2003–2004 (Table 2). The most abundant discoloration symptoms on Norway spruce at Level II were needle tip yellowing and apical yellowing and overall yellowing of more than one-year old needles. Table 1. The proportion of trees in different defoliation classes (amount of needle loss in assessable crown) on the mineral soil and peatland plots during 2001–2004. Taulukko 1. Kivennäis- ja turvemailla kasvavien puiden jakautuminen neljään eri harsuuntumisluokkaan vuosina 2001–2004. Tree species Year Number of trees Needle loss classes, % – Neulaskatoluokat, % Puulaji Vuosi Puiden lkm 0 – 10 11 – 25 26 – 60 > 60 Norway spruce 2001 797 33.9 54.4 11.2 0.5 Kuusi 2002 797 30.5 56.9 12.1 0.5 2003 797 26.2 62.1 11.1 0.6 2004 797 24.6 61.5 13.4 0.5 Scots pine 2001 722 74.8 22.3 2.9 0.0 Mänty 2002 722 71.8 25.3 2.9 0.0 2003 722 69.7 27.4 2.9 0.0 2004 722 67.5 28.9 3.6 0.0 Scots pine 2001 240 46.3 51.3 2.5 0.0 (peatland plots) 2002 240 50.4 46.7 2.9 0.0 Mänty 2003 240 45.8 52.1 2.1 0.0 (turvealat) 2004 240 52.1 47.1 0.8 0.0 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 43 Figure 1. The average defoliation degree of Norway spruce and Scots pine during 1995–2004 on the Level II mineral soil (m) and peatland (pl) plots in Finland. Kuva 1. Kuusen ja männyn keskimääräinen harsuuntumisaste tason II kivennäis- (m) ja turvemailla (pl) vuosina 1995–2004. The proportion of spruces with signs of damaging agents varied from 28% in 2001 to 11% in 2004, and of pine from 29.2% in 2001 to 1.5% in 2003 (Fig. 4). During 2001–2004 the most common biotic damaging agents were fungal pathogens on both tree species and insects on pine. Practically no insect damage was detected on spruce. The proportion of trees with fungal pathogens decreased clearly during the period 2001–2004 in both species. Abiotic (e.g. wind, snow) damage and man-made damage and competition (grouped as “other” damaging agents) were also an important group on both tree species. The proportion of trees with abiotic damage Table 2. The proportion of trees in different discoloration classes (amount of discoloured needle mass in assessable crown) on the mineral soil and peatland plots during 2001–2004. Taulukko 2. Kivennäis- ja turvemailla kasvavien puiden jakautuminen neljään eri värioireluokkaan vuosina 2001–2004. Tree species Year Number of trees Discoloration classes, % – Värioireluokat, % Puulaji Vuosi Puiden lkm 0 – 10 11 – 25 26 – 60 > 60 Norway Spruce 2001 792 94.6 5.2 0.2 0.0 Kuusi 2002 792 94.3 5.3 0.3 0.1 2003 792 95.7 3.7 0.4 0.0 2004 792 90.7 8.8 0.4 0.1 Scots pine 2001 721 100.0 0.0 0.0 0.0 Mänty 2002 721 99.9 0.1 0.0 0.0 2003 721 98.9 1.1 0.0 0.0 2004 721 99.6 0.3 0.0 0.1 Scots pine 2001 240 99.2 0.8 0.0 0.0 (peatland plots) 2002 240 100.0 0.0 0.0 0.0 Mänty 2003 240 99.6 0.4 0.0 0.0 (turvealat) 2004 240 98.7 1.3 0.0 0.0 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 44 Figure 2. The average defoliation of Norway spruce during 2001–2004 on the Level II plots in Finland. Kuva 2. Kuusen keskimääräinen harsuuntumisaste havaintoaloittain vuosina 2001–2004. Figure 3. The average defoliation of Scots pine during 2001–2004 on the Level II plots in Finland. Peatland sites are indicated with an asterisk. Kuva 3. Männyn keskimääräinen harsuuntumisaste havaintoaloittain vuosina 2001–2004. * = turvemaiden havaintoala. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 45 Figure 4. The proportion of trees in different damage classes on the Level II plots during 2001–2004. *Other includes e.g. competition and man-made damage. Due to fact that the assessed trees might have more than one type of damage, the sum of the proportions of trees might be more than 100 % in certain years. Kuva 4. Puiden jakautuminen eri tuholuokkiin tason II havaintoaloilla vuosina 2001–2004. *Luokka “Muut” sisältää esim. kilpailun ja ihmisen aiheuttamat tuhot. Koska arvioitavalla puulla voi olla enemmän kuin yksi tuho niin kuvan pylväiden summa saattaa jonakin vuonna ylittää 100 %. was highest in 2003 on spruce (ca. 8% of trees) and in 2002 on pine (ca. 3% of trees) (Fig. 4). For more comprehensive information about the abiotic and biotic damage in Finland, see Chapters 2.2, 4.2 and 4.3 in this volume. References Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. 2006. Part II. Visual assessment of crown condition. [Internet site]. UN-ECE. Programme coordination centre, Hamburg. Available at: http://www.icp-forests.org/manual. htm. 46 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 3.2 Needle chemistry on the intensive monitoring plots 1995–2003 Neulasten kemiallinen koostumus intensiiviseurannan havaintoaloilla vuosina 1995–2003 Päivi Merilä Finnish Forest Research Institute, Parkano Research Unit The elemental composition of needles has been monitored bi-annually on all the plots belonging to the Level II intensive monitoring network during 1995–2003. During this period, there were no drastic changes in the nutrient status of the trees on the plots. Consistently with the observed decrease in sulphur deposition, foliar sulphur concentrations showed a slight decreasing trend during the period. Neulasten alkuainekoostumusta on seurattu joka toinen vuosi metsien intensiiviseurannan (taso II) havaintoaloilla vuosina 1995–2003. Havaintojakson aikana puiden ravinnetilassa ei ole tapahtunut jyrkkiä muutoksia. Rikkilaskeumassa tapahtunut lasku näkyy lievästi laskevana trendinä myös neu- lasten rikkipitoisuudessa. Introduction The elemental composition of foliage represents an important tool when diagnosing nutrient deficiencies, excesses and imbalances in forest trees (Kimmins 1987, Walworth and Sumner 1988). When carefully applied, the chemical composition of leaves may qualify as a competent indicator of health status at the ecosystem level. Changes in the nutrient status of trees may result from several factors affecting nutrient availability and needle mass, such as the occurrence of abiotic and biotic damage, fluctuation in weather conditions, changes in anthropogenic deposition or, in general, changes in the nutrient pools in the ecosystem. In monitoring studies designed to detect temporal changes in the ecosystem, carefully standardized sampling design and consistent analysis, as well as regularly implemented quality control, are of utmost importance (e.g. Sulkava et al. 2007). In the ICP Forests/Forest Focus monitoring programmes, the analytical quality of needle elemental analysis is constantly monitored by means of method blanks and regular analysis of internal and certified reference samples. In addition, the laboratories participate in international and national inter-laboratory tests. The inter-laboratory comparisons have shown that Metla’s laboratories (Parkano and Vantaa), which are responsible for carrying out chemical leaf analysis in the Finnish ICP Forests/Forest Focus programme, are of high quality (e.g. Bartels 2002). In Finland, the nutritional status of the trees on the 31 Level II plots has been monitored biannually since 1995 (Raitio 1999). In this article we report the results of the surveys carried out in 1995/96, 1997, 1999, 2001 and 2003. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 47 Material and methods Two sets of 10 predominant or dominant sample trees are selected for needle chemistry analyses on each Level II plot. Sample branches are taken from 10 of these trees every second year. The two tree sets are sampled in rotation, i.e. each set is sampled every 4 years. This arrangement is being employed in order to minimise damage to the trees as a result of branch removal. The first tree set (18 plots) was sampled in 1995 (supplemented by two plots in 1996). Since then, all 31 plots in the Level II network were sampled in 1997, 1999, 2001 and 2003. The sample branches with current (C) and previous-year needles (C+1) were collected from the uppermost third of the living crown with a pruning device during October and November. The branches were stored in a freezer (-18°C) during the period between sampling and pre- treatment. In the pre-treatment procedure, the branches were cut in order to separate shoot sections bearing different needle-year classes. Shoots with the same needle-year class of each tree were pooled and subsequently treated as a separate sample. The shoots were dried at 40°C for 10 days and the needles then removed from the shoots. The dry needles were milled using an ultracentrifugal mill (Retsch type Zm 1, mesh size 1 mm). Unwashed needles from each tree (n = 10) from each plot were analysed separately for the nitrogen (N), sulphur (S), phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), zinc (Zn), manganese (Mn), iron (Fe), copper (Cu), and boron (B). The N concentration of the needles were determined without any further pre-treatment on a CHN analyser (1995, 1997 and 1999 samples: LECO CHN-600 Analyser, 2001 and 2003 samples: LECO CHN-2000 Analyser). The S, P, Ca, K, Mg, Zn, Mn, Fe and Cu concentrations in the needles were determined, following wet digestion in HNO3/H2O2, by inductively coupled plasma atomic emission spectroscopy (ICP/AES). For the 1995 and 1997 samples, digestion was performed by the open wet digestion method (Thermolyne 2200 Hot Plate), followed by determination on an ARL 3580 ICP emission spectrometer. For the 1999, 2001 and 2003 samples, the needle samples were digested by the closed wet digestion method in a microwave (CEM MDS 2000) and analysed on a TJA Iris Advantage ICP-emission spectrometer. For the samples of 1995 and 1997, B was determined by azomethin H-reagent on a UV-VIS spectrophotometer, and for samples of 1999 and onwards, B was determined by ICP/ AES after CEM digestion. The results are expressed as mean concentrations per plot per 105°C dry weight. Results and discussion During the period 1995–2003 the tree nutrient status on the Level II plots showed no drastic changes (Figs. 1–12). The observed annual variation in the nutrient concentrations primarily results from differences in the weather conditions between the years, and analytical variation plays only a minor or negligible role (Table 1). The overall mean ± S.D. (1995–2003) for the concentrations of each element in Scots pine and Norway spruce needles are presented in Table 2. Consistently with the observed decrease in S deposition (see Chapter 3.5) and in S concentrations in the needles on the Level I plots (Lorenz et al. 2003, Luyssaert et al. 2003), foliar S concentrations showed a slight, decreasing trend from 1995 to 2003 (Figs. 1, 4, 7 and 10). This trend may, however, be strengthened by the fact that there is a slight decreasing trend in the level of the foliar 48 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm S concentrations due to analytical reasons (Table 1, see also Luyssaert et al. 2004). In contrast, the foliar Cu concentrations showed an apparent increasing trend on several plots (Figs. 3, 6, 9 and 12). The increasing trend results, at least partly, from the improved accuracy and sensitivity of the analytical method for microelements such as Cu. The Cu concentrations on Plot nr. 1 (Sevettijärvi) clearly stand out from the concentrations on the other plots (Figs. 3 and 6). The elevated Cu concentrations may be due to Cu deposition originating from the copper-nickel smelter in Nikel, which is located on the Kola Peninsula in Russia at a distance of ca. 70 km from the Sevettijärvi plot. In general, the plots located near the coast show higher B concentrations than those in inland (Figs. 3, 6, 9 and 12). This difference is especially distinct in northern Finland; foliar B concentrations at Sevettijärvi (nr. 1) and Kevo (nr. 22) are clearly higher than on the other pine plots in northern Finland (Figs. 3 and 6), and indicates the significance of sea spray as a source of B deposition. Table 1. Average value (± 0.5 * 95% confidence interval) of certified reference material (spruce needles, CRM 101) from the Community Bureau of Reference for nitrogen (N), sulphur (S), phosphorus (P), calcium (Ca), magnesium (Mg), zinc (Zn) and manganese (Mn) analysed in conjunction with the needle material of this study. Number of repetitions for nitrogen/other elements is indicated. Taulukko 1. Tämän tutkimuksen neulasanalyysien yhteydessä analysoidun sertifioidun vertailumateriaalin (Community Bureau of Reference, kuusen neulaset, CRM101) typpi (N)-, rikki (S)-, fosfori (P)-, kalsium (Ca)-, magnesium (Mg)-, sinkki (Zn)- ja mangaani (Mn)-pitoisuuksien keskiarvot (± 0.5 * 95 % luottamusväli). n = toistojen määrä (typpi/muut alkuaineet). N S P Ca Mg Zn Mn mg g-1 mg kg-1 CRM 101 18.89 ± 1.70 ± 1.69 ± 4.28 ± 0.619 ± 35.3 ± 915 ± 0.18 0.04 0.04 0.08 0.009 2.3 11 1995 18.6 ± 1.79 ± 1.79 ± 4.20 ± 0.62 ± 32.8 ± 895 ± n = 10/15 0.06 0.08 0.10 0.33 0.04 1.8 51 1997 18.4 ± 1.67 ± 1.84 ± 4.32 ± 0.57 ± 30.9± 850 ± n = 4/3 0.26 0.05 0.03 0.07 0.13 1.5 3 1999 19.2 ± 1.66 ± 1.79 ± 4.22 ± 0.61 ± 31.5 ± 913 ± n = 23/23 0.10 0.10 0.12 0.32 0.04 1.9 72 2001 19.2 ± 1.70 ± 1.83 ± 4.24 ± 0.61 ± 32.6 ± 908 ± n = 19/13 0.04 0.04 0.06 0.16 0.02 1.4 26 2003 19.2 ± 1.54 ± 1.71 ± 4.12 ± 0.59 ± 30.3 ± 883 ± n = 11/12 0.03 0.06 0.06 0.12 0.02 1.0 49 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 49 Table 2. Overall average ± S.D. for nitrogen, sulphur, phosphorus, calcium, potassium, magnesium, zinc, manganese, iron and copper concentrations in current (C) and previous-year (C+1) Scots pine and Norway spruce needles. The samples were taken from Level II observation plots in 1995/96, 1997, 1999, 2001 and 2003. The four plots located on peatland (nrs. 27, 26, 29 and 30) have been excluded. Taulukko 2. Kuusen ja männyn nuorimpien (C) ja edellisenä kesänä syntyneiden (C+1) neulasten typpi-, rikki-, fosfori-, kalsium-, kalium-, magnesium-, sinkki-, mangaani-, rauta- ja kuparipitoisuuksien keskiarvot keskihajontoineen. Näytteet on kerätty II tason aloilta vuosina 1995/96, 1997, 1999, 2001 ja 2003. Turve- mailla sijaitsevien havaintoalojen (no. 27, 26, 29 ja 30) tuloksia ei ole sisällytetty keskiarvoihin. Element Scots pine – Mänty Norway spruce – Kuusi Alkuaine n = 65 n = 56 C needles C+1 needles C needles C+1 needles C-neulaset C+1 -neulaset C-neulaset C+1 -neulaset N, mg g-1 11.9 ± 11.7± 11.8 ± 10.8 ± 1.38 1.43 1.66 1.30 S, mg g-1 0.85 ± 0.86 ± 0.84 ± 0.83 ± 0.09 0.10 0.09 0.08 P, mg g-1 1.46 ± 1.31 ± 1.60 ± 1.31 ± 0.14 0.12 0.22 0.25 Ca, mg g-1 1.87 ± 3.08 ± 3.67 ± 6.41 ± 0.37 0.53 0.75 1.62 K, mg g-1 5.22 ± 4.58 ± 6.65 ± 4.99 ± 0.34 0.43 0.79 0.65 Mg, mg g-1 1.02 ± 0.89 ± 1.14 ± 1.05 ± 0.13 0.17 0.14 0.15 Zn, mg kg-1 39.1 ± 48.2 ± 33.8 ± 37.0 ± 5.0 7.1 6.9 13.1 Mn, mg g-1 406 ± 667 ± 673 ± 1037 ± 114 196 192 312 Fe, mg kg-1 28.5 ± 40.1 ± 26.1 ± 30.6 ± 5.7 9.7 5.3 6.5 Cu, mg kg-1 2.7 ± 2.2 ± 2.0 ± 1.7 ± 0.5 0.4 0.3 0.3 B, mg kg-1 11.3 ± 10.3 ± 11.3 ± 12.4 ± 4.32 4.71 3.75 6.10 50 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 1 . A ve ra ge n itr og en (N ), su lp hu r ( S ), ph os ph or us (P ) a nd c al ci um (C a) c on ce nt ra tio ns in c ur re nt -y ea r ( C ) S co ts p in e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 1 99 7, 1 99 9, 20 01 a nd 2 00 3. T he la st fo ur p lo ts a re lo ca te d on p ea tla nd (n rs . 2 7, 2 6, 3 0 an d 29 ). Th e pl ot s ar e ar ra ng ed in o rd er o f l at itu de (S to N ). K uv a 1. M än ny n nu or im pi en n eu la st en k es ki m ää rä is et ty pp i ( N )- , r ik ki (S )- , f os fo ri (P )- ja k al si um (C a) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa la t n o. 2 7, 2 6, 3 0 ja 2 9 (o ik .) si ja its ev at t ur ve m ai lla . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 51 Fi gu re 2 . A ve ra ge p ot as si um (K ), m ag ne si um (M g) , z in c (Z n) a nd m an ga ne se (M n) c on ce nt ra tio ns in c ur re nt -y ea r ( C ) S co ts p in e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 1 99 7, 19 99 , 2 00 1 an d 20 03 . T he la st fo ur p lo ts a re lo ca te d on p ea tla nd (n rs . 2 7, 2 6, 3 0 an d 29 ). Th e pl ot s ar e ar ra ng ed in o rd er o f l at itu de (S to N ). K uv a 2. M än ny n nu or im pi en n eu la st en k es ki m ää rä is et k al iu m (K )- , m ag ne si um (M g) -, si nk ki (Z n) - j a m an ga an i ( M n) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 19 99 , 2 00 1 ja 2 00 3. H av ai nt oa la t n o. 2 7, 2 6, 3 0 ja 2 9 (o ik .) si ja its ev at tu rv em ai lla . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . 52 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 3 . A ve ra ge ir on (F e) , c op pe r ( C u) a nd b or on (B ) c on ce nt ra tio ns in c ur re nt -y ea r ( C ) S co ts p in e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 1 99 7, 1 99 9, 2 00 1 an d 20 03 . T he la st fo ur p lo ts a re lo ca te d on p ea tla nd (n rs . 2 7, 2 6, 3 0 an d 29 ). Th e pl ot s ar e ar ra ng ed in o rd er o f l at itu de (S to N ). K uv a 3. M än ny n nu or im pi en n eu la st en k es ki m ää rä is et r au ta ( Fe )- , k up ar i ( C u) - ja b oo ri (B )- pi to is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa la t n o. 2 7, 2 6, 3 0 ja 2 9 (o ik .) si ja its ev at tu rv em ai lla . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 53 Fi gu re 4 . A ve ra ge n itr og en (N ), su lp hu r ( S ), ph os ph or us (P ) a nd c al ci um (C a) c on ce nt ra tio ns in p re vi ou s- ye ar (C +1 ) S co ts p in e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 1 99 7, 19 99 , 2 00 1 an d 20 03 . T he la st fo ur p lo ts a re lo ca te d on p ea tla nd (n rs . 2 7, 2 6, 3 0 an d 29 ). Th e pl ot s ar e ar ra ng ed in o rd er o f l at itu de (S to N ). K uv a 4. M än ny n ed el lis en ä ke sä nä s yn ty ne id en n eu la st en k es ki m ää rä is et ty pp i ( N )- , r ik ki (S )- , f os fo ri (P )- ja k al si um (C a) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 19 97 , 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa la t n o. 2 7, 2 6, 3 0 ja 2 9 (o ik .) si ja its ev at tu rv em ai lla . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . 54 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 5 . A ve ra ge p ot as si um (K ), m ag ne si um (M g) , z in c (Z n) a nd m an ga ne se (M n) c on ce nt ra tio ns in p re vi ou s- ye ar (C +1 ) S co ts p in e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 19 97 , 1 99 9, 2 00 1 an d 20 03 . T he la st fo ur p lo ts a re lo ca tin g on p ea tla nd (n rs . 2 7, 2 6, 3 0 an d 29 ). Th e pl ot s ar e ar ra ng ed in o rd er o f l at itu de (S to N ). K uv a 5. M än ny n ed el lis en ä ke sä nä s yn ty ne id en n eu la st en k es ki m ää rä is et k al iu m ( K )- , m ag ne si um ( M g) -, si nk ki ( Zn )- ja m an ga an i ( M n) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa la t n o. 2 7, 2 6, 3 0 ja 2 9 (o ik .) si ja its ev at tu rv em ai lla . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 55 Fi gu re 6 . A ve ra ge ir on (F e) , c op pe r ( C u) a nd b or on (B ) c on ce nt ra tio ns in p re vi ou s- ye ar (C +1 ) S co ts p in e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 1 99 7, 1 99 9, 2 00 1 an d 20 03 . Th e la st fo ur p lo ts a re lo ca tin g on p ea tla nd (n rs . 