Dry and low nutrient conditions shift conspecific interactions from negative to positive, and responses depend on trait.

Biotic interactions shape plant community dynamics, wherein demographic response to increasing density of conspecifics relative to heterospecifics–termed conspecific density dependence (CDD)–plays a critical role. CDD may vary with abiotic conditions, which remains understudied despite its relevance to plant community dynamics in spatiotemporally variable habitats. We examined how gradients of light, soil moisture and nutrient availability altered conspecific effects on seedling survival in a tropical humid forest of the Western Ghats (India), from 9148 seedlings of 39 tree species over three years. Then, we examined whether changes in CDD with abiotic conditions were explained by traits representing tradeoffs along fast-slow strategies: specific leaf area (SLA), leaf dry matter content (LDMC) and specific root length (SRL). Community-average CDD was negative at wetter and nutrient-rich sites and became positive at dry, nutrient-poor sites, and these shifts were driven by a few species. In bright, dry and nutrient rich conditions, resource-acquisitive species (high SLA, low LDMC and high SRL) had positive CDD while conservative species (low SLA, high LDMC and low SRL) had negative CDD. Synthesis: Drier and low nutrient conditions weakened CDD on average for the community, largely driven by changes to dominant species. Resource-acquisitive species escaped self-limitation and experienced positive conspecific interactions in brighter, drier, and nutrient-rich conditions. With global environmental change, drier conditions and nutrient deposition may diminish population constraints on resource-acquisitive species to modify community structure in the seedling bank of humid forests.

Interactions among plant neighbors can affect demographic performance of individual plants in a density-dependent manner (Comita et al., 2014; Hülsmann et al., 2024; Uriarte et al., 2004). Demographic response to increasing densities of conspecific neighbors, termed conspecific density dependence (henceforth CDD), when more detrimental than heterospecific neighbors (Chesson, 2000a; Detto et al., 2019), can lead to species limiting themselves more than they limit other species (Chesson, 2000b). This negative CDD aids coexistence (Chesson, 2000a; Comita & Stump, 2020). On the other hand, mutualists can improve performance around conspecifics to alleviate negative CDD or generate positive CDD (Zahra et al., 2021). Conspecific effects are especially prominent at the younger life-stages, i.e., seeds and seedlings (Comita et al., 2014; Song et al., 2021; Zhu et al., 2015), and shapes community structure of the seedling bank in closed-canopy forests (Comita & Hubbell, 2009; Lebrija-Trejos et al., 2023). Despite the plethora of work on CDD, few studies have assessed how neighborhood interactions vary with abiotic conditions (Fortunel et al., 2018). Understanding spatiotemporal variation in CDD will allow more robust predictions of plant community dynamics, and better forecast the effects of global environmental change (Schiffer et al., 2026). Density-dependent response to neighbors can be driven by competition for resources or mediated by herbivores, pathogens, and mutualists. Natural enemies such as insect herbivores and fungal pathogens, especially when host-specific, reduce per capita performance by a greater extent in neighborhoods with higher conspecific densities, causing negative CDD (Bagchi et al., 2014; Connell, 1971; Downey et al., 2018; Forrister et al., 2019; Janzen, 1970; Krishnadas et al., 2018; Krishnadas & Comita, 2018; Yamazaki et al., 2009). Natural enemy activity, plant response to enemies, and resource use by plants can change with abiotic context, potentially modifying CDD. For instance, light, water and soil nutrients are key abiotic factors in tropical forests that vary spatially, and along these gradients can vary the influence of natural enemies or mutualists on seedling survival (Craine & Dybzinski, 2013; Gaviria & Engelbrecht, 2015; Milici et al., 2020; Russo et al., 2008). Under closed