Flexible Methods for Species Distribution Modeling with Small Samples

Flexible Methods for Species Distribution Modeling with Small Samples | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecography This is a preprint and has not been peer reviewed. Data may be preliminary. 14 April 2025 V1 Latest version Share on Flexible Methods for Species Distribution Modeling with Small Samples Authors : Brian Maitner 0000-0002-2118-9880 [email protected] , Robert Richards , Ben Carlson , John Drake 0000-0003-4646-1235 , and Cory Merow 0000-0003-0561-053X Authors Info & Affiliations https://doi.org/10.22541/au.174462519.93620726/v1 Published Ecography Version of record Peer review timeline 300 views 202 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Species distribution models (SDMs) are used for understanding 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 includes most species on earth as well as most of those of the greatest conservation concern. Here, we evaluate the performance of three recently developed modeling approaches specifically designed for data-deficient situations: 1) plug-and-play modeling, 2) density-ratio modeling, and 3) environmental-range modeling. We compare the performance of these methods with Maxent, a widely used method. We compare model performance across sample sizes as well as comparisons limited to only data-poor species. We also ask to what extent model cross-validation performance on training data was correlated with model performance on independent, presence-absence data. We show that, across all species, one or more of the plug-and-play, density-ratio, or environmental-range algorithms outperformed Maxent in 72% of cases, with three of the algorithms having AUC distributions not significantly different from Maxent’s. For data-poor species (those with 20 or fewer occurrences), 24 of the algorithms considered had AUC distributions that were not significantly different from Maxent. However, despite these comparable AUC scores, we found that the algorithm outputs (when thresholded to predict presence vs absence) spanned a wide gradient of sensitivity vs. specificity. 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 had weak correlations. We found that only for 16% of species was the model that performed best on the training data the best performing model when evaluated on independent, presence-absence data. Finally, we show how ensembles of models that span the sensitivity-specificity gradient can represent model disagreement in poorly sampled species and improve model predictions. Supplementary Material File (small_sample_size_sdm_manuscript_ecography_main.docx) Download 814.55 KB Information & Authors Information Version history V1 Version 1 14 April 2025 Peer review timeline Published Ecography Version of Record 21 Jan 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecography Keywords kernel density estimation maxent niche models presence-only data range-bagging species distribution models Authors Affiliations Brian Maitner 0000-0002-2118-9880 [email protected] University of South Florida Department of Integrative Biology View all articles by this author Robert Richards Georgia Institute of Technology School of Biological Sciences View all articles by this author Ben Carlson Eversource Energy Center and Department of Ecology and Evolutionary Biology View all articles by this author John Drake 0000-0003-4646-1235 University of Georgia Department of Infectious Diseases View all articles by this author Cory Merow 0000-0003-0561-053X University of Connecticut View all articles by this author Metrics & Citations Metrics Article Usage 300 views 202 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Brian Maitner, Robert Richards, Ben Carlson, et al. Flexible Methods for Species Distribution Modeling with Small Samples. Authorea . 14 April 2025. DOI: https://doi.org/10.22541/au.174462519.93620726/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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