# Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively logged tropical forests

Daniela Nemetschek, Géraldine Derroire, Eric Marcon, Mélaine Aubry-Kientz, Johanna Auer, Vincyane Badouard, Christopher Baraloto, David Bauman, Quentin Le Blaye, Marion Boisseaux, et al.

► To cite this version:

Daniela Nemetschek, Géraldine Derroire, Eric Marcon, Mélaine Aubry-Kientz, Johanna Auer, et al. Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively logged tropical forests. Journal of Ecology, In press, 10.1111/1365-2745.14256. hal-04429206

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# Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively logged tropical forests

Daniela Nemetschek, Géraldine Derroire, Eric Marcon, Mélaine Aubry-Kientz, Johanna Auer, Vincyane Badouard, Christopher Baraloto, David Bauman, Quentin Le Blaye, Marion Boisseaux, et al.

► To cite this version:

Daniela Nemetschek, Géraldine Derroire, Eric Marcon, Mélaine Aubry-Kientz, Johanna Auer, et al. Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively logged tropical forests. Journal of Ecology, 2024, 112 (3), pp.590-612. 10.1111/1365-2745.14256. hal-04429206

HAL Id: hal-04429206

https://hal.inrae.fr/hal-04429206v1

Submitted on 20 Feb 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Climate anomalies and neighbourhood crowding interact

2 in shaping tree growth in old-growth and

3 selectively-logged tropical forests

4 Daniela Nemetschek¹,²*, Géraldine Derroire³, Eric Marcon¹,², Mélaine Aubry-Kientz², Johanna

5 Auer⁴, Vincyane Badouard¹,², Christopher Baraloto²,⁵, David Bauman¹,⁶, Quentin Le Blaye¹,

6 Marion Boisseaux², Damien Bonal⁷, Sabrina Coste², Elia Dardevet¹, Patrick Heuret¹, Peter

7 Hietz⁸, Sébastien Levionnois¹,², Isabelle Maréchaux¹, Sean M. McMahon⁹,¹⁰, Clément Stahl²,

8 Jason Vleminckx¹¹, Wolfgang Wanek³, Camille Ziegler², and Claire Fortunel¹

9 ¹AMAP (Botanique et Modélisation de l'Architecture des Plantes et des Végétations), Université de Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France

10 ²UMR EcoFoG (AgroParistech, CIRAD, CNRS, INRAE, Université des Antilles, Université de la Guyane), Kourou, France

11 ³CIRAD, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de la Guyane), Kourou, France

12 ⁴Center of Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria

13 ⁵Institute of Environment, Department of Biological Sciences, Florida International University, Miami, FL, USA

14 ⁶Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK

15 ⁷INRAE, Université de Lorraine, AgroParisTech, UMR SILVA, Nancy, France

16 ⁸Institute of Botany, University of Natural Resources and Life Sciences, Vienna, Austria

17 ⁹Smithsonian Environmental Research Center, Edgewater, MD, USA

18 ¹⁰Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City,
1 Climate anomalies and neighbourhood crowding interact

2 in shaping tree growth in old-growth and

3 selectively-logged tropical forests

4 Daniela Nemetschek¹,²*, Géraldine Derroire³, Eric Marcon¹,², Mélaine Aubry-Kientz², Johanna

5 Auer⁴, Vincyane Badouard¹,², Christopher Baraloto²,⁵, David Bauman¹,⁶, Quentin Le Blaye¹,

6 Marion Boisseaux², Damien Bonal⁷, Sabrina Coste², Elia Dardevet¹, Patrick Heuret¹, Peter

7 Hietz⁸, Sébastien Levionnois¹,², Isabelle Maréchaux¹, Sean M. McMahon⁹,¹⁰, Clément Stahl²,

8 Jason Vleminckx¹¹, Wolfgang Wanek³, Camille Ziegler², and Claire Fortunel¹

9 ¹AMAP (Botanique et Modélisation de l'Architecture des Plantes et des Végétations), Université de Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France

10 ²UMR EcoFoG (AgroParistech, CIRAD, CNRS, INRAE, Université des Antilles, Université de la Guyane), Kourou, France

11 ³CIRAD, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de la Guyane), Kourou, France

12 ⁴Center of Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria

13 ⁵Institute of Environment, Department of Biological Sciences, Florida International University, Miami, FL, USA

14 ⁶Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK

15 ⁷INRAE, Université de Lorraine, AgroParisTech, UMR SILVA, Nancy, France

16 ⁸Institute of Botany, University of Natural Resources and Life Sciences, Vienna, Austria

17 ⁹Smithsonian Environmental Research Center, Edgewater, MD, USA

18 ¹⁰Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City,
25 Panama, Brussels, Belgium

26 $^{11}$Université Libre de Bruxelles, Brussels, Belgium

27 *to whom correspondence should be addressed: Email: daniela.nemetschek@gmail.com
25 Panama, Brussels, Belgium

26 <sup>11</sup>Université Libre de Bruxelles, Brussels, Belgium

27 *to whom correspondence should be addressed: Email: daniela.nemetschek@gmail.com*
# Abstract

1. Climate extremes and biotic interactions at the neighbourhood scale affect tropical forest dynamics with long-term consequences for biodiversity, global carbon cycling and climate change mitigation. However, forest disturbance may change crowding intensity, and thus the relative contribution of climate extremes and neighbourhood interactions on tree growth, thereby influencing tropical forest resistance and resilience to climate change. Here, we aim to evaluate the separate and interactive effects of climate and neighbours on tree growth in old-growth and disturbed tropical forests.

2. We used 30 years of growth measurements for over 300 tropical tree species from 15 forest plots in French Guiana to investigate the separate and interactive effects of climate anomalies (in solar radiation, maximum temperature, vapor pressure deficit and climatic water deficit) and neighbourhood crowding on individual tree growth. Contrasting old-growth and selectively-logged forests, we also examined how disturbance history affects tree growth sensitivity to climate and neighbours. Finally, for the most abundant 100 species, we evaluated the role of 12 functional traits pertaining to water relations, light and carbon use in mediating tree growth sensitivity to climate anomalies, neighbourhood crowding, and their interactions.

3. Climate anomalies tied to heat and drought stress and neighbourhood crowding independently reduced tree growth, and showed positive interactive effects which attenuated their separate effects on tree growth. Their separate and interactive effects were stronger in disturbed than undisturbed forests. Fast-growing species (i.e. higher intrinsic growth rates) were more abundant in disturbed forests and more sensitive to climate anomalies and neighbourhood crowding. Traits related to water relations, light and carbon use captured species sensitivities to different climate anomalies and neighbourhood crowding levels, but were weak predictors of their interactions.

4. **Synthesis:** Our results demonstrate that climate anomalies and neighbourhood crowding can interact to shape tropical tree growth, suggesting that considering the biotic context may improve predictions of tropical forest dynamics facing altered climate regimes. Furthermore, species traits can capture tree growth sensitivity to the separate effects of climate and neighbours, suggesting
# Abstract

1. Climate extremes and biotic interactions at the neighbourhood scale affect tropical forest dynamics with long-term consequences for biodiversity, global carbon cycling and climate change mitigation. However, forest disturbance may change crowding intensity, and thus the relative contribution of climate extremes and neighbourhood interactions on tree growth, thereby influencing tropical forest resistance and resilience to climate change. Here, we aim to evaluate the separate and interactive effects of climate and neighbours on tree growth in old-growth and disturbed tropical forests.

2. We used 30 years of growth measurements for over 300 tropical tree species from 15 forest plots in French Guiana to investigate the separate and interactive effects of climate anomalies (in solar radiation, maximum temperature, vapor pressure deficit and climatic water deficit) and neighbourhood crowding on individual tree growth. Contrasting old-growth and selectively-logged forests, we also examined how disturbance history affects tree growth sensitivity to climate and neighbours. Finally, for the most abundant 100 species, we evaluated the role of 12 functional traits pertaining to water relations, light and carbon use in mediating tree growth sensitivity to climate anomalies, neighbourhood crowding, and their interactions.

3. Climate anomalies tied to heat and drought stress and neighbourhood crowding independently reduced tree growth, and showed positive interactive effects which attenuated their separate effects on tree growth. Their separate and interactive effects were stronger in disturbed than undisturbed forests. Fast-growing species (i.e. higher intrinsic growth rates) were more abundant in disturbed forests and more sensitive to climate anomalies and neighbourhood crowding. Traits related to water relations, light and carbon use captured species sensitivities to different climate anomalies and neighbourhood crowding levels, but were weak predictors of their interactions.

4. **Synthesis:** Our results demonstrate that climate anomalies and neighbourhood crowding can interact to shape tropical tree growth, suggesting that considering the biotic context may improve predictions of tropical forest dynamics facing altered climate regimes. Furthermore, species traits can capture tree growth sensitivity to the separate effects of climate and neighbours, suggesting
55

that better representing leading functional dimensions in tropical tree strategies offers a promising

56

way towards a better understanding of the underlying ecological mechanisms that govern tropical

57

forest dynamics.

58

**Keywords:** climate change, drought stress, functional traits, tropical forest, tree growth, water

59

relations
55

that better representing leading functional dimensions in tropical tree strategies offers a promising

56

way towards a better understanding of the underlying ecological mechanisms that govern tropical

57

forest dynamics.

58

**Keywords:** climate change, drought stress, functional traits, tropical forest, tree growth, water

59

relations
Résumé

1. Les extrêmes climatiques ainsi que les interactions biotiques à l'échelle du voisinage, affectent la dynamique des forêts tropicales, avec des conséquences à long terme pour la biodiversité, le cycle global du carbone et l'atténuation du changement climatique. Cependant, les perturbations forestières peuvent faire varier localement les voisinages, et ainsi modifier la contribution relative des extrêmes climatiques et des interactions de voisinage sur la croissance des arbres, ce qui peut impacter la résistance et la résilience des forêts tropicales au changement climatique. Nos travaux visent à évaluer les effets individuels et interactifs du climat et des voisins sur la croissance des arbres dans les forêts tropicales non-pertubées et perturbées.

2. Nous avons utilisé 30 ans de mesures de croissance pour plus de 300 espèces d'arbres tropicaux provenant de 15 parcelles forestières en Guyane française pour étudier les effets individuels et interactifs des anomalies climatiques (en termes de rayonnement solaire, de température maximale, de déficit de pression de vapeur, et de déficit hydrique climatique) et des interactions de voisinage sur la croissance individuelle des arbres. En comparant les forêts exploitées sélectivement aux forêts non-pertubées, nous avons également examiné comment l'historique des perturbations peut influencer la sensibilité de la croissance des arbres au climat et aux voisins. Enfin, pour les 100 espèces les plus abondantes, nous avons évalué le rôle de 12 traits fonctionnels reflétant les relations hydriques, l'utilisation de la lumière et du carbone sur la réponse de la croissance des arbres aux anomalies climatiques, aux interactions de voisinage et à leurs interactions.

3. Les anomalies climatiques liées aux stress thermique et hydrique, ainsi que les interactions de voisinage, réduisent la croissance des arbres. De plus, elles peuvent interagir positivement, ce qui atténue leurs effets individuels sur la croissance des arbres. Leurs effets individuels et interactifs sont plus forts dans les forêts perturbées que dans les forêts non-perturbées. Les espèces à croissance rapide (c'est-à-dire à taux de croissance intrinsèque plus élevé) sont plus abondantes dans les forêts perturbées, et aussi plus sensibles aux anomalies climatiques et aux interactions de voisinage. Les traits liées aux relations hydriques, à la lumière et à l'utilisation du carbone captent la sensibilité des espèces aux différentes anomalies climatiques et aux différents niveaux
Résumé

1. Les extrêmes climatiques ainsi que les interactions biotiques à l'échelle du voisinage, affectent la dynamique des forêts tropicales, avec des conséquences à long terme pour la biodiversité, le cycle global du carbone et l'atténuation du changement climatique. Cependant, les perturbations forestières peuvent faire varier localement les voisinages, et ainsi modifier la contribution relative des extrêmes climatiques et des interactions de voisinage sur la croissance des arbres, ce qui peut impacter la résistance et la résilience des forêts tropicales au changement climatique. Nos travaux visent à évaluer les effets individuels et interactifs du climat et des voisins sur la croissance des arbres dans les forêts tropicales non-pertubées et perturbées.

2. Nous avons utilisé 30 ans de mesures de croissance pour plus de 300 espèces d'arbres tropicaux provenant de 15 parcelles forestières en Guyane française pour étudier les effets individuels et interactifs des anomalies climatiques (en termes de rayonnement solaire, de température maximale, de déficit de pression de vapeur, et de déficit hydrique climatique) et des interactions de voisinage sur la croissance individuelle des arbres. En comparant les forêts exploitées sélectivement aux forêts non-pertubées, nous avons également examiné comment l'historique des perturbations peut influencer la sensibilité de la croissance des arbres au climat et aux voisins. Enfin, pour les 100 espèces les plus abondantes, nous avons évalué le rôle de 12 traits fonctionnels reflétant les relations hydriques, l'utilisation de la lumière et du carbone sur la réponse de la croissance des arbres aux anomalies climatiques, aux interactions de voisinage et à leurs interactions.

3. Les anomalies climatiques liées aux stress thermique et hydrique, ainsi que les interactions de voisinage, réduisent la croissance des arbres. De plus, elles peuvent interagir positivement, ce qui atténue leurs effets individuels sur la croissance des arbres. Leurs effets individuels et interactifs sont plus forts dans les forêts perturbées que dans les forêts non-perturbées. Les espèces à croissance rapide (c'est-à-dire à taux de croissance intrinsèque plus élevé) sont plus abondantes dans les forêts perturbées, et aussi plus sensibles aux anomalies climatiques et aux interactions de voisinage. Les traits liées aux relations hydriques, à la lumière et à l'utilisation du carbone captent la sensibilité des espèces aux différentes anomalies climatiques et aux différents niveaux
87 d'interactions de voisinage, mais ne prédisent pas leurs interactions.

88 **4. Synthèse:** Nos résultats montrent que les anomalies climatiques et les interactions de voisinage peuvent interagir pour façonner la croissance des arbres tropicaux, ce qui suggère que prendre en compte le contexte biotique peut améliorer les prévisions de la dynamique des forêts tropicales face aux changements climatiques. En outre, les traits des espèces peuvent capter la sensibilité de la croissance des arbres aux effets individuels du climat et des voisins, ce qui suggère que mieux représenter les principales dimensions des stratégies fonctionnelles des arbres tropicaux ouvre sur une meilleure compréhension des mécanismes écologiques qui régissent la dynamique des forêts tropicales.

96 **Mots-clés** : changement climatique, stress hydriqyue, traits fonctionnels, forêt tropicale, croissance des arbres, relations hydriques

97
87 d'interactions de voisinage, mais ne prédisent pas leurs interactions.

88 **4. Synthèse:** Nos résultats montrent que les anomalies climatiques et les interactions de voisinage peuvent interagir pour façonner la croissance des arbres tropicaux, ce qui suggère que prendre en compte le contexte biotique peut améliorer les prévisions de la dynamique des forêts tropicales face aux changements climatiques. En outre, les traits des espèces peuvent capter la sensibilité de la croissance des arbres aux effets individuels du climat et des voisins, ce qui suggère que mieux représenter les principales dimensions des stratégies fonctionnelles des arbres tropicaux ouvre sur une meilleure compréhension des mécanismes écologiques qui régissent la dynamique des forêts tropicales.

96 **Mots-clés** : changement climatique, stress hydriqyue, traits fonctionnels, forêt tropicale, croissance des arbres, relations hydriques
## Introduction

Tropical forests are biodiversity hotspots (Gatti et al. 2022, Pillay et al. 2022) and major contributors to global carbon and water cycles (Needham et al. 2018, Pan et al. 2011), but the services they provide to human populations are threatened by climate change and anthropogenic disturbance (Brodribb et al. 2020, Malhi et al. 2008, Nunes et al. 2022, Vancutsem et al. 2021). In particular, extreme droughts are linked to higher solar radiation, maximum temperatures, atmospheric evaporative demands and lower soil water availability, which have been shown to negatively impact tropical forest dynamics by reducing tree growth and increasing tree mortality (Bauman et al. 2022b,a, Rifai et al. 2018). As climate change is predicted to result in higher frequencies, intensities, and duration of extreme droughts in the tropics (Shukla et al. 2022), this may have important long-term implications for tropical tree dynamics and diversity, and therefore global carbon sequestration and residence time (Sullivan et al. 2020).

Tropical forest response to climate extremes is the compounded outcome of individual tree responses, which may depend on local biotic interactions, such as interactions between neighbouring trees. While crowding by neighbours generally reduces tropical tree growth (Dawkins 1959, Gourlet-Fleury and Houllier 2000, Fortunel et al. 2018, Uriarte et al. 2004, 2010), the net effects of neighbourhood interactions may change from negative to positive under specific environmental conditions (Grossiord 2020, Fichtner et al. 2020) such as with water levels (Aakala et al. 2018) and disturbance history. For instance, competition for water may accentuate the negative effects of water limitations on tree growth, as shown in temperate and Mediterranean forests (Bottero et al. 2017, Gómez-Aparicio et al. 2011, Astigarraga et al. 2020). Conversely, species interactions can alter the depth of soil water uptake (Grossiord et al. 2014a) or transpiration response to increasing drought stress (Grossiord et al. 2014b, Haberstroh and Werner 2022). Yet, the effects of climate on tree growth have rarely been evaluated together with the effects of neighbourhood crowding in tropical forests (but see: Uriarte et al. 2016a), and we still know little about the extent to which the interaction between climate extremes and neighbourhood interactions can attenuate or exacerbate their separate effects on tree growth, especially in tropical forests with different disturbance
## Introduction

Tropical forests are biodiversity hotspots (Gatti et al. 2022, Pillay et al. 2022) and major contributors to global carbon and water cycles (Needham et al. 2018, Pan et al. 2011), but the services they provide to human populations are threatened by climate change and anthropogenic disturbance (Brodribb et al. 2020, Malhi et al. 2008, Nunes et al. 2022, Vancutsem et al. 2021). In particular, extreme droughts are linked to higher solar radiation, maximum temperatures, atmospheric evaporative demands and lower soil water availability, which have been shown to negatively impact tropical forest dynamics by reducing tree growth and increasing tree mortality (Bauman et al. 2022b,a, Rifai et al. 2018). As climate change is predicted to result in higher frequencies, intensities, and duration of extreme droughts in the tropics (Shukla et al. 2022), this may have important long-term implications for tropical tree dynamics and diversity, and therefore global carbon sequestration and residence time (Sullivan et al. 2020).

