DOI: 10.5061/dryad.18931zd85
title: Integrated demographic strategies are more strongly associated with variation in conspecific density dependence than single traits in tropical tree seedlings

description: Conspecific negative density dependence (CNDD) is considered a key
 mechanism shaping species diversity in plant communities. However, species
 vary widely in CNDD strength, and the main ecological drivers of this
 variation remain unclear. We tested whether interspecific variation in
 CNDD is linked to (i) species’ demographic strategies related to
 growth–survival and stature–recruitment trade-offs, (ii) functional
 traits, including wood density, seed mass, maximum height, and four leaf
 traits, and (iii) species’ relative abundance. Using 18 years of seedling
 mortality data (145,768 individuals, 260 tree species) from a tropical
 moist forest in Panama and a robust modeling framework that accounts for
 potential biases due to non-linearities and variation in baseline
 mortality, we calculated species-specific estimates of  CNDD that
 quantify the effect of conspecific relative to heterospecific neighbors
 and tested for relationships between CNDD strength and demographic
 strategies, functional traits, and relative abundance. CNDD strength
 varied widely across species and was significantly related to species
 location along both the growth-survival and stature-recruitment trade-off
 axes. Fast-growing species and tall, long-lived pioneers exhibited
 stronger sensitivity to CNDD compared to slow-growing and short-lived
 species, respectively. With the exception of wood density, single
 functional traits were not significantly associated with CNDD strength.
 Rare species experienced stronger CNDD than common species. These findings
 underscore the importance of life-history strategies over isolated traits
 in shaping density-dependent effects, suggesting that CNDD variation could
 reinforce niche differences and influence community composition and
 species coexistence in tropical forests.   Additionally,
 our results confirm that rare species experience stronger CNDD than common
 species at the seedling stage, suggesting that CNDD may constrain species
 abundances and shape tropical tree diversity. Our results show that, with
 the exception of wood density, single traits were weak predictors of CNDD
 strength, while integrated demographic strategies captured meaningful
 variation. CNDD is strongest in rare, fast-growing, and long-lived pioneer
 species, suggesting that biotic interactions at early life stages align
 with key life-history trade-offs. Studies that assess and integrate links
 between CNDD and demographic trade-offs across multiple life stages are
 needed to understand the combined impact of these mechanisms on tree
 communities.
description: # Integrated demographic strategies are more strongly associated with
 variation in conspecific density dependence than single traits in tropical
 tree seedlings ## Overview This repository contains the data and R scripts
 required to reproduce the analyses from the manuscript titled
 **"**Integrated demographic strategies are more strongly associated
 with variation in conspecific density dependence than single traits in
 tropical tree seedlings.**"** The study quantifies species-specific
 CNDD (Conspecific Negative Density Dependence) using long-term seedling
 survival data and links variation in CNDD to species traits, life-history
 axes, and relative abundance via meta-regression. All files are contained
 in **Data_Script_NHN.zip**. --- #### Directory Structure and File
 Descriptions **1. Data Processing** * **File:** `Data_processing.R` *
 **Purpose:** Prepares the raw seedling census data (2001–2018) for
 modeling mortality. * **Variable Definitions (Main Census File)** Raw
 census variables (e.g., plot location, tag, species code, size, status,
 coordinates, and related fields) originate from the BCI seedling census
 dataset (Comita et al. 2023;
 [https://doi.org/10.1002/ecy.4140](https://doi.org/10.1002/ecy.4140)).
 Detailed variable definitions, units, and field protocols are provided in
 Supplementary Metadata S1 of that publication. Below, we provide a concise
 variable dictionary for the main census variables used in this study,
 derived from the original BCI seedling census dataset. | Variable |
 Description | Units | | ------------------------ |
 --------------------------------------- | ----------- | | `id` | Unique
 seedling identifier (q20.p5.tag) | – | | `q20` | 20 × 20 m quadrat ID | –
 | | `p5` | 5 × 5 m subquadrat ID | – | | `tag` | Tag number within plot |
 – | | `spp` | Six-letter species code | – | | `fecha` | Census date
 (MM/DD/YYYY) | – | | `censo` | Census number | – | | `alt` | Seedling
 height | mm | | `dbh` | Stem diameter at 1.3 m | mm | | `tallos` | Number
 of stems | count | | `hojas` | Number of leaves | count | | `status` |
 Seedling status (alive/dead/etc.) | categorical | | `x` | Plot
 x-coordinate | m | | `y` | Plot y-coordinate | m | | `genus` | Genus name
 | – | | `species` | Species name | – | | `family` | Plant family | – | |
 `census.count` | Number of censuses observed | count | |
 `time.since.last.census` | Interval since previous census | years |
 Additional administrative and field notes columns (e.g., `nov`, `ryan`,
 `notes_liza`, `sjw09.do.not.remeasure`) contain observer comments and
 quality-control flags. **Missing values:** Missing or unavailable
 measurements are coded as `NA` (e.g., leaf counts not recorded in a given
 census). --- #### 2. **Model Fitting** * **File:** `01_fit_model.R` *
 **Purpose:** Fits species-specific GAMs using conspecific and total
 seedling density, height, and plot/census as random smooth terms.
