Estimating occupancy probability of moose using hunter survey data
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Abstract
Monitoring rare species can be difficult, especially across large spatial extents, making conventional methods of population monitoring costly and logistically challenging. Citizen science has the potential to produce observational data across large areas that can be used to monitor wildlife distributions using occupancy models. We used citizen science (i.e., hunter surveys) to facilitate monitoring of moose (Alces alces) populations, an especially important endeavor because of their recent apparent declines in the northeastern and upper midwestern regions of the United States. To better understand patterns of occurrence of moose in New York, we used data collected through an annual survey of approximately 11,000 hunters between 2012 and 2014 that recorded detection–non-detection data of moose and other species. We estimated patterns of occurrence of moose in relation to land cover characteristics, climate effects, and interspecific interactions using occupancy models to analyze spatially referenced moose observations. Coniferous and deciduous forest with low prevalence of white-tailed deer (Odocoileus virginianus) had the highest probability of moose occurrence. This study highlights the potential of data collected using citizen science for understanding the spatial distribution of low-density species across large spatial extents and providing key information regarding where and when future research and management activities should be focused.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Estimating occupancy probability of moose using hunter survey data |
| Series title | Journal of Wildlife Management |
| DOI | 10.1002/jwmg.21207 |
| Volume | 81 |
| Issue | 3 |
| Year Published | 2017 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Coop Res Unit Leetown |
| Description | 14 p. |
| First page | 521 |
| Last page | 534 |
| Country | United States |
| State | New York |
| Google Analytic Metrics | Metrics page |