Landscape-scale patterns of beta-diversity in Amazonian canopy trees reveals environmental filtering beyond neutral expectations

This deposit contains the data and scripts associated with the manuscript, "Landscape-scale patterns of beta-diversity in Amazonian canopy trees reveals environmental filtering beyond neutral expectations." This work addresses a fundamental challenge in ecology: disentangling the roles of deterministic (niche) and stochastic (neutral) processes in structuring hyperdiverse tropical forests. This study quantifies community turnover (beta-diversity) of large canopy trees (≥40 cm DBH) across a vast 5,100 ha contiguous landscape of terra firme forest in the Brazilian Amazon. Critical Scale: The analysis reveals community turnover patterns operating at an intermediate spatial scale (4.8 km to 15 km) that is typically overlooked by traditional small forest plots (∼700 m) or large-scale, sparse plot networks. Data Resolution: The study uses a spatially explicit, species-level inventory of 377 canopy tree species and over 283,000 stems. Methodology The analysis employs a three-pronged approach to robustly test community assembly mechanisms: Scale Quantification: Mantel Correlograms were used to quantify the spatial scale of autocorrelation in canopy tree composition, revealing patterns persist up to 4.8 km and dissimilarity continues to 8.4 km. Variance Partitioning: Generalized Dissimilarity Models (GDM) were implemented to partition compositional variance into components explained by Geographic Distance (D) and Environmental factors (E), using high-resolution topographical proxies (Topographic Wetness Index, Elevation) Neutral Null Testing: Spatially-explicit neutral coalescent simulations (pycoalescence/rcoalescence) were used to test whether stochastic processes alone could reproduce the observed spatial turnover patterns and diversity levels. Data and Code Reproducibility This repository provides all necessary files to fully reproduce the results Analysis Scripts (Sequential Order): 01_Calculate_topographical_variables.R: Derives topographical rasters from the DEM. 02_Extract_block_topographical_variables.R: Extracts mean topographical metrics to 3 ha blocks. 03_Spatial_analyses.R: Calculates distance matrices, runs the Mantel correlogram, and performs the GDM analysis (Outputting R2 and GDM splines). 04_Prepare_rasters_for_neutral_sims.R: Creates the background, fine, and sample rasters for the coalescence model using the pre-processed density data. 05_Run_neutral_simulations.R: Defines the parameter space and sequentially runs the spatially explicit neutral simulations (rcoalescence) across all parameter sets and speciation rates. 06_Analyse_neutral_simulations.R: Calculates the observed baseline uncertainty (ϵ) and loops through all simulation results to perform the final comparisons against the observed splines and richness bounds, concluding the main analysis.