Tropical forest response to climate extremes is the compounded outcome of individual tree responses, which may depend on local biotic interactions, such as interactions between neighbouring trees. While crowding by neighbours generally reduces tropical tree growth (Dawkins 1959, Gourlet-Fleury and Houllier 2000, Fortunel et al. 2018, Uriarte et al. 2004, 2010), the net effects of neighbourhood interactions may change from negative to positive under specific environmental conditions (Grossiord 2020, Fichtner et al. 2020) such as with water levels (Aakala et al. 2018) and disturbance history. For instance, competition for water may accentuate the negative effects of water limitations on tree growth, as shown in temperate and Mediterranean forests (Bottero et al. 2017, Gómez-Aparicio et al. 2011, Astigarraga et al. 2020). Conversely, species interactions can alter the depth of soil water uptake (Grossiord et al. 2014a) or transpiration response to increasing drought stress (Grossiord et al. 2014b, Haberstroh and Werner 2022). Yet, the effects of climate on tree growth have rarely been evaluated together with the effects of neighbourhood crowding in tropical forests (but see: Uriarte et al. 2016a), and we still know little about the extent to which the interaction between climate extremes and neighbourhood interactions can attenuate or exacerbate their separate effects on tree growth, especially in tropical forests with different disturbance
125 histories.

126 More than half of the world's tropical forests are designated production forests (Blaser et al. 2011), and forest management such as selective logging and thinning has long-lasting effects on 127 forest composition, diversity and structure (Clark and Covey 2012, Cannon et al. 1994, Finegan 128 and Camacho 1999, Mirabel et al. 2021, Osazuwa-Peters et al. 2015), which may alter forest 129 resistance and resilience to extreme climate events (Hiltner et al. 2021). Specifically, disturbed 130 forests host more light-demanding tree species than old-growth forests (Carreño-Rocabado et al. 131 132 Baraloto et al. 2012, Kuusipalo et al. 1996, Mirabel et al. 2020, 2021, Whitmore 1989), 133 and these fast-growing species may be more sensitive to climate extremes (Bauman et al. 2022a, 134 Uriarte et al. 2016b) and neighbourhood crowding (Fortunel et al. 2016). Moreover, selective 135 logging and thinning creates gaps, thereby increasing the openness of forest canopies and changing 136 micro-climatic conditions (Ghuman and Lal 1987, Peña-Claros et al. 2008). These logging gaps 137 influence tree growth rates (Baraloto et al. 2005, Hérault et al. 2010, Peña-Claros et al. 2008, Silva 138 et al. 1995, Finegan et al. 1999), but it remains unclear how disturbance history may influence 139 tree growth responses to climate and neighbours.

140 Species differences in growth sensitivity to climate and neighbours likely depend on their functional 141 strategies. While commonly measured traits pertaining to light capture, nutrient and carbon use 142 such as specific leaf area and wood density can capture species differences in tree growth response 143 to neighbourhood crowding (Fortunel et al. 2016, Kunstler et al. 2016), they are not always good 144 proxies of species sensitivity to drought (Anderegg et al. 2018, Powell et al. 2017, Powers et al. 145 2020, Wagner et al. 2014b, but see: Uriarte et al. 2016a, Greenwood et al. 2017, Serra-Maluquer 146 et al. 2022). A promising way forward is to integrate leaf traits that directly link to plant carbon- 147 water relations or physiological responses to heat and water stress (Anderegg et al. 2018, Barros 148 et al. 2019, McGregor et al. 2021, Powell et al. 2017, Powers et al. 2020, Rowland et al. 2021, 149 Serra-Maluquer et al. 2022) (see: Table 1). For instance, stomatal density (Drake et al. 2013, 150 Machado et al. 2021) and oxygen and carbon isotope composition (Farquhar et al. 1989, 2007, 151 Moreno-Gutiérrez et al. 2012) can determine trade-offs between carbon assimilation and water loss 152 at the leaf level under water-limited conditions (Guerrieri et al. 2019, Prieto et al. 2018, Scheidegger
125 histories.

126 More than half of the world's tropical forests are designated production forests (Blaser et al. 2011), and forest management such as selective logging and thinning has long-lasting effects on 127 forest composition, diversity and structure (Clark and Covey 2012, Cannon et al. 1994, Finegan 128 and Camacho 1999, Mirabel et al. 2021, Osazuwa-Peters et al. 2015), which may alter forest 129 resistance and resilience to extreme climate events (Hiltner et al. 2021). Specifically, disturbed 130 forests host more light-demanding tree species than old-growth forests (Carreño-Rocabado et al. 131 132 2012, Baraloto et al. 2012, Kuusipalo et al. 1996, Mirabel et al. 2020, 2021, Whitmore 1989), 133 and these fast-growing species may be more sensitive to climate extremes (Bauman et al. 2022a, 134 135 logging and thinning creates gaps, thereby increasing the openness of forest canopies and changing 136 micro-climatic conditions (Ghuman and Lal 1987, Peña-Claros et al. 2008). These logging gaps 137 influence tree growth rates (Baraloto et al. 2005, Hérault et al. 2010, Peña-Claros et al. 2008, Silva 138 et al. 1995, Finegan et al. 1999), but it remains unclear how disturbance history may influence 139 tree growth responses to climate and neighbours.

140 Species differences in growth sensitivity to climate and neighbours likely depend on their functional 141 strategies. While commonly measured traits pertaining to light capture, nutrient and carbon use 142 such as specific leaf area and wood density can capture species differences in tree growth response 143 to neighbourhood crowding (Fortunel et al. 2016, Kunstler et al. 2016), they are not always good 144 proxies of species sensitivity to drought (Anderegg et al. 2018, Powell et al. 2017, Powers et al. 145 2020, Wagner et al. 2014b, but see: Uriarte et al. 2016a, Greenwood et al. 2017, Serra-Maluquer 146 et al. 2022). A promising way forward is to integrate leaf traits that directly link to plant carbon- 147 water relations or physiological responses to heat and water stress (Anderegg et al. 2018, Barros 148 et al. 2019, McGregor et al. 2021, Powell et al. 2017, Powers et al. 2020, Rowland et al. 2021, 149 Serra-Maluquer et al. 2022) (see: Table 1). For instance, stomatal density (Drake et al. 2013, 150 Machado et al. 2021) and oxygen and carbon isotope composition (Farquhar et al. 1989, 2007, 151 Moreno-Gutiérrez et al. 2012) can determine trade-offs between carbon assimilation and water loss 152 at the leaf level under water-limited conditions (Guerrieri et al. 2019, Prieto et al. 2018, Scheidegger
et al. 2000, Wang and Wen 2022b). Leaf water potential at turgor loss point is linked to species abilities to maintain key plant functions and growth during drought (Bartlett et al. 2012b, 2016, Maréchaux et al. 2018). Leaf minimum conductance (Blackman et al. 2019, Duursma et al. 2019, Machado et al. 2021) and leaf saturated water content (Blackman et al. 2019, Luo et al. 2021) may indicate species ability to resist or avoid heat- and drought-induced desiccation of plant tissue, and hence the potential for rapid post-drought growth. These leaf traits likely influence competition for water, but no study so far has evaluated their role in mediating tree growth response in the context of neighbourhood effects.

Here, we tested the separate and interactive effects of climate and neighbours by leveraging 30 years of individual tree diameter growth data for more than 300 tropical tree species from 15 tropical forest plots, including six old-growth forest plots and nine post-logging forest plots, located at the CIRAD (Centre de coopération Internationale en Recherche Agronomique pour le Développement) experimental site of Paracou in French Guiana (Gourlet-Fleury et al. 2004). For the most abundant species (ca. 100), we collected 12 traits pertaining to water relations, light capture and carbon use (Table 1) to examine their role in mediating tree growth response to climate anomalies and neighbourhood crowding. Specifically we asked:

1) How do climate anomalies and neighbourhood crowding influence tree growth? We expect tree growth to decline with positive anomalies in maximum temperature (Tmax), atmospheric evaporative demand (i.e. vapor pressure deficit, VPD) and climatic water deficit (CWD). As higher levels of solar radiation (SRad) lead to increased light availability, but also higher temperature and vapour pressure deficit, positive anomalies in SRad could either enhance or reduce tree growth. We further expect tree growth to decrease with higher neighbourhood crowding. Finally, we expect that climate anomalies and neighbourhood crowding can interact, thereby accentuating or attenuating their separate effects on tree growth.

2) Does tree growth response to climate and neighbours vary with forest disturbance history? We expect stronger climate-induced growth declines in disturbed forests as a more open canopy offers less buffer against climate anomalies. We also expect stronger sensitivity to climate anomalies and
et al. 2000, Wang and Wen 2022b). Leaf water potential at turgor loss point is linked to species abilities to maintain key plant functions and growth during drought (Bartlett et al. 2012b, 2016, Maréchaux et al. 2018). Leaf minimum conductance (Blackman et al. 2019, Duursma et al. 2019, Machado et al. 2021) and leaf saturated water content (Blackman et al. 2019, Luo et al. 2021) may indicate species ability to resist or avoid heat- and drought-induced desiccation of plant tissue, and hence the potential for rapid post-drought growth. These leaf traits likely influence competition for water, but no study so far has evaluated their role in mediating tree growth response in the context of neighbourhood effects.

Here, we tested the separate and interactive effects of climate and neighbours by leveraging 30 years of individual tree diameter growth data for more than 300 tropical tree species from 15 tropical forest plots, including six old-growth forest plots and nine post-logging forest plots, located at the CIRAD (Centre de coopération Internationale en Recherche Agronomique pour le Développement) experimental site of Paracou in French Guiana (Gourlet-Fleury et al. 2004). For the most abundant species (ca. 100), we collected 12 traits pertaining to water relations, light capture and carbon use (Table 1) to examine their role in mediating tree growth response to climate anomalies and neighbourhood crowding. Specifically we asked:

1) How do climate anomalies and neighbourhood crowding influence tree growth? We expect tree growth to decline with positive anomalies in maximum temperature (Tmax), atmospheric evaporative demand (i.e. vapor pressure deficit, VPD) and climatic water deficit (CWD). As higher levels of solar radiation (SRad) lead to increased light availability, but also higher temperature and vapour pressure deficit, positive anomalies in SRad could either enhance or reduce tree growth. We further expect tree growth to decrease with higher neighbourhood crowding. Finally, we expect that climate anomalies and neighbourhood crowding can interact, thereby accentuating or attenuating their separate effects on tree growth.

2) Does tree growth response to climate and neighbours vary with forest disturbance history? We expect stronger climate-induced growth declines in disturbed forests as a more open canopy offers less buffer against climate anomalies. We also expect stronger sensitivity to climate anomalies and
180 neighbourhood crowding in disturbed forests as they host more fast-growing species that may be
181 more vulnerable to both abiotic and biotic stressors.

182 3) Do leaf water-related traits and commonly-measured leaf and stem traits pertaining to light
183 and carbon use capture species differences in tree growth response to climate anomalies and neigh-
184 bourhood crowding? We expect greater tree growth sensitivity to heat and drought stress and
185 neighbourhood crowding in fast-growing, light-demanding, water-spender or drought intolerant
186 species.

187
# Materials and Methods

188
Study site

189 Our analyses used the permanent forest plot system of the CIRAD experimental site at the Paracou
190 Tropical Forest Research Station (5°18′N, 52°53′W) in French Guiana. Mean annual precipitation
191 at Paracou is 3041 mm yr-1, with a pronounced dry season (< 100 mm month-1) from August
192 through November. Paracou is characterised as a tropical lowland forest site consisting of old-
193 growth terra firme forest. Fifteen permanent forest plots of 6.25 ha each were established from
194 1984 to 1990, of which nine plots were subjected to different types (selective logging and thinning)
195 and intensities of sylvicultural treatments (Fig. 1), resulting in above-ground biomass (AGB) loss
196 (12-33% AGB loss in the three plots subjected to selective logging and 33-56% AGB loss in the six
197 plots that were subjected to selective logging and thinning) (Gourlet-Fleury et al. 2004, Hérault
198 and Piponiot 2018) and differences in species composition (Mirabel et al. 2020, 2021). In the 2021
199 inventory, there were 70,238 trees belonging to 591 species (including subspecies), 227 genera and
200 63 families.

201
Tree growth and neighbourhood data

202 In each of the 15 forest plots, all trees ≥ 10 cm DBH (diameter at breast height, i.e. 1.3 m) were
203 mapped to a precision of 0.5 m and were botanically identified. Tree inventories took place at
180 neighbourhood crowding in disturbed forests as they host more fast-growing species that may be
181 more vulnerable to both abiotic and biotic stressors.

182 3) Do leaf water-related traits and commonly-measured leaf and stem traits pertaining to light
183 and carbon use capture species differences in tree growth response to climate anomalies and neigh-
184 bourhood crowding? We expect greater tree growth sensitivity to heat and drought stress and
185 neighbourhood crowding in fast-growing, light-demanding, water-spender or drought intolerant
186 species.

187
# Materials and Methods

188
Study site

189 Our analyses used the permanent forest plot system of the CIRAD experimental site at the Paracou
190 Tropical Forest Research Station (5°18′N, 52°53′W) in French Guiana. Mean annual precipitation
191 at Paracou is 3041 mm yr⁻¹, with a pronounced dry season (< 100 mm month⁻¹) from August
192 through November. Paracou is characterised as a tropical lowland forest site consisting of old-
193 growth terra firme forest. Fifteen permanent forest plots of 6.25 ha each were established from
194 1984 to 1990, of which nine plots were subjected to different types (selective logging and thinning)
195 and intensities of sylvicultural treatments (Fig. 1), resulting in above-ground biomass (AGB) loss
196 (12–33% AGB loss in the three plots subjected to selective logging and 33-56% AGB loss in the six
197 plots that were subjected to selective logging and thinning) (Gourlet-Fleury et al. 2004, Hérault
198 and Piponiot 2018) and differences in species composition (Mirabel et al. 2020, 2021). In the 2021
199 inventory, there were 70,238 trees belonging to 591 species (including subspecies), 227 genera and
200 63 families.

201
Tree growth and neighbourhood data

202 In each of the 15 forest plots, all trees ≥ 10 cm DBH (diameter at breast height, i.e. 1.3 m) were
203 mapped to a precision of 0.5 m and were botanically identified. Tree inventories took place at
<figure><img src="image_2.png" /><figcaption>Figure 1. Spatial dimension of the 15 permanent forest plots and stand density in 10 m × 10 m quadrats, given as basal area (m² ha⁻¹), in a) 1991 and b) 2019. Disturbed plots are denoted by black contours. Box plots show differences in basal area between the 6 undisturbed (U) and the 9 disturbed (D) forest plots in c) 1991 and d) 2019. t value of Welch's two sample t-test, performed on log transformed basal area, and significance level (*** = p<0.001) are given in the left hand corner of the box plots.</figcaption></figure>

one or two year intervals during which the status (alive/dead) and circumference to a precision of 0.5 cm of every tree were recorded, from which we calculated DBH. New recruits (trees reaching the threshold DBH of 10 cm) were mapped and identified at each census (Gourlet-Fleury et al. 2004, Derroire et al. 2022a,b,c,d,e). To reduce noise in annual growth data (potentially caused by measurement/rounding errors), for each individual *i* of species *s*, we calculated the annualised absolute diameter growth rate (AGR, cm yr⁻¹) from DBH values between censuses *t* and *t* − 2 as:

$$ AGR_{i,s,t} = \frac{\mathrm{DBH}_{i,s,t} - \mathrm{DBH}_{i,s,t-2}}{t - (t-2)} \quad (\text{eqn 1}) $$
<figure><img src="image_2.png" /><figcaption>Figure 1. Spatial dimension of the 15 permanent forest plots and stand density in 10 m × 10 m quadrats, given as basal area (m² ha⁻¹), in a) 1991 and b) 2019. Disturbed plots are denoted by black contours. Box plots show differences in basal area between the 6 undisturbed (U) and the 9 disturbed (D) forest plots in c) 1991 and d) 2019. t value of Welch's two sample t-test, performed on log transformed basal area, and significance level (*** = p<0.001) are given in the left hand corner of the box plots.</figcaption></figure>

one or two year intervals during which the status (alive/dead) and circumference to a precision of 0.5 cm of every tree were recorded, from which we calculated DBH. New recruits (trees reaching the threshold DBH of 10 cm) were mapped and identified at each census (Gourlet-Fleury et al. 2004, Derroire et al. 2022a,b,c,d,e). To reduce noise in annual growth data (potentially caused by measurement/rounding errors), for each individual *i* of species *s*, we calculated the annualised absolute diameter growth rate (AGR, cm yr⁻¹) from DBH values between censuses *t* and *t* − 2 as:

$$ \text{AGR}_{i,s,t} = \frac{\text{DBH}_{i,s,t} - \text{DBH}_{i,s,t-2}}{t - (t-2)} \quad (\text{eqn 1}) $$
For each individual tree i, we thus calculated AGR for 15 two-year census intervals between 1991 and 2021. We excluded the following stems from the focal tree data: (a) Palm species due to their lack of secondary growth, (b) individuals with human-induced damage (e.g. through logging or thinning treatments), (c) missing or uncertain botanical determination, (d) with estimated circumference (e.g. due to buttresses), and (e) abnormal tree growth values (≤ −2 cm yr⁻¹, resulting from a shift in the point of measurement, and ≥ 5 cm yr⁻¹). The upper and lower thresholds for abnormal growth values were based on expert knowledge for Paracou site.

The growth of an individual tree *i* can be influenced by its *N*(*i*) neighbours within a given radius via their size (DBH²) and the inverse of their distance to the focal tree (*d*) (Canham et al. 2004, Fortunel et al. 2016, Uriarte et al. 2010). For each focal tree individual *i* at the start of the growth census interval *t* − 2, we calculated its neighbourhood crowding index (NCIᵢ,t₋₂) as the weighted sum of all neighbours *N*(*i*) within a 10 m radius of individual *i* (Fortunel et al. 2018, Lasky et al. 2014, Uriarte et al. 2016a), as:

$$ NCI_{i,t-2} = \sum_{j \in N(i)} \frac{\mathrm{DBH}_{j,t-2}^2}{d_{i,j}} \qquad (\text{eqn 2}) $$

We chose a 10 m radius to be able to compare with previous studies conducted in tropical forests (Fortunel et al. 2018, Lasky et al. 2014, Uriarte et al. 2016a), and well capture neighbourhood effects while keeping edge effects low (Zambrano et al. 2020). To avoid incomplete neighbourhoods, all focal tree individuals closer than 10 m to the plot edge were excluded. We kept as neighbours individuals with human-induced damage, missing or uncertain botanical determination, estimated circumferences, and corrected abnormal diameter increments (see: Supplementary Methods S1 for details).

## Climate data

We considered four climate variables that have been previously shown to influence tropical tree growth: solar radiation (SRad) representing light availability, maximum temperature (Tmax) cap-
For each individual tree i, we thus calculated AGR for 15 two-year census intervals between 1991 and 2021. We excluded the following stems from the focal tree data: (a) Palm species due to their lack of secondary growth, (b) individuals with human-induced damage (e.g. through logging or thinning treatments), (c) missing or uncertain botanical determination, (d) with estimated circumference (e.g. due to buttresses), and (e) abnormal tree growth values (≤ −2 cm yr⁻¹, resulting from a shift in the point of measurement, and ≥ 5 cm yr⁻¹). The upper and lower thresholds for abnormal growth values were based on expert knowledge for Paracou site.

