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• W3009190379 abstract "Abstract Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices." @default.
• W3009190379 created "2020-03-13" @default.
• W3009190379 creator A5011131670 @default.
• W3009190379 creator A5045899607 @default.
• W3009190379 creator A5052271280 @default.
• W3009190379 date "2020-03-02" @default.
• W3009190379 modified "2023-10-06" @default.
• W3009190379 title "Predictor species: Improving assessments of rare species occurrence by modeling environmental co‐responses" @default.
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• W3009190379 doi "https://doi.org/10.1002/ece3.6096" @default.
• W3009190379 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7140998" @default.
• W3009190379 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32273987" @default.
• W3009190379 hasPublicationYear "2020" @default.
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