Wolverine Occupancy, Spatial Distribution, and Monitoring Design

Lukacs, Paul M.; Mack, Diane Evans; Inman, Robert M.; Gude, Justin A.; Ivan, Jacob S.; Lanka, Robert P.; Lewis, Jeffrey C.; Long, Robert A.; Sallabanks, Rex; Walker, Zack; Courville, Stacy A.; Jackson, Scott M.; Kahn, Rick; Schwartz, Michael K.; Torbit, Stephen C.; Waller, John S.; Carroll, Kathleen A.

doi:10.1002/jwmg.21856

Cited by 22 publications

(33 citation statements)

References 32 publications

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“…Our model also suggested that asymptotic occupancy of wolverines in the Cascades is high, but short‐term use of any given area is low, consistent with estimates of moderate occurrence probability from previous studies conducted in the region (Lukacs et al. 2020). The difference in precision between continuous‐time and discrete‐time models is due to the need to discard >75% of detection events to create detection histories for the discrete‐time model (Guillera‐Arroita et al.…”

Section: Discussionsupporting

confidence: 89%

“…We found that this model improved inference relative to a discrete-time multi-scale occupancy model in our wolverine case study, decreasing standard deviation of estimated use probabilities by upwards of 50% (Table 1), and also provided more conservative use and instantaneous occupancy estimates. Our model also suggested that asymptotic occupancy of wolverines in the Cascades is high, but shortterm use of any given area is low, consistent with estimates of moderate occurrence probability from previous studies conducted in the region (Lukacs et al 2020). The difference in precision between continuous-time and discrete-time models is due to the need to discard >75% of detection events to create detection histories for the discrete-time model (Guillera-Arroita et al 2011).…”

Section: Discussionsupporting

confidence: 84%

“…The difference in precision between continuous-time and discrete-time models is due to the need to discard >75% of detection events to create detection histories for the discrete-time model (Guillera-Arroita et al 2011). Given that discrete-time occupancy models of highly mobile species such as wolverines may bin detections into detection histories as long as 30 d to estimate detection probability (Lukacs et al 2020), such differences in results may occur often. Inference from discrete-time models could be improved by creating longer detection histories with more tertiary occasions, each of shorter length (e.g., 10 3-d tertiary occasions instead of three 10-d tertiary occasions).…”

Section: Discussionmentioning

confidence: 99%

“…Given that discrete‐time occupancy models of highly mobile species such as wolverines may bin detections into detection histories as long as 30 d to estimate detection probability (Lukacs et al. 2020), such differences in results may occur often. Inference from discrete‐time models could be improved by creating longer detection histories with more tertiary occasions, each of shorter length (e.g., 10 3‐d tertiary occasions instead of three 10‐d tertiary occasions).…”

Section: Discussionmentioning

confidence: 99%

“…We used data collected in the Cascades Mountains of Washington State to estimate and interpret the parameters in our model. The data were detections of wolverines at 25 baited camera stations in a study area of about 48,000 km 2 operated from 1 December 2016 to 31 March 2017 as part of a larger, multi-state wolverine survey (Lukacs et al 2020). Each station was deployed before 1 December 2016.…”

Section: Wolverine Case Studymentioning

confidence: 99%

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Modeling multi‐scale occupancy for monitoring rare and highly mobile species

