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Using Modeling to Improve Monitoring of Structured Populations: Are We Collecting the Right Data?
Authors:TODD KATZNER‡  E J MILNER-GULLAND  EVGENY BRAGIN†
Institution:Division of Biology and Centre for Population Biology, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom;Science Department, Naurzum National Nature Reserve, Kustanay Oblast, Naurzumski Raijon, Karamendi 459730, Kazakhstan
Abstract:Abstract:  Population monitoring is central to most demographic studies and conservation efforts, but it may not always be directed at the most appropriate life stage. We used stochastic simulation modeling to evaluate the effectiveness of a monitoring program for a well-studied population of Eastern Imperial Eagles ( Aquila heliaca ) in Kazakhstan. Specifically, we asked whether the most appropriate data were being collected to understand system state and population dynamics. Our models were parameterized with data collected over the course of 25 years of study of this population. We used the models to conduct simulation experiments to evaluate relationships between monitored or potentially monitored parameters and the demographic variables of interest—population size ( N ) and population growth (λ). Static analyses showed that traditional territory-based monitoring was a poor indicator of eagle population size and growth and that monitoring survivorship would provide more information about these parameters. Nevertheless, these same traditionally monitored territory-based parameters had greater power to detect long-term changes in population size than did survivorship or population structure. Regardless of the taxa considered, threats can have immediate impacts on population size and growth or longer-term impacts on population dynamics. Prudently designed monitoring programs for any species will detect the demographic effects of both types of threats.
Keywords:Aquila heliaca            demography  Eastern Imperial Eagle  life-stage simulation analysis  population monitoring  sensitivity analysis  stochastic simulation modeling
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