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Assumption-versus data-based approaches to summarizing species' ranges.

Authors
  • Peterson, A Townsend1
  • Navarro-Sigüenza, Adolfo G2
  • Gordillo, Alejandro2
  • 1 Biodiversity Institute, University of Kansas, Lawrence, KS, 66045, U.S.A.
  • 2 Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico, D.F, 04510, Mexico. , (Mexico)
Type
Published Article
Journal
Conservation Biology
Publisher
Wiley (Blackwell Publishing)
Publication Date
Jun 01, 2018
Volume
32
Issue
3
Pages
568–575
Identifiers
DOI: 10.1111/cobi.12801
PMID: 27488935
Source
Medline
Keywords
License
Unknown

Abstract

For conservation decision making, species' geographic distributions are mapped using various approaches. Some such efforts have downscaled versions of coarse-resolution extent-of-occurrence maps to fine resolutions for conservation planning. We examined the quality of the extent-of-occurrence maps as range summaries and the utility of refining those maps into fine-resolution distributional hypotheses. Extent-of-occurrence maps tend to be overly simple, omit many known and well-documented populations, and likely frequently include many areas not holding populations. Refinement steps involve typological assumptions about habitat preferences and elevational ranges of species, which can introduce substantial error in estimates of species' true areas of distribution. However, no model-evaluation steps are taken to assess the predictive ability of these models, so model inaccuracies are not noticed. Whereas range summaries derived by these methods may be useful in coarse-grained, global-extent studies, their continued use in on-the-ground conservation applications at fine spatial resolutions is not advisable in light of reliance on assumptions, lack of real spatial resolution, and lack of testing. In contrast, data-driven techniques that integrate primary data on biodiversity occurrence with remotely sensed data that summarize environmental dimensions (i.e., ecological niche modeling or species distribution modeling) offer data-driven solutions based on a minimum of assumptions that can be evaluated and validated quantitatively to offer a well-founded, widely accepted method for summarizing species' distributional patterns for conservation applications.

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