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Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Managing wildlife for ecological, socioeconomic, and evolutionary sustainability
Author(s): Bunnefeld, Nils
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Issue Date: Sep-2014
Citation: Bunnefeld N (2014) Managing wildlife for ecological, socioeconomic, and evolutionary sustainability, Proceedings of the National Academy of Sciences, 111 (36), pp. 12964-12965.
Abstract: Selective harvesting of animals is widespread throughout the marine, freshwater, and terrestrial environments and affects a diverse list of species, including fish, mammals, birds, and reptiles (1). Such harvesting can cause changes in the distribution of phenotypic traits within target populations, often with undesirable biological and economic consequences. For example, selective harvesting has been linked to declines in the size of trophy horns in two antelope species in Zimbabwe (2) and of antlers in red deer (Cervus elaphus) in Europe (3, 4), as well as to earlier maturation in some fish species (5). However, the extent to which these changes are the result of ecological or evolutionary mechanisms has been much debated (1). In PNAS, Traill et al. (6) approach this question from a novel angle by developing stochastic two-sex integral projection models (IPMs) capable of differentiating between the ecological and evolutionary effects of selective harvest. Their finding that evolutionary mechanisms contribute relatively little to observed changes in the body mass of bighorn sheep (Ovis canadensis) is an intriguing contribution to the debate over the evolutionary consequences of selective offtake, contradicting earlier studies (7). In addition, Traill et al. (6) suggest that their method could be adopted more widely to allow wildlife managers and conservation practitioners to incorporate the potential evolutionary effects of selective harvesting into their management planning. Here, we explore this suggestion by discussing key challenges that would need to be addressed to translate the approach by Traill et al. (6) from a purely biological model to an effective management model, focusing particularly on issues of data availability and the incorporation of different forms of uncertainty.
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