# Global patterns and environmental drivers of forest functional composition

Elise Bouchard, Eric B Searle, Pierre Drapeau, Jingjing Liang, Javier G P Gamarra, Meinrad Abegg, Giorgio Alberti, Angelica Almeyda Zambrano, Esteban Alvarez-davila, Luciana F Alves, et al.

► To cite this version:

Elise Bouchard, Eric B Searle, Pierre Drapeau, Jingjing Liang, Javier G P Gamarra, et al. Global patterns and environmental drivers of forest functional composition. Global Ecology and Biogeography, 2024, 33, pp.303 - 324. 10.1111/geb.13790. hal-04593987

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Submitted on 30 May 2024

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Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

Global patterns and environmental drivers of forest functional
composition

Elise Bouchard<fnref n="1" /> ID | Eric B. Searle<fnref n="1" />,<fnref n="2" /> ID | Pierre Drapeau<fnref n="1" /> ID | Jingjing Liang<fnref n="3" /> ID |
Javier G. P. Gamarra<fnref n="4" /> ID | Meinrad Abegg ID | Giorgio Alberti ID | Angelica Almeyda Zambrano |
Esteban Alvarez-Davila ID | Luciana F. Alves ID | Valerio Avitabile ID | Gerardo Aymard ID |
Jean-François Bastin ID | Philippe Birnbaum ID | Frans Bongers ID | Olivier Bouriaud ID |
Pedro Brancalion ID | Eben Broadbent | Filippo Bussotti ID | Roberto Cazzolla Gatti ID |
Goran Češljar ID | Chelsea Chisholm ID | Emil Cienciala ID | Connie J. Clark |
José Javier Corral-Rivas ID | Thomas W. Crowther ID | Selvadurai Dayanandan |
Mathieu Decuyper ID | André L. de Gasper ID | Sergio de-Miguel ID | Géraldine Derroire ID |
Ben DeVries | Ilija Djordjević ID | Tran Van Do ID | Jiri Dolezal ID | Tom M. Fayle ID |
Jonas Fridman ID | Lorenzo Frizzera ID | Damiano Gianelle ID | Andreas Hemp |
Bruno Hérault ID | Martin Herold ID | Nobuo Imai | Andrzej M. Jagodziński ID |
Bogdan Jaroszewicz ID | Tommaso Jucker ID | Sebastian Kepfer-Rojas ID |
Gunnar Keppel ID | Mohammed Latif Khan ID | Hyun Seok Kim ID | Henn Korjus ID |
Florian Kraxner ID | Diana Laarmann | Simon Lewis ID | Huicui Lu ID | Brian S. Maitner ID |
Eric Marcon ID | Andrew R. Marshall ID | Sharif A. Mukul ID | Gert-Jan Nabuurs ID |
María Guadalupe Nava-Miranda ID | Elena I. Parfenova ID | Minjee Park ID | Pablo L. Peri ID |
Sebastian Pfautsch ID | Oliver L. Phillips ID | Maria Teresa F. Piedade ID | Daniel Piotto ID |
John R. Poulsen ID | Axel Dalberg Poulsen | Hans Pretzsch ID | Peter B. Reich ID |
Mirco Rodeghiero ID | Samir Rolim ID | Francesco Rovero | Purabi Saikia ID |
Christian Salas-Eljatib ID | Peter Schall ID | Dmitry Schepaschenko ID | Jochen Schöngart |
Vladimír Šebeň ID | Plinio Sist ID | Ferry Slik ID | Alexandre F. Souza ID |
Krzysztof Stereńczak ID | Miroslav Svoboda ID | Nadezhda M. Tchebakova ID | Hans ter Steege ID |
Elena V. Tikhonova | Vladimir A. Usoltsev | Fernando Valladares | Helder Viana ID |
Alexander C. Vibrans ID | Hua-Feng Wang | Bertil Westerlund ID | Susan K. Wiser ID |
Florian Wittmann ID | Verginia Wortel ID | Tomasz Zawiła-Niedźwiecki | Mo Zhou |
Zhi-Xin Zhu | Irié C. Zo-Bi ID | Alain Paquette<fnref n="1" /> ID

<footnote n="1">¹Department of Biological Sciences, Centre for Forest Research (CFR), Université du Québec à Montréal, Montreal, Quebec, Canada</footnote>

<footnote n="2">²Ontario Forest Research Institute, Ontario Ministry of Natural Resources and Forestry, Sault Ste. Marie, Ontario, Canada</footnote>

<footnote n="3">³Forest Advanced Computing and Artificial Intelligence Lab (FACAI), Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA</footnote>

<footnote n="4">⁴Forestry Division, Food and Agriculture Organization of the United Nations, Rome, Italy</footnote>

<footnote>This is an open access article under the terms of the [Creative Commons Attribution-NonCommercial-NoDerivs License](https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2023 The Authors. *Global Ecology and Biogeography* published by John Wiley & Sons Ltd.</footnote>
# Global patterns and environmental drivers of forest functional composition

Elise Bouchard<fnref n="1" /> ID | Eric B. Searle<fnref n="1" />,<fnref n="2" /> ID | Pierre Drapeau<fnref n="1" /> ID | Jingjing Liang<fnref n="3" /> ID |
Javier G. P. Gamarra<fnref n="4" /> ID | Meinrad Abegg ID | Giorgio Alberti ID | Angelica Almeyda Zambrano |
Esteban Alvarez-Davila ID | Luciana F. Alves ID | Valerio Avitabile ID | Gerardo Aymard ID |
Jean-François Bastin ID | Philippe Birnbaum ID | Frans Bongers ID | Olivier Bouriaud ID |
Pedro Brancalion ID | Eben Broadbent ID | Filippo Bussotti ID | Roberto Cazzolla Gatti ID |
Goran Češljar ID | Chelsea Chisholm ID | Emil Cienciala ID | Connie J. Clark |
José Javier Corral-Rivas ID | Thomas W. Crowther ID | Selvadurai Dayanandan |
Mathieu Decuyper ID | André L. de Gasper ID | Sergio de-Miguel ID | Géraldine Derroire ID |
Ben DeVries | Ilija Djordjević ID | Tran Van Do ID | Jiri Dolezal ID | Tom M. Fayle ID |
Jonas Fridman ID | Lorenzo Frizzera ID | Damiano Gianelle ID | Andreas Hemp |
Bruno Hérault ID | Martin Herold ID | Nobuo Imai | Andrzej M. Jagodziński ID |
Bogdan Jaroszewicz ID | Tommaso Jucker ID | Sebastian Kepfer-Rojas ID |
Gunnar Keppel ID | Mohammed Latif Khan ID | Hyun Seok Kim ID | Henn Korjus ID |
Florian Kraxner ID | Diana Laarmann | Simon Lewis ID | Huicui Lu ID | Brian S. Maitner ID |
Eric Marcon ID | Andrew R. Marshall ID | Sharif A. Mukul ID | Gert-Jan Nabuurs ID |
María Guadalupe Nava-Miranda ID | Elena I. Parfenova ID | Minjee Park ID | Pablo L. Peri ID |
Sebastian Pfautsch ID | Oliver L. Phillips ID | Maria Teresa F. Piedade ID | Daniel Piotto ID |
John R. Poulsen ID | Axel Dalberg Poulsen | Hans Pretzsch ID | Peter B. Reich ID |
Mirco Rodeghiero ID | Samir Rolim ID | Francesco Rovero | Purabi Saikia ID |
Christian Salas-Eljatib ID | Peter Schall ID | Dmitry Schepaschenko ID | Jochen Schöngart |
Vladimír Šebeň ID | Plinio Sist ID | Ferry Slik ID | Alexandre F. Souza ID |
Krzysztof Stereńczak ID | Miroslav Svoboda ID | Nadezhda M. Tchebakova ID | Hans ter Steege ID |
Elena V. Tikhonova | Vladimir A. Usoltsev | Fernando Valladares | Helder Viana ID |
Alexander C. Vibrans ID | Hua-Feng Wang | Bertil Westerlund ID | Susan K. Wiser ID |
Florian Wittmann ID | Verginia Wortel ID | Tomasz Zawiła-Niedźwiecki | Mo Zhou |
Zhi-Xin Zhu | Irié C. Zo-Bi ID | Alain Paquette<fnref n="1" /> ID

<footnote n="1">¹Department of Biological Sciences, Centre for Forest Research (CFR), Université du Québec à Montréal, Montreal, Quebec, Canada</footnote>

<footnote n="2">²Ontario Forest Research Institute, Ontario Ministry of Natural Resources and Forestry, Sault Ste. Marie, Ontario, Canada</footnote>

<footnote n="3">³Forest Advanced Computing and Artificial Intelligence Lab (FACAI), Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA</footnote>

<footnote n="4">⁴Forestry Division, Food and Agriculture Organization of the United Nations, Rome, Italy</footnote>
**Correspondence**

Elise Bouchard, Department of Biological Sciences, Centre for Forest Research (CFR), Université du Québec à Montréal, 141 Av. du Président-Kennedy, Montréal, QC H2X 1Y4, Canada.

Email: bouchard.elise.3@courrier.uqam.ca

**Funding information**

Natural Sciences and Engineering
Research Council of Canada

Handling Editor: Arndt Hampe

**Abstract**

**Aim:** To determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships.

**Location:** Global.

**Time period:** Recent.

**Major taxa studied:** Trees.

**Methods:** We integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community-weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes).

**Results:** Our models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within-biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models.

**Main conclusions:** Worldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within-biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

**KEYWORDS**

biogeography, climate, environmental gradients, functional traits, seed mass, species abundance, specific leaf area, trees, wood density

# 1 | INTRODUCTION

The global distribution of forest biomes is well explained by few climatic variables (Holdridge, 1947; Suzuki et al., 2006) and transitions from one biome to the next are generally accompanied by an increase in species richness from the poles to the equator (Hillebrand, 2004). However, our understanding of how species differ in terms of adaptive strategies (e.g. survival, reproduction and resource acquisition) and their related functional traits along global abiotic gradients such as climate is still underdeveloped. Due to the importance of forests worldwide in providing essential ecosystem services (Gamfeldt et al., 2013), understanding the mechanisms that

underpin forest community function and assemblage along environmental gradients is needed to predict changes in their composition resulting from shifts in climate and land use.

Abiotic gradients should affect the trait composition of forest communities through complex interplays among temperature, precipitation and soil properties (Joswig et al., 2022; Maire et al., 2015; Ordoñez et al., 2009; Simpson et al., 2016). However, many large-scale studies have assessed trait-environment relationships using univariate models (Bruelheide et al., 2018; Moles et al., 2014; Šímová et al., 2015; Swenson et al., 2012; Swenson & Enquist, 2007; Swenson & Weiser, 2010; Wieczynski et al., 2019), thereby assuming that one environmental variable
**Correspondence**

Elise Bouchard, Department of Biological Sciences, Centre for Forest Research (CFR), Université du Québec à Montréal, 141 Av. du Président-Kennedy, Montréal, QC H2X 1Y4, Canada.

Email: bouchard.elise.3@courrier.uqam.ca

**Funding information**

Natural Sciences and Engineering
Research Council of Canada

Handling Editor: Arndt Hampe

**Abstract**

**Aim:** To determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships.

**Location:** Global.

**Time period:** Recent.

**Major taxa studied:** Trees.

**Methods:** We integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community-weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes).

**Results:** Our models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within-biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models.

**Main conclusions:** Worldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within-biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

**KEYWORDS**

biogeography, climate, environmental gradients, functional traits, seed mass, species abundance, specific leaf area, trees, wood density

# 1 | INTRODUCTION

The global distribution of forest biomes is well explained by few climatic variables (Holdridge, 1947; Suzuki et al., 2006) and transitions from one biome to the next are generally accompanied by an increase in species richness from the poles to the equator (Hillebrand, 2004). However, our understanding of how species differ in terms of adaptive strategies (e.g. survival, reproduction and resource acquisition) and their related functional traits along global abiotic gradients such as climate is still underdeveloped. Due to the importance of forests worldwide in providing essential ecosystem services (Gamfeldt et al., 2013), understanding the mechanisms that

underpin forest community function and assemblage along environmental gradients is needed to predict changes in their composition resulting from shifts in climate and land use.

Abiotic gradients should affect the trait composition of forest communities through complex interplays among temperature, precipitation and soil properties (Joswig et al., 2022; Maire et al., 2015; Ordoñez et al., 2009; Simpson et al., 2016). However, many large-scale studies have assessed trait-environment relationships using univariate models (Bruelheide et al., 2018; Moles et al., 2014; Šímová et al., 2015; Swenson et al., 2012; Swenson & Enquist, 2007; Swenson & Weiser, 2010; Wieczynski et al., 2019), thereby assuming that one environmental variable
over time, only the most recent measurement was used in the analysis. Tree ferns and bamboos were removed from the analyses.

## 2.2 | Functional traits

Functional trait values were extracted from the TRY database (Kattge et al., 2020) and completed via a literature review (Appendix 1). We cleaned data from all sources to remove duplicates, values beyond reported ranges (Pérez-Harguindeguy et al., 2016), and measurements from seedlings in greenhouses and experiments evaluating stress response (Appendix S1). Depending on the functional trait, the total number of documented species varied between 3923 and 7252 (Table 1). A mean per species per trait was computed and attributed to the individuals of this species in the forest inventories. Then, community-weighted means (CWM) of traits in each plot were calculated using the number of stems per species as the weight. Community unweighted means (CM) were computed by giving the same weight to each species in the plot. We included only those plots with a minimum of 80% of constituent species with trait information based on the number of stems following other studies (Garnier et al., 2004; Grime, 1998; Pakeman & Quested, 2007). In the final analysis, we retained between 99,953 and 149,285 plots, which covered all of Earth's major biomes (Table 1, Figure 1, Appendix S2).

## 2.3 | Climatic and pedologic variables

Precipitation-, temperature- and solar radiation-related variables were obtained through the WorldClim database as 30-year averages (1970 to 2000) at a resolution of 30 arcseconds (~1 km² at the equator) (Fick & Hijmans, 2017). We compiled seven soil variables from the SoilGrids database for a depth of 0 to 30 cm at a resolution of 250 m (Hengl et al., 2017). These variables were selected to represent three broad environmental gradients: energy availability (including temperature and solar radiation), precipitation and soil properties (Table 2). Climatic and pedologic values were extracted using plot spatial coordinates.

## 2.4 | Data analysis

We conducted multiple linear regressions with second-order polynomials to account for the polynomial or linear relationships observed

in raw data between traits and environmental variables. We built four models per trait with data fitted at different scales and biomes: global forests (all data), boreal forests, temperate forests and tropical forests. Biome-related results were obtained using biome-level models trained on data coming solely from the studied biome. Global patterns were assessed using the full dataset. Biomes were based on the World Wildlife Fund classification (Olson et al., 2001). Subtropical forests were grouped with tropical forests, whereas temperate forests of broad-leaves and/or conifers were included in the temperate biome along with Mediterranean forests. The complete list of terrestrial ecoregions included in larger biomes (boreal, temperate and tropical) is available in Appendix S3. All analyses were conducted in R statistical software, version 4.1.0 (R Core Team, 2020).

To control for multicollinearity, we conducted variable selection based on standardized generalized variance inflation factors (GVIF) implemented in the 'car' package (Fox & Weisberg, 2019). Predictors were removed until no more variables would exceed a threshold of $GVIF=2$ in the model, which is equivalent to a variance inflation factor (VIF) threshold of 4. When selecting variables, we prioritized keeping at least two variables per gradient, since we were interested in the combined effects of all gradients. Seed mass was transformed by a natural logarithm because this trait is inherently multiplicative and spans orders of magnitude (Kerkhoff & Enquist, 2009). All assumptions related to regression models were respected (normality, homogeneity of variance, linearity and independence) except for normality in seed mass models. Therefore, seed mass models were bootstrapped using 10,000 iterations to compute confidence intervals (percentile) to ensure that deviations from normality would not underestimate error terms. We tested two-way interactions within gradients (three intra-gradient interactions) and two-way interactions between gradients (three inter-gradient interactions) (Appendix S4 for more details on data analysis).

Initial variable selection was performed for each trait at both global and biome scales. In some cases, models could differ in the inclusion of specific variables within each gradient. To ease interpretation and comparability between models, we also examined models using identical variables between global and biome scale models (although polynomials could differ). We found similar predictions of community means for each trait between the biome- and global-scale models (Appendix S5). For ease of interpretation, here we opted to display the models that include the same variables. All selected variables and interactions appear at the left of the panels in Figure 3. Variable importance was assessed using dominance analysis with the 'dominanceanalysis' package (Bustos Navarrete & Coutinho Soares, 2020). To ensure that the uneven plot

<table><caption>TABLE 1 Documented species per trait and number of plots per trait and biome.</caption><thead><tr><th rowspan="2">Functional traits</th><th colspan="4">Taxonomy</th><th colspan="4">Number of plots</th></tr><tr><th>Species</th><th>Angiosperm</th><th>Gymnosperm</th><th>Families</th><th>Global</th><th>Boreal</th><th>Temperate</th><th>Tropical</th></tr></thead><tbody><tr><td>Wood density</td><td>7252</td><td>7045</td><td>213</td><td>194</td><td>149,285</td><td>8783</td><td>138,081</td><td>2393</td></tr><tr><td>Specific leaf area</td><td>4930</td><td>4794</td><td>136</td><td>205</td><td>131,979</td><td>4870</td><td>125,677</td><td>1416</td></tr><tr><td>Seed mass</td><td>3923</td><td>3687</td><td>239</td><td>203</td><td>99,953</td><td>4831</td><td>94,058</td><td>1050</td></tr></tbody></table>
over time, only the most recent measurement was used in the analysis. Tree ferns and bamboos were removed from the analyses.

