Article https://doi.org/10.1038/s41467-024-53160-1
Mapping multi-dimensional variability in
water stress strategies across temperate
forests
Daijun Liu 1,2,3 , Adriane Esquivel-Muelbert 1,2, Nezha Acil 1,2,4,5,
Julen Astigarraga 6, Emil Cienciala 7,8, Jonas Fridman 9,
Georges Kunstler 10, Thomas J. Matthews1,2,11, Paloma Ruiz-Benito6,12,
Jonathan P. Sadler 1,2, Mart-Jan Schelhaas 13, Susanne Suvanto1,2,14,
Andrzej Talarczyk 15, Christopher W. Woodall 16, Miguel A. Zavala 6,
Chao Zhang17 & Thomas A. M. Pugh 1,2,18
Increasingwater stress is emerging as a global phenomenon, and is anticipated
to have a marked impact on forest function. The role of tree functional stra-
tegies is pivotal in regulating forest fitness and their ability to cope with water
stress. However, how the functional strategies found at the tree or species level
scale up to characterise forest communities and their variation across regions
is not yet well-established. By combining eight water-stress-related functional
traits with forest inventory data from the USA and Europe, we investigated the
community-level trait coordination and the biogeographic patterns of trait
associations forwoodyplants, and analysed the relationships between the trait
associations and climate factors. We find that the trait associations at
the community level are consistentwith those found at the species level. Traits
associated with acquisitive-conservative strategies forms one dimension of
variation, while leaf turgor loss point, associated with stomatal water regula-
tion strategy, loads along a second dimension. Surprisingly, spatial patterns of
community-level trait association are better explained by temperature than by
aridity, suggesting a temperature-driven adaptation. These findings provide a
basis to build predictions of forest response underwater stress, with particular
potential to improve simulations of tree mortality and forest biomass accu-
mulation in a changing climate.
Forests across the world are facing increasing water stress because of
climate change1–3. Forest responses to water stress will strongly
determine their future ability to deliver key ecosystem functions and
services such as carbon sequestration, microclimatic amelioration and
wood production4. These responses to water stress will be largely
driven by the ecological strategies of the trees that compose them5–7.
Ecological strategies describe the set of adaptations and behaviours
that allow a species to maintain a population8–10 and we use this term
here in terms of adaptations to do so in the face of water stress. These
water stress adaptation strategies include closing leaf stomata early,
investing in stronger water transport structures, dropping leaves,
storing water and developing deeper roots7,11–14 (Fig. 1a). These stra-
tegies themselves emerge from anatomical or physiological properties
that can be characterised by functional traits7,15, closely linked to sur-
vival, development, growth and reproduction10,16,17. The values of these
traits are often constrained by trade-offs between them13,18,19. Whilst
Received: 21 September 2023
Accepted: 2 October 2024
Check for updates
A full list of affiliations appears at the end of the paper. e-mail: cqliudaijun@gmail.com
Nature Communications |         (2024) 15:8909 1
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the knowledge of water stress strategies at the tree or species level is
increasingly understood7,11,12, we lack large-scale assessments of the
trait associations at the level of forest communities, which emerge
from the strategies across their constituent individuals. To understand
how responses to water stress vary across the world’s forests, it is first
necessary to characterise these trait associations and to identify how
they vary in space.
Tree functional strategies have been widely characterised as a
continuum between acquisitive species, which take risks in order to
win contests for resources, and conservative species, which invest
in tolerance of stress in order to thrive in harsh conditions8–10,17–19. For
example, a treemay grow in a way that indicates light acquisition plays
a more important role than the ability to tolerate water stress, hence
suggesting carbon allocation strategies focused on height growth
relative to the area of conductive sapwood, for a given leaf area.
Assuming the same sapwood conductivity and robustness of xylem to
cavitation, during a drought, this high leaf area to sapwood ratio may
lead to excessively negative pressures in the xylem, causing hydraulic
failure and death, whereas species favouring a lower ratio of leaf to
sapwood area may survive (Fig. 1b). The combination of structural
traits influences whether a tree is structurally more acquisitive or
conservative. Other adaptations to water stress (e.g., isohydricity or
water regulation) may not map so directly onto this continuum6,14,20.
For instance, isohydric species, which close their stomata under
moderate water stress, may avoid putting stress on their xylem but
also impair their ability to photosynthesise. Anisohydric species keep
stomata open under greater water potential gradients, but either need
to invest in more robust water transport structures or risk hydraulic
failure. Furthermore, water storage and deeper rooting may allow
trees to endure dry conditions by avoiding water stress21,22. The func-
tional strategy space is, therefore, complex and where an individual
tree is situated within it has implications for the function and ecolo-
gical services it provides.
To move from tree-level responses to forest community-level
responses, it is necessary to characterise how water stress strategies
assemble into communities. When species assemble into commu-
nities, the strategies that come to dominate depend on niche com-
plementarity and resource competition23–25. As water resources
become less limiting, competitionmay tend to favourmore acquisitive
strategies, resulting in convergence towards a limited range of water-
demanding strategies9,10,17,26. In water-limited environments, a wide
range of water stress strategies may be presented. These strategies
have been shown to be ecologically partitioned according to specific
combinations of environmental conditions11,27,28. How the trait asso-
ciation at the community level varies as a function ofwater stress is not
well established. Understanding trait associations is, however, funda-
mental to being able to predict the likely response of the forest com-
munity to ongoing environmental changes and to generating accurate
predictions by vegetationmodels, which are used tomake projections
of future forest function2. These models are currently taking big steps
forward in their capacity to simulate physiological processes relevant
to water stress responses29–32, but require precise data on the dis-
tribution of forest water stress strategies. An exploration of the trait
associations along climatic gradients, allowing us to infer the water
stress strategy within forest communities, would provide a new per-
spective on forest functional biogeography and open upopportunities
to support large-scale projections of forest function using vegetation
models.
