Species traits mediate environmental responses but not conspecific density dependence in tropical tree saplings

Understanding how functional traits mediate species-specific responses to environmental variation and neighborhood interaction is fundamental for linking individual performance to community assembly. We used a hierarchical framework to examine how functional traits along the acquisitive-conservative spectrum mediate growth responses to environmental effects and density dependence. We monitored the growth of 16,717 saplings from 115 tree species over five years in a tropical rainforest and measured 10 functional traits reflecting the acquisitive-conservative spectrum. We employed Bayesian hierarchical models to quantify the relative importance of environmental and density factors on sapling growth and investigate how functional traits mediate species-specific responses to these factors. Sapling growth rates were primarily influenced by soil conditions, light availability (canopy closure), and conspecific adult neighbor density. Acquisitive species exhibited enhanced growth under high light, favorable soil resources, and low aluminum conditions compared to conservative species. However, we found no significant relationship between functional traits and conspecific density dependence. Functional traits mediate environmental responses through divergent resource-use strategies rather than conspecific density dependence. Trait-based mechanisms underlying species coexistence may operate through pathways beyond the acquisitive-conservative spectrum. Our hierarchical modeling provides a framework for disentangling the complex relationships between functional traits, environmental responses, and density dependence in diverse tropical forests.

Methods
Tree census

The tree censuses data were collected from the 20-ha (400 m × 500 m) Tropical Seasonal Rainforest Dynamics Plots in 2017 and 2021. The plot located in Naban River Watershed National Nature Reserve, Yunnan Province, Southwestern China (100°36′E, 22°14′N).  All woody stems ≥ 1 cm DBH (diameter at breast height, 1.3 m above ground) in the plot were measured, tagged, mapped and identified to species. 

Functional traits measurement

We measured functional traits following the standardized protocol (Cornelissen et al. 2003, Pérez-Harguindeguy et al. 2016). These included key whole-plant traits (wood density, WD, g/cm3; seed mass, SM, g) and leaf traits (specific leaf area, SLA, mm2/mg; leaf area, LA, mm2; leaf thickness, LT, mm; leaf dry mass content, LDMC, mg/g; leaf carbon content, LCC, mg/g; leaf phosphorus content, LPC, mg/g; leaf nitrogen content, LNC, mg/g; leaf potassium content, LKC, mg/g). These traits were measured in in the 20-ha (400 m × 500 m) Xishuangbanna Forest Dynamics Plot in Southwest China, located in Yunnan Province, Southwestern China (101°34′ E, 21°36′ N). For each tree species, 3 - 5 healthy adult individuals were randomly selected in the plot, and 3 intact leaves (including petioles) were collected from each individual during September-October 2013, placed in plastic bags, and transported to the laboratory. Each fresh leaf was scanned and the area was measured using IMAGEJ software (Abràmoff et al. 2004). Leaf thickness (mm) was measured at the center of the leaf lamina, avoiding major veins, using electronic digital micrometer (CANY Co., Shanghai, China) on fresh leaves. The fresh mass of each leaf was measured using electronic balance with a precision of 0.001 g. Leaves were placed in paper envelopes, dried at 70 ℃ for 72 hours to a constant weight, and then weighed. Petioles were removed, leaves were grounded, and carbon and nitrogen contents were determined using a carbon-nitrogen analyzer (Vario MAX CN). Total phosphorus and potassium contents in the leaves were determined according to the LY/T 1270-1999 standard, using ICP-AES and iCAP6300 elemental analyzer, respectively. Specific leaf area was calculated as LA/leaf dry mass, and leaf dry matter content was calculated as leaf dry mass/leaf fresh mass. In 2014, wood density was measured by extracting tree cores from live trees using a sharp increment borer, and their volume was determined using the water displacement method. The cores were oven-dried at 70°C for 72 hours to achieve constant weight and then weighed. Wood density was calculated as the dry weight of the cores divided by their volume (Zhou et al. 2020). Seed mass was measured by collecting seeds within the forest for most focal tree species. For a small number of species, seed mass data were obtained from the "Seeds of Woody Plants in China" (State Forestry Administration, National Forest Farm and Tree Seed and Seedling Workstation, 2001).

Topographic factors were measured by subdividing the plot into 20 m × 20 m quadrats and calculating aspect, slope, elevation, convexity and topographic wetness index (TWI) based on elevations from 10-m stakes placed across the NFDP (Harms et al. 2001; Valencia et al. 2004). TWI is the ratio of upslope contributing area to the local slope (Tarboton 1997; Sørensen et al. 2006). These calculations were performed using the "CTFS" and "RSAGA" packages (Hall 2006; Brenning et al. 2018) in R (R Core Team 2021).

Soil Physical and Chemical Properties

Soil properties were measured using a grid of 40 m × 40 m cells established across NFDP. For each cell, a basic sampling point was established at coordinate (10 m, 10 m) in the cell, and a random sampling point was selected at a random distance of 5, 10, or 20 m along a random x, y or xy direction. Two soil samples were collected from each of the 130 cells (totaling 260 samples) at a depth of 10 cm, excluding litter and humus. For each sample, 12 soil physical and chemical properties were measured: pH, total nitrogen content, total carbon content, total phosphorus content, available iron content, available aluminum content, available potassium content, available calcium content, available manganese content, available magnesium content, available sulfur content, and available phosphorus content . Soil pH was measured using a pH meter in a 1:2.5 (w/v) suspension. Total carbon and nitrogen contents were analyzed using an elemental analyzer (Vario MAX CN). Total Phosphorus (TP) was determined by digestion with nitric acid-hydrochloric acid-hydrofluoric acid and measured using an elemental analyzer (iCAP 7400 ICP-OES). Available iron, aluminum, potassium, calcium, manganese, magnesium, sulfur, and phosphorus were measured using the Mehlich-3 method (Ziadi and Tran 2007).

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