Data and code from: Drivers of species richness in Amazonian amphibians and reptiles: Testing diversity hypotheses across taxonomic groups
Data files
Nov 20, 2025 version files 4.59 MB
-
analysis_code.R.txt
58.13 KB
-
bd_ab.txt
4.53 MB
-
README.md
6.75 KB
Abstract
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.
Authors
López-Rojas, Jhon Jairo1,2 (ORCID: 0000-0001-6726-5095)
López-Rojas, Carlos Marcial3
Solé, Mirco2,4 (ORCID: 0000-0001-7881-6227)
Lourenço-de-Moraes, Ricardo5 (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
- 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.
- 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.
- 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).
- Eta (actual evapotranspiration): SSEBop V6.1 model (FEWS NET, 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).
- NPP: MODIS-derived Net Primary Productivity (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). 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.
- 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.
- 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.
- 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
- CONTACT
For questions or clarifications, please contact:
Jhon Jairo López-Rojas
lopezrojasjj@gmail.com
ORCID: 0000-0001-6726-5095