Here, we combine a large dataset of functional traits for woody
plants with forest inventory plot data across regions of the United
States of America and Europe, including Spain, France, Germany,
Poland, Czechia and Sweden.We consider eight continuous functional
traits ofwoodyplants (shrubs and trees) related topotential functional
strategies for dealing with water stress (Table 1). We concentrate on
acquisitive-conservative, structural, stomatal and water storage stra-
tegies, as the information on rooting traits is very limited. The choice
of these traits is driven particularly by their relevance for informing
process-based modelling of forest form and function. We aggregate
these forest plots using a 0.25° × 0.25° grid (grid-cell level statistics are
hereafter referred to as community level) to reduce stochasticity and
then calculate community-weighted mean traits for each community
separately. The community-weighted means encapsulate the trait
associations at the community level. With the exception of commu-
nities including two ormore equally prevalent but opposite strategies,
they can also be expected to be representative of the strategies across
the individuals that compose that community. We use data for 12,452
forest communities across the USA and Europe, for which we also
extract climate variables relating to water availability and temperature
information based on their geographic locations (details of the vari-
ables in the Methods). We first assess the axes of variation of
community-level trait associations with respect to water stress traits
based on principal component analysis (PCA), and then compare them
to the axes of variation of plant strategies at the species level. Finally,
we relate the community-level associations to the geographic and
Fig. 1 | Conceptual diagramof how tree level adaptationswith respect towater
stress scale up to biogeographic patterns of trait associations at the level of
forest communities. a Trees have several possible functional pathways that affect
their response to water stress7,9,17. b These pathways combine into different
potential species-level strategies, which include structurally conservative, early
stomatal closure to avoid water loss, water storage and deep rooting. c Species
assemble into communities, where the combination of their strategies controls
community form and function. Community-level functional traits are calculated as
their community-weighted mean of each species within a community. Co-variation
of these traits, as assessed using principal component analysis (PCA), can identify
the trait associations at the community level, which in most cases are expected to
reflect the underlying strategies within each community. d Biogeographic patterns
of these associations are expected to emerge as a function of broad-scale patterns
in climate65. This figure is created in BioRender. Zhang, C. (2024) BioRender.com/
s74n436.
Article https://doi.org/10.1038/s41467-024-53160-1
Nature Communications |         (2024) 15:8909 2
climatic space of temperate forest communities (Fig. 1c, d). We
hypothesise that: (1) the strategic variation at the species level will be
reflected in the variation of trait associations at the community level;
(2) there is a tendency towards communities dominated by more
structurally acquisitive strategies (and thus community-level trait
associations) as the climate becomes less arid, reflecting a shift
towards competition for light rather than water; and (3) there is more
divergence in community-level associations across water-limited
habitats, reflecting the broad range of options that trees can pursue
to survive when water is limiting.
Results and discussion
Variation in trait associations at the community level
Clear differences in trait associations along the two main axes were
identified. The principal component analysis (PCA) revealed that the
traits of leaf nitrogen content and leaf mass per area were primarily
loaded along the first principal component (PC1) dimension (Fig. 2a).
These two traits are associatedwith the acquisitive-conservative trade-
off of the leaf economics spectrum (LES), with higher PC1 indicating a
tendency towards more acquisitive strategies in the species compris-
ing the community9,10,17. Hydraulic traits of xylem embolism resistance,
sapwood conductivity and leaf area to sapwood area ratio also loaded
along this same axis. Each of these functional traits contributed
15.8–20.8% to the variation in PC1, and they were strongly correlated
with each other (Pearson correlation coefficient = 0.46–0.79; all
p <0.001; Fig. 2b). These community-level associations are consistent
with the link between acquisitive-conservative strategies at the leaf-
level and the wider plant structure reported at the species level13,33.
The other aspects of hydraulic strategy displayed associations
largely orthogonal to PC1. Leaf turgor loss point, wood density and
xylem cavitation slope primarily loaded onto PC2 (Fig. 2a, b).We relate
this axis to the isohydricity spectrum (Table 1), with higher PC2 values
associatedwith amore anisohydric strategy (morenegative leaf turgor
loss and higher wood density). Communities with a more negative
Table 1 | The functional traits considered in this study
Functional traits (abbreviation) Units Explanation
Leaf nitrogen content (N) mgg−1 Proxy for Rubisco content of leaves and thereby photosynthetic capacity and growth9,10,17. More
acquisitive strategies are expected to have higher N.
Maximum xylem conductivity per unit
sapwood area (Ks)
kgm−1 s−1 MPa−1 Key structural determinants of the xylem water potential need to be tolerated to keep the canopy
supplied with water7,13,18. High Ks and/or LS are expected to be associated with more acquisitive
strategies.Leaf area to sapwood area ratio (LS) mm2 mm−2
Leaf mass per area (LMA) gm−2 Investment in leaf tissue relates to defensive allocation and helps dissipate energy from high
radiation inputs, protecting against high temperature9,10,17. More conservative strategies have higher
values.
Xylem water potential at 50% loss of
conductivity (P50)
MPa A critical determinant of tolerance to large water potential gradients and closely related to tree
mortality7,11,12. Lower P50 values indicate more tolerance to water stress.
