Model bigger to avoid unrepresentative species distribution models from truncated spatial extents, with the modulating influence of prevalence

2026/06/17 by Brice B. Hanberry · 1 voice
Environmental Science · Mathematics · #COVID-19 epidemiological studies #Census and Population Estimation #Species Distribution and Climate Change

paper · doi:10.1016/j.envc.2026.101551

openalex publication_date 2026/06/17 · crossref created 2026/06/17 · openalex created_date 2026/06/18 · openalex updated_date 2026/06/21 · crossref deposited 2026/07/30 · crossref indexed 2026/07/30 · crossref issued 2026/09/01 · crossref published 2026/09/01 · crossref published-print 2026/09/01

Citations

• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
• Measuring and comparing the accuracy of species distribution models with presence–absence data
• Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data
• Terrestrial Ecoregions of the World: A New Map of Life on Earth
• Without quality presence–absence data, discrimination metrics such as TSS can be misleading measures of model performance
• A standard protocol for reporting species distribution models
• Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code
• Spatially nested species distribution models (N‐ SDM ): An effective tool to overcome niche truncation for more robust inference and projections

Discussions

• Model bigger to avoid unrepresentative species distribution models from truncated spatial extents, with the modulating influence of prevalence doi.org/10.1016/j.en... [bsky, 0 points, 0 comments]