Confronting uncertainty in wildlife management: performance of grizzly bear management

Confronting uncertainty in wildlife management: performance of grizzly bear management

Confronting Uncertainty in Wildlife Management – Performance of Grizzly Bear Management, Artelle et al 2013 – (PDF)

See also the paper at http://dx.plos.org/10.1371/journal.pone.0078041

Citation

Kyle A. Artelle, C. Anderson, B. Cooper, Paul C. Paquet, John D. Reynolds and Chris T. Darimont. 2013. Confronting Uncertainty in Wildlife Management: Performance of Grizzly Bear Management. PLOS ONE.  DOI: 10.1371/journal.pone.0078041

Kyle A. Artelle, Paul C. Paquet, Chris T. Darimont
Raincoast Conservation Foundation, Sidney, British Columbia, Canada

Abstract

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone – discrepancy between expected and realized mortality levels – led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.

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