## 2.2 | Functional traits

Functional trait values were extracted from the TRY database (Kattge et al., 2020) and completed via a literature review (Appendix 1). We cleaned data from all sources to remove duplicates, values beyond reported ranges (Pérez-Harguindeguy et al., 2016), and measurements from seedlings in greenhouses and experiments evaluating stress response (Appendix S1). Depending on the functional trait, the total number of documented species varied between 3923 and 7252 (Table 1). A mean per species per trait was computed and attributed to the individuals of this species in the forest inventories. Then, community-weighted means (CWM) of traits in each plot were calculated using the number of stems per species as the weight. Community unweighted means (CM) were computed by giving the same weight to each species in the plot. We included only those plots with a minimum of 80% of constituent species with trait information based on the number of stems following other studies (Garnier et al., 2004; Grime, 1998; Pakeman & Quested, 2007). In the final analysis, we retained between 99,953 and 149,285 plots, which covered all of Earth's major biomes (Table 1, Figure 1, Appendix S2).

## 2.3 | Climatic and pedologic variables

Precipitation-, temperature- and solar radiation-related variables were obtained through the WorldClim database as 30-year averages (1970 to 2000) at a resolution of 30 arcseconds (~1 km² at the equator) (Fick & Hijmans, 2017). We compiled seven soil variables from the SoilGrids database for a depth of 0 to 30 cm at a resolution of 250 m (Hengl et al., 2017). These variables were selected to represent three broad environmental gradients: energy availability (including temperature and solar radiation), precipitation and soil properties (Table 2). Climatic and pedologic values were extracted using plot spatial coordinates.

## 2.4 | Data analysis

We conducted multiple linear regressions with second-order polynomials to account for the polynomial or linear relationships observed

in raw data between traits and environmental variables. We built four models per trait with data fitted at different scales and biomes: global forests (all data), boreal forests, temperate forests and tropical forests. Biome-related results were obtained using biome-level models trained on data coming solely from the studied biome. Global patterns were assessed using the full dataset. Biomes were based on the World Wildlife Fund classification (Olson et al., 2001). Subtropical forests were grouped with tropical forests, whereas temperate forests of broad-leaves and/or conifers were included in the temperate biome along with Mediterranean forests. The complete list of terrestrial ecoregions included in larger biomes (boreal, temperate and tropical) is available in Appendix S3. All analyses were conducted in R statistical software, version 4.1.0 (R Core Team, 2020).

To control for multicollinearity, we conducted variable selection based on standardized generalized variance inflation factors (GVIF) implemented in the 'car' package (Fox & Weisberg, 2019). Predictors were removed until no more variables would exceed a threshold of $GVIF=2$ in the model, which is equivalent to a variance inflation factor (VIF) threshold of 4. When selecting variables, we prioritized keeping at least two variables per gradient, since we were interested in the combined effects of all gradients. Seed mass was transformed by a natural logarithm because this trait is inherently multiplicative and spans orders of magnitude (Kerkhoff & Enquist, 2009). All assumptions related to regression models were respected (normality, homogeneity of variance, linearity and independence) except for normality in seed mass models. Therefore, seed mass models were bootstrapped using 10,000 iterations to compute confidence intervals (percentile) to ensure that deviations from normality would not underestimate error terms. We tested two-way interactions within gradients (three intra-gradient interactions) and two-way interactions between gradients (three inter-gradient interactions) (Appendix S4 for more details on data analysis).

Initial variable selection was performed for each trait at both global and biome scales. In some cases, models could differ in the inclusion of specific variables within each gradient. To ease interpretation and comparability between models, we also examined models using identical variables between global and biome scale models (although polynomials could differ). We found similar predictions of community means for each trait between the biome- and global-scale models (Appendix S5). For ease of interpretation, here we opted to display the models that include the same variables. All selected variables and interactions appear at the left of the panels in Figure 3. Variable importance was assessed using dominance analysis with the 'dominanceanalysis' package (Bustos Navarrete & Coutinho Soares, 2020). To ensure that the uneven plot

<table><caption>TABLE 1 Documented species per trait and number of plots per trait and biome.</caption><thead><tr><th rowspan="2">Functional traits</th><th colspan="4">Taxonomy</th><th colspan="4">Number of plots</th></tr><tr><th>Species</th><th>Angiosperm</th><th>Gymnosperm</th><th>Families</th><th>Global</th><th>Boreal</th><th>Temperate</th><th>Tropical</th></tr></thead><tbody><tr><td>Wood density</td><td>7252</td><td>7045</td><td>213</td><td>194</td><td>149,285</td><td>8783</td><td>138,081</td><td>2393</td></tr><tr><td>Specific leaf area</td><td>4930</td><td>4794</td><td>136</td><td>205</td><td>131,979</td><td>4870</td><td>125,677</td><td>1416</td></tr><tr><td>Seed mass</td><td>3923</td><td>3687</td><td>239</td><td>203</td><td>99,953</td><td>4831</td><td>94,058</td><td>1050</td></tr></tbody></table>
<figure><img src="image_1.png" /><figcaption>FIGURE 1 Geographical distribution of plots for wood density (total number of plots: 149,285), specific leaf area (total number of plots: 131,979) and seed mass (total number of plots: 99,953). While most of the plots illustrated on this map are used for all three functional traits, there are small differences in single-trait distributions due to the requirement that trait values needed to be available for 80% of the stems in a plot (see methods). Specific trait distribution maps are available in Appendix S2. Map projection = WGS 1984 Equal Earth (world).</figcaption></figure>

<table><caption>TABLE 2 Variables names, abbreviations, units, ranges and means.</caption><thead><tr><th>Variable</th><th>Abbreviation</th><th>Units</th><th>Range</th><th>Mean</th></tr></thead><tbody><tr><td colspan="5"><strong>Functional traits</strong></td></tr><tr><td>Wood density</td><td>WD</td><td>mg/mm<sup>3</sup></td><td>0.15 to 1.24</td><td>0.55</td></tr><tr><td>Specific leaf area</td><td>SLA</td><td>m<sup>2</sup>/kg</td><td>1.3 to 169.8</td><td>16.4</td></tr><tr><td>Seed mass</td><td>SM</td><td>mg</td><td>0.01 to 576,800</td><td>860</td></tr><tr><td colspan="5"><strong>Environmental variables</strong></td></tr><tr><td colspan="5"><strong>Energy gradient</strong></td></tr><tr><td>Mean annual temperature</td><td>MAT</td><td>°C</td><td>-7.7 to 27.8</td><td>12.2</td></tr><tr><td>Temperature seasonality</td><td>TS</td><td>°C * 100</td><td>27 to 1734</td><td>842</td></tr><tr><td>Annual mean solar radiation</td><td>SR</td><td>kJ/m<sup>2</sup>·day</td><td>7296 to 21,786</td><td>14,369</td></tr><tr><td colspan="5"><strong>Precipitation gradient</strong></td></tr><tr><td>Annual precipitation</td><td>AP</td><td>mm</td><td>68 to 4712</td><td>1175</td></tr><tr><td>Precipitation seasonality</td><td>PS</td><td></td><td>5.5 to 120.2</td><td>26</td></tr><tr><td colspan="5"><strong>Soil properties gradient</strong></td></tr><tr><td>Percentage in clay</td><td>CLAY</td><td>%</td><td>0 to 58.3</td><td>18</td></tr><tr><td>Percentage in sand</td><td>SAND</td><td>%</td><td>0 to 96.4</td><td>43.6</td></tr><tr><td>Available water storage capacity</td><td>AWC</td><td>v%</td><td>5.8 to 51.4</td><td>44.5</td></tr><tr><td>Cation exchange capacity</td><td>CEC</td><td>cmol<sup>+</sup>/kg</td><td>1.75 to 192.92</td><td>19.12</td></tr><tr><td>Percentage of organic carbon</td><td>OC</td><td>%</td><td>4.2 to 372.6</td><td>40.8</td></tr><tr><td>pH measured in a soil-water solution</td><td>pH</td><td>pH*10</td><td>37 to 81</td><td>53</td></tr><tr><td>Bulk density</td><td>BULK</td><td>kg/m<sup>3</sup></td><td>606.5 to 1696.2</td><td>1320.4</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>FIGURE 1 Geographical distribution of plots for wood density (total number of plots: 149,285), specific leaf area (total number of plots: 131,979) and seed mass (total number of plots: 99,953). While most of the plots illustrated on this map are used for all three functional traits, there are small differences in single-trait distributions due to the requirement that trait values needed to be available for 80% of the stems in a plot (see methods). Specific trait distribution maps are available in Appendix S2. Map projection = WGS 1984 Equal Earth (world).</figcaption></figure>

<table><caption>TABLE 2 Variables names, abbreviations, units, ranges and means.</caption><thead><tr><th>Variable</th><th>Abbreviation</th><th>Units</th><th>Range</th><th>Mean</th></tr></thead><tbody><tr><td colspan="5"><strong>Functional traits</strong></td></tr><tr><td>Wood density</td><td>WD</td><td>mg/mm<sup>3</sup></td><td>0.15 to 1.24</td><td>0.55</td></tr><tr><td>Specific leaf area</td><td>SLA</td><td>m<sup>2</sup>/kg</td><td>1.3 to 169.8</td><td>16.4</td></tr><tr><td>Seed mass</td><td>SM</td><td>mg</td><td>0.01 to 576,800</td><td>860</td></tr><tr><td colspan="5"><strong>Environmental variables</strong></td></tr><tr><td colspan="5"><strong>Energy gradient</strong></td></tr><tr><td>Mean annual temperature</td><td>MAT</td><td>°C</td><td>-7.7 to 27.8</td><td>12.2</td></tr><tr><td>Temperature seasonality</td><td>TS</td><td>°C * 100</td><td>27 to 1734</td><td>842</td></tr><tr><td>Annual mean solar radiation</td><td>SR</td><td>kJ/m<sup>2</sup>·day</td><td>7296 to 21,786</td><td>14,369</td></tr><tr><td colspan="5"><strong>Precipitation gradient</strong></td></tr><tr><td>Annual precipitation</td><td>AP</td><td>mm</td><td>68 to 4712</td><td>1175</td></tr><tr><td>Precipitation seasonality</td><td>PS</td><td></td><td>5.5 to 120.2</td><td>26</td></tr><tr><td colspan="5"><strong>Soil properties gradient</strong></td></tr><tr><td>Percentage in clay</td><td>CLAY</td><td>%</td><td>0 to 58.3</td><td>18</td></tr><tr><td>Percentage in sand</td><td>SAND</td><td>%</td><td>0 to 96.4</td><td>43.6</td></tr><tr><td>Available water storage capacity</td><td>AWC</td><td>v%</td><td>5.8 to 51.4</td><td>44.5</td></tr><tr><td>Cation exchange capacity</td><td>CEC</td><td>cmol<sup>+</sup>/kg</td><td>1.75 to 192.92</td><td>19.12</td></tr><tr><td>Percentage of organic carbon</td><td>OC</td><td>%</td><td>4.2 to 372.6</td><td>40.8</td></tr><tr><td>pH measured in a soil-water solution</td><td>pH</td><td>pH*10</td><td>37 to 81</td><td>53</td></tr><tr><td>Bulk density</td><td>BULK</td><td>kg/m<sup>3</sup></td><td>606.5 to 1696.2</td><td>1320.4</td></tr></tbody></table>
distribution would not compromise the regression slopes, we tested the models for spatial autocorrelation among residuals using variograms built with the 'gstat' package (Pebesma, 2004; Pebesma & Heuvelink, 2016) and k-fold cross validation (Ploton et al., 2020) using the 'blockCV' package in R (Valavi et al., 2019). The resulting figures showed no pattern of spatial autocorrelation across all models (Appendices S6 and S7). We ran selected models using weighted or unweighted community means as response variable. To determine the effect of abundance weighting on the strength of the relationships between forest communities' mean traits and environmental gradients, we compared model fits using adjusted R² and normalized root mean square error (nRMSE). We refrained from presenting any inferential statistics in the main text due to the high level of observations resulting in most coefficients being significant. Instead, we interpret results based on effect size and variance partitioning. However, regression coefficients and associated errors are presented in Appendix S4 for all models.

Finally, we built estimated biogeographic distribution maps of each trait to illustrate global scale patterns along with their associated uncertainty maps (Appendix S8). We derived estimates of global climate and soil properties from the same databases as our training data (WorldClim and SoilGrids) in forested areas identified through the Global Forest Change database (Hansen et al., 2013) since our training dataset was limited to forests. We then used biome-scale models to estimate the trait values for each grid cell, because they feature more detailed changes within biomes than maps fitted using global models. Grid cells that had climate and soil properties values outside the range of our observed values were omitted from mapping.

## 3 | RESULTS

### 3.1 | Species abundance

Eleven of twelve models performed better with community unweighted means than with community-weighted means based on nRMSE (Appendix S9) and adjusted R². The absolute increases in explained variance for the global models were 11%, 10% and 3% for WD, SLA and SM respectively. Further analyses were conducted using community unweighted means.

### 3.2 | Effect of environmental gradients on traits

For all trait values, boreal forests had the lowest medians and narrowest ranges (Figure 2). Trait expression varied more in tropical and temperate forests, with higher medians of WD in tropical forests and higher medians of SM and SLA in temperate forests. At global scale, SM showed the strongest relationships with environmental gradients (adjusted R² = 60%), followed by WD (adjusted R² = 46%) and SLA (adjusted R² = 45%). The explained variance of the biome models was 24% (WD), 26% (SLA) and 31% (SM) in boreal forests; 38% (WD), 45% (SLA) and 46% (SM) in temperate forests; and 38% (WD), 49% (SLA) and 26% (SM) in tropical forests (Figure 3).

We produced global prediction maps fitted with our biome-level models to simultaneously visualize the effect of all variables and their interactions (Figure 4). These maps revealed that SLA is the only trait reaching the most acquisitive strategies (high values, that is, leaves with large surface to weight ratios) in temperate forests (Figure 4). In contrast, both WD and SM generally followed a latitudinal gradient from acquisitive strategies (light wood and seeds) in boreal forests to conservative strategies (dense wood and heavy seeds) in tropical forests.

The effect of mean annual temperature on SM and WD was generally positive or unimodal, increasing to an optimum and decreasing thereafter. An exception in tropical forests is that SM decreased slightly along mean annual temperature. Relationships between SLA and solar radiation varied across biomes (Figure 5). Effects of temperature seasonality on traits depended on the biome (Figure 5). Higher temperature seasonality favoured conservative strategies in some instances (SLA in tropical forests and SM in temperate forests) or acquisitive strategies in others (WD and SM in boreal forests, WD in tropical forests and SLA in temperate forests). More nuanced responses by traits to increasing temperature seasonality occurred, with a unimodal response including either an optimum or a minimum (SM in tropical forests, WD in temperate forests) or no clear association (SLA in boreal forests).

Higher annual precipitation favoured conservative strategies of SM in boreal and tropical forests but did not affect temperate forests (Figure 5). Responses of WD to annual precipitation were variable across biomes (Figure 5). As precipitation increased, SLA increased in temperate forests but decreased slightly or remained stable in tropical and boreal forests. The lack of response from SM to precipitation seasonality was consistent across biomes, whereas WD and SLA generally decreased with precipitation seasonality.

The effect of soil properties on SLA was very consistent: soils with more favourable structures (low sand content and high bulk density) favoured acquisitive strategies. Results for SM were also consistent and indicated weak effect of cation exchange capacity (CEC) and soil pH. Wood density was slightly influenced by sand content across biomes. However, higher WD may be favoured by increasing CEC in tropical forests relative to temperate forests, whereas CEC had no notable effect in boreal forests (Figure 5).

These direct effects were modulated by interactions. The high number of variables and interactions prevented us from presenting all direct and interacting effects (but see appendices S10-S12). We selected the most notable ones to display in Figures 5 and 6.

### 3.3 | Importance of environmental gradients on traits

Globally, the energy gradient dominated the explained variance for WD and SM, with precipitation explaining the smallest amount of variance. Specific leaf area was more evenly influenced by the three gradients, although the energy gradient still explained most of the variance (Figure 2). Interactions between and among gradients strongly influenced trait distributions: the cumulative
distribution would not compromise the regression slopes, we tested the models for spatial autocorrelation among residuals using variograms built with the 'gstat' package (Pebesma, 2004; Pebesma & Heuvelink, 2016) and k-fold cross validation (Ploton et al., 2020) using the 'blockCV' package in R (Valavi et al., 2019). The resulting figures showed no pattern of spatial autocorrelation across all models (Appendices S6 and S7). We ran selected models using weighted or unweighted community means as response variable. To determine the effect of abundance weighting on the strength of the relationships between forest communities' mean traits and environmental gradients, we compared model fits using adjusted R² and normalized root mean square error (nRMSE). We refrained from presenting any inferential statistics in the main text due to the high level of observations resulting in most coefficients being significant. Instead, we interpret results based on effect size and variance partitioning. However, regression coefficients and associated errors are presented in Appendix S4 for all models.