2 7, 2 6, 3 0 an d 29 ). Th e pl ot s ar e ar ra ng ed in o rd er o f l at itu de (S to N ). K uv a 6. M än ny n ed el lis en ä ke sä nä s yn ty ne id en n eu la st en k es ki m ää rä is et ra ut a (F e) -, ku pa ri (C u) - j a bo or i ( B )- pi to is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 20 01 ja 2 00 3. H av ai nt oa la t n o. 2 7, 2 6, 3 0 ja 2 9 (o ik .) si ja its ev at tu rv em ai lla . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . 56 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 7 . A ve ra ge n itr og en (N ), su lp hu r ( S ), ph os ph or us (P ) a nd c al ci um (C a) c on ce nt ra tio ns in c ur re nt -y ea r ( C ) N or w ay s pr uc e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 1 99 7, 19 99 , 2 00 1 an d 20 03 . T he p lo ts a re a rr an ge d in o rd er o f l at itu de (S to N ). K uv a 7. K uu se n nu or im pi en n eu la st en k es ki m ää rä is et ty pp i ( N )- , r ik ki (S )- , f os fo ri (P )- ja k al si um (C a) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 ja 20 03 . H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 57 Fi gu re 8 . A ve ra ge p ot as si um (K ), m ag ne si um (M g) , z in c (Z n) a nd m an ga ne se (M n) c on ce nt ra tio ns in c ur re nt -y ea r ( C ) N or w ay s pr uc e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 19 97 , 1 99 9, 2 00 1 an d 20 03 . T he p lo ts a re a rr an ge d in o rd er o f l at itu de (S to N ). K uv a 8. K uu se n nu or im pi en n eu la st en k es ki m ää rä is et k al iu m (K )- , m ag ne si um (M g) -, si nk ki (Z n) - j a m an ga an i ( M n) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 19 99 , 2 00 1 ja 2 00 3. H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . 58 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 9 . A ve ra ge ir on ( Fe ), co pp er ( C u) a nd b or on ( B ) co nc en tra tio ns in c ur re nt -y ea r (C ) N or w ay s pr uc e ne ed le s ta ke n on th e Le ve l I I o bs er va tio n pl ot s in 1 99 5/ 96 , 1 99 7, 19 99 , 2 00 1 an d 20 03 . T he p lo ts a re a rr an ge d in o rd er o f l at itu de (S to N ). K uv a 9. K uu se n nu or im pi en n eu la st en k es ki m ää rä is et r au ta ( Fe )- , k up ar i ( C u) - ja b oo ri (B )- pi to is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 59 Fi gu re 1 0. A ve ra ge n itr og en (N ), su lp hu r ( S ), ph os ph or us (P ) a nd c al ci um (C a) c on ce nt ra tio ns in p re vi ou s- ye ar (C +1 ) N or w ay s pr uc e ne ed le s on th e Le ve l I I p lo ts in 1 99 5/ 96 , 19 97 , 1 99 9, 2 00 1 an d 20 03 . T he p lo ts a re a rr an ge d in o rd er o f l at itu de (S to N ). K uv a 10 . K uu se n ed el lis en ä ke sä nä s yn ty ne id en n eu la st en k es ki m ää rä is et ty pp i ( N )- , r ik ki (S )- , f os fo ri (P )- ja k al si um (C a) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 19 97 , 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . 60 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Fi gu re 1 1. A ve ra ge p ot as si um ( K ), m ag ne si um ( M g) , zi nc ( Zn ) an d m an ga ne se ( M n) c on ce nt ra tio ns in p re vi ou s- ye ar ( C +1 ) N or w ay s pr uc e ne ed le s on t he L ev el I I pl ot s in 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 an d 20 03 . T he p lo ts a re a rr an ge d ru nn in g fro m s ou th to n or th . K uv a 11 . K uu se n ed el lis en ä ke sä nä s yn ty ne id en n eu la st en k es ki m ää rä is et k al iu m ( K )- , m ag ne si um ( M g) -, si nk ki ( Zn )- ja m an ga an i ( M n) -p ito is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 1 99 9, 2 00 1 ja 2 00 3. H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 61 Fi gu re 1 2. A ve ra ge ir on (F e) , c op pe r ( C u) a nd b or on (B ) c on ce nt ra tio ns in p re vi ou s- ye ar (C +1 ) N or w ay s pr uc e ne ed le s on th e Le ve l I I o bs er va tio n pl ot s in 1 99 5/ 96 , 1 99 7, 1 99 9, 20 01 a nd 2 00 3. T he p lo ts a re a rr an ge d in o rd er o f l at itu de (S to N ). K uv a 12 . K uu se n ed el lis en ä ke sä nä s yn ty ne id en n eu la st en k es ki m ää rä is et r au ta ( Fe )- , k up ar i ( C u) - ja b oo ri (B )- pi to is uu de t I I t as on h av ai nt oa lo ill a vu os in a 19 95 /9 6, 1 99 7, 19 99 , 2 00 1 ja 2 00 3. H av ai nt oa lo je n jä rje st ys e te lä st ä po hj oi se en . 62 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm References Bartels, U. 2002. ICP-Forests 4th needle/leaf interlaboratory test 2001/2002. Results. North Rhine-Westfalia State Environment Agency, Essen. 128 p. Kimmins, J.P. 1987. Forest Ecology. Macmillan Publishing Company, New York. 531 p. Lorenz, M., Mues, V., Becher, G., Müller-Edzards, C., Luyssaert, S., Raitio, H., Fürst, A. & Langouche, D. 2003. Forest condition in Europe. Results of the 2002 large-scale survey. Technical Report. EC, UN/ ECE 2003, Brussels, Geneve. 171 p. Luyssaert, S., Raitio, H. & Fürst, A. 2003. Elemental foliar composition indicates environmental changes. In: The condition of forests in Europe. 2003 Executive Report. Federal Research Centre for Forestry and Forest Protection. p. 21. Luyssaert, S., Sulkava, M., Raitio, H. & Hollmén, J. 2004. Evaluation of forest nutrition based on large- scale foliar surveys: are nutrition profiles the way of the future? Journal of Environmental Monitoring 6: 160–167. Raitio, H. 1999. Needle chemistry. In: Raitio, H. & Kilponen, T. (eds.). Forest Condition Monitoring in Finland. National report 1998. The Finnish Forest Research Institute, Research Papers 743: 51. Sulkava, M., Luyssaert, S., Rautio, P., Janssens, I.A. & Hollmén, J. 2006. Improved data quality can advance trend detection in environmental monitoring by decades. Submitted manuscript. Walworth, J.L. & Sumner, M.E. 1988. Foliar Diagnosis: A Review. In: Tinker, B. & Läuchli, A. (eds.). Advances in Plant Nutrition 3. Praeger Publishers, New York. p. 193–241. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 63 3.3 Litterfall production on 14 Level II plots during 1996 – 2003 Karikesato 14 havaintoalalla (taso II) vuosina 1996 – 2003 Liisa Ukonmaanaho Forest Research Institute, Vantaa Research Unit Litterfall production was monitored on 8 Norway spruce plots and 6 Scots pine plots during 1996–2003. The annual litterfall production varied considerably between the years and plots. The mean annual litterfall sum on the spruce plots ranged from 61 to 503 g m-2, whereas on the pine plots it ranged from 123 to 342 g m-2. The average needle litterfall production varied from 29% to 87% of the total litterfall flux on the spruce plots and from 52% to 69% on the pine plots. The highest litter production on the pine plots occurred in the autumn, while on the spruce plots litterfall production was more evenly distributed throughout the year. Karikesatoa seurattiin Metsien intensiiviseurannan kahdeksalla kuusi- ja kuudella mäntyalalla vuosina 1996–2003. Karikesato vaihteli runsaasti sekä vuosien että havaintoalojen välillä. Kuusi- koissa keskimääräinen vuosittainen karikesato vaihteli 61–503 g m-2, vastaavasti männiköissä 123–342 g m-2. Neulaskarikkeen osuus kokonaiskarikesadosta oli kuusialoilla 29–87 % ja mänty- aloilla 52–69 %. Männiköissä karikesadossa esiintyi selkeä vuodenaikaisvaihtelu määrän ollessa suurimmillaan syksyisin, sen sijaan kuusikoissa karikesato oli tasaisemmin jakautunut ympäri vuo- den. Introduction A substantial proportion of terrestrial net primary production is removed from the trees as litterfall on the forest floor and subsequently to the detritus food web. Litterfall represents a major pathway through which soils, depleted by nutrient uptake and leaching, are replenished (Morrison 1991). Furthermore, litterfall represents one of the primary links between producer and decomposer organisms (Fyles et al. 1986). Therefore litterfall plays a key role in understanding the dynamics of nutrient cycling within forest ecosystems. Litterfall production is correlated strongly with site, stand and climate factors. Albrektson (1988), for instance, found that needle litterfall production in Scots pine stands increased with improving site quality and decreased with latitude. Annual litterfall can vary considerably, which is related to the fact that the weather conditions differ year to year. In addition, there is variation between seasons, e.g. deciduous trees shed most of their foliar biomass in the autumn. In boreal coniferous forests, foliar litter comprises the main part of the litterfall flux to the forest floor. This report presents the results of litterfall production on 14 Level II sites during 1996 to 2003. Material and methods The study was carried out in 8 Norway spruce (Picea abies L. Karst.) and 6 Scots pine (Pinus sylvestris L.) plots during 1996 to 2003; sampling on some of the plots started later than 1996. Litterfall was collected using 12 traps located systematically on a 20 x 20 m grid on one plot (30 x 30 m) in each stand. The top of the funnel-shaped traps, with a collecting area of 0.5 m2, 64 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm was located at a height of 1.5 m above the forest floor (Fig. 1). The litterfall was collected in a replaceable cotton bag attached to the bottom of the litterfall trap. Litterfall was sampled at two- week intervals during the snow-free period (May to November, depending on the latitude of the plot), and once at the end of winter. After collection, all the litter samples were air-dried and sorted into at least four fractions: pine green needles/brown needles, spruce needles and the remaining material. The mass of each fraction was weighed and nutrient analyses were performed. Litterfall production (dry mass per unit area) was calculated by dividing the total and needle litterfall masses by the total surface area of the traps. Results and discussion The annual litterfall production varied between years and plots considerably (Table 1 and 2, Fig. 2a, b). The mean annual litterfall sum on the spruce plots ranged from 61 to 503 g m-2, whereas for pine it ranged from 123 to 342 g m-2 (Table 1 and 2, Fig. 2a, b). The lowest litterfall production in both pine and spruce stands was at the northernmost plots at Kivalo. There was a clearly decreasing south-north gradient in litterfall production, which obviously is related to the stand characteristics, climate and latitude, e.g. the height of the trees is the lowest on the northern plots (see Table 2, p. 18), indicating the impact of tree height on litterfall production. Saarsalmi et al. (in press) found that stand height was the stand characteristic (tree height, breast diameter, basal area, stem volume, age) with the strongest correlation with litterfall production. Litterfall production also showed a slightly increasing trend over time, being greater in 2003 than in the beginning of the sampling period at most of the plots. The amount of needles, branches, bark and cones in litterfall usually increase with stand age (Mälkönen 1974, Flower-Ellis 1985, Finér 1996). This trend was also seen on our plots. The average litterfall production was greater on the spruce plots (282 g m-2) than on the pine plots (222 g m-2). The higher crown ratio of spruce (averaging 0.76 in Finland; Hynynen et al. 2002, Saarsalmi et al. in press) compared to pine partly explains the higher spruce canopy litterfall production. Figure 1. Photo of a litterfall trap at the Scots pine plot Hietajärvi (nr. 20). (Photo: Johan Stendahl). Kuva 1. Karikesatokeräin Hietajärven männikköalalla (no. 20). (Kuva: Johan Stendahl). Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 65 Figure 2. Mean annual sum and standard deviation of total and needle litterfall production on a) the Norway spruce plots, and b) the Scots pine plots during 1996–2003. Note, on the Evo, Oulanka, Punkaharju and Uusikaarlepyy plots sampling was started in 1999 and at Pallasjärvi in 2001. Kuva 2. Keskimääräinen vuosittainen karikesato ja vuosien välinen keskihajonta a) kuusialoilla ja b) mänty- aloilla vuosina 1996–2003. Huom., Evolla, Oulangalla, Punkaharjulla ja Uudessakaarlepyyssä karikesadon keräys aloitettiin vasta 1999, Pallasjärvellä 2001. Table 1. Annual a) total litterfall and b) needle litterfall production on the Norway spruce plots. Taulukko 1. Vuosittainen a) kokonaiskarikesato ja b) neulaskarikesato kuusialoilla. Plot Pallas- Kivalo Juupa- Tammela Punka- Evo (19) Oulanka Uusikaarle- Havaintoala järvi (3) (5) joki (11) (12) harju (17) (21) pyy (23) a) Year – Vuosi Total litterfall, g m-2 (dw) – Kokonaiskarikesato, g m-2 (kp) 1996 114 297 240 1997 120 372 305 1998 116 372 318 1999 124 360 327 293 382 122 482 2000 92 403 342 365 368 104 440 2001 141 434 351 367 316 119 587 2002 61 136 425 362 369 339 128 461 2003 61 127 463 388 476 380 145 548 x 61 121 391 329 374 357 124 503 sd 0 15 52 45 66 29 15 62 b) Year – Vuosi Needle litterfall, g m-2 (dw) – Neulaskarike, g m-2 (kp) 1996 84 172 136 1997 99 225 198 1998 91 225 204 1999 100 231 211 151 149 40 339 2000 69 217 193 162 116 28 277 2001 122 245 166 213 108 41 400 2002 55 107 241 193 212 123 32 254 2003 50 102 301 266 350 179 38 369 x 53 97 232 196 218 135 36 328 sd 3 16 36 37 79 29 5 61 66 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm There were also considerable variation in needle litterfall production between the plots and years. A similar type of decreasing south-north gradient, and an increasing trend over time, was observed in needle litterfall production as in total litterfall production (Table 1 and 2, Fig. 2a, b). However, no clear cycle in needle litter production was found, indicating that there is overlap of different needle age-classes in the trees. The average needle litterfall production varied from 29% (Oulanka) to 87% (Pallasjärvi) of the total litterfall flux on the spruce plots, and from 52% (Miehikkälä) to 69% (Kivalo) on the pine plots. On the average there was a slightly higher needle litterfall production on the spruce plots (59%) than on the pine plots (57%). In boreal coniferous forests needle litter constitutes the main part of the litterfall flux to the forest floor. The rest of the litterfall consisted of leaves, the reproductive organs of trees such as seeds, cones and flower parts, and branches and to a lesser extent dead insects, faeces of animals and, on rare occasions, dead squirrels and birds. There was a clear seasonal pattern in litterfall production on the pine plots (Fig 3a). The highest litter production occurred in the autumn, which is connected with needle senescence. The oldest needle age-glass of pine is usually shed in August-October (Salemaa and Lindgren 2000). Litter production was lowest during the winter and early summer. A similar seasonal pattern for Scots pine has earlier been reported in many other studies (e.g. Viro 1955, Mälkönen 1974). Spruce litterfall production was more evenly distributed throughout the year than that of pine, and only a small peak was observed in early summer and autumn (Fig. 3b), which is typical of Norway spruce according to earlier studies (Viro 1955, Mälkönen 1974, Finér 1996). Table 2. Annual a) total litterfall and b) needle litterfall production on the Scots pine plots. Taulukko 1. Vuosittainen a) kokonaiskarikesato ja b) neulaskarikesato mäntyaloilla. Plot Kivalo (6) Juupa- Tammela Punka- Miehik- Lieksa (20) Havaintoala joki (10) (13) harju (16) kälä (18) a) Year – Vuosi Total litterfall, g m-2 (dw) – Kokonaiskarikesato, g m-2 (kp) 1996 83 102 229 1997 157 176 292 1998 132 168 343 1999 146 206 388 382 196 2000 117 213 328 255 195 2001 92 223 416 284 161 2002 125 206 373 324 204 141 2003 134 209 365 325 190 154 x 123 188 342 314 197 169 sd 25 39 59 48 10 25 b) Year – Vuosi Needle litterfall, g m-2 (dw) – Neulaskarikesato, g m-2 (kp) 1996 65 51 139 1997 85 86 171 1998 83 96 221 1999 113 120 222 226 105 2000 89 108 186 143 90 2001 56 139 273 190 101 2002 101 109 205 177 121 74 2003 91 106 207 165 84 75 x 86 102 203 180 102 89 sd 18 26 40 31 26 14 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 67 Conclusions Both total and needle litterfall production during 1996–2003 varied substantially between years, plots, tree species and season. There was also a clear increasing trend in litterfall production over time, and a decreasing trend in latitude. The variation in litterfall production is mainly related to latitude, stand characteristics, site type and weather conditions. Figure 3. Total monthly litterfall (g m-2) during 1996–2003 on a) the Norway spruce plots, and b) on the Scots pine plots. Note, on the Evo, Oulanka, Punkaharju and Uusikaarlepyy plots sampling was started in 1999 and at Pallasjärvi in 2001. Kuva 3. Vuosijakson 1996–2003 yhteenlaskettu karikesato (g m-2) kuukausittain a) kuusialoilla ja b) mänty- aloilla. Huom., Evolla, Oulangalla, Punkaharjulla ja Uudessakaarlepyyssä karikesadon keräys aloitettiin vasta 1999, Pallasjärvellä 2001. 68 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm References Albrektson, A. 1988. Needle litterfall in stands of Pinus sylvestris L. in Sweden, in relation to site quality, stand age and latitude. Scandinavian Journal of Forest Research 3: 333–342. Finér, L. 1996. Variation in the amount and quality of litterfall in a Pinus sylvestris L. stand growing on a bog. Forest Ecology and Management 80: 1–11. Flower-Ellis, J. 1985. Litterfall in an age series of Scots pine stands: summary results for the period 1973–1983. In: Lindroth, A. (ed.). Klimat, fotosyntes och förnafall I tallskog på Sedimentmark – ekologiska basmätningar i Jädraås. Institutionen för Ekologi och Miljövård, Sveriges Lantbruksuniversitet, Uppsala. Rapport 19: 75–74. Fyles, J.W., La Roi, G.H. & Ellis, R.A. 1986. Litter production in Pinus banksiana dominated stands in northern Alberta. Canadian Journal of Forest Research 16: 772–777. Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H. & Haapala, P., 2002. Models for prediciting stand development in MELA System. The Finnish Forest Research Institute, Research Papers 835. 116 p. Saarsalmi, A., Starr, M. Hokkanen, T. Ukonmaanaho, L., Kukkola, M., Nöjd, P. & Sievänen, R. Predicting annual canopy litterfall production for Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management (in press). Mälkönen, E., 1974. Annual primary production and nutrient cycle in some Scots pine stands. Communicationes Instituti Forestalis Fenniae 84(5): 1–87. Morrison, I.K. 1991. Addition of organic matter and elements to the forest floor of an old growth Acer saccharum forest in the annual litter fall. Canadian Journal of Forest Research 21: 462–468. Salemaa, M. & Lindgren, M. 2000. Crown condition. In: Mälkönen, E. (ed.). Forest Condition in a Changing Environment – The Finnish Case. Forestry Sciences 65. Kluwer Academic Publishers, Dordrecht. p. 121–132. Viro, P.J. 1955. Investigations in forest litter. Communicationes Instituti Forestalis Fenniae 45(6): 1–65. