Overview
GeoThinneR is an R package designed for efficientspatial thinning of species occurrence records and other geospatialpoint data. It integrates three primary thinning methods(distance-based, grid-based, and precision-basedthinning) into a single package, eliminating the need to switchbetween multiple tools. GeoThinneR implements algorithms based onkd-tree structures for nearest-neighbor searches, significantlyimproving performance andscalability for largedatasets. Additionally, the package provides customfunctionalities useful for species distributionmodeling (SDM), such as thinning by group (e.g., multiple species),retaining an exact number of points, and prioritizing records based onuser-defined variables. These features make GeoThinneR a valuable toolfor handling large-scale occurrence datasets.
GeoThinneR has been developed as an alternative tool for spatialthinning to mitigate the effects of sampling bias in SDM. Variousapproaches exist to address sampling bias, each suited to differentscenarios. Below are some references discussing methods for biascorrection and spatial thinning:
- Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P.(2014). Spatial filtering to reduce sampling bias can improve theperformance of ecological niche models.Ecological modelling,275, 73-77.https://doi.org/10.1016/j.ecolmodel.2013.12.012
- Veloz, S. D. (2009). Spatially autocorrelated sampling falselyinflates measures of accuracy for presence‐only niche models.Journal of biogeography, 36(12), 2290-2299.https://doi.org/10.1111/j.1365-2699.2009.02174.x
- Moudrý, V., Bazzichetto, M., Remelgado, R., Devillers, R., Lenoir,J., Mateo, R.G., Lembrechts, J.J., Sillero, N., Lecours, V., Cord, A.F.,Barták, V., Balej, P., Rocchini, D., Torresani, M., Arenas-Castro, S.,Man, M., Prajzlerová, D., Gdulová, K., Prošek, J., Marchetto, E.,Zarzo-Arias, A., Gábor, L., Leroy, F., Martini, M., Malavasi, M.,Cazzolla Gatti, R., Wild, J. and Šímová, P. (2024), Optimisingoccurrence data in species distribution models: sample size, positionaluncertainty, and sampling bias matter.Ecography, 2024: e07294.https://doi.org/10.1111/ecog.07294
Getting started
You can installGeoThinneR from CRAN with:
install.packages("GeoThinneR")To install thedevelopment version from GitHub,use:
# install.packages("devtools")devtools::install_github("jmestret/GeoThinneR")Using GeoThinneR is simple. The main function,thin_points(), applies spatial thinning using auser-specified method and thinning constraint.
library(GeoThinneR)# Distance-based thinning (minimum separation of 10 km)thin_points(data,method ="distance",thin_dist =10)# Grid-based thinning (grid resolution of 0.1 degrees)thin_points(data,method ="grid",resolution =0.1)# Precision-based thinning (rounding coordinates to 1 decimal place)thin_points(data,method ="precision",precision =1)Documentation
For detailed documentation, guides, and usage examples, please visittheofficial packagedocumentation.
Contributing
We welcome contributions! If you have suggestions for improvements ornew features, please open an issue or submit a pull request on ourGitHub repository.
How to cite GeoThinneR
The GeoThinneR manuscript is currently in progress. In the meantime,you can cite thepreprint asfollows:
Mestre-Tomás, J. (2025). GeoThinneR: An R Package for EfficientSpatial Thinning of Species Occurrences and Point Data. arXiv preprintarXiv:2505.07867. DOI:https://doi.org/10.48550/arXiv.2505.07867
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