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7 results for “mark-resight”
Fig. 1 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 1. Map showing the location of the Central Catchment Nature Reserve on mainland Singapore. All 27 camera points are indicated with a red circle. Black squares indicate the three camera points added to the 1 km2 grid. The six cage traps are marked with a blue cross. Dotted circles indicate areas the last remaining patches of primary forest in Singapore.
Fig. 3 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 3. Map of the Central Catchment Nature Reserve showing the day and night fixes of the collared pig. The home ranges are calculated from the monthly 95% Kernel Density Estimate (KDE), while the aggregate home range was calculated from the 99% KDE from all six months. The Seletar Expressway (SLE) is pointed out on the map and the satellite overlay was adapted from Google Earth.
Fig. 2 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 2. The density map showing the number of activity centres per kilometer square, the locations of the camera points (circles), 143 out of 856 GPS locations from the collared pig (black dots) and the boundary of the Central Catchment Nature Reserve. Only a fraction of the GPS locations was plotted to prevent the colored pixels from being obscured. Each pixel is 1 km2. X and Y coordinates are in kilometers.
Data from: One-stage spatial mark-resight analysis reveals an increasing grizzly bear population with declining density near roads
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Data from: Generalized spatial mark-resight models with an application to grizzly bears
1. 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 when marked and unmarked animals had differing spatial distributions. In contrast, generalized SMR models produced unbiased density estimates with correct credible interval coverage 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 radio-collars, 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 km2) 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. 5. Synthesis and application. Conventional SMR models that ignore the marking process should only be used when marked and unmarked animals share the same spatial distribution, such as when a subset of the population has natural marks. Generalized SMR models that include the marking process are much more widely applicable. They represent a promising new approach for reducing the costs of studies aimed at understanding spatial and temporal variation in density.24-May-2017
Data from: Generalized spatial mark-resight models with an application to grizzly bears
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Fig. 4 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 4. Frequency of Sus scrofa group sizes observed in 167 unique camera trap observations from May 2016 to August 2016.
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