Synthesizing multiple data types for biological conservation using integrated population models

Citation data:

Biological Conservation, ISSN: 0006-3207, Vol: 217, Page: 240-250

Publication Year:
2018
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DOI:
10.1016/j.biocon.2017.10.017
Author(s):
Elise F. Zipkin; Sarah P. Saunders
Publisher(s):
Elsevier BV
Tags:
Agricultural and Biological Sciences; Environmental Science
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review description
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.