The growth of an individual tree i can be influenced by its N(i) neighbours within a given radius via their size (DBH²) and the inverse of their distance to the focal tree (d) (Canham et al. 2004, Fortunel et al. 2016, Uriarte et al. 2010). For each focal tree individual i at the start of the growth census interval t − 2, we calculated its neighbourhood crowding index (NCIᵢ,t⁻₂) as the weighted sum of all neighbours N(i) within a 10 m radius of individual i (Fortunel et al. 2018, Lasky et al. 2014, Uriarte et al. 2016a), as:

$$ NCI_{i,t-2} = \sum_{j \in N(i)} \frac{\mathrm{DBH}_{j,t-2}^2}{d_{i,j}} \qquad (\text{eqn 2}) $$

We chose a 10 m radius to be able to compare with previous studies conducted in tropical forests (Fortunel et al. 2018, Lasky et al. 2014, Uriarte et al. 2016a), and well capture neighbourhood effects while keeping edge effects low (Zambrano et al. 2020). To avoid incomplete neighbourhoods, all focal tree individuals closer than 10 m to the plot edge were excluded. We kept as neighbours individuals with human-induced damage, missing or uncertain botanical determination, estimated circumferences, and corrected abnormal diameter increments (see: Supplementary Methods S1 for details).

## Climate data

We considered four climate variables that have been previously shown to influence tropical tree growth: solar radiation (SRad) representing light availability, maximum temperature (Tmax) cap-
turing heat stress, vapour pressure deficit (VPD) relating to atmospheric evaporative demands and climatic water deficit (CWD) capturing soil water availability by relating precipitation to evaporation (Bauman et al. 2022a, Choat et al. 2018, Cunningham 2005, Doughty and Goulden 2008, Grossiord et al. 2020, Wagner et al. 2012). We extracted climate variables from TerraClimate, a high-resolution global data set of monthly climate variables and climatic water balance (Abatzoglou et al. 2018).

We identified anomalous years over the study period by calculating mean climate anomalies, expressed as the deviation of climate variables from their 30-year historical average, over each of the two-year census intervals. To this aim, we calculated the 30-year baseline (historical mean, $\mu_{m,baseline}$) and standard deviation ($\sigma_{m,baseline}$) for each month of the year (1-12) for the 1991-2021 period. We then calculated standardised anomalies for each month (1-12) $m$ at time $t$ as the deviation of the mean monthly climate variable $\mu_{m,t}$ from the long-term monthly baseline $\mu_{m,baseline}$ (Bauman et al. 2022a, Rifai et al. 2018). To investigate the effect of mean yearly anomalies on tree growth at two-year census interval ($CA_t$), we averaged the standardised monthly anomalies over the $M = 24$ months prior to census $t$ (July$_t$-July$_{t-2}$) as (Aubry-Kientz et al. 2015, Bauman et al. 2022a, Rifai et al. 2018):

$$CA_t = \frac{1}{M} \sum_{m=1}^{M} \frac{\mu_{m,t} - \mu_{m,baseline}}{\sigma_{m,baseline}} \quad (\text{eqn 3})$$

Using this approach, two-year periods with positive anomalies can directly be interpreted as periods in which trees experienced higher solar radiation (SRad), heat (Tmax), atmospheric (VPD) or soil water (CWD) drought stress than usual.

## Functional trait data

We determined six leaf traits pertaining to water relations (Table 1): stomatal density (SD), oxygen ($\delta^{18}$O) and carbon ($\delta^{13}$C) stable isotope composition, water potential at turgor loss point ($\pi_{tlp}$), minimum conductance ($g_{min}$) and saturated water content (LSWC). We sampled 645 trees belonging to 102 of the most abundant species (from 76 genera and 34 families) that together
turing heat stress, vapour pressure deficit (VPD) relating to atmospheric evaporative demands and climatic water deficit (CWD) capturing soil water availability by relating precipitation to evapo-transpiration (Bauman et al. 2022a, Choat et al. 2018, Cunningham 2005, Doughty and Goulden 2008, Grossiord et al. 2020, Wagner et al. 2012). We extracted climate variables from TerraClimate, a high-resolution global data set of monthly climate variables and climatic water balance (Abatzoglou et al. 2018).

We identified anomalous years over the study period by calculating mean climate anomalies, expressed as the deviation of climate variables from their 30-year historical average, over each of the two-year census intervals. To this aim, we calculated the 30-year baseline (historical mean, $\mu_{m,baseline}$) and standard deviation ($\sigma_{m,baseline}$) for each month of the year (1-12) for the 1991-2021 period. We then calculated standardised anomalies for each month (1-12) $m$ at time $t$ as the deviation of the mean monthly climate variable $\mu_{m,t}$ from the long-term monthly baseline $\mu_{m,baseline}$ (Bauman et al. 2022a, Rifai et al. 2018). To investigate the effect of mean yearly anomalies on tree growth at two-year census interval ($CA_t$), we averaged the standardised monthly anomalies over the $M = 24$ months prior to census $t$ (July$_t$-July$_{t-2}$) as (Aubry-Kientz et al. 2015, Bauman et al. 2022a, Rifai et al. 2018):

$$CA_t = \frac{1}{M} \sum_{m=1}^{M} \frac{\mu_{m,t} - \mu_{m,baseline}}{\sigma_{m,baseline}} \quad (\text{eqn 3})$$

Using this approach, two-year periods with positive anomalies can directly be interpreted as periods in which trees experienced higher solar radiation (SRad), heat (Tmax), atmospheric (VPD) or soil water (CWD) drought stress than usual.

## Functional trait data

We determined six leaf traits pertaining to water relations (Table 1): stomatal density (SD), oxygen ($\delta^{18}$O) and carbon ($\delta^{13}$C) stable isotope composition, water potential at turgor loss point ($\pi_{tlp}$), minimum conductance ($g_{min}$) and saturated water content (LSWC). We sampled 645 trees belonging to 102 of the most abundant species (from 76 genera and 34 families) that together
<figure><img src="image_3.png" /><figcaption>Figure 2. Mean standardised climate anomalies at Paracou for the two-year census intervals over the study period for a) solar radiation, b) maximum temperature, c) vapour pressure deficit and d) climatic water deficit. Red and blue lines and circles represent positive and negative anomalies respectively.</figcaption></figure>

257 represent 70% of all unique individual stems and 72% of all growth measurements at Paracou. We 258 randomly-selected 645 individuals located within the permanent 15 forest plots at Paracou, using 259 the following criteria: (i) botanical identification at the species level, (ii) DBH within species- 260 specific 10th and 90th percentile (to avoid sampling unusually small and large tree individuals), 261 (iii) topographic position corresponding with the species topographic preference (i.e. individuals 262 located at a certain topographic position (bottomland, slope, ridge) were only sampled if more than 263 10% of distinct stems of that species occur on the given topographic position). As the 102 target 264 species include understory, mid-story and canopy species, we sampled only leaves from branches 265 that were exposed to medium light conditions (Dawkins index: 3-4, Alder and Sunnott 1992), thus 266 excluding branches in full sun or shade. For each of the 645 individuals, we collected one fully 267 expanded and healthy leaf from a healthy branch in the dry season in 2020-2021 for each leaf trait 268 measurement. After harvest with a sling shot in the field, leaves were immediately put into ziplock 269 bags with a moist paper towel and kept in the dark in a cooler to minimize transpiration during 270 transport to the lab. No fieldwork permit was required.

271 To measure leaf SD, we used nail polish or silicon-based dental paste (Speedex Universal Silicon
<figure><img src="image_3.png" /><figcaption>Figure 2. Mean standardised climate anomalies at Paracou for the two-year census intervals over the study period for a) solar radiation, b) maximum temperature, c) vapour pressure deficit and d) climatic water deficit. Red and blue lines and circles represent positive and negative anomalies respectively.</figcaption></figure>

257 represent 70% of all unique individual stems and 72% of all growth measurements at Paracou. We 258 randomly-selected 645 individuals located within the permanent 15 forest plots at Paracou, using 259 the following criteria: (i) botanical identification at the species level, (ii) DBH within species- 260 specific 10th and 90th percentile (to avoid sampling unusually small and large tree individuals), 261 (iii) topographic position corresponding with the species topographic preference (i.e. individuals 262 located at a certain topographic position (bottomland, slope, ridge) were only sampled if more than 263 10% of distinct stems of that species occur on the given topographic position). As the 102 target 264 species include understory, mid-story and canopy species, we sampled only leaves from branches 265 that were exposed to medium light conditions (Dawkins index: 3-4, Alder and Sunnott 1992), thus 266 excluding branches in full sun or shade. For each of the 645 individuals, we collected one fully 267 expanded and healthy leaf from a healthy branch in the dry season in 2020-2021 for each leaf trait 268 measurement. After harvest with a sling shot in the field, leaves were immediately put into ziplock 269 bags with a moist paper towel and kept in the dark in a cooler to minimize transpiration during 270 transport to the lab. No fieldwork permit was required.

271 To measure leaf SD, we used nail polish or silicon-based dental paste (Speedex Universal Silicon
272 Activator and Speedex Light Body, Coltène/Whaledent AG, Switzerland) to take imprints from
273 the abaxial leaf surface at three positions of the widest part of the leaf blade, while avoiding major
274 veins (Voleníková and Tichá 2001). Imprints were mounted on microscope slides, and pictures were
275 taken at a magnification of 300x using a light microscope (Keyence VHX 7000, Osaka, Japan). We
276 took a minimum of 3 pictures, 1 per imprint when possible, for each sampled leaf. We used Fiji
277 ImageJ (Schindelin et al. 2012) to count all complete stomata per image area. Imprint quality and
278 resulting picture quality varied strongly between species and individuals, so we obtained stomata
279 counts for 470 individuals and 87 species.

280 Leaf osmotic potential was measured using a vapor pressure osmometer (VAPRO 5520, Wescor,
281 Logan, UT, USA). Leaf water potential at turgor loss point (πtlp) was linearly estimated from
282 osmotic potential at full hydration, following the standard protocol of Bartlett et al. (2012b,a),
283 which has been validated for species in French Guiana (Maréchaux et al. 2016).

284 Leaf gmin was estimated from leaf drying curves. We monitored leaf weight loss over time, following
285 the protocol of Sack et al. (2003). Time intervals for weight measurements were set to 30 min.
286 Leaves of tropical species lose water very rapidly, so that consecutive measurements over 3 to 4 h
287 were sufficient to assess minimum conductance.

288 We measured leaf saturated and dry weights with a digital scale at a 0.0001 g precision (Mettler
289 Toledo, Switzerland). We obtained saturated weights following the 'partial' rehydration protocol of
290 Vendramini et al. (2002), which provides comparable measurements to the 'complete' rehydration
291 protocol by Garnier et al. (2001) (Vaieretti et al. 2007). We wrapped fresh leaves into paper towels
292 that were previously soaked with de-ionised water, and kept them for 24 hours in the dark and at
293 low temperature (4°C) for rehydration. Dry weights were measured after drying leaves for at least
294 72 hours at 60°C (Sapes and Sala 2021). We calculated LSWC from saturated and dry weights
295 (Barrs and Weatherley 1962).

296 Bulk leaf δ¹⁸O and δ¹³C were calculated from a composite sample of dried and coarsely chopped
297 leaves, including those used for πtlp, LSWC and gmin measurements. Samples were ground to
298 fine powder using a ball mill (Retsch MM200, Hanau, Germany) for five minutes at maximum
272 Activator and Speedex Light Body, Coltène/Whaledent AG, Switzerland) to take imprints from
273 the abaxial leaf surface at three positions of the widest part of the leaf blade, while avoiding major
274 veins (Voleníková and Tichá 2001). Imprints were mounted on microscope slides, and pictures were
275 taken at a magnification of 300x using a light microscope (Keyence VHX 7000, Osaka, Japan). We
276 took a minimum of 3 pictures, 1 per imprint when possible, for each sampled leaf. We used Fiji
277 ImageJ (Schindelin et al. 2012) to count all complete stomata per image area. Imprint quality and
278 resulting picture quality varied strongly between species and individuals, so we obtained stomata
279 counts for 470 individuals and 87 species.

280 Leaf osmotic potential was measured using a vapor pressure osmometer (VAPRO 5520, Wescor,
281 Logan, UT, USA). Leaf water potential at turgor loss point (πtlp) was linearly estimated from
282 osmotic potential at full hydration, following the standard protocol of Bartlett et al. (2012b,a),
283 which has been validated for species in French Guiana (Maréchaux et al. 2016).

284 Leaf gmin was estimated from leaf drying curves. We monitored leaf weight loss over time, following
285 the protocol of Sack et al. (2003). Time intervals for weight measurements were set to 30 min.
286 Leaves of tropical species lose water very rapidly, so that consecutive measurements over 3 to 4 h
287 were sufficient to assess minimum conductance.

288 We measured leaf saturated and dry weights with a digital scale at a 0.0001 g precision (Mettler
289 Toledo, Switzerland). We obtained saturated weights following the 'partial' rehydration protocol of
290 Vendramini et al. (2002), which provides comparable measurements to the 'complete' rehydration
291 protocol by Garnier et al. (2001) (Vaieretti et al. 2007). We wrapped fresh leaves into paper towels
292 that were previously soaked with de-ionised water, and kept them for 24 hours in the dark and at
293 low temperature (4°C) for rehydration. Dry weights were measured after drying leaves for at least
294 72 hours at 60°C (Sapes and Sala 2021). We calculated LSWC from saturated and dry weights
295 (Barrs and Weatherley 1962).

296 Bulk leaf δ¹⁸O and δ¹³C were calculated from a composite sample of dried and coarsely chopped
297 leaves, including those used for πtlp, LSWC and gmin measurements. Samples were ground to
298 fine powder using a ball mill (Retsch MM200, Hanau, Germany) for five minutes at maximum
speed (100 rotations per minute) and further dried in a drying oven for 24 h at 85 °C. Aliquots of
0.2 – 0.4 mg per sample were weighed into silver capsules and δ¹⁸O signatures were measured with
a high temperature pyrolysis unit (TCEA) coupled to a Delta V isotope ratio mass spectrometer
instrument (Thermo Scientific, Vienna, Austria). Aliquots of 1 – 2 mg per sample were weighed
into tin capsules and δ¹³C signatures were subsequently determined using an elemental analyser
(Flash EA), linked by a Conflo IV Universal Interface to a Delta V isotope ratio mass spectrometer.

We combined our measurements for the six leaf traits with data from previous field campaigns
conducted at Paracou (Levionnois et al. 2021, Maréchaux et al. 2015, 2019, Ziegler et al. 2019).

We further compiled trait data for the 102 focal tree species for six leaf and wood traits that relate
to light capture and carbon use from previous work conducted in French Guiana (Baraloto et al.
2010, Fortunel et al. 2012, Vleminckx et al. 2021). We included leaf area (LA), specific leaf area
(SLA), leaf thickness (L<sub>thick</sub>), leaf toughness (L<sub>though</sub>), bark thickness (Bark<sub>thick</sub>) and wood specific
gravity (WSG, hereafter referred to as wood density) (see: Table 1). For all subsequent analyses
including trait values, we considered species means.
speed (100 rotations per minute) and further dried in a drying oven for 24 h at 85 °C. Aliquots of
0.2 – 0.4 mg per sample were weighed into silver capsules and δ¹⁸O signatures were measured with
a high temperature pyrolysis unit (TCEA) coupled to a Delta V isotope ratio mass spectrometer
instrument (Thermo Scientific, Vienna, Austria). Aliquots of 1 – 2 mg per sample were weighed
into tin capsules and δ¹³C signatures were subsequently determined using an elemental analyser
(Flash EA), linked by a Conflo IV Universal Interface to a Delta V isotope ratio mass spectrometer.

We combined our measurements for the six leaf traits with data from previous field campaigns
conducted at Paracou (Levionnois et al. 2021, Maréchaux et al. 2015, 2019, Ziegler et al. 2019).

We further compiled trait data for the 102 focal tree species for six leaf and wood traits that relate
to light capture and carbon use from previous work conducted in French Guiana (Baraloto et al.
2010, Fortunel et al. 2012, Vleminckx et al. 2021). We included leaf area (LA), specific leaf area
(SLA), leaf thickness (L<sub>thick</sub>), leaf toughness (L<sub>though</sub>), bark thickness (Bark<sub>thick</sub>) and wood specific
gravity (WSG, hereafter referred to as wood density) (see: Table 1). For all subsequent analyses
including trait values, we considered species means.
<table><caption>Table 1. Functional traits for the most abundant 100 species at Paracou, French Guiana</caption><thead><tr><th>Organ</th><th>Trait type</th><th>Trait</th><th>Abbreviation (Unit)</th><th>Function</th><th>Description</th><th>References</th><th>N</th></tr></thead><tbody><tr><td>Leaf</td><td>Anatomy</td><td>Stomata density</td><td>SD (n mm<sup>-2</sup>)</td><td>Gas exchange</td><td>SD relates to potential maximum stomatal conductance, promoting gas exchange and evaporative cooling, but increasing water loss through transpiration.</td><td>Bertolino et al. 2019, Drake et al. 2013, Machado et al. 2021</td><td>87</td></tr><tr><td>Leaf</td><td>Chemistry</td><td>Bulk leaf oxygen stable isotope</td><td>δ<sup>18</sup>O (%<sub>o</sub>)</td><td>Carbon-water use</td><td>Plants with high δ<sup>18</sup>O have low stomatal conductance and transpiration rates and thus conserve water at the expense of carbon assimilation.</td><td>Farquhar et al. 2007, Moreno-Gutierrez et al. 2012, Scheidegger et al. 2000</td><td>81</td></tr><tr><td>Leaf</td><td>Chemistry</td><td>Bulk leaf carbon stable isotope</td><td>δ<sup>13</sup>C (%<sub>o</sub>)</td><td>Carbon-water use</td><td>Plants with high δ<sup>13</sup>C have high intrinsic water-use efficiency (high photosynthetic rates relative to stomatal conductance) and thus conserve water at the expense of carbon assimilation.</td><td>Farquhar et al. 1989, Cernusak et al. 2013, Scheidegger et al. 2000</td><td>81</td></tr><tr><td>Leaf</td><td>Physiology</td><td>Water potential at turgor loss point</td><td>πt<sub>lp</sub> (MPa)</td><td>Drought tolerance</td><td>Plants with low πt<sub>lp</sub> maintain stomatal conductance, hydraulic conductance and photosynthetic gas exchange at lower soil water potential.</td><td>Bartlett et al. 2012, Marechaux et al. 2018</td><td>100</td></tr><tr><td>Leaf</td><td>Physiology</td><td>Minimum conductance</td><td>g<sub>min</sub> (mmol m<sup>-2</sup> s<sup>-1</sup>)</td><td>Water conservation</td><td>Low g<sub>min</sub> limits residual water loss after stomatal closure through leaf cuticle and incompletely closed stomata, thereby increasing dry-down time.</td><td>Blackman et al. 2019, Duursma et al. 2019, Machado et al. 2021</td><td>102</td></tr><tr><td>Leaf</td><td>Physiology</td><td>Leaf saturated water content</td><td>LSWC (%)</td><td>Water storage</td><td>LSWC translates into water reserves that may allow maintenance of leaf water potential when water supply becomes limiting.</td><td>Blackman et al. 2019, Gleason et al. 2014, Luo et al. 2021</td><td>100</td></tr><tr><td>Leaf</td><td>Structure</td><td>Leaf area</td><td>LA (cm<sup>2</sup>)</td><td>Light capture</td><td>Large leaves intercept more light, have thick leaf boundary layer that limits heat exchange with surrounding air, but are at risk of heat damage when stomatal closure prevents transpirational cooling.</td><td>Wright et al. 2017</td><td>101</td></tr><tr><td>Leaf</td><td>Structure</td><td>Specific leaf area</td><td>SLA (m<sup>2</sup> kg<sup>-1</sup>)</td><td>Light capture and carbon use</td><td>High SLA reflects greater allocation of dry mass/carbon to light interception than physical resistance, and is associated with resource acquisitive strategies along the leaf economic spectrum.</td><td>Osnas et al. 2013, Wright et al. 2004</td><td>101</td></tr><tr><td>Leaf</td><td>Structure</td><td>Leaf thickness</td><td>L<sub>thick</sub> (mm)</td><td>Carbon use</td><td>L<sub>thick</sub> relates to structural support, physical resistance and leaf lifespan.</td><td>Vile et al. 2005</td><td>100</td></tr><tr><td>Leaf</td><td>Structure</td><td>Leaf toughness</td><td>L<sub>tough</sub> (N)</td><td>Carbon use</td><td>L<sub>tough</sub> relates to structural support, physical resistance and leaf lifespan.</td><td>Kitajima and Poorter 2010</td><td>100</td></tr><tr><td>Wood</td><td>Structure</td><td>Stem bark thickness</td><td>Bark<sub>thick</sub> (mm)</td><td>Mechanical support, water transport and storage capacity</td><td>Plants with thick bark have low respiration rates, but high mechanical strength, resistance to abiotic and biotic threats, and stem water storage.</td><td>Loram-Lourenço et al. 2022, Poorter et al. 2014, Rosell et al. 2014</td><td>100</td></tr><tr><td>Wood</td><td>Structure</td><td>Stem wood specific gravity</td><td>WSG</td><td>Mechanical support, water transport and storage capacity</td><td>Dense wood increases mechanical strength and resistance to abiotic and biotic threats, and is related to high embolism resistance, but is more costly to produce per volume.</td><td>Chave et al. 2009, Poorter et al. 2010, Serra-Maluquer et al. 2022</td><td>95</td></tr></tbody></table>

