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Wiley InterScience

Conservation Biology

Conservation Biology

Volume 21 Issue 2, Pages 387 - 399

Published Online: 20 Nov 2006

©2010, Society for Conservation Biology



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Optimizing Allocation of Management Resources for Wildlife
HELENE MARSH*, ANDREW DENNIS, HARRY HINES, ALEX KUTT§, KEITH McDONALD**, ELLEN WEBER††, STEPHEN WILLIAMS‡‡, AND JOHN WINTER§§
  *School of Tropical Environment Studies and Geography, James Cook University, Townsville, QLD 4811, Australia, email helene.marsh@jcu.edu.au   CSIRO Tropical Forest Research Centre, Maunds Road, Atherton, QLD 4883, Australia   Queensland Parks and Wildlife Service, P.O. Box 64 Bellbowrie, QLD 4070, Australia   §CSIRO Sustainable Ecosystems, Davies Laboratory, Private Mail Bag P.O., Aitkenvale, QLD 4814, Australia   **Queensland Parks and Wildlife Service, P.O. Box 975, Atherton, QLD 4833, Australia   ††Wet Tropics Management Authority, P.O. Box 2050, Cairns, QLD 4870, Australia   ‡‡School of Tropical Biology, James Cook University, Townsville, QLD 4811, Australia   §§P.O. Box 151, Ravenshoe, QLD 4888, Australia
Copyright 2007 Society for Conservation Biology
KEYWORDS
anurans • conservation priorities • decision support • mammals • management effectiveness • management resources • species conservation
KEYWORDS
anuros • conservación de especies • efectividad de la gestión • mamíferos • prioridades de conservación • recursos para la gestión • soporte de decisión

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.

ABSTRACT

Optimización de la Asignación de Recursos para la Gestión de Vida Silvestre

Resumen:  La asignación de dinero para la conservación de especies sobre la base de las listas de especies amenazadas no es la forma más rentable de promover la recuperación o de minimizar las tasas de extinción. Utilizando factores ecológicos y sociales adicionalmente a las categorías de amenaza, diseñamos un proceso de soporte de decisiones para asistir a los políticos en la asignación de recursos para la gestión de vida silvestre nativa y para clarificar las consideraciones conducentes a una lista de prioridades. Cada especie es calificada con base en tres criterios en la escala de la jurisdicción relevantes: (1) categoría de amenaza, (2) consecuencias de la extinción, y (3) potencial de recuperación exitosa. Este método proporciona oportunidades para la aportación de datos por los tomadores de decisiones y otros actores (que evalúan la importancia relativa de los criterios) y de los científicos (que evalúan a las especies en función de los criterios). Por lo tanto, el proceso explícitamente separa los valores sociales de los aspectos técnicos del proceso de toma de decisiones, mientras reconoce la legitimidad de ambas aportaciones, Aplicamos nuestra técnica en dos casos de estudio australianos en diferentes escalas espaciales: las ranas de Queensland (1,728,000 km2; 116 especies) y los mamíferos de la bioregión del Trópico Húmedo (18,500 km2; 96 especies). Identificamos 7 especies de ranas y 10 de mamíferos como prioritarias para la conservación. Las ranas incluyeron 1 de las 9 especies clasificadas en peligro por la legislación de Queensland, 3 de las 10 especies clasificadas como vulnerables, 2 de las 22 especies clasificadas como raras y 1 de las 75 especies clasificadas como de menor preocupación. Los mamíferos identificados incluyeron 3 de las 6 especies clasificadas en peligro, 1 de las 4 especies clasificadas como vulnerables, 5 de las 11 especies clasificadas como raras y 1 de las 75 especies clasificadas como de menor preocupación. Los métodos que utilizamos para identificar especies fueron robustos a comparaciones en los dos grupos taxonómicos. Concluimos que (1) nuestro proceso facilita la comparación de datos que se requiere para tomar decisiones transparentes, rentables y estratégicas para la gestión de grupos taxonómicos y (2) el proceso debería ser utilizado para la selección de especies para mayor discusión y no para la asignación de recursos per se.


Paper submitted December 19, 2005; revised manuscript accepted July 13, 2006.

DIGITAL OBJECT IDENTIFIER (DOI)
10.1111/j.1523-1739.2006.00589.x About DOI

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