Spatial Capture–Mark–Resight Estimation of Animal Population Density

Efford, Murray G.; Hunter, Christine M.

doi:10.1111/biom.12766

Cited by 33 publications

(69 citation statements)

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“…However, an unsolved problem remained: the requirement that the marked animals should be a random subset of the population (Royle et al, 2014). This requirement was overcome with advent of generalized SMR (Gen-SMR) models (Efford & Hunter, 2018;Whittington, Hebblewhite, & Chandler, 2017) which include sub-models for both marking and resighting processes. The model for the marking process describes the distribution of the marked individual, thereby relaxing the SMR assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities (Whittington et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Jímenez

Chandler

Tobajas

et al. 2019

Ecology and Evolution

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The estimation of abundance of wildlife populations is an essential part of ecological research and monitoring. Spatially explicit capture–recapture (SCR) models are widely used for abundance and density estimation, frequently through individual identification of target species using camera‐trap sampling. Generalized spatial mark–resight (Gen‐SMR) is a recently developed SCR extension that allows for abundance estimation when only a subset of the population is recognizable by artificial or natural marks. However, in many cases, it is not possible to read the marks in camera‐trap pictures, even though individuals can be recognized as marked. We present a new extension of Gen‐SMR that allows for this type of incomplete identification. We used simulation to assess how the number of marked individuals and the individual identification rate influenced bias and precision. We demonstrate the model's performance in estimating red fox ( Vulpes vulpes ) density with two empirical datasets characterized by contrasting densities and rates of identification of marked individuals. According to the simulations, accuracy increases with the number of marked individuals ( m ), but is less sensitive to changes in individual identification rate (δ). In our case studies of red fox density estimation, we obtained a posterior mean of 1.60 (standard deviation SD: 0.32) and 0.28 ( SD : 0.06) individuals/km 2 , in high and low density, with an identification rate of 0.21 and 0.91, respectively. This extension of Gen‐SMR is broadly applicable as it addresses the common problem of incomplete identification of marked individuals during resighting surveys.

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Section: Introductionmentioning

confidence: 99%

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Jímenez

Chandler

Tobajas

et al. 2019

Ecology and Evolution

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“…(, Chap. 19) and with a complementary methods developed by Efford and Hunter (in press). In their analysis of rat ( Rattus rattus ) data, Efford and Hunter (in press) found that conventional SMR models underestimated density compared to generalized SMR models and that modelling resight data as counts or binary detections produced similar results.…”

Section: Discussionmentioning

confidence: 99%

“…19) and with a complementary methods developed by Efford and Hunter (in press). In their analysis of rat ( Rattus rattus ) data, Efford and Hunter (in press) found that conventional SMR models underestimated density compared to generalized SMR models and that modelling resight data as counts or binary detections produced similar results. Both their study and our study found that biases occurred even when marked animals were captured and marked on a regular grid of traps because the ratio of marked to unmarked animals decreased from the centre of the study area to the perimeter of the state‐space.…”

Section: Discussionmentioning

confidence: 99%

“…Efford and Hunter (in press) concurrently developed a generalized SMR model, which they term spatial capture–mark–resight, that accounts for both the marking and resighting processes. This model, which is currently available in the r secr 2.10.4 package (Efford, ), is less computationally demanding than our Bayesian approach, but ignores spatial autocorrelation in the counts of unmarked animals.…”

Section: Discussionmentioning

confidence: 99%

“…This model, which is currently available in the r secr 2.10.4 package (Efford, ), is less computationally demanding than our Bayesian approach, but ignores spatial autocorrelation in the counts of unmarked animals. Rather than conditioning on the spatial point process, the counts are assumed to be independent Poisson outcomes and the spatial and temporal correlation in counts can result in poor confidence interval coverage (Efford & Hunter, in press). Quasi‐likelihood methods are thus used to minimize bias and to estimate approximate confidence intervals.…”

Section: Discussionmentioning

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

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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Occupancy, density, and activity patterns of a Critically Endangered leopard population on the Kawthoolei‐Thailand border

Montgomery¹,

Stokes²,

K'lu³

et al. 2023

Population Ecology

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Large carnivores have been largely extirpated from Southeast Asia due to deforestation, habitat fragmentation, and poaching. Estimating the density of endangered carnivore populations, and identifying relationships between species occupancy and both environmental and anthropogenic factors, is essential for effective conservation planning. Recently, the IUCN conservation status of the Indochinese leopard (Panthera pardus delacouri) was upgraded to “Critically Endangered.” We surveyed Kweekoh Wildlife Sanctuary in Kawthoolei, an area administered by the Karen ethnic group in eastern Myanmar, to quantify (1) leopard population density using spatially explicit mark‐resight (SMR) models, (2) leopard occupancy as influenced by important ecological variables, and (3) potential differences in activity between melanistic and spotted leopard morphs. Leopard density was estimated to be 1.39 ± SE 0.22/100 km2. Leopard occupancy (ψ = 0.43; 95% credible interval: 0.26–0.67) increased further from roads, at relatively higher elevations, and in areas with higher relative abundance of wild boar. Leopard activity was cathemeral, with higher activity during night hours, and significant overlap (Δ = 0.84; 95% confidence interval: 0.71–0.96) between melanistic and spotted morphs. However, melanistic leopards were more active during twilight hours than spotted individuals whose activity did not significantly vary throughout the day. Indochinese leopard density estimates in Kweekoh were among the lowest reported from Southeast Asia. Leopard occupancy was highest in the sanctuary's core areas, suggesting the presence of negative anthropogenic impacts along the sanctuary borders. We suggest our low density estimates warrant immediate and decisive conservation action, including better protection for leopards, their habitat, and their prey.

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Spatial Capture–Mark–Resight Estimation of Animal Population Density

Cited by 33 publications

References 24 publications

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

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

Occupancy, density, and activity patterns of a Critically Endangered leopard population on the Kawthoolei‐Thailand border

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