Note: For a given trait N indicates the number of species for which we had available trait information.
<table><caption>Table 1. Functional traits for the most abundant 100 species at Paracou, French Guiana</caption><thead><tr><th>Organ</th><th>Trait type</th><th>Trait</th><th>Abbreviation (Unit)</th><th>Function</th><th>Description</th><th>References</th><th>N</th></tr></thead><tbody><tr><td>Leaf</td><td>Anatomy</td><td>Stomata density</td><td>SD (n mm<sup>-2</sup>)</td><td>Gas exchange</td><td>SD relates to potential maximum stomatal conductance, promoting gas exchange and evaporative cooling, but increasing water loss through transpiration.</td><td>Bertolino et al. 2019, Drake et al. 2013, Machado et al. 2021</td><td>87</td></tr><tr><td>Leaf</td><td>Chemistry</td><td>Bulk leaf oxygen stable isotope</td><td>δ<sup>18</sup>O (%<sub>o</sub>)</td><td>Carbon-water use</td><td>Plants with high δ<sup>18</sup>O have low stomatal conductance and transpiration rates and thus conserve water at the expense of carbon assimilation.</td><td>Farquhar et al. 2007, Moreno-Gutierrez et al. 2012, Scheidegger et al. 2000</td><td>81</td></tr><tr><td>Leaf</td><td>Chemistry</td><td>Bulk leaf carbon stable isotope</td><td>δ<sup>13</sup>C (%<sub>o</sub>)</td><td>Carbon-water use</td><td>Plants with high δ<sup>13</sup>C have high intrinsic water-use efficiency (high photosynthetic rates relative to stomatal conductance) and thus conserve water at the expense of carbon assimilation.</td><td>Farquhar et al. 1989, Cernusak et al. 2013, Scheidegger et al. 2000</td><td>81</td></tr><tr><td>Leaf</td><td>Physiology</td><td>Water potential at turgor loss point</td><td>πt<sub>lp</sub> (MPa)</td><td>Drought tolerance</td><td>Plants with low πt<sub>lp</sub> maintain stomatal conductance, hydraulic conductance and photosynthetic gas exchange at lower soil water potential.</td><td>Bartlett et al. 2012, Marechaux et al. 2018</td><td>100</td></tr><tr><td>Leaf</td><td>Physiology</td><td>Minimum conductance</td><td>g<sub>min</sub> (mmol m<sup>-2</sup> s<sup>-1</sup>)</td><td>Water conservation</td><td>Low g<sub>min</sub> limits residual water loss after stomatal closure through leaf cuticle and incompletely closed stomata, thereby increasing dry-down time.</td><td>Blackman et al. 2019, Duursma et al. 2019, Machado et al. 2021</td><td>102</td></tr><tr><td>Leaf</td><td>Physiology</td><td>Leaf saturated water content</td><td>LSWC (%)</td><td>Water storage</td><td>LSWC translates into water reserves that may allow maintenance of leaf water potential when water supply becomes limiting.</td><td>Blackman et al. 2019, Gleason et al. 2014, Luo et al. 2021</td><td>100</td></tr><tr><td>Leaf</td><td>Structure</td><td>Leaf area</td><td>LA (cm<sup>2</sup>)</td><td>Light capture</td><td>Large leaves intercept more light, have thick leaf boundary layer that limits heat exchange with surrounding air, but are at risk of heat damage when stomatal closure prevents transpirational cooling.</td><td>Wright et al. 2017</td><td>101</td></tr><tr><td>Leaf</td><td>Structure</td><td>Specific leaf area</td><td>SLA (m<sup>2</sup> kg<sup>-1</sup>)</td><td>Light capture and carbon use</td><td>High SLA reflects greater allocation of dry mass/carbon to light interception than physical resistance, and is associated with resource acquisitive strategies along the leaf economic spectrum.</td><td>Osnas et al. 2013, Wright et al. 2004</td><td>101</td></tr><tr><td>Leaf</td><td>Structure</td><td>Leaf thickness</td><td>L<sub>thick</sub> (mm)</td><td>Carbon use</td><td>L<sub>thick</sub> relates to structural support, physical resistance and leaf lifespan.</td><td>Vile et al. 2005</td><td>100</td></tr><tr><td>Leaf</td><td>Structure</td><td>Leaf toughness</td><td>L<sub>tough</sub> (N)</td><td>Carbon use</td><td>L<sub>tough</sub> relates to structural support, physical resistance and leaf lifespan.</td><td>Kitajima and Poorter 2010</td><td>100</td></tr><tr><td>Wood</td><td>Structure</td><td>Stem bark thickness</td><td>Bark<sub>thick</sub> (mm)</td><td>Mechanical support, water transport and storage capacity</td><td>Plants with thick bark have low respiration rates, but high mechanical strength, resistance to abiotic and biotic threats, and stem water storage.</td><td>Loram-Lourenço et al. 2022, Poorter et al. 2014, Rosell et al. 2014</td><td>100</td></tr><tr><td>Wood</td><td>Structure</td><td>Stem wood specific gravity</td><td>WSG</td><td>Mechanical support, water transport and storage capacity</td><td>Dense wood increases mechanical strength and resistance to abiotic and biotic threats, and is related to high embolism resistance, but is more costly to produce per volume.</td><td>Chave et al. 2009, Poorter et al. 2010, Serra-Maluquer et al. 2022</td><td>95</td></tr></tbody></table>

Note: For a given trait N indicates the number of species for which we had available trait information.
313 Models

We used Bayesian hierarchical models to evaluate the separate and interactive effects of climate
anomalies and neighbourhood crowding on individual absolute growth rates (AGR). We modeled
the natural logarithm of absolute growth rates (log(AGR)) because of the high heteroscedasticity
within the growth data set and to reduce the influence of outliers (Bauman et al. 2022a, Fortunel
et al. 2018, Hérault et al. 2011). To deal with negative and zero growth before taking the logarithm,
we added to each observed growth value the constant 1.1|AGR<sub>min</sub>|, where |AGR<sub>min</sub>| is the absolute
minimum value of observed growth of the dataset (Bauman et al. 2022a).
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The hierarchies of the models consist of modelling individual-level growth rates to simultaneously
fit a community-level regression and a species-level regression. The community level modelled AGR
responses to covariates via hyperparameters (i.e. statistical distributions from which species-level
intercepts and slope coefficients arose), while the species level captured species-specific AGR sensi-
tivities to covariates (i.e. slope parameters) and species-level intercepts (hereafter intrinsic AGR)
- i.e. species-level deviations from the community average parameters. While the community-level
regression (eqn 4a) was kept the same across all models, the species-level regressions (capturing
species-specific differences in intrinsic growth rates and growth responses to model covariates)
only depend on species identity in M1-models (eqn 4c), while species parameters are a function of
species traits in M2-models (eqn 5). The models further include a covariance matrix (eqn 4e) to
estimate correlations ρ among species-level parameters. To capture part of the unexplained growth
variation related to individuals and plots we allowed intercepts to vary by plots γp and individuals
εi (eqn 4f) (Bauman et al. 2022a, Fortunel et al. 2018).
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We modeled individual log(AGR_t) as a linear function of (i) tree size at the beginning of the census
interval (DBH<sub>t-2</sub>), (ii) monthly climate anomalies averaged over the census interval (CA_t), (iii)
neighbourhood crowding at the beginning of the census interval (NCI<sub>t-2</sub>) and (iv) the interaction
between climate anomalies and neighbourhood crowding (CA_t × NCI<sub>t-2</sub>) (Bauman et al. 2022a,
Fortunel et al. 2018). To allow direct comparison between covariates and ease assignment of
plausible weakly-informative prior to the parameters, log(AGR_t) and all covariates, except for
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313 Models

We used Bayesian hierarchical models to evaluate the separate and interactive effects of climate anomalies and neighbourhood crowding on individual absolute growth rates (AGR). We modeled the natural logarithm of absolute growth rates (log(AGR)) because of the high heteroscedasticity within the growth data set and to reduce the influence of outliers (Bauman et al. 2022a, Fortunel et al. 2018, Hérault et al. 2011). To deal with negative and zero growth before taking the logarithm, we added to each observed growth value the constant 1.1|AGR<sub>min</sub>|, where |AGR<sub>min</sub>| is the absolute minimum value of observed growth of the dataset (Bauman et al. 2022a). 314
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The hierarchies of the models consist of modelling individual-level growth rates to simultaneously
fit a community-level regression and a species-level regression. The community level modelled AGR
responses to covariates via hyperparameters (i.e. statistical distributions from which species-level
intercepts and slope coefficients arose), while the species level captured species-specific AGR sensi-
tivities to covariates (i.e. slope parameters) and species-level intercepts (hereafter intrinsic AGR)
- i.e. species-level deviations from the community average parameters. While the community-level
regression (eqn 4a) was kept the same across all models, the species-level regressions (capturing
species-specific differences in intrinsic growth rates and growth responses to model covariates)
only depend on species identity in M1-models (eqn 4c), while species parameters are a function of
species traits in M2-models (eqn 5). The models further include a covariance matrix (eqn 4e) to
estimate correlations ρ among species-level parameters. To capture part of the unexplained growth
variation related to individuals and plots we allowed intercepts to vary by plots γp and individuals
εi (eqn 4f) (Bauman et al. 2022a, Fortunel et al. 2018). 321
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We modeled individual log(AGR_t) as a linear function of (i) tree size at the beginning of the census
interval (DBH<sub>t-2</sub>), (ii) monthly climate anomalies averaged over the census interval (CA_t), (iii)
neighbourhood crowding at the beginning of the census interval (NCI<sub>t-2</sub>) and (iv) the interaction
between climate anomalies and neighbourhood crowding (CA_t × NCI<sub>t-2</sub>) (Bauman et al. 2022a,
Fortunel et al. 2018). To allow direct comparison between covariates and ease assignment of
plausible weakly-informative prior to the parameters, log(AGR_t) and all covariates, except for
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climate anomalies (Bauman et al. 2022a), were standardised to mean zero and unit standard deviation (Bauman et al. 2022a, Fortunel et al. 2018, McElreath 2020, Uriarte et al. 2016a). As we assumed tree growth to have a non-linear, multiplicative relationship with DBH and NCI (Canham et al. 2004, Fortunel et al. 2016, Uriarte et al. 2010), we log transformed DBH and NCI prior to standardisation to achieve an additive and linear relationship (Kunstler et al. 2016, Uriarte et al. 2016a, Fortunel et al. 2018). As species strongly varied in their mean tree size, we standardised DBH to mean zero and unit standard deviation within species, to prevent confounding species differences in growth response to tree size with interspecific variation in mean DBH (Fortunel et al. 2018, Uriarte et al. 2016a).

## M1: "no trait models"

To manage model complexity in assessing tree growth response to the separate and interactive effects of climate anomalies and neighbourhood crowding and examine the influence of forest disturbance history, we fitted models separately for each of the four climate variables (SRad, Tmax, VPD and CWD) and for (i) all 15 plots combined, (ii) the six undisturbed plots and (iii) the nine disturbed plots, resulting in a total of 12 M1-models. To ensure sufficient statistical power, M1-models were run on observed growth for all species with at least 100 growth measurements. M1-models were therefore based on 48,203, 20,688 and 27,515 individuals, belonging to 310, 306 and 307 species, respectively. To allow for direct comparison of parameter estimates between control and disturbed forest, we standardised and centered growth and all model covariates before splitting the data set (Ren et al. 2021). For each individual i of species s between censuses t − 2 and t, we modeled tree growth as:

$$ \log(\text{AGR}_{i,s,t,p}) \sim \mathcal{N}(\mu_{i,s,t,p}, \sigma) \qquad (\text{eqn 4a}) $$

$$ \mu_{i,s,t,p} = \alpha_s + \beta_{1s} \times \log(\text{DBH}_{i,t-2}) + \beta_{2s} \times \text{CA}_t + \beta_{3s} \times \log(\text{NCI}_{i,t-2}) \\ + \beta_{4s} \times \text{CA}_t \times \log(\text{NCI}_{i,t-2}) + \gamma_p + \epsilon_i \qquad (\text{eqn 4b}) $$
climate anomalies (Bauman et al. 2022a), were standardised to mean zero and unit standard deviation (Bauman et al. 2022a, Fortunel et al. 2018, McElreath 2020, Uriarte et al. 2016a). As we assumed tree growth to have a non-linear, multiplicative relationship with DBH and NCI (Canham et al. 2004, Fortunel et al. 2016, Uriarte et al. 2010), we log transformed DBH and NCI prior to standardisation to achieve an additive and linear relationship (Kunstler et al. 2016, Uriarte et al. 2016a, Fortunel et al. 2018). As species strongly varied in their mean tree size, we standardised DBH to mean zero and unit standard deviation within species, to prevent confounding species differences in growth response to tree size with interspecific variation in mean DBH (Fortunel et al. 2018, Uriarte et al. 2016a).

## M1: "no trait models"

To manage model complexity in assessing tree growth response to the separate and interactive effects of climate anomalies and neighbourhood crowding and examine the influence of forest disturbance history, we fitted models separately for each of the four climate variables (SRad, Tmax, VPD and CWD) and for (i) all 15 plots combined, (ii) the six undisturbed plots and (iii) the nine disturbed plots, resulting in a total of 12 M1-models. To ensure sufficient statistical power, M1-models were run on observed growth for all species with at least 100 growth measurements. M1-models were therefore based on 48,203, 20,688 and 27,515 individuals, belonging to 310, 306 and 307 species, respectively. To allow for direct comparison of parameter estimates between control and disturbed forest, we standardised and centered growth and all model covariates before splitting the data set (Ren et al. 2021). For each individual i of species s between censuses t − 2 and t, we modeled tree growth as:

$$ \log(\text{AGR}_{i,s,t,p}) \sim \mathcal{N}(\mu_{i,s,t,p}, \sigma) \qquad (\text{eqn 4a}) $$

$$ \mu_{i,s,t,p} = \alpha_s + \beta_{1s} \times \log(\text{DBH}_{i,t-2}) + \beta_{2s} \times \text{CA}_t + \beta_{3s} \times \log(\text{NCI}_{i,t-2}) \\ + \beta_{4s} \times \text{CA}_t \times \log(\text{NCI}_{i,t-2}) + \gamma_p + \epsilon_i \qquad (\text{eqn 4b}) $$
$$ \begin{pmatrix} \alpha_s \\ \beta_{1s} \\ \beta_{2s} \\ \beta_{3s} \\ \beta_{4s} \end{pmatrix} \sim \text{MVNormal} \left[ \begin{pmatrix} \alpha_0 \\ \beta_{1,0} \\ \beta_{2,0} \\ \beta_{3,0} \\ \beta_{4,0} \end{pmatrix}, S \right] \qquad (\text{eqn 4c}) $$

$$ S = \begin{pmatrix} \sigma_{\alpha_s} & 0 & 0 & 0 & 0 \\ 0 & \sigma_{\beta_{1s}} & 0 & 0 & 0 \\ 0 & 0 & \sigma_{\beta_{2s}} & 0 & 0 \\ 0 & 0 & 0 & \sigma_{\beta_{3s}} & 0 \\ 0 & 0 & 0 & 0 & \sigma_{\beta_{4s}} \end{pmatrix} \times R \times \begin{pmatrix} \sigma_{\alpha_s} & 0 & 0 & 0 & 0 \\ 0 & \sigma_{\beta_{1s}} & 0 & 0 & 0 \\ 0 & 0 & \sigma_{\beta_{2s}} & 0 & 0 \\ 0 & 0 & 0 & \sigma_{\beta_{3s}} & 0 \\ 0 & 0 & 0 & 0 & \sigma_{\beta_{4s}} \end{pmatrix} \qquad (\text{eqn 4d}) $$

