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Author(s): Mladen Ognjenović, Ivan Seletković, Mia Marušić, Mathieu Jonard, Pasi Rautio, 
Volkmar Timmermann, Melita Perčec Tadić, Miran Lanšćak, Damir Ugarković & 
Nenad Potočić 

Title: The Effect of Environmental Factors on the Nutrition of European Beech (Fagus 
sylvatica L.) Varies with Defoliation 

Year: 2023 

Version: Published version 

Copyright:   The Author(s) 2023 

Rights: CC BY 4.0 

Rights url: http://creativecommons.org/licenses/by/4.0/ 

 

Please cite the original version: 

Ognjenović, M.; Seletković, I.; Marušić, M.; Jonard, M.; Rautio, P.; Timmermann, V.; Tadić, M.P.; 
Lanšćak, M.; Ugarković, D.; Potočić, N. The Effect of Environmental Factors on the Nutrition of 
European Beech (Fagus sylvatica L.) Varies with Defoliation. Plants 2023, 12, 168. 
https://doi.org/10.3390/plants12010168 



Citation: Ognjenović, M.; Seletković,

I.; Marušić, M.; Jonard, M.; Rautio, P.;

Timmermann, V.; Tadić, M.P.;

Lanšćak, M.; Ugarković, D.; Potočić,

N. The Effect of Environmental

Factors on the Nutrition of European

Beech (Fagus sylvatica L.) Varies with

Defoliation. Plants 2023, 12, 168.

https://doi.org/10.3390/

plants12010168

Academic Editor: Aneta Helena

Baczewska-Dąbrowska

Received: 31 October 2022

Revised: 7 December 2022

Accepted: 23 December 2022

Published: 30 December 2022

Copyright: © 2022 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

plants

Article

The Effect of Environmental Factors on the Nutrition of
European Beech (Fagus sylvatica L.) Varies with Defoliation
Mladen Ognjenović 1, Ivan Seletković 2, Mia Marušić 2,* , Mathieu Jonard 3 , Pasi Rautio 4 ,
Volkmar Timmermann 5, Melita Perčec Tadić 6, Miran Lanšćak 2, Damir Ugarković 7 and Nenad Potočić 2

1 Department of BI, Analytics and Research, Njuškalo Ltd., 10000 Zagreb, Croatia
2 Croatian Forest Research Institute, 10450 Jastrebarsko, Croatia
3 Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
4 Natural Resources Institute Finland, 00790 Helsinki, Finland
5 Norwegian Institute of Bioeconomy Research, 1433 Ås, Norway
6 Meteorological and Hydrological Service, 10000 Zagreb, Croatia
7 Faculty of Forestry and Wood Technology, University of Zagreb, 10000 Zagreb, Croatia
* Correspondence: miam@sumins.hr

Abstract: Despite being adapted to a wide range of environmental conditions, the vitality of European
beech is expected to be significantly affected by the projected effects of climate change, which we
attempted to assess with foliar nutrition and crown defoliation, as two different, yet interlinked
vitality indicators. Based on 28 beech plots of the ICP Forests Level I network, we set out to investigate
the nutritional status of beech in Croatia, the relation of its defoliation and nutrient status, and the
effects of environmental factors on this relation. The results indicate a generally satisfactory nutrition
of common beech in Croatia. Links between defoliation and nutrition of beech are not very direct
or very prominent; differences were observed only in some years and on limited number of plots.
However, the applied multinomial logistic regression models show that environmental factors affect
the relationship between defoliation and nutrition, as climate and altitude influence the occurrence of
differences in foliar nutrition between defoliation categories.

Keywords: tree vitality; foliar composition; stoichiometry; climate change; ICP Forests

1. Introduction

Meteorological and climate conditions affect vegetation directly and indirectly. Direct
effects include responses to temperature; indirect effects occur primarily as soil-mediated
phenomena, such as the influence of precipitation on soil moisture regimes [1–4]. Probably
the greatest threat to existing forest ecosystems in Europe comes from a changing climate [5].
Extreme climatic events such as droughts are thought to be important in initiating changes
in forest ecosystems [6]. According to IPCC [7] predictions, extreme climate events such as
heat waves and long-lasting summer droughts will occur more frequently. Such climate
changes will negatively impact the stability, structure and biodiversity of forest ecosystems
throughout Europe [8]. The region of southeast Europe is one of the most vulnerable
regions in Europe when it comes to climate change impacts, primarily through intensified
severity and duration of droughts and heat waves. As these impacts should be stronger
and faster than on the rest of the continent [9,10] this region is an ideal model for studying
the future impact of changing climatic conditions.

A prerequisite for healthy growth and balanced metabolism is the maintenance of
adequate concentrations and relatively stable ratios of nutrients in plant tissues [11]. Con-
centrations of nutrients and their relationships in the leaves of forest trees are important
indicators of their functioning. It is crucial to account for nutrient limitation when studying
the forest response to climate change [12], as nutritional status has a significant and diverse
impact on tree vitality [13]. The concentration of nutrients in leaves depends on many

Plants 2023, 12, 168. https://doi.org/10.3390/plants12010168 https://www.mdpi.com/journal/plants

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https://creativecommons.org/
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https://creativecommons.org/licenses/by/4.0/
https://www.mdpi.com/journal/plants
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https://orcid.org/0000-0002-5635-2115
https://orcid.org/0000-0002-9680-792X
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https://orcid.org/0000-0002-5192-9974
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https://doi.org/10.3390/plants12010168
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Plants 2023, 12, 168 2 of 17

factors: the amount and distribution of precipitation and the length of the growing season,
the presence of nutrients in the soil, ion antagonisms, ion mobility and uptake capacity,
etc. [14]. It seems that the nutritional status of European beech (Fagus sylvatica L.) is deteri-
orating: Jonard et al. [12] and Talkner et al. [15] both report on the negative trend in beech
phosphorus (P) nutrition; furthermore, trends of concentrations of calcium and magnesium
in the foliage of beech trees in Europe were also found to be negative [12]. Nutrient contents
of tree foliage, which reflect atmospheric and soil-related influences, are an important part
of the UNECE ICP Forests monitoring scheme [16]. Beech forests in Croatia have a very
wide edaphic amplitude [17], but baseline data on the chemical composition of foliage in
beech stands has so far been missing.

Tree vitality is defined as the ability of a tree to assimilate, to survive stress, to react
to changing conditions, and to reproduce [18]. As vitality cannot be measured directly,
various indicators can be used to describe it [19]. Crown defoliation is a commonly used
tree vitality indicator [20–22], which can be obtained cost-effectively and relatively quickly
in field surveys [23]. Defoliation is defined as the loss of needles or leaves in the assessable
part of the crown compared to a reference tree [24]. Trees with defoliation above 25% are
regarded as considerably defoliated and are thus defined as damaged [25]. A recent study
showed that there are significant morphological and, in this context even more importantly,
physiological differences in beech leaves between healthy and considerably defoliated
trees [22]. Although European beech had the lowest mean defoliation (20.9%) of all main
tree species in the ICP Forests 2021 survey, the 20-year trend in mean plot defoliation
shows that defoliation of beech is constantly on the rise (3.4% in 20 years), and that larger
deviations from this trend can be linked to drought events [26]. A good example is year
2004, when the annual mean plot defoliation was higher than the trend as a result of the
drought in the preceding year which affected large parts of Europe [27–29]. In Croatia,
the trend in mean defoliation of beech is much steeper with an 8.6 % rise in the period of
1996–2017 [30].

European beech is often mentioned in the context of sensitivity to high temperatures
and drought [31,32], as beech grows best in areas marked by moderately warm summers
and high quantities of precipitation [33]. Beech forms vast forest communities which
provide multiple ecosystem goods and services, and is the dominant tree species in many
deciduous forests in Europe under temperate climate conditions [34]. The projected effects
of climate change, particularly drought, are expected to significantly affect the vitality of
beech, which we attempted to assess with foliar nutrition and crown defoliation, as two
different, yet closely interlinked vitality indicators. Although considered a nonspecific
indicator of vitality, defoliation appears to depend on the nutritional status of trees. How-
ever, research on the relationship between tree defoliation and nutritional status has so far
been limited, especially for beech: Jonard et al. [35] found that defoliation was associated
with lower concentrations of calcium (Ca) and magnesium (Mg) in beech leaves. A study
conducted in Switzerland did not find a link between nitrogen (N) concentrations and
defoliation [36], but Ferretti et al. [37] report that the proportion of common beech trees
with more than 25% defoliation increases with elevated nitrogen to calcium and potassium
(K) ratios in the leaves, indicating that crown status depends on the balance of nutrients in
leaves. Toigo et al. [38] found that defoliation increased due to drought effects, while the
influence of nutritional status was less consistent. The study by Ognjenović et al. [39] on a
limited sample of 26 beech trees showed that defoliation and calcium leaf concentrations
show similar susceptibility to drought, but with different responses in time. Obviously,
besides the overall tree health condition, the concentration of nutrients in leaves depends
on many outside factors. In this context, we set out to better characterize the link between
defoliation and foliar nutrition and determine the influence of environmental factors on the
foliar composition of beech trees of two distinct defoliation categories, on a large sample of
beech-dominated ICP Forests Level I plots in Croatia, with the hypotheses that (I) environ-
mental factors have a significant influence over the defoliation/nutrition relationship and
(II) trees with higher defoliation have lower concentrations of nutrients in leaves. In the



Plants 2023, 12, 168 3 of 17

process, we investigated whether tree nutrition plays a role in the loss of vitality of beech
trees (and vice versa); and if the link between tree nutrition and vitality loss varies from
year to year due to interannual climate variations.

