Daniela Nemetschek
email: daniela.nemetschek@gmail.com

Géraldine Derroire

Eric Marcon

Mélaine Aubry-Kientz

Johanna Auer

Vincyane Badouard

Christopher Baraloto

David Bauman

Quentin Le Blaye

Marion Boisseaux

Damien Bonal

Sabrina Coste

Elia Dardevet

Patrick Heuret

Peter Hietz

Sébastien Levionnois

Isabelle Maréchaux

Sean M Mcmahon

Clément Stahl

Jason Vleminckx

Wolfgang Wanek

Camille Ziegler

Claire Fortunel

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

Keywords: climate change, drought stress, functional traits, tropical forest, tree growth, water relations changement climatique, stress hydriqyue, traits fonctionnels, forêt tropicale, croissance des arbres, relations hydriques

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.

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 forêtsnon-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

Introduction

Tropical forests are biodiversity hotspots (Gatti et al. 2022[START_REF] Pillay | Tropical forests are home to over half of the world's vertebrate species[END_REF]) and major contributors to global carbon and water cycles [START_REF] Needham | Inferring forest fate from demographic data: from vital rates to population dynamic models[END_REF][START_REF] Pan | A large and persistent carbon sink in the world's forests[END_REF]), but the services they provide to human populations are threatened by climate change and anthropogenic disturbance [START_REF] Brodribb | Hanging by a thread? Forests and drought[END_REF][START_REF] Malhi | Climate Change, Deforestation, and the Fate of the Amazon[END_REF][START_REF] Nunes | Linking land-use and land-cover transitions to their ecological impact in the Amazon[END_REF][START_REF] Vancutsem | monitoring of forest cover changes in the humid tropics[END_REF]. 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[START_REF] Rifai | ENSO Drives interannual variation of forest woody growth across the tropics[END_REF]. As climate change is predicted to result in higher frequencies, intensities, and duration of extreme droughts in the tropics [START_REF] Shukla | IPCC: Climate Change 2022: Mitigation of Climate Change Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF], 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 [START_REF] Dawkins | The volume increment of natural tropical high-forest and limitations on its improvements[END_REF][START_REF] Gourlet-Fleury | Modelling diameter increment in a lowland evergreen rain forest in French Guiana[END_REF][START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF][START_REF] Uriarte | A neighborhood analysis of tree growth and survival in a hurricane-driven tropical forest[END_REF], 2010), the net effects of neighbourhood interactions may change from negative to positive under specific environmental conditions [START_REF] Grossiord | Having the right neighbors: how tree species diversity modulates drought impacts on forests[END_REF][START_REF] Fichtner | Neighbourhood diversity mitigates drought impacts on tree growth[END_REF]) such as with water levels [START_REF] Aakala | The roles of competition and climate in tree growth variation in northern boreal old-growth forests[END_REF]) 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 [START_REF] Bottero | Density-dependent vulnerability of forest ecosystems to drought[END_REF][START_REF] Gómez-Aparicio | Disentangling the relative importance of climate, size and competition on tree growth in Iberian forests: implications for forest management under global change[END_REF][START_REF] Astigarraga | Evidence of non-stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests[END_REF]. 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[START_REF] Haberstroh | The role of species interactions for forest resilience to drought[END_REF]). 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 histories.

More than half of the world's tropical forests are designated production forests [START_REF] Blaser | Status of Tropical Forest Management[END_REF], and forest management such as selective logging and thinning has long-lasting effects on forest composition, diversity and structure [START_REF] Clark | Tree species richness and the logging of natural forests: A metaanalysis[END_REF][START_REF] Cannon | The structure of lowland rainforest after selective logging in West Kalimantan, Indonesia[END_REF], Finegan and Camacho 1999[START_REF] Mirabel | 30 Years of postdisturbance recruitment in a Neotropical forest[END_REF][START_REF] Osazuwa-Peters | Selective logging: does the imprint remain on tree structure and composition after 45 years?[END_REF], which may alter forest resistance and resilience to extreme climate events [START_REF] Hiltner | Climate change alters the ability of neotropical forests to provide timber and sequester carbon[END_REF]. Specifically, disturbed forests host more light-demanding tree species than old-growth forests [START_REF] Carreño-Rocabado | Effects of disturbance intensity on species and functional diversity in a tropical forest[END_REF][START_REF] Baraloto | Contrasting taxonomic and functional responses of a tropical tree community to selective logging[END_REF][START_REF] Kuusipalo | Population dynamics of tree seedlings in a mixed dipterocarp rainforest before and after logging and crown liberation[END_REF][START_REF] Mirabel | Diverging taxonomic and functional trajectories following disturbance in a Neotropical forest[END_REF][START_REF] Core | R: A language and environment for statistical computing[END_REF][START_REF] Whitmore | Canopy Gaps and the Two Major Groups of Forest Trees[END_REF], and these fast-growing species may be more sensitive to climate extremes (Bauman et al. 2022a, Uriarte et al. 2016b) and neighbourhood crowding [START_REF] Fortunel | Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest[END_REF]. Moreover, selective logging and thinning creates gaps, thereby increasing the openness of forest canopies and changing micro-climatic conditions [START_REF] Ghuman | Effects of partial clearing on microclimate in a humid tropical forest[END_REF]Lal 1987, Peña-Claros et al. 2008). These logging gaps influence tree growth rates [START_REF] Baraloto | Performance trade-offs among tropical tree seedlings in contrasting microhabitats[END_REF], Hérault et al. 2010, Peña-Claros et al. 2008[START_REF] Silva | Growth and yield of a tropical rain forest in the Brazilian Amazon 13 years after logging[END_REF], Finegan et al. 1999), but it remains unclear how disturbance history may influence tree growth responses to climate and neighbours.