Embolism vulnerability curve between
P50-P88 (Slope)
% MPa−1 Extent to which xylem cavitation is a gradual or threshold-like response to water potential11. All else
being equal, a steeper slope indicates more sensitivity to water stress.
Leaf turgor loss point (TLP) MPa Closely correlated to isohydricity14,74. A less negative TLP is associatedwithmore isohydricbehaviour.
Wood density (WD) g cm−3 Associated with the investment in defence and water storage34,75,76 and a fundamental link between
carbon investment and size growth. It has been linked to stomatal regulation and water storage
strategies14.
Fig. 2 | Key trait associations along the two dimensions for the forest com-
munities. a Biplot resulting from the principal component analysis (PCA) of eight
functional traits at the community level. There were 12,452 forest communities
used in our analysis. The community weighted-mean trait was calculated based on
all species-level trait values weighted by the proportions of the species basal area
within a community. The dominant forest type was identified according to the
relative proportion of dominant types representing more than 50% of the com-
munity basal area: BD (broadleaved deciduous, blue, 5584 communities); BE
(broadleaved evergreen, green, 407 communities) and NC (needleleaved conifer,
red, 6461 communities). b The Pearson correlation coefficients between the func-
tional traits at the community level (all p values < 0.001). The Pearson correlation
tests are two-sided. Abbreviations of functional traits: N leaf nitrogen content, Ks
maximum xylem conductivity per unit sapwood area, LS leaf area to sapwood area
ratio, LMA leafmassper area, P50 xylemwater potential at 50% loss of conductivity,
Slope for the embolism vulnerability curve between P50–P88, TLP leaf turgor loss
point and WD wood density. Traits explanations are provided in Table 1. The ele-
ments in panel a are created in BioRender. Zhang, C. (2024) BioRender.com/
u64o243.
Article https://doi.org/10.1038/s41467-024-53160-1
Nature Communications |         (2024) 15:8909 3
average leaf turgor loss point and a higher average wood density
scored higher on this axis, while communities with a lower average
slope of embolism vulnerability curve had lower scores (Fig. 2a). More
robust xylem (i.e., more negative P50) was associated with higher
wood density, but the correlation was relatively weak and P50 and
wood density largely loaded orthogonally in the PCA, consistent with
wood density being a complex trait which is associated with many
ecological processes10,34,35. The cavitation slope had an extremely weak
correlation (Pearson correlation coefficient = −0.03) with wood den-
sity and also had a relatively low correlation (Pearson correlation
coefficient = −0.32) with leaf turgor loss (Fig. 2b). We observed a
relatively weak, but significant, relationship between slope and leaf
turgor loss, which was consistent with an earlier finding at the species
level that stomatal closure is closely coordinatedwith hydraulic failure
due to xylem embolism36. Altogether, this indicates less tight coupling
of the traits along PC2 than those along PC1.
Species to community scaling
The community-level PCA displayed similar loadings of traits along
two main dimensions of the PCA to that at the species level (Figs. S2a
and S2b). This result is consistent with our first hypothesis, H1, that
“the strategic variation at the species level will be reflected in the
variation of trait associations at the community level” (Figs. S3 and S4).
There was, however, 9.1% (64.2% vs. 55.1% for the community level and
species level, respectively)more explained varianceon the community
level (Fig. 2a) when compared to the species level (Fig. S2a), which
resulted from a higher loading contribution along the first dimension
when scaling up. Leaf nitrogen content was more closely coupled to
structural strategy (e.g., xylem conductivity and leaf area to sapwood
area ratio) at the community level (Fig. 2a) thanat the species level (Fig.
S2a). This may reflect the optimisation of resource usage (e.g., com-
petition for light and forest growth) at the species level affecting forest
community-level properties13,37. That the axis associating leaf eco-
nomics with wider plant structure and the axis we link to the isohydric
spectrum are both found at the species and community levels appears
to suggest that the process of community assembly does not allow
communities to diverge substantially from the same basic trade-offs
that govern individual plant strategy. Therefore, the functional trait
space that needs to be taken into account in large-scale assessments of
forest properties and ecosystem functions remains consistent at
individual and community extents38,39.
The functional trait space at the community level (Figs. 2a and
S4a) showed a clear split among three traditional plant functional
groups based on leaf type and habitat (broadleaved evergreen,
broadleaved deciduous and needle-leaved conifer), which was less
apparent at the species level (Fig. S4). This split was especially clear
between conifer and broadleaf trees, with broadleaf deciduous almost
universally falling on the acquisitive side of PC1 and broadleaf ever-
green on the anisohydric end of PC2. These general patterns were also
reflected at the species level butwithmoreoverlap. These species-level
patterns of more acquisitive trait associations in broadleaved decid-
uous trees and more conservative ones in conifers are consistent with
previous studies9,17. However, our results suggest that environmental
filtering accentuates this differentiation among the trait associations
of functional groups when assessed at the community level.
We tested the gap-filling method used for each trait across
species by validating different proportions of missingness (0.2–0.8
for the species that have observed trait values), finding high-
coefficients of determination (mean R2 = 0.62−0.94; Fig. S5a) and low
root mean squared error (RMSE) (mean RMSE < 1; 0.3−0.86; Fig. S5b)
for all comparisons. Generally, we found the differences to be rela-
tively small compared to the trait values from the observations.
Additionally, the trait coordination principles of forest communities
reported abovewere robust with respect to the threemethods used to
gap-fill traits after species matching (Fig. S6) and to the grid cell size
used (details in the “Methods” section; Fig. S7). Furthermore, Pro-
crustes tests demonstrate the loading values of the main axes of
the PCA were similar for the comparisons between different grid sizes
and different gap-filling methods at the species level (all p values <
0.001; Fig. S8) and at the community level (all p values < 0.001; Fig.
S9). They were also robust to excluding forest communities with <3
plots (19% of the 12,452 communities), which led to similar patterns of
trait variation (Fig. S10a), contribution to the two principal compo-
nents, and Pearson correlation coefficients between the traits
(Fig. S10b).
Geographic variation of trait associations
We found distinct geographic patterns of the trait association var-
iation across the regions (Fig. 3). Trait associations consistent with
an underlying acquisitive strategy (or higher PC1 scores) were
mainly distributed in central and eastern USA and Northern France.
Whilst associations linked to a conservative strategy (or lower
PC1 scores) were distributed in thewestern USA, boreal regions, and
the Mediterranean Basin (Fig. 3). More extreme values of PC1 were
found in the USA than in Europe, which might be a result of the
broader variation in environmental conditions (e.g., more water
stress and higher temperatures)40, a homogenising effect of
potentially more pervasive forest management in Europe41 or dif-
ferent evolutionary histories. Higher PC2 scores, linked with ani-
sohydricity, were broadly distributed across southwest-south USA
and south-western Spain, the areas where broadleaved evergreen
species tended to dominate the forest composition (Fig. S11). The
tendency towards more anisohydric strategies identified in these
areas differed from a previous study, based on diurnal variations in
vegetation optical depth from satellite data (VOD), which found
them to be more isohydric in character42. This disparity may arise
from scale-induced differences in the ecosystems considered, with
our results only focusing on tree communities and based on mea-
surements at the tree scale, whilst the 25 km scale of the VOD pixels
integrates across all vegetation types. Lower PC2 scores, suggesting
a prevalence of more isohydric trait associations, were widely dis-
tributed in the forest ecosystems across the two continents, espe-
cially in central USA and Sweden, where conifers and broadleaved
deciduous trees dominate the forests. This suggests that forest
communities in these regions may be more inclined to chronic
responses to water stress, characterised by resource limitation
following stomatal closure6,20. There are, however, large areas in the
eastern USA and Central Europe that do not tend towards either
strongly anisohydric or isohydric trait associations at the commu-
nity level. This result may reflect underlying species-level strategies
that sit in the middle of the spectrum or may arise from the coex-
istence of species with different isohydric strategies making up
similar proportions of the same community.
Climatic factors drive variation of trait associations
We found that water demand due to rising temperatures could be
more crucial for temperate forest communities than water avail-
ability (Fig. 4). We carried out a relative importance analysis to
assess the contribution of five climate variables related to water
availability and demand to the two dimensions of the PC scores
(Fig. 4a, d). Contrary to our expectations (Hypothesis 2), aridity, as
quantified by the aridity index (AI), was not a strong determinant of
the community-level trait averages (Fig. 4b, e). Instead, we found
that higher mean summer temperatures were positively associated
with PC1 scores (conservative to acquisitive strategy) (Fig. 4c),
whilst higher mean annual temperatures were positively associated
with PC2 scores (water storage capacity to anisohydricity) (Fig. 4f)).
These positive associations with temperature variables may indi-
cate that the adaptation of temporal forest communities reliesmore
onwater demand than water availability (or aridity index). However,
Article https://doi.org/10.1038/s41467-024-53160-1
Nature Communications |         (2024) 15:8909 4
the relationships between PC scores and climate factors were also
relatively weak, aligning with the patterns observed in global
trait–climate relationships43. These weak relationships with climate
variables suggest that the trait associations of temperate forest
communities that emergemay result from functional adaptations to
other aspects of environmental conditions. One such candidate
variable would be nutrient availability, as recently reported for the
Amazon forest44.
Although aridity was not a strong predictor of PC1 or PC2, we
observed a divergence in trait variation of both PC1 and PC2 inmore
arid habitats (low aridity index, Fig. 4b, e), while there was a ten-
dency to converge on a relatively narrow functional trait space with
increasing water availability (higher aridity index, Fig. 4b, e). This
supports Hypothesis 3 that the trait associations of forest commu-
nities diverge when water is limiting, reflecting an underlying broad
range of species-specific water stress strategies to survive in these
conditions. Local conditions of temperature, elevation and soil
nutrient availability have been previously shown to explain the
community-weighted variance of functional traits45,46 and trade-offs
related to thesemay be influencing the trait combinations emerging
at the community level19. Higher spatial resolution work will be
necessary to identify and distinguish which factors cause one water
stress strategy to prevail over another, leading to these community-
level patterns. Additionally, a larger coverage of species with
observations of functional traits, in particular hydraulic traits, will
be beneficial for evaluating the adaptations of forest communities
under water stress. The same applies to niches within communities;
we found a similar or higher variance of individual traits within
communities as opposed to across them (Fig. S12). This, in turn, will
be essential for understanding how forest function is likely to
change under increasing water stress in the future.
Future challenges
Although the dataset here is one of the broadest-reaching for water
stress-relevant traits yet assembled, we still found data gaps (ca.
3–28%; detailed trait missing in Table S1) after trait imputation by
genus and family for the forest tree species, even in these well-studied
North American and European forests. Although our sensitivity tests
showed robust results with respect to the trait imputations based on
phylogenetic signal, further collection and compilation of field mea-
surements will reduce uncertainty in upscaling assessments such as
those presented here. Such measurements would not only help to
better quantify intraspecific or interspecific trait variation across bio-
geographic regions47,48 but also allow better parameterisation of
process-based models to explore linkages with forest function. Our
selection of traits, being driven by considerations of modelling tree
form and function from first principles, did not include those that
clearly map onto the height dimension that has been identified by
other large-scale trait analyses17,49. However, the height dimension is
one that is hypothesised to emerge from the application of traits
related to resourceacquisition, resource allocation and tissue turnover
and mortality (Table 1) within a process-based vegetation model.