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 69 3.4 Understorey vegetation on the Level II plots during 1998–2004 Aluskasvillisuus tason II havaintoaloilla vuosina 1998–2004 Maija Salemaa & Leena Hamberg Finnish Forest Research Institute, Vantaa Research Unit A complete vegetation survey of the Level II plots is undertaken every fifth year. Here we present an overview of the second inventory of 31 Level II plots (year 2003) and a new birch plot, which was inventoried for the first time in 2004. The main compositional gradient represented the site fertility gradient, combined with the variation in soil moisture and location along the south-north axis in Non-metric Multidimensional Scaling (NMDS) of the vegetation of the mineral soil plots (year 2003). The number of vascular plant species decreased towards the north in both the pine and spruce stands. In contrast, the number of bryophyte and lichen species increased from south to north on the pine plots, whereas there was no south-north trend on the spruce plots. The cover percentages of the understorey plant species have remained relatively constant on six of the Level II plots that were surveyed every year during the period 1998–2003. The largest annual changes in the coverage of vascular plants and bryophytes were 10–15% -units. An increasing trend in the coverage of dwarf shrubs was found on two of the southern plots, but the cover of bryophytes simultaneously decreased. Between-year variation in the amount of precipitation and needle/leaf litter appeared to regulate the coverage of the bryophyte layer. Positive correlation between the cover of bilberry (Vaccinium myrtillus) and annual precipitation was also found on two of the northern plots. Metsäekosysteemien intensiiviseurannan (ICP metsäohjelma/taso II) havaintoalojen aluskasvilli- suus tutkitaan viiden vuoden välein. Tässä raportissa esitämme yhteenvedon toisesta inventoinnis- ta (v. 2003) sekä tuloksia uudelta koivualalta, joka inventoitiin ensimmäisen kerran vuonna 2004. Kivennäismaiden havaintoalojen aineistossa (v. 2003) tärkein kasvillisuuden rakennetta kuvaava vaihtelusuunta ilmensi kasvupaikan ravinteisuutta, maaperän kosteutta ja havaintoalan sijaintia etelä-pohjoissuunnassa (ei-metrinen moniuloitteinen skaalaus, NMDS). Putkilokasvilajien luku- määrä vähentyi pohjoiseen päin sekä männiköissä että kuusikoissa. Toisaalta sammal- ja jäkälälajien lukumäärä lisääntyi männiköissä pohjoiseen päin, mutta vastaavaa vaihtelua ei havaittu kuusikois- sa. Kasvilajien peittävyysprosentit ovat pysyneet suhteellisen vakaina kuudella vuosittain tutkitulla taso II:n havaintoalalla seurantajakson 1998–2003 aikana. Putkilokasvien ja sammalten peittävyyk- sien vuosittaiset muutokset ovat olleet suurimmillaan 10–15 %-yksikköä. Varpujen peittävyydet lisääntyivät kahdella eteläisellä havaintoalalla, mutta samanaikaisesti sammalten peittävyys pieneni. Vuosien väliset erot sade- ja neulas/lehtikarikkeen määrissä näyttivät säätelevän sammalkerroksen peittävyyttä. Myös mustikan (Vaccinium myrtillus) peittävyyden ja sademäärän välillä havaittiin po- sitiivinen korrelaatio kahdella pohjoisella havaintoalalla. Introduction Understorey vegetation makes an important contribution to the annual biomass production (Havas and Kubin 1983), nutrient cycling (Mälkönen 1974) and biodiversity (Reinikainen et al. 2000) of boreal forests. Changes in plant populations and communities have great indicative value in the monitoring of forest ecosystems. Long time series on the occurrence and abundance of plant species, connected to relevant environmental variables, offer the possibility to relate changes in vegetation to e.g. climatic and anthropogenic-derived changes (Økland 1995, Seidling 2005). 70 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm The main aims of vegetation monitoring in the Level II programme are 1) to characterize the current state of forest vegetation on the basis of the floristic composition, and 2) to detect temporal changes in the vegetation in relation to natural and anthropological environmental factors. A complete vegetation survey of the 31 sample plots was carried out for the first time in 1998 and repeated in 2003. In this report we present the general state (mean coverage of species groups and the number of species) of the understorey vegetation on all of the plots in 2003. A summary of the vegetation on a new birch plot at Punkaharju (surveyed in 2004) is also given. The vegetation pattern on the mineral soil plots was related to the chemical composition of the organic layer and a number of stand characteristics. In addition, we analyse annual changes in the coverage of the understorey vegetation on six plots during 1998–2003. Methods In 2003 there was a total of 31 Level II plots: 27 plots on mineral soil sites and 4 on peatland (Table 1). The sampling design for monitoring understorey vegetation is based on a pilot study carried out in 1996 (Salemaa et al. 1999). In general, the vegetation inventory is carried out according to the methods of the ICP Forests monitoring programme (Manual on methods... 2002). One of the three sub-plots was selected for vegetation monitoring (see Fig. 3, p. 15). The size of the sub-plot is 30 x 30 m. Altogether 16 sample quadrats, each 2 m2 (1.41 x 1.41 m) in area, were marked out systematically (4 x 4 design) on the sub-plot. The location of the quadrat was moved only in cases where there was an exceptional surface (e.g. path or large stone) occupying more than 20% of the area. In addition, four 10 x 10 m quadrats (A–D) were marked out to give four 100 m2 areas (Fig. 1). These areas provide vegetation data representing the Common Sample Area (= 400 m2), which is used in all countries participating in the ICP Forest monitoring programme. Estimation of plant species coverage % The vegetation inventory was carried out during July–August. The cover percentage of the individual plant species was assessed visually using the following scale: 0.01 (solitary or very sparsely growing shoots), 0.1, 0.2, 0.5, 1, 2, …99, 100%. The bottom layer (mosses, liverworts and lichens), the field layer (< 50 cm vascular plants: herbs, grasses, sedges, dwarf shrubs and tree seedlings) and the shrub layer (50–150 cm) were inventoried. Plants growing on stones, stumps or fallen stems were excluded. The cover of needle and leaf litter, dead plant material, dead branches, fallen tree stems, stumps, bare soil and stones was also assessed. Additional species, i.e. species occurring on the monitoring area (400 m2 and 900 m2) but not on the sample quadrats, were recorded. One team (2–4 botanists) performed the surveys on all the plots. Field tests were carried out to check the between-observer assessment level, and to calibrate it when necessary. A 2 m2 frame divided into 100 small quadrats by a net of elastic strings was used in the assessment of the plant cover (Fig. 2). “An open frame” without a net was placed on sites where a tree, shrubs or high vegetation were growing (Fig. 3). The cover of withered early summer species (e.g. Anemone nemoralis) was assessed according to their probable maximum biomass. The height of the field and shrub layers was measured at 10 points in different parts of the monitoring sub-plot. Samples of unknown plant species (mainly bryophytes and lichens) were later identified on the basis of microscopic characteristics. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 71 A B C D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Figure 2. The frame with a net of small quadrats used in vegetation analysis (1.41 x 1.41 m) (Sevettijärvi_P Nr. 1 in 2003). (Photo: Maija Salemaa). Kuva 2. Kasvillisuuden inventoinnissa käytettävä verkkokehikko (Sevettijärvi_P no. 1 vuonna 2003). (Kuva: Maija Salemaa). Figure 1. The sub-plot (30 x 30 m = 900 m2) used for the inventory of understorey vegetation. Coverage (%) of the plant species was assessed on the small sample quadrats (16 x 2 m2). The larger quadrats (A–D) were 10 x 10 m =100 m2 in area. Additional plant species growing outside the small quadrats were recorded within areas of 4 x 100 m2 (A–D) and 900 m2 (whole plot). Kuva 1. Metsäekosysteemin intensiiviseurannan kasvillisuusala (30 x 30 m = 900 m2). Kasvilajien peittävyy- det arvioitiin pieniltä näyteruuduilta (16 x 2 m2). Suuremmat ruudut (A–D) olivat kooltaan 10 x 10 m = 100 m2. Näyteruutujen ulkopuolella kasvavat lajit kirjattiin 4 x 100 m2:n (A–D) ja 900 m2:n alueelta. 72 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Ta bl e 1. T he n um be r o f s pe ci es o cc ur rin g on th e co m m on s am pl e ar ea (C S A = 40 0 m 2 ) o f t he v eg et at io n su bp lo ts , a nd th e ev en ne ss (E ) a nd d iv er si ty (S ha nn on H ’) in di ce s of th e pl an t c om m un iti es . A dd . = a dd iti on al s pe ci es fo un d ou ts id e C S A , b ut in si de th e ar ea o f 9 00 m 2 . D at a fro m 2 00 3, e xc ep t f or p lo t N r. 33 (P un ka ha rju _B ) f ro m 2 00 4. Ta ul uk ko 1 . L aj ie n lu ku m ää rä t k as vi lli su us al oi lle p er us te tu ill a 40 0 m 2 su ur ui si lla n äy te al oi lla (C S A ), se kä k as vi yh te is öj en ta sa is uu s- (E ) j a di ve rs ite et ti- (S ha nn on H ’) in de ks it. Y lim . = y lim ää rä is et la jit , j ot ka k as vo iv at C S A :n u lk op uo le lla , m ut ta 9 00 m 2 : n si sä llä . A in ei st o vu od el ta 2 00 3, p ai ts i h av ai nt oa la n o. 3 3 (P un ka ha rju _B ) v uo de lta 2 00 4. P lo t H ea th Va sc ul ar p la nt s – P ut ki lo ka sv it (< 50 c m ) B ry op hy te s – S am m al et Li ch en s – Jä kä lä t H av ai nt oa la si te ty pe Tr ee s & D w ar f H er bs G ra ss es & T ot H ep at ic s M os se s To t C la do ni a O th er To t A ll E H ’ A dd . sh ru bs sh ru bs se dg es K as vu p. P uu t & Va rv ut R uo ho t H ei nä t & To t M ak sa - Le ht i- To t To rv i- M uu t To t K ai kk i Y lim . ty yp pi pe ns aa t sa ra t sa m m . sa m m . jä kä lä t 1 S ev et tij är vi _P xe ric 1 4 0 0 5 9 11 20 15 6 21 46 0. 57 3 2. 19 5 1 2 P al la sj är vi _P su b- xe ric 2 8 2 1 13 7 14 21 15 6 21 55 0. 55 1 2. 20 8 3 3 P al la sj är vi _S m es ic 3 5 3 1 12 4 13 17 2 4 6 35 0. 48 0 1. 70 7 7 4 S od an ky lä _P su b- xe ric 3 6 0 0 9 9 10 19 13 4 17 45 0. 45 4 1. 72 7 0 5 K iv al o_ S m es ic 1 3 3 1 8 12 13 25 9 3 12 45 0. 49 7 1. 89 1 2 6 K iv al o_ P su b- xe ric 4 6 2 1 13 3 13 16 6 5 11 40 0. 40 6 1. 49 8 0 7 O ul an ka _S m es ic 1 4 5 3 13 6 12 18 1 0 1 32 0. 52 7 1. 82 5 2 8 O ul an ka _P m es ic 6 9 6 2 23 3 12 15 0 0 0 38 0. 48 1 1. 74 9 1 9 Y lik iim in ki _P xe ric 2 4 0 0 6 2 9 11 11 5 16 33 0. 49 4 1. 72 8 0 10 J uu pa jo ki _P su b- xe ric 4 3 6 3 16 0 13 13 8 2 10 39 0. 45 5 1. 66 8 4 11 J uu pa jo ki _S he rb -r ic h 4 2 15 7 28 11 20 31 1 0 1 60 0. 62 1 2. 54 3 5 12 T am m el a_ S m es ic 3 2 11 4 20 3 17 20 1 0 1 41 0. 52 7 1. 95 7 4 13 T am m el a_ P su b- xe ric 5 6 7 3 21 0 6 6 8 2 10 37 0. 50 8 1. 83 3 1 14 L ap in jä rv i_ P su b- xe ric 1 3 5 3 12 1 8 9 3 2 5 26 0. 38 3 1. 24 8 3 15 L ap in jä rv i_ S he rb -r ic h 5 3 27 7 42 4 16 20 2 0 2 64 0. 61 9 2. 57 6 1 16 P un ka ha rju _P su b- xe ric 2 5 4 0 11 0 5 5 1 0 1 17 0. 45 2 1. 28 1 8 17 P un ka ha rju _S he rb -r ic h 5 3 12 4 24 2 20 22 1 0 1 47 0. 39 7 1. 53 0 6 18 M ie hi kk äl ä_ P xe ric 2 4 1 0 7 0 6 6 15 4 19 32 0. 41 3 1. 43 0 2 19 E vo _S im he rb -r ic h 3 3 16 4 26 9 23 32 5 0 5 63 0. 41 1 1. 70 1 4 20 L ie ks a_ P im su b- xe ric 3 4 1 0 8 0 8 8 1 5 6 22 0. 56 9 1. 75 7 3 21 O ul an ka _S im m es ic 3 6 5 1 15 5 12 17 5 1 6 38 0. 43 4 1. 58 0 1 22 K ev o_ P im su b- xe ric 1 7 0 1 9 13 14 27 18 9 27 63 0. 59 9 2. 48 2 0 23 U us ik aa rle py y_ S he rb -r ic h 4 0 6 2 12 4 9 13 0 0 0 25 0. 48 4 1. 55 9 0 24 N är pi ö_ S m es ic 9 4 10 5 28 2 17 19 1 2 3 50 0. 54 8 2. 14 3 2 25 V ilp pu la _S pr o he rb -r ic h 4 3 19 8 34 2 18 20 1 0 1 55 0. 68 8 2. 75 8 1 26 Ik aa lin en _P (p ea tla nd ) 4 6 1 2 13 2 9 11 6 1 7 31 0. 64 8 2. 22 7 2 27 Ik aa lin en _P fe r (p ea tla nd ) 4 9 2 2 17 1 11 12 6 2 8 37 0. 56 6 2. 04 2 1 28 S ol bö le _S pr o he rb -r ic h 6 2 24 12 44 2 20 22 0 0 0 66 0. 57 9 2. 42 5 2 29 P yh än tä _P (p ea tla nd ) 4 9 1 2 16 4 20 24 7 5 12 52 0. 47 8 1. 88 7 1 30 P yh än tä _P fe r (p ea tla nd ) 4 9 1 2 16 5 17 22 4 3 7 45 0. 54 0 2. 05 4 1 31 K iv al o_ S pr o m es ic 3 3 4 3 13 5 16 21 1 1 2 36 0. 43 0 1. 54 2 3 33 P un ka ha rju _B he rb -r ic h 5 2 27 10 44 2 17 19 0 0 0 63 0. 61 2 2. 53 4 5 To ta l n um be r 19 14 59 23 11 5 35 66 10 1 21 13 34 25 0 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 73 Data analysis The number of species (S) and the mean coverage of the species groups were calculated for each vegetation plot. The diversity of the plant communities was measured by the Shannon diversity (H’ = – ∑ = S i 1 pi ln pi, i = proportion of the ith species of the total coverage) and the evenness (E = H’/ln S) indices. The data of all plots on mineral soil sites (year 2003) were ordinated by global non-metric multidimensional scaling (NMDS) in order to find the main compositional gradients of the vegetation (R programme version 2.4.1, Vegan package, Oksanen 2007). Two- dimensional solution using the Wisconsin squareroot transformation and Bray-Curtis coefficients as a measure of dissimilarity in floristic composition between the sample plots was chosen for the final method. Environmental vectors depicting the chemical properties of the organic layer (year 2003) and stand variables (years 1999–2000) were fitted to the ordination configuration. Results and discussion Vegetation surveys in 2003 and 2004 The number of species varied from 25 to 66 in the mesic and herb-rich spruce heaths, and from 17 to 63 in the xeric and sub-xeric pine heaths (Table 1). A total of 44 vascular plant and 19 bryophyte species were found in the birch stand at Punkaharju (Nr. 33). The number of species, Shannon diversity, as well as the evenness indices, were highest in the herb-rich spruce heaths (Table 1). The two exceptions were Uusikaarlepyy (Nr. 23) and Punkaharju (Nr. 17), where the number of species was low due to shading of the dense canopy of the spruce stands. The most northern plot Kevo (Nr. 22) formed an interesting case in the sub-xeric pine heaths: here the diversity of the species (H’ = 2.482) rose to the same level as that found in southern herb-rich heaths. Although the number of vascular plants was low (9) on this plot, the number of liverwort (13), moss (14) and Figure 3. An open frame (1.41 x 1.41 m) is used in sites where trees or high vegetation are growing (Tammela_P Nr. 13 in 2000). (Photo: Maija Salemaa). Kuva 3. Kulmasta auki olevaa verkotonta avokehikkoa käytetään paikoissa, jossa kasvaa puita tai alus- kasvillisuus on korkeaa (Tammela_P no. 13 vuonna 2000 ). (Kuva: Maija Salemaa). 74 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm lichen (27) species was high. The number of vascular plant species in the pine stands decreased, whereas that of bryophytes and lichens increased towards the north (Fig. 4a). In the spruce stands the number of vascular plants species was also higher in southern than in northern Finland, but there was no clear south-north trend in the species number of the bottom layer (Fig. 4b). In addition to the latitude and site fertility level, the species number is affected by the successional age of the stand (Tonteri 1994), and many other biotic and abiotic factors. The sum coverage of all species in the bottom and field layers exceeded 100% on almost all the plots (Table 2). Clear exceptions to this were the spruce plots at Uusikaarlepyy (Nr. 23), Evo (Nr. 19) and Juupajoki (Nr. 11), as well as the birch plot at Punkaharju (Nr. 33). On all these plots the large amounts of needle or leaf litter on the ground suppressed the growth of bryophytes especially. Plot and species scores of NMDS were displayed in two separate diagrams, but they were examined together in order to interpret the ordination (Fig. 5a, b). The more similar the plant species composition in the plots, the closer they were located to each other in the ordination diagram (Fig. 5a). Species scores were calculated as cover-weighted averages of the sample scores. As a result, the species points were located in the same part of the ordination space as the plots on which they were most likely to have a high abundance (Fig. 5b). The main compositional gradient in the ordination of the mineral soil plots (n = 27) represented the change in site fertility, combined with the variation in soil moisture and location along the south-north axis. The plots were located in accordance with the fertility level of the forest site types (Fig. 5a). Herb-rich heaths were located on the right, mesic heaths in the centre, followed by sub-xeric and xeric heaths on the left. In general, the ordination configuration was strongly related to the C/N ratio in the organic layer (r = 0.823, P < 0.01), which increased towards the north. The second gradient in the ordination indicated the combined effect of soil moisture and latitude. The northern plots, rich in bryophyte species, (upper part of the configuration), were divided from the drier southern stands. The exchangeable Ca concentrations and pH of the organic layer increased towards the southern herb-rich heaths (Fig. 5a). The arrangement of the species scores corresponded to the general pattern of fertility and moisture level of the plots. The demanding bryophyte species (e.g. Rhodobryum roseum and Brachythecium spp.) on the right were replaced by generalist bryophytes in the middle (e.g. Pleurozium schreberi and Dicranum polysetum) and by drought-tolerant lichens (e.g. Cladina rangiferina) on the left in the species ordination (Fig. 5b). The number and abundance of liverworts (e.g. Barbilophozia spp.) increased towards the northern plots, indicating increasing soil moisture. Plot-wise differences were mainly based on the variation in the vascular plant communities in the fertile, but in bryophytes and lichens at the infertile end of the site-type gradient (c.f. Tonteri et al. 2005). This confirms the importance of including all vegetation groups in the monitoring programmes of boreal forests. Vegetation change on six plots during 1998–2003 The annual variation in the total cover of vascular plants during 1998–2003 was relatively small (Table 3). The change in the cover of woody plants (dwarf shrubs and tree seedlings < 50 cm) was greatest on the Tammela plots (Nrs. 12 and 13): about 14% units higher in 2003 than in 1998. This was mainly caused by the increase in the cover of Vaccinium myrtillus at Tammela_S (Nr. 12) and Vaccinium vitis-idaea at Tammela_P (Nr. 13) (Fig. 6). Slight decreasing trend in the cover of V. myrtillus was found on the Pallasjärvi plots (Nrs. 2 and 3) (Fig. 6). Annual bulk precipitation Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 75 Figure 4. The number of species in the understorey vegetation (CSA = 400 m2) in heath forests in 2003. The plots run from left to right in accordance with the South-North gradient. a) Scots pine stands represent sub- xeric and xeric heaths, and b) Norway spruce stands mesic and herb-rich heaths. The Solböle and Närpiö sites are no longer part of the Level II network. Kuva 4. Aluskasvillisuuden lajimäärät kangasmetsien havaintoaloilla (CSA = 400 m2) vuonna 2003. Havainto- alat järjestetty vasemmalta oikealle vastaamaan etelä-pohjoisgradienttia Suomessa. a) Männiköt edustavat kuivahkoja ja kuivia kankaita, b) kuusikot lehtomaisia ja tuoreita kankaita. Solböle ja Närpiö eivät ole enää mukana tason II seurannassa. 