canopies, humid conditions can increase pathogen transmission and light limitation can make it harder for plants to recoup damage from enemies. Nutrient-rich soils increase the abundance and activity of microbial pathogens, increasing disease occurrence in plants (Erlandson et al., 2018; Van Der Heijden et al., 2008; Yamanaka, 1999). Herbivory increases in nutrient rich soils (Endara & Coley, 2011; Oliveira et al., 2025). Consequently, CDD may be more negative in wetter and nutrient rich conditions. By contrast, drier conditions may decrease pathogen load (Erlandson et al., 2018) or reduce transmission (Jiang et al., 2021). Brighter and nutrient rich conditions could allow better recovery from disease or herbivory (Augspurger, 1984; Hillebrand et al., 2007; Salgado-Luarte & Gianoli, 2010), making CDD less negative or neutral. Generally, higher light, moisture and nutrients provide more conducive conditions for survival and growth of plants (Brenes-Arguedas et al., 2011; Craine & Dybzinski, 2013; Zaret et al., 2023), which may decrease competition among neighbors to weaken negative CDD (Dostál, 2021; Wright, 2002). Plant responses to abiotic conditions and natural enemies relate to traits. Traits such as specific leaf area (SLA, area per unit mass) and specific root length (SRL, length per unit mass) array species along a resource acquisitive vs conservative strategy (Reich, 2014). Species with higher SLA or SRL rapidly acquire resources but this may come at the cost of stress-tolerance, including defence against enemies (Coley et al., 1985). For instance, tree species with higher SLA experienced stronger declines in growth than low SLA species when surrounded by more conspecifics (Kunstler et al., 2016). Species traits may thus mediate spatiotemporal variation in response to neighbors by affecting species’ resource-use and susceptibility to agents of CDD such as pathogens and herbivores, but this has not been tested for seedlings. Assuming poorer defence in acquisitive species (Coley et al., 1985), they may have more negative CDD than conservative species in shaded conditions because low light may limit growth, reducing the ability to compensate for losses to natural enemies. Brighter conditions may allow acquisitive species to capitalize on their growth advantage to alleviate negative CDD by a greater degree than conservative species. Similarly, where moisture and soil nutrients increase, greater preponderance of natural enemies may intensify negative CDD (Dostál, 2021; Hillebrand et al., 2007; LaManna et al., 2016), and this may be more prominent for species with acquisitive traits with poorer defences (Zhu et al., 2018). When conditions become drier and nutrient poor, all species may experience a weakening of CDD regardless of traits, due to fewer pathogens and diminished pathogen transmission (Erlandson et al., 2018; Lebrija-Trejos et al., 2023; Milici et al., 2025). We examined the role of plant traits in modulating density-dependent performance of tree seedlings across a gradient of abiotic conditions in a tropical humid forest situated in the Western Ghats, a global biodiversity hotspot in peninsular India. Across a 30 km² landscape, we monitored seedling survival for three years to test whether CDD varied with gradients of light, soil moisture and soil nutrients, and if the variation was associated with species traits. Specifically, we asked: 1. To what extent does CDD vary with light, soil moisture and nutrients and does this differ among species? 2. Do traits related to the resource acquisitive vs conservative spectrum explain variation in CDD with light, moisture and nutrients? We expected stronger negative CDD in conditions of low light, high soil moisture. and high nutrients. Resource-acquisitive species were expected to face more negative CDD in moist, shaded and nutrient poor conditions because of higher vulnerability to herbivory and pathogens compared to resource-conservative species. CDD for acquisitive species was expected to weaken in drier conditions due to lower enemy pressure, and in higher light and nutrients by virtue of being able to replace lost tissues via rapid growth.