Exploration of the structural and behavioural outcomes of functional
strategies by coupling large-scale trait analyses with suitable process-
based models50–52 could facilitate a more complete picture of plant
form and function.
Identifying how functional traits respond towater stress is crucial,
which may lead to apparent discrepancies in the exploration of trait
trade-offs and main functional strategies. The eight traits in our study
were selected on the basis of potential mechanisms outlined the
previous literature7,17, and data availability53. Here we have examined
the strategies of woody plants, however, herbaceous species were not
taken into account. Additionally, we excluded some other traits that
Fig. 3 | Geographic distribution of the trait associations of forest communities.
Therewere 12,452 forest communities used in our analysis. The arrows indicate the
trait associations of a conservative (Conse) to acquisitive (Acqui) and b Isohydric
(Isohy) to Anisohydric (Aniso) separately. The principal component (PC) scores
were calculated using the PCA coordinates in themain two components (Fig. 2a) to
obtain the eigenvalue for each community.
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Nature Communications |         (2024) 15:8909 5
we anticipated to be important for water stress strategies, such as
rooting depth54, root-to-shoot ratio55 and minimum leaf conductance,
the latter influencing the rate of plant desiccation56. The difficulty of
collecting these traits is reflected in the low sample size available in the
literature. While wood density is widely studied, it is only a weak
indicator of potential capacitance35, because it is still not known how
much storedwater is accessible. Likewise, wewere unable to usedirect
measurements of plant capacitance due to the limited availability of
suitable observations. Information on rooting depth, minimum leaf
conductance and capacitance may bring a new angle to the study of
the functional trait space in response to water stress, asmay trade-offs
with other aspects of plant function44.
Scaling up individual or species-level traits to the community level
may only capture the average of trait values, which approximate local
strategies. The intraspecific trait variation in response to environ-
mental conditions was not included within the method of community-
weighted mean traits. New approaches (e.g., normalising plant traits
per unit land area) to integrate community traits and their contextual
information are necessary38. Finally, it is possible that the results in
some regions may be strongly influenced by forest management. For
instance, the south-eastern US, which is an area with intensive plan-
tations and regular cutting, might be expected to alter the natural
selection of strategies57. Identifying a broad gradient of plots with a
long history of being unmanaged would be valuable to separate nat-
ural biogeography from human management decisions. Further
research is required to address these above-mentioned challenges and
to improve the understanding of functional strategies with respect to
global-scale climate change in the future.
While the mechanisms of tree- or species-level strategies to water
stress have been increasingly clarified in recent research7, upscaling
these strategies into associations at the level of forest communities
andmapping these across regional scale has beenmissing to date. Our
study integrated species-level traits into forest communities across the
regions to characterise the emerging functional trait associations and
to explore the variability across two continents. Our results demon-
strate consistency in trait coordination at both the species level and
community level, which highlights the potential of applying these
functional traits to explore forest biodiversity patterns and ecosystem
functions at the community level or ecosystem level38,39. The identified
geographical patterns of trait associations could be used as a basis for
parameterising forest hydraulic function in process-based vegetation
models, allowing us to make predictions of forest functions that are
tightly grounded in observed community trait associations. This could
include, for instance, assessing how water stress affects the current
and future forest carbon sink.
We found that traits relating to xylem embolism resistance, sap-
woodconductivity and tree structural strategies collapsed into a single
axis of variation, loading into the widely reported acquisitive-
conservative spectrum (or fast-slow continuum). However, the xylem
cavitation slope, leaf turgor loss point and wood density loaded onto
an orthogonal axis, are associated with the spectrum of isohydricity
and, tentatively, water storage. This complexity in functional strategy
Fig. 4 | Effect of climatic factors on the trait associations of forest communities
(Principal component (PC) scores: PC1 in upper panels and PC2 in lower
panels). a and d Result of the relative importance analysis of the individual climate
variable for the trait associations along PC1 or PC2. A total of 12,413 forest com-
munities are selected and the number of bootstrap samples is 1000 in the relative
importance analysis. Violin plots show the probability density of the bootstrapped
importance values. The boxplots indicate the interquartile range (the first quartile
(Q1) and the third quartile (Q3)), which includes themedian (50th percentile) of the
bootstrapped estimates of the relative importance values for the five climatic
variables. The whiskers represent the minimum and maximum values within 1.5
times the interquartile range from Q1 and Q3, respectively. b and e The linear
regressions with the aridity index, as hypothesised in the introduction. A total of
12,433 forest communities are used in the analysis. c and f The regressions with the
most important climatic variable for the PC1 (mean summer temperature: Sum-
mer_T) and PC2 (mean annual temperature: MAT) separately. The regression tests
are two-sided and the regression lines and shading are presented as themeans and
standard errors, respectively. A total of 12,434 forest communities are used in the
analysis. The shading in panels b, c, e and f follows the same colour legends as in
Fig. 3 for PC1 and PC2, respectively.