76 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Ta bl e 2. T he m ea n co ve r ( % ) o f t he s pe ci es g ro up s, d ec ay in g w oo d an d ne ed le /le af li tte r o n th e ve ge ta tio n pl ot s. D at a fro m 2 00 3, e xc ep t f or p lo t N r. 33 (P un ka ha rju _B ) f ro m 20 04 . Ta ul uk ko 2 . K as vi la jir yh m ie n, la ho pu un , n eu la s- ja le ht ik ar ik ke en k es ki m ää rä is et p ei ttä vy yd et (% ) k as vi lli su ud en h av ai nt oa lo ill a. A in ei st o on v uo de lta 2 00 3, p ai ts i h av ai nt oa la no . 3 3. (P un ka ha rju _B ), jo ka in ve nt oi tii n vu on na 2 00 4. P lo t Va sc . p la nt s B ry op hy te s - S am m al et Li ch en s - J äk äl ät A ll W oo d Li tte r - K ar ik e H av ai nt oa la P ut ki lo ka sv it H ep at ic s M os se s S um C la di na C la do ni a O th er S um de br is N ee dl es Le av es M ak sa sa m m . Le ht is am m . S um m a P or on jä k. To rv ijä k. M uu t S um m a K ai kk i La ho pu u N eu la se t Le hd et 1 S ev et tij är vi _P 46 .7 4. 1 11 .6 15 .7 38 .3 4. 0 0. 4 42 .7 10 5. 0 2. 0 29 .3 0. 8 2 P al la sj är vi _P 37 .2 2. 3 47 .0 49 .3 6. 9 3. 4 0. 8 11 .0 97 .5 3. 5 33 .3 3. 1 3 P al la sj är vi _S 57 .3 2. 0 84 .5 86 .6 0. 0 0. 0 0. 1 0. 2 14 4. 0 3. 1 7. 3 0. 0 4 S od an ky lä _P 37 .2 0. 1 74 .0 74 .1 4. 4 1. 2 0. 5 6. 1 11 7. 4 5. 0 15 .0 0. 0 5 K iv al o_ S 32 .0 8. 6 76 .1 84 .6 0. 0 0. 1 0. 0 0. 1 11 6. 7 5. 6 11 .0 0. 0 6 K iv al o_ P 35 .5 0. 0 91 .5 91 .5 1. 8 0. 1 0. 2 2. 1 12 9. 2 4. 2 9. 5 0. 0 7 O ul an ka _S 68 .0 1. 8 86 .1 87 .9 0. 0 0. 0 0. 0 0. 0 15 5. 9 3. 6 1. 4 20 .8 8 O ul an ka _P 79 .6 0. 1 86 .3 86 .4 0. 0 0. 0 0. 0 0. 0 16 6. 0 2. 8 13 .3 3. 4 9 Y lik iim in ki _P 33 .3 0. 0 51 .3 51 .3 16 .4 5. 2 0. 2 21 .9 10 6. 5 2. 7 23 .9 0. 0 10 J uu pa jo ki _P 47 .4 0. 0 91 .2 91 .2 0. 0 0. 1 0. 0 0. 2 13 8. 9 4. 1 7. 2 0. 4 11 J uu pa jo ki _S 34 .1 0. 7 43 .6 44 .3 0. 0 0. 0 0. 0 0. 0 78 .4 16 .4 26 .8 18 .0 12 T am m el a_ S 53 .9 0. 0 51 .7 51 .8 0. 0 0. 0 0. 0 0. 0 10 5. 7 6. 2 15 .4 26 .3 13 T am m el a_ P 45 .2 0. 0 46 .9 46 .9 0. 1 0. 2 0. 0 0. 4 92 .5 6. 1 39 .6 4. 4 14 L ap in jä rv i_ P 31 .1 0. 0 73 .8 73 .8 0. 0 0. 0 0. 0 0. 1 10 4. 9 7. 1 23 .3 1. 8 15 L ap in jä rv i_ S 50 .6 3. 5 66 .5 70 .0 0. 0 0. 0 0. 0 0. 0 12 0. 6 2. 6 7. 3 25 .4 16 P un ka ha rju _P 21 .8 0. 0 89 .0 89 .0 0. 0 0. 0 0. 0 0. 0 11 0. 8 7. 1 16 .9 0. 2 17 P un ka ha rju _S 17 .7 0. 0 68 .3 68 .3 0. 0 0. 0 0. 0 0. 0 86 .0 11 .0 28 .9 0. 2 18 M ie hi kk äl ä_ P 23 .4 0. 0 70 .1 70 .1 3. 1 0. 4 0. 3 3. 9 97 .3 5. 9 14 .8 2. 8 19 E vo _S im 18 .9 0. 1 41 .3 41 .3 0. 0 0. 2 0. 0 0. 2 60 .4 6. 7 12 .3 50 .0 20 L ie ks a_ P im 62 .3 0. 0 93 .2 93 .2 0. 5 0. 0 0. 2 0. 7 15 6. 1 4. 1 2. 5 10 .8 21 O ul an ka _S im 59 .1 0. 3 89 .3 89 .6 0. 0 0. 0 0. 0 0. 0 14 8. 7 5. 2 5. 4 16 .1 22 K ev o_ P im 42 .0 3. 1 42 .0 45 .1 4. 7 3. 2 4. 9 12 .7 99 .8 3. 0 24 .0 13 .8 23 U us ik aa rle py y_ S 4. 6 0. 1 15 .8 15 .9 0. 0 0. 0 0. 0 0. 0 20 .5 21 .9 85 .1 0. 0 24 N är pi ö_ S 25 .5 0. 0 69 .2 69 .2 0. 1 0. 0 0. 0 0. 1 94 .8 7. 0 22 .5 0. 6 25 V ilp pu la _S pr o 70 .9 6. 0 37 .2 43 .2 0. 0 0. 0 0. 0 0. 0 11 4. 1 9. 6 18 .5 0. 2 26 Ik aa lin en _P 59 .4 0. 0 84 .9 84 .9 0. 0 0. 1 0. 0 0. 2 14 4. 5 2. 0 8. 4 0. 2 27 Ik aa lin en _P fe r 31 .8 0. 0 78 .3 78 .4 0. 2 0. 5 0. 0 0. 7 11 0. 8 3. 2 8. 1 2. 0 28 S ol bö le _S pr o 73 .1 0. 0 69 .3 69 .3 0. 0 0. 0 0. 0 0. 0 14 2. 4 2. 8 12 .4 1. 6 29 P yh än tä _P 70 .7 0. 0 82 .4 82 .5 1. 8 0. 3 0. 3 2. 3 15 5. 4 4. 0 3. 6 0. 9 30 P yh än tä _P fe r 76 .6 0. 1 91 .1 91 .1 0. 4 0. 0 0. 1 0. 6 16 8. 2 3. 4 3. 8 1. 5 31 K iv al o_ S pr o 56 .7 2. 2 79 .2 81 .4 0. 0 0. 0 0. 0 0. 0 13 8. 1 7. 5 8. 9 0. 8 33 P un ka ha rju _B 44 .9 0. 0 2. 6 2. 6 0. 0 0. 0 0. 0 0. 0 47 .5 8. 1 0. 2 82 .2 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 77 −1 .0 −0 .5 0. 0 0. 5 1. 0 −1.0−0.50.00.51.0 N M D S 1 NMDS2 1S ev 2P al 3P al 4S od 5K iv 6K iv 7O ul 8O ul 9Y li 10 Ju u 11 Ju u 12 Ta m 13 Ta m 14 La p 15 La p 16 Pu n 17 Pu n 18 M ie 19 Ev o 20 Li e 21 O ul 22 K ev 23 U us 24 N är 25 Vi l 28 So l 31 K iv C _N C up lic h_ S A ge B ry o_ S pH C a Vo l N Tr ee _h ei gh t −2 −1 0 1 2 −1012 N M D S 1 NMDS2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ++ + + + + + + + + + + + + + + + + + + + + + + + ++ ++ + + + + + + + + + + + ++ + PL EU SC H RV AC CM YR T H YL O SP LE D IC RP O LY EM PE N IG R D IC RM AJ U M AI AB IF O D ES CF LE X CL AD RA N G CL AD AR BU BR AC _S U M CA LL VU LG O X AL AC ET D IC RF U SC BA RB _S U M G YM N D RY O TR IE EU RO D RY O CA RT H EP A_ SU M CL AD ST EL CA LA AR U N SO RB AU CU D IC RD RU M PO LY ST RI LE D U PA LU PE LT SC AB EQ U IS YL V PO LY JU N I N EP H AR CT D ES CC ES P PE LT AP H T LY CO AN N O RH O D RO SE PI N U SY LV AR CT AL PI PO H LN U TA CA LA EP IG RH YT TR IQ CE TR IS LA ST ER PA SC VE RO O FF I CA RE G LO B CE TR ER IC AR CT U VA U H IE RS P PO PU TR EM JU N IC O M M H YP N CU PR SA N IU N CI PL AG D EN T PE LT N EO P EQ U IA RV E G AL ES P M O EH TR IN D IC RF LA G IC M AE RI C PY RO CH LO KI AE BL YT D IC RS PA D Fi gu re 5 . G lo ba l n on m et ric m ul tid im en si on al s ca lin g (N M D S ) of th e ve ge ta tio n da ta o n th e m in er al s oi l p lo ts ( n = 27 ) in 2 00 3. a ) O rd in at io n of th e sa m pl e pl ot s an d th e fit te d ve ct or s of s el ec te d en vi ro nm en ta l v ar ia bl es . C _N = C /N in o rg an ic s oi l, C up lic h_ S = n um be r o f c up li ch en s pe ci es , B ry o_ S = n um be r o f b ry op hy te s pe ci es , V ol = S te m v ol um e) . S ite ty pe s: re d = he rb -r ic h he at hs , b lu e = m es ic h ea th s, g re en = s ub -x er ic h ea th s an d bl ac k = xe ric h ea th s. b ) W ei gh te d av er ag es o f t he s pe ci es . A bb re vi at io n of th e sp ec ie s na m es = fi rs t f ou r l et te rs fr om g en er ic a nd s pe ci es n am es . T he m os t a bu nd an t s pe ci es la be le d w ith n am es , o th er m ar ke d as c ro ss es . K uv a 5. G lo ba al i e i-m et rin en m on iu lo itt ei ne n sk aa la us ( N M D S ) ki ve nn äi sm ai de n nä yt ea lo je n ka sv ill is uu de st a vu od el ta 2 00 3. a) N äy te al oj en o rd in aa tio ja e rä id en y m pä - ris tö m uu ttu jie n so vi te tu t ve kt or it. C _N = C /N o rg aa ni se ss a ke rr ok se ss a, C up lic h_ S = t or vi jä kä lä la jie n lu ku m ää rä , B ry o_ S = s am m al aj ie n lu ku m ää rä , Vo l = r un ko til av uu s. K as vu pa ik ka ty yp it: p un ai ne n = le ht om ai se t, si ni ne n = tu or ee t, vi hr eä = k ui va hk ot ja m us ta = k ui va t k an ka at . b) L aj ie n pa in ot et ut k es ki ar vo t. La jin im ie n ly he nt ee t = n el jä e ns im - m äi st ä ki rja in ta s uv un ja la jin ti et ee lli si st ä ni m is tä . R un sa im m at la jit m er ki tty n im el lä , m uu t r is til lä . a) b) 78 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Table 3. The mean cover (%) of the woody species (dwarf shrubs and tree seedlings), herbs and grasses, bryophytes, lichens, needle and leaf litter during 1998–2003 on the six vegetation plots. Taulukko 3. Puuvartisten (varvut ja puiden taimet), ruohojen ja heinien summan, sammalten, jäkälien ja neu- las- ja lehtikarikkeen peittävyydet (%) kuudella kasvillisuuden havaintoalalla vuosina 1998–2003. Plot Year Woody Herbs & Bryophytes Lichens Needle Leaf species grasses sum sum litter litter Havaintoala Vuosi Puu- Ruohot & Sammalet Jäkälät Neulas- Lehti- vartiset heinät summa summa karike karike 2 Pallasjärvi_P 1998 45.1 0.3 35.5 13.9 51.0 5.6 1999 42.9 0.1 46.2 14.2 33.4 5.0 2000 53.8 0.2 41.3 11.5 36.5 3.6 2001 43.1 0.2 37.5 10.0 30.4 2.5 2002 42.2 0.3 49.5 9.6 36.8 2.8 2003 37.0 0.2 49.1 11.0 33.3 3.1 3 Pallasjärvi_S 1998 67.2 2.9 87.3 0.4 6.8 0.1 1999 56.8 1.2 84.0 0.2 6.4 0.1 2000 69.4 1.6 89.4 0.1 9.5 0.0 2001 63.7 2.1 87.0 0.1 5.1 0.1 2002 58.8 3.3 85.2 0.2 6.8 0.1 2003 55.2 2.2 86.5 0.2 7.3 0.0 7 Oulanka_S 1998 66.3 5.8 88.0 0.0 4.7 28.4 1999 64.9 3.9 90.8 0.0 1.5 25.9 2000 69.3 6.2 99.3 0.0 2.9 29.4 2001 64.5 6.2 85.9 0.0 1.0 18.2 2002 66.8 6.3 88.4 0.0 1.9 16.6 2003 62.0 6.0 87.8 0.0 1.4 20.8 8 Oulanka_P 1998 76.0 7.3 82.5 0.0 23.9 6.8 1999 74.9 4.9 87.4 0.0 9.9 7.0 2000 79.8 7.5 89.3 0.0 11.2 7.6 2001 74.0 5.5 90.5 0.0 5.1 3.6 2002 77.7 5.1 88.5 0.0 9.1 2.7 2003 75.8 3.8 86.4 0.0 13.3 3.4 12 Tammela_S 1998 31.2 16.1 77.4 0.0 15.7 8.4 1999 33.4 16.3 71.5 0.0 13.9 5.7 2000 30.6 15.8 68.7 0.0 12.5 12.9 2001 36.7 19.6 73.4 0.0 9.4 9.1 2002 47.2 16.0 65.8 0.0 13.1 13.3 2003 43.9 9.9 51.7 0.0 15.4 26.3 13 Tammela_P 1998 22.4 11.1 86.1 0.6 21.4 1.1 1999 25.4 14.8 80.9 0.6 19.8 1.1 2000 31.6 14.4 72.0 0.4 20.3 0.5 2001 36.0 14.5 65.2 0.3 20.3 0.3 2002 38.2 19.4 62.9 0.3 35.8 2.0 2003 36.3 8.9 46.9 0.3 39.6 4.4 at Pallasjärvi_S (Nr. 3) was lower during the years 2001–2003 (mean 482 mm) than in 1998 (647 mm) (Lindroos et al. 2000, and pages 84, 86 and 88 in this volume), and this may have restricted the growth of V. myrtillus. In fact, positive correlation was found between the cover of V. myrtillus and the amount of precipitation (r = 0.857, p = 0.029, n = 6). Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 79 Figure 6. The change in the mean cover of Vaccinium myrtillus (bilberry), V. vitis-idaea (cowberry) and a bryophyte Pleurozium schreberi on the six vegetation plots during 1998–2003. Kuva 6. Mustikan (Vaccinium myrtillus), puolukan (V. vitis-idaea) sekä seinäsammalen (Pleurozium schreberi) keskimääräisten peittävyyksien muutos kuudella kasvillisuuden havaintoalalla jaksolla 1998–2003. 80 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm The total cover of bryophytes and lichens changed only slightly on the northern plots (Nrs. 2, 3, 7, 8), whereas the cover of bryophytes decreased on the plots at Tammela (Nrs. 12, 13) during 1998–2003 (Table 3). The decreasing trend was expressed especially in the dominant moss species Pleurozium schreberi at Tammela (Fig. 6). At the same time the cover of dwarf shrubs, as well as the cover of needle and leaf litter on the forest floor, increased which may have suppressed the moss layer. The cover of bryophytes correlated negatively with the cover of needle litter at Tammela_P (Nr. 13) (r = –0.805, p = 0.053, n = 6) and with the cover of leaf litter at Tammela_S (Nr. 12) (r = –0.940, p = 0.005, n = 6). In addition, a positive correlation was found between the annual precipitation and the cover of bryophytes in the combined data of the two plots at Tammela (r = 0.592, p = 0.043, n = 12). Acknowledgements We thank Leila Korpela and Anna-Maija Kokkonen for participating in the inventory during 1998–2000, and Liisa Sierla, Tiina Tonteri, Anneli Viherä-Aarnio and Mari Viitamäki in the year 2003. Nijole Kalinauskaite identified the bryophyte and Sampsa Lommi the lichen samples collected in the field. References Havas, P. & Kubin, E. 1983. Structure, growth and organic matter content in the vegetation cover of an old spruce forest in Northern Finland. Annales Botanici Fennici 20: 115–149. Lindroos, A.-J., Derome, J., Derome, K. & Niska, K. 2000. Deposition. In: Ukonmaanaho, L. & Raitio, H. (eds.) Forest condition monitoring in Finland. National Report 1999. The Finnish Forest Research Insitute, Research Papers 782: 61–69. Mälkönen, E. 1974. Annual primary production and nutrient cycle in some Scots pine stands. Communicationes Instituti Forestalis Fenniae 84(5): 1–87. Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. 2002. Part VIII. Assessment of ground vegetation. 19 p. [Internet site]. UN-ECE. Programme coordination centre, Hamburg. Available at: http://www.icp-forests.org/manual. htm. Økland, R.H. 1995. Changes in the occurrence and abundance of plant species in a Norwegian boreal coniferous forest, 1988–1993. Nordic Journal of Botany 15: 415–438. Oksanen, J. 2007. Multivariate Analysis of Ecological Communities in R: vegan tutorial. 39 p. [Internet site]. Available at: http://cc.oulu.fi/~jarioksa/softhelp/vegan.html. Reinikainen, A., Mäkipää, R., Vanha-Majamaa, I. & Hotanen, J.-P. (eds.) 2000. Kasvit muuttuvassa metsäluonnossa. (English summary: Changes in the frequency and abundance of forest and mire plants in Finland since 1950). Tammi, Helsinki. 384 p. Salemaa, M., Monni, S., Royo Peris, F. & Uhlig, C. 1999. Sampling strategy for the assessment of temporal changes in ground vegetation in boreal forests. In: Raitio, H. & Kilponen, T. (eds.). Forest condition monitoring in Finland. National report 1998. The Finnish Forest Research Institute, Research Papers 743: 117–127. Seidling, W. 2005. Ground floor vegetation assessment within the intesive (Level II) monitoring of forest ecosystems in Germany: chances and challenges. European Journal of Forest Research 124: 301–312. Tonteri, T. 1994. Species richness of boreal understorey forest vegetation in relation to site type and successional factors. Annales Zoologici Fennici 31: 53–60. Tonteri, T., Hotanen, J.-P., Mäkipää, R., Nousiainen, H., Reinikainen, A. & Tamminen, M. 2005. Metsäkasvit kasvupaikoillaan – kasvupaikkatyypin, kasvillisuusvyöhykkeen, puuston kehitysluokan ja puulajin yhteys kasvilajien runsaussuhteisiin. Metsäntutkimuslaitoksen tiedonantoja 946. 106 p. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 81 3.5 Open area bulk deposition and stand throughfall in Finland during 2001–2004 Avoimen paikan ja metsikkö­sadannan laskeuma Suomessa vuosina 2001–2004 Antti-Jussi Lindroos1, John Derome2 & Kirsti Derome2 Finnish Forest Research Institute; 1) Vantaa Research Unit, 2) Rovaniemi Research Unit The results of deposition (open area bulk and stand throughfall) monitoring on 8 Norway spruce and 8 Scots pine Level II plots during 2001–2004 are presented in this report. Mean total N and SO4-S deposition were clearly higher in southern Finland than in northern Finland. Sulphur deposition in the open and in stand throughfall during 2001–2004 was clearly lower than that measured in earlier years (monitoring started in 1996), especially on the plots in southern Finland. There was no corresponding decrease in the deposition of nitrogen compounds in either bulk deposition or in stand throughfall. The lowest SO4-S deposition in stand throughfall was recorded on the spruce plot at Pallasjärvi (N Finland), 94 mg m-2 in 2002, and the highest value on the spruce plot in Tammela (S Finland), 507 mg m-2 in 2003. The lowest total N deposition in the open occurred on the pine plot at Sevettijärvi (N Finland), 62 mg m-2 in 2002, while the corresponding highest deposition load was recorded on the plot at Miehikkälä (S Finland), 456 mg m-2 in 2004. The annual values for many of the other deposition parameters were also higher in the southern part of Finland compared to the north. Tässä raportissa esitetään avoimen paikan ja metsikkösadannan laskeuman tulokset kahdeksalle kuu- si- ja mäntyalalle vuosille 2001–2004. Totaalitypen ja SO4-S:n keskiarvolaskeumat olivat selvästi suurempia Etelä-Suomessa verrattuna Pohjois-Suomeen vuosina 2001–2004. Verrattaessa vuosien 2001–2004 tuloksia aikaisempiin vuosiin (seuranta alkoi 1996) havaittiin, että avoimen paikan ja metsikkösadannan rikkilaskeuma on alentunut etenkin Etelä-Suomen havaintoaloilla. Vastaavaa las- keuman vähentymistä ei ollut havaittavissa typen yhdisteille avoimella paikalla tai metsikkösadan- nassa. Alhaisin metsikkösadannan SO4-S -laskeuma mitattiin Pallasjärven kuusikkoalalla Pohjois- Suomessa, 94 mg m-2 vuonna 2002, ja suurin laskeuma Tammelan kuusikkoalalla Etelä-Suomessa, 507 mg m-2 vuonna 2003. Alhaisin avoimen paikan totaalitypen laskeuma mitattiin Sevettijärven alalla Pohjois-Suomessa, 62 mg m-2 vuonna 2002, ja suurin laskeuma Miehikkälän havaintoalalla Etelä-Suomessa, 456 mg m-2 vuonna 2004. Myös useiden muiden laskeumatunnusten arvot olivat suurimmat maan eteläosissa pohjoiseen verrattuna. Introduction Deposition monitoring started in Finland as a part of the EU/ICP Forests programmes in 1995. During the first year, 18 Level II intensive monitoring plots were established for this purpose in different parts of Finland. The number of the Level II plots with deposition monitoring increased to 24 plots during 1996 but, from 1998 onwards, the number of intensively monitored plots was reduced to 16. In 2004, one plot was replaced with two new plots, i.e. the total number of the plots was 17. The results concerning deposition on the forests and forest floor have been published in the national reports (Lindroos et al. 1999, 2000, 2001, 2002). The deposition monitoring was carried out according to the relevant sub-manual of the EU-funded Forest Focus programme. In this report, the deposition results of the 16 monitoring plots for the years 2001–2004 are presented. 82 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Material and methods Deposition on the forests (bulk deposition in the open area, BD) and on the forest floor (stand throughfall, TF) were monitored in 8 Norway spruce and 8 Scots pine stands during 2001–2003. In 2004, deposition was monitored on 8 Norway spruce, 7 Scots pine and 2 birch plots. The BD and TF samples were collected at 4-week intervals during the winter, and at 2-week intervals during the snow-free period. There were 20 systematically located bulk deposition collectors (ø = 20 cm, h = 0.4 m) within the stand (TF) during the snow-free period, and 6–10 snow collectors (ø = 36 cm, h = 1.8 m) during the wintertime depending on the structure of the stand. The number of snow collectors in each stand was based on a pre-study using 20 snow collectors located systematically on each plot. From this 20-collector network, 6–10 collectors were selected for sampling such that the mean deposition value was approximately the same as the result obtained with the 20 collectors. The number of collectors in the open area was 3 (bulk deposition) and 2 (snow collectors). The samples were pre- treated and analysed according to the sub-manual of the ICP Forests Programme (current version: Manual on methods... 