Camera-based occupancy monitoring at large scales: Power to detect trends in grizzly bears across the Canadian Rockies

Steenweg, Robin; Whittington, Jesse; Hebblewhite, Mark; Forshner, Anne; Johnston, Barb; Petersen, Derek; Shepherd, Brenda; Lukacs, Paul M.

doi:10.1016/j.biocon.2016.06.020

Cited by 76 publications

(84 citation statements)

References 57 publications

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“…Cameras were located on Trans‐Canada Highway wildlife overpasses and open span bridges ( n = 15), wildlife underpasses ( n = 25), grizzly bear rub trees ( n = 35) and on human use or wildlife trails ( n = 139). At least one camera was located in each 10 × 10 km cell (Steenweg et al., ). We defined our study area based on the minimum convex polygon around remote cameras that detected marked grizzly bears (4,050 km 2 , Figure ).…”

Section: Methodsmentioning

confidence: 99%

“…We defined our study area based on the minimum convex polygon around remote cameras that detected marked grizzly bears (4,050 km 2 , Figure ). Cameras extending beyond this area (Steenweg et al., ) were excluded from the analysis. Cameras operated year round and were serviced 1–4 times per year.…”

Section: Methodsmentioning

confidence: 99%

“…Occasion was centred so that values of other parameters would reflect July encounter probabilities. We included a covariate for the type of camera location (overpass‐bridge, underpass, rub tree or trail) based on grizzly bear responses to these features in previous studies (Clevenger & Waltho, ; Steenweg et al., ).…”

Section: Methodsmentioning

confidence: 99%

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Generalized spatial mark–resight models with an application to grizzly bears

Whittington

Hebblewhite

Chandler

2017

Journal of Applied Ecology

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126

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Abstract1. The high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently developed spatial mark-resight (SMR) models were proposed as a cost-effective alternative because they only require a single marking event. However, existing SMR models ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. This assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals.2. We developed a generalized SMR model that includes sub-models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities.3. Our simulation study demonstrated that conventional SMR models produce biased density estimates with low credible interval coverage (CIC) when marked and unmarked animals had differing spatial distributions. In contrast, generalized SMR models produced unbiased density estimates with correct CIC in all scenarios.4. We applied our SMR model to grizzly bear (Ursus arctos) data where the marking process occurred along a transportation route through Banff and Yoho National Parks, Canada. Twenty-two grizzly bears were trapped, fitted with radiocollars and then detected along with unmarked bears on 214 remote cameras. Closed population density estimates (posterior median ± 1 SD) averaged from 2012 to 2014 were much lower for conventional SMR models (7.4 ± 1.0 bears per 1,000 km 2 ) than for generalized SMR models (12.4 ± 1.5). When compared to previous DNA-based estimates, conventional SMR estimates erroneously suggested a 51% decline in density. Conversely, generalized SMR estimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. Synthesis and applications.Mark-resight studies often cost less than capture-recapture studies, but require that marked and unmarked animals have equal encounter rates. Generalized spatial mark-resight models relax this assumption by including sub-models for both the marking and resighting processes. They produce unbiased density estimates even when marked and unmarked animals have differing spatialThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Generalized spatial mark–resight models with an application to grizzly bears

Whittington

Hebblewhite

Chandler

2017

Journal of Applied Ecology

Self Cite

126

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“…) or occupancy (Strayer , Steenweg et al. , Latif et al. ), while accounting for observation errors such as imperfect detection (Guillera‐Arroita and Lahoz‐Monfort ).…”

Section: Introductionmentioning

confidence: 99%

Spatially explicit power analysis for detecting occupancy trends for multiple species

Southwell

Einoder²,

Lahoz‐Monfort

et al. 2019

Ecological Applications

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Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species‐level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long‐term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera‐trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision‐makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods.

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“…Monitoring of elusive species, in general, yields a low power (e.g. Jones et al 2015;Steenweg et al 2016;Williams and Thomas 2009). Unfortunately, only a few power analysis studies on the populations trends of terrestrial invertebrates have been published in the peer reviewed literature (Bried and Pellet 2012;Lang et al 2016;Schmucki et al 2016, all on butterflies).…”

Section: Introductionmentioning

confidence: 99%

Can we successfully monitor a population density decline of elusive invertebrates? A statistical power analysis on Lucanus cervus

Thomaes¹,

Verschelde²,

Mader³

et al. 2017

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Monitoring global biodiversity is essential for understanding and countering its current loss. However, monitoring of many species is hindered by their difficult detection due to crepuscular activity, hidden phases of the life cycle, short activity period and low population density. Few statistical power analyses of declining trends have been published for terrestrial invertebrates. Consequently, no knowledge exists of the success rate of monitoring elusive invertebrates. Here data from monitoring transects of the European stag beetle, Lucanus cervus, is used to investigate whether the population trend of this elusive species can be adequately monitored. Data from studies in UK, Switzerland and Germany were compiled to parameterize a simulation model explaining the stag beetle abundance as a function of temperature and seasonality. A Monte-Carlo simulation was used to evaluate the effort needed to detect a population abundance decline of 1%/year over a period of 12 years. To reveal such a decline, at least 240 1-hour transect walks on 40 to 100 transects need to be implemented in weekly intervals during warm evenings. It is concluded that monitoring of stag beetles is feasible and the effort is not greater than that which has been found for other invertebrates. Based on this example, it is assumed that many other elusive species with similar life history traits can be monitored with moderate efforts. As saproxylic invertebrates account for a large share of the forest biodiversity, although many are elusive, it is proposed that at least some flagship species are included in monitoring programmes.

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Camera-based occupancy monitoring at large scales: Power to detect trends in grizzly bears across the Canadian Rockies

Cited by 76 publications

References 57 publications

Generalized spatial mark–resight models with an application to grizzly bears

Generalized spatial mark–resight models with an application to grizzly bears

Spatially explicit power analysis for detecting occupancy trends for multiple species

Can we successfully monitor a population density decline of elusive invertebrates? A statistical power analysis on Lucanus cervus

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