$$ R = \begin{pmatrix} 1 & \rho_{\alpha_s, \beta_{1s}} & \rho_{\alpha_s, \beta_{2s}} & \rho_{\alpha_s, \beta_{3s}} & \rho_{\alpha_s, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{1s}} & 1 & \rho_{\beta_{1s}, \beta_{2s}} & \rho_{\beta_{1s}, \beta_{3s}} & \rho_{\beta_{1s}, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{2s}} & \rho_{\beta_{1s}, \beta_{2s}} & 1 & \rho_{\beta_{2s}, \beta_{3s}} & \rho_{\beta_{2s}, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{3s}} & \rho_{\beta_{1s}, \beta_{3s}} & \rho_{\beta_{2s}, \beta_{3s}} & 1 & \rho_{\beta_{3s}, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{4s}} & \rho_{\beta_{1s}, \beta_{4s}} & \rho_{\beta_{2s}, \beta_{4s}} & \rho_{\beta_{3s}, \beta_{4s}} & 1 \end{pmatrix} \qquad (\text{eqn 4e}) $$

using the following weakly informative priors:

$$ \gamma_p \sim \mathcal{N}(0, \sigma_\gamma) $$

$$ \epsilon_i \sim \mathcal{N}(0, \sigma_{\epsilon}) $$

$$ \alpha_0, \beta_{1-4,0} \sim \mathcal{N}(0, 0.5) \qquad (\text{eqn 4f}) $$

$$ \sigma_{\alpha_s}, \sigma_{\beta_{1-4s}}, \sigma_{\gamma}, \sigma_{\epsilon}, \sigma \sim \exp(1) $$

$$ R \sim \text{LKJcorr}(2) $$

where $\alpha_s$ represents the species-level intrinsic growth and $\beta_{1s}$, $\beta_{2s}$, $\beta_{3s}$ and $\beta_{4s}$ represent species-level

growth responses to tree size (DBH), climate anomalies (CA), neighbourhood crowding (NCI) and

the interactive effect of climate anomalies and neighbourhood crowding (eqn 4a). Negative values

of $\alpha_s$ indicate species whose growth is lower and positive values indicate species whose growth
$$ \begin{pmatrix} \alpha_s \\ \beta_{1s} \\ \beta_{2s} \\ \beta_{3s} \\ \beta_{4s} \end{pmatrix} \sim \text{MVNormal} \left[ \begin{pmatrix} \alpha_0 \\ \beta_{1,0} \\ \beta_{2,0} \\ \beta_{3,0} \\ \beta_{4,0} \end{pmatrix}, S \right] \qquad (\text{eqn 4c}) $$

$$ S = \begin{pmatrix} \sigma_{\alpha_s} & 0 & 0 & 0 & 0 \\ 0 & \sigma_{\beta_{1s}} & 0 & 0 & 0 \\ 0 & 0 & \sigma_{\beta_{2s}} & 0 & 0 \\ 0 & 0 & 0 & \sigma_{\beta_{3s}} & 0 \\ 0 & 0 & 0 & 0 & \sigma_{\beta_{4s}} \end{pmatrix} \times R \times \begin{pmatrix} \sigma_{\alpha_s} & 0 & 0 & 0 & 0 \\ 0 & \sigma_{\beta_{1s}} & 0 & 0 & 0 \\ 0 & 0 & \sigma_{\beta_{2s}} & 0 & 0 \\ 0 & 0 & 0 & \sigma_{\beta_{3s}} & 0 \\ 0 & 0 & 0 & 0 & \sigma_{\beta_{4s}} \end{pmatrix} \qquad (\text{eqn 4d}) $$

$$ R = \begin{pmatrix} 1 & \rho_{\alpha_s, \beta_{1s}} & \rho_{\alpha_s, \beta_{2s}} & \rho_{\alpha_s, \beta_{3s}} & \rho_{\alpha_s, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{1s}} & 1 & \rho_{\beta_{1s}, \beta_{2s}} & \rho_{\beta_{1s}, \beta_{3s}} & \rho_{\beta_{1s}, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{2s}} & \rho_{\beta_{1s}, \beta_{2s}} & 1 & \rho_{\beta_{2s}, \beta_{3s}} & \rho_{\beta_{2s}, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{3s}} & \rho_{\beta_{1s}, \beta_{3s}} & \rho_{\beta_{2s}, \beta_{3s}} & 1 & \rho_{\beta_{3s}, \beta_{4s}} \\ \rho_{\alpha_s, \beta_{4s}} & \rho_{\beta_{1s}, \beta_{4s}} & \rho_{\beta_{2s}, \beta_{4s}} & \rho_{\beta_{3s}, \beta_{4s}} & 1 \end{pmatrix} \qquad (\text{eqn 4e}) $$

using the following weakly informative priors:

$$ \gamma_p \sim \mathcal{N}(0, \sigma_\gamma) $$

$$ \epsilon_i \sim \mathcal{N}(0, \sigma_\epsilon) $$

$$ \alpha_0, \beta_{1-4,0} \sim \mathcal{N}(0, 0.5) \qquad (\text{eqn 4f}) $$

$$ \sigma_{\alpha_s}, \sigma_{\beta_{1-4s}}, \sigma_{\gamma}, \sigma_{\epsilon}, \sigma \sim \exp(1) $$

$$ R \sim \text{LKJcorr}(2) $$

where $\alpha_s$ represents the species-level intrinsic growth and $\beta_{1s}$, $\beta_{2s}$, $\beta_{3s}$ and $\beta_{4s}$ represent species-level growth responses to tree size (DBH), climate anomalies (CA), neighbourhood crowding (NCI) and the interactive effect of climate anomalies and neighbourhood crowding (eqn 4a). Negative values of $\alpha_s$ indicate species whose growth is lower and positive values indicate species whose growth
370 is higher than the average AGR across all species. Negative values of $\beta_{1-3s}$ indicate decreases in growth with increasing model covariate. The interpretation of $\beta_{4s}$ depends on the respective directions of $\beta_{2s}$ and $\beta_{3s}$. If the sign of $\beta_{4s}$ is the same as the signs of $\beta_{2s}$ and $\beta_{3s}$, the interaction between neighbourhood crowding and climate can accentuate their separate effects. Conversely, if the sign of $\beta_{4s}$ is opposite from both signs of $\beta_{2s}$ and $\beta_{3s}$, the interaction between climate anomalies and neighbourhood crowding can attenuate their separate effects. The strength and implications of the interactions usually require a visual exploration to provide a full interpretation.
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377 Species intrinsic growth $\alpha_s$ and growth response to covariates $\beta_{1-4s}$ for the $s$ species were modeled as in eqn 4c, where $\alpha_0$ represents the community-level intrinsic growth rate and $\beta_{1-4,0}$ the overall effect of covariates on growth across all species. To assess correlations ($\rho$) among species-level intrinsic growth $\alpha_s$ and growth response to model covariates $\beta_{1-4s}$, we fitted a matrix of correlation coefficients among all pairs of species-level parameters. In addition, modeling all species-level parameters as a multivariate normal distribution (eqn 4c) allows to share information across species, thus improving the fit for poorly represented species, while preventing overfitting (Bauman et al. 2022a, McElreath 2020). The variance-covariance matrix $S$ was constructed as shown in eqn 4d, where $R$ is the correlation matrix of species-level parameters (eqn 4e). Varying intercepts for the $p$ plots $\gamma_p$ and the $i$ individuals $\epsilon_i$ were modeled as given in eqn 4f with a mean centered on 0 (i.e. mean intercept across all plots and individuals respectively). We specified weakly informative priors which are given in eqn 4f.
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## M2: "trait models"

389 To assess the effect of traits on species intrinsic growth $\alpha_s$, and growth response to climate anomalies $\beta_{2s}$, neighbourhood crowding $\beta_{3s}$ and their joint effects $\beta_{4s}$, we fitted M2-models for a subset of 81-102 tree species (representing over 40,000 trees), for which we had available trait data (Table 1). To manage model complexity, M2-models were fitted separately for each of the four climate variables (SRad, Tmax, VPD and CWD) and for each of the 12 functional traits, resulting in a total of 48 M2-models. While the hierarchical structure, variance-covariance matrix and equation of the community level hierarchy of the M2-models is identical to M1-models (eqn 4a-eqn 4b; eqn
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370 is higher than the average AGR across all species. Negative values of $\beta_{1-3s}$ indicate decreases in growth with increasing model covariate. The interpretation of $\beta_{4s}$ depends on the respective directions of $\beta_{2s}$ and $\beta_{3s}$. If the sign of $\beta_{4s}$ is the same as the signs of $\beta_{2s}$ and $\beta_{3s}$, the interaction between neighbourhood crowding and climate can accentuate their separate effects. Conversely, if the sign of $\beta_{4s}$ is opposite from both signs of $\beta_{2s}$ and $\beta_{3s}$, the interaction between climate anomalies and neighbourhood crowding can attenuate their separate effects. The strength and implications of the interactions usually require a visual exploration to provide a full interpretation.
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377 Species intrinsic growth $\alpha_s$ and growth response to covariates $\beta_{1-4s}$ for the $s$ species were modeled as in eqn 4c, where $\alpha_0$ represents the community-level intrinsic growth rate and $\beta_{1-4,0}$ the overall effect of covariates on growth across all species. To assess correlations ($\rho$) among species-level intrinsic growth $\alpha_s$ and growth response to model covariates $\beta_{1-4s}$, we fitted a matrix of correlation coefficients among all pairs of species-level parameters. In addition, modeling all species-level parameters as a multivariate normal distribution (eqn 4c) allows to share information across species, thus improving the fit for poorly represented species, while preventing overfitting (Bauman et al. 2022a, McElreath 2020). The variance-covariance matrix $S$ was constructed as shown in eqn 4d, where $R$ is the correlation matrix of species-level parameters (eqn 4e). Varying intercepts for the $p$ plots $\gamma_p$ and the $i$ individuals $\epsilon_i$ were modeled as given in eqn 4f with a mean centered on 0 (i.e. mean intercept across all plots and individuals respectively). We specified weakly informative priors which are given in eqn 4f.
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## M2: "trait models"

389 To assess the effect of traits on species intrinsic growth $\alpha_s$, and growth response to climate anomalies $\beta_{2s}$, neighbourhood crowding $\beta_{3s}$ and their joint effects $\beta_{4s}$, we fitted M2-models for a subset of 81-102 tree species (representing over 40,000 trees), for which we had available trait data (Table 1). To manage model complexity, M2-models were fitted separately for each of the four climate variables (SRad, Tmax, VPD and CWD) and for each of the 12 functional traits, resulting in a total of 48 M2-models. While the hierarchical structure, variance-covariance matrix and equation of the community level hierarchy of the M2-models is identical to M1-models (eqn 4a-eqn 4b; eqn 4f
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397 4d-eqn 4f), all species-level parameters, except species-level growth response to tree size $\beta_{1s}$, were modeled in species-level regressions to capture species-specific differences, while simultaneously evaluating the effect of species-mean functional traits within these responses (eqn 5) (Fortunel et al. 2018, Uriarte et al. 2016a, Bauman et al. 2022a). More specifically, species-level parameters were modelled as:

$$ \begin{pmatrix} \alpha_s \\ \beta_{1s} \\ \beta_{2s} \\ \beta_{3s} \\ \beta_{4s} \end{pmatrix} \sim MVN \left[ \begin{pmatrix} \alpha_0 + \alpha_1 \times \text{Trait}_s \\ \beta_{1,0} \\ \beta_{2,0} + \beta_{2,1} \times \text{Trait}_s \\ \beta_{3,0} + \beta_{3,1} \times \text{Trait}_s \\ \beta_{4,0} + \beta_{4,1} \times \text{Trait}_s \end{pmatrix}, S \right] \quad (\text{eqn 5}) $$

402 where $\alpha_0$ represents the community-level intrinsic growth (i.e. grand intercept) and $\beta_{2-4,0}$ the community-level response to model covariates (i.e grand slopes) whose posterior distributions represent the effect of covariates on AGR across all species. $\alpha_1$ and $\beta_{2-4,1}$ represent the departure from the community level AGR and community level response to model covariates with one standard deviation change in the value of Trait$_s$ of species s. Traits were standardised to mean zero and unit standard deviation across species, thus implying that parameter $\beta_{2-4s}$ equals $\beta_{2-4,0}$ for the mean trait value of the data set. Given their skewed distributions, we log transformed values of stomatal density (SD), leaf saturated water content (LSWC), minimum conductance ($g_{min}$), leaf area (LA), leaf thickness ($L_{thick}$), leaf toughness ($L_{though}$) and bark thickness (Bark$_{thick}$), prior to standardisation, to prevent the tail of the distribution from dominating the model fit (Fortunel et al. 2018). Species AGR responses to model covariates can thus be accentuated (i.e. the sign of the trait effect $\beta_{2-4,1}$ is the same as that of the corresponding community level response $\beta_{2-4,0}$) or attenuated (i.e. the sign of the trait effect $\beta_{2-4,1}$ is the opposite of that of the corresponding community level response $\beta_{2-4,0}$) with increasing values of Trait$_s$ (Bauman et al. 2022a, Fortunel et al. 2018, Uriarte et al. 2016a).
397 4d-eqn 4f), all species-level parameters, except species-level growth response to tree size $\beta_{1s}$, were modeled in species-level regressions to capture species-specific differences, while simultaneously 398 evaluating the effect of species-mean functional traits within these responses (eqn 5) (Fortunel 399 et al. 2018, Uriarte et al. 2016a, Bauman et al. 2022a). More specifically, species-level parameters 400 were modelled as:

401

$$ \begin{pmatrix} \alpha_s \\ \beta_{1s} \\ \beta_{2s} \\ \beta_{3s} \\ \beta_{4s} \end{pmatrix} \sim MVN \left[ \begin{pmatrix} \alpha_0 + \alpha_1 \times \text{Trait}_s \\ \beta_{1,0} \\ \beta_{2,0} + \beta_{2,1} \times \text{Trait}_s \\ \beta_{3,0} + \beta_{3,1} \times \text{Trait}_s \\ \beta_{4,0} + \beta_{4,1} \times \text{Trait}_s \end{pmatrix}, S \right] \quad (\text{eqn 5}) $$

402 where $\alpha_0$ represents the community-level intrinsic growth (i.e. grand intercept) and $\beta_{2-4,0}$ the 403 community-level response to model covariates (i.e grand slopes) whose posterior distributions rep- 404 resent the effect of covariates on AGR across all species. $\alpha_1$ and $\beta_{2-4,1}$ represent the departure from 405 the community level AGR and community level response to model covariates with one standard 406 deviation change in the value of Trait$_s$ of species s. Traits were standardised to mean zero and 407 unit standard deviation across species, thus implying that parameter $\beta_{2-4s}$ equals $\beta_{2-4,0}$ for the 408 mean trait value of the data set. Given their skewed distributions, we log transformed values of 409 stomatal density (SD), leaf saturated water content (LSWC), minimum conductance ($g_{min}$), leaf 410 area (LA), leaf thickness ($L_{thick}$), leaf toughness ($L_{though}$) and bark thickness (Bark$_{thick}$), prior to 411 standardisation, to prevent the tail of the distribution from dominating the model fit (Fortunel 412 et al. 2018). Species AGR responses to model covariates can thus be accentuated (i.e. the sign of 413 the trait effect $\beta_{2-4,1}$ is the same as that of the corresponding community level response $\beta_{2-4,0}$) 414 or attenuated (i.e. the sign of the trait effect $\beta_{2-4,1}$ is the opposite of that of the corresponding 415 community level response $\beta_{2-4,0}$) with increasing values of Trait$_s$ (Bauman et al. 2022a, Fortunel 416 et al. 2018, Uriarte et al. 2016a).

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417 Model fitting

All models were fitted in R environment (R Core Team 2021, RStudio Team 2020), on the
418 Meso@LR HPC cluster using the package brms (Bürkner 2017). Bayesian updating of param-
419 eters was performed via the No-U-Turn Sampler (NUTS) in Stan (Carpenter et al. 2017) using
420 CmdStanR (Stan Development Team 2022), which allows intra-chain parallelisation in the back-
421 end. We used four chains and 4000 iterations (2000 warm up) per chain. Chains of all models mixed
422 well and generally converged within 2000 iterations (Rhat between 1 and 1.05). Model parame-
423 ter posteriors were summarised through their median and 90%-highest posterior density interval
424 (HPDI) using the packages tidyverse (Wickham et al. 2019) and tidybayes (Kay 2022). Model
425 covariates were considered to have a clear effect when their slope coefficients 90%-HPDIs did not
426 encompass zero. To assess the model goodness of fit, we calculated conditional and marginal R2
427 using the bayes_R2() function of the brms package (Bürkner 2017), which calculates a Bayesian
428 version of R2 for regression models (Gelman et al. 2019). M1- and M2-models had high explanatory
429 power, with a mean conditional R2 of 0.62 and 0.64, respectively. For more detailed information
430 on conditional and marginal R2 for each model fit see Table S1.
431

432 Results

433 Tree growth sensitivity to climate anomalies and neighbourhood crowd-
434 ing

Across the 15 plots, anomalies in all four climate variables influenced tree growth at Paracou (Fig. 3). Positive anomalies in solar radiation (SRad) enhanced tree growth, whereas positive anomalies in maximum temperature (Tmax), vapour pressure deficit (VPD) and climatic water deficit (CWD) reduced tree growth. Neighbourhood crowding reduced tree growth, and this effect was stronger than that of any of the climate anomalies. Moreover, neighbourhood crowding showed positive interactive effects with climate anomalies linked to heat and drought stress (Tmax, VPD and CWD) on tree growth, but negative interactive effects with anomalies in SRad on tree growth. More crowded trees suffered less from negative effects of Tmax, VPD and CWD anomalies, while
417 Model fitting

All models were fitted in R environment (R Core Team 2021, RStudio Team 2020), on the
Meso@LR HPC cluster using the package brms (Bürkner 2017). Bayesian updating of param-
eters was performed via the No-U-Turn Sampler (NUTS) in Stan (Carpenter et al. 2017) using
CmdStanR (Stan Development Team 2022), which allows intra-chain parallelisation in the back-
end. We used four chains and 4000 iterations (2000 warm up) per chain. Chains of all models mixed
well and generally converged within 2000 iterations (Rhat between 1 and 1.05). Model parame-
ter posteriors were summarised through their median and 90%-highest posterior density interval
(HPDI) using the packages tidyverse (Wickham et al. 2019) and tidybayes (Kay 2022). Model
covariates were considered to have a clear effect when their slope coefficients 90%-HPDIs did not
encompass zero. To assess the model goodness of fit, we calculated conditional and marginal R2
using the bayes_R2() function of the brms package (Bürkner 2017), which calculates a Bayesian
version of R2 for regression models (Gelman et al. 2019). M1- and M2-models had high explanatory
power, with a mean conditional R2 of 0.62 and 0.64, respectively. For more detailed information
on conditional and marginal R2 for each model fit see Table S1.

432 Results

433 Tree growth sensitivity to climate anomalies and neighbourhood crowd-
ing
434

Across the 15 plots, anomalies in all four climate variables influenced tree growth at Paracou (Fig. 3). Positive anomalies in solar radiation (SRad) enhanced tree growth, whereas positive anomalies in maximum temperature (Tmax), vapour pressure deficit (VPD) and climatic water deficit (CWD) reduced tree growth. Neighbourhood crowding reduced tree growth, and this effect was stronger than that of any of the climate anomalies. Moreover, neighbourhood crowding showed positive interactive effects with climate anomalies linked to heat and drought stress (Tmax, VPD and CWD) on tree growth, but negative interactive effects with anomalies in SRad on tree growth. More crowded trees suffered less from negative effects of Tmax, VPD and CWD anomalies, while
also benefiting less from the positive effects of SRad anomalies.