Species differences in growth sensitivity to climate and neighbours likely depend on their functional strategies. While commonly measured traits pertaining to light capture, nutrient and carbon use such as specific leaf area and wood density can capture species differences in tree growth response to neighbourhood crowding [START_REF] Fortunel | Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest[END_REF][START_REF] Kunstler | Plant functional traits have globally consistent effects on competition[END_REF], they are not always good proxies of species sensitivity to drought [START_REF] Anderegg | Hydraulic diversity of forests regulates ecosystem resilience during drought[END_REF][START_REF] Powell | Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees[END_REF], Powers et al. 2020, Wagner et al. 2014b, but see: Uriarte et al. 2016a[START_REF] Greenwood | Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area[END_REF][START_REF] Serra-Maluquer | Wood density and hydraulic traits influence species' growth response to drought across biomes[END_REF]. A promising way forward is to integrate leaf traits that directly link to plant carbonwater relations or physiological responses to heat and water stress [START_REF] Anderegg | Hydraulic diversity of forests regulates ecosystem resilience during drought[END_REF][START_REF] Barros | Hydraulic traits explain differential responses of Amazonian forests to the 2015 El Niño-induced drought[END_REF][START_REF] Mcgregor | Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest[END_REF][START_REF] Powell | Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees[END_REF], Powers et al. 2020[START_REF] Rowland | Plant traits controlling growth change in response to a drier climate[END_REF][START_REF] Serra-Maluquer | Wood density and hydraulic traits influence species' growth response to drought across biomes[END_REF]) (see: Table 1). For instance, stomatal density [START_REF] Drake | faster stomata: scaling of stomatal size, rate of response, and stomatal conductance[END_REF][START_REF] Machado | Where do leaf water leaks come from? Trade-offs underlying the variability in minimum conductance across tropical savanna species with contrasting growth strategies[END_REF]) and oxygen and carbon isotope composition [START_REF] Farquhar | Carbon Isotope Discrimination and Photosynthesis[END_REF], 2007[START_REF] Moreno-Gutiérrez | Isotopes reveal contrasting water use strategies among coexisting plant species in a Mediterranean ecosystem[END_REF]) can determine trade-offs between carbon assimilation and water loss at the leaf level under water-limited conditions [START_REF] Guerrieri | Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency[END_REF][START_REF] Prieto | Leaf carbon and oxygen isotopes are coordinated with the leaf economics spectrum in Mediterranean rangeland species[END_REF], 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[START_REF] Maréchaux | Dry-season decline in tree sapflux is correlated with leaf turgor loss point in a tropical rainforest[END_REF]. Leaf minimum conductance [START_REF] Blackman | Drought response strategies and hydraulic traits contribute to mechanistic understanding of plant dry-down to hydraulic failure[END_REF][START_REF] Duursma | On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls[END_REF][START_REF] Machado | Where do leaf water leaks come from? Trade-offs underlying the variability in minimum conductance across tropical savanna species with contrasting growth strategies[END_REF]) and leaf saturated water content [START_REF] Blackman | Drought response strategies and hydraulic traits contribute to mechanistic understanding of plant dry-down to hydraulic failure[END_REF][START_REF] Luo | Leaf Water Storage and Robustness to[END_REF]) 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 [START_REF] Gourlet-Fleury | Ecology and management of a neotropical rainforest: lessons drawn from Paracou, a long-term experimental research site in French Guiana[END_REF]). 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 neighbourhood crowding in disturbed forests as they host more fast-growing species that may be more vulnerable to both abiotic and biotic stressors.

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

Materials and Methods

Study site

Our analyses used the permanent forest plot system of the CIRAD experimental site at the Paracou Tropical Forest Research Station (5°18'N, 52°53'W) in French Guiana. Mean annual precipitation at Paracou is 3041 mm yr -1 , with a pronounced dry season (< 100 mm month -1 ) from August through November. Paracou is characterised as a tropical lowland forest site consisting of oldgrowth terra firme forest. Fifteen permanent forest plots of 6.25 ha each were established from 1984 to 1990, of which nine plots were subjected to different types (selective logging and thinning) and intensities of sylvicultural treatments (Fig. 1), resulting in above-ground biomass (AGB) loss (12-33% AGB loss in the three plots subjected to selective logging and 33-56% AGB loss in the six plots that were subjected to selective logging and thinning) (Gourlet-Fleury et al. 2004, Hérault and[START_REF] Hérault | Key drivers of ecosystem recovery after disturbance in a neotropical forest[END_REF] and differences in species composition [START_REF] Mirabel | Diverging taxonomic and functional trajectories following disturbance in a Neotropical forest[END_REF][START_REF] Mirabel | 30 Years of postdisturbance recruitment in a Neotropical forest[END_REF]. In the 2021 inventory, there were 70,238 trees belonging to 591 species (including subspecies), 227 genera and 63 families.

Tree growth and neighbourhood data

In each of the 15 forest plots, all trees ≥ 10 cm DBH (diameter at breast height, i.e. 1.3 m) were mapped to a precision of 0.5 m and were botanically identified. Tree inventories took place at 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 [START_REF] Gourlet-Fleury | Ecology and management of a neotropical rainforest: lessons drawn from Paracou, a long-term experimental research site in French Guiana[END_REF], 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 = DBH i,s,t -DBH i,s,t-2 t -(t -2) (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 (DBH 2 ) and the inverse of their distance to the focal tree (d) [START_REF] Canham | A neighborhood analysis of canopy tree competition: effects of shading versus crowding[END_REF][START_REF] Fortunel | Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest[END_REF][START_REF] Uriarte | Trait similarity, shared ancestry and the structure of neighbourhood interactions in a subtropical wet forest: implications for community assembly[END_REF]. For each focal tree individual i at the start of the growth census interval t -2, we calculated its neighbourhood crowding index (NCI i,t-2 ) as the weighted sum of all neighbours N (i) within a 10 m radius of individual i [START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF][START_REF] Lasky | Trait-mediated assembly processes predict successional changes in community diversity of tropical forests[END_REF], Uriarte et al. 2016a), as:

NCI i,t-2 = ∑ j∈N (i) DBH 2 j,t-2 d i,j (eqn 2)
We chose a 10 m radius to be able to compare with previous studies conducted in tropical forests [START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF][START_REF] Lasky | Trait-mediated assembly processes predict successional changes in community diversity of tropical forests[END_REF], Uriarte et al. 2016a), and well capture neighbourhood effects while keeping edge effects low [START_REF] Zambrano | The scale dependency of trait-based tree neighborhood models[END_REF]. 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 evapotranspiration (Bauman et al. 2022a, Choat et al. 2018[START_REF] Cunningham | Photosynthetic responses to vapour pressure deficit in temperate and tropical evergreen rainforest trees of Australia[END_REF][START_REF] Doughty | Are tropical forests near a high temperature threshold[END_REF], Grossiord et al. 2020[START_REF] Wagner | Water Availability Is the Main Climate Driver of Neotropical Tree Growth[END_REF]. We extracted climate variables from TerraClimate, a high-resolution global data set of monthly climate variables and climatic water balance [START_REF] Abatzoglou | a highresolution global dataset of monthly climate and climatic water balance from 1958-2015[END_REF].