2006). Results and discussion The amount of precipitation in the open area varied from 278 to 822 mm/year during 2001–2004. The corresponding range for the amount of stand throughfall was 191–731 mm/year. The lowest annual SO4-S deposition load in the open was recorded on the Oulanka plot in northern Finland, 81 mg m-2 in 2003, and the highest load on the Miehikkälä plot in southern Finland, 320 mg m-2 in 2004. The corresponding lowest value in stand throughfall was recorded on the spruce plot at Pallasjärvi (N Finland), 94 mg m-2 in 2002, and the highest value on the spruce plot at Tammela (S Finland), 507 mg m-2 in 2003. For the total N deposition in the open, the lowest value occurred on the plot at Sevettijärvi (N Finland), 62 mg m-2 in 2002, while the corresponding highest deposition load on the plot at Miehikkälä (S Finland), 456 mg m-2 in 2004 (Tables 1–8). The annual deposition values for many of the parameters were higher in the southern part of Finland than in the north. Throughfall deposition was generally higher than that in the open for all the parameters except for nitrogen compounds. The bulk deposition values for NH4-N, NO3-N and Ntot were generally higher than those in stand throughfall (Tables 1–8). There were two exceptions to these general trends: the pine plot at Sevettijärvi and the spruce plot at Uusikaarlepyy. On the Sevettijärvi plot in NE Finland, the Na and Cl deposition values were very high due to the proximity of the Barents Sea. On the Uusikaarlepyy plot, located on the west coast of Finland, local NH3 emissions were reflected in deposition, and the stand throughfall values for nitrogen compounds were generally higher than those in the open (Tables 1–8). Sulphur deposition in bulk deposition and stand throughfall in 2003 and 2004 were lower than the values recorded in earlier years (monitoring started in 1996), especially on the plots in southern Finland (Lindroos et al. 2006). This decrease is in accordance with the results for the whole European monitoring network (The Condition of...2005). On the other hand, there was no corresponding decrease in the deposition of nitrogen compounds in either bulk deposition or in stand throughfall. Mean total N in bulk deposition and SO4-S deposition in stand throughfall were clearly higher in southern Finland compared to northern Finland during 2001–2004 (Figs. 1 and 2). Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 83 Figure 1. Mean SO4-S deposition (mg m-2 yr-1) in stand throughfall for the period 2001–2004. Kuva 1. Keskimääräinen SO4-S -laskeuma (mg m-2 v-1) metsikkö­sadannassa tutkimusjak- solla 2001–2004. Figure 2. Mean total N deposition (mg m-2 yr-1) in bulk deposition for the period 2001–2004. Kuva 2. Keskimääräinen totaali-N -laskeuma (mg m-2 v-1) avoimella paikalla tutkimusjaksolla 2001–2004. Miehikkälä Punkaharju Juupajoki Tammela Oulanka Pallasjärvi Sevettijärvi Uusikaarlepyy Kivalo Evo Lieksa Ylikiiminki 163 117 142 215 148 275 169 301 163 209 219 457 382 240 357 394 Scots pine Mänty Norway spruce Kuusi 200 200 Miehikkälä Punkaharju Juupajoki Tammela Oulanka Pallasjärvi Sevettijärvi Uusikaarlepyy Kivalo Evo Lieksa Ylikiiminki Scots pine Mänty Norway spruce Kuusi 83 148 207 300 130 293 195 328 266 293 273 372 273 385 386 200 301 200 84 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Ta bl e 1. B ul k de po si tio n in th e op en ( B D ) an d de po si tio n in s ta nd th ro ug hf al l ( TF ) on 8 N or w ay s pr uc e pl ot s in 2 00 1. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 1 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la k uu si al al la (p lo t) vu on na 2 00 1. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta am aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at . = le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 P al la sj är vi 3 67 B D 57 5 11 5 10 9 61 69 14 2 61 20 31 76 66 10 41 TF 48 7 13 1 14 6 21 41 12 7 96 34 37 7 94 14 1 30 13 K iv al o 5 66 B D 56 1 15 6 17 7 66 96 20 3 81 27 23 65 59 10 36 TF 50 0 17 4 19 5 24 55 13 5 10 2 35 43 5 87 10 5 35 36 O ul an ka 21 66 B D 39 4 12 1 13 3 46 62 12 3 58 19 17 62 62 78 7 TF 38 8 15 8 16 3 24 35 96 10 3 37 29 1 97 11 2 24 14 U us ik aa rle py y 23 63 B D 54 3 16 7 17 7 16 9 12 2 36 9 97 34 70 12 6 13 1 10 28 TF 34 0 31 0 38 6 22 1 10 4 57 2 14 1 75 13 88 20 1 53 6 71 57 Ju up aj ok i 11 61 B D 72 7 24 7 26 9 12 1 16 8 34 7 14 9 37 63 11 5 12 0 13 26 TF 49 5 28 7 36 4 33 58 25 0 20 7 67 13 97 14 7 29 1 74 01 P un ka ha rju 17 61 B D 48 7 18 5 20 3 85 13 3 26 6 12 6 29 43 65 65 10 21 TF 31 3 38 3 43 2 24 62 20 9 19 5 77 10 06 87 27 0 50 69 E vo 19 61 B D 64 2 21 8 24 3 12 3 16 0 34 1 12 6 33 40 90 92 12 47 TF 44 1 36 4 42 4 33 93 26 4 30 3 94 10 63 18 4 46 6 67 24 Ta m m el a 12 60 B D 63 2 23 1 26 0 12 7 17 6 35 3 14 6 39 52 11 9 14 1 12 88 TF 42 0 46 1 49 6 23 80 29 8 29 5 10 7 12 41 27 1 68 4 82 06 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 85 Ta bl e 2. B ul k de po si tio n in th e op en a re a (B D ) a nd d ep os iti on in s ta nd th ro ug hf al l ( TF ) o n 8 S co ts p in e pl ot s in 2 00 1. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 2 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la m än ty al al la (p lo t) vu on na 2 00 1. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta a- m aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at . = le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 S ev et tij är vi 1 69 B D 45 4 11 3 11 9 18 39 68 37 36 24 27 7 44 9 70 4 TF 41 4 19 3 20 1 13 35 81 88 56 11 5 37 6 62 8 19 82 K iv al o 6 66 B D 56 1 15 1 15 9 87 97 22 4 67 20 29 97 62 10 87 TF 43 2 14 9 15 8 34 60 14 7 87 30 20 1 81 92 29 67 Y lik iim in ki 9 64 B D 54 8 16 8 19 1 11 3 11 6 31 6 92 26 40 84 83 11 60 TF 44 2 16 5 18 4 39 79 19 5 12 5 44 20 5 94 12 4 36 91 Li ek sa 20 63 B D 50 1 17 1 24 9 94 11 8 27 8 97 20 38 88 73 94 0 TF 44 4 21 2 23 8 12 6 93 35 4 14 7 51 25 2 93 12 0 38 17 Ju up aj ok i 10 61 B D 72 7 24 7 26 9 12 1 16 8 34 7 14 9 37 63 11 5 12 0 13 26 TF 60 0 26 2 29 7 38 10 5 23 9 18 6 61 55 2 16 8 24 4 53 13 P un ka ha rju 16 61 B D 48 7 18 5 20 3 85 13 3 26 6 12 6 29 43 64 65 10 21 TF 34 1 23 1 26 0 30 74 18 3 17 0 48 37 0 99 13 2 43 27 Ta m m el a 13 60 B D 68 0 24 9 28 4 14 4 19 2 39 5 14 2 37 46 12 4 12 2 12 71 TF 49 9 25 6 30 3 34 12 0 28 1 22 1 73 53 5 16 7 26 9 59 63 M ie hi kk äl ä 18 60 B D 67 7 30 5 33 1 13 2 17 8 35 9 18 0 31 36 97 11 1 12 47 TF 53 8 39 3 42 8 80 16 3 36 2 28 6 63 32 7 13 2 20 3 46 21 86 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Ta bl e 3. B ul k de po si tio n in th e op en ( B D ) an d de po si tio n in s ta nd th ro ug hf al l ( TF ) on 8 N or w ay s pr uc e pl ot s in 2 00 2. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 3 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la k uu si al al la (p lo t) vu on na 2 00 2. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta am aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at . = le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 P al la sj är vi 3 67 B D 41 7 82 93 57 49 13 2 84 80 46 66 77 85 6 TF 38 1 94 10 6 21 32 86 95 81 29 2 12 2 19 1 26 04 K iv al o 5 66 B D 49 8 10 8 12 0 64 70 15 5 10 1 58 65 87 99 10 77 TF 44 3 11 9 14 7 11 37 10 8 12 3 64 40 3 11 7 12 9 30 69 O ul an ka 21 66 B D 35 1 90 10 5 66 50 16 6 83 57 41 60 77 58 9 TF 38 9 12 7 14 1 9 34 74 14 5 71 26 4 11 8 11 8 24 57 U us ik aa rle py y 23 63 B D 27 8 13 4 14 8 10 3 88 28 4 13 4 71 10 3 27 1 35 5 67 4 TF 23 4 28 3 35 0 13 6 12 0 52 6 16 7 86 17 72 29 1 86 0 72 01 Ju up aj ok i 11 61 B D 48 7 14 7 16 6 10 2 12 0 23 8 12 0 65 54 71 96 88 3 TF 35 7 22 4 27 5 18 50 17 1 19 5 82 10 01 11 5 22 9 46 54 P un ka ha rju 17 61 B D 46 3 15 4 17 3 69 11 4 23 1 13 5 73 95 79 11 4 95 0 TF 29 0 34 4 40 6 24 56 20 5 23 0 10 1 11 69 12 5 28 5 58 52 E vo 19 61 B D 53 9 18 0 20 3 13 5 14 0 30 4 18 6 74 63 98 12 7 23 24 TF 38 6 37 3 45 5 29 10 9 28 1 32 9 11 6 11 07 26 7 44 1 68 27 Ta m m el a 12 60 B D 54 5 21 4 23 9 20 7 17 5 41 6 19 9 90 59 13 2 18 8 11 68 TF 40 6 45 1 55 4 55 92 32 3 38 4 16 0 13 57 33 0 60 4 89 91 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 87 Ta bl e 4. B ul k de po si tio n in th e op en a re a (B D ) a nd d ep os iti on in s ta nd th ro ug hf al l ( TF ) o n 8 S co ts p in e pl ot s in 2 00 2. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 4 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la m än ty al al la (p lo t) vu on na 2 00 2. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta a- m aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at . = le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 S ev et tij är vi 1 69 B D 35 7 11 7 12 6 27 32 62 81 10 1 40 50 1 81 4 57 6 TF 30 1 15 4 16 3 13 24 60 13 1 10 3 12 0 64 7 10 92 16 83 K iv al o 6 66 B D 48 0 99 11 0 46 68 13 5 89 52 57 71 81 10 84 TF 38 9 10 7 12 8 31 49 11 9 10 6 51 18 7 10 9 12 5 26 15 Y lik iim in ki 9 64 B D 50 6 12 5 13 9 60 98 21 4 10 1 42 56 78 95 94 0 TF 42 8 12 8 15 6 33 65 16 5 15 7 59 24 4 12 0 14 2 37 64 Li ek sa 20 63 B D 54 9 15 8 17 6 10 1 11 4 26 7 11 1 54 58 95 11 7 11 42 TF 44 7 17 2 20 4 49 77 19 4 22 2 79 31 9 13 0 16 9 46 68 Ju up aj ok i 10 61 B D 48 7 14 7 16 6 10 2 12 0 23 8 12 0 65 54 71 96 88 3 TF 39 8 16 1 19 3 54 10 2 22 7 16 1 78 38 4 11 9 17 5 40 67 P un ka ha rju 16 61 B D 46 3 15 4 17 3 69 11 4 23 1 13 5 73 95 79 11 4 95 0 TF 31 1 18 7 21 9 27 72 18 5 18 2 57 38 7 10 5 13 0 38 36 Ta m m el a 13 60 B D 53 6 21 5 25 0 21 4 17 2 43 4 19 5 86 57 12 9 17 7 11 19 TF 40 4 22 8 26 9 11 1 13 0 31 5 25 5 10 3 48 3 16 5 26 7 54 31 M ie hi kk äl ä 18 60 B D 53 9 26 8 29 7 14 4 17 1 35 2 17 5 52 62 11 1 15 3 12 60 TF 40 2 31 0 35 3 78 16 2 32 9 24 5 81 31 9 15 0 23 6 37 85 88 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Ta bl e 5. B ul k de po si tio n in th e op en ( B D ) an d de po si tio n in s ta nd th ro ug hf al l ( TF ) on 8 N or w ay s pr uc e pl ot s in 2 00 3. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 5 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la k uu si al al la (p lo t) vu on na 2 00 3. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta am aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at . = le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 P al la sj är vi 3 67 B D 45 4 82 90 61 48 17 0 62 21 52 69 10 3 73 5 TF 42 5 10 2 11 6 5 27 77 72 27 28 1 14 3 22 8 32 24 K iv al o 5 66 B D 57 2 15 2 16 2 99 96 19 5 69 23 33 68 94 78 0 TF 56 2 21 0 24 5 42 62 15 7 10 7 38 42 8 12 7 16 8 43 97 O ul an ka 21 66 B D 36 3 81 81 32 52 97 53 27 26 41 59 42 0 TF 40 3 14 6 15 8 15 33 93 78 37 34 8 10 4 16 3 32 01 U us ik aa rle py y 23 63 B D 32 6 12 1 11 9 16 3 81 31 1 58 32 58 74 10 0 65 6 TF 19 1 35 2 40 0 19 8 12 8 50 3 11 5 74 17 26 22 3 79 6 69 91 Ju up aj ok i 11 61 B D 57 8 19 1 19 5 15 7 14 6 30 9 85 23 49 64 99 95 7 TF 40 8 35 2 37 7 24 42 20 6 17 9 51 14 10 15 5 36 4 79 13 P un ka ha rju 17 61 B D 59 5 19 3 18 8 12 8 14 1 27 9 12 8 48 59 70 10 5 99 2 TF 37 8 41 4 46 6 39 55 23 2 20 8 99 14 32 13 8 43 1 93 12 E vo 19 61 B D 58 8 19 3 18 6 11 6 15 0 28 2 14 8 55 47 72 11 1 10 02 TF 47 0 47 5 52 0 55 16 4 32 7 31 5 94 13 83 26 8 48 7 88 93 Ta m m el a 12 60 B D 51 7 23 4 22 4 22 8 15 7 34 0 10 2 35 67 16 4 16 1 88 3 TF 34 6 50 7 55 7 30 89 27 8 28 4 10 2 11 75 28 3 62 0 93 45 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 89 Ta bl e 6. B ul k de po si tio n in th e op en a re a (B D ) a nd d ep os iti on in s ta nd th ro ug hf al l ( TF ) o n 8 S co ts p in e pl ot s in 2 00 3. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 6 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la m än ty al al la (p lo t) vu on na 2 00 3. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta a- m aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at . = le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 S ev et tij är vi 1 69 B D 39 7 10 0 10 3 32 43 93 54 53 24 23 5 40 4 60 0 TF 37 5 13 4 14 3 17 33 82 97 68 11 7 44 7 67 0 21 98 K iv al o 6 66 B D 59 0 14 7 14 9 94 98 21 5 73 20 39 77 95 85 0 TF 46 3 16 4 18 0 38 66 15 0 86 27 21 5 10 3 14 1 34 93 Y lik iim in ki 9 64 B D 54 0 18 1 17 6 11 5 13 3 26 7 58 19 42 71 10 0 90 1 TF 45 1 19 7 21 7 61 92 19 9 98 41 21 5 11 0 15 8 35 49 Li ek sa 20 63 B D 58 1 19 1 20 1 14 0 13 0 34 0 93 25 49 77 11 3 98 3 TF 49 6 22 8 24 3 61 88 23 6 14 2 45 25 6 12 1 17 1 44 70 Ju up aj ok i 10 61 B D 57 8 19 1 19 5 15 7 14 6 30 9 85 23 49 64 99 95 7 TF 47 3 23 3 24 7 57 12 6 25 9 15 4 39 39 0 13 0 20 7 52 95 P un ka ha rju 16 61 B D 59 5 19 3 18 8 12 8 14 1 27 9 12 8 48 59 70 10 5 99 2 TF 39 3 24 2 25 1 75 99 21 6 15 9 42 44 6 11 3 17 0 56 33 Ta m m el a 13 60 B D 53 1 22 6 22 9 17 6 17 2 35 0 12 1 32 34 11 4 15 4 87 7 TF 36 8 25 9 26 5 96 11 9 26 8 20 9 61 45 5 16 0 32 2 53 34 M ie hi kk äl ä 18 60 B D 66 8 29 0 30 2 17 3 18 8 37 8 12 2 26 47 12 9 16 6 10 63 TF 49 6 34 8 37 2 96 17 2 34 3 23 6 77 36 2 20 8 23 1 51 49 90 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Ta bl e 7. B ul k de po si tio n in th e op en ( B D ) an d de po si tio n in s ta nd th ro ug hf al l ( TF ) on 8 N or w ay s pr uc e pl ot s in 2 00 4. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 7 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la k uu si al al la (p lo t) vu on na 2 00 4. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta am aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at .= le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 P al la sj är vi 3 67 B D 71 2 13 0 11 3 42 72 14 8 82 29 42 79 79 11 21 TF 65 6 14 2 15 1 29 51 14 9 11 7 35 38 0 14 4 17 8 41 31 K iv al o 5 66 B D 64 5 14 9 14 8 63 93 22 8 73 23 60 82 10 2 11 06 TF 60 4 17 3 18 0 23 67 18 2 95 36 47 6 12 5 14 9 43 36 O ul an ka 21 66 B D 49 0 12 4 10 6 47 66 13 2 69 24 33 69 80 66 7 TF 52 5 15 9 16 3 49 51 13 3 10 6 34 26 7 91 12 2 26 92 U us ik aa rle py y 23 63 B D 45 3 13 2 13 9 16 7 10 0 34 7 78 42 11 0 14 4 18 1 77 5 TF 26 2 25 7 30 6 16 5 93 46 7 14 3 74 16 08 24 2 70 7 69 04 Ju up aj ok i 11 61 B D 64 0 16 8 14 3 10 0 13 1 27 8 92 28 43 96 12 0 89 5 TF 47 5 23 5 27 3 28 62 22 4 15 1 46 10 51 19 9 31 1 59 65 P un ka ha rju 17 61 B D 63 7 19 1 20 2 11 1 14 3 31 7 13 0 42 85 95 10 2 10 38 TF 39 7 38 7 39 9 53 58 26 4 19 8 74 10 99 17 2 27 7 71 96 E vo 19 61 B D 76 9 17 3 16 9 10 4 12 7 27 8 16 4 56 44 11 1 12 2 11 10 TF 53 0 36 2 38 7 27 92 27 3 28 6 92 11 13 30 2 43 9 69 63 Ta m m el a 12 60 B D 82 1 22 0 22 5 13 7 20 0 38 0 14 9 49 49 16 8 20 6 11 08 TF 54 4 40 9 46 2 33 76 30 9 26 4 91 10 41 38 2 57 4 83 27 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 91 Ta bl e 8. B ul k de po si tio n in th e op en a re a (B D ) a nd d ep os iti on in s ta nd th ro ug hf al l ( TF ) o n 8 S co ts p in e pl ot s in 2 00 4. T he p lo ts a re li st ed in th e ta bl e fro m n or th to s ou th Fi nl an d. Ta ul uk ko 8 . A vo im en p ai ka n la sk eu m a (B D ) j a m et si kk ö­s ad an ta la sk eu m a (T F) k ah de ks al la m än ty al al la (p lo t) vu on na 2 00 4. H av ai nt oa la t o n jä rje st et ty ta ul uk os sa v as ta a- m aa n et el ä- po hj oi sg ra di en tti a S uo m en lä pi (L at .= le ve ys as te ). P re c. = s ad em ää rä . S am pl e pl ot P lo t La t. P re c. S O 4- S S to t N H 4- N N O 3- N N to t C a M g K N a C l D O C nr . m m m g m -2 S ev et tij är vi 1 69 B D 46 5 11 4 11 6 31 52 10 7 67 34 24 15 3 23 2 60 7 TF 40 5 17 1 18 3 28 42 10 9 98 50 13 0 33 2 52 2 21 96 K iv al o 6 66 B D 63 4 14 8 13 4 81 10 7 25 2 73 22 42 73 87 10 02 TF 50 0 14 9 14 4 49 70 17 4 90 29 25 0 97 12 7 36 95 Li ek sa 20 63 B D 82 2 21 6 19 3 11 3 13 8 31 3 14 2 36 85 12 0 13 4 14 09 TF 73 1 24 7 24 6 14 6 11 5 31 7 17 7 52 28 5 15 8 16 0 46 56 Ju up aj ok i 10 61 B D 64 0 16 8 14 3 10 0 13 1 27 8 92 28 43 96 12 0 89 5 TF 51 5 18 1 19 6 59 94 23 9 14 8 49 32 6 16 0 22 5 46 93 P un ka ha rju 16 61 B D 63 7 19 1 20 2 11 1 14 3 31 7 13 0 42 85 95 10 2 10 38 TF 41 5 21 4 22 5 49 74 22 1 13 9 38 45 7 11 6 15 4 47 72 Ta m m el a 13 60 B D 75 8 21 2 21 7 13 9 19 7 35 9 13 1 41 49 17 5 21 6 99 8 TF 54 1 21 6 23 0 79 11 3 26 4 16 6 53 46 0 21 4 31 5 57 25 M ie hi kk äl ä 18 60 B D 81 7 32 0 30 7 20 7 21 7 45 6 20 3 53 54 18 5 25 0 11 35 TF 60 0 37 8 38 4 12 6 18 3 40 3 27 6 76 42 2 28 1 42 4 47 51 92 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm References Lindroos, A-J., Derome, J., Derome, K. & Niska, K. 1999. Deposition. In: Raitio, H. & Kilponen, T. (eds.). Forest Condition Monitoring in Finland. National Report 1998. The Finnish Forest Research Institute, Research Papers 743: 72–77. Lindroos, A-J., Derome, J., Derome, K. & Niska, K. 2000. Deposition. In: Ukonmaanaho, L. & Raitio, H. (eds.). Forest Condition Monitoring in Finland. National Report 1999. The Finnish Forest Research Institute, Research Papers 782: 61–69. Lindroos, A-J., Derome, J., Derome, K. & Niska, K. 2001. Deposition on the forests and forest floor in 1999. In: Ukonmaanaho,L. & Raitio, H. (eds.). Forest Condition Monitoring in Finland. National Report 2000. The Finnish Forest Research Institute, Research Papers 824: 78–88. Lindroos, A-J., Derome, J., Derome, K. & Niska, K. 2002. Deposition on the forests and forest floor in 2000. In: Rautjärvi, H., Ukonmaanaho, L. & Raitio, H. (eds.). Forest Condition Monitoring in Finland. National Report 2001. The Finnish Forest Research Institute, Research Papers 879: 63–69. Lindroos, A-J., Derome, J., Derome, K. & Lindgren, M. 2006. Trends in sulphate deposition on the forests and forest floor and defoliation degree in 16 intensively studied forest stands in Finland during 1996– 2003. Boreal Environment Research 11(6): 451–461. Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. 2006. Part VI. Sampling and analysis of deposition. 78 p. [Internet site]. UN-ECE. Programme coordination centre, Hamburg. Available at: http://www.icp-forests.org/ manual.htm. The Condition of Forests in Europe. 2005. Executive Report. United Nations Economic Commission for Europe. Federal Research Centre for Forestry and Forest Products (BFH). 33 p. 93 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 3.6 Soil percolation water quality during 2001–2004 on 11 Level II plots Vajoveden kemiallinen koostumus 11 havaintoalalla (taso II) vuosina 2001–2004 John Derome1, Antti-Jussi Lindroos2 & Kirsti Derome1 Finnish Forest Research Institute; 1) Rovaniemi Research Unit, 2) Vantaa Research Unit This report presents the results of monitoring carried out on percolation water quality during 2001–2004 on 6 Level II plots located in Scots pine stands and 5 plots in Norway spruce stands. Percolation water was collected at 4-week-intervals during the snowfree period in 2001, 2002, 2003 and 2004 using zero tension lysimeters located at a depth of 5 cm from the ground surface. Overall, there were no signs of an increase in acidification caused by the deposition of acidifying sulphur and nitrogen compounds during the period 2001–2004, nor of a decrease in sulphate concentrations resulting from the decrease in sulphur deposition during the past decade. Nitrate concentrations were extremely low, indicating that there are currently no signs of nitrogen saturation in these ecosystems. DOC concentrations increased strongly on most of the plots in 2003, which was an extremely hot and dry summer, and therefore DOC concentrations could potentially be used as a relatively sensitive indicator of the effects of climate change on carbon fluxes. Concentrations of important base cations (Ca, Mg and K) remained relatively constant throughout the monitoring period on most of the plots. Tässä raportissa esitetään maavesiseurannan tuloksia tason II kuudelta mänty- ja viideltä kuusi- alalta jaksolta 2001–2004. Vesinäytteet kerättiin 5 cm:n syvyydelle asennetuilla vajovesilysi- metreillä lumettomana aikana neljän viikon välein. Jaksolla 2001–2004 ei havaittu merkkejä rikki- ja typpilaskeuman aiheuttamasta happamoitumisesta, eikä toisaalta merkkejä vajoveden sulfaattipitoisuuksien alenemisesta, vaikka rikkilaskeuma on vähentynyt viime vuosikymmenen aikana. Nitraattipitoisuudet olivat erittäin alhaisia, mikä osoittaa, ettei kyseisissä ekosysteemeis- sä ole tällä hetkellä nähtävissä viitteitä typpikyllästymisestä. Liukoisen hiilen (DOC) pitoisuudet kohosivat voimakkaasti suurimmalla osalla seuranta-aloista vuonna 2003, jolloin kesä oli erittäin lämmin ja kuiva. Vajoveden DOC-pitoisuutta voitaisiin mahdollisesti käyttää suhteellisen herkkänä indikaattorina osoittamaan hiilivirroissa ilmastomuutoksen seurauksena tapahtuvia muutoksia. Suu- rimmalla osalla havaintoaloista tärkeiden emäskationien (kalsium, magnesium ja kalium) pitoisuu­ det pysyivät seurantajakson aikana suhteellisen vakaina. Introduction Soil solution has been monitored on 16 Level II plots since 1998. The results of percolation water quality monitoring using zero-tension lysimeters located at 5 cm depth on 6 pine plots and 5 spruce plots in different parts of Finland during 2001–2004 are presented in the report. The chemical composition of percolation water sampled with zero-tension lysimeters provides important information about the passage of ions and dissolved organic carbon down the soil profile and, together with information about the chemical composition of throughfall, enables fluxes to be calculated for the most important elements (e.g. carbon and nitrogen) in forest ecosystems. The main purpose of the report is to provide forest researchers with an overall view of the range of percolation water parameters in pine and spruce stands in different parts of the country. Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 94 Material and methods Percolation water was collected at 4-week-intervals during the snowfree period in 2001–2004 using zero tension lysimeters (diam. 20 cm) located at a depth of 5 cm from the ground surface. The installation and construction of the lysimeters have been described in detail in Derome et al. (1991). There were 5 replicate lysimeters at a depth of 5 cm on each plot. The soil type on the plots is podzolic; most of the pine plots are located on sorted glaciofluvial material, and the spruce plots on till soils. The pH was measured on unfiltered samples. The samples were filtered through membrane filters (0.45 µm) under positive pressure by means of a peristaltic pump. An aliquot of the filtrate was preserved with concentrated HNO3 prior to the determination of Altot by inductively coupled plasma atomic emission spectrometry (ICP­AES). Dissolved organic carbon (DOC) was determined on the unpreserved filtrate on a TOC analyser, and Ca, Mg, K, SO4 and NO3 by ion chromatography (IC). Results and discussion Acidity parameters (pH, Altot, SO4 and NO3) The average pH on the pine plots was relatively constant during 2001–2004 (Fig. 1), and percolation water pH was clearly the highest on the northernmost plot (P_1). On two of the spruce plots (S_3, S_17), on the other hand, there was a statistically significant decrease in pH over time. One of the plots (S_3) is located in northern Finland where the load of acidifying deposition is the lowest, and also biomass accumulation in the stand is relatively slow owing to the low stand density and slow growth rate. Thus neither acidifying deposition nor higher uptake of base cations is likely to be the explanation for the decrease in pH. The other plot is located in south­east Finland in an area which receives considerable amounts of acidifying deposition from the St. Petersburg area. The percolation water on this site was also the most acidic. However, the relatively high acidity on this plot may be related to the fact that it is relatively paludified, and the site was subjected to slash­and­burn agriculture during the late 19th century. Figure 1. Average (S.E.) annual pH in percolation water sampled using zero-tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 1. Keskimääräinen vajoveden pH 5 cm:n syvyydessä kuudella männikköalalla ja viidellä kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keskiarvon keskivirhe (S.E.). 95 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm The total aluminium (Altot) concentration on the pine plots, as well as all but one (S_17) of the spruce plots, were relatively constant throughout the period (Fig. 2). The average Al concentration was the highest on the two plots (P_16 and S_17) in Punkaharju in southern Finland, and the year­ to­year variation was also very large on one of these plots (S_17). Aluminium concentrations are usually strongly correlated with pH, and in fact Plot S_17 was the most acidic of all the plots. It has been proposed that Al concentrations above a threshold value of 1.8 mg L-1 may cause damage to the fine root systems of trees (de Vries et al. 1995). The annual average Altot concentration on only two of the plots (P_16 and S_17) exceeded this critical value. However, the percolation water collected under the organic layer on these two plots also had the highest DOC concentrations, and there are strong grounds to assume that only a relatively small proportion of the aluminium was in the form (Al3+) toxic to plant roots. The average sulphate concentration on all the pine plots and all but one (S_17) of the spruce plots remained extremely constant throughout the period, with no apparent decreasing trend (Fig. 3). Figure 2. Average (S.E.) annual total aluminium (Altot) concentration in percolation water sampled using zero- tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 2. Keskimääräinen vajoveden kokonaisalumiinipitoisuus (Altot) 5 cm:n syvyydessä kuudella männikkö- alalla ja viidellä kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keskiarvon keskivirhe (S.E.). Figure 3. Average (S.E.) annual sulphate concentration (SO4-S) in percolation water sampled using zero- tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 3. Keskimääräinen vajoveden sulfaattipitoisuus (SO4-S) 5 cm:n syvyydessä kuudella männikköalalla ja viidellä kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keski- arvon keskivirhe (S.E.). Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 96 Figure 4. Average (S.E.) annual nitrate concentration (NO3-N) in percolation water sampled using zero- tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001– 2004. The number of the plots runs in ascending order from north to south. Kuva 4. Keskimääräinen vajoveden nitraattipitoisuus (NO3-N) 5 cm:n syvyydessä kuudella männikköalalla ja viidellä kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keski- arvon keskivirhe (S.E.). Figure 5. Average (S.E.) annual dissolved organic carbon (DOC) concentration in percolation water sampled using zero-tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 5. Keskimääräinen vajoveden liuenneen orgaanisen hiilen pitoisuus (DOC) 5 cm:n syvyydessä kuudella männikköalalla ja viidella kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keskiarvon keskivirhe (S.E.). The nitrate concentrations on all the plots were extremely low (below 0.25 mg NO3-N L-1), thus indicating that the leaching of nitrate derived from nitrification is of little importance on these plots (Fig. 4). Dissolved organic carbon (DOC) The average DOC concentrations on the pine and spruce stands did not show any clear trends as regards the location of the plots (e.g. north, central and south Finland), nor between the type of tree species on the plots (Fig. 5). There was a clear increase in the DOC concentrations in percolation water on 8 of the 11 plots in 2003. This year had an exceptionally warm and dry summer, and the increase may have been caused by a higher rate of decomposition in the organic layer, with resulting higher DOC concentrations in autumn when precipitation as rain was at a 97 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Figure 7. Average (S.E.) annual magnesium concentration in percolation water sampled using zero-tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 7. Keskimääräinen vajoveden magnesiumpitoisuus 5 cm:n syvyydessä kuudella männikköalalla ja vii- dellä kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keskiarvon keskivirhe (S.E.). peak. This is an interesting finding, and further work should be carried out on the relationship between soil temperature, precipitation and DOC concentrations below the organic layer, as DOC concentrations could potentially be a relatively sensitive indicator of the effects of climate change on carbon fluxes. Fertility parameters (Ca, Mg, K) The year­to­year variation in the average Ca, Mg and K concentrations were relatively small (apart from plot S_11 for Ca and K, and S_17 for Mg), and there were no clear trends as regards the overall level of the Ca, Mg and K concentrations in different parts of the country nor for the type of tree species (Figs. 6, 7 and 8). There were no logical explanations for the large year­to­ year variation on two of the plots. Figure 6. Average (S.E.) annual calcium concentration in percolation water sampled using zero-tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 6. Keskimääräinen vajoveden kalsiumpitoisuus 5 cm:n syvyydessä kuudella männikköalalla ja viidellä kuusikkoalalla vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keskiarvon kes- kivirhe (S.E.). Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 98 References Derome, J., Niska, K., Lindroos, A.­J. & Välikangas, P. 1991. Ion­balance monitoring plots and bulk deposition in Lapland during July 1989 – June 1990. In: Tikkanen, E. & Varmola, M. (eds.). Research into forest damage connected with air pollution in Finnish Lapland and the Kola Peninsula of the U.S.S.R. The Finnish Forest Research Institute, Research Papers 373: 49–76. de Vries, W., van Grinsven, J.J.M., van Breemen, N., Leeters, E.E.J.M. & Jansen, P.C. 1995. Impacts of acid deposition on concentrations and fluxes of solutes in acid sandy forest soils in the Netherlands. Geoderma 67: 17–43. Figure 8. Average (S.E.) annual potassium concentration in percolation water sampled using zero-tension lysimeters at a depth of 5 cm on 6 Scots pine plots and 5 Norway spruce plots during 2001–2004. The number of the plots runs in ascending order from north to south. Kuva 8. Keskimääräinen vajoveden kaliumpitoisuus 5 cm:n syvyydessä kuudella männikköalalla ja viidellä kuusikkoalalla eri vuosina 2001–2004. Alojen numerointi pohjoisesta etelään. Kuvissa esitetty keskiarvon keskivirhe (S.E.). 99 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 3.7 Phenological assessments on the intensive monitoring plots Fenologinen seuranta intensiiviseurannan havaintoaloilla Boy Possen & Egbert Beuker Finnish Forest Research Institute, Punkaharju Research Unit Phenological observations have been conducted on four of the intensive monitoring plots since 2000. Ten trees per plot were observed and growth onset was recorded on a 5-point scale. Data on environmental variables were obtained from weather stations installed on the plots. The onset of growth was correlated to several environmental parameters (temperature sum, photoperiod, accumulated photoperiod, soil temperature and night frost). Differences between the sites and years were found for all the parameters, indicating the variability of weather conditions on a spatial and temporal scale. The results clearly showed that the trees on the northern plots flushed later than those on the southern plots, but did so at a lower temperature sum. Furthermore, the results also showed that Norway spruce (Picea abies) flushed earlier and at a lower temperature sum than Scots pine (Pinus sylvestris). No correlation was found between growth onset and any of the tested environmental parameters, indicating that a 5-year series covers too short a time to predict shifts in growth onset and that the measurements should be continued. Suomessa fenologista havainnointia on tehty neljällä intensiiviseurannan havaintoalalla vuodesta 2000 lähtien. Kullakin havaintoalalla on seurattu kasvuunlähtöä, ts. silmujen puhkeamista kymme- nellä puulla käyttäen asteikkoa 0–5. Säähavainnot on saatu havaintoaloille asennetuilta sääasemilta. Kasvuunlähdön yhteisvaihtelua tutkittiin useiden säätekijöiden kanssa (lämpösumma, valoperiodi, kertynyt valoperiodi, maan lämpötila ja yöpakkaset). Kaikissa säämuuttujissa ilmeni koealojen ja vuosien välistä vaihtelua. Pohjoisilla havaintoaloilla silmut puhkeavat selvästi myöhemmin kuin etelässä, mutta silmujen puhjetessa lämpösummakertymä on pohjoisessa alhaisempi kuin etelässä. Kasvuunlähtö tapahtuu aikaisemmin kuusella kuin männyllä. Kasvuunlähdön ja säätekijöiden välil- lä ei havaittu merkitsevää yhteisvaihtelua, mikä osoittaa, että viiden vuoden aikasarja on liian lyhyt kasvuunlähdon ennustamiseksi. Havainnointia tulisi siis jatkaa. Introduction Phenology has been included in the intensive monitoring programme as an optional activity since 2000. Knowledge about the timing and duration of certain life cycle events provides valuable information about the condition of the trees and the effects of climate fluctuations and changes on trees (Beuker 2002). Two levels of phenological observations are included in the programme, monitoring at the plot level and at the individual tree level. This chapter summarizes the results obtained at the individual tree level and focuses on 1) trends in growth onset over the years, 2) correlations between growth onset and environmental parameters, and 3) the identification of differences between species with respect to growth onset. 100 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Material and methods Phenological observations from 3 Norway spruce (Picea abies) plots (Pallasjärvi, Punkaharju and Solböle) and on one Scots pine (Pinus sylvestris; Punkaharju) plot were used. The plot at Pallasjärvi was classified as a northern site, while the plots at Punkaharju and Solböle were classified as southern sites. Data availability for each plot can be found in Table 1. Ten trees per plot were selected following a stratified random sampling method. Only trees where the upper two-thirds of the living crown were visible from the ground were selected. During the period of shoot development the trees were observed three times a week (Monday, Wednesday and Friday). The same trees were observed in all the years. Binoculars were used to assess the development of growth onset. The development was assessed on a 5-point scale ranging from 0 (none of the buds are open) to 4 (100% of the buds are open). Growth onset was assumed to have started when development had reached stage 1. For spruce, stage 1 is reached when the new growth is visible outside the bud. Pine has reached stage 1 when the orientation of the needles changes from along the axis of the shoot to perpendicular to the axis of the shoot. To prevent bias due to different observers, the same observers conducted the observations on each plot as much as possible. The meteorological data were collected from weather stations installed on the plots. Minimum, mean and maximum air temperature were recorded above and within the crown (heights ranging from 6 to 20 m for within crown measurements, and from 16.5 to 29 m for above crown measurements) on an hourly basis and corrected for missing values using data from the Finnish Meteorological Institute. Temperature data on a daily basis, corrected for missing values, were available for all years, except 2005. For 2005 uncorrected data were available up until the 14th of November. Missing values had a negligible influence on the temperature sum before the date of bud burst and therefore the data could be used. Temperature data corrected for missing values (on a daily basis) from the above crown measurements were used to calculate the temperature sum for different thresholds. Temperature data on an hourly basis were used to estimate night frost parameters (e.g. difference in days between the last night frost and the start of growth onset, difference in days between first frost free period (>0 days, >3 days and >14 days) and the start of growth onset and the total number of frost free days (counted from the first of January) before the start of growth onset). See Table 2 for an overview. Soil temperature was measured within the plot at various depths (ranging from 10 to 100 cm below ground level) on an hourly basis. Most of the root mass of spruce and pine occurs at depths of between 0 and 30 cm (Makkonen and Helmisaari 1999, Cronan 2003). Therefore, the data for a depth of 20 cm were used in the analysis. Three replicates were available at this depth on each Table 1. Years with growth onset observations on Level II observation plots. Taulukko 1. Kasvuunlähdön havainnointivuodet tason II havaintoaloilla. Tree species Plot 2000 2001 2002 2003 2004 2005 Puulaji Havaintoala Norway spruce – Kuusi Pallasjärvi X X X X X Punkaharju X X X X X Solböle X X X X X Scots pine – Mänty Punkaharju X X X X 101 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm S pe ci es La ji S am pl e pl ot H av ai nt oa la Ye ar Vu os i Te m pe ra tu re su m Lä m pö -­­ su m m a dd (> 5° C ) S oi l te m pe ra tu re su m M aa n lä m pö su m m a dd P ho to pe rio d at b ud b ur st ��h�� h Va lo ja ks o si lm uj en pu hj et es sa , t A cc um ul at ed ph ot op er io d at b ud b ur st �� h Va lo su m m a si lm uj en pu hj et es sa , t Fr os t f re e da ys p er y ea r P ak ka se tto -­­ m ie n pä iv ie n lk m v uo de ss a D ay s fro m la te st fr os t f re e pe rio d (le ng th in p ar en th es es ) at b ud b ur st P äi vi ä ed el lis es tä p ak ka se tto -­­ m as ta ja ks os ta (p itu us s ul ui ss a) si lm uj en p uh je te ss a D ay s fro m la st fr os t at b ud b ur st P äi vi ä vi im ei se st ä pa kk as es ta si lm uj en pu hj et es sa (> 0d ) (> 3d ) (> 14 d) N or w ay P al la sj är vi 20 00 79 7 15 0 24 21 25 27 85 56 42 17 sp ru ce 20 01 80 0 15 4 24 20 96 37 14 9 14 9 51 18 K uu si 20 02 88 7 13 8 24 18 74 45 14 5 70 45 10 20 03 84 0 15 4 20 04 75 3 15 8 24 21 98 54 11 7 62 51 29 20 05 62 0 15 8 24 21 44 46 82 51 34 29 P un ka ha rju 20 00 14 45 11 2 20 01 15 35 11 3 18 16 96 59 13 6 55 39 39 20 02 16 01 11 3 18 16 01 67 13 1 41 31 41 20 03 14 59 19 17 46 56 13 0 62 48 29 20 04 13 70 12 1 19 17 61 62 97 61 54 33 20 05 14 44 12 9 19 18 47 65 14 5 63 39 32 S ol bö le 20 00 15 13 96 18 16 91 86 14 3 65 50 50 20 01 16 19 18 16 83 77 14 0 75 55 38 20 02 16 83 17 15 22 80 12 3 44 37 37 20 03 14 96 10 2 18 18 40 75 13 7 70 53 36 20 04 96 18 15 34 56 12 3 61 40 32 S co ts P un ka ha rju 20 02 16 01 11 2 19 18 12 76 14 0 50 40 50 pi ne 20 03 14 59 19 19 48 66 14 0 72 58 39 M än ty 20 04 13 70 12 1 18 16 38 57 92 56 49 28 20 05 14 44 12 9 19 19 73 70 15 0 68 44 37 Ta bl e 2. O ve rv ie w o f t he v ar ia bl es u se d in th e an al ys is fo r g ro w th o ns et o f N or w ay s pr uc e an d S co ts p in e. Ta ul uk ko 2 . K uu se n ja m än ny n ka sv uu nl äh dö n an al ys oi nn is sa k äy te ty t t au st am uu ttu ja t. Va lo su m m a = ke rty ny t v al oi st en tu nt ie n su m m a. 102 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm site. The difference in days between the date of an exponential increase in soil temperature and the date of growth onset was evaluated. Due to technical problems not all the data were available for the analysis (Table 2). Accumulated photoperiod was calculated from light measurements made above the crown level using a LI-200SZ photometer. The height varied from 16.5 to 29 m depending on the plot. The data were available on an hourly basis. Calculations were carried out in the same way as for the temperature sum calculations, but without a threshold value. The length of the photoperiod (day length) was calculated as the time difference in hours between sunrise and sunset (Table 2). Sunrise and sunset data for the individual plots were obtained using a sunset calculator (Mundall 2002). All the parameters were evaluated on a temperature sum as well as a Julian day (the running number of the day from January 1st) basis. Statistical analysis The likelihood of a normal distribution was evaluated using a Shapiro-Wilcoxon test or a Kolmogorov-Smirnov test, depending on the number of available cases. Most of the data did not follow a normal distribution even after transformation. In these cases, differences between groups were evaluated using a Kruskal-Wallis test, followed by a Duncan post-hoc test. When there were only two groups a Mann-Whitney-U test was used. A one-way ANOVA followed by a Tukey post-hoc test was used to evaluate differences between groups in cases where the data followed a normal distribution and the requirements for homogeneity of variance were met. A non-parametric (two tailed) Spearman r test was used to evaluate trends. Due to the non- normal distribution of the data and the small number of observations, it was not possible to build regression models allowing evaluation of the relative importance of all the factors together. For pair-wise comparisons between the deviation from the average for Julian day and temperature sum, the independent samples t-test was used for years (normal distribution). A Mann-Whitney U test (non-parametric) was used with respect to the sites. Results An attempt was made to predict the date of bud burst on the basis of the average date of bud burst over all five years following Rousi and Pusenius (2005). No agreement between the plots was obtained for any of the calculated thresholds (–2 to +5 °C with a 1 °C increase). Likewise, predicting the date of bud burst on the basis of the average temperature sum at bud burst over all five years did not result in any agreement between the plots. Since no agreement between the plots was obtained, the widely accepted threshold for bud burst of +5 °C (Sarvas 1972) was used for further analysis. Spruce When all the plots were pooled, it became clear that there were significant differences between years on both a Julian day (p < 0.001) and temperature sum (p < 0.001) scale (Fig. 1). The difference between the earliest and the latest year was 9.8 days and 67.3 temperature units. Annual variation in the temperature sum and Julian day at bud burst clearly followed a different pattern, but a significant trend over the years was not found for either parameter. 