Study site: The study was conducted in a 30-km 2 tropical humid forest in central Western Ghats, a biodiversity hotspot in southern India, Kadumane village (12°53'03.4"N, 75°41'25.3"E, 900 to 1100 m.a.s.l., Fig. S1). The site receives ca. 4100 mm of rainfall annually with a dry season of about 6 months from November to April with monthly precipitation less than 100 mm. This human-modified landscape consists of forest fragments, grassland, tea plantations, roads, and small human settlements. The forest fragments offer a gradient in abiotic conditions from forest edge to interiors. To capture the variability in canopy openness, soil moisture, and nutrients across the landscape, we established 250 2-m X 2-m plots (demarcated into four 1-m X 1-m sub plots) across 30 transects laid from the edge of roads or tea plantations into the forest interior (Fig. S1). Seedling census: In November 2021, we tagged, identified and recorded tree seedlings of height 5-50 cm within each plot. We censused seedlings twice a year, pre-monsoon (March – April) and post-monsoon (November – December), totalling seven censuses until November 2024. During each census, all the new seedling recruits were also tagged and monitored, totalling 9613 seedlings of 80 tree species across 3.5 years. Abiotic variables : In each plot, we assessed canopy openness as a proxy for understorey light availability using 180-degree hemispherical photographs. In March 2023, canopy images were taken at 50 cm above the ground in the center of each plot (details in Methods S1). We analysed the percent of open canopy from these images using Gap Light Analyzer version 2.0 (Frazer et al., 1999). Gravimetric soil moisture was assessed monthly for each plot every year during the dry season (January to April, details in Methods S2). While CDD varies with wet season moisture (Lebrija-Trejos et al., 2023), this will play out primarily for the seed-to-seedling transition and early seedling recruitment in this system (Krishnadas et al., 2018; Krishnadas & Comita, 2018), whereas we wanted to examine CDD in older seedlings. Moreover, CDD has been observed during the dry season here (Krishnadas et al., 2020; Krishnadas & Comita, 2018) and evidence from seasonal forests suggests that CDD effects on seedlings in tropical wet forests can be strong in the dry season (Lin et al., 2012). For soil nutrients, soil from the top 10 cm of each 1 m 2 plot was collected during October 2022 to quantify the concentration of Nitrate (NO 3 -, mg Kg -1 ), Ammonium (NH 4 +, mg Kg -1 ) and plant-available phosphorus (P, mg Kg -1 ) (Details in Methods S3). Nutrient analysis was outsourced to the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India, and performed accoring to standard lab protocols. Trait measurements: We measured specific leaf area (SLA, mm 2 mg -1 ), leaf dry matter content (LDMC, mg g -1 ) and specific root length (SRL, mm g -1 ) as the key traits relevant to classifying species along a spectrum of fast-growing, resource-acquisitive to slow growing, resource-conservative strategies (Laliberté, 2017; Reich, 2014), which is in turn associated with a growth-defence trade-off (Coley et al., 1985). Higher SLA, higher SRL and lower LDMC values reflect a resource-acquisitive strategy and low defenses. SLA and LDMC were measured for 28 most common species (making up more than 95% of seedling abundance, Table S1), and SRL was measured for 21 common species (making up 90% of seedling abundance Table S1) following Pérez-Harguindeguy et al. (2013). Refer to supplementary Methods S4 for more details of measurements. Statistical analys is Seedling survival models: Quantifying CDD in a robust and reliable manner has been the subject of much recent debate (Chesson, 2000a; Detto et al., 2019), and we followed the recent best practices to estimate CDD (Hülsmann et al., 2024). Specifically, to account for nonlinearity and saturation in the effects of neighbours on seedling survival we used Generalized Additive Mixed Models (GAMM) with thin plate splines (more details below). Furthermore, conspecifics must have a stronger effect on the plant performance as compared to heterospecifics for stabilising effects to shape the community (Chesson, 2000a). To estimate the difference between the effect of conspecific and heterospecific density dependence (HDD), we included the density of conspecific