2. Results
2.1. Foliar Nutrient Concentrations

Mean N foliar concentrations in the study area level were within normal range [40]
in both defoliation categories and across sampling years. Repeated Measures ANOVA
indicated that there was no difference in N concentrations between LD and HD trees,
while there was a difference between individual years (F(2, 556) = 30.4, p < 0.0001).
In 2018, significantly lower N concentrations were recorded for both defoliation categories
compared to 2019 and 2020 (Table 1).

Table 1. Results of Holm pairwise comparisons of foliar nutrient concentrations and sample dry mass
within a defoliation category and between sampling years (NS–not significant, * p < 0.05, ** p < 0.01,
*** p < 0.001).

Defoliation Category Year 1 Year 2 N P K Ca Mg Sample Dry Mass

LD 2018 2019 *** ns *** ** *** ***
LD 2018 2020 *** *** *** *** ns *
LD 2019 2020 ns *** ns *** * *
HD 2018 2019 *** *** *** ns *** ***
HD 2018 2020 *** *** *** *** *** ***
HD 2019 2020 ns * ** *** ns *

Although there is no significant main effect of defoliation category on P concentrations,
the pairwise comparisons between LD and HD trees indicated significant differences in
2019, when LD trees had a higher concentration than HD trees, but not in 2018 and 2020
(Figure 1). Significant differences in P concentrations (F(2, 556) = 74.9, p < 0.001) were also
observed between individual sampling years (Table 1) with a decrease during the study
period. However, values of P on the plot level were mostly normal throughout the study
period except for the year 2020, when P concentrations were slightly below normal range
on 53% of the research plots (Figure S3).

No significant differences were established for potassium (K) between defoliation cate-
gories and mean concertation values stayed within the normal range in each year (Figure 1).

Repeated Measures ANOVA showed no significant main effect of defoliation category
on Mg concentrations. Nevertheless, pairwise comparisons between LD and HD trees
revealed significant differences in 2018, with HD trees having higher Mg concentrations
(Figure 1). Mean Mg concentrations were within normal range in each year, although they
were lower in 2019 and 2020 compared to 2018 for HD trees.

Similar to other elements, there was no significant main effect of defoliation category
on calcium (Ca) concentrations (F(2, 556) = 3.4, p = 0.06). Pairwise comparisons, however,
showed that HD trees in 2018 and 2019 had significantly higher Ca concentrations than LD
trees (Figure 1). For these years, values for both groups fall within normal Ca concentration
range for beech. In 2020, both categories had significantly higher concentrations than in
previous years, bordering on surplus (luxury) values, but no significant difference was
found between them (Figure 1).

Although there was no significant main effect of defoliation category on dry leaf
mass, the pairwise comparisons between LD and HD trees indicated significant differences
in 2018, when LD trees had a higher dry leaf mass than HD trees (Figure 1). However,
there was a statistically significant main effect of the sampling year on dry leaf mass
(F(2, 556) = 56.3, p < 0.001) and pairwise comparisons showed differences between all years
for both defoliation categories (Table 1).



Plants 2023, 12, 168 4 of 17

Plants 2023, 12, x FOR PEER REVIEW 4 of 17 
 

 

Table 1. Results of Holm pairwise comparisons of foliar nutrient concentrations and sample dry 
mass within a defoliation category and between sampling years (NS–not significant, * p < 0.05, ** p 
< 0.01, *** p < 0.001). 

Defoliation 
Category 

Year 1 Year 2 N P K Ca Mg 
Sample 

Dry 
Mass 

LD 2018 2019  *** ns  *** **  ***  *** 
LD 2018 2020  ***  ***  ***  *** ns * 
LD 2019 2020 ns  *** ns  *** * * 
HD 2018 2019  ***  ***  *** ns  ***  *** 
HD 2018 2020  ***  ***  ***  ***  ***  *** 
HD 2019 2020 ns * **  *** ns * 

 
Figure 1. Mean foliar concentrations and sample dry mass (dots) with confidence intervals (vertical 
bars) for LD (green color) and HD trees (orange color) on all plots within sampling years. Dotted 
lines represent lower and upper critical value for normal range of foliar N concentrations for com-
mon beech according to Mellert and Goettlein [40]. Only significant differences in foliar concentra-
tions between LD and HD categories determined by the Holm–Bonferroni method are indicated (* 
p < 0.05, ** p < 0.01, *** p < 0.001). 

On the plot level, mean Ca concentrations were mostly in the normal range during 
the first two years, while excessive Ca concentrations were observed on all plots located 

Figure 1. Mean foliar concentrations and sample dry mass (dots) with confidence intervals (vertical
bars) for LD (green color) and HD trees (orange color) on all plots within sampling years. Dotted
lines represent lower and upper critical value for normal range of foliar N concentrations for common
beech according to Mellert and Goettlein [40]. Only significant differences in foliar concentrations
between LD and HD categories determined by the Holm–Bonferroni method are indicated (* p < 0.05,
** p < 0.01, *** p < 0.001).

On the plot level, mean Ca concentrations were mostly in the normal range during
the first two years, while excessive Ca concentrations were observed on all plots located in
the alpine region in 2020 (Figure S5), and most of the alpine region plots had insufficient
P concentrations (Figure S3). In the same year, excessive K concentrations were found on
eleven plots (39%), most of them located in the continental region (Figure S4). N values are
rather plot dependent and relatively stable in time (Figure S2), while Mg concentrations
show a very broad range of values, independent of the area, defoliation category, or
sampling year (Figure S6)

Overall, the share of plots with differences in foliar concentrations between defoliation
categories was relatively low for most elements during the entire sampling period (Table 2).
The highest share of plots with differences in foliar concentrations between defoliation
categories was determined for Ca (21.4%) in 2018, followed by Mg (17.9%) in the same year.



Plants 2023, 12, 168 5 of 17

Table 2. Number of plots with significantly higher foliar concentrations in each defoliation category
within sampling years determined by the independent samples t-test (total number of plots: 28).

N P K Ca Mg

2018
HD higher 1 - - 6 4
LD higher 1 - 2 - 1

2019
HD higher - - 1 - 2
LD higher 1 4 1 1 1

2020
HD higher - 2 1 1 1
LD higher 2 - - - 1

2.2. Foliar Nutrient Ratios

N/P ratios were especially high and above the upper limit for both LD and HD trees
in 2020 at the study area level, mostly due to very low P values that approached deficiency
values (Figure 2). Repeated Measures ANOVA did not show a significant main effect of
defoliation category on N/P ratio. However, pairwise comparison between LD and HD
trees indicated significant differences in N/P ratios in 2019, with values out of optimal
range for HD trees due to low P concentration values (Figure 2). The main effect of the
sampling year was significant (F(2556) = 13.4, p < 0.001) with an increase in N/P ratio
during the monitoring period.

Plants 2023, 12, x FOR PEER REVIEW 6 of 17 
 

 

 
Figure 2. Mean foliar ratios (dots) and confidence intervals (vertical bars) for LD (green color) and 
HD trees (orange color) on all plots within sampling years. Dotted lines represent lower and upper 
critical value for normal range of foliar ratios for common beech according to Mellert and Goettlein 
[40]. Only significant differences in foliar ratios between LD and HD categories determined by the 
Holm–Bonferroni method are indicated (* p < 0.05, ** p < 0.01, *** p < 0.001). 

Table 3. Results of Holm pairwise comparisons of foliar nutrient ratios within a defoliation category 
and between sampling years (ns–not significant, * p < 0.05, ** p < 0.01, *** p < 0.001). 

Defoliation  
Category 

Year 1 Year 2 N/P N/K N/Ca N/Mg 

LD 2018 2019 ns ns ns ns 
LD 2018 2020 *** ns *** ns 
LD 2019 2020 *** ns *** ns 
HD 2018 2019 ns ns ns *** 
HD 2018 2020 * * *** *** 
HD 2019 2020 ns ns *** ns 

Mean N/P, N/K and N/Ca ratios on the plot level were mostly in normal range during 
the entire study (Figures S7–S9). However, in 2020, most of the plots located in the alpine 
biogeographic region had excessive N/P and insufficient N/Ca ratios. 

The highest share of plots with differences between defoliation categories (5 out of 
28, or 17.9%) was determined for N/Ca in 2018 and N/P in 2019. The share of plots in which 
we found differences hardly passed 10% for other ratios and years (Table 4). 

Table 4. Number of plots with significantly higher foliar ratios in each defoliation category within 
sampling years determined by the independent samples t-test (total number of plots: 28). 