103 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Further analysis showed a significant influence of plot (p < 0.001) and plot * year (p < 0.001) in both the temperature sum and Julian day. The temperature sum at bud burst over the years for each plot is shown in Fig. 2. Due to missing values, year 2000 at Solböle had to be omitted from the analysis. Figure 2 shows that, with respect to temperature sum, the patterns over the years were different for each plot. The differences between the years were significant at p < 0.001. A significant, but weak, negative trend over the years was found for the plot at Punkaharju (r = –0.292, p = 0.044), indicating that bud burst started at a lower temperature sum each year. For the plots at Pallasjärvi and Solböle there was no significant trend. Significant differences between trees with respect to the temperature sum at bud burst were only found for the trees in Punkaharju (p = 0.007). Three groups could be separated, but the differences between the groups were small. For Pallasjärvi and Solböle there were no differences between trees. Analysis of bud burst on a Julian day basis over the years for each plot showed that the patterns were different for each plot (Fig. 3). The differences between the years were all significant at p < 0.001. With respect to Julian day there was a significant, but weak, positive trend over the years for the plots at Pallasjärvi and Punkaharju (r = 0.313, p = 0.036 and r = 0.571, p < 0.001, respectively), indicating that bud burst started at a later date each year. No trend was found for Solböle. Based on Julian day at bud burst, there were no differences between trees at any of the plots. Although the differences between trees were not significant, ranking of individual trees over the years showed that the same trees were among the first or last to start flushing each year. Figures 2 and 3 show that temperature sum and Julian day at bud burst followed a different pattern over the years when analysed at the plot level. Furthermore, the variation in temperature sum at bud burst between the plots seemed to be larger than the variation in Julian day at bud burst over the plots (Table 3). Figure 1. Average temperature sum (a) and Julian day (b) at bud burst over all the plots. Bars show means. Error bars show SEM. Letters indicate significantly different groups. Kuva 1. Keskimääräinen lämposumma (a) ja juliaaninen päivä (b), jolloin silmut puhkeavat (havaintoalojen keskiarvo ± keskarvon keskivirhe). Tilastollisesti merkisevästi poikkeavat ryhmät on osoitettu eri kirjaimin. 104 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm The standard error of the mean (SEM) seemed to be larger on a temperature sum basis compared to that on a Julian day basis for both plots and years (Table 3). SEM was significantly smaller for Julian day compared to the temperature sum (p < 0.001). There was significant correlation between environmental parameters and the pooled plots, indicating differences between the northern and southern plots. Correlations within plots or years are necessary in order to find correlations between environmental parameters and the start of growth onset. The correlations within the plots for temperature sum were variable. For Pallasjärvi a negative relationship was found between growth onset and temperature sum (r = –0.398, p = 0.007), while for Solböle there was a positive relationship (r = 0.706, p < 0.001). No relationship was found for Punkaharju. Due to the interdependent nature of Julian day and day length, it was not possible to determine correlations for the environmental variables, photoperiod and accumulated photoperiod. The number of observations of night frost were too few to perform correlation analysis. The results of correlation analysis on soil temperature were variable. On a Julian day scale, there was a significant positive relationship for the plots at Pallasjärvi and Punkaharju (r = 0.861 and r = 0.946, respectively with p < 0.001 for both). This indicates that growth onset Figure 2. Temperature sum at bud burst over the years for Norway spruce in Pallasjärvi (a)�� Punkaharju (b) and Solböle (c). Bars show means. Error bars show SEM. Small letters represent significantly different groups. Kuva 2. Lämpösumman keskiarvo (± keskiarvon keskivirhe) silmujen puhjetessa Pallasjärven (a), Punka-­­ harjun (b) ja Solbölen (c) kuusihavaintoaloilla. Tilastollisesti merkitsevästi toisistaan eroavat vuodet on merkitty eri kirjaimin. 105 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm starts later when the exponential increase in soil temperature starts later. No significant relationship was found for the plot at Solböle. On a temperature sum scale, none of the correlations with soil temperature over the plots were significant. Comparison between northern and southern plots It was evident that, on the average, on the northern plot flushing occurred 19 days later than on the southern plots (p < 0.001; Table 4), but this occurred at a 69 units lower temperature sum (p < 0.001). Furthermore, there was a significant year * orientation interaction for Julian day (p < 0.001). This interaction was not present for temperature sum (p = 0.100). The years 2000 and 2003 were excluded from these analyses because of incomplete data sets. Scots pine For pine, bud burst on the basis of Julian day and the temperature sum differed significantly over the years (p < 0.001; Fig. 4). The magnitude of the variation over the years was 133 temperature units and 15 days. The year 2004 clearly stood out as a particularly early year. Figure 3. Julian day at bud burst over years for Norway spruce in Pallasjärvi (a)�� Punkaharju (b) and Solböle (c). Bars show means. Error bars show SEM. Small letters represent significantly different groups. Kuva 3. Juliaaninen päivä (keskiarvo ± keskiarvon keskivirhe) silmujen puhjetessa Pallasjärven (a), Punka-­­ harjun (b) ja Solbölen (c) kuusihavaintoaloilla. Tilastollisesti merkitsevästi eroavat vuodet on merkitty eri kirjaimin. 106 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm A significant, but weak, positive trend over the years was found for Julian day at bud burst (r = 0.253; p = 0.001). For the temperature sum at bud burst there was a significant, but weak, negative trend over the years (r = –0.514; p < 0.001). No differences between trees were found with respect to temperature sum at bud burst or Julian day at bud burst. Although the differences between trees were not significant, ranking of the individual trees over the years showed that the same trees were always among the first or last to flush. The number of observations was too small to determine correlation for night frost within the plots. Neither was it possible to determine correlations for the environmental variables, photoperiod and accumulated photoperiod, due to their interdependent nature. Too few observations were available for night frost to justify carrying out correlation analysis. A significant positive correlation was found between soil temperature and Julian day (r = 0.493; p = 0.006). This indicates that growth Table 4. Julian day at bud burst in the northern and southern plots and their difference. Taulukko 4. Silmujen puhkeamisen juliaaninen päivä pohjoisilla ja eteläisillä havaintoalolla keskimäärin sekä niiden erotus. Year North South Difference Vuosi Pohjoinen Eteläinen Erotus 2001 162 144 18 2002 156 137 19 2004 168 143 25 2005 165 152 13 Table 3. The means and their standard errors for Julian day and temperature sum at bud burst for Norway spruce in Pallasjärvi�� Punkaharju and Solböle in 2000–2005. Taulukko 3. Juliaanisen päivän ja lämpösumman keskiarvo keskivirheineen silmujen puhjetessa Pallasjär-­­ ven, Punkaharjun ja Solbölen kuusialoilla jaksolla 2000–2005. Site Year Temperature sum Julian day Havaintoala Vuosi Lämpösumma Juliaaninen päivä Average SEM Average SEM Keskiarvo Keskiarvo Pallasjärvi 2000 97 3.03 165 1.12 2001 74 0.85 162 0.16 2002 123 0.62 155 0.33 2004 77 168 0.00 2005 87 165 0.00 Punkaharju 2001 166 2.00 145 1.39 2002 189 7.60 140 1.85 2003 189 3.80 148 0.47 2004 156 5.83 149 1.26 2005 158 9.70 152 1.51 Solböle 2000 185 145 2001 140 1.62 143 0.58 2002 140 2.72 134 0.35 2003 151 0.00 153 0.00 2004 140 1.63 136 0.65 107 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm onset starts later when the exponential increase in soil temperature starts later. No significant correlation was found on a temperature sum basis, but a significant positive relationship with growth onset was found for temperature sum (r = 0.672; p < 0.001). Species comparison In Punkaharju spruce flushed, on the average, 6 days earlier than pine at a 36 units lower temperature sum (p < 0.001 in both cases) (Fig. 5), but the difference varied significantly over the years (p < 0.001). When individual years were analysed, spruce always flushed earlier than pine on both a Julian day and a temperature sum basis, except for the year 2004 when pine flushed 4.1 days earlier and at a 21.9 units lower temperature sum than spruce (Table 5). Figure 4. Temperature sum (a) and Julian day (b) at bud burst over years for Scots pine in Punkaharju. Bars show means. Error bars show SEM. Letters represent significantly different groups. Kuva 4. Lämpösumma (a) ja juliaaninen päivä (b) silmujen puhjetessa Punkaharjun männikössä (keskiarvo ± keskiarvon keskivirhe). Tilastollisesti merkitsevästi eroavat vuodet on merkitty eri kirjaimin. Figure 5. Growth onset of the two species in Punkaharju compared on a temperature sum (a) and a Julian day (b) basis. Bars show mean. Error bars show SEM. Kuva 5. Kasvuunlähdön lämpösumma (a) ja juliaaninen päivä (b) männyllä ja kuusella Punkaharjulla (keski-­­ arvo ± keskiarvon keskivirhe). 108 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Discussion Differences between the plots Weather is variable in both a spatial and a temporal sense, and causes large variation in the growth onset of trees. These variations in the prevailing weather conditions underlie the differences found between the northern and southern plots. The result that, under natural conditions, trees growing on the more northern plots flushed later than trees growing on more southern plots, and do so at lower temperature sums, is in agreement with earlier reports (e.g. Beuker 1994), and shows that the trees growing on the different plots are genetically adapted to the prevailing climatic conditions (Sarvas 1967). Trends in growth onset Following the generally accepted hypothesis that temperature is the forcing factor in breaking bud quiescence, it has been predicted that global warming will cause growth onset to start earlier in the year (Cannell and Smith 1986, Murray et al. 1989). In their meta-analysis, Root et al. (2003) found that the spring phenology of trees currently begins three days (sem ± 0.1) earlier as a result of global warming. Chmielewski and Rötzer (2001) found that the beginning of the growing season advanced 8 days over the period 1969–1998, due to an almost European-wide warming. There are, however, large fluctuations between years (Häkkinen 1999, Rousi and Pusenius 2005). In contrast, Murray et al. (1989) concluded in their modeling study that the date of growth onset would not show a marked shift towards earlier dates. For some scenarios Cannell and Smith (1986) found similar results. Both Murray et al. (1989) and Cannell and Smith (1986) state that an increase in temperature also leads to an increased autumn temperature that, through chilling requirements, could delay the onset of growth. Among others, Partanen et al. (1998) found evidence that the length of the photoperiod has an influence on growth onset in Norway spruce, and that it is possible that premature growth onset may be prevented through the influence of the photoperiod. Because of the unpredictability of the weather conditions, the likelihood of a light signal being involved seems plausible. It has to be stressed however, that global warming does not only force its effects through temperature, but also through interaction with other factors (Root et al. 2003), for example, precipitation (Caldwell et al. 2003). The significant trends found so far in our study were weak and showed contradicting patterns, indicating that the few significant correlations are most likely due to chance. These results are Table 5. Julian day and temperature sum at bud burst for Scots pine and Norway spruce in Punkaharju. Taulukko 5. Juliaaninen päivä ja lämpösumma silmujen puhjetessa kuusella ja männyllä Punkaharjulla. Year Julian day Temperature sum Vuosi Juliaaninen päivä Lämpösumma Scots pine Norway spruce Scots pine Norway spruce Mänty Kuusi Mänty Kuusi 2002 150 140 246 189 2003 259 148 255 189 2004 145 149 134 156 2005 158 152 199 158 109 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm similar to the results of Chmielewski and Rötzer (2001), who found weak and contradicting trends for Betula pubescens, Prunus avium, Sorbus aucuparia and Ribes alpinum over a 30-year study period in Scandinavia and Eastern Europe. However, care must be taken when interpreting the results of the current study because the time series used are short (max. 5 years) and variation between years is high. Rousi and Pusenius (2005) concluded that their six-year study period was not long enough to reveal any significant advance in the date of growth onset for European white birch (Betula pendula). No clear trends were found in the data, which seems to indicate that global warming does not cause a shift in the timing of growth onset, and that temperature alone is not enough to explain the variance. The fact that the SEM on a Julian day basis seemed to be smaller than the SEM on a temperature sum basis may support the hypothesis that temperature is not the only forcing variable for growth onset. If temperature sum is used as the basis for the analysis then only the influence of temperature is analysed, but Julian day includes the whole complex of relevant factors, which apparently decreases variation. This indicates the influence of other factors on growth onset. It must be stressed, however, that the variation in temperature sum accumulation between years is considerable and that temperature sum and Julian day are interacting variables. Nevertheless, based on these results it seems credible that growth onset consists of a complex of several regulatory factors that are still poorly understood. Patterns between species This study indicates that growth onset occurs earlier (on both a Julian day and a temperature sum basis) in spruce than in pine. On the average, growth onset started 6 days (and 36 temperature units) earlier in spruce. This result is in line with the generally observed pattern. Rötzer et al. (2004) found that the average date of height growth onset was May 12th (S.D. ± 7.8) in Norway spruce compared to May 15th (S.D. ± 8.9) in Scots pine in southern Germany. Kramer (1996) found that the average date of height growth onset was May 10th (SD ± 8.1) for Norway spruce, compared to May 13th (S.D. ± 7.0) for Scots pine during the period 1951–1990 in Germany and the Netherlands. In 2004, however, pine started growth onset 4 days (and 22 temperature units) earlier than spruce. This indicates that both species react differently to the environmental signals relevant for growth onset. Temperature sum accumulation in 2004 was characterized by exponential accumulation during a short period, followed by low temperatures (the last night frost occurred on the 24th of April), which are likely to have influenced bud development. In addition, the year 2003 was characterized by relatively high temperatures in autumn and early winter. Warm autumns and winters can delay growth onset because a larger temperature sum accumulation is needed to compensate for the unfulfilled chilling requirements (Cannell and Smith 1986, Murray et al. 1989). Hänninen and Pelkonen (1989) found that warm periods during chilling decreased the dormancy release ratio if they occurred in a later part of the chilling period (4 to 7 weeks). In an early part of the chilling period (1 to 3 weeks) the response of the dormancy release ratio was variable. Chilling requirements vary between species (Smith and Kefford 1964), and therefore it is possible that both species were affected in a different