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Nature Communications |         (2024) 15:8909 6
implies a similar complexity in the response of forest functions to
water stress. Our results suggest that trait associations at the com-
munity level are more closely related to water demand than water
availability. However, the relationships with climate are relatively
weak, and the proliferation of different strategies in more arid envir-
onments, along with the limited explanatory power of climate vari-
ables, raises the question of what factors exert the primary filter
governing which water stress strategy comes to dominate a forest
community. Whilst, to some extent, this filter may be determined by
small-scale heterogeneity in edaphic and climatic conditions, it also
indicates that the water stress strategymay be influenced by the trade-
offs among multiple functional traits across a much wider range of
environmental conditions. Unpacking this unexpectedly complex
environmental filter will be a major challenge for functional biogeo-
graphy, but one that is necessary to overcome if we are to understand
how forest composition and functions are likely to adapt to novel
environmental stress.
Methods
Forest inventory data and plot aggregation
We used forest inventory data from the USA and six countries in Eur-
ope (Spain, France, Germany, Czechia, Poland and Sweden) (Table S1).
These forest inventory data included the approximate geographic
locations of the forest plots and, for each individual living tree, the
species and diameter at breast height (1.3m). The large spatial-scale
data covers the temperate, Mediterranean (Spain and western USA)
and boreal biomes (Sweden and Alaska) across their climate gradients.
To keep the forest inventory data temporally consistent between
countries, censuses closest to the year 2010 ( ± 3 years) were selected
since the majority of the forest inventory data were available at that
period.Weused a totalof 219,787 forest inventoryplots across theUSA
and Europe in our analyses. We selected the living trees in each plot
and included only those with a diameter at breast height larger than
12.7 cm across all the datasets. We aggregated inventory plots into
0.25° grid cells to dampen variation induced by the small sizes of plots
and to provide a consistent spatial unit across all the countries. We
term the sample of trees in a grid cell as a community (mean plot
number per community = 17.3; Fig. S1). We selected all the woody
plants (trees and shrubs) in each community based on the woodiness
information from TRY and the Woody Plant Database (http://
woodyplants.cals.cornell.edu). Species scientific names were standar-
dised to the Plant List (http://www.theplantlist.org/) according to the
plantlist package in R (version 0.8.0)58. The cactus species Pilosocereus
royeniiwas removed. In total, 643 woody species were retained for the
analysis. To further distinguish the different strategies of woody
plants, broad leaves were split into two groups according to their leaf
phenological types (evergreen and deciduous). For the conifers, the
species count is not as rich as for the broadleaf trees, with only eight
conifer species being deciduous.We thus classifiedwoody species into
3 functional groups: (1) Broadleaved Deciduous (BD); (2) Broadleaved
Evergreen (BE) and (3) needleleaved Conifer (NC). Of these 643 spe-
cies, there are 307, 215 and 121 for BE, BD and NC, respectively.
Calculating functional traits at the species level and
community level
Eight key functional traits of woody species at the species level were
selected for this analysis (Table 1). The trait data at the species level
were requested from the TRY dataset53 and supplemented by a litera-
ture reviewof 75 researchpublications searched inWebof Science and
Google Scholar until May 2020 using the search terms “Xylem water
potential at 50% loss of conductivity”, “Turgor loss point”, “slope of the
vulnerability curve”, “Xylem conductivity” and “Leaf area to sapwood
area ratio” separately (Supplementary Data 1). Similar to the woody
species in the forest inventory data, species scientific names were
standardised to the Plant List according to the plantlist package
(version 0.8.0) in R58. We then combined the traits with the species
composition in each community, and median values for each species-
level trait were matched to the species present in the forest inventory
data. After this initial species matching, the amount of missing trait
data differed greatly across the eight selected traits (Table S2). For
well-studied traits such as leaf nitrogen content and wood density,
about 40% of the woody species were missing, rising to 60%-80% for
the hydraulic traits. For the species-level functional traits that were
missing, we used the median trait values of the same genus instead.
And if the trait was still missing, we used the median values of the
family to fill the gaps59. However, there were still some species for
which the trait data weremissing (3%-25%). We imputed the remaining
trait values for the species using the funspace package in R (version
0.1.1)60, which is coupled with phylogenetic information to impute
missing trait data61. We carried out sensitivity analyses to explore the
consequences of this imputation for our results (see the “Statistical
analysis” section below).
We calculated mean values for each functional trait for each
community, weighted by the relative basal area of the species in that
community25. The functional traits, except wood density, were log-
transformed to increase the symmetry of their distribution before the
calculation of weighted mean values. For turgor loss point (TLP) and
xylem embolism vulnerability (P50), we first multiplied trait values by
−1 to obtain positive values before log-transformation. Furthermore,
we calculated the basal area for each functional group in a community
and compared this to the total basal area to obtain the relative pro-
portion for each functional group. Based on this, forest functional
types at the community level could be identified according to the
relative proportion of dominant groups representing more than 50%
of the gridbasal area62. 215 forest communitieswere removed from the
analyses because the dominant functional type was <50%. In total, we
used 12,452 communities in our analyses.
Climate factors across regions. Water availability and temperature
are crucial in determining forest function, diversity and productivity63.
Here we considered the pattern of trait associations that are strongly
related to thewater availability based on annual precipitation amounts
and summerprecipitation, aswell as to the aridity index (ratio between
precipitation and evapotranspiration) and to water demand caused by
evapotranspiration based on mean annual temperatures and summer
temperatures. The climatic variables of mean annual precipitation
(MAP), precipitation of driest quarter (Summer_P), mean annual tem-
perature (MAT) and mean temperature of warmest quarter (Sum-
mer_T) were derived from the WorldClim dataset (https://www.
worldclim.org/data/bioclim.html)64. The aridity index (AI) was
obtained from the Global Aridity and PET Dataset (https://cgiarcsi.
community/data/global-aridity-and-pet-database/), which provides
local water availability (ca. 1 km resolution) considering both evapo-
transpiration processes and rainfall deficits (version 3)65. Higher AI
values indicate more water availability (less water stress), while lower
AI values indicate less water availability (more water stress). Because
the accuracy of forest plot locations is fuzzed, typically within ca.