  N/P N/Ca N/Mg N/K 

2018 HD higher - - 1 2 
LD higher - 5 2 - 

2019 HD higher 5 1 1 2 
LD higher - 2 1 1 

2020 HD higher 1 - - - 
LD higher 1 2 1 - 

2.3. Effects of Environmental Factors on Differences in Foliar Nutrition 
While the fitted MLR (multinomial logistic regression) models for N and P did not 

indicate a significant influence of any of the tested factors, we found that environmental 
factors influence the occurrence of differences in foliar concentrations of K, Ca, and Mg 
between defoliation categories. 

Figure 2. Mean foliar ratios (dots) and confidence intervals (vertical bars) for LD (green color)
and HD trees (orange color) on all plots within sampling years. Dotted lines represent lower and
upper critical value for normal range of foliar ratios for common beech according to Mellert and
Goettlein [40]. Only significant differences in foliar ratios between LD and HD categories determined
by the Holm–Bonferroni method are indicated (* p < 0.05, ** p < 0.01, *** p < 0.001).

No significant differences were found for N/K ratio between defoliation groups or
sampling years and values were within normal range in all years.

Repeated Measures ANOVA identified a significant main effect of defoliation cat-
egories on N/Ca ratios (F(2556) = 109.9, p < 0.0001). Pairwise comparisons confirmed
differences in N/Ca ratios between defoliation groups in 2018 and 2019 (Figure 2). These
differences were dictated primarily by Ca concentrations, which showed higher values
in HD trees in 2018 and 2019. Sampling year also had a significant effect (F(2556) = 13.4,
p < 0.001) and pairwise comparison established that values within defoliation categories
were lower in 2020 compared to 2018 and 2019 (Table 3).

Due to low Mg concentrations in 2019 and 2020, the N/Mg ratios were mostly in the
surplus range in those years (Figure 2). No significant effect of defoliation categories on
N/Mg ratios was found. However, pairwise comparisons showed a significant difference
between LD and HD trees in 2018, with higher values for LD trees.



Plants 2023, 12, 168 6 of 17

Table 3. Results of Holm pairwise comparisons of foliar nutrient ratios within a defoliation category
and between sampling years (ns–not significant, * p < 0.05, *** p < 0.001).

Defoliation
Category Year 1 Year 2 N/P N/K N/Ca N/Mg

LD 2018 2019 ns ns ns ns
LD 2018 2020 *** ns *** ns
LD 2019 2020 *** ns *** ns
HD 2018 2019 ns ns ns ***
HD 2018 2020 * * *** ***
HD 2019 2020 ns ns *** ns

Mean N/P, N/K and N/Ca ratios on the plot level were mostly in normal range
during the entire study (Figures S7–S9). However, in 2020, most of the plots located in the
alpine biogeographic region had excessive N/P and insufficient N/Ca ratios.

The highest share of plots with differences between defoliation categories (5 out of 28,
or 17.9%) was determined for N/Ca in 2018 and N/P in 2019. The share of plots in which
we found differences hardly passed 10% for other ratios and years (Table 4).

Table 4. Number of plots with significantly higher foliar ratios in each defoliation category within
sampling years determined by the independent samples t-test (total number of plots: 28).

N/P N/Ca N/Mg N/K

2018
HD higher - - 1 2
LD higher - 5 2 -

2019
HD higher 5 1 1 2
LD higher - 2 1 1

2020
HD higher 1 - - -
LD higher 1 2 1 -

2.3. Effects of Environmental Factors on Differences in Foliar Nutrition

While the fitted MLR (multinomial logistic regression) models for N and P did not
indicate a significant influence of any of the tested factors, we found that environmental
factors influence the occurrence of differences in foliar concentrations of K, Ca, and Mg
between defoliation categories.

Altitude (alt) and mean annual temperature (MAT) are the only environmental factors
that influence the occurrence of Differences in Foliar Concentrations (DFC) of K. Keeping
all other variables constant, the odds for LD trees having higher K concentration is 0.5%
higher for every meter increase in altitude. On the other hand, odds are 35% lower for HD
trees to have higher K concentrations when the mean temperature increases.

Trees on higher altitudes are more likely to have higher Ca concentrations in HD trees
(Table 5). An opposite effect can be seen with mean annual precipitation (MAP) where,
keeping all other variables constant, with an increase in precipitation the odds are 90%
higher for LD trees and 2% lower for HD trees to have higher Ca concentrations. With
increasing maximum temperatures (Tmax) it is 1.3 times likely that HD trees will have
significantly higher Ca concentrations.

Beech trees on higher altitudes are also more likely to have higher Mg concentrations
in HD trees, but the effect on LD trees is not significant. Keeping all other variables
constant, with a 1 ◦C increase in mean temperature it is 6.6 times likely that HD trees will
have higher Mg concentrations, while at the same time the likelihood of LD having higher
concentrations is negligible (Table 5).



Plants 2023, 12, 168 7 of 17

Table 5. Results of multinomial logistic regression for Differences in Foliar Concentrations (DFC) of
potassium, calcium and magnesium and corresponding model AIC. Odds ratios are reported for each
environmental factor while the confidence intervals are given in the brackets (* p < 0.1, ** p < 0.05,
*** p < 0.01).

DFC

Element Environmental
Factor 1 HD Higher LD Higher

K

Alt 1.000 1.005 ***
(0.996, 1.003) (1.002, 1.008)

MAT 0.644 *** 0.884
(0.480, 0.809) (0.659, 1.108)

AIC 48.042 48.042

Ca

Tmax 1.298 *** 0.00002 ***
(1.112, 1.485) (0.00002, 0.00002)

sand 0.926 0.048 ***
(0.809, 1.043) (0.048, 0.048)

MAP 0.988 ** 1.900 ***
(0.977, 0.999) (1.900, 1.900)

Alt 1.005 ** 0.372 ***
(1.001, 1.009) (0.372, 0.372)

AIC 56.908 56.908

Mg

MAT 6.578 *** 1.671 *
(6.330, 6.826) (1.149, 2.194)

MAP 1.002 1.006
(0.996, 1.007) (0.998, 1.015)

Alt 1.009 *** 1.001
(1.005, 1.013) (0.992, 1.009)

sand 0.991 1.121 **
(0.885, 1.097) (1.020, 1.222)

pH 2.600 ** 1.015
(1.708, 3.492) (−0.815, 2.846)

AIC 80.311 80.311

Environmental factors 1 Alt—altitude, MAT—Mean Annual Temperature, MAP—Mean Annual Precipitation,
sand—soil sand fraction, pH—pH value, AIC—Akaike information criterion.

The results of the fitted MLR models indicate that beech on higher altitudes is less
likely to have significant differences in N/P ratios and increasing maximum temperature
has the same effect (Table 6). A similar effect of maximum temperatures can be seen for
differences in N/Ca. Holding all other variables constant, with an increase in precipitation
the odds are 0.6% higher that HD trees will have higher N/P ratios and 0.8% higher N/Mg
ratios. When mean temperatures and soil pH increase, it is 2.5 and 3.7 times, respectively,
more likely that LD trees will have higher N/Mg ratios. Keeping all other variables
constant, with a unit increase in PDSI, i.e., less drought, the odds are 83.5% higher that
LD trees will have higher N/Ca ratios. The fitted MLR models for N/K did not indicate a
significant influence of any of the tested environmental factors on the occurrence probability
of differences in foliar nutrient ratios.



Plants 2023, 12, 168 8 of 17

Table 6. Results of multinomial logistic regression for Differences in Foliar Ratios (DFR) of potassium,
calcium and magnesium. Odds ratios are reported for each environmental factor while the confidence
intervals are given in the brackets (* p < 0.1, ** p < 0.05, *** p < 0.01).

DFC

Environmental
Factor 1 HD Higher LD Higher

N/P

MAP 1.006 * 0.989
(1.000, 1.012) (0.967, 1.011)

Tmax 0.820 *** 0.006 ***
(0.696, 0.943) (−0.438, 0.449)

Alt 0.992 ** 0.960 ***
(0.986, 0.999) (0.937, 0.983)

AIC 59.378 59.378

N/Ca

Tmax 0.070 *** 0.402 ***
(−1.553, 1.693) (0.029, 0.775)

PDSI 1.401 1.835 **
(−0.956, 3.758) (1.251, 2.419)

Ca2+ 0.534 0.909 **
(−0.314, 1.381) (0.829, 0.988)

MAP 0.996 0.993 ***
(0.957, 1.036) (0.989, 0.998)

silt 0.741 1.158 *
(−0.353, 1.835) (0.996, 1.319)

AIC 72.131 72.131

N/Mg

MAP 0.999 1.008 ***
(0.989, 1.009) (1.002, 1.014)

MAT 1.070 2.502 ***
(0.648, 1.493) (2.198, 2.805)

pH 0.631 3.708 ***
(−1.419, 2.681) (2.770, 4.646)

SPEI 2.613 0.071 **
(−1.249, 6.474) (−2.192, 2.333)

AIC 65.799 65.799

Environmental factors 1 Alt—altitude, MAT—Mean Annual Temperature, Tmax—Mean annual maximum
temperature, MAP—Mean Annual Precipitation, silt—soil silt fraction, SPEI—Standardised Precipitation-
Evapotranspiration Index, PDSI—Palmer Drought Severity Index, Ca2+—Soil exchangeable calcium, AIC—Akaike
information criterion.

