Skip to main content
ARS Home » Plains Area » Sidney, Montana » Northern Plains Agricultural Research Laboratory » Pest Management Research » Research » Publications at this Location » Publication #399017

Research Project: Forecasting, Outbreak Prevention, and Ecology of Grasshoppers and Other Rangeland and Crop Insects in the Great Plains

Location: Pest Management Research

Title: Habitat and climatic associations of climate-sensitive species along a southern range boundary

Author
item WILSON, EVAN - University Of Michigan
item COUSINS, STELLA - University Of Michigan
item ETTER, DWAYNE - Michigan Department Of Natural Resources
item Humphreys Jr, John
item ROLOFF, GARY - Michigan State University
item CARTER, NEIL - University Of Michigan

Submitted to: Ecology and Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/27/2023
Publication Date: 5/17/2023
Citation: Wilson, E.C., Cousins, S., Etter, D., Humphreys Jr, J.M., Roloff, G., Carter, N.H. 2023. Habitat and climatic associations of climate-sensitive species along a southern range boundary. Ecology and Evolution. 13. Article e100083. https://doi.org/10.1002/ece3.10083.
DOI: https://doi.org/10.1002/ece3.10083

Interpretive Summary: Climate change and habitat loss are recognized as important drivers of shifts in wildlife species’ geographic distributions. While often considered independently, there is considerable overlap between these drivers, and understanding how they contribute to range shifts can predict future species assemblages and inform effective management. The research objective was to evaluate the impacts of habitat, climatic and anthropogenic effects on the distributions of climate-sensitive vertebrates along a southern range boundary. The research revealed that direct and indirect effects from climate change are likely to impact the distribution of climate-sensitive species in the future and the use of multiple data types and sources in the modeling of species distributions can result in more accurate predictions resulting in improved management at policy-relevant scales.

Technical Abstract: Aim: Climate change and habitat loss are recognized as important drivers of shifts in wildlife species’ geographic distributions. While often considered independently, there is considerable overlap between these drivers, and understanding how they contribute to range shifts can predict future species assemblages and inform effective management. Our objective was to evaluate the impacts of habitat, climatic and anthropogenic effects on the distributions of climate-sensitive vertebrates along a southern range boundary. Location: Northern Michigan, USA. Methods: We combined multiple sources of occurrence data, including harvest and citizen-science data, then used hierarchical Bayesian spatial models to determine habitat and climatic associations for four climate-sensitive vertebrate species [American marten (Martes americana), snowshoe hare (Lepus americanus), ruffed grouse (Bonasa umbellus) and moose (Alces alces)]. We used total basal area of at-risk forest types to represent habitat, and used temperature and winter habitat indices to represent climate. Results: Marten associated with upland spruce-fir and lowland riparian forest types, hares with lowland conifer and aspen-birch, grouse with lowland riparian hardwood, and moose with upland spruce-fir. Species differed in climatic drivers with hares positively associated with cooler annual temperatures, moose with cooler summer temperatures and grouse with colder winter temperatures. Contrary to expectations, temperature variables outperformed winter habitat indices. Main conclusions: Model performance varied greatly among species, as did predicted distributions along the southern edge of the Northwoods region. As multiple species were associated with lowland riparian and upland spruce-fir habitats, these results provide potential for efficient prioritization of habitat management. Both direct and indirect effects from climate change are likely to impact the distribution of climate-sensitive species in the future and the use of multiple data types and sources in the modeling of species distributions can result in more accurate predictions resulting in improved management at policy-relevant scales.