Is my species distribution model fit for purpose? Matching data and models to applications

Guillera-Arroita, G, Lahoz-Monfort, J, Elith, J, Gordon, A, Kujalah, H, Lentini, P, McCarthy, M, Tingley, R and Wintle, B 2015, 'Is my species distribution model fit for purpose? Matching data and models to applications', Global Ecology and Biogeography, vol. 24, no. 3, pp. 276-292.


Document type: Journal Article
Collection: Journal Articles

Title Is my species distribution model fit for purpose? Matching data and models to applications
Author(s) Guillera-Arroita, G
Lahoz-Monfort, J
Elith, J
Gordon, A
Kujalah, H
Lentini, P
McCarthy, M
Tingley, R
Wintle, B
Year 2015
Journal name Global Ecology and Biogeography
Volume number 24
Issue number 3
Start page 276
End page 292
Total pages 17
Publisher Blackwell Publishing Ltd
Abstract Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.
Subject Conservation and Biodiversity
Environmental Management
Keyword(s) Ecological niche model
Habitat model
Imperfect detection
Presence-absence
Presence-background
Presence-only
Prevalence
Sampling bias
DOI - identifier 10.1111/geb.12268
Copyright notice © 2015 John Wiley & Sons Ltd
ISSN 1466-822X
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