2021

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Occupancy-based methods are commonly used to model the distribution, habitat use, and relative abundance of species. In particular, occupancy-based methods are often recommended for monitoring species, allowing researchers to track population trends using detection-non-detection data alone. Occupancy models, however, have proven difficult to apply to rare, highly mobile species. Species' movements outside of sampling areas may lead to violation of the geographic closure assumption of occupancy models and overestimated occupancy probability. Low detection probability may further inflate occupancy probability estimates. We developed a novel continuous-time, multi-scale occupancy model to simultaneously account for closure assumption violations and low detection probability. We used a simulation study to test our model relative to a discrete-time multi-scale model, and we conducted a power analysis to assess the ability of an instantaneous occupancy parameter to detect trends in abundance, relative to standard occupancy alone. The continuous-time model was competitive with the discrete-time model and was generally computationally faster than and outperformed the discrete-time model when detection probability was low. The instantaneous occupancy parameter outperformed occupancy in terms of power to detect trends when we used an implicit (i.e., estimating occupancy independently in each primary occasion) dynamic occupancy model, but performed no better when we used an explicit (i.e., estimating colonization and extinction) dynamic occupancy model. We applied both discrete-time and continuous-time multi-scale occupancy models to a case study of data collected on wolverines (Gulo gulo) in Washington, USA. We found improved precision in estimates with the continuous-time model and that asymptotic occupancy of wolverines was high, but short-term use of any given area was low. Our multi-scale, continuous-time occupancy model can be used to detect trends in abundance of rare, highly mobile species, regardless of how occupancy dynamics are modeled. Furthermore, our model can allow for more efficient data collection and analysis than traditional discrete-time or spatially multi-scale approaches, as our model uses all available detections and requires only one detector per sampling unit by substituting time for space.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Wolverine Case Studymentioning

confidence: 99%

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Modeling multi‐scale occupancy for monitoring rare and highly mobile species

2021

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An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers

Long,

MacKay,

Sauder

et al. 2024

Ecology and Evolution

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Camera traps deployed with olfactory attractants are used to survey rare and elusive carnivores. Study areas with deep snowpack and rugged terrain present challenges and risks to field personnel, who traditionally must revisit camera stations regularly to refresh attractants. In such locations, alternative overwinter survey protocols that include a persistent attractant would improve both the safety and efficiency of camera‐trap surveys. We present a protocol for installing camera traps and automated scent dispensers on trees at above‐average maximum snow depth to eliminate the need for interim service visits and to enable standardized surveys to be conducted throughout the year. Our protocol proved to be effective at attracting and detecting numerous and repeated visits by wolverines, fishers, and other carnivores in two montane regions of the western contiguous United States. The volume, timing, and composition of liquid scent lure released by automated scent dispensers can be varied to target multiple species of interest, and the dispenser can be used in situations where bait rewards may influence the behavior of target species and/or pose human safety concerns.

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Comparison of daily activity patterns across seasons using GPS telemetry and camera trap data for a widespread mammal

Wolfson,

Schlichting,

Boughton

et al. 2023

Ecosphere

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Daily activity patterns of animals can be dynamic across seasons in response to changing environmental conditions. Daily activity, though, has rarely been evaluated in relation to multiple factors (e.g., seasons, demographics, and data collection methods), which could be important for understanding what drives activity patterns. Here, we evaluated the daily activity patterns of a widespread invasive species, wild pigs (Sus scrofa), across two ecologically different study areas at Buck Island Ranch, FL, and Tejon Ranch, CA, from 2015 to 2018. Using GPS telemetry data (62 individuals in FL, 21 individuals in CA) and detections from grids of systematically placed motion‐activated cameras (44 cameras in FL, 48 cameras in CA), we compared activity patterns among seasons, sexes, and data collection methods (GPS and camera traps). Overall, wild pigs were mostly active during crepuscular and nocturnal periods, with their lowest activity during diurnal periods; however, activity patterns varied due to several factors. Daily activity patterns were similar between methods, among seasons, and between sexes in the subtropical climate of Florida. In contrast, daily activity patterns exhibited greater differences between methods and among seasons in California, where seasonal differences in temperature and precipitation were more pronounced. Overall, daily activity patterns estimated using GPS telemetry and camera‐based methods not only exhibited a high degree of overlap in several comparisons but also exhibited marked differences that should be recognized. Given the increasing evaluation of daily activity patterns of animals using multiple types of datasets, our study provides ecologists with valuable information to consider when designing ecological studies, interpreting their results, and comparing research across systems and studies.

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Wolverine Occupancy, Spatial Distribution, and Monitoring Design

Cited by 22 publications

References 32 publications

Modeling multi‐scale occupancy for monitoring rare and highly mobile species

Modeling multi‐scale occupancy for monitoring rare and highly mobile species

An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers

Comparison of daily activity patterns across seasons using GPS telemetry and camera trap data for a widespread mammal

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