<figure><img src="image_4.png" /></figure>

Figure 3. Community-level effects of climate anomaly ($\beta_{2,0}$), neighbourhood crowding (NCI, $\beta_{3,0}$), and their interaction (Climate anomaly×NCI, $\beta_{4,0}$) on tree growth (M1-models). Standardised coefficients from a) SRad, b) Tmax, c) VPD and d) CWD models are shown for all 15 plots combined, the 6 undisturbed plots and the 9 disturbed plots respectively. Circles show posterior medians of standardised coefficients (community level responses, see: $\beta_{2-4,0}$ in eqn 4c), and lines indicate 90% highest posterior density intervals [HPDI]. Colored circles indicate clear negative (red) and positive (blue) effects (i.e. slope coefficient 90%-HPDIs not encompassing zero), while white circles indicate no effects. Positive $\beta_{2-3,0}$ values indicate faster growth, while negative $\beta_{2-3,0}$ values indicate slower growth with increasing model covariate (details in Table S2). The interpretation of $\beta_{4,0}$ depends on the direction of the climate effect $\beta_{2,0}$. If the sign of $\beta_{4,0}$ is the same as the sign of $\beta_{2,0}$, neighbourhood crowding accentuates the effect of climate, inversely if the sign of $\beta_{4,0}$ is in the opposite of the sign of $\beta_{2,0}$, neighbourhood crowding attenuates the effect of climate. Community-level intercepts ($\alpha_0$) and tree size effects (DBH, $\beta_{1,0}$) are shown in Fig. S1 A.

Tree growth sensitivity to climate anomalies ($\beta_{2s}$) and neighbourhood crowding ($\beta_{3s}$) varied among the focal 310 species: 39% showed clear responses to SRad anomalies (of which 98% showed positive and only 2% negative responses). Moreover, 45% responded to Tmax anomalies (of which 99% responded negatively and only 1% positively) and 21% responded to VPD anomalies (of which 86% responded negatively and 14% positively). Finally, 21% responded to anomalies in CWD (of which 94% responded negatively and 6% positively). On average across the four climate models, 88% of all species showed marked responses to NCI, all of them suppressed by higher crowding intensities.
also benefiting less from the positive effects of SRad anomalies.

<figure><img src="image_4.png" /></figure>

Figure 3. Community-level effects of climate anomaly ($\beta_{2,0}$), neighbourhood crowding (NCI, $\beta_{3,0}$), and their interaction (Climate anomaly×NCI, $\beta_{4,0}$) on tree growth (M1-models). Standardised coefficients from a) SRad, b) Tmax, c) VPD and d) CWD models are shown for all 15 plots combined, the 6 undisturbed plots and the 9 disturbed plots respectively. Circles show posterior medians of standardised coefficients (community level responses, see: $\beta_{2-4,0}$ in eqn 4c), and lines indicate 90% highest posterior density intervals [HPDI]. Colored circles indicate clear negative (red) and positive (blue) effects (i.e. slope coefficient 90%-HPDIs not encompassing zero), while white circles indicate no effects. Positive $\beta_{2-3,0}$ values indicate faster growth, while negative $\beta_{2-3,0}$ values indicate slower growth with increasing model covariate (details in Table S2). The interpretation of $\beta_{4,0}$ depends on the direction of the climate effect $\beta_{2,0}$. If the sign of $\beta_{4,0}$ is the same as the sign of $\beta_{2,0}$, neighbourhood crowding accentuates the effect of climate, inversely if the sign of $\beta_{4,0}$ is in the opposite of the sign of $\beta_{2,0}$, neighbourhood crowding attenuates the effect of climate. Community-level intercepts ($\alpha_0$) and tree size effects (DBH, $\beta_{1,0}$) are shown in Fig. S1 A.

Tree growth sensitivity to climate anomalies ($\beta_{2s}$) and neighbourhood crowding ($\beta_{3s}$) varied among the focal 310 species: 39% showed clear responses to SRad anomalies (of which 98% showed positive and only 2% negative responses). Moreover, 45% responded to Tmax anomalies (of which 99% responded negatively and only 1% positively) and 21% responded to VPD anomalies (of which 86% responded negatively and 14% positively). Finally, 21% responded to anomalies in CWD (of which 94% responded negatively and 6% positively). On average across the four climate models, 88% of all species showed marked responses to NCI, all of them suppressed by higher crowding intensities.
Overall, individual-level tree growth response to the interaction between climate anomalies and neighbourhood crowding was weakly related to species identity. Yet, species showed a consistent directional response (i.e. no species showed an opposite effect) to the interactive effects of neighbourhood crowding and anomalies ($\beta_{4s}$) in SRad, Tmax and CWD, but less in VPD. Specifically, neighbourhood crowding clearly attenuated positive effects (negative $\beta_{4s}$) of SRad anomalies in 18%, and negative effects (positive $\beta_{4s}$) of Tmax and CWD anomalies in 27% and 8% of all species. Neighbourhood crowding modulated negative effects of VPD anomalies in 7% of all species, either attenuating (81% of positive $\beta_{4s}$) or reinforcing (19% of negative $\beta_{4s}$) these effects. For details see Table S3.

Species-level intrinsic AGR (M1, $\alpha_s$) were negatively associated with AGR response to anomalies (M1, $\beta_{2s}$), as shown by the median $\tilde{\rho}$, lower 5% quantile $q_5(\rho)$ and upper 5% quantile $q_{95}(\rho)$ of the highest posterior density, in SRad ($\tilde{\rho} = -0.20$, $q_5(\rho) = -0.32$, $q_{95}(\rho) = -0.09$), Tmax ($\tilde{\rho} = -0.69$, $q_5(\rho) = -0.76$, $q_{95}(\rho) = -0.62$), VPD ($\tilde{\rho} = -0.59$, $q_5(\rho) = -0.66$, $q_{95}(\rho) = -0.52$) and CWD ($\tilde{\rho} = -0.55$, $q_5(\rho) = -0.65$, $q_{95}(\rho) = -0.46$) as well as neighbourhood crowding ($\tilde{\rho} = -0.43$, $q_5(\rho) = -0.51$, $q_{95}(\rho) = -0.33$, mean across climate models). Tree growth sensitivity to neighbourhood crowding (M1, $\beta_{3s}$) was negatively associated with the response to SRad anomalies ($\tilde{\rho} = -0.17$, $q_5(\rho) = -0.30$, $q_{95}(\rho) = -0.04$) and positively with the response to anomalies in Tmax ($\tilde{\rho} = 0.26$, $q_5(\rho) = 0.13$, $q_{95}(\rho) = 0.37$) and CWD ($\tilde{\rho} = 0.28$, $q_5(\rho) = 0.15$, $q_{95}(\rho) = 0.41$) For details see Table S4: All plots).

## Variation in tree growth response to climate and neighbours with forest disturbance history

The effects of climate anomalies (M1, $\beta_{2,0}$) and neighbourhood crowding (M1, $\beta_{3,0}$) were consistent between the six undisturbed and nine disturbed plots, but were usually stronger in disturbed plots (except for VPD anomalies). In particular, tree growth sensitivity to neighbourhood crowding was twice as strong in disturbed plots than undisturbed plots. In addition, the interactive effects between climate anomalies and neighbourhood crowding were usually greater in disturbed plots, further attenuating their separate effects in these forests. More crowded trees in disturbed plots
Overall, individual-level tree growth response to the interaction between climate anomalies and neighbourhood crowding was weakly related to species identity. Yet, species showed a consistent directional response (i.e. no species showed an opposite effect) to the interactive effects of neighbourhood crowding and anomalies ($\beta_{4s}$) in SRad, Tmax and CWD, but less in VPD. Specifically, neighbourhood crowding clearly attenuated positive effects (negative $\beta_{4s}$) of SRad anomalies in 18%, and negative effects (positive $\beta_{4s}$) of Tmax and CWD anomalies in 27% and 8% of all species. Neighbourhood crowding modulated negative effects of VPD anomalies in 7% of all species, either attenuating (81% of positive $\beta_{4s}$) or reinforcing (19% of negative $\beta_{4s}$) these effects. For details see Table S3.

Species-level intrinsic AGR (M1, $\alpha_s$) were negatively associated with AGR response to anomalies (M1, $\beta_{2s}$), as shown by the median $\tilde{\rho}$, lower 5% quantile $q_5(\rho)$ and upper 5% quantile $q_{95}(\rho)$ of the highest posterior density, in SRad ($\tilde{\rho} = -0.20$, $q_5(\rho) = -0.32$, $q_{95}(\rho) = -0.09$), Tmax ($\tilde{\rho} = -0.69$, $q_5(\rho) = -0.76$, $q_{95}(\rho) = -0.62$), VPD ($\tilde{\rho} = -0.59$, $q_5(\rho) = -0.66$, $q_{95}(\rho) = -0.52$) and CWD ($\tilde{\rho} = -0.55$, $q_5(\rho) = -0.65$, $q_{95}(\rho) = -0.46$) as well as neighbourhood crowding ($\tilde{\rho} = -0.43$, $q_5(\rho) = -0.51$, $q_{95}(\rho) = -0.33$, mean across climate models). Tree growth sensitivity to neighbourhood crowding (M1, $\beta_{3s}$) was negatively associated with the response to SRad anomalies ($\tilde{\rho} = -0.17$, $q_5(\rho) = -0.30$, $q_{95}(\rho) = -0.04$) and positively with the response to anomalies in Tmax ($\tilde{\rho} = 0.26$, $q_5(\rho) = 0.13$, $q_{95}(\rho) = 0.37$) and CWD ($\tilde{\rho} = 0.28$, $q_5(\rho) = 0.15$, $q_{95}(\rho) = 0.41$) For details see Table S4: All plots).

## Variation in tree growth response to climate and neighbours with forest disturbance history

The effects of climate anomalies (M1, $\beta_{2,0}$) and neighbourhood crowding (M1, $\beta_{3,0}$) were consistent between the six undisturbed and nine disturbed plots, but were usually stronger in disturbed plots (except for VPD anomalies). In particular, tree growth sensitivity to neighbourhood crowding was twice as strong in disturbed plots than undisturbed plots. In addition, the interactive effects between climate anomalies and neighbourhood crowding were usually greater in disturbed plots, further attenuating their separate effects in these forests. More crowded trees in disturbed plots
479 were more buffered against climate anomalies (except for the VPD anomalies:NCI interaction).

480 However, while negative effects of CWD anomalies on growth were mitigated by neighbourhood

481 crowding in disturbed plots, they were reinforced in undisturbed plots (Fig. 3).

482 Although species intrinsic growth rates varied slightly between undisturbed and disturbed plots

483 (Fig. S2), correlations between species intrinsic growth rates (M1, intercept αs) and their growth

484 response to climate anomalies (M1, β2s) and neighbourhood crowding (M1, β3s) were overall con-

485 sistent between undisturbed and disturbed plots (Fig. 4 a-b). However, while species that suffered

486 stronger growth declines due to neighbourhood crowding (M1, β3s) were also more sensitive to pos-

487 itive CWD anomalies (M1, β2s) in disturbed plots, we found no clear relationship in undisturbed

488 plots (Fig. 4 c). For details on correlations of species-level parameters see: Table S4.
479 were more buffered against climate anomalies (except for the VPD anomalies:NCI interaction).

480 However, while negative effects of CWD anomalies on growth were mitigated by neighbourhood

481 crowding in disturbed plots, they were reinforced in undisturbed plots (Fig. 3).

482 Although species intrinsic growth rates varied slightly between undisturbed and disturbed plots

483 (Fig. S2), correlations between species intrinsic growth rates (M1, intercept αs) and their growth

484 response to climate anomalies (M1, β2s) and neighbourhood crowding (M1, β3s) were overall con-

485 sistent between undisturbed and disturbed plots (Fig. 4 a-b). However, while species that suffered

486 stronger growth declines due to neighbourhood crowding (M1, β3s) were also more sensitive to pos-

487 itive CWD anomalies (M1, β2s) in disturbed plots, we found no clear relationship in undisturbed

488 plots (Fig. 4 c). For details on correlations of species-level parameters see: Table S4.
<figure><img src="image_5.png" /><figcaption>Figure 4. Correlations between a) species intrinsic growth rates $\alpha_s$ and species-level growth response to CWD anomalies ($\beta_{2s}$), b) species intrinsic growth rates $\alpha_s$ and species-level growth response to neighbourhood crowding (NCI, $\beta_{3s}$) and c) species-level growth response to NCI ($\beta_{3s}$) and CWD anomalies ($\beta_{2s}$), estimated by the CWD M1-models for undisturbed (left) and disturbed (right) plots, respectively (eqn 4c). Circles represent species coefficients (i.e. species median posteriors). Vertical and horizontal lines represent coefficients 90%-highest posterior density intervals (HPDI). Blue and red regression lines indicate positive and negative correlations, respectively. $\rho$ values were obtained from the variance-covariance matrix (see: eqn 4e). Median, lower and upper 90%-HPDI are given in the upper right-hand or left-hand corner of the figures.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 4. Correlations between a) species intrinsic growth rates $\alpha_s$ and species-level growth response to CWD anomalies ($\beta_{2s}$), b) species intrinsic growth rates $\alpha_s$ and species-level growth response to neighbourhood crowding (NCI, $\beta_{3s}$) and c) species-level growth response to NCI ($\beta_{3s}$) and CWD anomalies ($\beta_{2s}$), estimated by the CWD M1-models for undisturbed (left) and disturbed (right) plots, respectively (eqn 4c). Circles represent species coefficients (i.e. species median posteriors). Vertical and horizontal lines represent coefficients 90%-highest posterior density intervals (HPDI). Blue and red regression lines indicate positive and negative correlations, respectively. $\rho$ values were obtained from the variance-covariance matrix (see: eqn 4e). Median, lower and upper 90%-HPDI are given in the upper right-hand or left-hand corner of the figures.</figcaption></figure>
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# Trait effects on tree growth response to climate anomalies and neighbourhood crowding

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Parameter estimates of community-level tree growth regressions were consistent between the M1-models (310 focal species) and M2-models (subset of species with available trait information, Table S2 and Table S5). Fast-growing tree species (i.e. with high intrinsic growth rates, $\alpha_s$) had higher SD, but lower $\delta^{18}$O, LA, $L_{\text{thick}}$, $L_{\text{though}}$ and WSG (Fig. S1 B and Table S5). Species traits influenced tree growth response to climate anomalies, neighbourhood crowding and their interaction, either reinforcing (values of $\beta_{2-4,1}$ moved $\beta_{2-4s}$ further away from zero with increasing trait value) or attenuating them (values of $\beta_{2-4,1}$ moved $\beta_{2-4s}$ closer to zero with increasing trait value) (Fig. 5).

Species with lower LSWC and SLA, but higher $\delta^{13}$C, $L_{\text{thick}}$ and $\text{Bark}_{\text{thick}}$ grew faster with increasing SRad anomalies (Fig. 5 a). Species with lower SD, but higher $\delta^{18}$O, $L_{\text{though}}$ and WSG were less sensitive to anomalies in Tmax and VPD (Fig. 5 b-c). Additionally, species with higher $\delta^{13}$C, $g_{\text{min}}$, LA and $L_{\text{thick}}$ showed lower sensitivities to VPD anomalies (Fig. 5 c). Species with lower $\pi_{\text{tlp}}$ but higher WSG were less sensitive to CWD anomalies. (Fig. 5 d). Species with lower SD, but higher $\delta^{18}$O, $L_{\text{though}}$ and WSG were less sensitive to neighbourhood crowding (Fig. 5 a-d).

Finally, species with lower $\delta^{13}$C but higher SLA benefited more from SRad anomalies when found in crowded environments (Fig. 5 a). Species with higher WSG profited less from the mitigating effects of neighbours against Tmax anomalies (Fig. 5 b), while species with higher SD and SLA, but lower LA, $L_{\text{thick}}$, $L_{\text{though}}$ were less buffered against VPD anomalies by their neighbours (Fig. 5 c). Species traits did not mediate the interactive effects of CWD anomalies and neighbourhood crowding (Fig. 5 d).
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Parameter estimates of community-level tree growth regressions were consistent between the M1-models (310 focal species) and M2-models (subset of species with available trait information, Table S2 and Table S5). Fast-growing tree species (i.e. with high intrinsic growth rates, $\alpha_s$) had higher SD, but lower $\delta^{18}$O, LA, $L_{\text{thick}}$, $L_{\text{though}}$ and WSG (Fig. S1 B and Table S5). Species traits influenced tree growth response to climate anomalies, neighbourhood crowding and their interaction, either reinforcing (values of $\beta_{2-4,1}$ moved $\beta_{2-4s}$ further away from zero with increasing trait value) or attenuating them (values of $\beta_{2-4,1}$ moved $\beta_{2-4s}$ closer to zero with increasing trait value) (Fig. 5).

Species with lower LSWC and SLA, but higher $\delta^{13}$C, $L_{\text{thick}}$ and $\text{Bark}_{\text{thick}}$ grew faster with increasing SRad anomalies (Fig. 5 a). Species with lower SD, but higher $\delta^{18}$O, $L_{\text{though}}$ and WSG were less sensitive to anomalies in Tmax and VPD (Fig. 5 b-c). Additionally, species with higher $\delta^{13}$C, $g_{\text{min}}$, LA and $L_{\text{thick}}$ showed lower sensitivities to VPD anomalies (Fig. 5 c). Species with lower $\pi_{\text{tlp}}$ but higher WSG were less sensitive to CWD anomalies. (Fig. 5 d). Species with lower SD, but higher $\delta^{18}$O, $L_{\text{though}}$ and WSG were less sensitive to neighbourhood crowding (Fig. 5 a-d).