Finally, we built estimated biogeographic distribution maps of each trait to illustrate global scale patterns along with their associated uncertainty maps (Appendix S8). We derived estimates of global climate and soil properties from the same databases as our training data (WorldClim and SoilGrids) in forested areas identified through the Global Forest Change database (Hansen et al., 2013) since our training dataset was limited to forests. We then used biome-scale models to estimate the trait values for each grid cell, because they feature more detailed changes within biomes than maps fitted using global models. Grid cells that had climate and soil properties values outside the range of our observed values were omitted from mapping.

## 3 | RESULTS

### 3.1 | Species abundance

Eleven of twelve models performed better with community unweighted means than with community-weighted means based on nRMSE (Appendix S9) and adjusted R². The absolute increases in explained variance for the global models were 11%, 10% and 3% for WD, SLA and SM respectively. Further analyses were conducted using community unweighted means.

### 3.2 | Effect of environmental gradients on traits

For all trait values, boreal forests had the lowest medians and narrowest ranges (Figure 2). Trait expression varied more in tropical and temperate forests, with higher medians of WD in tropical forests and higher medians of SM and SLA in temperate forests. At global scale, SM showed the strongest relationships with environmental gradients (adjusted R² = 60%), followed by WD (adjusted R² = 46%) and SLA (adjusted R² = 45%). The explained variance of the biome models was 24% (WD), 26% (SLA) and 31% (SM) in boreal forests; 38% (WD), 45% (SLA) and 46% (SM) in temperate forests; and 38% (WD), 49% (SLA) and 26% (SM) in tropical forests (Figure 3).

We produced global prediction maps fitted with our biome-level models to simultaneously visualize the effect of all variables and their interactions (Figure 4). These maps revealed that SLA is the only trait reaching the most acquisitive strategies (high values, that is, leaves with large surface to weight ratios) in temperate forests (Figure 4). In contrast, both WD and SM generally followed a latitudinal gradient from acquisitive strategies (light wood and seeds) in boreal forests to conservative strategies (dense wood and heavy seeds) in tropical forests.

The effect of mean annual temperature on SM and WD was generally positive or unimodal, increasing to an optimum and decreasing thereafter. An exception in tropical forests is that SM decreased slightly along mean annual temperature. Relationships between SLA and solar radiation varied across biomes (Figure 5). Effects of temperature seasonality on traits depended on the biome (Figure 5). Higher temperature seasonality favoured conservative strategies in some instances (SLA in tropical forests and SM in temperate forests) or acquisitive strategies in others (WD and SM in boreal forests, WD in tropical forests and SLA in temperate forests). More nuanced responses by traits to increasing temperature seasonality occurred, with a unimodal response including either an optimum or a minimum (SM in tropical forests, WD in temperate forests) or no clear association (SLA in boreal forests).

Higher annual precipitation favoured conservative strategies of SM in boreal and tropical forests but did not affect temperate forests (Figure 5). Responses of WD to annual precipitation were variable across biomes (Figure 5). As precipitation increased, SLA increased in temperate forests but decreased slightly or remained stable in tropical and boreal forests. The lack of response from SM to precipitation seasonality was consistent across biomes, whereas WD and SLA generally decreased with precipitation seasonality.

The effect of soil properties on SLA was very consistent: soils with more favourable structures (low sand content and high bulk density) favoured acquisitive strategies. Results for SM were also consistent and indicated weak effect of cation exchange capacity (CEC) and soil pH. Wood density was slightly influenced by sand content across biomes. However, higher WD may be favoured by increasing CEC in tropical forests relative to temperate forests, whereas CEC had no notable effect in boreal forests (Figure 5).

These direct effects were modulated by interactions. The high number of variables and interactions prevented us from presenting all direct and interacting effects (but see appendices S10-S12). We selected the most notable ones to display in Figures 5 and 6.

### 3.3 | Importance of environmental gradients on traits

Globally, the energy gradient dominated the explained variance for WD and SM, with precipitation explaining the smallest amount of variance. Specific leaf area was more evenly influenced by the three gradients, although the energy gradient still explained most of the variance (Figure 2). Interactions between and among gradients strongly influenced trait distributions: the cumulative
<figure><img src="image_2.png" /><figcaption>FIGURE 2 Density plots of community unweighted means (CM) per trait and biome. The dashed lines display the median of the distribution.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>FIGURE 3 Results of variance partitioning from dominance analysis. Bar colours represent different models (boreal, tropical, temperate, global), whereas pictograms on the y axis emphasize the gradient to which the variables belong (energy availability, soil properties, precipitation or an interaction between gradients). Interactions are symbolized by an asterisk * between the variable abbreviations. The total explained variance of a model (adjusted R²) is displayed in the label box and equals the cumulative weight of all bars from a same model.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>FIGURE 2 Density plots of community unweighted means (CM) per trait and biome. The dashed lines display the median of the distribution.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>FIGURE 3 Results of variance partitioning from dominance analysis. Bar colours represent different models (boreal, tropical, temperate, global), whereas pictograms on the y axis emphasize the gradient to which the variables belong (energy availability, soil properties, precipitation or an interaction between gradients). Interactions are symbolized by an asterisk * between the variable abbreviations. The total explained variance of a model (adjusted R²) is displayed in the label box and equals the cumulative weight of all bars from a same model.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>FIGURE 4 Biogeographical representations of the explained distribution of functional traits of trees. The colour palette represents a gradient from acquisitive strategies (red-yellow) to conservative strategies (green-blue). Map projection, WGS 1984 Equal Earth (world).</figcaption></figure>

explained variance due to interactions in global-scale models were
18% (WD), 11% (SLA) and 29% (SM). Within biomes, the cumu-
lative explained variance due to interactions were 7% (WD), 8%
(SLA) and 10% (SM) in boreal forests; 17% (WD), 10% (SLA) and

22% (SM) in temperate forests; and 16% (WD), 24% (SLA) and 12%
(SM) in tropical forests.

Boreal forest functional trait responses varied under the influ-
ence of environmental gradients. Wood density was best predicted
<figure><img src="image_5.png" /><figcaption>FIGURE 4 Biogeographical representations of the explained distribution of functional traits of trees. The colour palette represents a gradient from acquisitive strategies (red-yellow) to conservative strategies (green-blue). Map projection, WGS 1984 Equal Earth (world).</figcaption></figure>

explained variance due to interactions in global-scale models were
18% (WD), 11% (SLA) and 29% (SM). Within biomes, the cumu-
lative explained variance due to interactions were 7% (WD), 8%
(SLA) and 10% (SM) in boreal forests; 17% (WD), 10% (SLA) and

22% (SM) in temperate forests; and 16% (WD), 24% (SLA) and 12%
(SM) in tropical forests.

Boreal forest functional trait responses varied under the influ-
ence of environmental gradients. Wood density was best predicted
<figure><img src="image_5.png" /><figcaption>FIGURE 5 Effect plots of environmental variables on community unweighted means (CM) of traits. Lines and colours represent different scales and biomes. Bands around lines represent 95% confidence intervals.</figcaption></figure>

by energy and precipitation with less influence from soils. Seed mass was also primarily influenced by energy, followed by soil properties and precipitation. For SLA, soil properties dominated the explained variance along with energy, while precipitation had less influence. For temperate forests, energy had a dominant effect on all traits with precipitation having the least influence (WD and SM) or affected equally by soils and precipitation (SLA). In tropical forests, WD was best explained by precipitation and soil properties with energy the least important gradient. Specific leaf area was strongly influenced by soils and energy, with precipitation still explaining much of the variance. Seed mass was almost equally influenced by energy and precipitation with less influence from soil properties.

# 4 | DISCUSSION

## 4.1 | Global patterns

On a global scale, the studied functional traits responded strongly to macroenvironmental gradients, with about half the variance among traits explained. This strong signal demonstrates that local communities of tree species respond to large-scale fluctuations in climate and soil properties, contradicting results from Bruelheide et al. (2018) who studied univariate trait-environment relationships in vegetation

plots at large scale. This difference may arise from the inclusion of simultaneous effects of multiple environmental variables in our study since environmental variables act jointly in filtering tree strategies. Also, trees as long-lived organisms may be more responsive to macro environmental gradients compared to other plant life forms. Plants with shorter life spans operate at smaller temporal scales and could be more sensitive to local climate and biotic conditions (Levin, 1992).

Our prediction maps revealed that WD and SM generally followed a latitudinal gradient, whereas SLA did not, although we did not test directly for latitude but rather for associated variables (energy availability). Traits related to organ size are believed to respond to latitude due to their dependence on available energy (Joswig et al., 2022; Moles et al., 2006), which was validated for SM in our study. On the other hand, SLA is considered an economic trait and should respond mostly to latitude-independent soil and climate variables (Joswig et al., 2022), which is consistent with our results. Wood density is considered an intermediate trait (Díaz et al., 2016; Joswig et al., 2022) that clusters more closely with economic traits (Joswig et al., 2022). However, WD displayed a latitudinal gradient in our prediction maps, indicating that trees rely on available energy to produce dense wood, even though this trait is not directly linked to tree size but mostly to plant performance (Chave et al., 2009).

Globally, the effects of energy availability on traits distribution were unimodal, suggesting the existence of optima for tree organ
<figure><img src="image_6.png" /><figcaption>FIGURE 5 Effect plots of environmental variables on community unweighted means (CM) of traits. Lines and colours represent different scales and biomes. Bands around lines represent 95% confidence intervals.</figcaption></figure>

by energy and precipitation with less influence from soils. Seed mass was also primarily influenced by energy, followed by soil properties and precipitation. For SLA, soil properties dominated the explained variance along with energy, while precipitation had less influence. For temperate forests, energy had a dominant effect on all traits with precipitation having the least influence (WD and SM) or affected equally by soils and precipitation (SLA). In tropical forests, WD was best explained by precipitation and soil properties with energy the least important gradient. Specific leaf area was strongly influenced by soils and energy, with precipitation still explaining much of the variance. Seed mass was almost equally influenced by energy and precipitation with less influence from soil properties.

# 4 | DISCUSSION

## 4.1 | Global patterns

On a global scale, the studied functional traits responded strongly to macroenvironmental gradients, with about half the variance among traits explained. This strong signal demonstrates that local communities of tree species respond to large-scale fluctuations in climate and soil properties, contradicting results from Bruelheide et al. (2018) who studied univariate trait-environment relationships in vegetation

plots at large scale. This difference may arise from the inclusion of simultaneous effects of multiple environmental variables in our study since environmental variables act jointly in filtering tree strategies. Also, trees as long-lived organisms may be more responsive to macro environmental gradients compared to other plant life forms. Plants with shorter life spans operate at smaller temporal scales and could be more sensitive to local climate and biotic conditions (Levin, 1992).

Our prediction maps revealed that WD and SM generally followed a latitudinal gradient, whereas SLA did not, although we did not test directly for latitude but rather for associated variables (energy availability). Traits related to organ size are believed to respond to latitude due to their dependence on available energy (Joswig et al., 2022; Moles et al., 2006), which was validated for SM in our study. On the other hand, SLA is considered an economic trait and should respond mostly to latitude-independent soil and climate variables (Joswig et al., 2022), which is consistent with our results. Wood density is considered an intermediate trait (Díaz et al., 2016; Joswig et al., 2022) that clusters more closely with economic traits (Joswig et al., 2022). However, WD displayed a latitudinal gradient in our prediction maps, indicating that trees rely on available energy to produce dense wood, even though this trait is not directly linked to tree size but mostly to plant performance (Chave et al., 2009).

Globally, the effects of energy availability on traits distribution were unimodal, suggesting the existence of optima for tree organ
<figure><img src="image_6.png" /><figcaption>FIGURE 6 Predicted graphs of interactions among climatic and soil variables in relation to community unweighted mean (CM) of traits for different scales or biomes. Bands around lines represent 95% confidence intervals. The categories of explanatory variables (low, mid, high) denote the 10th, 50th and 90th percentile of their distribution. AP, annual precipitation (mm); MAT, mean annual temperature (°C); PS, precipitation seasonality; SAND, sand content (%); TS, temperature seasonality (°C*100).</figcaption></figure>

functioning. Specific leaf area reached the most acquisitive strategies at intermediate values of solar radiation (around 13,000 kJ m⁻² day⁻¹). Conservative strategies of WD and SM reached an optimum at mean annual temperatures around 15°C, consistent with results of Maynard et al. (2022). However, tropical forests did exhibit the highest values of WD and SM. The decline observed after these temperature optimums seems to reflect a transition away from energy to other limiting factors in tropical forests (soils properties and precipitation) rather than a theoretical optimum of temperature after which traits values are constrained by further increases in energy availability.

Energy availability had the strongest influence on functional traits on a global scale. Indeed, mean annual temperature and solar radiation are the variables that best align with latitude. Precipitation and soil properties show greater heterogeneity along latitudes compared to energy, so it is plausible that their effect is more easily detectable at biome scales, as reflected in our data. This might explain why precipitation and soil effects on traits distribution are seldom strongly detected at global scale compared to temperature patterns

(De Frenne et al., 2013; Maire et al., 2015; Maynard et al., 2022; Moles et al., 2014; Wieczynski et al., 2019). In sum, few trait-environment relationships were consistent across scales and biomes, confirming the necessity to investigate the biogeography of traits at the biome level.

## 4.2 | Effects of environmental gradients on forest functional composition

### 4.2.1 | Wood density

Wood density is an important component of tree longevity, mechanical strength, hydraulic safety, and growth rate (Chave et al., 2009). The lowest median and narrowest range of WD were found in broad forests, reflecting the dominance of conifers. Their wood is mostly composed of tracheids, which allows a greater resistance to frost-induced cavitation than the vessels of broad-leaf species at
<figure><img src="image_7.png" /><figcaption>FIGURE 6 Predicted graphs of interactions among climatic and soil variables in relation to community unweighted mean (CM) of traits for different scales or biomes. Bands around lines represent 95% confidence intervals. The categories of explanatory variables (low, mid, high) denote the 10th, 50th and 90th percentile of their distribution. AP, annual precipitation (mm); MAT, mean annual temperature (°C); PS, precipitation seasonality; SAND, sand content (%); TS, temperature seasonality (°C*100).</figcaption></figure>

functioning. Specific leaf area reached the most acquisitive strategies at intermediate values of solar radiation (around 13,000 kJ m⁻² day⁻¹). Conservative strategies of WD and SM reached an optimum at mean annual temperatures around 15°C, consistent with results of Maynard et al. (2022). However, tropical forests did exhibit the highest values of WD and SM. The decline observed after these temperature optimums seems to reflect a transition away from energy to other limiting factors in tropical forests (soils properties and precipitation) rather than a theoretical optimum of temperature after which traits values are constrained by further increases in energy availability.

Energy availability had the strongest influence on functional traits on a global scale. Indeed, mean annual temperature and solar radiation are the variables that best align with latitude. Precipitation and soil properties show greater heterogeneity along latitudes compared to energy, so it is plausible that their effect is more easily detectable at biome scales, as reflected in our data. This might explain why precipitation and soil effects on traits distribution are seldom strongly detected at global scale compared to temperature patterns

(De Frenne et al., 2013; Maire et al., 2015; Maynard et al., 2022; Moles et al., 2014; Wieczynski et al., 2019). In sum, few trait-environment relationships were consistent across scales and biomes, confirming the necessity to investigate the biogeography of traits at the biome level.

## 4.2 | Effects of environmental gradients on forest functional composition

### 4.2.1 | Wood density

Wood density is an important component of tree longevity, mechanical strength, hydraulic safety, and growth rate (Chave et al., 2009). The lowest median and narrowest range of WD were found in broad forests, reflecting the dominance of conifers. Their wood is mostly composed of tracheids, which allows a greater resistance to frost-induced cavitation than the vessels of broad-leaf species at
similar WD (Hacke et al., 2001). Temperature seasonality was the most limiting factor in the boreal biome: shorter growing seasons came with lower wood densities. In contrast, we observed a decline in the importance of temperature seasonality relative to mean annual temperature in temperate forests compared to boreal forests, possibly reflecting a threshold in the influence of seasonality on broad leaf species presence. Broad leaf species may out-compete conifers where there are fewer than about two months of frost per year (Crawford, 1989) leading to higher community WD. Temperate species invest more in their wood as mean annual temperature increases. However, this positive relationship between WD and temperature reached an optimum, and subsequent increases in mean annual temperature in tropical forests had much less impact than in other biomes relative to soil properties and precipitation gradients. In the tropical biome, temperature seasonality became slightly more important than mean temperatures again, with lower wood density values towards subtropical forests, as also observed by Pinho et al. (2021).

Worldwide, sand content impacted consistently wood density in forest communities, with a small shift of similar magnitude (±0.01 mg/mm3 per 20% change in sand content). In the northern biomes (boreal and temperate forests), trees tended to develop denser woods on finer soil texture (lower sand content). Many areas of these forests, characterized by coarser soils, host coniferous species (mostly pines) explaining the trend towards lighter woods. On the contrary, WD slightly increased with sand content in tropical forests, confirming patterns observed in Amazonia (Fortunel et al., 2014; Ter Steege et al., 2006; Vleminckx et al., 2021) and African tropical forests (Réjou-Méchain et al., 2021) where broadleaf species tend to adopt conservative resource-use strategies in poorer soil habitats. In addition to soil texture, cation exchange capacity was highly influential in explaining variation of WD in tropical forests, independently and in interaction with sand content and annual precipitation. Tropical forests are characterized by infertile and acidic soils (Kricher, 2017) explaining why soil properties might be more of a constraint towards conservative strategies in this biome.

