DOI: 10.5061/dryad.stqjq2cgv
title: Data and code from: Drivers of species richness in Amazonian amphibians and reptiles: Testing diversity hypotheses across taxonomic groups

description: Investigating the ecological drivers of species richness in the Amazon
 Basin enhances our understanding of the eco-evolutionary processes shaping
 biodiversity across taxonomic groups. Our study investigates the roles of
 energy availability, water balance, and habitat heterogeneity in shaping
 the distribution of amphibians and reptiles by testing five ecological
 hypotheses: energy-environment, energy-water dynamics, productivity,
 metabolic theory, and habitat heterogeneity. We found distinct patterns
 across taxa. The energy-environment hypothesis received the strongest
 support overall, with temperature exerting a positive effect on species
 richness in all groups, while potential evapotranspiration negatively
 impacted Gymnophiona and snakes. The energy-water dynamics hypothesis
 showed moderate, taxon-specific support, performing best for Anura,
 snakes, Crocodylia, and Testudines. Precipitation had a positive effect
 across groups, whereas actual evapotranspiration had mixed impacts, with
 positive impacts on Anura, lizards, and snakes but negatively impacting
 Crocodylia and Testudines. The productivity hypothesis received weak
 overall support; net primary productivity positively influenced Anura and
 snakes but negatively affected lizards. The metabolic theory was
 moderately supported, with the inverse of absolute temperature (1/kT)
 consistently showing a negative effect across groups. The habitat
 heterogeneity hypothesis was generally poorly supported, except for
 Gymnophiona; landscape heterogeneity negatively influenced Anura, Caudata,
 and snakes but positive influences on the remaining taxa. Our findings
 highlight that, although the energy-environment hypothesis has emerged as
 the most robust across taxa, species richness patterns in amphibians and
 reptiles are best understood when considering the joint influence of
 energy, environment, and water availability (i.e.
 energy+environment+water). This integrated understanding underscores the
 need for conservation strategies that account for the unique ecological
 requirements of different taxonomic groups. Our findings highlight that
 sustaining biodiversity in the Amazon Basin depends primarily on the
 balance of energy and water dynamics, while the role of landscape
 heterogeneity appears to be taxon-specific and context-dependent.
description: --- # README for Dataset: Drivers of Species Richness in Amazonian
 Amphibians and Reptiles: Testing Diversity Hypotheses Across Taxonomic
 Groups **Authors**\ López-Rojas, Jhon Jairo^1,2^ (ORCID:
 0000-0001-6726-5095)\ López-Rojas, Carlos Marcial^3^ Solé, Mirco^2,4^
 (ORCID: 0000-0001-7881-6227)\ Lourenço-de-Moraes, Ricardo^5^ (ORCID:
 0000-0001-6055-5380) ^1^ Facultad de Ecología, Universidad Nacional de San
 Martín, Peru\ ^2^ Programa de Pós-graduação em Zoologia, Universidade
 Estadual de Santa Cruz, Brazil\ ^3^ Oficina Zonal Loreto, Organismo de
 Formalización de la Propiedad Informal, Peru\ ^4^ Museum Koenig Bonn,
 Leibniz Institute for the Analysis of Biodiversity Change, Germany\ ^5^
 Departamento de Botânica e Zoologia, Universidade Federal do Rio Grande do
 Norte, Brazil **Article DOI** 10.1002/oik.11319 **Article Date of
 publication** November 2025 --- 1. DATASET SUMMARY --- This dataset
 provides species richness estimates for seven major ectothermic vertebrate
 groups — Anura, Caudata, Gymnophiona, Lizards, Snakes, Turtles, and
 Crocodilians — across the Amazon Basin at a spatial resolution of 10 km²
 (0.1°). It also includes key environmental and land-cover variables used
 to model drivers of richness patterns. The data were compiled to test
 macroecological hypotheses on the determinants of species richness in
 Amazonian amphibians and reptiles. The original dataset was produced at a
 spatial resolution of 10 km (0.1°). However, to allow computational
 feasibility in modeling and correlation analyses, the data were aggregated
 to 20 km (0.2°) resolution using the accompanying R script
 (analysis_code.R). Each model run required approximately 5–7 hours of
