DOI: 10.5061/dryad.5x69p8dcz
title: Species traits mediate environmental responses but not conspecific density dependence in tropical tree saplings

description: 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.
description: <strong>Tree census</strong> 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. 
 <strong>Functional traits
 measurement</strong> 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/cm<sup>3</sup>; seed mass, SM, g) and
 leaf traits (specific leaf area, SLA, mm<sup>2</sup>/mg; leaf
 area, LA, mm<sup>2</sup>; 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). <strong>Soil Physical and
 Chemical Properties</strong> 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).
 <strong>Reference</strong>
 Abràmoff, M. D., P. J. Magalhães, and S. J. Ram. 2004. Image
 processing with ImageJ. Biophotonics International 11:36–42.
 Brenning, A., D. Bangs, M. Becker, P. Schratz, and F. Polakowski.
 2018. SAGA Geoprocessing and terrain analysis.
 Cornelissen, J. H. C., S. Lavorel, E. Garnier, S. Díaz, N.
 Buchmann, D. E. Gurvich, P. B. Reich, et al. 2003. A handbook of protocols
 for standardised and easy measurement of plant functional traits
 worldwide. Australian Journal of Botany 51:335–380.
 Hall, P. 2006. The CTFS large plot forest dynamics
 analyses. Harms, K. E., R. Condit, S. P. Hubbell, and
 R. B. Foster. 2001. Habitat associations of trees and shrubs in a 50-ha
 neotropical forest plot. Journal of Ecology 89:947–959.
 Perez-Harguindeguy, N., S. Diaz, E. Garnier, S. Lavorel, H.
 Poorter, P. Jaureguiberry, M. S. Bret-Harte, et al. 2016. New handbook for
 standardised measurement of plant functional traits worldwide. Australian
 Journal of Botany 61:715–716. R Core Team. 2021. R: A
 language and environment for statistical computing.
 State-owned forest farm and tree seedling work station of state
 forestry administration. 2001. Seeds of woody plants in China. China
 Forestry Publishing House, Beijing. Sørensen, R., U.
 Zinko, and J. Seibert. 2006. On the calculation of the topographic wetness
 index: evaluation of different methods based on field observations.
 Hydrology and Earth System Sciences 10:101–112.
 Tarboton, D. G. 1997. A new method for the determination of flow
 directions and upslope areas in grid digital elevation models. Water
 Resources Research 33:309–319. Valencia, R., R. B.
 Foster, G. Villa, R. Condit, J. C. Svenning, C. Hernandez, K. Romoleroux,
 et al. 2004. Tree species distributions and local habitat variation in the
 Amazon: large forest plot in eastern Ecuador. Journal of Ecology
 92:214–229. Zhou C., B. Wang, Y. Deng, J. Wu, M. Cao,
 and L. Lin. 2020. Canopy structure is an important factor driving
 local-scale woody plant functional beta diversity. Biodiversity Science
 28:1546–1557. Ziadi, N., and T. S. Tran. 2007. Mehlich
 3-extractable elements. Pages 81–88 <em>in</em> M. R. Carter
 and E. G. Gregorich, eds. Soil sampling and methods of analysis (2nd ed.).
 CRC Press, Boca Raton, USA.
