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14 - Using the Species–Area Relationship to Predict Extinctions Resulting from Habitat Loss

from Part IV - The Species–Area Relationship in Applied Ecology

Published online by Cambridge University Press:  11 March 2021

By
Simone Fattorini ,
Werner Ulrich  and
Thomas J. Matthews
Edited by
Thomas J. Matthews ,
Kostas A. Triantis  and
Robert J. Whittaker
Thomas J. Matthews
Affiliation:
University of Birmingham
Kostas A. Triantis
Affiliation:
National and Kapodistrian University of Athens
Robert J. Whittaker
Affiliation:
University of Oxford
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Summary

It is widely acknowledged that we are in the midst of an extinction crisis and habitat loss is generally considered the primary driver. However, providing accurate estimates of extinction rates has proven to be problematic and a range of extinction estimates have been published. Arguably, the most commonly used method for predicting extinctions resulting from habitat loss has been application of the species–area relationship (SAR). The purpose of this chapter is to provide a review of the many ways in which the SAR has been used to predict the number of extinctions resulting from habitat loss. By doing so, we highlight the pitfalls of using the SAR in such a way and discuss how the SAR has been argued to both over-predict and under-predict extinctions. We also provide examples of the myriad ways in which studies have extended and built on standard SAR models and approaches to better model and predict extinctions. We conclude by arguing that there is a need to recognize that any approach based on a single variable (i.e. area), such as the SAR, is unlikely to provide a perfect extinction prediction, regardless of the specific details.

Keywords

Extinctionhabitat lossbiodiversity conservationconservation biogeographyfragmentationspecies–area relationshipendemics–area relationshipmatrix effectextinction debtpower functionextinction rates
Type
Chapter
Information
The Species–Area Relationship
Theory and Application
 , pp. 345 - 367
Publisher: Cambridge University Press
Print publication year: 2021

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