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, µ m,baseline ) and standard deviation (σ m,baseline ) for each month of the year (1)(2)(3)[START_REF] Loram-Lourenço | Variations in bark structural properties affect both water loss and carbon economics in neotropical savanna trees in the Cerrado region of Brazil[END_REF](5)(6)(7)(8)(9)(10)(11)(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 µ m,t from the long-term monthly baseline µ m,baseline (Bauman et al. 2022a[START_REF] Rifai | ENSO Drives interannual variation of forest woody growth across the tropics[END_REF]. 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 [START_REF] Aubry-Kientz | Identifying climatic drivers of tropical forest dynamics[END_REF], Bauman et al. 2022a[START_REF] Rifai | ENSO Drives interannual variation of forest woody growth across the tropics[END_REF]:

CA t = 1 M M ∑ m=1 µ m,t -µ m,baseline σ m,baseline (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 (δ 18 O) and carbon (δ 13 C) stable isotope composition, water potential at turgor loss point (π 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 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5 2 0 1 7 2 0 1 9 2 0 2 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5 2 0 1 7 2 0 1 9 2 0 2 represent 70% of all unique individual stems and 72% of all growth measurements at Paracou. We randomly-selected 645 individuals located within the permanent 15 forest plots at Paracou, using the following criteria: (i) botanical identification at the species level, (ii) DBH within speciesspecific 10th and 90th percentile (to avoid sampling unusually small and large tree individuals), (iii) topographic position corresponding with the species topographic preference (i.e. individuals located at a certain topographic position (bottomland, slope, ridge) were only sampled if more than 10% of distinct stems of that species occur on the given topographic position). As the 102 target species include understory, mid-story and canopy species, we sampled only leaves from branches that were exposed to medium light conditions (Dawkins index: 3-4, Alder and Sunnott 1992), thus excluding branches in full sun or shade. For each of the 645 individuals, we collected one fully expanded and healthy leaf from a healthy branch in the dry season in 2020-2021 for each leaf trait measurement. After harvest with a sling shot in the field, leaves were immediately put into ziplock bags with a moist paper towel and kept in the dark in a cooler to minimize transpiration during transport to the lab. No fieldwork permit was required.

To measure leaf SD, we used nail polish or silicon-based dental paste (Speedex Universal Silicon the abaxial leaf surface at three positions of the widest part of the leaf blade, while avoiding major veins [START_REF] Voleníková | Insertion Profiles in Stomatal Density and Sizes in Nicotiana Tabacum L. Plantlets[END_REF]. Imprints were mounted on microscope slides, and pictures were taken at a magnification of 300x using a light microscope (Keyence VHX 7000, Osaka, Japan). We took a minimum of 3 pictures, 1 per imprint when possible, for each sampled leaf. We used Fiji ImageJ [START_REF] Schindelin | Fiji: an open-source platform for biological-image analysis[END_REF] to count all complete stomata per image area. Imprint quality and resulting picture quality varied strongly between species and individuals, so we obtained stomata counts for 470 individuals and 87 species.

Leaf osmotic potential was measured using a vapor pressure osmometer (VAPRO 5520, Wescor, Logan, UT, USA). Leaf water potential at turgor loss point (π tlp ) was linearly estimated from osmotic potential at full hydration, following the standard protocol of Bartlett et al. (2012b,a), which has been validated for species in French Guiana [START_REF] Maréchaux | Causes of variation in leaflevel drought tolerance within an Amazonian forest[END_REF]).

Leaf g min was estimated from leaf drying curves. We monitored leaf weight loss over time, following the protocol of [START_REF] Sack | The 'hydrology' of leaves: co-ordination of structure and function in temperate woody species[END_REF]. Time intervals for weight measurements were set to 30 min.

Leaves of tropical species loose water very rapidly, so that consecutive measurements over 3 to 4 h were sufficient to assess minimum conductance.

We measured leaf saturated and dry weights with a digital scale at a 0.0001 g precision (Mettler Toledo, Switzerland). We obtained saturated weights following the 'partial' rehydration protocol of [START_REF] Vendramini | Leaf traits as indicators of resource-use strategy in floras with succulent species[END_REF], which provides comparable measurements to the 'complete' rehydration protocol by [START_REF] Garnier | A standardized protocol for the determination of specific leaf area and leaf dry matter content[END_REF] [START_REF] Vaieretti | Two measurement methods of leaf dry matter content produce similar results in a broad range of species[END_REF]). We wrapped fresh leaves into paper towels that were previously soaked with de-ionised water, and kept them for 24 hours in the dark and at low temperature (4 • C) for rehydration. Dry weights were measured after drying leaves for at least 72 hours at 60 • C (Sapes and Sala 2021). We calculated LSWC from saturated and dry weights [START_REF] Barrs | A Re-Examination of the Relative Turgidity Technique for Estimating Water Deficits in Leaves[END_REF].

Bulk leaf δ 18 O and δ 13 C were calculated from a composite sample of dried and coarsely chopped leaves, including those used for π tlp , LSWC and g min measurements. Samples were ground to fine powder using a ball mill (Retsch MM200, Hanau, Germany) for five minutes at maximum 0.2 -0.4 mg per sample were weighed into silver capsules and δ 18 O 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 δ 13 C 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 [START_REF] Levionnois | Is vulnerability segmentation at the leaf-stem transition a drought resistance mechanism? A theoretical test with a trait-based model for Neotropical canopy tree species[END_REF][START_REF] Maréchaux | Drought tolerance as predicted by leaf water potential at turgor loss point varies strongly across species within an Amazonian forest[END_REF], 2019[START_REF] Ziegler | Large hydraulic safety margins protect Neotropical canopy rainforest tree species against hydraulic failure during drought[END_REF].

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 [START_REF] Baraloto | Decoupled leaf and stem economics in rain forest trees[END_REF], Fortunel et al. 2012[START_REF] Vleminckx | Resolving whole-plant economics from leaf, stem and root traits of 1467 Amazonian tree species[END_REF]. We included leaf area (LA), specific leaf area (SLA), leaf thickness (L thick ), leaf toughness (L though ), bark thickness (Bark thick ) 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. Note: For a given trait N indicates the number of species for which we had available trait information.