3. Discussion

Assessing the nutritional status of trees using the values of element concentrations in
needles or leaves is a common diagnostic practice in forestry [40–42]. In Croatia there have
been only a few studies into the nutritional status of beech [41,43,44], however these studies
were performed on a limited research area. The results obtained in this study indicate a
generally satisfactory nutrition of common beech in Croatia. Mean concentrations on the
study and plot levels were within the normal range for beech [40]. Similar results were
reported by Ognjenović et al. [39] from a long-term monitoring based case study of beech
foliar composition on one ICP Forests intensive monitoring plot.

Large deviations from the mean concentration values of longer periods in individ-
ual years have been reported for several mineral nutrients [45–47]. In contrast, foliar K
concentrations were reported to be relatively stable [39], and this finding is repeated in
our study, although concentrations of all investigated elements stayed mostly within limit
values [40]. Very similar relationships of N and K to limit values has also been observed
in other studies [48,49]. On the other hand, P foliar levels are a reason for concern at
the European level [12,15]. While beech P nutrition on a study area level seemed to be
sufficient [40], on average 33% of the plots had insufficient P nutrition during the study



Plants 2023, 12, 168 9 of 17

period (Figure S3). Additionally, the ratio of nitrogen to phosphorus shows that beech
nutrition in 2019 and 2020 was not balanced in this respect, which is partly caused by
diminishing P, and partly by increasing N concentrations, respectively.

The fact that we found surplus N/Mg ratios in 2019 and 2020, both due to increasing
N and decreasing Mg values, echoes results of other studies that report an unbalanced Mg
nutrition on national [48,50] and European level [12].

Excessive Ca concentrations on an extremely high proportion of plots (90%) were
determined in a study of beech nutrition in northern Spain, but since no symptoms of un-
balanced nutrition have been identified in the field, the authors believe that the deviations
of the obtained values may be based on ecological and geological differences between the
studied forests and central European forests on the basis of which reference values were
obtained [51]. This consideration is consistent with the hypothesis that the optimal leaf
composition of each species depends on the specific ecological biogeochemical niche that
the species occupies [52]. Taking into account the wide ecological amplitude of common
beech, there is a possibility that the optimal range of concentrations of nutrients is adapted
to the specific habitat in which a particular stand develops. Thus Sardans and Peñuelas [53]
assume that plant species have a certain degree of flexibility in changing stoichiometry in
response to changes in environmental conditions such as climate gradients.

Nutritional status and tree crown defoliation, as indicators of vitality, can provide
a basis for monitoring the effects of long-term environmental changes on forest ecosys-
tems [22,48]. According to Simon and Wild [54], if the concentration of a certain element
remains in the normal range, a decrease in mineral nutrition should be regarded more as
a consequence than the cause of damage. If, on the other hand, the concentrations are
inadequate, we can suspect nutrition to be the cause of damage. Although we did not find
a universal relationship between the defoliation categories and nutritional status of beech
trees, subsequent comparisons revealed differences in the element concentrations and ratios
for certain years. For instance, we found significantly higher P concentrations in LD trees
in year 2019. On the contrary, a study in northern Spain recorded higher P concentrations
in more damaged trees [51]. The most pronounced differences in concentrations between
defoliation categories were determined for Ca, with HD trees having significantly higher
concentrations in 2018 and 2019. Higher concentrations in HD trees were also recorded for
Mg in 2018. Conversely, Jonard et al. [35] report that higher defoliation values are associated
with lower Ca and Mg concentrations in a liming and P/K fertilization experiment of beech
stands in Belgium.

A case study investigating interrelations of various common beech vitality indicators
did not find significant links between foliar nutrient concentrations and defoliation [39].
It should be taken into account that (i) the study was performed on a limited area, (ii)
element concentrations were mostly within the normal range, and (iii) the range of defo-
liation values was narrow and seldom exceeded 25%. The lack of association between N
concentrations and defoliation values, also recorded in this study, Thimonier et al. [36]
attribute to the same kind of limitations. Unlike Ouimet and Moore [55], who observed
that an increase in K concentration in needles resulted in a decrease in defoliation in balsam
fir stands, we found no significant differences in K concentrations between defoliation
categories. Compared to fertilization experiments, the analysis of the nutritional status in
natural stands is more demanding due to the high variability of data and the inability to
control many factors that may affect the nutritional status. Ewald [56] states heterogeneous
environmental conditions as the reason for the lack of significant relationship between
nutrient concentrations and spruce defoliation status in a study conducted in Bavaria. On
the other hand, with this study we do have a full picture of beech nutritional status in
Croatia, including the insight into the significant variations between years and defolia-
tion categories. Significant differences in concentrations only during certain years are not
a phenomenon specific to this research alone. High year-to-year variability in nutrient
concentrations is characteristic of nutritional studies [15,35,48,57].



Plants 2023, 12, 168 10 of 17

The quantification of forest ecosystem response in a climate driven changing envi-
ronment is fundamental for maintenance, enhancement and restoration of future forest
ecosystem goods and services. The results presented in this study are a novel approach in in-
vestigating the effects of climate and environmental properties on the defoliation/nutrition
relationship. Both tree nutrition and defoliation have been reported to depend on climate
properties in various studies. A study by Braun [58] showed that air temperature and pre-
cipitation have a considerable impact on the foliar nutrient concentration. Jonard et al. [49]
observed a positive relationship between precipitation and foliar Ca concentrations, while
insufficient water supply during dry spells have a negative impact on the uptake of calcium,
according to Bergmann [59]. Our results indicate that the increase in precipitation is better
utilized in low defoliation trees since the odds are 90% higher for LD trees to have higher
Ca concentrations with an increase in precipitation. Lukac et al. [60] state that elements
with primarily biologically controlled cycles such as nitrogen can show a different reaction
to temperature changes than elements whose cycles are controlled by both biological and
geological processes (P and K) or predominantly by geological processes (Ca and Mg).
Therefore, it is not surprising that we did not determine a significant effect of environmental
factors on the occurrence of differences in N concentrations between defoliation categories.
A study of beech nutrient dynamics along a precipitation gradient found that N concen-
trations remain constant with decreasing precipitation while P concentrations increase,
resulting in a decrease in N/P ratio [61]. However, in this study increasing precipitation
increases the odds of higher N/P ratios in high defoliation trees suggesting a disturbed
uptake and utilization of P in beech with higher defoliation.

From our results it becomes quite clear that an unequivocal association of foliar nutri-
tion and crown status should not be expected for beech. It seems plausible that various
pressures influence this relationship, affecting the physiological status of beech trees in a
multitude of ways. For instance, Gottardini et al. [22] found that various morphological
and physiological characteristics of beech leaves and canopy such as leaf volume and
photosynthetic activity, had a significant negative association with the extent of dam-
age. Additionally, Ferretti et al. [62] showed that the 25%-defoliation threshold can be a
reasonable approximation for tree classification indicated by the effect of slight and mod-
erate variations in defoliation on tree growth, which is in contrast to the results obtained
by Tallieu [63]. Highly differing research results on this topic are likely depending on
the research area, management practices, climate influences and other unknown factors.
Nevertheless, we established that environmental factors, especially climate, influence the
occurrence of differences in foliar nutrition between defoliation categories.

4. Materials and Methods
4.1. Study Area and Plot Design

The ICP Forests Level I monitoring plots in Croatia are established on intersections of
a 16 × 16 km grid that contain forest cover. These plots do not have a fixed area; rather,
24 trees are chosen for defoliation assessments using a cross-cluster system with six trees in
each cluster [64]. Only plots with a minimum of five beech trees in the sample in 2018 were
selected to ensure that beech was significantly represented in the mixture of tree species,
resulting in total of 28 plots (Figure 3).



Plants 2023, 12, 168 11 of 17

Plants 2023, 12, x FOR PEER REVIEW 11 of 17 
 

 

4. Materials and Methods 
4.1. Study Area and Plot Design 

The ICP Forests Level I monitoring plots in Croatia are established on intersections 
of a 16 × 16 km grid that contain forest cover. These plots do not have a fixed area; rather, 
24 trees are chosen for defoliation assessments using a cross-cluster system with six trees 
in each cluster [64]. Only plots with a minimum of five beech trees in the sample in 2018 
were selected to ensure that beech was significantly represented in the mixture of tree 
species, resulting in total of 28 plots (Figure 3). 

 
Figure 3. Location of research plots and distribution of European beech in Europe [65]. Selected 
Level I ICP Forests monitoring plots (orange dots); remaining Level I plots in Croatia (black dots); 
distribution of European beech forests in Croatia [66] (gray polygon); biogeographic region in the 
study area (shaded polygon) [67]. 

4.2. Climate Data 
Climate monitoring stations are generally situated at considerable distances from the 

forest research plots. Therefore, the data they provide are not always representative of the 
research locations. To overcome this, we used gridded data produced by regression 
kriging (RK), which is a hybrid method of interpolation carried out in four steps [68,69]. 
The method was validated with leave-one-out cross-validation, while the root mean 
square error (RMSE) was calculated between observed and interpolated values. Mean 
RMSEs are for mean monthly temperature from 0.5 °C to 0.9 °C, for minimum tempera-
ture from 1.1 °C to 1.5 °C, for maximum temperature from 0.7 °C to 1.1 °C, and for precip-
itation from 18 to 30 mm, averaged by months. 

