Increasing Detection by Reducing Disturbance and Excluding Nontarget Small Mammal Species: An Occupancy Study Approach

Oddy, Donna M.; Stolen, Eric D.; Gann, Shanon L.; Legare, Stephanie A.; Weiss, Stephanie K.; Holloway-Adkins, Karen G.

doi:10.3996/072017-jfwm-057

Cited by 2 publications

(2 citation statements)

References 24 publications

(26 reference statements)

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“…A site was considered to have a detection during a given track tube session if the footprint of a southeastern beach mouse was recorded at any of the five track tubes. Details of track tube and paper design can be found in Oddy et al (2018).…”

Section: Occupancy Samplingmentioning

confidence: 99%

Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

et al. 2019

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Habitat occupancy models, designed to deal with non‐detection of a target species in occupied sites, have been expanded to allow for false‐positive detections when species are mistakenly detected in unoccupied sites. When a subset of the data are unambiguous detections, such occupancy models can produce reliable results. However, if not properly accounted for, heterogeneity in the rate of false‐positive detection between sites may bias estimates of habitat occupancy. We studied habitat occupancy of the southeastern beach mouse (Peromyscus polionotus niveiventris), a species along Florida's Atlantic coast that is threatened due to the reduction of its core range by over 79% since 1950. Southeastern beach mice were detected based on their footprints in track tubes, but because of co‐occurrence of the cotton mouse (Peromyscus gossypinus) with overlapping footprint size, we had to allow for possible false‐positive detections. Differences in the relative abundance of these two species between habitats may have resulted in heterogeneity in the false‐positive rate. By combining uncertain (track tube) and certain (live capture) detection methods, we were able to use covariates to account for heterogeneity in false‐positive detection rates between habitats. Southeastern beach mouse habitat occupancy was higher in coastal dune and strand habitat than in coastal scrub or interior scrub habitats. The rate of false‐positive detections was much higher in coastal scrub habitat than in coastal dune and strand habitat or interior scrub habitat, reinforcing the need for methods that accommodate heterogeneity in the rate of false positives to reduce bias in estimates of habitat occupancy. Using simulations, we show that heterogeneity in the false‐positive detection rate leads to bias in habitat occupancy estimates unless properly accounted for. We also discuss how differences in relative abundance between the target species and a confusing species can directly impact the heterogeneity of false‐positive detections.

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Section: Occupancy Samplingmentioning

confidence: 99%

Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

et al. 2019

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“…We selected the sampling sites to minimize the correlations between forest amount, fragmentation and patch connectivity. We sampled small mammals using plastic footprint-tracking tubes lined with paper with a central ink pad (Rytwinski, 2006;Oddy et al, 2018;Nams & Gillis, 2003). As a small mammal crosses the inkpad, its tracks are left on the paper (Fig.…”

Section: Overviewmentioning

confidence: 99%

The Independent Effects of Forest Amount, Fragmentation and Structural Connectivity on Small Mammals' Diversity, Abundance and Occurrence

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Although habitat amount, fragmentation, and connectivity are thought to be important drivers of biodiversity, their independent effects have not been evaluated. We selected 70 forested sites in Ontario, Canada, such that forest amount, fragmentation (number of patches), and structural connectivity (treed corridors) in the surrounding x within multiple spatial extents around 70 sampling sites. All relative abundances were standardized. A: relative abundance (number of papers with presence) of P. leucopus per site. B: the relative abundance of T. striatus per site. C: the relative abundance of M. gapperi per site. D: the relative abundance of B. brevicauda per site. E: the relative abundance of T. hudsonicus per site. Forest amount and number of patches are, respectively, the amount of treed area (m2) and the number of distinct patches of treed area in the landscapes surrounding the sampling sites. Patch connectivity is the percentage of patches in the surrounding landscapes connected to the sampled patch (the "focal patches") through corridors. Points and lines indicate landscape variable coefficients. Grey bars indicate delta AICc's for the models containing the landscape variables at each spatial extent. The scale of effect is taken as the scale where the model fit is best, i.e., delta AICc = 0.

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Increasing Detection by Reducing Disturbance and Excluding Nontarget Small Mammal Species: An Occupancy Study Approach

Cited by 2 publications

References 24 publications

Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

The Independent Effects of Forest Amount, Fragmentation and Structural Connectivity on Small Mammals' Diversity, Abundance and Occurrence

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