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

Gurutzeta Guillera-Arroita, José J. Lahoz-Monfort, Jane Elith, Ascelin Gordon, Heini Kujala, Pia E. Lentini, Michael A. Mccarthy, Reid Tingley, Brendan A. Wintle

Research output: Contribution to journalArticleResearchpeer-review

330 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)276-292
Number of pages17
JournalGlobal Ecology and Biogeography
Volume24
Issue number3
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • Ecological niche model
  • Habitat model
  • Imperfect detection
  • Presence-absence
  • Presence-background
  • Presence-only
  • Prevalence
  • Sampling bias

Cite this