Monthly temperature and precipitation values from the gridded dataset on 1 km spa-
tial resolution for Croatia [30] were used to calculate Mean Annual Temperature (MAT), 
mean annual minimum (Tmin) and mean annual maximum (Tmax) temperature, Mean 
Annual Precipitation (MAP) as well as the Palmer Drought Severity Index (PDSI) [70] and 
Standardized Precipitation Evapotranspiration Index (SPEI) [71]. Lower values of scPDSI 

Figure 3. Location of research plots and distribution of European beech in Europe [65]. Selected
Level I ICP Forests monitoring plots (orange dots); remaining Level I plots in Croatia (black dots);
distribution of European beech forests in Croatia [66] (gray polygon); biogeographic region in the
study area (shaded polygon) [67].

4.2. Climate Data

Climate monitoring stations are generally situated at considerable distances from the
forest research plots. Therefore, the data they provide are not always representative of
the research locations. To overcome this, we used gridded data produced by regression
kriging (RK), which is a hybrid method of interpolation carried out in four steps [68,69].
The method was validated with leave-one-out cross-validation, while the root mean square
error (RMSE) was calculated between observed and interpolated values. Mean RMSEs
are for mean monthly temperature from 0.5 ◦C to 0.9 ◦C, for minimum temperature from
1.1 ◦C to 1.5 ◦C, for maximum temperature from 0.7 ◦C to 1.1 ◦C, and for precipitation from
18 to 30 mm, averaged by months.

Monthly temperature and precipitation values from the gridded dataset on 1 km
spatial resolution for Croatia [30] were used to calculate Mean Annual Temperature (MAT),
mean annual minimum (Tmin) and mean annual maximum (Tmax) temperature, Mean
Annual Precipitation (MAP) as well as the Palmer Drought Severity Index (PDSI) [70] and
Standardized Precipitation Evapotranspiration Index (SPEI) [71]. Lower values of scPDSI
and SPEI indicate a stronger drought intensity while higher values indicate a higher degree
of humidity. SPEI was calculated on a time scale of 3 months.

4.3. Tree Selection Procedures, Foliar Sampling and Analysis

Sampling was conducted annually from 2018 to 2020 during late July and August.
At a distance of less than 50 m from the centre of the plot, a stratified sampling of trees
based on the percentage of defoliation was carried out, forming two groups of five trees:
(i) “LOW DEFOLIATION” (LD) trees with defoliation lower than 25% and (ii) ‘HIGH
DEFOLIATION’ (HD) trees with defoliation higher than 25%. Defoliation assessments were



Plants 2023, 12, 168 12 of 17

performed during July and August according to the ICP Forests methodology [24] in steps
of 5%, ranging from 0 to 100%. An absolute reference tree, defined as the best/healthiest
possible beech tree, was used in the assessments, regardless of local factors that may
affect the crown condition. Only dominant or codominant trees were selected, without
any wounds or fruiting bodies of fungi on the trunk. In cases when trees changed their
defoliation percentage and thus changed their group status (LD/HD), new, replacement
trees were selected instead, and the discarded trees were not used in further sampling.
Stratified sampling allowed us to circumvent the irregular distribution of tree defoliation
status on plots and focus on determining whether concentrations/contents of nutrients
in leaves differ in trees of different defoliation categories on each plot. Leaves were
sampled from the upper third of the crown using a shotgun rifle or a rope climbing
technique [72,73]. Samples from each tree were combined into a composite sample accord-
ing to fresh weight and analyzed in the Laboratory for Physical and Chemical Testing (LFKI)
of the Croatian Forest Research Institute. Upon arrival to the laboratory, samples of plant
material were dried at 105 ◦ C to constant mass, ground in a Fritsch Pulverisette 14 mill
and prepared for analysis in a Milestone Ethos One microwave oven [74]. Concentrations
of total nitrogen were determined on a Leco CNS 2000 elemental analyzer (LECO, 2000).
Phosphorus (P) concentration was determined colorimetrically on a LaboMed UV/VIS
spectrophotometer [74], and potassium, calcium and magnesium by atomic absorption on
the Perkin-Elmer Aanalyst 700 absorption spectrophotometer [74].

4.4. Soil Sampling and Analysis

Soil sampling was performed in 2019 on five points located within each of the research
plots. One point was located within each of the four clusters of trees that are assessed for
defoliation during regular monitoring activities, and an additional fifth point was located
in the centre of each plot. Soil samples were taken with a pedological drill from a depth of
0–10 cm, 10–20 cm, 20–40 cm, and 40–80 cm. Collected samples were pooled by sampling
depth. Soil mechanical properties were determined by [75] and chemical parameters were
analysed according to the following protocols and methods:

• soil pH (CaCl2) [76]
• exchangeable K, Ca and Mg according to [77]
• exchangeable acidity (free H+) according to [78]
• total N with an elemental analyzer Leco CNS 2000 [79]
• available P and K with the AL method [80]; P by spectrophotometry using molybdate

blue method, on UV/VIS spectrophotometer LaboMed and K directly from filtrate on
flame photometer Buck scientific PFP−7 [81].

4.5. Data Analysis

Descriptive statistics of foliar concentration and ratios were performed for each el-
ement. Foliar data distribution was inspected visually and by Shapiro-Wilk test. Slight
deviations from normality were determined, especially for potassium, calcium and magne-
sium. However, studies have shown that slight deviations from normality do not affect the
rate of false positive results [82].

Therefore, a two-factor repeated measures analysis of variance (ANOVA) was used to
determine the difference in foliar concentrations and ratios between defoliation categories
(LD/HD) of common beech on a study area level. The data of all elements met the assump-
tion of homoscedasticity, which was confirmed by Levene’s test. The Holm–Bonferroni
method [83] was used to perform pairwise comparisons to determine differences in foliar
concentrations (i) between LD and HD categories in single years and (iii) across defoliation
categories between sampling years.

To determine differences in foliar concentrations and ratios between defoliation cat-
egories on a plot level, we used the independent samples t-test [84]. The results were
then categorized for each plot and sampling year to reflect three cases: (i) HD_higher if
HD trees had significantly higher foliar concentrations (p < 0.05), (ii) LD_higher if LD trees



Plants 2023, 12, 168 13 of 17

had significantly higher foliar concentrations (p < 0.05) and (iii) NO_diff if there were no
significant differences in foliar concentrations between defoliation categories, thus creating
a variable which represents Differences in Foliar Concentrations or Ratios for each element
(DFC/DFR).

Multinomial logistic regression (MLR) was used to determine which environmental
factors influence the occurrence probability of a certain DFC/DFR category, except no_dif
which was set as the reference category. Separate odds ratios, which are the exponentiation
of model coefficients, were determined for all independent variables for each DFC/DFR
category. The odds ratio of a coefficient indicates the occurrence probability of either
HD or LD trees having significantly higher foliar concentrations over the probability
of no difference between defoliation categories (NO_diff ). An odds ratio > 1 indicates
that the occurrence probability of a certain DFC/DFR category relative to the occurrence
of NO_diff increases as the independent variable increases, whereas an odds ratio < 1
indicates that the occurrence of a certain DFC/DFR category decreases. Of the potential
independent variables, those with the highest variable importance were selected with the
random forest algorithm [85]. The final model selection process was based on diagnostic
diagrams and a procedure defined by Johnson et al. [86]. All analyses were conducted in
an R programming environment [87].

5. Conclusions

Our final view is that links between defoliation and nutrition of beech in Croatia are
not very direct or very prominent; this can be seen from (I) the fact that differences in
nutrition of LD and HD trees were found for only a few nutrients (II) these effects are
mostly not universal, but present only in some years and on a limited number of plots and
(III) nutrient concentrations mostly stay within normal values regardless of tree defoliation
status. However, it is clear that environmental factors, especially climate properties, do
affect the relationship between defoliation and nutrition. Discovering mechanisms by
which environmental factors affect foliar nutrition should complement current research on
the defoliation–nutrition relationships.

Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/plants12010168/s1, Figure S1. Comparison of study area climate
properties during the vegetation period with the climate normal (1980–2011) of the vegetation period
(March -September); Figure S2. Foliar nitrogen concentrations on research plots; Figure S3. Foliar
phosphorus concentrations on research plots; Figure S4. Foliar potassium concentrations on research
plots; Figure S5. Foliar calcium concentrations on research plots; Figure S6. Foliar magnesium
concentrations on research plots; Figure S7. Foliar N/P ratios on research plots; Figure S8. Foliar
N/Ca ratios on research plots; Figure S9. Foliar N/K ratios on research plots; Figure S10. Foliar
N/Mg ratios on research plots; Figure S11. Defoliation of common beech on research plots; Figure S12.
Defoliation of common beech on all plots during the study period.

Author Contributions: Conceptualization, M.O., I.S. and N.P.; methodology, M.O.; formal analysis,
M.O.; investigation, M.O., I.S., P.R. and N.P.; resources, N.P.; data curation, M.O.; writing—original
draft preparation, M.O., I.S. and N.P.; writing—review and editing, M.O., I.S., M.M., P.R., M.J., V.T.,
M.L., M.P.T., D.U. and N.P.; visualization, M.O.; supervision, N.P.; project administration, M.O. and
M.M.; funding acquisition, M.O., I.S. and N.P. All authors have read and agreed to the published
version of the manuscript.