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[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF][START_REF] Hérault | Functional traits shape ontogenetic growth trajectories of rain forest tree species[END_REF]). To deal with negative and zero growth before taking the logarithm, we added to each observed growth value the constant 1.1|AGR min |, where |AGR min | is the absolute minimum value of observed growth of the dataset (Bauman et al. 2022a).

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 sensitivities 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 γ p and individuals ϵ i (eqn 4f) (Bauman et al. 2022a[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF].

We modeled individual log(AGR t ) as a linear function of (i) tree size at the beginning of the census interval (DBH t-2 ), (ii) monthly climate anomalies averaged over the census interval (CA t ), (iii) neighbourhood crowding at the beginning of the census interval (NCI t-2 ) and (iv) the interaction between climate anomalies and neighbourhood crowding (CA t × NCI t-2 ) (Bauman et al. 2022a[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF]. 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 climate anomalies (Bauman et al. 2022a), were standardised to mean zero and unit standard deviation (Bauman et al. 2022a[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF][START_REF] Mcelreath | Statistical rethinking: A Bayesian course with examples in R and Stan[END_REF], Uriarte et al. 2016a). As we assumed tree growth to have a non-linear, multiplicative relationship with DBH and NCI [START_REF] Canham | A neighborhood analysis of canopy tree competition: effects of shading versus crowding[END_REF][START_REF] Fortunel | Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest[END_REF][START_REF] Uriarte | Trait similarity, shared ancestry and the structure of neighbourhood interactions in a subtropical wet forest: implications for community assembly[END_REF], we log transformed DBH and NCI prior to standardisation to achieve an additive and linear relationship [START_REF] Kunstler | Plant functional traits have globally consistent effects on competition[END_REF], Uriarte et al. 2016a[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF]. 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 [START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF], 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 [START_REF] Ren | Tree growth response to soil nutrients and neighborhood crowding varies between mycorrhizal types in an old-growth temperate forest[END_REF]. For each individual i of species s between censuses t -2

and t, we modeled tree growth as:

log(AGR i,s,t,p ) ∼ N (µ i,s,t,p , σ) (eqn 4a) µ i,s,t,p = α s + β 1s × log(DBH i,t-2 ) + β 2s × CA t + β 3s × log(NCI i,t-2 ) + β 4s × CA t × log(NCI i,t-2 ) + γ p + ϵ i (eqn 4b)             α s β 1s β 2s β 3s β 4s             ∼ M V N ormal                         α 0 β 1,0 β 2,0 β 3,0 β 4,0             , S             (eqn 4c) S =             σ αs 0 0 0 0 0 σ β 1s 0 0 0 0 0 σ β 2s 0 0 0 0 0 σ β 3s 0 0 0 0 0 σ β 4s             × R ×             σ αs 0 0 0 0 0 σ β 1s 0 0 0 0 0 σ β 2s 0 0 0 0 0 σ β 3s 0 0 0 0 0 σ β 4s             (eqn 4d) R =             1 ρ αs,β 1s ρ αs,β 2s ρ αs,β 3s ρ αs,β 4s ρ αs,β 1s 1 ρ β 1s ,β 2s ρ β 1s ,β 3s ρ β 1s ,β 4s ρ αs,β 2s ρ β 1s ,β 2s 1 ρ β 2s ,β 3s ρ β 2s ,β 4s ρ αs,β 3s ρ β 1s ,β 3s ρ β 2s ,β 3s 1 ρ β 3s ,β 4s ρ αs,β 4s ρ β 1s ,β 4s ρ β 2s ,β 4s ρ β 3s ,β 4s 1             (eqn 4e)
using the following weakly informative priors:

γ p ∼ N (0, σ γ ) ϵ i ∼ N (0, σ ϵ ) α 0 , β 1-4,0 ∼ N (0, 0.5) σ αs , σ β 1-4s , σ γ , σ ϵ , σ ∼ exp(1)
R ∼ LKJcorr( 2) Species intrinsic growth α s and growth response to covariates β 1-4s for the s species were modeled as in eqn 4c, where α 0 represents the community-level intrinsic growth rate and β 1-4,0 the overall effect of covariates on growth across all species. To assess correlations (ρ) among species-level intrinsic growth α s and growth response to model covariates β 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[START_REF] Mcelreath | Statistical rethinking: A Bayesian course with examples in R and Stan[END_REF]. 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 γ p and the i individuals ϵ 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.

(

M2: "trait models"

To assess the effect of traits on species intrinsic growth α s , and growth response to climate anomalies β 2s , neighbourhood crowding β 3s and their joint effects β 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 4d-eqn 4f), all species-level parameters, except species-level growth response to tree size β 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) [START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF], Uriarte et al. 2016a, Bauman et al. 2022a). More specifically, species-level parameters were modelled as:

            α s β 1s β 2s β 3s β 4s             ∼ M V N                         α 0 + α 1 × T rait s β 1,0 β 2,0 + β 2,1 × T rait s β 3,0 + β 3,1 × T rait s β 4,0 + β 4,1 × T rait s             , S             (eqn 5)
where α 0 represents the community-level intrinsic growth (i.e. grand intercept) and β 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. α 1 and β 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 T rait s of species s. Traits were standardised to mean zero and unit standard deviation across species, thus implying that parameter β 2-4s equals β 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 [START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF]. Species AGR responses to model covariates can thus be accentuated (i.e. the sign of the trait effect β 2-4,1 is the same as that of the corresponding community level response β 2-4,0 )

or attenuated (i.e. the sign of the trait effect β 2-4,1 is the opposite of that of the corresponding community level response β 2-4,0 ) with increasing values of T rait s (Bauman et al. 2022a[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF], Uriarte et al. 2016a).

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 [START_REF] Bürkner | brms: An R Package for Bayesian Multilevel Models Using Stan[END_REF]. Bayesian updating of parameters was performed via the No-U-Turn Sampler (NUTS) in Stan [START_REF] Carpenter | A Probabilistic Programming Language[END_REF]) using CmdStanR (Stan Development Team 2022), which allows intra-chain parallelisation in the backend. 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 parameter posteriors were summarised through their median and 90%-highest posterior density interval (HPDI) using the packages tidyverse [START_REF] Wickham | Welcome to the tidyverse[END_REF]) and tidybayes [START_REF] Kay | tidybayes: Tidy Data and Geoms for Bayesian Models[END_REF]. 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 [START_REF] Bürkner | brms: An R Package for Bayesian Multilevel Models Using Stan[END_REF], which calculates a Bayesian version of R2 for regression models [START_REF] Gelman | R-squared for Bayesian Regression Models[END_REF]). 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.