seedlings as well as total seedlings in our models (Chesson, 2000a; Hülsmann et al., 2024; LaManna et al., 2024). Including the total density gives us the conspecific effects adjusted for the heterospecific density effects (Hülsmann et al., 2024), and can be interpreted as stabilizing CDD. Conspecific seedling density and the total seedling density for each focal seedling was calculated as the number of conspecific seedlings and the total number of seedlings present in the plot in the census preceding the one when seedling survival was recorded. We then filtered our data to include species that occurred in at least three plots and had at least three levels of conspecific densities recorded. Of 80 species of tree seedlings found across 250 plots (2 X 2m), 39 species qualified for inclusion in models (Table S1), giving us 9148 seedlings over seven censuses. To test if the effects of neighbor density varied with light, soil moisture and nutrients, we modelled individual seedling survival as a function of canopy openness, minimum dry-season soil moisture, nitrate, ammonium, total available nitrogen (sum of nitrate and ammonium) or available phosphorus in interaction with conspecific density and total seedling density (Box 1 Equation 1). Although the wet season can be a time of strong CDD for seedlings due to an increase in pathogens, there may be little spatial variation as it is unlikely that soil moisture is limiting anywhere. Because our goal was to understand spatial variation in CDD across an abiotically heterogeneous landscape, we chose to assess CDD with the dry season soil moisture (November through April), when there is marked spatial variation in soil moisture acting as an abiotic filter (Wilson et al. in preparation). At the same time, seedlings experience CDD in the dry season here (Krishnadas & Comita, 2018) as well as in other tropical forests (Lin et al., 2012), which may vary spatially with soil moisture. We set up GAMMs with Bernoulli distribution and complementary log-log (cloglog) link to allow for asymmetric fit since our data was skewed with nearly 70% survival (Hardin & Hilbe, 2007). Abiotic variables were correlated (Fig. S2), so we implemented separate models for each variable. Seedling survival is known to depend on seedling height (Johnson et al., 2017), hence log-transformed initial height of the seedling was included as a predictor. All models had random intercepts and slopes per species for the effects of abiotic variables, conspecific and total densities, and their interaction with the abiotic predictor. To account for temporal and spatial autocorrelation, we added the census date and plot identity as random intercepts, respectively. All the models were fitted using the restricted maximum likelihood estimation (REML) using gam() function from mgcv package (Wood, 2025). Model performance was assessed from simulated residuals using package DHARMa (Hartig et al., 2024). Equation 1: cloglog(Survival) ~ season + log(height) + Con + Tot + Env + Con : Env + Tot : Env + (1 + Con * Env + Tot * Env | Species) + (1 | census) + (1 | plot) Equation 2: cloglog(Survival) ~ season + log(height) + Con + Tot + Env + trait + Env : trait + Con : Env + Con : trait + Con : trait : Env + Tot : trait + Tot : Env + Tot : trait : Env + (1+ Con * Env + Tot * Env | Species) + (1 | census) + (1 | plot) Box 1: Generalised additive mixed effect model (GAMM) equations in Wilkinson-Rogers notation. Final GAMM model structure after trouble-shooting for convergence errors described in Wilkinson-Rogers notation. Con is conspecific seedling density, Tot is total seedling density, Env is either canopy openness , scaled minimum soil moisture or soil nutrients (nitrate, ammonium, total nitrogen or available phosphorus) and trait is either SLA , LDMC or SRL. Finally, we tested whether SLA, LDMC and SRL explained the variation in CDD and its change with light, soil moisture, and soil nutrients. The 28 species with SLA and LDMC included 8752 seedlings and 21 species with SRL included 8302 seedlings (Table S1). For this, we fitted fifteen separate models, to predict seedling survival for the combinations of each trait and abiotic variable in a three-way interaction with conspecific and total densities (Box 1 Equation 2). For example, to test the effects of neighbour density with SLA and light, we modelled survival as a function of