2026-10-11T13:13:40Z https://www.scidb.cn/oai

10.57760/sciencedb.37492 2026-02-24T17:37:32Z

2026-02-24 Cascading Temporal, Spatial and Metabolomic Drivers of Tropical Seedling Community Structure and Dynamics doi:10.57760/sciencedb.37492 en The code provided in this repository facilitates the replication of analyses for the study "Cascading Temporal, Spatial and Metabolomic Drivers of Tropical Seedling Community Structure and Dynamics." It is structured to process the associated dataset and generate the key results and figures presented in the manuscript. The R code performs integrated analyses of long-term seedling demographic data and leaf metabolomic profiles. Its core functions include:Temporal Niche Analysis:​ Quantifies inter-annual and intra-annual covariation in seedling recruitment time series using Pearson correlation and stochastic block modeling to identify species groups with synchronous recruitment patterns.Spatial Encounter Analysis:​ Calculates standardized effect sizes (SES) for conspecific and heterospecific spatial encounter rates, comparing observed patterns to null models of random dispersal.Metabolomic Data Processing:​ Handles species-level chemical similarity calculations using the Chemical Structural and Compositional Similarity (CSCS) index for the full metabolomic dataset and specific compound classes (alkaloids, polyketides, shikimates and phenylpropanoids, and terpenoids).Neighborhood Survival Models:​ Implements Bayesian Generalized Linear Mixed Models (GLMMs) to evaluate the effects of conspecific density and the phytochemical similarity of heterospecific neighbors on seedling survival. The models account for species identity, plot, and census interval as random effects. Temporal Niche Analysis; Spatial Encounter Analysis; Metabolomic Data Processing; Neighborhood Survival Models Yang Jie Nathan G. Swenson PUBLIC https://api.github.com/licenses/mit dataset Science Data Bank