 processing time on a standard workstation. --- 1. FILE CONTENTS --- The
 dataset is provided as a single comma-separated values file: * `bd_ab.txt`
 Columns: | Column | Description | Unit | | ------------------- |
 ------------------------------------------------- | --------------- | | ID
 | Unique grid cell identifier | — | | x | Longitude (centroid of grid
 cell) | Decimal degrees | | y | Latitude (centroid of grid cell) | Decimal
 degrees | | Temperatura | Mean annual temperature | °C | | PET | Potential
 evapotranspiration | mm/year | | Precipitation | Annual precipitation |
 mm/year | | Eta | Actual evapotranspiration | mm/year | | NPP | Net
 Primary Productivity | g C/m²/year | | anura | Species richness of frogs
 and toads (Anura) | Count | | caudata | Species richness of salamanders
 (Caudata) | Count | | gymno | Species richness of caecilians (Gymnophiona)
 | Count | | lizard | Species richness of lizards (Squamata) | Count | |
 snakes | Species richness of snakes (Squamata) | Count | | turtle |
 Species richness of turtles (Testudines) | Count | | croco | Species
 richness of crocodilians (Crocodilia) | Count | | rangeland | Percentage
 of rangeland within each cell | % | | bare\_ground | Percentage of bare
 ground within each cell | % | | flooded\_vegetation | Percentage of
 flooded vegetation within each cell | % | | water | Percentage of water
 cover within each cell | % | | trees | Percentage of tree cover within
 each cell | % | Missing values are coded as `NA`. --- 1. DATA SOURCES AND
 PROCESSING --- **Species distributions** * Amphibians and Crocodilia: IUCN
 Red List (2023.1), range polygons (SHP). * Squamata (lizards &
 snakes): Global Assessment of Reptile Distributions (GARD v1.7; Roll et
 al. 2017. * Testudines: Rhodin et al. (2021), *Turtles of the World* (9th
 Ed.), digital range maps provided by the authors. **Environmental
 variables** * Climate (BIO1, BIO12): WorldClim v2.1 (30 arc-seconds). *
 PET: Zomer et al. 2022 (DOI:
 [10.1038/s41597-022-01493-1](https://doi.org/10.1038/s41597-022-01493-1)).
 * Eta (actual evapotranspiration): SSEBop V6.1 model (FEWS NET,
 [https://earlywarning.usgs.gov/fews/product/458](https://earlywarning.usgs.gov/fews/product/458)). * LULC (Land Cover): Sentinel-2 Global Land Use 2021 ([https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d](https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d)). * NPP: MODIS-derived Net Primary Productivity ([https://lpdaac.usgs.gov/products/mod17a3hgfv061/](https://lpdaac.usgs.gov/products/mod17a3hgfv061/)). **Spatial processing**\ All layers were clipped to the Amazon Basin using the `amzbasin.tif` mask from Mayorga et al. (2012) (DOI: [10.3334/ORNLDAAC/1086](https://doi.org/10.3334/ORNLDAAC/1086)). Species range maps were rasterized to a 0.1° (~10 km) grid, and presence/absence was overlaid with environmental layers in ArcGIS Pro to generate richness counts per cell. --- 1. CODE FILE --- Code file: * `analysis_code.R.txt` This R script contains all procedures for data aggregation, variable computation, and spatial analyses. Key steps include the rescaling of 10 km base data to 20 km resolution, calculation of the Boltzmann temperature variable (1/kT), generation of land-cover metrics (Shannon diversity index), and implementation of spatial autoregressive and error models (SAR and SEM), and impact estimates. --- 1. USAGE NOTES --- * This dataset is intended for research on macroecology, biodiversity drivers, conservation prioritization, and climate–land use impacts on Amazonian herpetofauna. * As a courtesy, users are suggested to cite the associated article and original data sources when reusing this dataset. * The dataset complies with the data transparency policy of *Oikos*. --- 1. CITATION --- López-Rojas, J. J., López-Rojas, C. M., Solé, M., & Lourenço-de-Moraes, R. (2025). Drivers of Species Richness in Amazonian Amphibians and Reptiles: Testing Diversity Hypotheses Across Taxonomic Groups. Oikos. [https://doi.org/10.1002/oik.11319](https://doi.org/10.1002/oik.11319) --- 1. CONTACT --- For questions or clarifications, please contact: Jhon Jairo López-Rojas [lopezrojasjj@gmail.com](mailto:lopezrojasjj@gmail.com)\ ORCID: 0000-0001-6726-5095