Results

Tree growth sensitivity to climate anomalies and neighbourhood crowding

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. S2). The interpretation of β 4,0 depends on the direction of the climate effect β 2,0 . If the sign of β 4,0 is the same as the sign of β 2,0 , neighbourhood crowding accentuates the effect of climate, inversely if the sign of β 4s is in the opposite of the sign of β 2,0 , neighbourhood crowding attenuates the effect of climate. Community-level intercepts (α 0 ) and tree size effects (DBH, β 1,0 ) are shown in Fig. S1 A.

Tree growth sensitivity to climate anomalies (β 2s ) and neighbourhood crowding (β 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 (β 4s ) in SRad, Tmax and CWD, but less in VPD. Specifically, neighbourhood crowding clearly attenuated positive effects (negative β 4s ) of SRad anomalies in 18%, and negative effects (positive β 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 β 4s ) or reinforcing (19% of negative β 4s ) these effects. For details see Table S3.

Species-level intrinsic AGR (M1, α s ) were negatively associated with AGR response to anomalies (M1, β 2s ), as shown by the median ρ, lower 5% quantile q 5 (ρ) and upper 5% quantile q 95 (ρ) of the highest posterior density, in SRad (ρ = -0.20, q 5 (ρ) = -0.32, q 95 (ρ) = -0.09), Tmax (ρ = -0.69, q 5 (ρ) = -0.76, q 95 (ρ) = -0.62), VPD (ρ = -0.59, q 5 (ρ) = -0.66, q 95 (ρ) = -0.52) and CWD (ρ = -0.55, q 5 (ρ) = -0.65, q 95 = -0.46) as well as neighbourhood crowding (ρ = -0.43, q 5 (ρ) = -0.51, q 95 (ρ) = -0.33, mean across climate models). Tree growth sensitivity to neighbourhood crowding (M1, β 3s ) was negatively associated with the response to SRad anomalies (ρ = -0.17, q 5 (ρ) = -0.30, q 95 (ρ) = -0.04) and positively with the response to anomalies in Tmax (ρ = 0.26, q 5 (ρ) = 0.13, q 95 (ρ) = 0.37) and CWD (ρ = 0.28, q 5 (ρ) = 0.15, q 95 (ρ) = 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, β 2,0 ) and neighbourhood crowding (M1, β 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 However, while negative effects of CWD anomalies on growth were mitigated by neighbourhood crowding in disturbed plots, they were reinforced in undisturbed plots (Fig. 3).

Although species intrinsic growth rates varied slightly between undisturbed and disturbed plots (Fig. S2), correlations between species intrinsic growth rates (M1, intercept α s ) and their growth response to climate anomalies (M1, β 2s ) and neighbourhood crowding (M1, β 3s ) were overall consistent between undisturbed and disturbed plots (Fig. 4 a-b). However, while species that suffered stronger growth declines due to neighbourhood crowding (M1, β 3s ) were also more sensitive to positive CWD anomalies (M1, β 2s ) in disturbed plots, we found no clear relationship in undisturbed plots (Fig. 4 c). For details on correlations of species-level parameters see: Table S4. Trait effects on tree growth response to climate anomalies and neigh-

bourhood crowding

Parameter estimates of community-level tree growth regressions were consistent between the M1models (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, α s ) had higher SD, but lower δ 18 O, LA, L thick , L 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 β 2-4,1 moved β 2-4s further away from zero with increasing trait value) or attenuating them (values of β 2-4,1 moved β 2-4s closer to zero with increasing trait value) (Fig. 5).

Species with lower LSWC and SLA, but higher δ 13 C , L thick and Bark thick grew faster with increasing SRad anomalies (Fig. 5 a). Species with lower SD, but higher δ 18 O, L though and WSG were less sensitive to anomalies in Tmax and VPD (Fig. 5 b-c). Additionally, species with higher δ 13 C, g min , LA and L thick showed lower sensitivities to VPD anomalies (Fig. 5 c). Species with lower π tlp but higher WSG were less sensitive to CWD anomalies. (Fig. 5 d). Species with lower SD, but higher δ 18 O, L though and WSG were less sensitive to neighbourhood crowding (Fig. 5 a-d).

Finally, species with lower δ 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 thick , L 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). S5).

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 29 permanent forest plots, with contrasting disturbance histories. For the most abundant species (ca. 100), we further evaluated the effect of 12 traits pertaining to water relations, light capture and carbon use (Table 1), in mediating tree growth response to climate and neighbours.

Climate anomalies and neighbourhood crowding reduce tropical tree growth

High temperatures (Tmax) were associated with tree growth decline in half of the 300 species at Paracou (β 2s ; 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[START_REF] Doughty | Are tropical forests near a high temperature threshold[END_REF] and gross primary production [START_REF] Aguilos | What drives long-term variations in carbon flux and balance in a tropical rainforest in French Guiana?[END_REF][START_REF] Crous | Temperature responses of photosynthesis and respiration in evergreen trees from boreal to tropical latitudes[END_REF], which can lead to inter-annual declines in tropical woody productivity (Sullivan et al. 2020, Way and[START_REF] Way | Differential responses to changes in growth temperature between trees from different functional groups and biomes : a review and synthesis of data[END_REF] and carbon sinks (i.e. net ecosystem exchange) [START_REF] Anderegg | Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink[END_REF]. As global temperatures are predicted to increase in the tropics with ongoing climate change [START_REF] Shukla | IPCC: Climate Change 2022: Mitigation of Climate Change Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF], this thermal sensitivity of tropical species may lead to important reductions in tree growth in the future, which may alter long-term forest dynamics [START_REF] Aubry-Kientz | Temperature rising would slow down tropical forest dynamic in the Guiana Shield[END_REF], 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 [START_REF] Grossiord | Plant responses to rising vapor pressure deficit[END_REF][START_REF] Sanginés De Cárcer | Vapor-pressure deficit and extreme climatic variables limit tree growth[END_REF], it does not affect respiration. Conversely, positive anomalies in CWD strongly reduced tree growth at Paracou [START_REF] Wagner | Water Availability Is the Main Climate Driver of Neotropical Tree Growth[END_REF][START_REF] Aubry-Kientz | Identifying climatic drivers of tropical forest dynamics[END_REF], 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[START_REF] Choat | Global convergence in the vulnerability of forests to drought[END_REF][START_REF] Rifai | ENSO Drives interannual variation of forest woody growth across the tropics[END_REF]. 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 [START_REF] Aguilos | What drives long-term variations in carbon flux and balance in a tropical rainforest in French Guiana?[END_REF][START_REF] Wagner | Water Availability Is the Main Climate Driver of Neotropical Tree Growth[END_REF], 2014a), a typical light-limited tropical forest [START_REF] Guan | Photosynthetic seasonality of global tropical forests constrained by hydroclimate[END_REF], 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 [START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF], 2015[START_REF] Hammond | Global field observations of tree die-off reveal hotter-drought fingerprint for Earth's forests[END_REF], 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 [START_REF] Dawkins | The volume increment of natural tropical high-forest and limitations on its improvements[END_REF], Canham et al. 2004[START_REF] Fortunel | Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest[END_REF], Uriarte et al. 2016a[START_REF] Zambrano | Local neighbourhood and regional climatic contexts interact to explain tree performance[END_REF].

