DOI: 10.5061/dryad.0vt4b8hc2
title: Data from: Flexible methods for species distribution modeling with small samples

description: 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 but was outperformed by one or more of the plug-and-play,
 density-ratio, or environmental-range algorithms in 72 % of cases. Six of
 the other algorithms had the area under the receiver operating
 characteristic curve (AUC) distributions not significantly different from
 MaxEnt’s, and for data-poor species (those with 20 or fewer occurrences),
 24 of the algorithms considered had AUC distributions not significantly
 different from MaxEnt’s. However, we found that the algorithm outputs
 (when thresholded to predict presence vs absence) spanned a wide
 sensitivity-specificity gradient. Specificity and prediction accuracy
 assessed on training data were strongly correlated with specificity and
 prediction accuracy assessed on independent presence-absence data.
 However, AUC and sensitivity were weakly correlated in training vs testing
 sets, with only 22 % of species having the same model perform best when
 evaluated on training and independent, presence absence data. Finally, we
 show how ensembles of algorithms that span the sensitivity-specificity
 gradient can represent model disagreement in poorly sampled species and
 improve model predictions.
description: # 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 run in
 the corresponding order. The **R folder** contains R functions written for
 use in this manuscript, with documentation provided in
 [Roxygen](https://roxygen2.r-lib.org/) syntax. The **outputs** folder
 contains data on model performances. Files beginning with "temp"
 are temporary files used to resume interrupted workflows. In file names,
 "dr" refers to density-ratio models while "pnp" refers
 to plug-and-play models plus environmental-range models and
 "all" includes density-ratio, plug-and-play models, and
 environmental range models. "Full" refers to models fitted with
 the full set of training data while "fold" refers to data
 pertaining to k-fold cross-validation of model. The folder
 **model_predictions** contains parquet files which describe, for each
 species and model algorithm combination, which locations were predicted to
 be within a species' range (1) vs. outside of a species' range
 (0). The .RDS files in the outputs folders can be opened in R using the
 function readRDS. The parquet files in the model_predictions folder can be
 opened with the [arrow package](https://cran.r-project.org/package=arrow)
 for R, using either the function read_parquet to open a single file or
 open_dataset to access multiple files at once. The **figures** and
 **tables** folders contain figures and tables (respectively) that are
 generated from the numbered scripts. ## Sharing/Access information Links
 to other publicly accessible locations of the data: * [Access this dataset
 on Zenodo](https://doi.org/10.5281/zenodo.14902719) * [Access this dataset
 on Github](https://github.com/bmaitner/small_sample_size_sdms) Data were
 derived from the following sources: * [Elith, J., Graham, C., Valavi, R.,
 Abegg, M., Bruce, C., Ford, A., Guisan, A., Hijmans, R. J., Huettmann, F.,
 Lohmann, L., & Others. (2020). Presence-only and presence-absence data
 for comparing species distribution modeling methods. Biodiversity
 Informatics, 15(2), 69–80.
 https://doi.org/10.17161/bi.v15i2.13384](https://doi.org/10.17161/bi.v15i2.13384) * [Maitner, B. S., Boyle, B., Casler, N., Condit, R., Donoghue, J., Durán, S. M., Guaderrama, D., Hinchliff, C. E., Jørgensen, P. M., Kraft, N. J. B., McGill, B., Merow, C., Morueta-Holme, N., Peet, R. K., Sandel, B., Schildhauer, M., Smith, S. A., Svenning, J.-C., Thiers, B., … Enquist, B. J. (2017). The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution / British Ecological Society. https://doi.org/10.1111/2041-210X.12861](https://doi.org/10.1111/2041-210X.12861) * [Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas: NEW CLIMATE SURFACES FOR GLOBAL LAND AREAS. International Journal of Climatology: A Journal of the Royal Meteorological Society, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086](https://doi.org/10.1002/joc.5086) ## Code/Software The key scripts for the analyses begin with a number and are meant to be run in the corresponding order. Files 1 and 2 contain analyses which evaluate the performances of different species distribution modelling algorithms. 3 is focused on understanding how well model performances evaluated with fitting or cross-validation data compare with independent, presence-absence performance. 4 evaluates the performance of model ensembles. 5 evaluates how range model predicitons converge as sample size increases and provides an example visualization of an ensemble of three algorithms. 6 creates a figure depicting the counter-intuitive shape of relative occurrence rate funcitons when combining gaussian and range-bagging algorithms. 7 contains code used to track the packages used in analyses. 8 contains code underlying figures that were added at the suggestions of reviewers in the final update of the manuscript. [![DOI](https://zenodo.org/badge/381449808.svg)](https://doi.org/10.5281/zenodo.14902719)