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Published on: November 17, 2023
Forging a Bayesian link between habitat selection and avoidance behavior in a grassland grouse.
Michael A Patten1,2, Alexandra A Barnard3,4, Claire M Curry3,5
1Ecology Research Group, Faculty of Biosciences and Aquaculture, Nord University, 7729, Steinkjer, Norway. michael.a.patten@nord.no.
Lesser Prairie-Chickens avoid large and tall human-made structures, but not low fences. This habitat selection study quantifies avoidance of anthropogenic features, informing conservation strategies for grassland grouse.
Area of Science:
- Ecology
- Wildlife Conservation
- Behavioral Ecology
Background:
- Habitat selection is a fundamental ecological process, often confused with habitat preference and use.
- A clear conceptual framework is needed to differentiate these terms and analyze animal responses to environmental features.
Purpose of the Study:
- To differentiate habitat selection, preference, and use within a Bayesian framework.
- To estimate prior probability profiles for habitat selection by the Lesser Prairie-Chicken (Tympanuchus pallidicinctus).
- To quantify the avoidance of various anthropogenic features by this species.
Main Methods:
- Utilized long-term occupancy data of the Lesser Prairie-Chicken.
- Analyzed avoidance patterns relative to anthropogenic features (roads, powerlines, wells, fences, buildings) in areas with varying feature densities.
- Employed a Bayesian approach to model habitat selection and estimate prior probabilities.
Main Results:
- Lesser Prairie-Chickens strongly avoided large (e.g., outbuildings) and tall (e.g., powerlines) anthropogenic features.
- Low, unobtrusive features like fences showed little to no avoidance.
- Co-location of features influenced avoidance, with birds being "pushed toward" some features due to avoidance of others.
Conclusions:
- The study provides a method to incorporate avoidance behavior into habitat selection analyses.
- Understanding species' responses to anthropogenic features is crucial for effective conservation planning.
- The Bayesian framework allows inference of selection processes from use data, given prior preference data.
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