Neighbourhood crowding overall reduced tree growth across the 300 focal species, in line with previous work across forest systems [START_REF] Kunstler | Plant functional traits have globally consistent effects on competition[END_REF]. 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[START_REF] Ghuman | Effects of partial clearing on microclimate in a humid tropical forest[END_REF], but simultaneously providing thermal insulation [START_REF] Fetcher | Vegetation effects on microclimate in lowland tropical forest in Costa Rica[END_REF][START_REF] Gaudio | Impact of tree canopy on thermal and radiative microclimates in a mixed temperate forest: A new statistical method to analyse hourly temporal dynamics[END_REF][START_REF] Ghuman | Effects of partial clearing on microclimate in a humid tropical forest[END_REF], thereby sustaining a more favourable micro-climate during extreme macro-climatic events [START_REF] De Frenne | Microclimate moderates plant responses to macroclimate warming[END_REF], 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[START_REF] Grossiord | Plant responses to rising vapor pressure deficit[END_REF][START_REF] Hafner | Friendly neighbours: Hydraulic redistribution accounts for one quarter of water used by neighbouring drought stressed tree saplings[END_REF].

Previous studies have also shown mitigating effects of neighbourhood diversity or species richness on tree growth response to drought across forest biomes [START_REF] Fichtner | Neighbourhood diversity mitigates drought impacts on tree growth[END_REF][START_REF] Gillerot | Tree Neighbourhood Diversity Has Negligible Effects on Drought Resilience of European Beech , Silver Fir and Norway Spruce[END_REF], Grossiord et al. 2014a[START_REF] O'brien | Resistance of tropical seedlings to drought is mediated by neighbourhood diversity[END_REF][START_REF] Pardos | The greater resilience of mixed forests to drought mainly depends on their composition: Analysis along a climate gradient across Europe[END_REF], in contrast with findings showing exacerbating effects of stand or neighbourhood densities on tree demographic response to climatic stresses in temperate [START_REF] Bottero | Density-dependent vulnerability of forest ecosystems to drought[END_REF] and Mediterranean [START_REF] Astigarraga | Evidence of non-stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests[END_REF][START_REF] Gómez-Aparicio | Disentangling the relative importance of climate, size and competition on tree growth in Iberian forests: implications for forest management under global change[END_REF]) 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 [START_REF] Hérault | Growth responses of neotropical trees to logging gaps[END_REF][START_REF] Hérault | Key drivers of ecosystem recovery after disturbance in a neotropical forest[END_REF][START_REF] Hiltner | Climate change alters the ability of neotropical forests to provide timber and sequester carbon[END_REF][START_REF] Fargeon | Vulnerability of Commercial Tree Species to Water Stress in Logged Forests of the Guiana Shield[END_REF][START_REF] Piponiot | Carbon recovery dynamics following disturbance by selective logging in amazonian forests[END_REF]. 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 [START_REF] Mirabel | Diverging taxonomic and functional trajectories following disturbance in a Neotropical forest[END_REF][START_REF] Mirabel | 30 Years of postdisturbance recruitment in a Neotropical forest[END_REF], 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 (α 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 [START_REF] Reich | The world-wide ' fast -slow ' plant economics spectrum: A traits manifesto[END_REF], 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 [START_REF] De Frenne | Microclimate moderates plant responses to macroclimate warming[END_REF], 2019[START_REF] Ghuman | Effects of partial clearing on microclimate in a humid tropical forest[END_REF][START_REF] Von Arx | Spatio-temporal effects of forest canopy on understory microclimate in a long-term experiment in Switzerland[END_REF].

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 [START_REF] Bertness | Positive interactions in communities[END_REF][START_REF] Brooker | Facilitation in plant communities: the past, the present, and the future[END_REF][START_REF] Callaway | Positive interactions among plants[END_REF][START_REF] Callaway | Competition and facilitation: A synthetic approach to interactions in plant communities[END_REF][START_REF] Isbell | Biodiversity increases the resistance of ecosystem productivity to climate extremes[END_REF].

Neutral crowding indices have recently been suggested to quantify silvicultural treatments [START_REF] Yue | Effects of neighborhood interaction on tree growth in a temperate forest following selection harvesting[END_REF]), 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 [START_REF] Astigarraga | Evidence of non-stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests[END_REF][START_REF] Gómez-Aparicio | Disentangling the relative importance of climate, size and competition on tree growth in Iberian forests: implications for forest management under global change[END_REF]), 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 [START_REF] Blaser | Status of Tropical Forest Management[END_REF], 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 [START_REF] Hiltner | Climate change alters the ability of neotropical forests to provide timber and sequester carbon[END_REF] Traits mediate tree growth response to climate anomalies and neigh-

bourhood crowding

In line with expectations from the 'fast-slow' economic spectrum [START_REF] Reich | The world-wide ' fast -slow ' plant economics spectrum: A traits manifesto[END_REF], 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 δ 18 O, highlighting the key role of stomatal conductance and transpiration rates for efficient carbon assimilation [START_REF] Drake | faster stomata: scaling of stomatal size, rate of response, and stomatal conductance[END_REF][START_REF] Hepworth | Manipulating stomatal density enhances drought tolerance without deleterious effect on nutrient uptake[END_REF][START_REF] Jarvis | Stomatal conductance, gaseous exchange and transpiration[END_REF][START_REF] Poorter | Leaf traits are good predictors of plant performance across 53 rain forest species[END_REF][START_REF] Wong | Stomatal conductance correlates with photosynthetic capacity[END_REF]. Fast-growing species also exhibited leaf and wood traits pertaining to acquisitive strategies such as lower L thick , L though , and WSG, supporting previous findings that a species investment into tissue longevity comes at higher construction costs [START_REF] Hérault | Functional traits shape ontogenetic growth trajectories of rain forest tree species[END_REF][START_REF] Philipson | A trait-based trade-off between growth and mortality: Evidence from 15 tropical tree species using size-specific relative growth rates[END_REF][START_REF] Poorter | Are functional traits good predictors of demographic rates? Evidence from five neotropical forests[END_REF], 2010[START_REF] Reich | The world-wide ' fast -slow ' plant economics spectrum: A traits manifesto[END_REF][START_REF] Wright | Functional traits and the growth-mortality trade-off in tropical trees[END_REF].