Finally, species with lower $\delta^{13}$C but higher SLA benefited more from SRad anomalies when found in crowded environments (Fig. 5 a). Species with higher WSG profited less from the mitigating effects of neighbours against Tmax anomalies (Fig. 5 b), while species with higher SD and SLA, but lower LA, $L_{\text{thick}}$, $L_{\text{though}}$ were less buffered against VPD anomalies by their neighbours (Fig. 5 c). Species traits did not mediate the interactive effects of CWD anomalies and neighbourhood crowding (Fig. 5 d).
<figure><img src="image_6.png" /><figcaption>Figure 5. M2-models: trait effects on tree growth response to climate anomalies β<sub>2,1</sub>, neighbourhood crowding β<sub>3,1</sub> and the climate anomaly-crowding interaction β<sub>4,1</sub> (see: eqn 5). Standardised coefficients from a) SRad, b) Tmax, c) VPD and d) CWD models are shown for each of the 12 trait models: stomatal density (SD), leaf oxygen (δ<sup>18</sup>O) and carbon (δ<sup>13</sup>C) isotope composition, water potential at turgor loss point (π<sub>tlp</sub>), leaf saturated water content (LSWC), minimum conductance (g<sub>min</sub>), leaf area (LA), specific leaf area (SLA), leaf thickness (L<sub>thick</sub>), leaf toughness (L<sub>though</sub>), bark thickness (Bark<sub>thick</sub>) and wood specific gravity (WSG). Circles show posterior medians of standardised coefficients, and lines indicate 90% HPDIs. Filled circles indicate negative and positive effects (i.e. slope coefficient 90%-HPDI not encompassing zero) and empty circles indicate no clear effects. Positive β<sub>2-4,1</sub> values indicate faster growth with increasing trait values, while negative β<sub>2-4,1</sub> values indicate slower growth with increasing trait values (details in Table S5).</figcaption></figure>

510 Discussion

511

511

512

In this study, we examined the separate and interactive effects of climate anomalies and neighbours
on tree growth, combining 30 years of climate and tree growth data for over 300 species from 15
<figure><img src="image_6.png" /><figcaption>Figure 5. M2-models: trait effects on tree growth response to climate anomalies $\beta_{2,1}$, neighbourhood crowding $\beta_{3,1}$ and the climate anomaly-crowding interaction $\beta_{4,1}$ (see: eqn 5). Standardised coefficients from a) SRad, b) Tmax, c) VPD and d) CWD models are shown for each of the 12 trait models: stomatal density (SD), leaf oxygen ($\delta^{18}$O) and carbon ($\delta^{13}$C) isotope composition, water potential at turgor loss point ($\pi_{tlp}$), leaf saturated water content (LSWC), minimum conductance ($g_{min}$), leaf area (LA), specific leaf area (SLA), leaf thickness ($L_{thick}$), leaf toughness ($L_{though}$), bark thickness ($Bark_{thick}$) and wood specific gravity (WSG). Circles show posterior medians of standardised coefficients, and lines indicate 90% HPDIs. Filled circles indicate negative and positive effects (i.e. slope coefficient 90%-HPDI not encompassing zero) and empty circles indicate no clear effects. Positive $\beta_{2-4,1}$ values indicate faster growth with increasing trait values, while negative $\beta_{2-4,1}$ values indicate slower growth with increasing trait values (details in Table S5).</figcaption></figure>

510 Discussion

In this study, we examined the separate and interactive effects of climate anomalies and neighbours on tree growth, combining 30 years of climate and tree growth data for over 300 species from 15
permanent forest plots, with contrasting disturbance histories. For the most abundant species (ca. 513 100), we further evaluated the effect of 12 traits pertaining to water relations, light capture and 514 carbon use (Table 1), in mediating tree growth response to climate and neighbours. 515

Climate anomalies and neighbourhood crowding reduce tropical tree
growth
516
517

High temperatures (Tmax) were associated with tree growth decline in half of the 300 species at 518 Paracou (β₂s; Table S3), indicating that in years without strong positive anomalies, some species 519 at the site may already operate near their temperature optimum. Indeed, higher Tmax (and 520 minimum night time temperatures) can increase respiration but decrease stomatal conductance and 521 photosynthesis, thus reducing carbon assimilation (Anderegg et al. 2015, Doughty and Goulden 522 2008) and gross primary production (Aguilos et al. 2018, Crous et al. 2022), which can lead to 523 inter-annual declines in tropical woody productivity (Sullivan et al. 2020, Way and Oren 2010) 524 and carbon sinks (i.e. net ecosystem exchange) (Anderegg et al. 2015). As global temperatures 525 are predicted to increase in the tropics with ongoing climate change (Shukla et al. 2022), this 526 thermal sensitivity of tropical species may lead to important reductions in tree growth in the 527 future, which may alter long-term forest dynamics (Aubry-Kientz et al. 2019, Sullivan et al. 2020). 528
Although Tmax directly influences VPD (Table S6 and Fig. S2), VPD was a less important 529 driver of tree growth at Paracou. While positive VPD anomalies likely reduce tree growth by 530 reducing stomatal conductance and thereby carbon assimilation (Grossiord et al. 2020, Sanginés de 531 Cárcer et al. 2018), it does not affect respiration. Conversely, positive anomalies in CWD strongly 532 reduced tree growth at Paracou (Wagner et al. 2012, Aubry-Kientz et al. 2015), in contrast to 533 previous work in the Australian wet tropics indicating that VPD may limit tree growth before 534 soil water becomes scarce (Bauman et al. 2022a, Choat et al. 2012, Rifai et al. 2018). Our results 535 suggest that trees at Paracou may be more sensitive to soil water stress than to atmospheric water 536 stress. Alternatively, atmospheric water stress may not be severe enough at Paracou to lead to 537 important growth reductions. In addition, trees allocated more to diameter growth with higher 538 anomalies in solar radiation (SRad), which contrasts with recent work showing negative effects of 539
permanent forest plots, with contrasting disturbance histories. For the most abundant species (ca. 513 100), we further evaluated the effect of 12 traits pertaining to water relations, light capture and 514 carbon use (Table 1), in mediating tree growth response to climate and neighbours. 515

Climate anomalies and neighbourhood crowding reduce tropical tree
growth
516
517

High temperatures (Tmax) were associated with tree growth decline in half of the 300 species at Paracou (β₂s; Table S3), indicating that in years without strong positive anomalies, some species at the site may already operate near their temperature optimum. Indeed, higher Tmax (and minimum night time temperatures) can increase respiration but decrease stomatal conductance and photosynthesis, thus reducing carbon assimilation (Anderegg et al. 2015, Doughty and Goulden 2008) and gross primary production (Aguilos et al. 2018, Crous et al. 2022), which can lead to inter-annual declines in tropical woody productivity (Sullivan et al. 2020, Way and Oren 2010) and carbon sinks (i.e. net ecosystem exchange) (Anderegg et al. 2015). As global temperatures are predicted to increase in the tropics with ongoing climate change (Shukla et al. 2022), this thermal sensitivity of tropical species may lead to important reductions in tree growth in the future, which may alter long-term forest dynamics (Aubry-Kientz et al. 2019, Sullivan et al. 2020).
Although Tmax directly influences VPD (Table S6 and Fig. S2), VPD was a less important driver of tree growth at Paracou. While positive VPD anomalies likely reduce tree growth by reducing stomatal conductance and thereby carbon assimilation (Grossiord et al. 2020, Sanginés de Cárcer et al. 2018), it does not affect respiration. Conversely, positive anomalies in CWD strongly reduced tree growth at Paracou (Wagner et al. 2012, Aubry-Kientz et al. 2015), in contrast to previous work in the Australian wet tropics indicating that VPD may limit tree growth before soil water becomes scarce (Bauman et al. 2022a, Choat et al. 2012, Rifai et al. 2018). Our results suggest that trees at Paracou may be more sensitive to soil water stress than to atmospheric water stress. Alternatively, atmospheric water stress may not be severe enough at Paracou to lead to important growth reductions. In addition, trees allocated more to diameter growth with higher anomalies in solar radiation (SRad), which contrasts with recent work showing negative effects of
light anomalies on tropical tree growth in Australia's wet tropics (Bauman et al. 2022a). Light availability is an important driver of tree growth and primary production at Paracou (Aguilos et al. 2018, Wagner et al. 2012, 2014a), a typical light-limited tropical forest (Guan et al. 2015, Wagner et al. 2016). Furthermore, years with positive anomalies in SRad, did not necessarily translate into years with increased heat stress and atmospheric drought stress (Table S6 and Fig. S2), therefore reducing confounding and opposing effects of light availability and heat or atmospheric drought stress on tree growth. We explored the effects of different climate drivers separately, but there could be interactive effects between climate factors because they are causally linked. Future studies could further improve our understanding of tropical forest sensitivity to climate by embedding the causal associations among climate variables into the model structure, and by explicitly modelling their interactive effects (Allen et al. 2010, 2015, Hammond et al. 2022, Zuidema et al. 2022). Neighbourhood crowding was the strongest driver of tree growth at Paracou, confirming the importance of neighbourhood interactions for tropical forest dynamics (Dawkins 1959, Canham et al. 2004, Fortunel et al. 2018, Uriarte et al. 2016a, Zambrano et al. 2017).

Neighbourhood crowding overall reduced tree growth across the 300 focal species, in line with previous work across forest systems (Kunstler et al. 2016). Importantly, we provide first evidence that climate anomalies can interact with neighbourhood crowding to shape tropical tree growth, thereby attenuating their respective separate effects. In particular, the negative (Tmax, VPD, CWD) and positive (SRad) effects of climate anomalies on tree growth were attenuated in more crowded neighbourhoods. Denser neighbourhoods may result into higher levels of canopy closure, decreasing light availability (Gaudio et al. 2017, Ghuman and Lal 1987), but simultaneously providing thermal insulation (Fetcher et al. 1985, Gaudio et al. 2017, Ghuman and Lal 1987), thereby sustaining a more favourable micro-climate during extreme macro-climatic events (De Frenne et al. 2013, 2019). Contrary to our expectation, more crowded trees were also less sensitive to CWD anomalies, indicating that buffering effects might out-weigh increased competition for soil water during drought. During periods of lower soil water content, more crowded trees may have benefited from complementary neighbourhood water uptake, hydraulic redistribution or improved water retention through higher root densities (Grossiord et al. 2014c,b, Grossiord 2020, Hafner et al. 2021).
light anomalies on tropical tree growth in Australia's wet tropics (Bauman et al. 2022a). Light availability is an important driver of tree growth and primary production at Paracou (Aguilos et al. 2018, Wagner et al. 2012, 2014a), a typical light-limited tropical forest (Guan et al. 2015, Wagner et al. 2016). Furthermore, years with positive anomalies in SRad, did not necessarily translate into years with increased heat stress and atmospheric drought stress (Table S6 and Fig. S2), therefore reducing confounding and opposing effects of light availability and heat or atmospheric drought stress on tree growth. We explored the effects of different climate drivers separately, but there could be interactive effects between climate factors because they are causally linked. Future studies could further improve our understanding of tropical forest sensitivity to climate by embedding the causal associations among climate variables into the model structure, and by explicitly modelling their interactive effects (Allen et al. 2010, 2015, Hammond et al. 2022, Zuidema et al. 2022). Neighbourhood crowding was the strongest driver of tree growth at Paracou, confirming the importance of neighbourhood interactions for tropical forest dynamics (Dawkins 1959, Canham et al. 2004, Fortunel et al. 2018, Uriarte et al. 2016a, Zambrano et al. 2017).

Neighbourhood crowding overall reduced tree growth across the 300 focal species, in line with previous work across forest systems (Kunstler et al. 2016). Importantly, we provide first evidence that climate anomalies can interact with neighbourhood crowding to shape tropical tree growth, thereby attenuating their respective separate effects. In particular, the negative (Tmax, VPD, CWD) and positive (SRad) effects of climate anomalies on tree growth were attenuated in more crowded neighbourhoods. Denser neighbourhoods may result into higher levels of canopy closure, decreasing light availability (Gaudio et al. 2017, Ghuman and Lal 1987), but simultaneously providing thermal insulation (Fetcher et al. 1985, Gaudio et al. 2017, Ghuman and Lal 1987), thereby sustaining a more favourable micro-climate during extreme macro-climatic events (De Frenne et al. 2013, 2019). Contrary to our expectation, more crowded trees were also less sensitive to CWD anomalies, indicating that buffering effects might out-weigh increased competition for soil water during drought. During periods of lower soil water content, more crowded trees may have benefited from complementary neighbourhood water uptake, hydraulic redistribution or improved water retention through higher root densities (Grossiord et al. 2014c,b, Grossiord 2020, Hafner et al. 2021).
Previous studies have also shown mitigating effects of neighbourhood diversity or species richness on tree growth response to drought across forest biomes (Fichtner et al. 2020, Gillerot et al. 2021, Grossiord et al. 2014a, O’Brien et al. 2017, Pardos et al. 2021), in contrast with findings showing exacerbating effects of stand or neighbourhood densities on tree demographic response to climatic stresses in temperate (Bottero et al. 2017) and Mediterranean (Astigarraga et al. 2020, Gómez-Aparicio et al. 2011) forests. Our results suggest that accounting for the interactive effects of climate and neighbours offers a promising way towards an improved understanding of tropical forest resistance and resilience to climate change.

Disturbance increases tropical forest sensitivity to climate anomalies
and neighbourhood crowding

As expected, disturbed plots were more sensitive (i.e. showed greater tree growth reductions) to separate and joint effects of climate anomalies and neighbourhood crowding than undisturbed plots at Paracou (Fig. 3 a-d), highlighting the lasting impact of human activities such as logging and thinning on tropical forest dynamics (Hérault et al. 2010, Hérault and Piponiot 2018, Hiltner et al. 2021, Fargeon et al. 2016, Piponiot et al. 2016). Differences between disturbed and undisturbed plots in effect sizes of climate anomalies and neighbourhood crowding may originate from differences in tree community composition. Indeed, disturbed plots at Paracou harbor more fast-growing species (Mirabel et al. 2020, 2021), which tend to be more sensitive to climate anomalies, as shown in other tropical forests (Bauman et al. 2022a, Esquivel-Muelbert et al. 2020). The higher abundance of fast growing species and individuals on disturbed plots was also reflected in the higher community-level intrinsic growth rate ($\alpha_0$) of disturbed plots (Fig. S1 and Table S2). While higher sensitivity of disturbed communities to environmental drivers is consistent with expectations from the 'fast-slow' spectrum (Reich 2014), we show that this trend extends to increased susceptibility to biotic stressors such as neighbourhood crowding (Fig. 4 a-b, Table S4). In addition, greater canopy openness in disturbed plots may lead to more severe climate effects through tighter coupling of micro- and macro-climatic conditions (De Frenne et al. 2013, 2019, Ghuman and Lal 1987, Von Arx et al. 2012).
Previous studies have also shown mitigating effects of neighbourhood diversity or species richness on tree growth response to drought across forest biomes (Fichtner et al. 2020, Gillerot et al. 2021, Grossiord et al. 2014a, O’Brien et al. 2017, Pardos et al. 2021), in contrast with findings showing exacerbating effects of stand or neighbourhood densities on tree demographic response to climatic stresses in temperate (Bottero et al. 2017) and Mediterranean (Astigarraga et al. 2020, Gómez-Aparicio et al. 2011) forests. Our results suggest that accounting for the interactive effects of climate and neighbours offers a promising way towards an improved understanding of tropical forest resistance and resilience to climate change.

Disturbance increases tropical forest sensitivity to climate anomalies
and neighbourhood crowding

As expected, disturbed plots were more sensitive (i.e. showed greater tree growth reductions) to separate and joint effects of climate anomalies and neighbourhood crowding than undisturbed plots at Paracou (Fig. 3 a-d), highlighting the lasting impact of human activities such as logging and thinning on tropical forest dynamics (Hérault et al. 2010, Hérault and Piponiot 2018, Hiltner et al. 2021, Fargeon et al. 2016, Piponiot et al. 2016). Differences between disturbed and undisturbed plots in effect sizes of climate anomalies and neighbourhood crowding may originate from differences in tree community composition. Indeed, disturbed plots at Paracou harbor more fast-growing species (Mirabel et al. 2020, 2021), which tend to be more sensitive to climate anomalies, as shown in other tropical forests (Bauman et al. 2022a, Esquivel-Muelbert et al. 2020). The higher abundance of fast growing species and individuals on disturbed plots was also reflected in the higher community-level intrinsic growth rate ($\alpha_0$) of disturbed plots (Fig. S1 and Table S2). While higher sensitivity of disturbed communities to environmental drivers is consistent with expectations from the 'fast-slow' spectrum (Reich 2014), we show that this trend extends to increased susceptibility to biotic stressors such as neighbourhood crowding (Fig. 4 a-b, Table S4). In addition, greater canopy openness in disturbed plots may lead to more severe climate effects through tighter coupling of micro- and macro-climatic conditions (De Frenne et al. 2013, 2019, Ghuman and Lal 1987, Von Arx et al. 2012).
We further demonstrate that interactive effects of climate and neighbours (i.e. $\beta_{4,0}$) can shift from negative to positive depending on forest disturbance history at Paracou. In particular, denser neighbourhoods reinforced soil water stress in undisturbed plots, while they acted as a buffer against soil water stress in disturbed plots. This suggests that in disturbed plots, positive biotic interactions (e.g. complementary resource use between neighbours and direct or indirect facilitative effects of neighbouring trees) can mitigate competitive effects for soil water during drought. Disturbed plots typically have more and larger canopy gaps than undisturbed plots, and soil drying (and thus water availability) is controlled by evaporation from the soil surface in large forest gaps, but by root water extraction in the dense understory and small forest gaps (Marthews et al. 2008). In line with the stress gradient hypothesis, our results suggest that positive biotic interactions may become more important under more stressful environmental conditions at Paracou (Bertness and Callaway 1994, Brooker et al. 2007, Callaway 1995, Callaway and Walker 1997, Isbell et al. 2015).
Neutral crowding indices have recently been suggested to quantify silvicultural treatments (Yue et al. 2022), but in this study they were insufficient to capture all dimensions in disturbance history. Logging effects on forest communities involve not only alterations in forest structure, but also in forest composition. In addition, previous work in temperate and Mediterranean forests suggested that forest management via thinning may help to alleviate the negative impact of climate change on forest dynamics (Astigarraga et al. 2020, Gómez-Aparicio et al. 2011), but our study provides a more nuanced perspective in tropical forests where the interactive effects of neighbourhoods and climate may change from negative to positive depending on the specific climatic variable. Given that more than half of the world's tropical forests are designated production forests (Blaser et al. 2011), our results warrant closer examination of how logging and land use practices affect neighbourhood interactions and the resistance and resilience of tropical forests to future climate change (Hiltner et al. 2021)
We further demonstrate that interactive effects of climate and neighbours (i.e. $β_{4,0}$) can shift from negative to positive depending on forest disturbance history at Paracou. In particular, denser neighbourhoods reinforced soil water stress in undisturbed plots, while they acted as a buffer against soil water stress in disturbed plots. This suggests that in disturbed plots, positive biotic interactions (e.g. complementary resource use between neighbours and direct or indirect facilitative effects of neighbouring trees) can mitigate competitive effects for soil water during drought. Disturbed plots typically have more and larger canopy gaps than undisturbed plots, and soil drying (and thus water availability) is controlled by evaporation from the soil surface in large forest gaps, but by root water extraction in the dense understory and small forest gaps (Marthews et al. 2008). In line with the stress gradient hypothesis, our results suggest that positive biotic interactions may become more important under more stressful environmental conditions at Paracou (Bertness and Callaway 1994, Brooker et al. 2007, Callaway 1995, Callaway and Walker 1997, Isbell et al. 2015).
Neutral crowding indices have recently been suggested to quantify silvicultural treatments (Yue et al. 2022), but in this study they were insufficient to capture all dimensions in disturbance history. Logging effects on forest communities involve not only alterations in forest structure, but also in forest composition. In addition, previous work in temperate and Mediterranean forests suggested that forest management via thinning may help to alleviate the negative impact of climate change on forest dynamics (Astigarraga et al. 2020, Gómez-Aparicio et al. 2011), but our study provides a more nuanced perspective in tropical forests where the interactive effects of neighbourhoods and climate may change from negative to positive depending on the specific climatic variable. Given that more than half of the world's tropical forests are designated production forests (Blaser et al. 2011), our results warrant closer examination of how logging and land use practices affect neighbourhood interactions and the resistance and resilience of tropical forests to future climate change (Hiltner et al. 2021)
## 619 Traits mediate tree growth response to climate anomalies and neighbourhood crowding

620

In line with expectations from the 'fast-slow' economic spectrum (Reich 2014), high species intrinsic growth rates were related to acquisitive strategies for carbon and water (Fig. S1 B). Fast growing species had high SD and low δ¹⁸O, highlighting the key role of stomatal conductance and transpiration rates for efficient carbon assimilation (Drake et al. 2013, Hepworth et al. 2015, Jarvis 1981, Poorter and Bongers 2006, Wong et al. 1979). Fast-growing species also exhibited leaf and wood traits pertaining to acquisitive strategies such as lower Lₜhick, Lₜhough, and WSG, supporting previous findings that a species investment into tissue longevity comes at higher construction costs (Hérault et al. 2011, Philipson et al. 2014, Poorter et al. 2008, 2010, Reich 2014, Wright et al. 2010).

