Novel methods improve prediction of species’ distributions from occurrence data

Abstract

Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence‐only data to fit models, and independent presence‐absence data to evaluate the predictions. Along with well‐established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species’ distributions. These include machine‐learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species’ occurrence data. Presence‐only data were effective for modelling species’ distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Keywords

Affiliated Institutions

• University of California, Berkeley US
• University of Missouri US
• Swiss Federal Institute for Forest, Snow and Landscape Research CH
• The University of Melbourne AU
• City University of New York US
• AT&T (United States) US
• Centro de Referência em Informação Ambiental BR
• Aarhus University DK
• Centre Suisse de Cartographie de la Faune CH
• Consejo Nacional de Ciencia y Tecnología GT
• Princeton University US
• McGill University CA
• James Cook University AU
• University of Alaska Fairbanks US
• Universidade de São Paulo BR
• University of Kansas US
• Cadre Research US
• Stony Brook University US
• Commonwealth Scientific and Industrial Research Organisation AU
• Manaaki Whenua – Landcare Research NZ
• University of Lausanne CH

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