We found that the precipitation gradient had minimal effect on WD at global scale, as also reported by others (Costa-Saura et al., 2016; de la Riva et al., 2018; Šímová et al., 2015; Wiemann & Williamson, 2002; Zhang et al., 2013). Despite the main effects being unimportant, the interaction between precipitation and temperature was notable. Higher WD was associated with more arid environments as expected because of greater resistance to drought-induced cavitation associated with high WD in broad-leaf species (Greenwood et al., 2017; O'Brien et al., 2017). Precipitation was the most influential gradient driving the distribution of WD in tropical forests. Wood was denser at higher temperatures when annual precipitation was low, which can indicate an arid climate. However, WD seemed to be higher in tropical forests that are either drier or wetter throughout the year, than in habitats that are periodically arid with high seasonal changes in precipitation regimes. This effect may be due to other protection mechanisms to drought cavitation in seasonal tropical forests, such as leaf shedding during the dry

season. These interactions between annual means and seasonality could explain the mixed results reported in the literature, with reports of higher values of WD in areas with high rainfall (Ordoñez et al., 2009; Swenson et al., 2012; Swenson & Weiser, 2010; Zhang et al., 2011), in arid environments (Cornwell & Ackerly, 2009; Preston et al., 2006; Swenson & Enquist, 2007) or at average precipitation regimes (Wiemann & Williamson, 2002). In boreal forests, higher precipitation allowed for more conservative strategies, which may be associated with a lower risk of forest fires. This reduced risk of fire gives more opportunity for establishment of late successional species which typically have high WD.

The WD of trees was associated with survival strategies that are increasingly conservative and diversified as we moved from boreal to tropical forests (Stahl et al., 2014), probably due to fewer constraints from temperature and its seasonality, and increasing biotic pressure leading to more divergent strategies (Swenson & Enquist, 2007; Terborgh, 1973) and higher constraints on wood mechanical strength (Kricher, 2017). Regardless of biome or scale, our results were consistent with the role of WD related to hydraulic safety in trees (Chave et al., 2009; Swenson & Enquist, 2007). Lower values were found where the growing season is short and temperature is low (the presence of conifers and frost-cavitation avoidance), and higher values where the climate is drier (drought cavitation avoidance).

### 4.2.2 | Specific leaf area

Specific leaf area is the amount of biomass invested in building a given light-intercepting leaf area (Wright et al., 2004). Our results for SLA are consistent with a dominant role of resource investment in governing this trait, since we observed a more balanced importance of environmental gradients linked to resources availability (light, precipitation and soil properties) than in the other functional traits. Specific leaf area is the only trait for which solar radiation was selected over mean annual temperature, confirming the strong links between light availability and this functional trait (Wilson et al., 1999). Specific leaf area is expected to decrease with higher irradiance following a non-linear relationship (Poorter et al., 2009). Instead, our large-scale analysis showed a unimodal relationship with an optimum (acquisitive strategies) found at average solar radiation in temperate forests. This is likely due to light-independent stresses that limit leaf productivity in other biomes. Evergreen species are known to have lower SLA in trees than deciduous species (Villar & Merino, 2001). Indeed, trees in both boreal and tropical forests likely invest more heavily in their leaves than their temperate counterparts since they both include evergreen species with leaves highly adapted to survive winter (boreal forests) or are subjected to herbivory that require higher levels of investment in defence (tropical forests; Sedio et al., 2018). On the other hand, in many areas temperate forests are characterized by seasonal leaf shedding, which is highly influential in the dynamics of leaf development and longevity. The acquisitive strategies
similar WD (Hacke et al., 2001). Temperature seasonality was the most limiting factor in the boreal biome: shorter growing seasons came with lower wood densities. In contrast, we observed a decline in the importance of temperature seasonality relative to mean annual temperature in temperate forests compared to boreal forests, possibly reflecting a threshold in the influence of seasonality on broad leaf species presence. Broad leaf species may out-compete conifers where there are fewer than about two months of frost per year (Crawford, 1989) leading to higher community WD. Temperate species invest more in their wood as mean annual temperature increases. However, this positive relationship between WD and temperature reached an optimum, and subsequent increases in mean annual temperature in tropical forests had much less impact than in other biomes relative to soil properties and precipitation gradients. In the tropical biome, temperature seasonality became slightly more important than mean temperatures again, with lower wood density values towards subtropical forests, as also observed by Pinho et al. (2021).

Worldwide, sand content impacted consistently wood density in forest communities, with a small shift of similar magnitude (±0.01 mg/mm3 per 20% change in sand content). In the northern biomes (boreal and temperate forests), trees tended to develop denser woods on finer soil texture (lower sand content). Many areas of these forests, characterized by coarser soils, host coniferous species (mostly pines) explaining the trend towards lighter woods. On the contrary, WD slightly increased with sand content in tropical forests, confirming patterns observed in Amazonia (Fortunel et al., 2014; Ter Steege et al., 2006; Vleminckx et al., 2021) and African tropical forests (Réjou-Méchain et al., 2021) where broadleaf species tend to adopt conservative resource-use strategies in poorer soil habitats. In addition to soil texture, cation exchange capacity was highly influential in explaining variation of WD in tropical forests, independently and in interaction with sand content and annual precipitation. Tropical forests are characterized by infertile and acidic soils (Kricher, 2017) explaining why soil properties might be more of a constraint towards conservative strategies in this biome.

We found that the precipitation gradient had minimal effect on WD at global scale, as also reported by others (Costa-Saura et al., 2016; de la Riva et al., 2018; Šímová et al., 2015; Wiemann & Williamson, 2002; Zhang et al., 2013). Despite the main effects being unimportant, the interaction between precipitation and temperature was notable. Higher WD was associated with more arid environments as expected because of greater resistance to drought-induced cavitation associated with high WD in broad-leaf species (Greenwood et al., 2017; O'Brien et al., 2017). Precipitation was the most influential gradient driving the distribution of WD in tropical forests. Wood was denser at higher temperatures when annual precipitation was low, which can indicate an arid climate. However, WD seemed to be higher in tropical forests that are either drier or wetter throughout the year, than in habitats that are periodically arid with high seasonal changes in precipitation regimes. This effect may be due to other protection mechanisms to drought cavitation in seasonal tropical forests, such as leaf shedding during the dry

season. These interactions between annual means and seasonality could explain the mixed results reported in the literature, with reports of higher values of WD in areas with high rainfall (Ordoñez et al., 2009; Swenson et al., 2012; Swenson & Weiser, 2010; Zhang et al., 2011), in arid environments (Cornwell & Ackerly, 2009; Preston et al., 2006; Swenson & Enquist, 2007) or at average precipitation regimes (Wiemann & Williamson, 2002). In boreal forests, higher precipitation allowed for more conservative strategies, which may be associated with a lower risk of forest fires. This reduced risk of fire gives more opportunity for establishment of late successional species which typically have high WD.

The WD of trees was associated with survival strategies that are increasingly conservative and diversified as we moved from boreal to tropical forests (Stahl et al., 2014), probably due to fewer constraints from temperature and its seasonality, and increasing biotic pressure leading to more divergent strategies (Swenson & Enquist, 2007; Terborgh, 1973) and higher constraints on wood mechanical strength (Kricher, 2017). Regardless of biome or scale, our results were consistent with the role of WD related to hydraulic safety in trees (Chave et al., 2009; Swenson & Enquist, 2007). Lower values were found where the growing season is short and temperature is low (the presence of conifers and frost-cavitation avoidance), and higher values where the climate is drier (drought cavitation avoidance).

### 4.2.2 | Specific leaf area

Specific leaf area is the amount of biomass invested in building a given light-intercepting leaf area (Wright et al., 2004). Our results for SLA are consistent with a dominant role of resource investment in governing this trait, since we observed a more balanced importance of environmental gradients linked to resources availability (light, precipitation and soil properties) than in the other functional traits. Specific leaf area is the only trait for which solar radiation was selected over mean annual temperature, confirming the strong links between light availability and this functional trait (Wilson et al., 1999). Specific leaf area is expected to decrease with higher irradiance following a non-linear relationship (Poorter et al., 2009). Instead, our large-scale analysis showed a unimodal relationship with an optimum (acquisitive strategies) found at average solar radiation in temperate forests. This is likely due to light-independent stresses that limit leaf productivity in other biomes. Evergreen species are known to have lower SLA in trees than deciduous species (Villar & Merino, 2001). Indeed, trees in both boreal and tropical forests likely invest more heavily in their leaves than their temperate counterparts since they both include evergreen species with leaves highly adapted to survive winter (boreal forests) or are subjected to herbivory that require higher levels of investment in defence (tropical forests; Sedio et al., 2018). On the other hand, in many areas temperate forests are characterized by seasonal leaf shedding, which is highly influential in the dynamics of leaf development and longevity. The acquisitive strategies
of leaves observed in this biome are likely adapted to their short
residence time, which is confirmed by a pronounced importance of
temperature seasonality over solar radiation in temperate forests
relative to boreal and tropical forests.

Specific leaf area increased (acquisitive strategies) with annual
precipitation and decreased with precipitation seasonality, which
tends to confirm the conservative strategies of this trait reported
for xeric environments (Cornwell & Ackerly, 2009; Costa-Saura
et al., 2016; Wright et al., 2005) as an adaptation to withstand
drought (Greenwood et al., 2017; O'Brien et al., 2017). The negative
effect of precipitation seasonality was generalized across biomes,
whereas annual precipitation had smaller effects on SLA in tropi-
cal and boreal forests than in temperate and global forests, provid-
ing further evidence that these biomes are possibly constrained by
freezing and herbivore defence respectively.

Specific leaf area tended to exhibit more acquisitive strategies
on soil with a favourable structure (i.e. higher bulk densities and
lower sand content), especially when precipitation inputs were sta-
ble throughout the year. This trend is consistent across the three
biomes, but most prominent in tropical forests where soil variables
explained more variance in SLA than energy variables. This finding
is consistent with the hypothesis that SLA responds strongly to soil
variables when soil fertility is low, such as in tropical rainforests
(Fortunel et al., 2014; Vleminkx et al., 2021; Wright et al., 2004).

Surprisingly, almost all models of SLA had high predictive power
(45% to 49% R²<sub>adj</sub>) and large effect size, with the possible exception
of boreal forests (26% R²<sub>adj</sub>). Typically, evidence of relationships
between SLA and abiotic factors such as soil fertility, temperature
and precipitation is extremely weak or inconsistent across studies
(Moles, 2017). It is possible that SLA responds more strongly in
trees than in other plant life forms; thus, when all plants are pooled,
the signal may be weaker or lost as previously observed (Bruelheide
et al., 2018; Gong & Gao, 2019). Our results validated that in stress-
ful environments where plant growth is limited whether by cold,
drought or soil properties, tree species tend to invest in tougher,
longer lived leaves, maximizing reserves rather than resource acqui-
sition, at the expense of growth (Grime, 1977; Poorter et al., 2009).

4.2.3 | Seed mass

Seed mass is related to the reproductive effort in plants, through
its negative relationships with seed production, dormancy, persis-
tence in the soil and positive relationships with seedling size and
survival (Moles, 2017). It has been suggested that temperature and
precipitation may be important drivers of the latitudinal gradient in
SM (Lord et al., 1997). At global scale, we observed a strong effect
of both mean temperature and its seasonality on SM, but very little
effect of precipitation. Others have also associated this latitudinal
gradient with broad vegetation types (Moles et al., 2007). The light-
est seeds were indeed found in boreal forests with a low range of
values and the dominance of wind dispersal strategies. The heavi-
est seeds occurred in tropical forests, as previously observed (Lord

et al., 1997; Moles et al., 2007), with community means fifteen times
heavier than in temperate forests, but interestingly the median was
lower due to the broad range of seed strategies (e.g. wind vs. animal
dispersal) encountered in this biome (Malhado et al., 2015).

Seed mass was the most consistent trait across scale and biomes and responded best to the energy gradient. The literature attributing heavier seeds to the warmest temperatures is vast (Malhado et al., 2015; Moles et al., 2014; Šímová et al., 2015; Simpson et al., 2016; Swenson et al., 2012; Swenson & Weiser, 2010; Wieczynski et al., 2019). Our global and temperate models validated a positive relationship of SM with mean annual temperature but only to a certain point, after which it declined. This decrease at very warm temperatures seems surprising, but analysis in tropical forests showed that the direction of this relationship in this biome depends on precipitation regimes and mean annual temperature alone explained little of the variance relative to temperature seasonality. In fact, at both temperature extremes (cold in boreal forest and warm in tropical forests), the variation of temperature across seasons was more limiting on reproduction strategies than mean annual temperature alone. Trees tended to invest in heavier seeds in boreal forests if temperature across seasons was more even, probably due to more energy available to invest in reproduction with longer growing seasons. Whereas in tropical forests, the impact of mean annual temperature was pronounced only if it fluctuated significantly over the year (as in subtropical forests). Seeds were generally heavier in these seasonal forests, possibly because there are less diverse strategies than in warmer tropical forests, and their mass decreased along mean annual temperature up to 17°C. In warmer tropical zones (>18°C of mean annual temperature), SM increased slightly with temperature.

The importance and the effect of soil pH were minimal, indi-
cating that throughout the world similar values of seed masses are
found under different pH values, as previously observed in temper-
ate forests (Simpson et al., 2016). Selected soil variables had weak
importance, or minimal effects in all biomes. In light of these results,
it is challenging to identify trends in the effect of soil properties on
SM, confirming the mixed results (Moles, 2017) or the complex in-
terplays between soil fertility and this trait (Simpson et al., 2016)
reported in literature.

The global effect of precipitation on SM was weak, which contra-
dicts results of studies at broad geographic scales in which precipita-
tion was tested against traits alone, without the possible interactions
with other drivers (Moles et al., 2014; Swenson et al., 2012; Swenson
& Weiser, 2010). Nevertheless, models fitted at biome scale revealed
that in boreal and tropical forests the effect of annual precipitation
on SM was positive. Tropical forests contrasted with other biomes
by being strongly influenced by precipitation regimes. Seed mass
increased with annual precipitation and this relationship was mod-
ulated by precipitation seasonality. When annual precipitation was
low, the presence of a dry season favoured higher seed mass, proba-
bly because many trees in seasonal tropical forests drop their seeds
at the onset of the rainy season. Therefore, the seeds are not subject
to as much water stress as those in tropical forests that are drier
year-round. The hypothesis of higher seed mass in arid environments
of leaves observed in this biome are likely adapted to their short residence time, which is confirmed by a pronounced importance of temperature seasonality over solar radiation in temperate forests relative to boreal and tropical forests.

Specific leaf area increased (acquisitive strategies) with annual precipitation and decreased with precipitation seasonality, which tends to confirm the conservative strategies of this trait reported for xeric environments (Cornwell & Ackerly, 2009; Costa-Saura et al., 2016; Wright et al., 2005) as an adaptation to withstand drought (Greenwood et al., 2017; O'Brien et al., 2017). The negative effect of precipitation seasonality was generalized across biomes, whereas annual precipitation had smaller effects on SLA in tropical and boreal forests than in temperate and global forests, providing further evidence that these biomes are possibly constrained by freezing and herbivore defence respectively.

Specific leaf area tended to exhibit more acquisitive strategies on soil with a favourable structure (i.e. higher bulk densities and lower sand content), especially when precipitation inputs were stable throughout the year. This trend is consistent across the three biomes, but most prominent in tropical forests where soil variables explained more variance in SLA than energy variables. This finding is consistent with the hypothesis that SLA responds strongly to soil variables when soil fertility is low, such as in tropical rainforests (Fortunel et al., 2014; Vleminkx et al., 2021; Wright et al., 2004).

Surprisingly, almost all models of SLA had high predictive power (45% to 49% R²<sub>adj</sub>) and large effect size, with the possible exception of boreal forests (26% R²<sub>adj</sub>). Typically, evidence of relationships between SLA and abiotic factors such as soil fertility, temperature and precipitation is extremely weak or inconsistent across studies (Moles, 2017). It is possible that SLA responds more strongly in trees than in other plant life forms; thus, when all plants are pooled, the signal may be weaker or lost as previously observed (Bruelheide et al., 2018; Gong & Gao, 2019). Our results validated that in stressful environments where plant growth is limited whether by cold, drought or soil properties, tree species tend to invest in tougher, longer lived leaves, maximizing reserves rather than resource acquisition, at the expense of growth (Grime, 1977; Poorter et al., 2009).

### 4.2.3 | Seed mass

Seed mass is related to the reproductive effort in plants, through its negative relationships with seed production, dormancy, persistence in the soil and positive relationships with seedling size and survival (Moles, 2017). It has been suggested that temperature and precipitation may be important drivers of the latitudinal gradient in SM (Lord et al., 1997). At global scale, we observed a strong effect of both mean temperature and its seasonality on SM, but very little effect of precipitation. Others have also associated this latitudinal gradient with broad vegetation types (Moles et al., 2007). The lightest seeds were indeed found in boreal forests with a low range of values and the dominance of wind dispersal strategies. The heaviest seeds occurred in tropical forests, as previously observed (Lord

et al., 1997; Moles et al., 2007), with community means fifteen times heavier than in temperate forests, but interestingly the median was lower due to the broad range of seed strategies (e.g. wind vs. animal dispersal) encountered in this biome (Malhado et al., 2015).