conspecific density, SLA and canopy openness in a three-way interaction as well as total density, SLA and canopy openness in a three-way interaction. The random effects were fitted similar to Equation 1 (Box 1). SLA, LDMC and SRL were not correlated in our dataset (Fig. S3). Conspecific density dependence estimates: CDD was interpreted with predicted survival probabilities at representative density values, using predict.gam() function from package mgcv (Wood, 2025). Survival probabilities were generated depending on the question (see details below), and assessed or compared based on 95% confidence intervals (CI). For our first question, we assessed the difference (i.e. effect size) in survival probability between seedlings without conspecific neighbors (zero) and (a) at mean conspecific density (referred to as mean CDD effect) and (b) at the 95 th percentile of conspecific density (referred to as high CDD effect). Mean and high CDD effects were calculated at low (5 th percentile) and high (95 th percentile) levels of each of the respective environmental conditions (i.e. canopy openness, soil moisture, nitrate, ammonium, total nitrogen and available phosphorus), with other model terms held at their median. Significance of CDD was assessed based on the overlap with zero of 95% CIs of the difference in survival probability, and this was compared between each low and high level of the abiotic variable to assess change in CDD. Negative and positive effect sizes denote significant negative and positive CDD, respectively. For the second part of question 1, we evaluated species-level CDD by predicting effects of conspecific density as detailed above at species-specific low (5 th percentile), mean, and high (95 th percentile) values of conspecific seedling density, at low (5 th percentile) and high (95 th percentile) levels of each of the respective environmental conditions. For question two, we estimated the contribution of traits in explaining CDD by calculating CDD effect size, as mentioned above, for low and high values of each trait (5 th and 95 th percentile respectively): SLA, LDMC and SRL, repeated at low (5 th percentile) and high (95 th percentile) levels of each of the respective environmental conditions. All analysis was done in R version 4.5.1 (R Core Team, 2025), using tidyverse (Wickham et al., 2019) for data management and ggplot2 (Wickham, 2016), ggpubr (Kassambara, 2025), ggrepel (Slowikowski et al., 2024), gratia (Simpson, 2024) and cowplot (Wilke, 2025) for plotting.

Context-dependent CDD: In lower light (5 th percentile canopy openness = 0.45%), community-average seedling survival decreased between zero and mean conspecific density, indicating negative CDD (Fig. S3a, Table S2), which persisted in more open canopies (95 th percentile canopy openness = 2.63%, S3a, Table S2). At high conspecific densities, we found positive CDD in lower light where seedling survival increased relative to no conspecific seedlings (S3a, Table S2) and no CDD under more open canopies (S3a, Table S2). In wetter conditions (95 th percentile soil moisture = 35.78%), survival decreased between zero and mean conspecific density, indicating negative CDD (Fig. 1a, Table S3), but negative CDD disappeared at high conspecific density (Fig. 1a, Table S3). By comparison, in drier conditions (5 th percentile soil moisture = 3.76%), survival probability increased considerably at high density relative to no conspecifics, indicating positive CDD (Fig. 1a, Table S3), with no discernible CDD at mean conspecific density (Fig. 1a, Table S3). We also fitted models using average dry season soil moisture. However, these had higher AIC than models using minimum dry season soil moisture (Table S4). In high nitrate soils (95 th percentile nitrate = 72.3 mg Kg -1 ), survival probability decreased between zero and mean as well as zero and high conspecific density, indicating negative CDD (Fig. 1b, Table S5). In lower nitrate soils by comparison (5 th percentile nitrate = 17.5 mg Kg -1 ), survival probability increased between zero to high conspecific density, indicating positive CDD (Fig. 1b, Table S5). In higher ammonium soils (95 th percentile ammonium = 17.95 mg Kg -1 ), seedling survival decreased between zero and mean conspecific density, indicating negative CDD (Fig. S4c, Table S6), which became positive between zero and high conspecific density (Fig. S4c, Table S6). In lower ammonium soils (5 th percentile ammonium = 10.35 mg Kg -1 ), we found negative CDD between zero and mean conspecific density (Fig. S4c, Table S6), but no discernible CDD between zero and high conspecific density. Patterns of CDD with total available nitrogen (sum of ammonium and nitrate) was similar as compared to nitrate (Fig. 1b, S4d, Table S5, S7). In higher phosphorus soils (95 th percentile phosphorus = 3.31 mg Kg -1 ), survival probability decreased between zero and mean as well as zero and high conspecific density, indicating negative CDD (Fig. S4b, Table S8). At lower phosphorus levels by comparison (5 th percentile phosphorus = 0.2 mg Kg -1 ), there was no discernible CDD (Fig. S4b, Table S8). Species variation in CDD: Seedlings of seven out of thirty nine species were found in conspecific densities higher than community mean (of 26 seedlings, Table S1), and these species showed variation in CDD with varying abiotic conditions (Fig. 2, S5 - S16, Table S9-14). Canopy openness changed CDD for only one species; at high density, Reinwardtiodendron anamalaiense had negative CDD in lower light conditions (Fig. S6, Table S9). Soil moisture changed CDD for six species, consistently being more negative in wetter conditions (Fig. 2a-f, S7, S8, Table S10). Notably, Dimocarpus longan and Symplocos racemosa had positive CDD at high conspecific densities in drier conditions (Fig. 2e,f, S8, Table S10). In nitrate rich soils, seven species had negative CDD, surviving better at low conspecific density compared to mean or high conspecific density (Fig. 2g-l, S9, S10, Table S11). As in drier soil, D. longan, S. racemosa and Psychotria nigra had positive CDD in nitrate poor soils at high conspecific densities (Fig. 2j-l, S10, Table S11). Soil ammonium and available phosphorus changed CDD for fewer species (Fig. S11-S14, Table S12, S13) and total nitrogen changed CDD for species in a similar manner as with nitrate (Fig. S9, S10, S15, S16, Table S11, S14). Traits mediate variation in CDD across gradients: Under more open canopy (Canopy openness = 2.63 %), species with low SLA (5 th percentile, 14.78 mm 2 mg -1 ) had negative CDD whereas species with high SLA (95 th percentile, 26.77 mm 2 mg -1 ) exhibited positive CDD (Fig. 3a; Table S15). Under more shaded conditions (Canopy openness = 0.45 %), there was no evidence for CDD for either low or high SLA species (Fig. 3b, Table S15). Low LDMC (5 th percentile, 175.95 mg g -1 ) and high SRL (95 th percentile, 1466.2 mm g -1 ) species similarly had positive CDD at high conspecific densities when in open canopy (Fig. S17a,b, S18a,b, Table S16, S17), but there were no other trait-mediated changes in CDD across light availability. Under high soil moisture (Soil moisture = 35.79%), species with high SLA, low LDMC, and high SRL experienced negative CDD at mean conspecific density (Fig. 3c, S17c, S18c, Table S18, S19, S20), but this effect was not significant at higher conspecific densities (Fig. 3c, S17c, S18c, Table S18, S19, S20). In dry conditions (Soil moisture = 3.77%), species with high SLA, low LDMC, and high SRL had positive CDD at high conspecific density (Fig. 3d, S17d, S18d, Table S18, S19, S20). Low SLA, high LDMC (95 th percentile, 587.73 mg g -1 ), and low SRL (5 th percentile, 123.03 mm g -1 ) species had no discernible CDD in either wet or dry conditions (Fig. 3c,d, S17c,d, S18c,d, Table S18, S19, S20). Where soil nitrate increased (Nitrate = 72.26 mg Kg -1 ), species with high SLA and low LDMC experienced positive CDD at higher conspecific densities (Fig 3e, S17e, Table S21, S22), while low SLA and high LDMC species experienced negative CDD (Fig 3e, S17e, Table S21, S22). In low nitrate soils (Nitrate = 17.48 mg Kg -1 ), species with high SLA experienced negative CDD (Fig. 3f, Table S21). Unexpectedly, low SLA and high LDMC species experienced positive CDD at higher conspecific densities in low nitrate conditions (Fig. Fig. 3f, S17f, Table S21, S22). CDD did not vary with SRL in either high or low nitrate conditions (Fig. S18e,f, Table S23). Trait-mediated CDD variation was explained better with soil nitrate as compared to ammonium and phosphorus (Fig. 3c,d, S17 - S19, Table S21 - S29), and patterns with total available nitrogen (sum of ammonium and nitrate) reflected patterns with nitrate (Fig. S17-19, Table S21 - S23, S30 - S32).