Funding: This work has been fully supported by Croatian Science Foundation under the project
VitaClim (IP-2018-01-5222).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to legal reasons.

https://www.mdpi.com/article/10.3390/plants12010168/s1
https://www.mdpi.com/article/10.3390/plants12010168/s1


Plants 2023, 12, 168 14 of 17

Acknowledgments: We highly appreciate the work of researchers and technicians of the Croatian
Forest Research Institute that participated in field research and laboratory analysis for this study.

Conflicts of Interest: The authors declare no conflict of interest.

References
1. Centritto, M.; Tognetti, R.; Leitgeb, E.; Střelcová, K.; Cohen, S. Above Ground Processes: Anticipating Climate Change Influences.

In Forest Management and the Water Cycle: An Ecosystem-Based Approach; Bredemeier, M., Cohen, S., Godbold, D.L., Lode, E., Pichler,
V., Schleppi, P., Eds.; Springer: Dordrecht, The Netherlands, 2011; pp. 31–64. ISBN 978-90-481-9834-4.

2. Trumbore, S.; Brando, P.; Hartmann, H. Forest Health and Global Change. Science 2015, 349, 814–818. [CrossRef]
3. Stocker, T.F.; Qin, D.; Plattner, G.-K.; Alexander, L.V.; Allen, S.K.; Bindoff, N.L.; Bréon, F.-M.; Church, J.A.; Cubasch, U.; Emori, S.;

et al. Technical Summary. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J.,
Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013.

4. de Vries, W.; Dobbertin, M.H.; Solberg, S.; van Dobben, H.F.; Schaub, M. Impacts of Acid Deposition, Ozone Exposure and
Weather Conditions on Forest Ecosystems in Europe: An Overview. Plant Soil 2014, 380, 1–45. [CrossRef]

5. Ferretti, M.; Waldner, P.; Verstraeten, A.; Schmitz, A.; Michel, A.; Žlindra, D.; Marchetto, A.; Hansen, K.; Pitar, D.; Gottardini,
E.; et al. Criterion 2: Maintenance of Forest EcosystemHealth and Vitality. In Forest Europe, 2020: State of Europe’s Forests 2020;
Ministerial Conference on the Protection of Forests in Europe—Liaison Unit Bratislava: Zvolen, Slovak Republic, 2020.

6. Zierl, B. A Simulation Study to Analyse the Relations between Crown Condition and Drought in Switzerland. For. Ecol. Manag.
2004, 188, 25–38. [CrossRef]

7. IPCC. Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan,
C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York,
NY, USA, 2021; pp. 3–32. [CrossRef]

8. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears,
D.D.; Hogg, E.H.; et al. A Global Overview of Drought and Heat-Induced Tree Mortality Reveals Emerging Climate Change
Risks for Forests. For. Ecol. Manag. 2010, 259, 660–684. [CrossRef]

9. Giorgi, F. Climate Change Hot-Spots. Geophys. Res. Lett. 2006, 33. [CrossRef]
10. Beniston, M.; Stephenson, D.B.; Christensen, O.B.; Ferro, C.A.T.; Frei, C.; Goyette, S.; Halsnaes, K.; Holt, T.; Jylhä, K.; Koffi, B.;

et al. Future Extreme Events in European Climate: An Exploration of Regional Climate Model Projections. Clim. Change 2007, 81,
71–95. [CrossRef]

11. Marschner, H. 9—Functions of Mineral Nutrients: Micronutrients. In Marschner’s Mineral Nutrition of Higher Plants, 2nd ed.;
Academic Press: San Diego, CA, USA, 2002; pp. 313–404. ISBN 978-0-12-473543-9.

12. Jonard, M.; Fürst, A.; Verstraeten, A.; Thimonier, A.; Timmermann, V.; Potočić, N.; Waldner, P.; Benham, S.; Hansen, K.; Merilä, P.;
et al. Tree Mineral Nutrition Is Deteriorating in Europe. Glob. Chang. Biol. 2015, 21, 418–430. [CrossRef]

13. Hallenbarter, D.; Landolt, W.; Bucher, J. Nutrition and Vitality: Phenological Investigation in Forest under Different Fertiliser
Treatments. In Trends in European Forest Tree Physiology Research: Cost Action E6: EUROSILVA; Springer: Dordrecht, The Netherlands,
2001; pp. 9–12.

14. Kreuzwieser, J.; Gessler, A. Global Climate Change and Tree Nutrition: Influence of Water Availability. Tree Physiol. 2010, 30,
1221–1234. [CrossRef]

15. Talkner, U.; Meiwes, K.J.; Potočić, N.; Seletković, I.; Cools, N.; De Vos, B.; Rautio, P. Phosphorus Nutrition of Beech (Fagus sylvatica
L.) Is Decreasing in Europe. Ann. For. Sci. 2015, 72, 919–928. [CrossRef]

16. Potočić, N.; Timmermann, V.; Ognjenović, M.; Kirchner, T.; Prescher, A.K.; Ferretti, M. Tree Health Is Deteriorating in the European
Forests; ICP Forests: 2021; Programme Coordinating Centre of ICP Forests, Thünen, Institute of Forest Ecosystems: Eberswalde,
Germany, 2021. [CrossRef]

17. Pernar, N.; Bakšić, D. The Soils of Beech Forests. In Common Beech (Fagus sylvatica L.) in Croatia; Matić, S., Ed.; Academy of Forestry
Sciences: Zagreb, Croatia, 2003; ISBN 953-98571-1-2.

18. Dobbertin, M.; Brang, P. Crown Defoliation Improves Tree Mortality Models. For. Ecol. Manag. 2001, 141, 271–284. [CrossRef]
19. Cherubini, P.; Battipaglia, G.; Innes, J.L. Tree Vitality and Forest Health: Can Tree-Ring Stable Isotopes Be Used as Indicators?

Curr. For. Rep. 2021, 7, 69–80. [CrossRef]
20. De Vries, W.; Klap, J.M.; Erisman, J.W. Effects of Environmental Stress on Forest Crown Condition in Europe. Part I: Hypotheses

and Approach to the Study. Water. Air. Soil Pollut. 2000, 119, 317–333. [CrossRef]
21. De Marco, A.; Proietti, C.; Cionni, I.; Fischer, R.; Screpanti, A.; Vitale, M. Future Impacts of Nitrogen Deposition and Climate

Change Scenarios on Forest Crown Defoliation. Environ. Pollut. 2014, 194, 171–180. [CrossRef]
22. Gottardini, E.; Cristofolini, F.; Cristofori, A.; Pollastrini, M.; Camin, F.; Ferretti, M. A Multi-Proxy Approach Reveals Common and

Species-Specific Features Associated with Tree Defoliation in Broadleaved Species. For. Ecol. Manag. 2020, 467, 118151. [CrossRef]
23. Dobbertin, M. Tree Growth as Indicator of Tree Vitality and of Tree Reaction to Environmental Stress: A Review. Eur. J. For. Res.

2005, 124, 319–333. [CrossRef]

http://doi.org/10.1126/science.aac6759
http://doi.org/10.1007/s11104-014-2056-2
http://doi.org/10.1016/j.foreco.2003.07.019
http://doi.org/10.1017/9781009157896.001
http://doi.org/10.1016/j.foreco.2009.09.001
http://doi.org/10.1029/2006GL025734
http://doi.org/10.1007/s10584-006-9226-z
http://doi.org/10.1111/gcb.12657
http://doi.org/10.1093/treephys/tpq055
http://doi.org/10.1007/s13595-015-0459-8
http://doi.org/10.3220/ICP1638780772000
http://doi.org/10.1016/S0378-1127(00)00335-2
http://doi.org/10.1007/s40725-021-00137-8
http://doi.org/10.1023/A:1005157509454
http://doi.org/10.1016/j.envpol.2014.07.027
http://doi.org/10.1016/j.foreco.2020.118151
http://doi.org/10.1007/s10342-005-0085-3


Plants 2023, 12, 168 15 of 17

24. Eichhorn, J.; Roskams, P.; Potočić, N.; Timmermann, V.; Ferretti, M.; Mues, V.; Szepesi, A.; Durrant, D.; Seletković, I.; Schroeck,
H.-W.; et al. Part IV: Visual Assessment of Crown Condition and Damaging Agents. Version 2020-3. In Manual on Methods and
Criteria For harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air pollution on Forests; Thünen Institute of
Forest Ecosystems: Eberswalde, Germany, 2020; p. 49. ISBN 978-3-86576-162-0.

25. Lorenz, M. International Co-Operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests-ICP Forests.
Water. Air. Soil Pollut. 1995, 85, 1221–1226. [CrossRef]

26. Timmermann, V.; Potočić, N.; Ognjenović, M.; Kirchner, T. Tree Crown Condition in 2021. Forest Condition in Europe: The 2022
Assessment. ICP Forests technical Report under the UNECE Convention on Long-Range Transboundary Air Pollution (Air Convention);
Thünen Institute: Eberswalde, Germany, 2022; pp. 41–55.

27. Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogée, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A.; et al.
Europe-Wide Reduction in Primary Productivity Caused by the Heat and Drought in 2003. Nature 2005, 437, 529–533. [CrossRef]

28. Seidling, W. Signals of Summer Drought in Crown Condition Data from the German Level I Network. Eur. J. For. Res. 2007, 126,
529–544. [CrossRef]

29. Seletković, I.; Potočić, N.; Ugarković, D.; Jazbec, A.; Pernar, R.; Seletković, A.; Benko, M. Climate and Relief Properties Influence
Crown Condition of Common Beech (Fagus sylvatica L.) on the Medvednica Massif. Period. Biol. 2009, 111, 435–441.

30. Ognjenović, M.; Seletković, I.; Potočić, N.; Marušić, M.; Tadić, M.P.; Jonard, M.; Rautio, P.; Timmermann, V.; Lovreškov, L.;
Ugarković, D. Defoliation Change of European Beech (Fagus Sylvatica L.) Depends on Previous Year Drought. Plants 2022,
11, 730. [CrossRef]

31. Geßler, A.; Keitel, C.; Kreuzwieser, J.; Matyssek, R.; Seiler, W.; Rennenberg, H. Potential Risks for European Beech (Fagus Sylvatica
L.) in a Changing Climate. Trees-Struct. Funct. 2007, 21, 1–11. [CrossRef]

32. Leuschner, C. Drought Response of European Beech (Fagus sylvatica L.)—A Review. Perspect. Plant Ecol. Evol. Syst. 2020,
47, 125576. [CrossRef]

33. Seletković, Z.; Tikvić, I.; Prpić, B. Ekološka Konstitucija Obične Bukve. In Obična Bukva (Fagus sylvatica L.) u Hrvatskoj; Matić, S.,
Ed.; Akademija Šumarskih Znanosti: Zagreb, Croatia, 2003.

34. Bolte, A.; Czajkowski, T.; Cocozza, C.; Tognetti, R.; De Miguel, M.; Pšidová, E.; Ditmarová, L.; Dinca, L.; Delzon, S.; Cochard,
H.; et al. Desiccation and Mortality Dynamics in Seedlings of Different European Beech (Fagus sylvatica L.) Populations under
Extreme Drought Conditions. Front. Plant Sci. 2016, 7, 751. [CrossRef] [PubMed]

35. Jonard, M.; André, F.; Giot, P.; Weissen, F.; Van der Perre, R.; Ponette, Q. Thirteen-Year Monitoring of Liming and PK Fertilization
Effects on Tree Vitality in Norway Spruce and European Beech Stands. Eur. J. For. Res. 2010, 129, 1203–1211. [CrossRef]

36. Thimonier, A.; Graf Pannatier, E.; Schmitt, M.; Waldner, P.; Walthert, L.; Schleppi, P.; Dobbertin, M.; Kräuchi, N. Does Exceeding
the Critical Loads for Nitrogen Alter Nitrate Leaching, the Nutrient Status of Trees and Their Crown Condition at Swiss Long-Term
Forest Ecosystem Research (LWF) Sites? Eur. J. For. Res. 2010, 129, 443–461. [CrossRef]

37. Ferretti, M.; Calderisi, M.; Marchetto, A.; Waldner, P.; Thimonier, A.; Jonard, M.; Cools, N.; Rautio, P.; Clarke, N.; Hansen, K.;
et al. Variables Related to Nitrogen Deposition Improve Defoliation Models for European Forests. Ann. For. Sci. 2015, 72,
897–906. [CrossRef]

38. Toïgo, M.; Nicolas, M.; Jonard, M.; Croisé, L.; Nageleisen, L.M.; Jactel, H. Temporal Trends in Tree Defoliation and Response to
Multiple Biotic and Abiotic Stresses. For. Ecol. Manag. 2020, 477, 118476. [CrossRef]

39. Ognjenović, M.; Levanič, T.; Potočić, N.; Ugarković, D.; Indir, K.; Seletković, I. Interrelations of Various Tree Vitality Indicators and
Their Reaction to Climatic Conditions on a European Beech (Fagus sylvatica L.) Plot. Šumarski List 2020, 144, 351–365. [CrossRef]

40. Mellert, K.H.; Göttlein, A. Comparison of New Foliar Nutrient Thresholds Derived from van Den Burg’s Literature Compilation
with Established Central European References. Eur. J. For. Res. 2012, 131, 1461–1472. [CrossRef]

41. Seletković, I.; Potočić, N. Stanje Ishrane Obične Bukve (Fagus sylvatica L.) i Obične Jele (Abies alba Mill.) Na Području Medvednice.
Rad. Šum. Inst. Izvanredno Izd. 2006, 9, 117–125.

42. Raitio, H. Chemical Needle Analysis as a Diagnostic and Monitoring Method. In Nutrient Uptake and Cycling in Forest Ecosystems;
Nilsson, L.O., Hüttl, R.F., Johansson, U.T., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1993; Volume 197–202,
ISBN 0792330307.

43. Komlenović, N.; Gračan, J.; Pezdric, N.; Rastovski, P. Utjecaj Polutanata Na Bukove Šume i Kulture Smreke u Sjeverozapadnoj
Hrvatskoj. Šumarski List 1988, 112, 12.

44. Komlenović, N.; Matković, N.; Moćan, D.; Rastovski, P. Unos Onečišćenja Iz Zraka u Šumu Bukve i Jele (Abieti-Fagetum
“Dinaricum”) u Predjelu Lividrage u Zapadnoj Hrvatskoj. Šumarski List 1997, 121, 8.

45. Sauter, U. Zeitliche Variationen Des Ernährungszustands Nordbayerischer Kiefernbestände. Forstwissenschaftliches Cent. Ver. mit
Tharandter Forstl. Jahrb. 1991, 110, 13–33. [CrossRef]

46. Prietzel, J.; Kolb, E.; Rehfuess, K.E. Langzeituntersuchung Ehemals Streugenutzter Kiefernökosysteme in der Oberpfalz: Verän-
derungen von Bodenchemischen Eigenschaften und der Nährelementversorgung der Bestände. Forstwissenschaftliches Cent. Ver.
mit Tharandter Forstl. Jahrb. 1997, 116, 269–290. [CrossRef]

47. Stefan, K.; Fürst, A.; Hacker, R.; Bartels, U. Forest Foliar Condition in Europe. Results of Large-Scale Foliar Chemistry Surveys.;
European Commission—United Nations/Economic Commission for Europe: Brussels, Belgium, 1997.

48. Duquesnay, A.; Dupouey, J.-L.; Clement, A.; Ulrich, E.; Le Tacon, F. Spatial and Temporal Variability of Foliar Mineral Concentra-
tion in Beech (Fagus Sylvatica) Stands in Northeastern France. Tree Physiol. 2000, 20, 13–22. [CrossRef]

http://doi.org/10.1007/BF00477148
http://doi.org/10.1038/nature03972
http://doi.org/10.1007/s10342-007-0174-6
http://doi.org/10.3390/plants11060730
http://doi.org/10.1007/s00468-006-0107-x
http://doi.org/10.1016/j.ppees.2020.125576
http://doi.org/10.3389/fpls.2016.00751
http://www.ncbi.nlm.nih.gov/pubmed/27379105
http://doi.org/10.1007/s10342-010-0410-3
http://doi.org/10.1007/s10342-009-0328-9
http://doi.org/10.1007/s13595-014-0445-6
http://doi.org/10.1016/j.foreco.2020.118476
http://doi.org/10.31298/sl.144.7-8.2
http://doi.org/10.1007/s10342-012-0615-8
http://doi.org/10.1007/BF02741237
http://doi.org/10.1007/BF02766904
http://doi.org/10.1093/treephys/20.1.13


Plants 2023, 12, 168 16 of 17

49. Jonard, M.; André, F.; Dambrine, E.; Ponette, Q.; Ulrich, E. Temporal Trends in the Foliar Nutritional Status of the French, Walloon
and Luxembourg Broad-Leaved Plots of Forest Monitoring. Ann. For. Sci. 2009, 66, 412. [CrossRef]

50. Amores, G.; Santamaría, J.M. Beech Foliar Chemical Composition: A Bioindicator of Air Pollution Stress. In Developments in
Environmental Science; Elsevier: Amsterdam, The Netherlands, 2003; Volume 3, pp. 301–313. ISBN 1474-8177.

51. Amores, G.; Bermejo, R.; Elustondo, D.; Lasheras, E.; Santamaría, J.M. Nutritional Status of Northern Spain Beech Forests. Water
Air Soil Pollut. 2006, 177, 227–238. [CrossRef]

52. Penuelas, J.; Sardans, J.; Ogaya, R.; Estiarte, M. Nutrient Stoichiometric Relations and Biogeochemical Niche in Coexisting Plant
Species: Effect of Simulated Climate Change. Polish J. Ecol. 2008, 56, 613–622.