description: Data and R code from: Species traits mediate environmental responses but
 not conspecific density dependence in tropical tree saplings Author:
 Xiaohua Ma[1, 2#], Rong Gu[1, 2#], Guoshan Shi[2], Feng Liu[3, 4],
 Guanghong Cao[3], Yun Deng[2], Shangwen Xia[2], Xiaodong Yang[2], Zhiming
 Zhang[1*], Luxiang Lin [2, 5*] \[1] School of Ecology and Environment
 Science, Yunnan University, Kunming 650504, China \[2] Yunnan Key
 Laboratory of Forest Ecosystem Stability and Global Change, Xishuangbanna
 Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, Yunnan,
 666303, China \[3] Administration Bureau of Naban River Watershed National
 Nature Reserve, Jinghong, Yunnan 666100, China \[4] Yunnan Academy of
 Forestry and Grassland, Kunming, Yunnan 650204, China \[5] National Forest
 Ecosystem Research Station at Xishuangbanna, Mengla, Yunnan, 666303, China
 \[#] These authors have equal contributions. \[*] Corresponding authors:
 Zhiming Zhang: E-mail: [zzming76@ynu.edu.cn](mailto:zzming76@ynu.edu.cn);
 Luxiang Lin: E-mail: [linluxa@xtbg.ac.cn](mailto:linluxa@xtbg.ac.cn) #
 Species traits mediate environmental responses but not conspecific density
 dependence in tropical tree saplings This README.txt file was updated on
 2025-10-24 on "Species traits mediate environmental responses but not
 conspecific density dependence in tropical tree saplings" **Software
 Requirements** Data analyses were performed in R version 4.2.2. The
 following R packages were used in this analyses: dplyr (version 1.1.4)
 rstan (version 2.32.3) loo (version 2.6.0) cowplot (version 1.1.1) tibble
 (version 3.2.1) purrr (version 1.0.2) ggplot2 (version 3.5.1) ggh4x
 (version 0.3.0) vegan (version 2.7.1) factoextra (version 1.0.7)
 FactoMineR (version 2.12) ggpubr (version 0.6.1) # Species traits mediate
 environmental responses but not conspecific density dependence in tropical
 tree saplings This data repository consist of 3 data files, 4 code
 scripts, 101 stan scripts, 11 fitted stan model objects and this README
 document, with the following data and code filenames and variables ## Data
 files 1.**RGRAnalysisData.csv**: Data used for constructing Bayesian
 hierarchical models. * Family: family of a species. * species: scientific
 species name. * qua: The numbering of 20m × 20m plots. * dbh_2016 :
 Diameter (m) at breast height (1.3 m above ground) measured in 2016. *
 rgr: Relative growth rate calculated from 2016 to 2021. * Atotal_15: Total
 adult neighbor density(cm^2/m), calculated based on the sapling basal area
 within a 15-meter radius. * Stotal_15: Total sapling neighbor
 density(cm^2/m) , calculated based on the sapling basal area within a
 15-meter radius. * Acon_15: Conspecific adult neighbor density(cm^2/m) ,
 calculated based on the sapling basal area within a 15-meter radius. *
 Scon_15: Conspecific sapling neighbor density(cm^2/m) , calculated based
 on the sapling basal area within a 15-meter radius. * Scon_stem_number:
 Conspecific sapling neighbor density(ind. / m) , calculated based on the
 sapling stem count within a 15-meter radius. * Stotal_stem_number: Total
 sapling neighbor density(ind. / m) , calculated based on the sapling stem
 count within a 15-meter radius. * closure_10 & closure_15 &
 closure_20: Canopy closure within radius of 10m, 15m, 20m, respectively *
 envpc1 & envpc2 & envpc3: The first three principal component axes
 of physical and chemical properties of soil and topographic variables
 2.**trait_data.csv**: Trait file includes 10 species-specific functional
 traits and their first two principal components, which were used as
 group-level predictors in the two-level model. These traits were measured
 in 2013 (leaf economics traits) and 2014 (wood density) inin adjacent
 forests of the same type as our study plot (Zhou et al. 2020), following
 standardized protocols (Cornelissen et al. 2003; Perez-Harguindeguy et al.
 2016). 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) * LA: Leaf area (mm^2). * SLA: Specific leaf area (mm^2
 /mg). * LDMC: Leaf dry matter content (mg/g). * LT: Leaf thickness (mm). *
 LCC: Leaf carbon content (mg/g). * LNC: Leaf nitrogen content (mg/g). *
 LPC: Leaf phosphor content (mg/g). * LKC: Leaf kalium content (mg/g). *
 WD: Wood density (g/cm^3). * SM: Seed mass (g). * PC1: the first principal
 component axis of 10 functional traits. * PC2: the second principal
 component axis of 10 functional traits. 3.**EnvironmentData.csv**:
 contains 5 topographic factors and 12 soil physical and chemical
 properties, which were used in the first-level model to characterize the
 environment. * plot: plot ID of each 20m × 20m plots. * AAl: available
 aluminum content (mg/kg). * ACa: available calcium content (mg/kg). * AFe:
 available iron content (mg/kg). * AK: available potassium content (mg/kg).
 * AMg: available magnesium content (mg/kg). * AMn: available manganese
 content (mg/kg). * AP: available phosphorus content (mg/kg). * AS:
 available sulfur content (g/kg). * TC: total carbon content (g/kg). * TN:
 total nitrogen content (g/kg). * TP: total phosphorus content (g/kg). *
 Soil pH: Soil pH. * Elevation: elevation(m). * Convexity: convexity. *
 Slope: slope. * AspectSin: Aspect in Sin. * AspectCos: Aspect in Cos. *
 TWI: topographic wetness index. ## stan model scripts 1.
 **stanmodel.zip**: All stan model files for RGR parameter estimation
 **First-Level Models** **Models of canopy closure series**: *
 model_closure10.stan: The model with canopy closure within radii of 10m *
 model_closure15.stan: The model with canopy closure within radii of 15m *
 model_closure20.stan: The model with canopy closure within radii of 20m
 **Neighborhood density model**: models with different combinations of
 density variables including total and conspecific adult and sapling
 neighbor densities within radius of 15m from the focal saplings. *
 nei_model1.stan: The model with total sapling nieghbor density and
 conspecific sapling neighbor density that were calculated based on basal
 area. * nei_model2.stan: The model with total sapling nieghbor density and
 conspecific sapling neighbor density that were calculated based on stem
 count. **Second-Level Models** * model_FixFactor_trait.stan: file
 represents a Bayesian hierarchical model where functional traits are used
 as group-level predictors for species-specific coefficients of
 environmental or neighbor density variables. FixFactor represent fixed
 factor in Neighborhood density model, include "envpc1",
 "envpc2", "envpc3", "closure",
 "Acon", "Scon", "Atotal",
 "Stotal"; trait include "LA", "SLA",
 "LDMC", "LT", "LCC", "LNC",
 "LPC", "LKC", "WD", "SM",
 "PC1", "PC2". **E.g.** * model_closure_LA.stan: leaf
 area (LA) as the group level predictor for species-specific coefficients
 of canopy closure (closure). **Note**: Since this study involves a large
 number of Stan models, we have provided detailed annotations only in
 "model_closure_LA.stan". ## R scripts 1.
 **Bayesian_Model_Fitting.R**: Code for Bayesian parameter estimation of
 growth models using Stan. This script compiles and executes Stan models
 via the rstan package in R. Generates Table S4, Table S5. Note that the
 full execution may take approximately 78 hours to complete on a standard
 research workstation (varies by hardware; tested on 8-core CPU with 32GB
 RAM). 2. **Extract_result.R**: Code for extracting posterior coefficients
 and summary statistics from Bayesian model outputs for visualization and
 analysis. Generates Table 1, Table S1, Table S6, and Table S7. 3.
 **Figure_code.R**: R script for the drawing Figure1 to Figure 4 and Figure
 S5. 4. **PCA_trait_environment_visualization.R**: R script for principal
 component analysis of trait and environmental variables and visualization,
 including Figure S1, Figure S3, Table S2 and Table S3. ## Intermediate
 Stan model objects 1. **base_data.RData**: A serialized R data file
 containing a prepared and standardized dataset for Bayesian hierarchical
 modeling using Stan, including transformed response and predictor
 variables, species and plot indices, and standardized functional traits.
 2. **first_level_model.Rdata**: This file contains the fist-level model
 object (newfit1 and newfit2) resulting from a Bayesian hierarchical
 analysis. 3. **envpc1_model_list.RData**: Contains a list of 12 fitted
 Bayesian hierarchical models from the second-level analysis, examining how
 10 functional traits and their first two principal components influence
 species-specific responses to Soil PC1. 4. **envpc2_model_list.RData**:
 Contains a list of 12 fitted Bayesian hierarchical models from the
 second-level analysis, examining how 10 functional traits and their first
 two principal components influence species-specific responses to Soil PC2.
 5. **envpc3_model_list.RData**: Contains a list of 12 fitted Bayesian
 hierarchical models from the second-level analysis, examining how 10
 functional traits and their first two principal components influence
 species-specific responses to Soil PC3. 6. **closure10_model_list.RData**:
 Contains a list of 12 fitted Bayesian hierarchical models from the
 second-level analysis, examining how 10 functional traits and their first
 two principal components influence species-specific responses to light
 availability. 7. **Atotal15_model_list.RData**: Contains a list of 12
 fitted Bayesian hierarchical models from the second-level analysis,
 examining how 10 functional traits and their first two principal
 components influence species-specific responses to total adult neighbor
 density. 8. **Acon15_model_list.RData**: Contains a list of 12 fitted
 Bayesian hierarchical models from the second-level analysis, examining how
 10 functional traits and their first two principal components influence
 species-specific responses to conspecific adult neighbor density. 9.
 **Stotal15_model_list.RData**: Contains a list of 12 fitted Bayesian
 hierarchical models from the second-level analysis, examining how 10
 functional traits and their first two principal components influence
 species-specific responses to total sapling neighbor density. 10.
 **Scon15_model_list.RData**: Contains a list of 12 fitted Bayesian
 hierarchical models from the second-level analysis, examining how 10
 functional traits and their first two principal components influence
 species-specific responses to conspecific sapling neighbor density. 11.
 **FigureData.RData**: All outputs derived from Bayesian models for
 visualization.