1–2 km, we used the climate factors from WorldClim with the spatial
resolution of 2.5 arc minutes (~5 km at the equator). For the aridity
index, we aggregated the resolution from 30 arc second to 2.5 arc
minute resolution. We extracted the climate factors for each plot
individually and then aggregated them into a grid cell (0.25° × 0.25°)by
taking themeanof their values to study thepatterns of trait association
variation across the regions.
Statistical analysis
Principal component analysis (PCA). All eight functional traits were
selected a priori before the PCA test based on these traits being
strongly linked to acquisitive-conservative, structural, stomatal and
water storage strategies in the literature (Table 1). We also took into
Article https://doi.org/10.1038/s41467-024-53160-1
Nature Communications |         (2024) 15:8909 7
account the importance of these traits, or aspects of function that
these traits act as proxies for, in process-based vegetation
modelling50. The selected traits were scaled and centred before
running PCA tests. The PCAwas conducted to explore themain axes
of trait variation at the species level and community level using the
FactoMineR package in R (version 1.34)66. This procedure helps to
identify the main plant strategy spectrum among the functional
traits. After this, we checked the consistency of the functional trait
variation between the species level and community level based on
the percentage of explained variance. For the PCA test at the com-
munity level, we also calculated the contribution values of each trait
loading to the two main axes. In addition, we also calculated the
correlations between community-level functional traits using the
corrplot package in R (version 0.92)67 to visualise the correlations.
To further quantify the trait associations of forest communities
along the main axes, we calculated the PCA coordinates in the main
components to obtain the eigenvalue for each grid according to the
score function. The contribution of each community-level trait to
these two dimensions was separately calculated (PC1 and
PC2 scores).
Relative importance of the climate variables for the trait associa-
tions. The relative importance analysis (RIA) was used to evaluate the
relative importance of climatic variables (e.g., AI, MAP, MAT, Sum-
mer_T and Summer_P) in relation to the two forest trait associations at
the community level using the relaimpo package in R (version 2.2-6)68.
We removed the forest communities that did not have climate infor-
mation, leaving 12,409 communities in the relative importance analy-
sis. To determine the relative weights of these five climatic factors in a
linear regression model, we used the functions boot.relimp and
booteval.relimp from the relaimpo package to compute the boot-
strapped estimates of the relative importance of each climate factor
and the confidence interval separately. Here, the type parameter was
set to the “lmg” method69, which is a commonly suggested approach
for determining relative importance. The number of bootstrap sam-
ples was 1000 in our analysis.
Sensitivity analyses. In order to test the effectiveness of the gap-
filling method, for species for which we had observations for a
particular trait, we randomly selected seven different proportions
(from 0.2 to 0.8, increasing each time by 0.1) of the species for
whichwe had observations of trait values (missingness proportions)
and predicted their values from the remaining species for which we
had data for their trait values, following the gap-filling method
described above—i.e. the median of genus and family and phylo-
genetic relationship (default, as described above). Then, we ran-
domly ran this 100 times to obtain the coefficient of determination
(R2) and root mean squared error (RMSE) values of z-transformed
predicted versus observation trait values as an indicator of overall
prediction accuracy for the trait gap-filling70,71. Lastly, we evaluated
the mean R2 and RMSE values. Here, we did not use very low pro-
portions of missingness (<0.2) in order to avoid introducing bias
resulting from a low number of observations. We also tested gap-
filling the missing trait values using three different methods: (a) the
median of the genus and family and phylogenetic relationship, (b)
median of the genus and phylogenetic relationship and (c) only the
phylogenetic relationship. We used Procrustes analysis using
the vegan package (version 2.6-4)71 to check the similarity of PCA
tests by comparing the main PC loading values (1) among the dif-
ferent gap-filling methods described above and (2) between com-
munity level (0.25° grid) and species level. Moreover, we ran
sensitivity tests to assess the influence of different grid sizes (0.1°
and 0.5°) at the community-level trait means. Furthermore, we
conducted sensitivity tests to evaluate whether a low number of
forest plots in some grid cells influenced the trait variation along the
first two dimensions and trait correlations. For this, we re-ran the
PCA tests excluding 2,375 forest communities that had <3
forest plots.
All analyses in this study were conducted in R (version 4.2.3) (R
Core Team 2023) and figures were produced using the ggplot2 R
package (version 3.4.3)72,73.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The raw NFI data underlying the analyses are available for open access
for the United States of America (https://research.fs.usda.gov/products/
dataandtools/tools/fia-datamart), France (https://inventaire-forestier.ign.
fr/dataifn/?lang=en), Germany (https://bwi.info/Download/de/) and
Spain (https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-
nacionales/inventario-forestal-nacional/cuarto_inventario.html). Raw NFI
data for the other countries are available by concluding data access
agreementswith the agencies owning the data; Sweden (https://www.slu.
se/en/Collaborative-Centres-and-Projects/the-swedish-national-forest-
inventory/), Poland (https://buligl.pl/web/buligl-en/w/national-forest-
inventory) and Czechia (https://www.czechterra.cz/). The community-
level traits, PC scores (e.g., PC1 and PC2) and climate variables for this
analysis can be found in the Zenodo repository: https://zenodo.org/
records/13757078.