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 δ 18 O) 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 CO 2 assimilation during periods of elevated temperature and atmospheric evaporative demands [START_REF] Caine | Rice with reduced stomatal density conserves water and has improved drought tolerance under future climate conditions[END_REF][START_REF] Hepworth | Manipulating stomatal density enhances drought tolerance without deleterious effect on nutrient uptake[END_REF][START_REF] Reich | The world-wide ' fast -slow ' plant economics spectrum: A traits manifesto[END_REF], 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 [START_REF] Esteban | The other side of droughts: wet extremes and topography as buffers of negative drought effects in an Amazonian forest[END_REF][START_REF] Hammond | Global field observations of tree die-off reveal hotter-drought fingerprint for Earth's forests[END_REF]. Still, the growth of species with more negative π tlp was less impacted by soil drought stress. Lower π tlp (Table 1) captures species' drought-related mortality risks [START_REF] Powell | Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees[END_REF]) and distribution along aridity gradients (Bartlett et al. 2012b), but previous work from different biomes found mixed evidence for species drought-related growth reductions [START_REF] Mcgregor | Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest[END_REF][START_REF] Smith-Martin | Hydraulic traits are not robust predictors of tree species stem growth during a severe drought in a wet tropical forest[END_REF][START_REF] Song | Growth resilience of conifer species decreases with early, long-lasting and intense droughts but cannot be explained by hydraulic traits[END_REF]).

While we predicted species with higher g min would suffer stronger drought-related growth declines [START_REF] Blackman | Leaf photosynthetic, economics and hydraulic traits are decoupled among genotypes of a widespread species of eucalypt grown under ambient and elevated CO2[END_REF][START_REF] Duursma | On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls[END_REF], we found they were less vulnerable to atmospheric drought stress. As elevated VPD leads to stomatal closure [START_REF] Grossiord | Plant responses to rising vapor pressure deficit[END_REF], residual water loss could maintain evaporative cooling (given non limiting soil water conditions), which potentially prevents irreversible leaf tissue damage through overheating [START_REF] Drake | Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance[END_REF]. The maintainance 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 [START_REF] Drake | Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance[END_REF][START_REF] Powell | Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees[END_REF].

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 [START_REF] Poorter | The importance of wood traits and hydraulic conductance for the performance and life history strategies of 42 rainforest tree species[END_REF][START_REF] Nardini | Hard and tough: the coordination between leaf mechanical resistance and drought tolerance[END_REF]), while species with larger leaves have higher evaporative cooling through transpiration [START_REF] Gates | Transpiration and Leaf Temperature[END_REF][START_REF] Schuepp | Leaf boundary layers[END_REF][START_REF] Wright | Global climatic drivers of leaf size[END_REF], 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 [START_REF] Greenwood | Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area[END_REF], 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 [START_REF] Fortunel | Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest[END_REF][START_REF] Kunstler | Plant functional traits have globally consistent effects on competition[END_REF]). 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 interactive 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 [START_REF] Fortunel | Functional trait differences influence neighbourhood interactions in a hyperdiverse Amazonian forest[END_REF][START_REF] Kunstler | Plant functional traits have globally consistent effects on competition[END_REF]. 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.

Résumé 1 .

 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.

Figure 1 .

 1 Figure 1. Spatial dimension of the 15 permanent forest plots and stand density in 10 m × 10 m quadrats, given as basal area (m 2 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.

Figure 2 .

 2 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.

Figure 3 .

 3 Figure 3. Community-level effects of climate anomaly (β 2,0 ), neighbourhood crowding (NCI, β 3,0 ), and their interaction (Climate anomaly×NCI, β 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: β 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 β 2-3,0 values indicate faster growth, while negative β 2-3,0 values indicate slower growth with increasing model covariate (details in TableS2). The interpretation of β 4,0 depends on the direction of the climate effect β 2,0 . If the sign of β 4,0 is the same as the sign of β 2,0 , neighbourhood crowding accentuates the effect of climate, inversely if the sign of β 4s is in the opposite of the sign of β 2,0 , neighbourhood crowding attenuates the effect of climate. Community-level intercepts (α 0 ) and tree size effects (DBH, β 1,0 ) are shown in Fig.S1A.

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Figure 5 .

 5 Figure 5. M2-models: trait effects on tree growth response to climate anomalies β 2,1 , neighbourhood crowding β 3,1 and the climate anomaly-crowding interaction β 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 (δ 18 O) and carbon (δ 13 C) isotope composition, water potential at turgor loss point (π 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 β 2-4,1 values indicate faster growth with increasing trait values, while negative β 2-4,1 values indicate slower growth with increasing trait values (details in TableS5).

Table 1 .

 1 Functional traits for the most abundant 100 species at Paracou,

French Guiana Organ Trait type Trait Abbreviation (Unit) Function Description References N

	87			81				81				100				102				100			101				101			100	100	100	95
	Bertolino et al. 2019,	Drake et al. 2013,	Machado et al. 2021	Farquhar et al. 2007,	Moreno-Gutierrez et al.	2012, Scheidegger et al. 2000	Farquhar et al. 1989,	Cernusak et al. 2013,	Scheidegger et al. 2000	Bartlett et al. 2012,	Marechaux et al. 2018		Blackman et al. 2019,	Duursma et al. 2019,	Machado et al. 2021	Blackman et al.2019,	Gleason et al. 2014,	Luo et al. 2021	Wright et al. 2017				Osnas et al. 2013,	Wright et al. 2004		Vile et al. 2005	Kitajima and Poorter 2010	Loram-Lourenço et al. 2022,	Poorter et al. 2014,	Rosell et al. 2014	Chave et al. 2009,	Poorter et al. 2010,	Serra-Maluquer et al. 2022
	SD relates to potential maximum stomatal conductance,	promoting gas exchange and evaporative cooling, but increasing	water loss through transpiration.	Plants with high δ 18 O have low stomatal conductance and		transpiration rates and thus conserve water at the expense of	carbon assimilation.	Plants with high δ 13				Plants with low πtlp maintain stomatal conductance, hydraulic		conductance and photosynthetic gas exchange at lower soil water	potential.	Low gmin limits residual water loss after stomatal closure through		leaf cuticle and incompletely closed stomata, thereby increasing	dry-down time.	LSWC translates into water reserves that may allow maintenance	of leaf water potential when water supply becomes limiting.		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.	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.	Lthick relates to structural support, physical resistance and leaf	lifespan.	Ltough relates to structural support, physical resistance and leaf	lifespan.	Plants with thick bark have low respiration rates, but high	mechanical strength, resistance to abiotic and biotic threats, and	stem water storage.	Dense wood increases mechanical strength and resistance to
	Gas exchange			Carbon-water use				Carbon-water use				Drought tolerance				Water conservation				Water storage			Light capture				Light capture and	carbon use		Carbon use	Carbon use	Mechanical	support, water	transport and	storage capacity	Mechanical	support, water	transport and	storage capacity
	SD	(n mm -2 )		δ 18 O	(‰)	δ 13 C	(‰)	πtlp	(MPa)		gmin	) (mmol m-² s -1	LSWC	(%)		LA	(cm²)			SLA	(m² kg -1 )		Lthick	(mm)	Ltough	(N)	BarkThick	(mm)	WSG
	Stomata density			Bulk leaf oxygen	stable isotope		Bulk leaf carbon	stable isotope		Water potential at	turgor loss point		Minimum	conductance		Leaf saturated water	content		Leaf area				Specific leaf area			Leaf thickness	Leaf toughness	Stem bark thickness	Stem wood specific	gravity
	Anatomy			Chemistry				Chemistry				Physiology				Physiology				Physiology			Structure				Structure			Structure	Structure	Structure	Structure
	Leaf			Leaf				Leaf				Leaf				Leaf				Leaf			Leaf				Leaf			Leaf	Leaf	Wood	Wood

C have high intrinsic water-use efficiency (high photosynthetic rates relative to stomatal conductance) and thus conserve water at the expense of carbon assimilation. abiotic and biotic threats, and is related to high embolism resistance, but is more costly to produce per volume.

  the average AGR across all species. Negative values of β 1-3s indicate decreases in growth with increasing model covariate. The interpretation of β 4s depends on the respective directions of β 2s and β 3s . If the sign of β 4s is the same as the signs of β 2s and β 3s , the interaction between neighbourhood crowding and climate can accentuate their separate effects. Conversely, if the sign of β 4s is opposite from both signs of β 2s and β 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.

eqn 4f) where α s represents the species-level intrinsic growth and β 1s , β 2s , β 3s and β 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 α s indicate species whose growth is lower and positive values indicate species whose growth is higher than

Acknowledgements

We thank the many colleagues who participated in field and lab work for trait data collection in French Guiana and at the University of Vienna, especially Coralie Dalban-Pilon, Jocelyn Cazal, Stéphane Fourtier, Jean-Yves Goret, Paul Mischler, Gaëlle Jaouen, Laetitia Proux, Camille Girard-Tercieux, Jeanne Clément, Ghislain Vieilledent and Sylvain Schmitt. We thank the CIRAD fieldwork team for the tree inventory, and Pascal Petronelli, Giacomo Sellan and Julien Engel for botanical identification. The modeling work has been realised with the support of Meso@LR-Platform at the University of Montpellier. We thank the Meso@LR team, in particular Bertrand Pitollat, and Philippe Verley for technical support. We thank Emmanuel Delande for his assistance with L A T E X. We thank Charlotte Grossiord, Raphaël Pélissier, Stefan Dullinger and Marie-Pierre Etienne for helpful discussions. D., Haneca, K., Harley, G. L., Heinrich, I., Helle, G., Inga, J. G., Islam, M., Jiang, Y. m., Kaib, M., Khamisi, Z. H., Koprowski, M., Kruijt, B., Layme, E., Leemans, R., Leffler, A. J., Lisi, C. S., Loader, N. J., Locosselli, G. M., Lopez, L., López-Hernández, M. I., Lousada, J. L. P. C., Mendivelso, H. A., Mokria, M., Montóia, V. R., Moors, E., Nabais, C., Ngoma, J., Nogueira Júnior, F. d. C., Oliveira, J. M., Olmedo, G. M., Pagotto, M. A., Panthi, S., Pérez-De-Lis, G., Pucha-Cofrep, D., Pumijumnong, N., Rahman, M., Ramirez, J. A., Requena-Rojas, E. J., Ribeiro, A. d. S., Robertson, I., Roig, F. A., Rubio-Camacho, E. A., Sass-Klaassen, U., Schöngart, J., Sheppard, P. R., Slotta, F., Speer, J. H., Therrell, M. D., Toirambe, B., Tomazello-Filho, M., Torbenson, M. C., Touchan, R., Venegas-González, A., Villalba, R., Villanueva-Diaz, J., Vinya, R., Vlam, M., Wils, T., and Zhou, Z. K. Tropical tree growth driven by dry-season climate variability. Nature Geoscience, 15:269-276, 2022. doi: 10.1038/S41561-022-00911-8.

Funding

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

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

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0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] ρ = 0.07 [-0.12, 0.26] Response to NCI Response to CWD anomalies c) ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 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0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48] ρ = 0.32 [0.17, 0.48]

Author Contributions

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

Conflict of interest

The authors declare there are no competing interests.

List of Supporting Information

See PDF file SupportingInformation_1.pdf Supplementary Methods S2. R code for M1-and M2-models.

See Excel file SupportingInformation_2.xlsx Table S1. Conditional and margignal R2 estimates for M1-and M2-models Table S2. Standardised regression coefficients of community level parameters and group-level sigmas for M1-models.

Table S3. Summarised species-level responses to model covariates for M1-models.

Table S4. Estimates of correlations (ρ) between species-level parameters for M1-models.

Table S5. Standardised regression coefficients of community level parameters and group-level sigmas for M2-models.

Table S6. Pearson correlation coefficients among mean anomalies of the 4 climate variables.