Seed mass was the most consistent trait across scale and biomes and responded best to the energy gradient. The literature attributing heavier seeds to the warmest temperatures is vast (Malhado et al., 2015; Moles et al., 2014; Šímová et al., 2015; Simpson et al., 2016; Swenson et al., 2012; Swenson & Weiser, 2010; Wieczynski et al., 2019). Our global and temperate models validated a positive relationship of SM with mean annual temperature but only to a certain point, after which it declined. This decrease at very warm temperatures seems surprising, but analysis in tropical forests showed that the direction of this relationship in this biome depends on precipitation regimes and mean annual temperature alone explained little of the variance relative to temperature seasonality. In fact, at both temperature extremes (cold in boreal forest and warm in tropical forests), the variation of temperature across seasons was more limiting on reproduction strategies than mean annual temperature alone. Trees tended to invest in heavier seeds in boreal forests if temperature across seasons was more even, probably due to more energy available to invest in reproduction with longer growing seasons. Whereas in tropical forests, the impact of mean annual temperature was pronounced only if it fluctuated significantly over the year (as in subtropical forests). Seeds were generally heavier in these seasonal forests, possibly because there are less diverse strategies than in warmer tropical forests, and their mass decreased along mean annual temperature up to 17°C. In warmer tropical zones (>18°C of mean annual temperature), SM increased slightly with temperature.

The importance and the effect of soil pH were minimal, indicating that throughout the world similar values of seed masses are found under different pH values, as previously observed in temperate forests (Simpson et al., 2016). Selected soil variables had weak importance, or minimal effects in all biomes. In light of these results, it is challenging to identify trends in the effect of soil properties on SM, confirming the mixed results (Moles, 2017) or the complex interplays between soil fertility and this trait (Simpson et al., 2016) reported in literature.

The global effect of precipitation on SM was weak, which contradicts results of studies at broad geographic scales in which precipitation was tested against traits alone, without the possible interactions with other drivers (Moles et al., 2014; Swenson et al., 2012; Swenson & Weiser, 2010). Nevertheless, models fitted at biome scale revealed that in boreal and tropical forests the effect of annual precipitation on SM was positive. Tropical forests contrasted with other biomes by being strongly influenced by precipitation regimes. Seed mass increased with annual precipitation and this relationship was modulated by precipitation seasonality. When annual precipitation was low, the presence of a dry season favoured higher seed mass, probably because many trees in seasonal tropical forests drop their seeds at the onset of the rainy season. Therefore, the seeds are not subject to as much water stress as those in tropical forests that are drier year-round. The hypothesis of higher seed mass in arid environments
(Baker, 1972; Pakeman et al., 2008) was not supported by our data at any level. Low water input seems to limit conservative strategies of SM in trees. We would expect tree species to exhibit lower SM when subjected to harsh environmental conditions, because plants growing in stressful conditions tend to assign a lower proportion of annual carbon to reproduction (Grime, 1977). Our results validated this trend for energy and water limitations, but not for soil fertility.

## 4.3 | Importance of environmental gradients on forest functional composition

In boreal forests, energy availability was influential for all traits but sometimes was equal to soil properties (SLA) or followed closely by precipitation (WD). Therefore, the dominant effect of temperature was not as pronounced as expected. Some traits may be more influenced by environmental conditions that act during the growing season (precipitation and soils), rather than the dormant season (cold temperature). In summary, strategies of boreal trees did not respond as strongly as those in the other biomes to shifts in environmental gradients, suggesting that they might be proportionally less affected by changes along these gradients. On the other hand, a limited range of trait values may restrict their ability to cope with these changes. The trait composition of boreal forest communities could also be constrained by a disturbance gradient, such as large-scale insect epidemics (Gauthier et al., 2015) that was not considered in this study, relative to temperate and tropical forests that are both characterized by smaller scale disturbance patterns.

Trait variation in temperate forests was generally well explained by environmental gradients. Functional traits are known to differ greatly between broadleaves and conifers (Stahl et al., 2013). The alternation of pure or mixed coniferous and broad-leaf forests found in this biome may explain this result through substantial transitions in functional traits values. Temperate forests were the only biome for which a single gradient emerged as the most important across traits. We observed the expected strong influence of energy availability due to the large range of temperature and solar radiation encountered along with a weaker effect of precipitation and soils that are generally more stable and fertile in this biome. This finding suggests that temperate forests could be particularly sensitive to shifts in temperature.

Tropical forests reflected a different picture, with all traits responding strongly to precipitation regime. Moreover, SLA and WD both showed the expected smaller effect of energy availability in the Neotropics compared to other biomes, with a stronger influence of precipitation and soil properties. Therefore, with changes in climate altered precipitation regimes and feedbacks on soils might be important limiting factors on forest functional composition in tropical forests.

## 4.4 | Species abundance

Weighting trait means by individual species abundances did not improve the strength of the trait-environment relationships, suggesting that only information on species presence at any given

location is needed to investigate the distribution of functional traits of trees at broad scale. While surprising, this finding re-curs in previous research conducted over smaller areas (Bjorkman et al., 2018; Dubuis et al., 2013; Pakeman et al., 2008), although it is rarely discussed. We have attempted to fill this gap by putting forward a hypothesis that merits further development. Dominant species are assumed to be at the optimum of their ecological niche and growing under favourable environmental conditions. Therefore, it is unlikely that a small change in the environment at the core of their distribution would cause a huge shift in their abundance and the functional composition of the forest, compared to rare species that are already growing under stressful conditions and may be displaced by other species with contrasting life strategies. Consistent with this hypothesis, the effect of the environment on tree functional traits was stronger at the edge of species distribution range (where a species is rare) than at the core in North America (Stahl et al., 2014). This indicates that the turnover in rare species might be coupled with shifts in functional composition. Community unweighted means, by giving more importance to the trait values of rare species, possibly increase the strength of the relationships between forest community trait composition and the environment.

## 4.5 | Limitations

Forest inventory protocols and sample plot sizes are not consistent across countries (Liang et al., 2022), which may lead to bias in the variance of projected data. As well, data coverage is uneven with tropical forests under and temperate forests overrepresented in both inventories and functional trait measurements. Increased sampling effort and funding to establish permanent sample plots in poorly documented areas are needed to rectify this discrepancy (Nesha et al., 2022). We acknowledge that additional variation in forest functional composition may be present due to genetic variation and phenotypic plasticity within species (Fridley et al., 2007; Fridley & Grime, 2010), especially in species-poor communities dominated by conifers (Anderegg et al., 2018; Siefert et al., 2015). This greater share of intraspecific variability may explain why our models in conifer-dominated boreal forests had generally less explanatory power (24%-31%) than in other biomes (26%-49%). The resolution of climatic (1km²) and edaphic variables (~250m) prevented us from resolving very fine-grained variability at the scale of the local tree communities. Moreover, using averages for climatic conditions precluded getting data for the exact years of plots measurements and detecting possible effects of extreme climatic events (Waldock et al., 2018).

## 5 | CONCLUSION AND OUTLOOK

This study highlights several major trends in the distribution of tree functional traits. Wood density follows a latitudinal gradient aligned mostly with temperature, but other limiting factors (precipitation
(Baker, 1972; Pakeman et al., 2008) was not supported by our data at any level. Low water input seems to limit conservative strategies of SM in trees. We would expect tree species to exhibit lower SM when subjected to harsh environmental conditions, because plants growing in stressful conditions tend to assign a lower proportion of annual carbon to reproduction (Grime, 1977). Our results validated this trend for energy and water limitations, but not for soil fertility.

## 4.3 | Importance of environmental gradients on forest functional composition

In boreal forests, energy availability was influential for all traits but sometimes was equal to soil properties (SLA) or followed closely by precipitation (WD). Therefore, the dominant effect of temperature was not as pronounced as expected. Some traits may be more influenced by environmental conditions that act during the growing season (precipitation and soils), rather than the dormant season (cold temperature). In summary, strategies of boreal trees did not respond as strongly as those in the other biomes to shifts in environmental gradients, suggesting that they might be proportionally less affected by changes along these gradients. On the other hand, a limited range of trait values may restrict their ability to cope with these changes. The trait composition of boreal forest communities could also be constrained by a disturbance gradient, such as large-scale insect epidemics (Gauthier et al., 2015) that was not considered in this study, relative to temperate and tropical forests that are both characterized by smaller scale disturbance patterns.

Trait variation in temperate forests was generally well explained by environmental gradients. Functional traits are known to differ greatly between broadleaves and conifers (Stahl et al., 2013). The alternation of pure or mixed coniferous and broad-leaf forests found in this biome may explain this result through substantial transitions in functional traits values. Temperate forests were the only biome for which a single gradient emerged as the most important across traits. We observed the expected strong influence of energy availability due to the large range of temperature and solar radiation encountered along with a weaker effect of precipitation and soils that are generally more stable and fertile in this biome. This finding suggests that temperate forests could be particularly sensitive to shifts in temperature.

Tropical forests reflected a different picture, with all traits responding strongly to precipitation regime. Moreover, SLA and WD both showed the expected smaller effect of energy availability in the Neotropics compared to other biomes, with a stronger influence of precipitation and soil properties. Therefore, with changes in climate altered precipitation regimes and feedbacks on soils might be important limiting factors on forest functional composition in tropical forests.

## 4.4 | Species abundance

Weighting trait means by individual species abundances did not improve the strength of the trait-environment relationships, suggesting that only information on species presence at any given

location is needed to investigate the distribution of functional traits of trees at broad scale. While surprising, this finding re-curs in previous research conducted over smaller areas (Bjorkman et al., 2018; Dubuis et al., 2013; Pakeman et al., 2008), although it is rarely discussed. We have attempted to fill this gap by putting forward a hypothesis that merits further development. Dominant species are assumed to be at the optimum of their ecological niche and growing under favourable environmental conditions. Therefore, it is unlikely that a small change in the environment at the core of their distribution would cause a huge shift in their abundance and the functional composition of the forest, compared to rare species that are already growing under stressful conditions and may be displaced by other species with contrasting life strategies. Consistent with this hypothesis, the effect of the environment on tree functional traits was stronger at the edge of species distribution range (where a species is rare) than at the core in North America (Stahl et al., 2014). This indicates that the turnover in rare species might be coupled with shifts in functional composition. Community unweighted means, by giving more importance to the trait values of rare species, possibly increase the strength of the relationships between forest community trait composition and the environment.

## 4.5 | Limitations

Forest inventory protocols and sample plot sizes are not consistent across countries (Liang et al., 2022), which may lead to bias in the variance of projected data. As well, data coverage is uneven with tropical forests under and temperate forests overrepresented in both inventories and functional trait measurements. Increased sampling effort and funding to establish permanent sample plots in poorly documented areas are needed to rectify this discrepancy (Nesha et al., 2022). We acknowledge that additional variation in forest functional composition may be present due to genetic variation and phenotypic plasticity within species (Fridley et al., 2007; Fridley & Grime, 2010), especially in species-poor communities dominated by conifers (Anderegg et al., 2018; Siefert et al., 2015). This greater share of intraspecific variability may explain why our models in conifer-dominated boreal forests had generally less explanatory power (24%-31%) than in other biomes (26%-49%). The resolution of climatic (1km²) and edaphic variables (~250m) prevented us from resolving very fine-grained variability at the scale of the local tree communities. Moreover, using averages for climatic conditions precluded getting data for the exact years of plots measurements and detecting possible effects of extreme climatic events (Waldock et al., 2018).

## 5 | CONCLUSION AND OUTLOOK

This study highlights several major trends in the distribution of tree functional traits. Wood density follows a latitudinal gradient aligned mostly with temperature, but other limiting factors (precipitation
and soil properties) take a more important role within boreal and tropical forests. The links between wood density and precipitation regimes confirm the role of this trait in tree hydraulic safety globally. Our results demonstrate for the first time that worldwide, specific leaf area is more evenly influenced by major environmental gradients than traits linked to size, supporting the theory that this trait is involved in resource management. We show that its most acquisitive values are found in temperate forests, where leaf shedding is a ubiquitous process. Reproduction strategies are highly dependent on energy availability across the globe. Trees require large amount of energy to invest in their seeds, and to a lesser extent water, independent of soil properties.

In summary, forest community strategies are more conservative in stressful environments, but the limiting constraints that define these stressful environments, and thus the sensitivity of traits to environmental gradients, vary from biome to biome. This raises the question of the scale of study used to assess the response of community functional composition to global change. Tropical forests are a notable example, with greater importance on forest functional composition of gradients that are not captured by latitude, such as precipitation and soil properties, and whose effect can be founded on a large scale. Future global studies should ensure that the heterogeneity of gradients and large vegetation groups across spatial scales are well captured.

Our results show that even at local community level, tree functions respond to broad environmental gradients. Further studies could investigate why the inclusion of species dominance does not improve these relationships compared to the mere presence of species in local forest communities. Furthermore, the importance of interactions among and between energy, soil properties and precipitation gradients indicates that the interplay of climate and soil is essential to understanding the distribution of trees' life strategies. Moving forward, models including interactions are essential to fully elucidate trait-environment relationships.

The large scope of this study, analysing over 148 trait-environment relationships, enabled us to test, illustrate and standardize results from the vast literature on trees' functional trait distribution, heterogeneous across different spatial scales and methodologies. These relationships can be used to guide future research into the sensitivity of forest communities to global change and how to integrate multiple aspects of the environment into large-scale modelling.

### ACKNOWLEDGEMENTS

This work was funded in part by a Natural Sciences and Engineering Research Council of Canada Discovery Grant to Alain Paquette. This work was also made possible by the Global Forest Biodiversity Database (https://www.gfbinitiative.org/), which represents the work of over 200 independent investigators and their public and private funding agencies (see Appendix S13 Supplementary acknowledgements). The study was supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (MPI BGC, Germany) and supported by Future Earth and the German Centre for Integrative

Biodiversity Research (iDiv) Halle-Jena-Leipzig. We are thankful to Orane Mordacq and Cameron Daoust for their help in cleaning and processing the data as well as collecting functional trait values from literature, and Charlotte Langlois, Marine Fernandez and Maxime Paquette for preparing the manuscript. We would like to thank François Rousseu, Daniel Schoenig, Daniel Lesieur and Mélanie Desrochers, CFR professionals in statistics, database management and GIS.

### FUNDING INFORMATION

Natural Sciences and Engineering Research Council of Canada.

### CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interests.

### DATA AVAILABILITY STATEMENT

Data on community means of traits as well as climatic and pedologic variables are available through figshare: https://figshare.com/ Information regarding variable selection and model outputs is available as Supplementary material. The GFBI database is available upon written request at https://www.gfbinitiative.org/datar equest. The TRY database is available upon written request at https://www.try-db.org/TryWeb/Prop0.php Any other relevant data are available from the corresponding authors upon reasonable request.

### ORCID

Elise Bouchard ID https://orcid.org/0000-0001-7146-4820

Eric B. Searle ID https://orcid.org/0000-0002-3425-5998

Pierre Drapeau ID https://orcid.org/0000-0002-1115-2531

Jingjing Liang ID https://orcid.org/0000-0001-9439-9320

Javier G. P. Gamarra ID https://orcid.org/0000-0002-1290-9559

Meinrad Abegg ID https://orcid.org/0000-0001-5151-5889

Giorgio Alberti ID https://orcid.org/0000-0003-2422-3009

Esteban Alvarez-Davila ID https://orcid.org/0000-0001-9032-0099

Luciana F. Alves ID https://orcid.org/0000-0002-8944-1851

Valerio Avitabile ID https://orcid.org/0000-0003-3646-052X

Gerardo Aymard ID https://orcid.org/0000-0001-9405-0508

Jean-François Bastin ID https://orcid.org/0000-0003-2602-7247

Philippe Birnbaum ID https://orcid.org/0000-0003-4830-5500

Frans Bongers ID https://orcid.org/0000-0002-8431-6189

Olivier Bouriaud ID https://orcid.org/0000-0002-8046-466X

Pedro Brancalion ID https://orcid.org/0000-0001-8245-4062

Filippo Bussotti ID https://orcid.org/0000-0002-8353-4459

Roberto Cazzolla Gatti ID https://orcid.org/0000-0001-5130-8492

Goran Češljar ID https://orcid.org/0000-0003-0438-1050

Chelsea Chisholm ID https://orcid.org/0000-0002-7739-0508

Emil Cienciala ID https://orcid.org/0000-0002-1254-4254

José Javier Corral-Rivas ID https://orcid.org/0000-0002-2851-7517

Thomas W. Crowther ID https://orcid.org/0000-0001-5674-8913

Mathieu Decuyper ID https://orcid.org/0000-0002-1713-8562

André L. Gasper ID https://orcid.org/0000-0002-1940-9581

Sergio de-Miguel ID https://orcid.org/0000-0002-9738-0657
and soil properties) take a more important role within boreal and tropical forests. The links between wood density and precipitation regimes confirm the role of this trait in tree hydraulic safety globally. Our results demonstrate for the first time that worldwide, specific leaf area is more evenly influenced by major environmental gradients than traits linked to size, supporting the theory that this trait is involved in resource management. We show that its most acquisitive values are found in temperate forests, where leaf shedding is a ubiquitous process. Reproduction strategies are highly dependent on energy availability across the globe. Trees require large amount of energy to invest in their seeds, and to a lesser extent water, independent of soil properties.

In summary, forest community strategies are more conservative in stressful environments, but the limiting constraints that define these stressful environments, and thus the sensitivity of traits to environmental gradients, vary from biome to biome. This raises the question of the scale of study used to assess the response of community functional composition to global change. Tropical forests are a notable example, with greater importance on forest functional composition of gradients that are not captured by latitude, such as precipitation and soil properties, and whose effect can be founded on a large scale. Future global studies should ensure that the heterogeneity of gradients and large vegetation groups across spatial scales are well captured.

