Optimizing Allocation of Management Resources for Wildlife

Optimizing Allocation of Management Resources for Wildlife Abstract: Allocating money for species conservation on the basis of threatened species listings is not the most cost‐effective way of promoting recovery or minimizing extinction rates. Using ecological and social factors in addition to threat categories, we designed a decision‐support process to assist policy makers in their allocation of resources for the management of native wildlife and to clarify the considerations leading to a priority listing. Each species is scored on three criteria at the scale of the relevant jurisdiction: (1) threat category, (2) consequences of extinction, and (3) potential for successful recovery. This approach provides opportunity for independent input by policy makers and other stakeholders (who weight the relative importance of the criteria) and scientists (who score the species against the criteria). Thus the process explicitly separates societal values from the technical aspects of the decision‐making process while acknowledging the legitimacy of both inputs. We applied our technique to two Australian case studies at different spatial scales: the frogs of Queensland (1,728,000 km2; 116 species) and the mammals of the Wet Tropics bioregion (18,500 km2; 96 species). We identified 7 frog and 10 mammal species as priorities for conservation. The frogs included 1 of the 9 species classified as endangered under Queensland legislation, 3 of the 10 species classified as vulnerable, 2 of the 22 species classified as rare, and 1 of the 75 species classified as least concern. The mammals identified included 3 of the 6 species classified as endangered, 1 of the 4 species classified as vulnerable, 5 of the 11 species classified as rare, and 1 of the 75 species classified as least concern. The methods we used to identify species were robust to comparisons across the two taxonomic groups. We concluded that (1) our process facilitates comparisons of data required to make transparent, cost‐effective, and strategic management decisions across taxonomic groups and (2) the process should be used to short‐list species for further discussion rather than for allocating resources per se. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Conservation Biology Wiley

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Publisher
Wiley
Copyright
Copyright © 2007 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0888-8892
eISSN
1523-1739
DOI
10.1111/j.1523-1739.2006.00589.x
Publisher site
See Article on Publisher Site

Abstract

Abstract: Allocating money for species conservation on the basis of threatened species listings is not the most cost‐effective way of promoting recovery or minimizing extinction rates. Using ecological and social factors in addition to threat categories, we designed a decision‐support process to assist policy makers in their allocation of resources for the management of native wildlife and to clarify the considerations leading to a priority listing. Each species is scored on three criteria at the scale of the relevant jurisdiction: (1) threat category, (2) consequences of extinction, and (3) potential for successful recovery. This approach provides opportunity for independent input by policy makers and other stakeholders (who weight the relative importance of the criteria) and scientists (who score the species against the criteria). Thus the process explicitly separates societal values from the technical aspects of the decision‐making process while acknowledging the legitimacy of both inputs. We applied our technique to two Australian case studies at different spatial scales: the frogs of Queensland (1,728,000 km2; 116 species) and the mammals of the Wet Tropics bioregion (18,500 km2; 96 species). We identified 7 frog and 10 mammal species as priorities for conservation. The frogs included 1 of the 9 species classified as endangered under Queensland legislation, 3 of the 10 species classified as vulnerable, 2 of the 22 species classified as rare, and 1 of the 75 species classified as least concern. The mammals identified included 3 of the 6 species classified as endangered, 1 of the 4 species classified as vulnerable, 5 of the 11 species classified as rare, and 1 of the 75 species classified as least concern. The methods we used to identify species were robust to comparisons across the two taxonomic groups. We concluded that (1) our process facilitates comparisons of data required to make transparent, cost‐effective, and strategic management decisions across taxonomic groups and (2) the process should be used to short‐list species for further discussion rather than for allocating resources per se.

Journal

Conservation BiologyWiley

Published: Apr 1, 2007

References

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