This repository contains standardized forest inventory data and Bayesian modeling scripts used to analyze drivers of aboveground carbon (AGC) and forest structural traits—including height, basal area, and wood density—in Gabonese tropical forests. The study evaluates how environmental conditions, forest disturbance (e.g., logging), and elephant activity influence AGC across different tree size classes (small, medium, large).
Two modeling frameworks are implemented in R using JAGS:(1) a general model that predicts AGC based on latent traits and simulates outcomes under different scenarios; and(2) hierarchical models stratified by tree size class to evaluate size-specific drivers of carbon and structure.
The repository includes cleaned and standardized input datasets (in .csv format), reproducible modeling scripts, posterior summaries, diagnostics (DIC, Gelman R̂, effective sample size), and publication-ready figures.
Raw data are subject to third-party access restrictions but are available upon request with permission from Le Ministère des Eaux, de la Forêt, de la Mer, de l’Environnement (Gabon). Interested researchers may contact the corresponding author to initiate a data access request.