Focusing on ca. 100 species that make up >70% of total stems and basal area at Paracou, our study showed that the effects of climate on tree growth were mediated by different traits depending on the climate variable (Fig. 5 b-c). Leaf water-related traits overall mediated the negative effects of climate anomalies related to heat and drought stress. More specifically, species that prioritise water-conservation over carbon gain (e.g. low SD and high δ¹⁸O) were less sensitive to heat and atmospheric drought stress. These functional strategies reduced species' potential for fast growth, but may allow species to maintain photosynthesis and CO₂ assimilation during periods of elevated temperature and atmospheric evaporative demands (Caine et al. 2019, Hepworth et al. 2015, Reich 2014, Wang and Wen 2022a).

The mitigating effects of traits linked to carbon-water relations were less clear for tree growth response to soil water stress (CWD), potentially because individual topographic position may lead to a decoupling between growth sensitivity to soil water stress and water conservation capacity (Esteban et al. 2021, Hammond et al. 2022). Still, the growth of species with more negative π<sub>tlp</sub> was less impacted by soil drought stress. Lower π<sub>tlp</sub> (Table 1) captures species' drought-related mortality risks (Powell et al. 2017) and distribution along aridity gradients (Bartlett et al. 2012b), but previous work from different biomes found mixed evidence for species drought-related growth
619

## Traits mediate tree growth response to climate anomalies and neighbourhood crowding

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In line with expectations from the 'fast-slow' economic spectrum (Reich 2014), high species intrinsic growth rates were related to acquisitive strategies for carbon and water (Fig. S1 B). Fast growing species had high SD and low δ¹⁸O, highlighting the key role of stomatal conductance and transpiration rates for efficient carbon assimilation (Drake et al. 2013, Hepworth et al. 2015, Jarvis 1981, Poorter and Bongers 2006, Wong et al. 1979). Fast-growing species also exhibited leaf and wood traits pertaining to acquisitive strategies such as lower Lₜhick, Lₜhough, and WSG, supporting previous findings that a species investment into tissue longevity comes at higher construction costs (Hérault et al. 2011, Philipson et al. 2014, Poorter et al. 2008, 2010, Reich 2014, Wright et al. 2010).

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Focusing on ca. 100 species that make up >70% of total stems and basal area at Paracou, our study showed that the effects of climate on tree growth were mediated by different traits depending on the climate variable (Fig. 5 b-c). Leaf water-related traits overall mediated the negative effects of climate anomalies related to heat and drought stress. More specifically, species that prioritise water-conservation over carbon gain (e.g. low SD and high δ¹⁸O) were less sensitive to heat and atmospheric drought stress. These functional strategies reduced species' potential for fast growth, but may allow species to maintain photosynthesis and CO₂ assimilation during periods of elevated temperature and atmospheric evaporative demands (Caine et al. 2019, Hepworth et al. 2015, Reich 2014, Wang and Wen 2022a).

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The mitigating effects of traits linked to carbon-water relations were less clear for tree growth response to soil water stress (CWD), potentially because individual topographic position may lead to a decoupling between growth sensitivity to soil water stress and water conservation capacity (Esteban et al. 2021, Hammond et al. 2022). Still, the growth of species with more negative π<sub>tlp</sub> was less impacted by soil drought stress. Lower π<sub>tlp</sub> (Table 1) captures species' drought-related mortality risks (Powell et al. 2017) and distribution along aridity gradients (Bartlett et al. 2012b), but previous work from different biomes found mixed evidence for species drought-related growth
reductions (McGregor et al. 2021, Smith-Martin et al. 2023, Song et al. 2022).

While we predicted species with higher $g_{min}$ would suffer stronger drought-related growth declines (Blackman et al. 2016, Duursma et al. 2019), we found they were less vulnerable to atmospheric drought stress. As elevated VPD leads to stomatal closure (Grossiord et al. 2020), residual water loss could maintain evaporative cooling (given non limiting soil water conditions), which potentially prevents irreversible leaf tissue damage through overheating (Drake et al. 2018). The maintenance of evaporative cooling could thus allow species to quickly resume carbon assimilation after atmospheric drought stress, while simultaneously reducing post-drought respiration rates linked to tissue repair (Drake et al. 2018, Powell et al. 2017).

Leaf and wood morphological traits also captured tree growth response to climate anomalies at Paracou. Species with conservative tissues (i.e. lower SLA, higher $L_{thick}$ and $Bark_{thick}$) grew faster at higher solar radiation (Fig. 5 a). During atmospheric drought and heat stress, species with thicker and tougher leaves may experience less tissue damage through desiccation, thus reducing post-drought costs for tissue repair (Poorter et al. 2010, Nardini 2022), while species with larger leaves have higher evaporative cooling through transpiration (Gates 2003, Schuepp 1993, Wright et al. 2017), leading to less growth reductions. Moreover, species with high WSG were less vulnerable to heat stress, atmospheric and soil drought stress at Paracou, confirming previous findings (Greenwood et al. 2017, Wagner et al. 2014b).

Slow-growing, resource-conservative species tend to be more tolerant to competition at Paracou. In particular, species conservative for carbon (with higher $L_{though}$ and WSG) were less sensitive to neighbourhood crowding, as found in previous studies across forest biomes (Fortunel et al. 2016, Kunstler et al. 2016). In addition, species conservative for water (with lower SD, stomatal conductance and transpiration rates) were also less sensitive to neighbourhood crowding, highlighting the role of water limitation in these tropical wet forests. This is a similar suite of traits that mediated growth response to heat stress, suggesting that species suffering most from Tmax anomalies are also experiencing stronger neighbourhood effects (Table S4).

However, species traits poorly captured the interactive effects of climate anomalies and neighbour-
reductions (McGregor et al. 2021, Smith-Martin et al. 2023, Song et al. 2022).

While we predicted species with higher $g_{min}$ would suffer stronger drought-related growth declines (Blackman et al. 2016, Duursma et al. 2019), we found they were less vulnerable to atmospheric drought stress. As elevated VPD leads to stomatal closure (Grossiord et al. 2020), residual water loss could maintain evaporative cooling (given non limiting soil water conditions), which potentially prevents irreversible leaf tissue damage through overheating (Drake et al. 2018). The maintenance of evaporative cooling could thus allow species to quickly resume carbon assimilation after atmospheric drought stress, while simultaneously reducing post-drought respiration rates linked to tissue repair (Drake et al. 2018, Powell et al. 2017).

Leaf and wood morphological traits also captured tree growth response to climate anomalies at Paracou. Species with conservative tissues (i.e. lower SLA, higher $L_{thick}$ and $Bark_{thick}$) grew faster at higher solar radiation (Fig. 5 a). During atmospheric drought and heat stress, species with thicker and tougher leaves may experience less tissue damage through desiccation, thus reducing post-drought costs for tissue repair (Poorter et al. 2010, Nardini 2022), while species with larger leaves have higher evaporative cooling through transpiration (Gates 2003, Schuepp 1993, Wright et al. 2017), leading to less growth reductions. Moreover, species with high WSG were less vulnerable to heat stress, atmospheric and soil drought stress at Paracou, confirming previous findings (Greenwood et al. 2017, Wagner et al. 2014b).

Slow-growing, resource-conservative species tend to be more tolerant to competition at Paracou. In particular, species conservative for carbon (with higher $L_{though}$ and WSG) were less sensitive to neighbourhood crowding, as found in previous studies across forest biomes (Fortunel et al. 2016, Kunstler et al. 2016). In addition, species conservative for water (with lower SD, stomatal conductance and transpiration rates) were also less sensitive to neighbourhood crowding, highlighting the role of water limitation in these tropical wet forests. This is a similar suite of traits that mediated growth response to heat stress, suggesting that species suffering most from Tmax anomalies are also experiencing stronger neighbourhood effects (Table S4).

However, species traits poorly captured the interactive effects of climate anomalies and neighbour-
hood crowding on tree growth at Paracou (Fig. 5 a-d). Trait differences between neighbouring trees rather than the traits of the focal tree alone may be playing a key role in driving the inter-active effects of climate anomalies and neighbourhood crowding on tree growth, in particular as trait differences can reflect niche partitioning and competitive hierarchies between neighbouring individuals (Fortunel et al. 2016, Kunstler et al. 2016). To clarify this, future work could explore the relative contribution of focal tree traits and trait differences in mediating tree growth response to the interaction between climate extremes and neighbourhood crowding.

## Conclusion

Tropical trees are particularly sensitive to climate stress and interactions with neighbours, suggesting that ongoing climate change and associated changes in forest composition and structure may result in a long-term slowdown of carbon sequestration in tropical forests. Our study shows that climate anomalies and neighbourhood crowding can interact positively in shaping tropical tree growth, suggesting that trees growing in more crowded neighbourhood may be less sensitive to climate extremes. Logged forests suffered considerably stronger growth reductions in response to water stress, suggesting lower resistance of managed tropical forests to climate change. Our findings reveal that functional traits, in particular related to water relations, can capture differences in species sensitivities to atmospheric and soil water stress and neighbourhood crowding (though not their interactive effects), that can be implemented in models to improve predictions of the future of these highly diverse ecosystems.
hood crowding on tree growth at Paracou (Fig. 5 a-d). Trait differences between neighbouring trees rather than the traits of the focal tree alone may be playing a key role in driving the inter-active effects of climate anomalies and neighbourhood crowding on tree growth, in particular as trait differences can reflect niche partitioning and competitive hierarchies between neighbouring individuals (Fortunel et al. 2016, Kunstler et al. 2016). To clarify this, future work could explore the relative contribution of focal tree traits and trait differences in mediating tree growth response to the interaction between climate extremes and neighbourhood crowding.

## Conclusion

Tropical trees are particularly sensitive to climate stress and interactions with neighbours, suggesting that ongoing climate change and associated changes in forest composition and structure may result in a long-term slowdown of carbon sequestration in tropical forests. Our study shows that climate anomalies and neighbourhood crowding can interact positively in shaping tropical tree growth, suggesting that trees growing in more crowded neighbourhood may be less sensitive to climate extremes. Logged forests suffered considerably stronger growth reductions in response to water stress, suggesting lower resistance of managed tropical forests to climate change. Our findings reveal that functional traits, in particular related to water relations, can capture differences in species sensitivities to atmospheric and soil water stress and neighbourhood crowding (though not their interactive effects), that can be implemented in models to improve predictions of the future of these highly diverse ecosystems.
692 Acknowledgements

693 We thank the many colleagues who participated in field and lab work for trait data collection in
694 French Guiana and at the University of Vienna, especially Coralie Dalban-Pilon, Jocelyn Cazal,
695 Stéphane Fournier, Jean-Yves Goret, Paul Mischler, Gaëlle Jaouen, Laetitia Proux, Camille Girard-
696 Tercieux, Jeanne Clément, Ghislain Vieilledent and Sylvain Schmitt. We thank the CIRAD field-
697 work team for the tree inventory, and Pascal Petronelli, Giacomo Sellan and Julien Engel for
698 botanical identification. The modeling work has been realised with the support of Meso@LR-
699 Platform at the University of Montpellier. We thank the Meso@LR team, in particular Bertrand
700 Pitollat, and Philippe Verley for technical support. We thank Emmanuel Delande for his assistance
701 with LATEX. We thank Charlotte Grossiord, Raphaël Pélissier, Stefan Dullinger and Marie-Pierre
702 Etienne for helpful discussions.

703 Funding

704 Daniela Nemetschek was supported by a PhD grant co-funded by CIRAD and ‘Centre d’Etude de
705 la Biodiversité Amazonienne’, an ‘Investissements d’Avenir’ grant managed by Agence Nationale
706 de la Recherche (CEBA, ref. ANR-10-LABX- 25-01). David Bauman was funded by the European
707 Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant
708 agreement (no. 895799). This work has benefited from a grant (ManagForRes project) from
709 Office Français de la Biodiversité (OFB) and ‘Centre Méditerranéen de l’Environnement et de la
710 Biodiversité’, an ‘Investissements d’Avenir’ grant managed by Agence Nationale de la Recherche
711 (CeMEB, ref. ANR-10-LABX-04-01). This work was also supported by the Institut de Recherche
712 pour le Développement (IRD).

713 Author Contributions

714 Daniela Nemetschek, Claire Fortunel and Géraldine Derroire designed the study. Daniela Nemetschek,
715 Marion Boisseaux, Johanna Auer, Vincyane Badouard, Christopher Baraloto, Quentin Le Blaye,
692 Acknowledgements

693 We thank the many colleagues who participated in field and lab work for trait data collection in
694 French Guiana and at the University of Vienna, especially Coralie Dalban-Pilon, Jocelyn Cazal,
695 Stéphane Fourtier, Jean-Yves Goret, Paul Mischler, Gaëlle Jaouen, Laetitia Proux, Camille Girard-
696 Tercieux, Jeanne Clément, Ghislain Vieilledent and Sylvain Schmitt. We thank the CIRAD field-
697 work team for the tree inventory, and Pascal Petronelli, Giacomo Sellan and Julien Engel for
698 botanical identification. The modeling work has been realised with the support of Meso@LR-
699 Platform at the University of Montpellier. We thank the Meso@LR team, in particular Bertrand
700 Pitollat, and Philippe Verley for technical support. We thank Emmanuel Delande for his assistance
701 with LATEX. We thank Charlotte Grossiord, Raphaël Pélissier, Stefan Dullinger and Marie-Pierre
702 Etienne for helpful discussions.

703 Funding

704 Daniela Nemetschek was supported by a PhD grant co-funded by CIRAD and ‘Centre d’Etude de
705 la Biodiversité Amazonienne’, an ‘Investissements d’Avenir’ grant managed by Agence Nationale
706 de la Recherche (CEBA, ref. ANR-10-LABX- 25-01). David Bauman was funded by the European
707 Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant
708 agreement (no. 895799). This work has benefited from a grant (ManagForRes project) from
709 Office Français de la Biodiversité (OFB) and ‘Centre Méditerranéen de l’Environnement et de la
710 Biodiversité’, an ‘Investissements d’Avenir’ grant managed by Agence Nationale de la Recherche
711 (CeMEB, ref. ANR-10-LABX-04-01). This work was also supported by the Institut de Recherche
712 pour le Développement (IRD).

713 Author Contributions

714 Daniela Nemetschek, Claire Fortunel and Géraldine Derroire designed the study. Daniela Nemetschek,
715 Marion Boisseaux, Johanna Auer, Vincyane Badouard, Christopher Baraloto, Quentin Le Blaye,
Damien Bonal, Sabrina Coste, Elia Dardevet, Claire Fortunel, Patrick Heuret, Sébastien Levionnois, Isabelle Maréchaux, Clément Stahl, Jason Vleminckx, Wolfgang Wanek and Camille Ziegler collected or contributed trait data. Daniela Nemetschek formatted and vetted the plot census, climate and functional trait data, with help from Claire Fortunel, Géraldine Derroire, Eric Marcon and Marion Boisseaux. Daniela Nemetschek, Claire Fortunel and Géraldine Derroire designed the tree growth models, with input from Eric Marcon, David Bauman, Sean M. McMahon and Mélaine Aubry-Kientz. Daniela Nemetschek performed the analyses, with help from Claire Fortunel, Géraldine Derroire and Eric Marcon. Daniela Nemetschek and Claire Fortunel led the interpretation of the results and the writing of the first draft, with frequent input from Géraldine Derroire. All authors contributed to revisions and gave final approval for publication.

726

## Data Availability Statement

Plot census data was extracted from the Paracou Station database, for which access is available at https://dataverse.cirad.fr/dataverse/paracou, with corresponding DOIs: 10.18167/DVN1/NSCWF0; 10.18167/DVN1/Q8V2YI; 10.18167/DVN1/LIVCEK; 10.18167/DVN1/HWTD4U; 10.18167/DVN1/HIGNWQ. Species mean values for the six leaf water-related traits are available at 10.5281/zenodo.10396927

732

## Conflict of interest

The authors declare there are no competing interests.
Damien Bonal, Sabrina Coste, Elia Dardevet, Claire Fortunel, Patrick Heuret, Sébastien Levionnois, Isabelle Maréchaux, Clément Stahl, Jason Vleminckx, Wolfgang Wanek and Camille Ziegler collected or contributed trait data. Daniela Nemetschek formatted and vetted the plot census, climate and functional trait data, with help from Claire Fortunel, Géraldine Derroire, Eric Marcon and Marion Boisseaux. Daniela Nemetschek, Claire Fortunel and Géraldine Derroire designed the tree growth models, with input from Eric Marcon, David Bauman, Sean M. McMahon and Mélaine Aubry-Kientz. Daniela Nemetschek performed the analyses, with help from Claire Fortunel, Géraldine Derroire and Eric Marcon. Daniela Nemetschek and Claire Fortunel led the interpretation of the results and the writing of the first draft, with frequent input from Géraldine Derroire. All authors contributed to revisions and gave final approval for publication.

## Data Availability Statement

Plot census data was extracted from the Paracou Station database, for which access is available at https://dataverse.cirad.fr/dataverse/paracou, with corresponding DOIs: 10.18167/DVN1/NSCWF0; 10.18167/DVN1/Q8V2YI; 10.18167/DVN1/LIVCEK; 10.18167/DVN1/HWTD4U; 10.18167/DVN1/HIGNWQ. Species mean values for the six leaf water-related traits are available at 10.5281/zenodo.10396927

## Conflict of interest

The authors declare there are no competing interests.
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