Flexible methods for species distribution modeling with small samples

Species distribution models (SDMs) predict where species live or could potentially live and are a key resource for ecological research and conservation decision-making. However, current SDM methods often perform poorly for rare or inadequately sampled species, which include most species on earth, as well as most of those of the greatest conservation concern. Here, we evaluated the performance of three modeling approaches designed for data-deficient situations: plug-and-play modeling, density-ratio modeling, and environmental-range modeling. We compared the performance of algorithms within these approaches with the maximum entropy (MaxEnt) model, a widely used density-ratio algorithm, both for data-poor species and more generally. We also tested to what extent model cross-validation performance on training data predicts model performance on independent, presence-absence data. We found that no algorithm performed best in all situations. Across all species, MaxEnt performed best on average..., , # Data from: Flexible methods for species distribution modeling with small samples

This dataset contains the code, intermediate data products, and figures associated with the paper \"Flexible Methods for Species Distribution Modeling with Small Samples\" by Maitner *et al* (2025). This paper focuses on how best to model the distributions of species with small sample sizes (e.g., rare or poorly-sampled species), and compares the performances of several different approaches and algorithms. Data used in this study have been previously published, but intermediate data products and results are included in this repository in order to increase transparency and save any interested parties from having to re-run these time-consuming analyses.

## Description of the data and file structure

The file small_sample_size_sdms-main.zip contains a zipped version of the GitHub repository, the contents of which are described below.

The key scripts for the analyses begin with a number and are meant to be r...,