way by the warmer conditions in 2003. These factors together seem to indicate that, in pine, light is a more important forcing factor for growth onset than temperature accumulation. Patterns between trees Variation between genotypes has been observed for many species and traits. Beuker (1994) found differences with respect to growth onset for Scots pine. Differences in growth onset between 110 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm genotypes have also been reported for Norway spruce (e.g. Beuker 1994). In this study, significant but relatively small differences between trees were only observed for spruce on the plot in Punkaharju. The differences for the other plots were not significant, but ranking of the trees for each year revealed that individual trees showed similar patterns over the years, indicating that there are differences between the trees. It is likely that variable weather conditions and the small number of observations obscured the differences in growth onset between the trees. It must be kept in mind, however, that the trees used in this study are over 60 years old and therefore may show less variation compared to the younger trees (Hannerz 1999, Partanen et al. 2004) which are usually used in short-term experiments under controlled conditions (Hänninen et al. 2001). In addition, the stands on the plots in this study have been subjected to normal forestry practices such as thinning. Depending on the practice applied when thinning a stand, the trees with the best potential (e.g. the trees best adapted to local conditions) are usually released from competition. It is possible that this has resulted in decreased variation between the trees (Beuker 1994). Correlation with environmental variables Although the mechanisms underlying growth onset and cessation have been intensively studied they, as well as their interactions, are still not fully understood. In this study, the polarity of the correlation between growth onset and temperature sum were different, indicating that these correlations are most likely due to chance, even though model studies have shown that trees with different temperature sum requirements are affected by global warming in a different way (Murray et al. 1989). Correlations could not be determined for the environmental parameters, photoperiod and accumulated photoperiod, due to the strong relationship between Julian day and the respective parameters. Under controlled conditions, the influence of time on these parameters can be omitted by standardizing the length of the photoperiod. Under natural conditions, however, this is not possible. Correlations could not be determined for night frost parameters due to the small number of observations. The positive relationship between the exponential increase in soil temperature and the growth onset for spruce and pine is most likely the result of increasing root activity with increasing soil temperature. In the temperate zone, the optimum temperature range for root growth is approximately 10 to 30 oC (but growth may continue to around 0 oC; Lambers et al. 1998), while the optimum for shoot growth is higher (Deans and Ford 1986). This allows the roots to become active before the shoots. The short time series does not yet allow any conclusions to be drawn. References Beuker, E. 1994. Adaptation to climatic changes of the timing of bud burst in populations of Pinus sylvestris L. and Picea abies (L.) Karst. Tree Physiology 14: 961–970. Beuker, E. 2002. Phenological observations on Level II plots in 2001. In: Rautjärvi, H., Ukonmaanaho, L. & Raitio, H. (eds.). Forest Condition Monitoring in Finland. National report 2001. The Finnish Forest Research Institute, Research Papers 897: 89–94. Caldwell, M.M., Ballare, C.L., Bornman, J.F., Flint, S.D., Björn, L.O., Teramura, A.H., Kulandaivelu, G. & Tevini, M. 2003. Terrestrial ecosystems, increased solar ultraviolet radiation and interactions with other climatic change factors. Photochemical & Photobiological Sciences 2: 29–38. Cannell, M.G.R. & Smith, R.I. 1986. Climatic warming, spring bud burst and frost damage on trees. Journal of Applied Ecology 23: 177–193. Chmielewski, F.-M. & Rötzer, T. 2001. Response of tree phenology to climate change across Europe. Agricultural and Forest Meteorology 108: 101–112. 111 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm Cronan, C.S. 2003. Below ground biomass, production and carbon cycling in mature Norway spruce Maine, USA. Canadian Journal of Forest Research 33: 339–350. Deans, J.D. & Ford, E.D. 1986. Seasonal patterns of radial root growth and starch dynamics in plantation- grown Sitka spruce trees of different ages. Tree Physiology 1: 241–251. Häkkinen, R. 1999. Analysis of bud development theories based on long-term phenological and air temperature time series: application to Betula sp. leaves. Thesis University of Helsinki, Helsinki. 59 p. Hannerz, M. 1999. Evaluation of temperature models for predicting bud burst in Norway spruce. Canadian Journal of Forest Research 29: 9–19. Hänninen, H. & Pelkonen, P. 1989. Dormancy release in Pinus sylvestris L. and Picea abies (L.) Karst. seedlings: effects of intermittent warm periods during chilling. Trees 3: 179–184. Hänninen, H., Beuker, E., Johnson, Ø., Leinonen, I., Murray, M. & Skrøppa, T. 2001. Impacts of climate change on cold hardiness of conifers. In: Bigras, F.J. & Colombo, S.J. (eds.). Conifer cold hardiness. p. 305–333. Kramer, K. 1996. Phenology and growth of European trees in relation to climate change. Thesis. Landbouw Universiteit Wageningen. 210 p. Lambers, H., Chapin, F.S. & Pons, T.L. 1998. Plant Physiological Ecology. Springer-Verlag, New York. Makkonen, K. & Helmisaari, H.-S. 1999. Assessing fine-root biomass and production in a Scots pine stand – comparison of soil core and root ingrowth core methods. Plant and Soil 210: 43–50. Mundall, E. 2002. Sunste calculator. [Online publication] Available at: http://www.mundall.com/cgi/ sunsets.pl?countries 11–12–2005. Murray, M.B., Cannell, M.G.R. & Smith, R.I. 1989. Date of bud burst of fiftheen species in Britain following climatic warming. Journal of Applied Ecology 26: 693–700. Partanen, J., Koski, V. & Hänninen, H. 1998. Effects of photoperiod and temperature on the timing of bud burst in Norway spruce (Picea abies). Tree Physiology 18: 811–816. Partanen, J., Hänninen, H. & Häkkinen, R. 2004. Bud burst in Norway spruce (Picea abies): Preliminary evidence for age-specific rest patterns. Trees 19: 66–72. Root, T.L., Price, J.T., Hall, K.R., Schneiders, S.H., Rosenzweig, C. & Pounds, J. A. 2003. Fingerprints of global warming on wild animals and plants. Nature 421: 57–60. Rötzer, T., Grote, R. & Pretzsch, H. 2004. The timing of bud burst and its effect on tree growth. International Journal of Biometeorology 48: 109–118. Rousi, M. & Pusenius, J. 2005. Variation in phenology and growth of European white birch (Betula pendula) clones. Tree Physiology 25: 201–210. Sarvas, R. 1967. The annual period of development of forest trees. Proceedings of the Finnish Academy of Science and Letters 1965: 211–231. Sarvas, R. 1972. Investigations on the annual cycle of development of forest trees. Active period. Communicationes Instituti Forestalis Fenniae 76(3): 1–110. Smith, H. & Kefford, N.P. 1964. The chemical regulation of the dormancy phases of bud development. American Journal of Botany 51: 1002–1012. 112 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 3.8 Assessment of air quality on Level II plots Ilman laadun seuranta tason II havaintoaloilla Kirsti Derome Finnish Forest Research Institute, Rovaniemi Research Unit The air quality at a number of Level II plots was monitored using passive samplers during 2000–2001 and in 2004. The air quality parameters were ozone (O3), sulphur dioxide (SO2), nitrous oxide (NO2) and ammonia (NH3). During the first phase (2000–2001) air quality was monitored in open areas close to the plots at Pallasjärvi (Nr. 3), Uusikaarlepyy (Nr. 23) and Miehikkälä (Nr. 18), and in 2004 at Pallasjärvi, Juupajoki (Nr. 10) and Miehikkälä. Comparative measurements were performed close to the continuous air-quality monitoring stations of the Finnish Meteorological Institute in Virolahti (near Miehikkälä) and Matorova (near Pallasjärvi), and of the University of Helsinki in Hyytiälä (near Juupajoki). The first monitoring phase in 2000–2001 clearly showed that the SO2 and NO2 concentrations are higher in wintertime, and that the O3 concentration reached a maximum in early spring. The seasonal patterns for all three gases were similar all over the country, but the NO2 concentrations were very low at the northernmost monitoring site. In 2004 there was no clear difference between the monitoring sites for O3, but the SO2 and NO2 concentrations were higher at the sites in south-eastern Finland near to the Russian border, and lower at the remote sites in Lapland. The NH3 concentrations appeared to be relatively contradictory during both monitoring periods. Tässä raportissa kuvataan ilman laatua muutamilla intensiivikoealoilla esittämällä avoimille pai- koille sijoitetuilla passiivikeräimillä saatuja tuloksia vuosilta 2000–2001 ja 2004. Mitatut parametrit ovat otsoni (O3), rikkidioksidi (SO2), typpidioksidi (NO2) ja ammoniakki (NH3). Ensimmäisessä jaksossa 2000–2001 koealat olivat Pallasjärvi (no. 3), Uusikaarlepyy (no. 23) ja Miehikkälä (no. 18) ja vuonna 2004 Pallasjärvi, Juupajoki (no. 10) ja Miehikkälä. Vertailumittaukset tehtiin Ilmatieteen laitoksen mittausasemilla Matorovassa (lähellä Pallasjärveä) ja Virolahdella (lähellä Miehikkälää) sekä Helsingin yliopiston metsäasemalla Hyytiälässä (lähellä Juupajokea). Ensimmäisen mittaus- jakson (2000–2001) tuloksissa SO2- ja NO2-pitoisuudet olivat korkeimmat talviaikana, kun taas O3- pitoisuudet olivat korkeimmillaan aikaisin keväällä. Vuodenaikaisvaihtelu oli samansuuntaista kai- killa mittauspaikoilla, mutta NO2-pitoisuudet olivat hyvin matalia pohjoisimmalla mittauspaikalla. Vuoden 2004 O3-pitoisuuksissa ei havaittu selviä eroja eri mittauspaikkojen välillä, sen sijaan SO2- ja NO2-pitoisuudet olivat korkeimpia Kaakkois-Suomessa, lähellä Venäjän rajaa olevilla mittaus- paikoilla, ja matalimpia Lapissa sijaitsevilla mittauspaikoilla. Molemmilla mittausjaksoilla saadut NH3-pitoisuudet ovat jonkin verran ristiriitaisia. Introduction The lack of ozone data has been a serious limitation for the ICP Forests Intensive Monitoring (Level II) database. In addition to the obvious connections with the potential effects of ozone on forests, ozone data are also relevant in relation to other topics related to important international agreements, such as changes in tropospheric chemistry and regional ozone formation. These agreements include the Multi-Pollutant, Multi-effect Protocol of the Convention on Long-range Transboundary Air Pollution (CLRTAP), the UN Convention on Biological Diversity (CBD) the EU Habitat Directive, the EU Acidification Strategy, and the EU Air Quality directive. Since 1999, the Working group on Air Quality within the Expert Panel on Deposition has been working to improve our knowledge of the concentrations of various pollutants in the air, and the Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 113 effects of these pollutants across forested areas in Europe. The intention was to carry out air quality measurements using passive sampling in connection with visible injury assessment. The main gaseous pollutant being monitored is ozone, although other pollutants such as sulphur dioxide (SO2), nitrogen oxide (NO2) and ammonia (NH3) are also included in order to complement the deposition surveys being carried out by ICP-Forests on many of the Level II plots. The results of a test phase with different types of passive samplers, carried out in 2000 and 2001 in some European countries, were presented in the Level II Technical report 2003 (De Vries et al. 2003). The use of passive samplers is considered to be a reliable and comparatively inexpensive method of obtaining information on ambient air quality, especially in remote forest areas where it is not technically or economically feasible to operate continuous monitoring stations. As a result, this method was chosen for assessing ambient air quality at Level II sites. However, it is also necessary to determine the comparability of these data with data from continuous monitoring sites. This information is particularly important for quality assurance and quality control purposes, as well as for extending the database used for modelling ozone concentrations over Europe (e.g. EMEP). Passive sampling monitoring within the Working Group is being carried out in accordance with a sub-manual based on document nr. 264 of the European Committee of Standardization (CEN 2001). As a considerable amount of the ozone data collected at the European level are derived from monitoring devices situated in urban/sub-urban areas (e.g. De Leeuw et al. 2001), often at low altitudes, the collection of a comprehensive ozone dataset for forested sites will greatly increase our knowledge of ozone levels in remote areas, including a broad range of altitudes. Material and methods Ambient air quality was monitored as a part of the ICP Forests programme using passive samplers during 2000–2001, and again in 2004. During the first phase the monitoring was carried out at three Level II plots in different parts of Finland and, for comparison purposes, in the immediate vicinity of two air-quality monitoring stations of the Finnish Meteorological Institute (FMI) (Table 1). In 2004, passive sampler monitoring at one of the plots was changed to another Level II plot, and a comparison set of passive samplers installed near to the air-quality monitoring station of Helsinki University (HU). The measured parameters were ozone (O3), sulphur dioxide (SO2), nitrogen oxide (NO2) and ammonia (NH3). The passive samplers were supplied by IVL (Gothenburg, Sweden; Ferm 2001). The temperature data required in calculating the results were obtained from the weather station on the Level II plot or from a nearby FMI meteorological station. The samplers were located at a height of 3 m in the open area used for monitoring bulk deposition on the three Level II plots and at a suitable open location in the immediate vicinity of the air-quality monitoring stations. Monitoring started in July 2000 and continued until June 2001. The next stage was carried out from mid-April 2004 to the beginning of November 2004 (seven 4-week periods). During 2000–2001 there were a couple of 2- and 3-week periods at one Level II plot, and in 2004 two 2-week periods at one Level II plot and the nearby FMI station. All the measurements were performed in duplicates. The samplers were sent by IVL to Metla’s laboratory at Rovaniemi, and then to the field personnel 114 Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm prior to the start of each monitoring period. At the end of the four-week sampling period the samplers were sent first to Rovaniemi, and then to IVL in Gothenburg for analysis. IVL’s accredited laboratory performed the analyses and calculated the results as micrograms/m3 STP (standard air temperature and pressure, 20°C, 1013 hPa), with corrections for air temperature and altitude. The passive samplers manufactured by IVL are widely used, and the accuracy of the ozone measurements is within 5% over the range 30–90 µg m-3. The concentrations of gases can be expressed using a number of different units, but µg m-3 or ppb, are the most widely used units. The relationship between these two units is: ppb = µg m-3 * 2.14. Results and discussion The first monitoring stage in 2000–2001 clearly showed that the concentrations of SO2 and NO2 were higher in wintertime (Figs. 1 b–c), and that the concentration of O3 reached a maximum in early spring (Fig. 1a). This is in agreement with FMI measurements in Virolahti and Sammaltunturi (Leinonen 2001). In 2004 monitoring started in week 17, and therefore only the latter part of the typical episode of elevated O3 concentrations in early spring was observed (Fig. 2a). There were no clear differences between the monitoring sites for O3 during this period, but the concentrations of SO2 and NO2 were higher at the sites in south-eastern Finland near the Russian border, and lower at the remote sites in Lapland (Figs. 2b–c). The 12-month means, minimum and maximum values, for O3, SO2, NO2 and NH3 during the period July 2000–June 2001, as well as the detection limits for the individual gas samplers are presented in Table 2. The monitoring of NH3 proved to be relatively problematic: many of the values were below the detection limit or uncertain for technical reasons, and the variations between duplicate measurements were relatively large. The NH3 measurements at the comparison stations in some cases also varied considerably (Table 3). However, NH3 is mainly derived from local emission point Table 1. The latitude, altitude and distance between the Level II plots and air-quality monitoring stations of the Finnish Meteorological Institute (FMI) and Helsinki University (HU). Taulukko 1. Ilmalaadun seurannassa mukana olleiden tason II havaintoalojen sekä Ilmatieteen laitoksen (FMI) ja Helsingin yliopiston (HU) mittausasemien leveysaste, korkeus (mpy) ja jatkuvatoimisten mittaus­ asemien etäisyys tason II havaintoaloilta. Sample plot �r. Latitude �ltitude Distance to Sampling period a.s.l. Level II plot Havaintoala �o. �eveys­aste �orkeus �täisyys tason Mittausjakso mpy II hav.alalta °� m km ��������1 ���� Pallasjärvi �� ��� ���1 �� �� Uusikaarlepyy ��� ��� �� �� Juupajoki 1� �1 1��� �� Miehikkälä 1�� �� ��� �� �� Matorova, FMI (near plot nr. ��) ��� ���� �.��� �� (lähellä alaa no. 3) Hyytiälä, HU (near plot nr. 1�) �1 1�� � �� (lähellä alaa no. 10) Virolahti, FMI (near plot nr. 1��) �� � �1 �� �� (lähellä alaa no. 18) Working Papers of the Finnish Forest Research Institute 45 http://www.metla.fi/julkaisut/workingpapers/2007/mwp045.htm 115 sources, and the concentrations can vary considerably due e.g. to the wind direction. The NH4+ concentrations measured in deposition on the Level II plots were the highest in Uusikaarlepyy and Miehikkälä (See Chapter 3.5 in this volume). However, the same trend was not as clear in the passive sampler measurements (Figs. 1d, 2d). Table ��. Mean O��, SO�, �O� and �H�� concentrations and minimum and ma��imum values for the period �pril�October ����. The detection limits for the different passive gas samplers are given. The plots are arranged in the table to correspond to the latitude of the plots (��S). Taulukko 3. O3­, SO2­, �O2­ ja �H3­pitoisuuksien keskiarvot sekä minimi­ ja maksimiarvot mittausjaksolla huhtikuusta lokakuuhun 2004. �ri parametrien määritysrajat on esitetty taulukossa. Havaintoalat on järjes­ tetty taulukossa vastaamaan pohjois–etelä gradienttia Suomen läpi. Sample plot O�� , SO� , �O� , �H�� , Havaintoala µg m-�� µg m-�� µg m-�� µg m-�� Detection limit Määritysraja � n �.�� n �.1� n �.��� n Pallasjärvi Mean � �a. ������ �� �.����� �� �.��� � 1.��� �� min�ma�� ������ �.����1.�1