53. Sardans, J.; Peñuelas, J. Tree Growth Changes with Climate and Forest Type Are Associated with Relative Allocation of Nutrients,
Especially Phosphorus, to Leaves and Wood. Glob. Ecol. Biogeogr. 2013, 22, 494–507. [CrossRef]

54. Simon, A.; Wild, A. Mineral Nutrients in Leaves and Bast of Pedunculate Oak (Quercus robur L.) at Different States of Defoliation.
Chemosphere 1998, 36, 955–959. [CrossRef]

55. Ouimet, R.; Moore, J.-D. Effects of Fertilization and Liming on Tree Growth, Vitality and Nutrient Status in Boreal Balsam Fir
Stands. For. Ecol. Manag. 2015, 345, 39–49. [CrossRef]

56. Ewald, J. Ecological Background of Crown Condition, Growth and Nutritional Status of Picea Abies (L.) Karst. in the Bavarian
Alps. Eur. J. For. Res. 2005, 124, 9–18. [CrossRef]

57. Potočić, N.; Ćosić, T.; Pilaš, I. The Influence of Climate and Soil Properties on Calcium Nutrition and Vitality of Silver Fir (Abies
Alba Mill.). Environ. Pollut. 2005, 137, 596–602. [CrossRef] [PubMed]

58. Braun, S.; Schindler, C.; Rihm, B. Foliar Nutrient Concentrations of European Beech in Switzerland: Relations with Nitrogen
Deposition, Ozone, Climate and Soil Chemistry. Front. For. Glob. Chang. 2020, 3, 33. [CrossRef]

59. Bergmann, W. (Ed.) Nutritional Disorders of Plants; Gustav Fischer Verlag: Jena, Germany; Stuttgart, Germany; New York, NY,
USA, 1992.

60. Lukac, M.; Calfapietra, C.; Lagomarsino, A.; Loreto, F. Global Climate Change and Tree Nutrition: Effects of Elevated CO2 and
Temperature. Tree Physiol. 2010, 30, 1209–1220. [CrossRef]

61. Meier, I.C.; Leuschner, C. Nutrient Dynamics along a Precipitation Gradient in European Beech Forests. Biogeochemistry 2014, 120,
51–69. [CrossRef]

62. Ferretti, M.; Bacaro, G.; Brunialti, G.; Calderisi, M.; Croisé, L.; Frati, L.; Nicolas, M. Tree Canopy Defoliation Can Reveal Growth
Decline in Mid-Latitude Temperate Forests. Ecol. Indic. 2021, 127, 107749. [CrossRef]

63. Tallieu, C.; Badeau, V.; Allard, D.; Nageleisen, L.M.; Bréda, N. Year-to-Year Crown Condition Poorly Contributes to Ring Width
Variations of Beech Trees in French ICP Level I Network. For. Ecol. Manag. 2020, 465, 118071. [CrossRef]

64. Ferretti, M.; Fischer, R.; Mues, V.; Granke, O.; Lorenz, M.; Seidling, W.; Nicolas, M. Part II:Basic Design principlesfor the ICP
Forests Monitoring Networks. Version 2020-2. In Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring
and Analysis of the Effects of Air Pollution On forests; Thünen Institute of Forest Ecosystems: Eberswalde, Germany, 2020; p. 33.
ISBN 978-3-86576-162-0.

65. Caudullo, G.; Welk, E.; San-Miguel-Ayanz, J. Chorological Maps for the Main European Woody Species. Data Br. 2017, 12,
662–666. [CrossRef]

66. Pravilnik o Vrstama Stanišnih Tipova, Karti Staništa, Ugroženim i Rijetkim Stanišnim Tipovima Te o Mjerama Za Očuvanje
Stanišnih Tipova NN 7/2006. Available online: https://narodne-novine.nn.hr/clanci/sluzbeni/2006_01_7_156.html (accessed on
14 July 2022).

67. Cervellini, M.; Zannini, P.; Di Musciano, M.; Fattorini, S.; Jiménez-Alfaro, B.; Rocchini, D.; Field, R.; Vetaas, O.R.; Irl, S.D.H.;
Beierkuhnlein, C. A Grid-Based Map for the Biogeographical Regions of Europe. Biodivers. Data J. 2020, 8, e53720. [CrossRef]

68. Perčec Tadić, M. Gridded Croatian Climatology for 1961–1990. Theor. Appl. Climatol. 2010, 102, 87–103. [CrossRef]
69. Perčec Tadić, M.; Pasarić, Z.; Guijarro, J.A. Croatian High-Resolution Monthly Gridded Data Set of Homogenised Surface Air

Temperature. Theor. Appl. Climatol. 2022. [CrossRef]
70. Palmer, W.C. Meteorological Drought; US Department of Commerce, Weather Bureau: Washington, DC, USA, 1965; Volume 45.
71. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standard-

ized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718. [CrossRef]
72. Dial, R.; Tobin, S.C. Description of Arborist Methods for Forest Canopy Access and Movement. Selbyana 1994, 15, 24–37.
73. Jepson, J. The Tree Climber’s Companion; Beaver Tree Publishing: Longville, MI, USA, 2000.
74. Rautio, P.; Fürst, A.; Stefan, K.; Raitio, H.; Bartels, U. Part XII: Sampling and Analysis of Needles and Leaves. In Manual on

Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests; UNECE,
ICP Forests Programme Co-Ordinating Centre: Hamburg, Germany, 2016.

75. ISO 11277; Soil Quality—Determination of Particle Size Distribution in Mineral Soil Material. ISO: Geneva, Switzerland, 2020.
76. ISO-10390; Soil Quality—Determination of PH. ISO: Geneva, Switzerland, 2005.
77. ISO-11260; Soil Quality—Determination of Effective Cation Exchange Capacity and Base Saturation Level Using Barium Chloride

Solution4. ISO: Geneva, Switzerland, 1994.
78. ISO 14254; Soil Quality—Determination of Exchangeable Acidity Using Barium Chloride Solution as Extractant 2018. ISO:

Geneva, Switzerland, 2018.

http://doi.org/10.1051/forest/2009014
http://doi.org/10.1007/s11270-006-9157-3
http://doi.org/10.1111/geb.12015
http://doi.org/10.1016/S0045-6535(97)10154-0
http://doi.org/10.1016/j.foreco.2015.02.032
http://doi.org/10.1007/s10342-004-0051-5
http://doi.org/10.1016/j.envpol.2005.01.045
http://www.ncbi.nlm.nih.gov/pubmed/16005770
http://doi.org/10.3389/ffgc.2020.00033
http://doi.org/10.1093/treephys/tpq040
http://doi.org/10.1007/s10533-014-9981-2
http://doi.org/10.1016/j.ecolind.2021.107749
http://doi.org/10.1016/j.foreco.2020.118071
http://doi.org/10.1016/j.dib.2017.05.007
https://narodne-novine.nn.hr/clanci/sluzbeni/2006_01_7_156.html
http://doi.org/10.3897/BDJ.8.e53720
http://doi.org/10.1007/s00704-009-0237-3
http://doi.org/10.1007/s00704-022-04241-y
http://doi.org/10.1175/2009JCLI2909.1


Plants 2023, 12, 168 17 of 17

79. ISO-13878; Soil Quality—Determination of Total Nitrogen Content by Dry Combustion (“elemental Analysis”) 1998. ISO: Geneva,
Switzerland, 1998.

80. Egnér, H.; Riehm, H.; Domingo, W.R. Untersuchungen Über Die Chemische Bodenanalyse Als Grundlage Für Die Beurteilung
Des Nährstoffzustandes Der Böden. II. Chemische Extraktionsmethoden Zur Phosphor-Und Kaliumbestimmung. K. Lantbr. Ann.
1960, 26, 199–215.

81. Škorić, A. Priručnik Za Pedološka Istraživanja; Fakultet Poljoprivrednih Znanosti: Zagreb, Croatia, 1985.
82. Lix, L.M.; Keselman, J.C.; Keselman, H.J. Consequences of Assumption Violations Revisited: A Quantitative Review of Alterna-

tives to the One-Way Analysis of Variance F Test. Rev. Educ. Res. 1996, 66, 579–619. [CrossRef]
83. Holm, S. A Simple Sequentially Rejective Multiple Test Procedure. Scand. J. Stat. 1979, 6, 65–70.
84. Ross, A.; Willson, V.L. Independent Samples T-Test. In Basic and Advanced Statistical Tests; Springer: Rotterdam, The Netherlands,

2017; pp. 13–16.
85. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [CrossRef]
86. Johnson, J.B.; Omland, K.S. Model Selection in Ecology and Evolution. Trends Ecol. Evol. 2004, 19, 101–108. [CrossRef]
87. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2016.

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people or property resulting from any ideas, methods, instructions or products referred to in the content.

http://doi.org/10.3102/00346543066004579
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http://doi.org/10.1016/j.tree.2003.10.013

	Ognjenović et al 2021.pdf
	Ognjenovic et al 2023 Plants_Effect of Environmental Factors on the Nutrition of Beech
	Introduction 
	Results 
	Foliar Nutrient Concentrations 
	Foliar Nutrient Ratios 
	Effects of Environmental Factors on Differences in Foliar Nutrition 

	Discussion 
	Materials and Methods 
	Study Area and Plot Design 
	Climate Data 
	Tree Selection Procedures, Foliar Sampling and Analysis 
	Soil Sampling and Analysis 
	Data Analysis 

	Conclusions 
	References