Dataset Information

Confronting uncertainty in wildlife management: performance of grizzly bear management.

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.

SUBMITTER: Artelle KA

PROVIDER: S-EPMC3819331 | biostudies-other | 2013

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Confronting uncertainty in wildlife management: performance of grizzly bear management.

Artelle Kyle A KA Anderson Sean C SC Cooper Andrew B AB Paquet Paul C PC Reynolds John D JD Darimont Chris T CT

PloS one 20131106 11

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 Co ...[more]

PMID: 24223134

Similar Datasets

Project description:Managing wildlife populations for conservation, control or harvesting involves uncertainty. Nevertheless, decisions need to be made based on the available evidence. The two main sources of uncertainty in population modelling are parameter estimates and structural uncertainty. Structural uncertainty in models is not included as often as parameter uncertainty.We present an approach where parameter and structural uncertainty (strength of density dependence) is included within a model, using the over-wintering English population of cormorants Phalacrocorax carbo L. Because of the damage caused to inland fishery interests by cormorants, there was a change in UK government policy in autumn 2004, increasing the numbers of birds that can be shot under licence.A stochastic Monte Carlo annual population model was produced to examine the effect of changes to the numbers of birds shot each year. Indices of annual population size were converted to population estimates and used to determine annual growth rates and strength of density dependence.There is strong evidence for density dependence in the data, which suggests the population is currently slightly above carrying capacity, with a mean growth rate of 4-6% per annum. The 1300 birds shot under licence in 2004/05 represent about 4.5% of the English population, and if this level of culling continues, the population would be expected to decline by 9% by 2007, compared to the long-term average. The a priori preferred model, which included all uncertainty, gave predictions for 2004/05 and 2005/06 in close agreement with field data.The model was used to produce short-term population projections, with the understanding that Adaptive Resource Management (ARM) will be adopted to iteratively update the parameters and model each year, feeding back into the numbers of available licences.Synthesis and applications. We recommend the approach used in this study of including parameter and structural uncertainty within a single model, where possible, with the proportion of iterations that utilize a particular structure dependent on the weight of evidence for that structure. This will produce results with wider confidence intervals, but ensures that the evidence for any particular model is not over-interpreted.

Project description:Research productivity and impact are often considered in professional evaluations of academics, and performance metrics based on publications and citations increasingly are used in such evaluations. To promote evidence-based and informed use of these metrics, we collected publication and citation data for 437 tenure-track faculty members at 33 research-extensive universities in the United States belonging to the National Association of University Fisheries and Wildlife Programs. For each faculty member, we computed 8 commonly used performance metrics based on numbers of publications and citations, and recorded covariates including academic age (time since Ph.D.), sex, percentage of appointment devoted to research, and the sub-disciplinary research focus. Standardized deviance residuals from regression models were used to compare faculty after accounting for variation in performance due to these covariates. We also aggregated residuals to enable comparison across universities. Finally, we tested for temporal trends in citation practices to assess whether the "law of constant ratios", used to enable comparison of performance metrics between disciplines that differ in citation and publication practices, applied to fisheries and wildlife sub-disciplines when mapped to Web of Science Journal Citation Report categories. Our regression models reduced deviance by ¼ to ½. Standardized residuals for each faculty member, when combined across metrics as a simple average or weighted via factor analysis, produced similar results in terms of performance based on percentile rankings. Significant variation was observed in scholarly performance across universities, after accounting for the influence of covariates. In contrast to findings for other disciplines, normalized citation ratios for fisheries and wildlife sub-disciplines increased across years. Increases were comparable for all sub-disciplines except ecology. We discuss the advantages and limitations of our methods, illustrate their use when applied to new data, and suggest future improvements. Our benchmarking approach may provide a useful tool to augment detailed, qualitative assessment of performance.

Dataset Information

Confronting uncertainty in wildlife management: performance of grizzly bear management.

Publications

Confronting uncertainty in wildlife management: performance of grizzly bear management.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets