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July 12, 2022

Evaluating the best method for monitoring wildlife to understand biodiversity changes

Alex Wright is helping conservationists optimize data collection to answer complex biodiversity questions at large scales. While studying for his Ph.D. at Michigan State University in Elise Zipkin's Quantitative Ecology Lab, Wright and his Ph.D. advisors set out to determine the best way to monitor wildlife to understand how biodiversity changes through time and space. Their paper, “A comparison of monitoring designs to assess wildlife community parameters across spatial scales,” was recently published in Ecological Applications.     

To determine the optimal monitoring method for a given question, Wright and his team ran a simulation study using amphibian data from the National Capital Region Network—the 11 national parks within and around Washington, D.C. Biodiversity in this area is threatened by climate change and urbanization, and amphibians are considered an indicator species in these parks—their presence is presumed to correlate with the overall health of the ecosystem. Therefore, collecting amphibian data across this network has been a priority for more than two decades.

Amphibian data are used for multiple objectives across different scales, so they represent an ideal case study for evaluating monitoring alternatives. For example, the network is interested in the status and trends of biodiversity across all parks, but the management decisions needed to address the drivers of those trends are often made, and implemented, at local park-level scales.

“Evaluating multiple processes for multiple species simultaneously is a tricky endeavor,” said senior author Elise Zipkin, associate professor in Integrative Biology and director of MSU’s Ecology, Evolution, and Behavior Program. “But it’s increasingly important as biodiversity continues to decline and the objectives for conservation become more nuanced, varying within and across geographic regions."

Wright and his team compared commonly used monitoring protocols using a hierarchical community model. This model enabled them to understand information tradeoffs between local and regional scales. They evaluated five common monitoring designs — stratified random, weighted effort, indicator unit, rotating panel, and split panel — to estimate status (how many), trends (increasing or decreasing), and drivers (factors that influence the status and trends) of individual species and the whole community at two spatial scales (local and regional). Previous studies have accounted for spatial variation, but none have balanced the need to address multiple objectives at different scales.

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