Assessing the impacts of ongoing climate and anthropogenic-induced change on wildlife populations requires understanding species distributions and abundances across large spatial and temporal scales. For threatened or declining populations, collecting sufficient broad-scale data is challenging as sample sizes tend to be low because many such species are rare and/or elusive. As a result, demographic data are often piecemeal, leading to difficulties in determining causes of population changes and developing strategies to mitigate the effects of environmental stressors. Thus, the population dynamics of threatened species across spatio-temporal extents is typically inferred through incomplete, independent, local-scale studies. Emerging integrative modeling approaches, such as integrated population models (IPMs), combine multiple data types into a single analysis and provide a foundation for overcoming problems of sparse or fragmentary data. In this paper, we demonstrate how IPMs can be successfully implemented by synthesizing the elements, advantages, and novel insights of this modeling approach. We highlight the latest developments in IPMs that are explicitly relevant to the ecology and conservation of threatened species, including capabilities to quantify the spatial scale of management, source-sink dynamics, synchrony within metapopulations, and population density effects on demographic rates. Adoption of IPMs has led to improved detection of population declines, adaptation of targeted monitoring schemes, and refined management strategies. Continued methodological advancements of IPMs, such as incorporation of a wider set of data types (e.g., citizen science data) and coupled population-environment models, will allow for broader applicability within ecological and conservation sciences.
Bibliographical noteFunding Information:
We thank M. Schaub and M. Kéry for ideas and the IPM prospective analysis example and template code provided at their integrated population modeling workshop held at Patuxent Wildlife Research Center in August 2016 (attended by SPS). We greatly appreciate S. Converse, B. Gardner, E. Grant, A. Royle, and J. Thorson for useful feedback and ideas. We are also grateful for insightful written comments provided by T. Arnold, M. Schaub, and two anonymous reviewers. This research was supported by awards from the National Science Foundation ( EF-1702635 from the Macrosystems Biology Program) and the U.S. Fish and Wildlife Service (Cooperative Agreement Award F17AC00427 ).
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- Bayesian analysis
- Capture-recapture data
- Integrative modeling
- State-space model
- Threatened species