In a tropical humid forest, community-average CDD was negative in wet and nutrient rich conditions, turning positive in dry and nutrient poor conditions. A change in their response to conspecifics at different moisture and nutrient levels, probably due to their low seedling densities, which may have resulted from CDD at an earlier life stage (Krishnadas et al., 2018; Krishnadas & Comita, 2018, 2019). Traits explained variation in CDD with light, water and soil nutrients; resource acquisition strategies, which may trade-off with defence or stress tolerance, mediated the interaction of abiotic and biotic factors for seedling dynamics. Wetter soil may have led to more negative CDD due to higher prevalence of pathogenic microbes (de Vries et al., 2023; Erlandson et al., 2018), whereas drier conditions may have increased mutualist associations, creating positive CDD (de Vries et al., 2023; Porter et al., 2020; Semchenko et al., 2022). For species that experienced more negative CDD in wetter conditions, the shift was driven by survival without conspecifics improving in high moisture while survival at higher conspecific densities being similar to dry conditions (Fig 2, S11). Thus, stabilizing CDD strengthened at higher soil moisture, as seen during wetter years in another tropical forest (Lebrija-Trejos et al., 2023; Liu et al., 2026). Similarly, nitrate and phosphorus rich soils created stronger negative CDD for seedlings, consistent with patterns in a temperate forest (LaManna et al., 2016), and CDD became positive in drier and low nitrate conditions. Positive CDD occurred for species with the highest seedling densities (Fig. 2, S8b, S8c, Table S1), e.g., D. longan, P. nigra and S. racemosa, suggesting that drier and nitrate poor conditions may aid their dominance. Larger competitive differences in drier or nutrient poor conditions might further reduce the potential to maintain diversity, if species with a competitive advantage in these stressful conditions also experience weaker CDD (Comita & Stump, 2020). In contrast to nitrate, higher ammonium created positive CDD for these species (Fig S14). This could reflect trade-offs in uptake of nitrate and ammonium as found in grasses (Maire et al., 2009). Our results suggest that nutrient deposition in tropical forests may alter stabilization during seedling dynamics, but effects will vary with type of nutrient and species traits. Resource-acquisitive species - higher SLA and SRL - experienced more negative CDD where soil moisture was higher and soil nitrogen was lower. These patterns could have accrued from pathogens in wetter soils causing more detriment to acquisitive species than better-defended resource conservative species. On the other hand, nitrogen-rich soils may have supported recovery, allowing acquisitive species to overcome negative effects of conspecific neighbors (Coley et al., 1985). Acquisitive species had positive CDD in higher light conditions, perhaps due to enhanced growth (Reich, 2014) or harnessing mutualists to overcome enemy attack or competition. Some species may recruit mutualists when soil nutrients are lacking (Ma et al., 2021; Makarov, 2019; Porter et al., 2020), which may explain why resource-conservative species, with low SLA and SRL, showed positive CDD in nitrate poor conditions (Semchenko et al., 2022). Pathogens, implicated as key agents of negative CDD (Augspurger, 1984; Bagchi et al., 2014; Bell et al., 2006; Mangan et al., 2010), also in this landscape (Krishnadas et al., 2018; Krishnadas & Comita, 2018, 2019; Viswanathan et al., 2025), may have curtailed survival at higher soil nutrients and moisture (Saini et al., in revision). This happened only for the species with highest conspecific densities, suggesting that an increase in pathogen-driven mortality may require sufficient accumulation of local conspecific density (Bell et al., 2006; Milici et al., 2024). Similarly, positive CDD, potentially as a benefit from mutualists, became prominent only at higher densities. Accumulation of pathogens and mutualists also relates to densities of older individuals (Liang et al., 2021; Makarov, 2019), which we did not assess here. Experiments will help test how biotic agents mediate the strength and direction of CDD across abiotic gradients.

Overall, our results reveal that the contribution of biotic interactions to community structure will depend on abiotic context. Drier and nutrient poor conditions weakened negative interactions operating at early life-stages, which has implications for the maintenance of diversity in the seedling bank of closed-canopy forests subject to global environmental changes (e.g., nutrient deposition, drought, edge effects). Furthermore, trait-mediated spatial variation in neighborhood interactions may alter functional composition in the seedling bank or enforce species separation across abiotic gradients (Schiffer et al., 2026). Deeper understanding of the underlying drivers of density-dependent plant performance will reveal whether and how global change factors alter the relative importance of mechanisms that structure plant communities at different scales.

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