Assess the best wildlife monitoring method to understand changes in biodiversity | MSUToday


Alex Wright helps conservationists optimize data collection to answer complex questions about large-scale biodiversity. During his doctoral studies. at Michigan State University in Elise Zipkin’s Quantitative Ecology Lab, Wright and her Ph.D. Advisors set out to determine how best to monitor wildlife to understand how biodiversity changes over time and space. Their paper, “A Comparison of Monitoring Designs for Assessing 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 conducted 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 region is threatened by climate change and urbanization, and amphibians are considered indicator species in these parks – their presence is presumed to correlate with overall ecosystem health. Therefore, collecting amphibian data through this network has been a priority for more than two decades.

Amphibian data are used for multiple purposes at 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 in all parks, but the management decisions needed to address the drivers of these trends are often made and implemented at the scale of local parks. .

“Assessing multiple processes for multiple species simultaneously is a tricky business,” said lead author Elise Zipkin, associate professor of integrative biology and director of MSU’s ecology, evolution, and behavior program. “But it’s increasingly important as biodiversity continues to decline and conservation goals become more nuanced, varying within and between geographic regions.”

Wright and his team compared commonly used monitoring protocols using a hierarchical community model. This model allowed them to understand information trade-offs 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 (up or down), 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 considered spatial variation, but none have balanced the need to address multiple objectives at different scales.

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