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Open-File Report 2015-1029

Prepared in cooperation with the U.S. Fish and Wildlife Service

Resilience and Risk—A Demographic Model to Inform Conservation Planning for Polar Bears

By Eric V. Regehr, Ryan R. Wilson, Karyn D. Rode, and Michael C. Runge

Thumbnail of and link to report PDF (2.1 MB)Executive Summary

Climate change is having widespread ecological effects, including loss of Arctic sea ice. This has led to listing of the polar bear (Ursus maritimus) and other ice-dependent marine mammals under the U.S. Endangered Species Act (ESA). Methods are needed to evaluate the effects of climate change on population persistence to inform recovery planning for listed species. For polar bears, this includes understanding interactions between climate and secondary factors, such as subsistence harvest, which provide economic, nutritional, or cultural value to humans.

We developed a matrix-based demographic model for polar bears that can be used for population viability analysis and to evaluate the effects of human-caused removals. This model includes density-dependence (the potential for a declining environmental carrying capacity), density-independent limitation, and sex- and age-specific harvest vulnerabilities. We estimated values of adult female survival (0.93–0.96), recruitment (number of yearling cubs per adult female; 0.1–0.3), and carrying capacity (>250 animals) that must be maintained for a hypothetical population to achieve a 90-percent probability of persistence over 100 years.

We also developed a state-dependent management framework, based on harvest theory and the potential biological removal method, by linking the demographic model to simulated population assessments. This framework can be used to estimate the maximum sustainable rate of human-caused removals, including subsistence harvest, which maintains a population at its maximum net productivity level. The framework also can be used to calculate a recommended sustainable harvest rate, which generally is lower than the maximum sustainable rate and depends on management objectives, the precision and frequency of population data, and risk tolerance. The historical standard 4.5-percent harvest rate for polar bears, at a 2:1 male-to-female ratio, is reasonable under many biological and management conditions, although lower or higher rates may be appropriate in some cases.

Our modeling results suggest that harvest of polar bears is unlikely to accelerate population declines that result from declining carrying capacity caused by sea-ice loss, provided that several conditions are met: (1) the sustainable harvest rate reflects the population’s intrinsic growth rate, and the corresponding harvest level is obtained by applying this rate to an estimate of population size; (2) the sustainable harvest rate reflects the quality of population data (e.g., lower harvest when data are poor); and (3) the level of human-caused removals can be adjusted. Finally, our results suggest that stopgap measures (e.g., further reduction or cessation of harvest when the population size is less than a critical threshold) may be necessary to minimize the incremental risk associated with harvest, if environmental conditions are deteriorating rapidly. We suggest that the demographic model and approaches presented here can serve as a template for conservation planning for polar bears and other species facing similar challenges.

First posted March 3, 2015

For additional information, contact:
Director, Alaska Science Center
U.S. Geological Survey
4210 University Dr
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov/

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Suggested citation:

Regehr, E.V., Wilson, R.R., Rode, K.D., and Runge, M.C., 2015, Resilience and risk—A demographic model to inform conservation planning for polar bears: U.S. Geological Survey Open-File Report 2015-1029, 56 p., https://dx.doi.org/10.3133/ofr20151029.

ISSN 2331-1258 (online)



Contents

Executive Summary

Introduction

Methods

Results

Discussion

Appendix A. Methods to Adapt Published Vital Rates to the Polar Bear Life Cycle Graph

Appendix B. Methods to Generate Density-Dependent Functions for the Vital Rates

Appendix C. Methods to Generate a Sea Ice-Based Proxy for Carrying Capacity

Appendix D. Methods to Estimate Parameters Using Simulated Population Assessments


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