Our results show that even at local community level, tree functions respond to broad environmental gradients. Further studies could investigate why the inclusion of species dominance does not improve these relationships compared to the mere presence of species in local forest communities. Furthermore, the importance of interactions among and between energy, soil properties and precipitation gradients indicates that the interplay of climate and soil is essential to understanding the distribution of trees' life strategies. Moving forward, models including interactions are essential to fully elucidate trait-environment relationships.

The large scope of this study, analysing over 148 trait-environment relationships, enabled us to test, illustrate and standardize results from the vast literature on trees' functional trait distribution, heterogeneous across different spatial scales and methodologies. These relationships can be used to guide future research into the sensitivity of forest communities to global change and how to integrate multiple aspects of the environment into large-scale modelling.

## ACKNOWLEDGEMENTS

This work was funded in part by a Natural Sciences and Engineering Research Council of Canada Discovery Grant to Alain Paquette. This work was also made possible by the Global Forest Biodiversity Database (https://www.gfbinitiative.org/), which represents the work of over 200 independent investigators and their public and private funding agencies (see Appendix S13 Supplementary acknowledgements). The study was supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (MPI BGC, Germany) and supported by Future Earth and the German Centre for Integrative

Biodiversity Research (iDiv) Halle-Jena-Leipzig. We are thankful to Orane Mordacq and Cameron Daoust for their help in cleaning and processing the data as well as collecting functional trait values from literature, and Charlotte Langlois, Marine Fernandez and Maxime Paquette for preparing the manuscript. We would like to thank François Rousseu, Daniel Schoenig, Daniel Lesieur and Mélanie Desrochers, CFR professionals in statistics, database management and GIS.

## FUNDING INFORMATION

Natural Sciences and Engineering Research Council of Canada.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interests.

## DATA AVAILABILITY STATEMENT

Data on community means of traits as well as climatic and pedologic variables are available through figshare: https://figshare.com/ Information regarding variable selection and model outputs is available as Supplementary material. The GFBI database is available upon written request at https://www.gfbinitiative.org/datar equest. The TRY database is available upon written request at https://www.try-db.org/TryWeb/Prop0.php Any other relevant data are available from the corresponding authors upon reasonable request.

## ORCID

Elise Bouchard ID https://orcid.org/0000-0001-7146-4820

Eric B. Searle ID https://orcid.org/0000-0002-3425-5998

Pierre Drapeau ID https://orcid.org/0000-0002-1115-2531

Jingjing Liang ID https://orcid.org/0000-0001-9439-9320

Javier G. P. Gamarra ID https://orcid.org/0000-0002-1290-9559

Meinrad Abegg ID https://orcid.org/0000-0001-5151-5889

Giorgio Alberti ID https://orcid.org/0000-0003-2422-3009

Esteban Alvarez-Davila ID https://orcid.org/0000-0001-9032-0099

Luciana F. Alves ID https://orcid.org/0000-0002-8944-1851

Valerio Avitabile ID https://orcid.org/0000-0003-3646-052X

Gerardo Aymard ID https://orcid.org/0000-0001-9405-0508

Jean-François Bastin ID https://orcid.org/0000-0003-2602-7247

Philippe Birnbaum ID https://orcid.org/0000-0003-4830-5500

Frans Bongers ID https://orcid.org/0000-0002-8431-6189

Olivier Bouriaud ID https://orcid.org/0000-0002-8046-466X

Pedro Brancalion ID https://orcid.org/0000-0001-8245-4062

Filippo Bussotti ID https://orcid.org/0000-0002-8353-4459

Roberto Cazzolla Gatti ID https://orcid.org/0000-0001-5130-8492

Goran Češljar ID https://orcid.org/0000-0003-0438-1050

Chelsea Chisholm ID https://orcid.org/0000-0002-7739-0508

Emil Cienciala ID https://orcid.org/0000-0002-1254-4254

José Javier Corral-Rivas ID https://orcid.org/0000-0002-2851-7517

Thomas W. Crowther ID https://orcid.org/0000-0001-5674-8913

Mathieu Decuyper ID https://orcid.org/0000-0002-1713-8562

André L. Gasper ID https://orcid.org/0000-0002-1940-9581

Sergio de-Miguel ID https://orcid.org/0000-0002-9738-0657
Géraldine Derroire https://orcid.org/0000-0001-7239-2881

Ilija Djordjević https://orcid.org/0000-0001-9499-3565

Tran Van Do https://orcid.org/0000-0001-9059-5842

Jiri Dolezal https://orcid.org/0000-0002-5829-4051

Tom M. Fayle https://orcid.org/0000-0002-1667-1189

Jonas Fridman https://orcid.org/0000-0002-8295-665X

Lorenzo Frizzera https://orcid.org/0000-0003-3150-8395

Damiano Gianelle https://orcid.org/0000-0001-7697-5793

Bruno Hérault https://orcid.org/0000-0002-6950-7286

Martin Herold https://orcid.org/0000-0003-0246-6886

Andrzej M. Jagodziński https://orcid.org/0000-0001-6899-0985

Bogdan Jaroszewicz https://orcid.org/0000-0002-2042-8245

Tommaso Jucker https://orcid.org/0000-0002-0751-6312

Sebastian Kepfer-Rojas https://orcid.org/0000-0002-1681-2877

Gunnar Keppel https://orcid.org/0000-0001-7092-6149

Mohammed Latif Khan https://orcid.org/0000-0001-6849-0307

Hyun Seok Kim https://orcid.org/0000-0002-3440-6071

Henn Korjus https://orcid.org/0000-0001-8522-7869

Florian Kraxner https://orcid.org/0000-0003-3832-6236

Simon Lewis https://orcid.org/0000-0002-8066-6851

Huicui Lu https://orcid.org/0000-0001-9450-9681

Brian S. Maitner https://orcid.org/0000-0002-2118-9880

Eric Marcon https://orcid.org/0000-0002-5249-321X

Andrew R. Marshall https://orcid.org/0000-0002-3261-7326

Sharif A. Mukul https://orcid.org/0000-0001-6955-2469

Gert-Jan Nabuurs https://orcid.org/0000-0002-9761-074X

María Guadalupe Nava-Miranda https://orcid.org/0000-0001-5983-2053

Elena I. Parfenova https://orcid.org/0000-0002-3221-1457

Minjee Park https://orcid.org/0000-0001-9921-4890

Pablo L. Peri https://orcid.org/0000-0002-5398-4408

Sebastian Pfautsch https://orcid.org/0000-0002-4390-4195

Oliver L. Phillips https://orcid.org/0000-0002-8993-6168

Maria Teresa F. Piedade https://orcid.org/0000-0002-7320-0498

Daniel Piotto https://orcid.org/0000-0002-6505-0098

John R. Poulsen https://orcid.org/0000-0002-1532-9808

Hans Pretzsch https://orcid.org/0000-0002-4958-1868

Peter B. Reich https://orcid.org/0000-0003-4424-662X

Mirco Rodeghiero https://orcid.org/0000-0003-3228-4557

Samir Rolim https://orcid.org/0000-0002-9962-8708

Purabi Saikia https://orcid.org/0000-0001-5481-282X

Christian Salas-Eljatib https://orcid.org/0000-0002-8468-0829

Peter Schall https://orcid.org/0000-0003-4808-818X

Dmitry Schepaschenko https://orcid.org/0000-0002-7814-4990

Vladimír Šebeň https://orcid.org/0000-0003-3692-446X

Plinio Sist https://orcid.org/0000-0002-4565-4417

Ferry Slik https://orcid.org/0000-0003-3988-7019

Alexandre F. Souza https://orcid.org/0000-0001-7468-3631

Krzysztof Stereńczak https://orcid.org/0000-0002-9556-0144

Miroslav Svoboda https://orcid.org/0000-0003-4050-3422

Nadezhda M. Tchebakova https://orcid.org/0000-0002-4208-6792

Hans Steege https://orcid.org/0000-0002-8738-2659

Helder Viana https://orcid.org/0000-0003-4024-3472

Alexander C. Vibrans https://orcid.org/0000-0002-8789-5833

Bertil Westerlund https://orcid.org/0000-0002-1073-8434

Susan K. Wiser https://orcid.org/0000-0002-8938-8181

Florian Wittmann https://orcid.org/0000-0001-9180-356X

Verginia Wortel https://orcid.org/0000-0002-1224-8025

Irié C. Zo-Bi https://orcid.org/0000-0003-0982-8579

Alain Paquette https://orcid.org/0000-0003-1048-9674

## REFERENCES

Ackerly, D., Knight, C., Weiss, S., Barton, K., & Starmer, K. (2002). Leaf size, specific leaf area and microhabitat distribution of chaparral woody plants: Contrasting patterns in species level and community level analyses. *Oecologia*, 130(3), 449–457. https://doi.org/10.1007/s004420100805

Anderegg, L. D. L., Berner, L. T., Badgley, G., Sethi, M. L., Law, B. E., & HilleRisLambers, J. (2018). Within-species patterns challenge our understanding of the leaf economics spectrum. *Ecology Letters*, 21(5), 734–744. https://doi.org/10.1111/ele.12945

Baker, H. G. (1972). Seed weight in relation to environmental conditions in California. *Ecology*, 53(6), 997–1010. https://doi.org/10.2307/1935413

Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Normand, S., Rüger, N., Beck, P. S. A., Blach-Overgaard, A., Blok, D., Cornelissen, J. H. C., Forbes, B. C., Georges, D., Goetz, S. J., Guay, K. C., Henry, G. H. R., HilleRisLambers, J., Hollister, R. D., Karger, D. N., Kattge, J., Manning, P., ... Weiher, E. (2018). Plant functional trait change across a warming tundra biome. *Nature*, 562(7725), 57–62. https://doi.org/10.1038/s41586-018-0563-7

Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S. M., Botta-Dukát, Z., Chytrý, M., Field, R., Jansen, F., Kattge, J., Pillar, V. D., Schrodt, F., Mahecha, M. D., Peet, R. K., Sandel, B., van Bodegom, P., Altman, J., Alvarez-Dávila, E., ... Jandt, U. (2018). Global trait-environment relationships of plant communities. *Nature Ecology and Evolution*, 2(12), 1906–1917. https://doi.org/10.1038/s41559-018-0699-8

Bustos Navarrete, C., & Coutinho Soares, F. (2020). dominanceanalysis: Dominance Analysis. R package version 1.3.0.

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12(4), 351–366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

Cingolani, A. M., Cabido, M., Gurvich, D. E., Renison, D., & Díaz, S. (2007). Filtering processes in the assembly of plant communities: Are species presence and abundance driven by the same traits? *Journal of Vegetation Science*, 18(6), 911–920. https://doi.org/10.1111/j.1654-1103.2007.tb02607.x

Cornwell, W. K., & Ackerly, D. D. (2009). Community assembly and shifts in plant trait distributions across an environmental gradient in Coastal California. *Ecological Monographs*, 79(1), 109–126. https://doi.org/10.1890/07-1134.1

Costa-Saura, J. M., Martínez-Vilalta, J., Trabucco, A., Spano, D., & Mereu, S. (2016). Specific leaf area and hydraulic traits explain niche segregation along an aridity gradient in Mediterranean woody species. *Perspectives in Plant Ecology, Evolution and Systematics*, 21, 23–30. https://doi.org/10.1016/j.ppees.2016.05.001

Crawford, R. M. M. (1989). *Studies in plant survival*. Blackwell Scientific.

De Frenne, P., Graae, B. J., Rodríguez-Sánchez, F., Kolb, A., Chabrerie, O., Decocq, G., De Kort, H., De Schrijver, A., Diekmann, M., Eriksson, O., Gruwez, R., Hermy, M., Lenoir, J., Plue, J., Coomes, D. A., & Verheyen, K. (2013). Latitudinal gradients as natural laboratories to infer species' responses to temperature. *Journal of Ecology*, 101(3), 784–795. https://doi.org/10.1111/1365-2745.12074
Géraldine Derroire https://orcid.org/0000-0001-7239-2881

Ilija Djordjević https://orcid.org/0000-0001-9499-3565

Tran Van Do https://orcid.org/0000-0001-9059-5842

Jiri Dolezal https://orcid.org/0000-0002-5829-4051

Tom M. Fayle https://orcid.org/0000-0002-1667-1189

Jonas Fridman https://orcid.org/0000-0002-8295-665X

Lorenzo Frizzera https://orcid.org/0000-0003-3150-8395

Damiano Gianelle https://orcid.org/0000-0001-7697-5793

Bruno Hérault https://orcid.org/0000-0002-6950-7286

Martin Herold https://orcid.org/0000-0003-0246-6886

Andrzej M. Jagodziński https://orcid.org/0000-0001-6899-0985

Bogdan Jaroszewicz https://orcid.org/0000-0002-2042-8245

Tommaso Jucker https://orcid.org/0000-0002-0751-6312

Sebastian Kepfer-Rojas https://orcid.org/0000-0002-1681-2877

Gunnar Keppel https://orcid.org/0000-0001-7092-6149

Mohammed Latif Khan https://orcid.org/0000-0001-6849-0307

Hyun Seok Kim https://orcid.org/0000-0002-3440-6071

Henn Korjus https://orcid.org/0000-0001-8522-7869

Florian Kraxner https://orcid.org/0000-0003-3832-6236

Simon Lewis https://orcid.org/0000-0002-8066-6851

Huicui Lu https://orcid.org/0000-0001-9450-9681

Brian S. Maitner https://orcid.org/0000-0002-2118-9880

Eric Marcon https://orcid.org/0000-0002-5249-321X

Andrew R. Marshall https://orcid.org/0000-0002-3261-7326

Sharif A. Mukul https://orcid.org/0000-0001-6955-2469

Gert-Jan Nabuurs https://orcid.org/0000-0002-9761-074X

María Guadalupe Nava-Miranda https://orcid.org/0000-0001-5983-2053

Elena I. Parfenova https://orcid.org/0000-0002-3221-1457

Minjee Park https://orcid.org/0000-0001-9921-4890

Pablo L. Peri https://orcid.org/0000-0002-5398-4408

Sebastian Pfautsch https://orcid.org/0000-0002-4390-4195

Oliver L. Phillips https://orcid.org/0000-0002-8993-6168

Maria Teresa F. Piedade https://orcid.org/0000-0002-7320-0498

Daniel Piotto https://orcid.org/0000-0002-6505-0098

John R. Poulsen https://orcid.org/0000-0002-1532-9808

Hans Pretzsch https://orcid.org/0000-0002-4958-1868

Peter B. Reich https://orcid.org/0000-0003-4424-662X

Mirco Rodeghiero https://orcid.org/0000-0003-3228-4557

Samir Rolim https://orcid.org/0000-0002-9962-8708

Purabi Saikia https://orcid.org/0000-0001-5481-282X

Christian Salas-Eljatib https://orcid.org/0000-0002-8468-0829

Peter Schall https://orcid.org/0000-0003-4808-818X

Dmitry Schepaschenko https://orcid.org/0000-0002-7814-4990

Vladimír Šebeň https://orcid.org/0000-0003-3692-446X

Plinio Sist https://orcid.org/0000-0002-4565-4417

Ferry Slik https://orcid.org/0000-0003-3988-7019

Alexandre F. Souza https://orcid.org/0000-0001-7468-3631

Krzysztof Stereńczak https://orcid.org/0000-0002-9556-0144

Miroslav Svoboda https://orcid.org/0000-0003-4050-3422

Nadezhda M. Tchebakova https://orcid.org/0000-0002-4208-6792

Hans Steege https://orcid.org/0000-0002-8738-2659

Helder Viana https://orcid.org/0000-0003-4024-3472

Alexander C. Vibrans https://orcid.org/0000-0002-8789-5833

Bertil Westerlund https://orcid.org/0000-0002-1073-8434

Susan K. Wiser https://orcid.org/0000-0002-8938-8181

Florian Wittmann https://orcid.org/0000-0001-9180-356X

Verginia Wortel https://orcid.org/0000-0002-1224-8025

Irié C. Zo-Bi https://orcid.org/0000-0003-0982-8579

Alain Paquette https://orcid.org/0000-0003-1048-9674

## REFERENCES

Ackerly, D., Knight, C., Weiss, S., Barton, K., & Starmer, K. (2002). Leaf size, specific leaf area and microhabitat distribution of chaparral woody plants: Contrasting patterns in species level and community level analyses. *Oecologia*, 130(3), 449–457. https://doi.org/10.1007/s004420100805

Anderegg, L. D. L., Berner, L. T., Badgley, G., Sethi, M. L., Law, B. E., & HilleRisLambers, J. (2018). Within-species patterns challenge our understanding of the leaf economics spectrum. *Ecology Letters*, 21(5), 734–744. https://doi.org/10.1111/ele.12945

Baker, H. G. (1972). Seed weight in relation to environmental conditions in California. *Ecology*, 53(6), 997–1010. https://doi.org/10.2307/1935413

Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Normand, S., Rüger, N., Beck, P. S. A., Blach-Overgaard, A., Blok, D., Cornelissen, J. H. C., Forbes, B. C., Georges, D., Goetz, S. J., Guay, K. C., Henry, G. H. R., HilleRisLambers, J., Hollister, R. D., Karger, D. N., Kattge, J., Manning, P., ... Weiher, E. (2018). Plant functional trait change across a warming tundra biome. *Nature*, 562(7725), 57–62. https://doi.org/10.1038/s41586-018-0563-7

Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S. M., Botta-Dukát, Z., Chytrý, M., Field, R., Jansen, F., Kattge, J., Pillar, V. D., Schrodt, F., Mahecha, M. D., Peet, R. K., Sandel, B., van Bodegom, P., Altman, J., Alvarez-Dávila, E., ... Jandt, U. (2018). Global trait-environment relationships of plant communities. *Nature Ecology and Evolution*, 2(12), 1906–1917. https://doi.org/10.1038/s41559-018-0699-8

Bustos Navarrete, C., & Coutinho Soares, F. (2020). dominanceanalysis: Dominance Analysis. R package version 1.3.0.

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12(4), 351–366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

Cingolani, A. M., Cabido, M., Gurvich, D. E., Renison, D., & Díaz, S. (2007). Filtering processes in the assembly of plant communities: Are species presence and abundance driven by the same traits? *Journal of Vegetation Science*, 18(6), 911–920. https://doi.org/10.1111/j.1654-1103.2007.tb02607.x

Cornwell, W. K., & Ackerly, D. D. (2009). Community assembly and shifts in plant trait distributions across an environmental gradient in Coastal California. *Ecological Monographs*, 79(1), 109–126. https://doi.org/10.1890/07-1134.1

Costa-Saura, J. M., Martínez-Vilalta, J., Trabucco, A., Spano, D., & Mereu, S. (2016). Specific leaf area and hydraulic traits explain niche segregation along an aridity gradient in Mediterranean woody species. *Perspectives in Plant Ecology, Evolution and Systematics*, 21, 23–30. https://doi.org/10.1016/j.ppees.2016.05.001

Crawford, R. M. M. (1989). *Studies in plant survival*. Blackwell Scientific.

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Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Colin Prentice, I., Garnier, E., Bönisch, G., Westoby, M., Poorter, H., Reich, P. B., Moles, A. T., Dickie, J., Gillison, A. N., Zanne, A. E., ... Gorné, L. D. (2016). The global spectrum of plant form and function. *Nature*, 529, 167-171. https://doi.org/10.1038/nature16489

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Fridley, J. D., Grime, J. P., & Bilton, M. (2007). Genetic identity of interspecific neighbours mediates plant responses to competition and environmental variation in a species-rich grassland. *Journal of Ecology*, 95(5), 908-915. https://doi.org/10.1111/j.1365-2745.2007.01256.x

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Garnier, E., Cortez, J., Billès, G., Navas, M. L., Roumet, C., Debussche, M., Laurent, G., Blanchard, A., Aubry, D., Bellmann, A., Neill, C., & Toussaint, J. P. (2004). Plant functional markers capture ecosystem properties during secondary succession. *Ecology*, 85(9), 2630-2637. https://doi.org/10.1890/03-0799

Gauthier, S., Bernier, P., Kuuluvainen, T., Shvidenko, A. Z., & Schepaschenko, D. G. (2015). Boreal forest health and global change. *Science*, 349(6250), 819-822. https://doi.org/10.1126/science.aaa9092

Gong, H., & Gao, J. (2019). Soil and climatic drivers of plant SLA (specific leaf area). *Global Ecology and Conservation*, 20, e00696. https://doi.org/10.1016/j.gecco.2019.e00696

Greenwood, S., Ruiz-Benito, P., Martínez-Vilalta, J., Lloret, F., Kitzberger, T., Allen, C. D., Fensham, R., Laughlin, D. C., Kattge, J., Bönisch, G., Kraft, N. J. B., & Jump, A. S. (2017). Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. *Ecology Letters*, 20(4), 539-553. https://doi.org/10.1111/ele.12748

Grime, J. P. (1977). Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. *The American Naturalist*, 111(982), 40-1194. https://doi.org/10.1086/283244

Grime, J. P. (1998). Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. *Journal of Ecology*, 86(6), 902-910. https://doi.org/10.1046/j.1365-2745.1998.00306.x

Hacke, U. G., Sperry, J. S., Pockman, W. T., Davis, S. D., & McCulloh, K. A. (2001). Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. *Oecologia*, 126(4), 457-461. https://doi.org/10.1007/s004420100628

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justices, C. O., & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century Forest cover changer. *Science*, 342(6160), 850-853. https://doi.org/10.1126/science.1239552

Hengl, T., De Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. *PLoS ONE*, 12(2), 1-40. https://doi.org/10.1371/journal.pone.0169748

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Holdridge, L. R. (1947). Determination of world plant formations from simple climatic data. *Science*, 105(2727), 367-368. https://doi.org/10.1126/science.105.2727.367

Joswig, J. S., Wirth, C., Schuman, M. C., Kattge, J., Reu, B., Wright, I. J., Sippel, S. D., Rüger, N., Richter, R., Schaepman, M. E., van Bodegom, P. M., Cornelissen, J. H. C., Díaz, S., Hattingh, W. N., Kramer, K., Lens, F., Niinemets, Ü., Reich, P. B., Reichstein, M., ... Mahecha, M. D. (2022). Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. *Nature Ecology and Evolution*, 6(1), 36-50. https://doi.org/10.1038/s41559-021-01616-8

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., & Acosta, A. T. (2020). TRY plant trait database-enhanced coverage and open access. *Global Change Biology*, 26(1), 119-188. https://doi.org/10.1111/gcb.14904

Keppel, G., Craven, D., Weigelt, P., Smith, S. A., van der Sande, M. T., Sandel, B., Levin, S. C., Kreft, H., & Knight, T. M. (2021). Synthesizing tree biodiversity data to understand global patterns and processes of vegetation. *Journal of Vegetation Science*, 32(3), e13021. https://doi.org/10.1111/jvs.13021

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Liang, J., Gamarra, J. G. P., Picard, N., Zhou, M., Pijanowski, B., Jacobs, D. F., Reich, P. B., Crowther, T. W., Nabuurs, G.-J., de Miguel, S., Fang, J., Woodall, C. W., Svenning, J.-C., Jucker, T., Bastin, J.-F., Wiser, S. K., Slik, F., Hérault, B., Alberti, G., ... Hui, C. (2022). Co-limitation towards lower latitudes shapes global forest diversity gradients. *Nature Ecology & Evolution.*, 6, 1423-1437. https://doi.org/10.1038/s41559-022-01831-x

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Maire, V., Wright, I. J., Prentice, I. C., Batjes, N. H., Bhaskar, R., van Bodegom, P. M., Cornwell, W. K., Ellsworth, D., Niinemets, Ü., Ordonez, A., Reich, P. B., & Santiago, L. S. (2015). Global effects of soil and climate on leaf photosynthetic traits and rates. *Global Ecology and Biogeography*, 24(6), 706-717. https://doi.org/10.1111/geb.12296

Malhado, A. C. M., Oliveira-Neto, J. A., Stropp, J., Strona, G., Dias, L. C. P., Pinto, L. B., & Ladle, R. J. (2015). Climatological correlates of seed size in Amazonian forest trees. *Journal of Vegetation Science*, 26(5), 956-963. https://doi.org/10.1111/jvs.12301
de la Riva, E. G., Violle, C., Pérez-Ramos, I. M., Marañón, T., Navarro-Fernández, C. M., Olmo, M., & Villar, R. (2018). A multidimensional functional trait approach reveals the imprint of environmental stress in Mediterranean Woody Communities. *Ecosystems*, 21(2), 248-262. https://doi.org/10.1007/s10021-017-0147-7

Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Colin Prentice, I., Garnier, E., Bönisch, G., Westoby, M., Poorter, H., Reich, P. B., Moles, A. T., Dickie, J., Gillison, A. N., Zanne, A. E., ... Gorné, L. D. (2016). The global spectrum of plant form and function. *Nature*, 529, 167-171. https://doi.org/10.1038/nature16489

Dubuis, A., Rossier, L., Pottier, J., Pellissier, L., Vittoz, P., & Guisan, A. (2013). Predicting current and future spatial community patterns of plant functional traits. *Ecography*, 36(11), 1158-1168. https://doi.org/10.1111/j.1600-0587.2013.00237.x

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. *International Journal of Climatology*, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086

Fortunel, C., Paine, C. E. T., Fine, P. V. A., Kraft, N. J. B., & Baraloto, C. (2014). Environmental factors predict community functional composition in Amazonian forests. *Journal of Ecology*, 102(1), 145-155. https://doi.org/10.1111/1365-2745.12160

Fox, J., & Weisberg, S. (2019). An R companion to applied regression (3rd ed.). Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/

Fridley, J. D., & Grime, J. P. (2010). Community and ecosystem effects of intraspecific genetic diversity in grassland microcosms of varying species diversity. *Ecology*, 91(8), 2272-2283.

Fridley, J. D., Grime, J. P., & Bilton, M. (2007). Genetic identity of interspecific neighbours mediates plant responses to competition and environmental variation in a species-rich grassland. *Journal of Ecology*, 95(5), 908-915. https://doi.org/10.1111/j.1365-2745.2007.01256.x

Gamfeldt, L., Snäll, T., Bagchi, R., Jonsson, M., Gustafsson, L., Kjellander, P., Ruiz-Jaen, M. C., Fröberg, M., Stendahl, J., Philipson, C. D., Mikusiński, G., Andersson, E., Westerlund, B., Andrén, H., Moberg, F., Moen, J., & Bengtsson, J. (2013). Higher levels of multiple ecosystem services are found in forests with more tree species. *Nature Communications*, 4, 1340. https://doi.org/10.1038/ncomms2328

Garnier, E., Cortez, J., Billès, G., Navas, M. L., Roumet, C., Debussche, M., Laurent, G., Blanchard, A., Aubry, D., Bellmann, A., Neill, C., & Toussaint, J. P. (2004). Plant functional markers capture ecosystem properties during secondary succession. *Ecology*, 85(9), 2630-2637. https://doi.org/10.1890/03-0799

Gauthier, S., Bernier, P., Kuuluvainen, T., Shvidenko, A. Z., & Schepaschenko, D. G. (2015). Boreal forest health and global change. *Science*, 349(6250), 819-822. https://doi.org/10.1126/science.aaa9092

Gong, H., & Gao, J. (2019). Soil and climatic drivers of plant SLA (specific leaf area). *Global Ecology and Conservation*, 20, e00696. https://doi.org/10.1016/j.gecco.2019.e00696

Greenwood, S., Ruiz-Benito, P., Martínez-Vilalta, J., Lloret, F., Kitzberger, T., Allen, C. D., Fensham, R., Laughlin, D. C., Kattge, J., Bönisch, G., Kraft, N. J. B., & Jump, A. S. (2017). Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. *Ecology Letters*, 20(4), 539-553. https://doi.org/10.1111/ele.12748

Grime, J. P. (1977). Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. *The American Naturalist*, 111(982), 40-1194. https://doi.org/10.1086/283244

Grime, J. P. (1998). Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. *Journal of Ecology*, 86(6), 902-910. https://doi.org/10.1046/j.1365-2745.1998.00306.x

Hacke, U. G., Sperry, J. S., Pockman, W. T., Davis, S. D., & McCulloh, K. A. (2001). Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. *Oecologia*, 126(4), 457-461. https://doi.org/10.1007/s004420100628

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justices, C. O., & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century Forest cover changer. *Science*, 342(6160), 850-853. https://doi.org/10.1126/science.1239552

Hengl, T., De Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. *PLoS ONE*, 12(2), 1-40. https://doi.org/10.1371/journal.pone.0169748

Hillebrand, H. (2004). On the generality of the latitudinal diversity gradient. *The American Naturalist*, 163(2), 192-211. https://doi.org/10.1086/381004

Holdridge, L. R. (1947). Determination of world plant formations from simple climatic data. *Science*, 105(2727), 367-368. https://doi.org/10.1126/science.105.2727.367

Joswig, J. S., Wirth, C., Schuman, M. C., Kattge, J., Reu, B., Wright, I. J., Sippel, S. D., Rüger, N., Richter, R., Schaepman, M. E., van Bodegom, P. M., Cornelissen, J. H. C., Díaz, S., Hattingh, W. N., Kramer, K., Lens, F., Niinemets, Ü., Reich, P. B., Reichstein, M., ... Mahecha, M. D. (2022). Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. *Nature Ecology and Evolution*, 6(1), 36-50. https://doi.org/10.1038/s41559-021-01616-8

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., & Acosta, A. T. (2020). TRY plant trait database-enhanced coverage and open access. *Global Change Biology*, 26(1), 119-188. https://doi.org/10.1111/gcb.14904

Keppel, G., Craven, D., Weigelt, P., Smith, S. A., van der Sande, M. T., Sandel, B., Levin, S. C., Kreft, H., & Knight, T. M. (2021). Synthesizing tree biodiversity data to understand global patterns and processes of vegetation. *Journal of Vegetation Science*, 32(3), e13021. https://doi.org/10.1111/jvs.13021

Kerkhoff, A. J., & Enquist, B. J. (2009). Multiplicative by nature: Why logarithmic transformation is necessary in allometry. *Journal of Theoretical Biology*, 257(3), 519-521. https://doi.org/10.1016/j.jtbi.2008.12.026

Kricher, J. C. (2017). The new neotropical companion. In *The new neotropical companion*. Princeton University Press.

Levin, S. A. (1992). The problem of pattern and scale in ecology. *Ecology*, 73(6), 1943-1967. https://doi.org/10.2307/1941447

Liang, J., Gamarra, J. G. P., Picard, N., Zhou, M., Pijanowski, B., Jacobs, D. F., Reich, P. B., Crowther, T. W., Nabuurs, G.-J., de Miguel, S., Fang, J., Woodall, C. W., Svenning, J.-C., Jucker, T., Bastin, J.-F., Wiser, S. K., Slik, F., Hérault, B., Alberti, G., ... Hui, C. (2022). Co-limitation towards lower latitudes shapes global forest diversity gradients. *Nature Ecology & Evolution.*, 6, 1423-1437. https://doi.org/10.1038/s41559-022-01831-x

Lord, J., Egan, J., Clifford, T., Jurado, E., Leishman, M., Williams, D., & Westoby, M. (1997). Larger seeds in tropical floras: Consistent patterns independent of growth form and dispersal mode. *Journal of Biogeography*, 24(2), 205-211. https://doi.org/10.1046/j.1365-2699.1997.00126.x

Maire, V., Wright, I. J., Prentice, I. C., Batjes, N. H., Bhaskar, R., van Bodegom, P. M., Cornwell, W. K., Ellsworth, D., Niinemets, Ü., Ordonez, A., Reich, P. B., & Santiago, L. S. (2015). Global effects of soil and climate on leaf photosynthetic traits and rates. *Global Ecology and Biogeography*, 24(6), 706-717. https://doi.org/10.1111/geb.12296

Malhado, A. C. M., Oliveira-Neto, J. A., Stropp, J., Strona, G., Dias, L. C. P., Pinto, L. B., & Ladle, R. J. (2015). Climatological correlates of seed size in Amazonian forest trees. *Journal of Vegetation Science*, 26(5), 956-963. https://doi.org/10.1111/jvs.12301
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Moles, A. T., Ackerly, D. D., Tweddle, J. C., Dickie, J. B., Smith, R., Leishman, M. R., Mayfield, M. M., Pitman, A., Wood, J. T., & Westoby, M. (2007). Global patterns in seed size. *Global Ecology and Biogeography*, 16, 109–116. https://doi.org/10.1111/j.1466-822x.2006.00259.x

Moles, A. T., Perkins, S. E., Laffan, S. W., Flores-Moreno, H., Awasthy, M., Tindall, M. L., Sack, L., Pitman, A., Kattge, J., Aarssen, L. W., Anand, M., Bahn, M., Blonder, B., Cavender-Bares, J., Cornelissen, J. H. C., Cornwell, W. K., Díaz, S., Dickie, J. B., Freschet, G. T., ... Bonser, S. P. (2014). Which is a better predictor of plant traits: Temperature or precipitation? *Journal of Vegetation Science*, 25(5), 1167–1180. https://doi.org/10.1111/jvs.12190

Moles, A. T., Westoby, M., & Eriksson, O. (2006). Seed size and plant strategy across the whole life cycle. *Oikos*, 113(1), 91–105. https://doi.org/10.1111/j.0030-1299.2006.14194.x

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## BIOSKETCH

Elise Bouchard is a PhD student at the Université du Québec à Montréal. She is interested in patterns and underlying causes of plant distribution at multiple scales as well as forest community assemblage. Her work also covers tree physiology, especially water and carbohydrate dynamics in trees. The Centre for Forest Research (CFR) central mission is advanced training and research in forest sciences. It is a network of 75 forest scientists from 11 universities in Québec, Canada. The Global Forest Biodiversity Initiative is an international multi-stakeholder team of foresters and forest scientists. GFBI supports cutting-edge research and policy making in forest sciences and related areas.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Bouchard, E., Searle, E. B., Drapeau, P., Liang, J., Gamarra, J. G. P., Abegg, M., Alberti, G., Zambrano, A. A., Alvarez-Davila, E., Alves, L. F., Avitabile, V., Aymard, G., Bastin, J.-F., Birnbaum, P., Bongers, F., Bouriaud, O., Brancalion, P., Broadbent, E., Bussotti, F., ... Paquette, A. (2024). Global patterns and environmental drivers of forest functional composition. *Global Ecology and Biogeography*, 33, 303-324. https://doi.org/10.1111/geb.13790
Stahl, U., Reu, B., & Wirth, C. (2014). Predicting species' range limits from functional traits for the tree flora of North America. *Proceedings of the National Academy of Sciences of the United States of America, 111*(38), 13739-13744. https://doi.org/10.1073/pnas.1300673111

