To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

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Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, estimate extinction rates and plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterised, and over-reliance on a single model may lead to over-confidence in maps of future distributions.

The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so-called “bioclimatic” variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate.  The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.

Article first published online: 29 JUL 2013  - DOI: 10.1111/emr.12055

Volume 14, Issue 3, pages 230–234, September 2013


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Bibliography Entry

Type of Publication Journal Article
Year of Publication 2013
Type of Article Short Report
Authors Harris RMB, Porfirio LL, Hugh S, Lee G, Bindoff NL, Mackey B, Beeton NJ
Volume 14
Issue 3
Pagination 230–234
Date Published 07/2013
Journal Ecological Management & Restoration
This page was last updated on October 22, 2013