Novel methods improve prediction of species’ distributions from occurrence data

Elith*, Jane; H. Graham*, Catherine; P. Anderson, Robert; Dudík, Miroslav; Ferrier, Simon; Guisan, Antoine; J. Hijmans, Robert; Huettmann, Falk; R. Leathwick, John; Lehmann, Anthony; Li, Jin; G. Lohmann, Lucia; A. Loiselle, Bette; Manion, Glenn; Moritz, Craig; Nakamura, Miguel; Nakazawa, Yoshinori; McC. M. Overton, Jacob; Townsend Peterson, A.; J. Phillips, Steven; Richardson, Karen; Scachetti‐Pereira, Ricardo; E. Schapire, Robert; Soberón, Jorge; Williams, Stephen; S. Wisz, Mary; E. Zimmermann, Niklaus

doi:10.1111/j.2006.0906-7590.04596.x

Cited by 9,088Species Distribution and Climate ChangeEcology and Vegetation Dynamics StudiesWildlife Ecology and Conservation

Novel methods improve prediction of species’ distributions from occurrence data

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Elith*, Jane, H. Graham*, Catherine, P. Anderson, Robert, Dudík, Miroslav, Ferrier, Simon, Guisan, Antoine, J. Hijmans, Robert, Huettmann, Falk +19 more·2006·Ecography

DOI 10.1111/j.2006.0906-7590.04596.x

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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.

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“This method also has a predictive performance that is consistently competitive with other high-performing methods (Elith et al., 2006; Feng et al., 2019).”
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Authors: Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., J. Phillips, S., Richardson, K., Scachetti‐Pereira, R., E. Schapire, R., Soberón, J., Williams, S., S. Wisz, M., & E. Zimmermann, N.
Affiliations: University of New England (AU)
University of Lausanne (CH)
University of Alaska Fairbanks (US)
info fauna (CH)
Commonwealth Scientific and Industrial Research Organisation (AU)
University of California, Berkeley (US)
Secretaría de Ciencia, Humanidades, Tecnología e Innovación (MX)
University of Kansas (US)
McGill University (CA)
Centro de Referência em Informação Ambiental (BR)
James Cook University (AU)
ORCID: Ferrier, Simon
Guisan, Antoine
Huettmann, Falk
Lehmann, Anthony
Li, Jin
Moritz, Craig
Nakamura, Miguel
Nakazawa, Yoshinori
Soberón, Jorge
Williams, Stephen
Venue: Ecography
Publisher: Wiley
Published: 2006-03-29
Volume: 29
Issue: 2
Pages: 129-151
ISSN: 0906-7590 (ISSN-L)
1600-0587
DOI: 10.1111/j.2006.0906-7590.04596.x
OpenAlex: W2112315008
Cited by: 9,088
References: 119
Citations by year: 2026: 289 · 2025: 522 · 2024: 539 · 2023: 550 · 2022: 597 · 2021: 588 · 2020: 614 · 2019: 515 · 2018: 513 · 2017: 465 · 2016: 492 · 2015: 436 · 2014: 484 · 2013: 536 · 2012: 471
FWCI: 136.89
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Ecography (publishedVersion)
Archive ouverte UNIGE (University of Geneva) (publishedVersion)
ResearchOnline at James Cook University (James Cook University) (submittedVersion)
e-Publications@Marquette (Marquette University) (submittedVersion)
DORA WSL (Swiss Federal Institute for Forest, Snow and Landscape Research) (submittedVersion)
IRIS (submittedVersion)
SERVAL (Université de Lausanne) (submittedVersion)
Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
Topics: Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Keywords: species distribution modeling, presence-only data, species occurrence records, model comparison, machine learning, community model, generalized additive models, BIOCLIM, GARP, data sparsity, conservation science

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Novel methods improve prediction of species’ distributions from occurrence data

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