Abstract

BackgroundWildlife populations are difficult to monitor directly because of costs and logistical challenges associated with collecting informative abundance data from live animals. By contrast, data on harvested individuals (e.g., age and sex) are often readily available. Increasingly, integrated population models are used for natural resource management because they synthesize various relevant data into a single analysis.Methodology/Principal FindingsWe investigated the performance of integrated population models applied to black bears (Ursus americanus) in Minnesota, USA. Models were constructed using sex-specific age-at-harvest matrices (1980–2008), data on hunting effort and natural food supplies (which affects hunting success), and statewide mark–recapture estimates of abundance (1991, 1997, 2002). We compared this approach to Downing reconstruction, a commonly used population monitoring method that utilizes only age-at-harvest data. We first conducted a large-scale simulation study, in which our integrated models provided more accurate estimates of population trends than did Downing reconstruction. Estimates of trends were robust to various forms of model misspecification, including incorrectly specified cub and yearling survival parameters, age-related reporting biases in harvest data, and unmodeled temporal variability in survival and harvest rates. When applied to actual data on Minnesota black bears, the model predicted that harvest rates were negatively correlated with food availability and positively correlated with hunting effort, consistent with independent telemetry data. With no direct data on fertility, the model also correctly predicted 2-point cycles in cub production. Model-derived estimates of abundance for the most recent years provided a reasonable match to an empirical population estimate obtained after modeling efforts were completed.Conclusions/SignificanceIntegrated population modeling provided a reasonable framework for synthesizing age-at-harvest data, periodic large-scale abundance estimates, and measured covariates thought to affect harvest rates of black bears in Minnesota. Collection and analysis of these data appear to form the basis of a robust and viable population monitoring program.

Highlights

  • Age-at-harvest data are commonly collected for many wildlife populations, including those of ungulates and carnivores, and a variety of methods have been developed to assess population abundances and trends from these data [1]

  • Simulation Experiments We highlight the main results of the simulation study here, but refer the reader to Appendix S2, Table S1, and Figure S1 for a more detailed summary, including performance statistics for all six age-at-harvest model estimators and three Downing reconstruction estimators applied to each simulation scenario

  • Animal populations are notoriously difficult to monitor because of costs and logistical challenges associated with collecting informative data on population trends and abundance

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Summary

Introduction

Age-at-harvest data are commonly collected for many wildlife populations, including those of ungulates and carnivores, and a variety of methods have been developed to assess population abundances and trends from these data [1]. Several authors have suggested applying modern statistical age-at-harvest models to monitor population trends, either in concert or as an alternative to more labor intensive survey methods [1,2,3,4,5,6,7]. Integrated population models synthesize demography with multiple sources of data, such as age- and length-frequencies, abundance indices, annual harvest, and lifehistory information, into a comprehensive analysis. A relevant question of practicality is, ‘‘what assumptions or auxiliary data are necessary to reliably estimate population trends with age-at-harvest data?’’. Integrated population models are used for natural resource management because they synthesize various relevant data into a single analysis

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