 Calculates: * AUC for model fit * Pseudo-R² (compared to reduced model) *
 Average Marginal Effects (AME) and relative AMEs (rAME) for CNDD under
 multiple change scenarios --- #### CNDD Marginal Effects * **File:**
 `rAME_spp_equilibrium.csv` This file contains species-specific estimates
 of conspecific negative density dependence (CNDD) derived from generalized
 additive models fitted in `01_fit_model.R`. It summarizes relative average
 marginal effects (rAME) under defined density-change scenarios. | Variable
 | Description | Units | | ----------------------- |
 ----------------------------------------------------- | ----------- | |
 `N` | Row index | – | | `X` | Species code | – | | `term` | Model term
 (e.g., `con_dens` for conspecific density) | – | | `estimate` | Estimated
 marginal effect | model scale | | `std.error` | Standard error of estimate
 | model scale | | `offset` | Offset value used in model | – | |
 `change.value` | Density change value used in AME/rAME calculation | count
 | | `estimate_relative_log` | Relative marginal effect (log-transformed) |
 log scale | | `var_log` | Variance of log-transformed estimate | – | |
 `change` | Density-change scenario (e.g., equilibrium) | categorical | |
 `sp` | Species code | – | Estimates are derived from GAMs with a
 complementary log–log link and represent effects on mortality risk.
 **Missing values:** Missing values are coded as `NA`. --- #### Additional
 Derived and External Data Files The following external datasets are used
 in the meta-regression analyses described below. For transparency and
 reuse, we provide brief descriptions of key variables in each file. Full
 methodological and variable details are available in the original source
 publications cited. **1. Functional traits and life-history PCA dataset
 (`Traits_Browne.et.al_and_Westbrook.et.al.csv`)** This file contains
 species-level functional trait data compiled from Browne et al. (2023;
 [https://doi.org/10.5061/dryad.mkkwh713s](https://doi.org/10.5061/dryad.mkkwh713s); [https://doi.org/10.1086/659963](https://doi.org/10.1086/659963)), Westbrook et al. (2011), and life-history PCA axes from Rüger et al. (2020; [https://doi.org/10.1126/science.aaz4797](https://doi.org/10.1126/science.aaz4797)). These traits are used to explain interspecific variation in CNDD. | Column | Description | Units | | ------------ | ---------------------------------------------------------------------- | -------- | | `SPP` | Six-letter species code | – | | `wood_dens` | Mean wood specific gravity | g cm⁻³ | | `lma` | Leaf mass per area (indirect light; incl. petiole) | g m⁻² | | `seed_mass` | Mean seed dry mass (60°C) | g | | `leaf_area` | Mean leaf area (indirect light) | cm² | | `height_max` | Mean height of up to the six largest individuals in the BCI 50-ha plot | m | | `leaf_dmc` | Leaf dry matter content (indirect light) | g g⁻¹ | | `LamT` | Lamina thickness | mm | | `LamFT` | Lamina fracture toughness | J m⁻² | | `PC1_ruger` | Life-history PCA axis 1 (Rüger et al. 2020) | unitless | | `PC2_ruger` | Life-history PCA axis 2 (Rüger et al. 2020) | unitless | | `BA_m2` | Adult species basal area (if included) | m² | | `No_trees` | Number of adult trees (if included) | count | Trait measurement protocols and original units are described in Westbrook et al. (2011) and Wright et al. (2010). PCA axes are described in Rüger et al. (2020). **Missing values:** Missing or unavailable trait values are coded as `NA`. --- **2. Species-level demographic rates (`subset_demographic_Ruger.etal.csv`)** This file contains species-level demographic rate variables provided by N. Rüger and described in Rüger et al. (2018; [https://doi.org/10.1111/ele.12974](https://doi.org/10.1111/ele.12974)). Values are in transformed (but not centered or scaled) form and are used as covariates in the meta-regression analyses. | Variable | Description | Units | | ------------------ | ------------------------------------------ | ------------------------------ | | `sp` | Species code used to match across datasets | – | | `G1`–`G4` | Growth rates in four canopy layers | transformed scale (see source) | | `S1`–`S4` | Survival rates in four canopy layers | transformed scale (see source) | | `Rcapita` | Per-capita sapling recruitment | per capita (transformed) | | `seedlingGrowth` | Seedling growth rate | transformed scale (see source) | | `seedlingSurvival` | Seedling survival estimate | transformed scale (see source) | | `seedN` | Per-capita number of seeds | per capita (transformed) | | `seedlingN` | Per-capita number of seedling recruits | per capita (transformed) | For full methodological details and original units, see Rüger et al. (2018) and associated supplementary materials. **Missing values:** Missing or unavailable values are coded as `NA`. --- #### 3. **Meta-Regression Analyses** * **Script:** `Meta-regression.R` * **Purpose:** Tests whether variation in species-specific CNDD is explained by: * **Functional traits** * **PCA axes** representing major life-history trade-offs (growth-survival and stature-recruitmet). * **Species relative abundance**. * **Demographic rates** (growth, survival, recruitment from Rüger et al., 2018). * **Outputs:** Regression summaries, diagnostic plots, and figures. --- #### How to Reproduce the Analysis 1. Run `Data_processing.R` to generate the cleaned dataset. 2. Use `01_fit_model.R` to fit GAMs, compute AMEs/rAMEs, and save model summaries. 3. Use `Meta-regression.R` to evaluate how CNDD relates to species traits and demographic strategies. --- #### Notes on Access and Data Sharing * **Data Source (original):** 1\. Long-term seedling censuses in Barro Colorado Island, Panama (2001–2018) find it at [https://doi.org/10.1002/ecy.4140](https://doi.org/10.1002/ecy.4140) (Comita et al. 2023) 2\. Life-history PCA scores find it at [https://10.1126/science.aaz4797](https://10.1126/science.aaz4797) (Rüger et al. 2020) and species demographic rates find it at [https://doi.org/10.1111/ele.12974](https://doi.org/10.1111/ele.12974) (Rüger et al. 2018) 3: Functional traits: find it at [https://doi.org/10.5061/dryad.mkkwh713s](https://doi.org/10.5061/dryad.mkkwh713s) and [https://doi.org/10.1086/659963](https://doi.org/10.1086/659963) (Browne et al., 2023) and (Westbrook et al., 2011)