Code availability
The R code for the statistical analyses and generating the figures is
available in the Zenodo repository: https://zenodo.org/records/
13757078.
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Acknowledgements
We thank all the people who conducted field measurements and data
collection for the forest inventory dataset and thosewhomade their trait
data available through the TRY dataset (Request IDs 5395 and 8814). We
thank the teambehind theGlobal Forest Dynamics database, initiated by
the TreeMort project, onwhich this study is based. T.A.M.P., N.A, A.E.-M.,
M.-J.S. and D.L. were financially supported by the European Research
Council (ERC) under the European Union’s Horizon 2020 research and
innovation programme (grant agreement No. 758873, TreeMort). D.L.
was also financially supported by the FWF Austrian Science Fund (Lise
Meitner ProgrammeM2714-B29).A.E.-M.was further fundedby theRoyal
Society Standard Grant RGS/R1/21115 ‘MegaFlora’, the UKRI/NERC
TreeScapes NE/V021346/1 ‘MEMBRA’, the NERC/NSF Gigante NE/
Y003942/1 and the FRB/CESAB ‘Syntreesys’. N.A.was also fundedby the
Natural Environment Research Council (NERC, NE/R016518/1) and the
European Space Agency BIOMASS Climate Change Initiative+. E.C.
acknowledges funding from theprojectAdAgriF—Advancedmethodsof
greenhouse gases emission reduction and sequestration in agriculture
and forest landscape for climate change mitigation (CZ.02.01.01/00/
22_008/0004635). This paper is a contribution to the strategic research
areas BECC and MERGE funded by the Swedish government and the
Nature-Based Future Solutions profile area at Lund University. J.A.,
P.R.B., M.A.Z. and T.A.M.P. acknowledge funding from the CLIMB-
FOREST Horizon Europe Project (No. 101059888) that was funded by the
European Union and the Science and Innovation Ministry (Agencia
Estatal de Investigación subproject LARGE, No. PID2021-123675OB-C41).
C.Z. acknowledges the financial support from the Academy of Finland
(340744). S.S. acknowledges funding from the European Union’s Hor-
izon 2020 research and innovation programme under the Marie
Skłodowska-Curie grant agreement No 895158 (ForMMI). This research
was supported in part by the U.S. Department of Agriculture, Forest
Service. The findings and conclusions in this publication are those of the
authors and should not be construed to represent any official USDA or
U.S. Government determination or policy.
Author contributions
D.L. and T.A.M.P. designed the research with inputs from J.P.S., T.J.M.,
N.A., A.E.M. D.L. and T.A.M.P. led the writing of the manuscript. A.E.M.,
J.A. and M.J.-S. standardised the forest inventory data and assisted in
data preparation and cleaning, with contributions from S.S., J.A., C.Z.,
M.A.Z., and P.R.B. D.L. led the data combination and statistical analysis.
C.W.W., S.S., J.A., P.R.B., M.A.Z., E.C., J.F., G.K., A.T. and M.J.S. curated
the forest inventory data. All the co-authors commented on analytical
approaches and edited the draft and final manuscripts.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
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Correspondence and requests for materials should be addressed to
Daijun Liu.
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Article https://doi.org/10.1038/s41467-024-53160-1
Nature Communications |         (2024) 15:8909 10
1School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK. 2Birmingham Institute of Forest Research,
University of Birmingham, B15 2TTBirmingham,UK. 3Department of Botany andBiodiversity Research, University of Vienna, Rennweg 14, 1030Vienna, Austria.
4NationalCentre for EarthObservation, University of Leicester, LE45SP Leicester, UK. 5Institute for Environmental Futures, School ofGeography,Geology and
the Environment, University of Leicester, LE1 7RH Leicester, UK. 6Universidad de Alcalá, Departamento de Ciencias de la Vida, Grupo de Ecología y Res-
tauración Forestal (FORECO), 28805 Alcalá de Henares, Spain. 7IFER - Institute of Forest Ecosystem Research, Cs. Armady 655, 254 01, Jilove u Prahy, Czech
Republic. 8Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4b, 603 00, Brno, Czech Republic. 9Department of Forest
Resource Management, Swedish University of Agricultural Sciences, SE901-83 Umeå, Sweden. 10Univ. Grenoble Alpes, INRAE, LESSEM, F-38402 St-Martin-
d’Hères, France. 11Centre for Ecology, Evolution and Environmental Changes/Azorean Biodiversity Group/CHANGE—Global Change and Sustainability
Institute and Universidade dos Açores—Faculty of Agricultural Sciences and Environment, PT-9700-042 Angra do Heroísmo Azores, Portugal. 12Universidad
de Alcalá, Departamento de Geología, Geografía y Medio Ambiente, Grupo de Investigación en Teledetección Ambiental, 28801 Alcalá de
Henares Madrid, Spain. 13Wageningen University and Research, Wageningen Environmental Research (WENR), Droevendaalsesteeg 3, 6708PB
Wageningen, The Netherlands. 14Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland. 15Forest and Natural Resources
Research Centre/Taxus IT, ul. Płomyka 56A, 02-491 Warszawa, Poland. 16The United States Department of Agriculture (USDA) Forest Service, Northern
Research Station, NH 03824 Durham, USA. 17Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest
Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki 00014, Finland. 18Department of Physical Geography and Ecosystem Science, Lund
University, Sölvegatan 12, 22362 Lund, Sweden. e-mail: cqliudaijun@gmail.com
Article https://doi.org/10.1038/s41467-024-53160-1
Nature Communications |         (2024) 15:8909 11