Occupancy modeling is a logistic regression-based framework that estimates the probability of a species occupying sampled sites (occupancy) while accounting for the probability of detecting the species using the given sampling methods (detectability). Identifying such critical habitats often relies on occupancy modeling, which has become a common tool to monitor wildlife populations when insufficient captures prevent the use of mark-recapture analysis or when individual identification is impossible. Rapid and global environmental change increases the need to identify and conserve critical habitats for at-risk and endangered species. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. The work is made available under the Creative Commons CC0 public domain dedication.ĭata Availability: All data files for this paper are available from the University of Wyoming database ( ).įunding: This work was supported by: KH - Wyoming Game and Fish Department Grant #1002578, DK - Wyoming NASA Space Grant Consortium #1002741. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. Received: OctoAccepted: FebruPublished: March 5, 2019 PLoS ONE 14(3):Įditor: Bi-Song Yue, Sichuan University, CHINA Increasing detection probability of rare components of a community can improve the results and understanding of future studies.Ĭitation: Harkins KM, Keinath D, Ben-David M (2019) It’s a trap: Optimizing detection of rare small mammals. Our results show that simple changes to standard small-mammal trapping methods can dramatically increase the detectability of rare and elusive small mammals. We were also able to demonstrate that by deploying a combination of different traps and baits it is possible to overcome the potential effect of non-target species (e.g., deer mice, Peromyscus maniculatus) on the detection probability of pocket mice. Increasing grid size, while maintaining a similar trapping effort, resulted in higher detection probability, although our analyses showed that effective grids can be about three-quarters of the size we use to achieve similar results. We found that bait and trap type selection varied significantly by species, with pocket mice showing strongest selection for Havahart traps baited with bird seed. Regardless of species, trap success was higher for Havaharts. We also assessed the effect of captures of non-target species on detection probability of pocket mice. We used three trap and bait types and trapped an area 4.4 times larger than the standard grid. Our goal was to create a new small mammal trapping protocol that improved detection of rare species, such as the olive-backed pocket mouse ( Perognathus fasciatus). Improving detection probabilities for rare species is critical when assessing presence or habitat associations.
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