Suzuki, R., Xu, J., & Motoya, K. (2006). Global analyses of satellite-derived vegetation index related to climatological wetness and warmth. *International Journal of Climatology*, 26(4), 425-438. https://doi.org/10.1002/joc.1256

Swenson, N. G., & Enquist, B. J. (2007). Ecological and evolutionary determinants of a key plant functional trait: Wood density and its community-wide variation across latitude and elevation. *American Journal of Botany*, 94(3), 451-459. https://doi.org/10.3732/ajb.94.3.451

Swenson, N. G., Enquist, B. J., Pither, J., Kerkhoff, A. J., Boyle, B., Weiser, M. D., Elser, J. J., Fagan, W. F., Forero-Montaña, J., Fyllas, N., Kraft, N. J. B., Lake, J. K., Moles, A. T., Patiño, S., Phillips, O. L., Price, C. A., Reich, P. B., Quesada, C. A., Stegen, J. C., ... Nolting, K. M. (2012). The biogeography and filtering of woody plant functional diversity in North and South America. *Global Ecology and Biogeography*, 21(8), 798-808. https://doi.org/10.1111/j.1466-8238.2011.00727.x

Swenson, N. G., & Weiser, M. D. (2010). Plant geography upon the basis of functional traits: An example from eastern North American trees. *Ecology*, 91(8), 2234-2241. https://doi.org/10.1890/09-1743

Ter Steege, H., Pitman, N. C. A., Phillips, O. L., Chave, J., Sabatier, D., Duque, A., Molino, J. F., Prévost, M. F., Spichiger, R., Castellanos, H., Von Hildebrand, P., & Vásquez, R. (2006). Continental-scale patterns of canopy tree composition and function across Amazonia. *Nature*, 443(7110), 444-447. https://doi.org/10.1038/nature05134

Terborgh, J. (1973). On the notion of favorableness in plant ecology. *The American Naturalist*, 107(956), 481-501. https://doi.org/10.1086/282852

Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). *blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models*. *Methods in Ecology and Evolution*, 10(2), 225-232. https://doi.org/10.1111/2041-210X.13107

Villar, R., & Merino, J. (2001). Comparison of leaf construction costs in woody species with differing leaf life-spans in contrasting ecosystems. *New Phytologist*, 151(1), 213-226. https://doi.org/10.1046/j.1469-8137.2001.00147.x

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BIOSKETCH

Elise Bouchard is a PhD student at the Université du Québec à Montréal. She is interested in patterns and underlying causes of plant distribution at multiple scales as well as forest community assemblage. Her work also covers tree physiology, especially water and carbohydrate dynamics in trees. The Centre for Forest Research (CFR) central mission is advanced training and research in forest sciences. It is a network of 75 forest scientists from 11 universities in Québec, Canada. The Global Forest Biodiversity Initiative is an international multi-stakeholder team of foresters and forest scientists. GFBI supports cutting-edge research and policy making in forest sciences and related areas.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

How to cite this article: Bouchard, E., Searle, E. B., Drapeau, P., Liang, J., Gamarra, J. G. P., Abegg, M., Alberti, G., Zambrano, A. A., Alvarez-Davila, E., Alves, L. F., Avitabile, V., Aymard, G., Bastin, J.-F., Birnbaum, P., Bongers, F., Bouriaud, O., Brancalion, P., Broadbent, E., Bussotti, F., ... Paquette, A. (2024). Global patterns and environmental drivers of forest functional composition. *Global Ecology and Biogeography*, 33, 303-324. https://doi.org/10.1111/geb.13790
# APPENDIX 1

## DATA SOURCES

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Atkin, O. K., Bloomfield, K. J., Reich, P. B., Tjoelker, M. G., Asner, G. P., Bonal, D., Bönisch, G., Bradford, M., Cernusak, L. A., Cosio, E. G., Creek, D., Crous, K. Y., Domingues, T., Dukes, J. S., Egerton, J. J. G., Evans, J. R., Farquhar, G. D., Fyllas, N. M., Gauthier, P. P. G., ... Zaragoza-Castells, J. (2015). Global variability in leaf respiration among plant functional types in relation to climate and leaf traits. *New Phytologist*. https://doi.org/10.1111/nph.13253

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Auger, S., & Shipley, B. (2012). Interspecific and intraspecific trait variation along short environmental gradients in an old-growth temperate forest. *Journal of Vegetation Science*. 10.1111/j.1654-1103.2012.01473.x

Baraloto, C., Paine, C. E. T., Poorter, L., Beauchene, J., Bonal, D., Domenach, A.-M., Herault, B., Patino, S., Roggy, J.-C., & Chave, J. (2010). Decoupled leaf and stem economics in rainforest trees. *Ecology Letters*, 13, 1338–1347.

Blonder, B., Buzzard, B., Sloat, L., Simova, I., Lipson, R., Boyle, B., & Enquist, B. (2012). The shrinkage effect biases estimates of paleoclimate. *American Journal of Botany*, 99(11), 1756–1763.

Blonder, B., Violle, C., & Enquist, B. J. (2013). Assessing the causes and scales of the leaf economics spectrum using venation networks in *Populus tremuloides*. *Journal of Ecology*, 101, 981–989. https://doi.org/10.1111/1365-2745.12102

Blonder, B., Violle, C., Patrick, L., & Enquist, B. (2011). Leaf venation networks and the origin of the leaf economics spectrum. *Ecology Letters*, 14(2), 91–100.

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Bond-Lamberty, B., Wang, C., & Gower, S. T. (2002). Leaf area dynamics of a boreal black spruce fire chronosequence. *Tree Physiology*, 22(14), 993–1001.

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Burrascano, S., Copiz, R., Del Vico, E., Fagiani, S., Giarrizzo, E., Mei, M., Mortelliti, A., Sabatini, F. M., & Blasi, C. (2015). Wild boar rooting intensity determines shifts in understorey composition and functional traits. *Community Ecology*, 16(2), 244–253. https://doi.org/10.1556/168.2015.16.2.12

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Byun, C., de Blois, S., & Brisson, J. (2013). Plant functional group identity and diversity determine biotic resistance to invasion by an exotic grass. *Journal of Ecology*. https://doi.org/10.1111/1365-2745.12016

Campbell, C., Atkinson, L., Zaragoza-Castells, J., Lundmark, M., Atkin, O., & Hurry, V. (2007). Acclimation of photosynthesis and respiration is asynchronous in response to changes in temperature regardless of plant functional group. *New Phytologist*, 176, 375–389.

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Castro-Diez, P., Puyravaud, J. P., Cornelissen, J. H. C., & Villar-Salvador, P. (1998). Stem anatomy and relative growth rate in seedlings of a wide range of woody plant species and types. *Oecologia*, 116, 57–66.

Catford, J. A., Morris, W. K., Vesk, P. A., Gippel, C. J., & Downes, B. J. (2014). Species and environmental characteristics point to flow regulation and drought as drivers of riparian plant invasion. *Diversity and Distributions*, 20, 1084–1096. https://doi.org/10.1111/ddi.12225

Cavender-Bares, J., Keen, A., & Miles, B. (2006). Phylogenetic structure of floridan plant communities depends on taxonomic and spatial scale. *Ecology*, 87, S109–S122.

Chacón-Madrigal, E., Wolfgang, W., Hietz, P. & Dullinger, S. A stress-tolerance syndrome distinguishes endemic tropical trees from their widespread congeneres. Unpublished.

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12, 351–366.

Choaat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L., Lens, F., Maherali, H., Martinez-Vilalta, J., Mayr, S., Mencuccini, M., Mitchell, P. J., Nardini, A., Jarmila Pittermann, R., ... Zanne, A. E. (2012). Global convergence in the vulnerability of forests to drought. *Nature*, 491, 752–755. https://doi.org/10.1038/natur e11688

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Cornelissen, J. H. C., Cerabolini, B., Castro-Diez, P., Villar-Salvador, P., Montserrat-Martí, G., Puyravaud, J. P., Maestro, M., Werger, M. J. A., & Aerts, R. (2003). Functional traits of woody plants: Correspondence of species rankings between field adults and laboratory-grown seedlings? *Journal of Vegetation Science*, 14, 311–322.

Cornelissen, J. H. C., Diez, P. C., & Hunt, R. (1996). Seedling growth, allocation and leaf attributes in a wide range of woody plant species and types. *Journal of Ecology*, 84, 755–765.

Cornelissen, J. H. C., Quested, H. M., Gwynn-Jones, D., Van Logtestijn, R. S. P., De Beus, M. A. H., Kondratchuk, A., Callaghan, T. V., & Aerts, R. (2004). Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. *Functional Ecology*, 18, 779–786.

Cornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., Hobbie, S. E., Hoorens, B., Kurokawa, H., Pérez-Harguindeguy, N., Quested, H. M., Santiago, L. S., Wardle, D. A., Wright, I. J., Aerts, R., Allison, S. D., van Bodegom, P., Brovkin, V., Chatain, A., ... Westoby, M. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. *Ecology Letters*, 11, 1065–1071.

Craven, D., Braden, D., Ashton, M. S., Berlyn, G. P., Wishnie, M., & Dent, D. (2007). Between and within-site comparisons of structural and physiological characteristics and foliar nutrient content of 14 tree species at a wet, fertile site and a dry, infertile site in Panama. *Forest Ecology and Management*, 238, 335–346.

Dahlin, K. M., Asner, G. P., & Field, C. B. (2013). Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. *Proceedings of the National Academy of Sciences USA.*, 110(17), 6895–6900.
# APPENDIX 1

## DATA SOURCES

Adler, P. B., Salguero-Gómez, R., Compagnoni, A., Hsu, J. S., Ray-Mukherjee, J., Mbeau-Ache, C., & Franco, M. (2014). Functional traits explain variation in plant life history strategies. *Proceedings of the National Academy of Sciences*, 111(2), 740–745.

Atkin, O. K., Bloomfield, K. J., Reich, P. B., Tjoelker, M. G., Asner, G. P., Bonal, D., Bönisch, G., Bradford, M., Cernusak, L. A., Cosio, E. G., Creek, D., Crous, K. Y., Domingues, T., Dukes, J. S., Egerton, J. J. G., Evans, J. R., Farquhar, G. D., Fyllas, N. M., Gauthier, P. P. G., ... Zaragoza-Castells, J. (2015). Global variability in leaf respiration among plant functional types in relation to climate and leaf traits. *New Phytologist*. https://doi.org/10.1111/nph.13253

Atkin, O. K., Schortemeyer, M., McFarlane, N., & Evans, J. R. (1999). The response of fast- and slow-growing acacia species to elevated atmospheric CO2: An analysis of the underlying components of relative growth rate. *Oecologia*, 120, 544–554.

Auger, S., & Shipley, B. (2012). Interspecific and intraspecific trait variation along short environmental gradients in an old-growth temperate forest. *Journal of Vegetation Science*. 10.1111/j.1654-1103.2012.01473.x

Baraloto, C., Paine, C. E. T., Poorter, L., Beauchene, J., Bonal, D., Domenach, A.-M., Herault, B., Patino, S., Roggy, J.-C., & Chave, J. (2010). Decoupled leaf and stem economics in rainforest trees. *Ecology Letters*, 13, 1338–1347.

Blonder, B., Buzzard, B., Sloat, L., Simova, I., Lipson, R., Boyle, B., & Enquist, B. (2012). The shrinkage effect biases estimates of paleoclimate. *American Journal of Botany*, 99(11), 1756–1763.

Blonder, B., Violle, C., & Enquist, B. J. (2013). Assessing the causes and scales of the leaf economics spectrum using venation networks in *Populus tremuloides*. *Journal of Ecology*, 101, 981–989. https://doi.org/10.1111/1365-2745.12102

Blonder, B., Violle, C., Patrick, L., & Enquist, B. (2011). Leaf venation networks and the origin of the leaf economics spectrum. *Ecology Letters*, 14(2), 91–100.

Bocanegra-González, K. T., Fernandez-Mendez, F., & Galvis-Jiménez, J. F. (2013). Determinación de la resiliencia en bosques secundarios húmedos tropicales a través de la diversidad funcional de árboles en la región del Bajo Calima. Tesis, Universidad del Tolima, Facultad de Ingeniería Forestal, Ibagué.

Bond-Lamberty, B., Wang, C., & Gower, S. T. (2002). Leaf area dynamics of a boreal black spruce fire chronosequence. *Tree Physiology*, 22(14), 993–1001.

Brown, K. A., Flynn, D. F. B., Abram, N. K., Ingram, J. C., Johnson, S. E., & Wright, P. (2011). Assessing natural resource use by forest-reliant communities in madagascar using functional diversity and functional redundancy metrics. *PLoS ONE*, 6(9), e24107. https://doi.org/10.1371/journal.pone.0024107

Burrascano, S., Copiz, R., Del Vico, E., Fagiani, S., Giarrizzo, E., Mei, M., Mortelliti, A., Sabatini, F. M., & Blasi, C. (2015). Wild boar rooting intensity determines shifts in understorey composition and functional traits. *Community Ecology*, 16(2), 244–253. https://doi.org/10.1556/168.2015.16.2.12

Butterfield, B. J., & Briggs, J. M. (2011). Regeneration niche differentiates functional strategies of desert woody plant species. *Oecologia*, 165, 477–487.

Byun, C., de Blois, S., & Brisson, J. (2013). Plant functional group identity and diversity determine biotic resistance to invasion by an exotic grass. *Journal of Ecology*. https://doi.org/10.1111/1365-2745.12016

Campbell, C., Atkinson, L., Zaragoza-Castells, J., Lundmark, M., Atkin, O., & Hurry, V. (2007). Acclimation of photosynthesis and respiration is asynchronous in response to changes in temperature regardless of plant functional group. *New Phytologist*, 176, 375–389.

Campbell, G., Botta-Dukát, Z., Wellstein, C., Canullo, R., Gatto, S., Chelli, S., Mucina, L., & Bartha, S. (2011). Patterns of plant trait-environment relationships along a forest succession chronosequence. *Agriculture, Ecosystems & Environment*, 145(1), 38–48. https://doi.org/10.1016/j.agee.2011.06.025

Carswell, F. E., Meir, P., Wandelli, E. V., Bonates, L. C. M., Kruijst, B., Barbosa, E. M., Nobre, A. D., & Jarvis, P. G. (2000). Photosynthetic capacity in a central Amazonian rain forest. *Tree Physiology*, 20(3), 179–186. 8 p.

Castro-Diez, P., Puyravaud, J. P., Cornelissen, J. H. C., & Villar-Salvador, P. (1998). Stem anatomy and relative growth rate in seedlings of a wide range of woody plant species and types. *Oecologia*, 116, 57–66.

Catford, J. A., Morris, W. K., Vesk, P. A., Gippel, C. J., & Downes, B. J. (2014). Species and environmental characteristics point to flow regulation and drought as drivers of riparian plant invasion. *Diversity and Distributions*, 20, 1084–1096. https://doi.org/10.1111/ddi.12225

Cavender-Bares, J., Keen, A., & Miles, B. (2006). Phylogenetic structure of floridan plant communities depends on taxonomic and spatial scale. *Ecology*, 87, S109–S122.

Chacón-Madrigal, E., Wolfgang, W., Hietz, P. & Dullinger, S. A stress-tolerance syndrome distinguishes endemic tropical trees from their widespread congeneres. Unpublished.

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12, 351–366.

Choa, T., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L., Lens, F., Maherali, H., Martinez-Vilalta, J., Mayr, S., Mencuccini, M., Mitchell, P. J., Nardini, A., Jarmila Pittermann, R., ... Zanne, A. E. (2012). Global convergence in the vulnerability of forests to drought. *Nature*, 491, 752–755. https://doi.org/10.1038/natur e11688

Cornelissen, J. H. C. (1996). An experimental comparison of leaf decomposition rates in a wide range of temperate plant species and types. *Journal of Ecology*, 84, 573–582.

Cornelissen, J. H. C., Cerabolini, B., Castro-Diez, P., Villar-Salvador, P., Montserrat-Martí, G., Puyravaud, J. P., Maestro, M., Werger, M. J. A., & Aerts, R. (2003). Functional traits of woody plants: Correspondence of species rankings between field adults and laboratory-grown seedlings? *Journal of Vegetation Science*, 14, 311–322.

Cornelissen, J. H. C., Diez, P. C., & Hunt, R. (1996). Seedling growth, allocation and leaf attributes in a wide range of woody plant species and types. *Journal of Ecology*, 84, 755–765.

Cornelissen, J. H. C., Quested, H. M., Gwynn-Jones, D., Van Logtestijn, R. S. P., De Beus, M. A. H., Kondratchuk, A., Callaghan, T. V., & Aerts, R. (2004). Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. *Functional Ecology*, 18, 779–786.

Cornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., Hobbie, S. E., Hoorens, B., Kurokawa, H., Pérez-Harguindeguy, N., Quested, H. M., Santiago, L. S., Wardle, D. A., Wright, I. J., Aerts, R., Allison, S. D., van Bodegom, P., Brovkin, V., Chatain, A., ... Westoby, M. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. *Ecology Letters*, 11, 1065–1071.

Craven, D., Braden, D., Ashton, M. S., Berlyn, G. P., Wishnie, M., & Dent, D. (2007). Between and within-site comparisons of structural and physiological characteristics and foliar nutrient content of 14 tree species at a wet, fertile site and a dry, infertile site in Panama. *Forest Ecology and Management*, 238, 335–346.

Dahlin, K. M., Asner, G. P., & Field, C. B. (2013). Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. *Proceedings of the National Academy of Sciences USA.*, 110(17), 6895–6900.
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