Improving estimation of puma (Puma concolor) population density: clustered camera-trapping, telemetry data, and generalized spatial mark-resight models

Murphy, Sean M.; Wilckens, David T.; Augustine, Ben C.; Peyton, Mark A.; Harper, Glenn C.

doi:10.1038/s41598-019-40926-7

Cited by 39 publications

(38 citation statements)

References 66 publications

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“…bears and cougars). This can be accomplished with either a finely-spaced grid (this study) or through cameras that are clustered with variable spacing intervals (Rich et al 2019, Murphy et al 2019). Similarly, assumptions of demographic closure may be challenging to meet for larger home range species unless the extent of the study area is large enough to contain many home ranges.…”

Section: Discussionmentioning

confidence: 99%

Evaluating and integrating spatial capture-recapture models with data of variable individual identifiability

Ruprecht

Eriksson

Forrester

et al. 2020

Preprint

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Many applications in ecology depend on unbiased and precise estimates of animal population density. Spatial capture recapture models and their variants have become the preferred tool for estimating densities of carnivores. Within the spatial capture-recapture family are variants that require individual identification of all encounters (spatial capture-recapture), individual identification of a subset of a population (spatial mark-resight), or no individual identification (spatial count). In addition, these models can incorporate telemetry data (all models) and the marking process (spatial mark-resight). However, the consistency of results among methods and the relative precision of estimates in a real-world setting are unknown. Consequently, it is unclear how much and what type of data are needed to achieve satisfactory density estimates. We tested a suite of models to estimate population densities of black bears (Ursus americanus), bobcats (Lynx rufus), cougars (Puma concolor), and coyotes (Canis latrans). For each species we genotyped fecal DNA collected with detection dogs. A subset of individuals from each species were affixed with GPS collars bearing unique markings to be resighted by remote cameras set on a 1 km grid. We fit 10 models for each species ranging from those requiring no animals to be individually recognizable to others that necessitate full individual recognition. We then assessed the contribution of incorporating telemetry data to each model and the marking process to the mark-resight model. Finally, we developed an integrated hybrid model that combines camera, physical capture, genetic, and GPS data into a single hierarchical model. Importantly, we find that spatial count models that do not individually identify animals fail in all cases whether or not telemetry data are included. Results improved as models contained more information on individual identity. Models where a subset of individuals were identifiable yielded qualitatively similar results, but can produce quantitatively divergent estimates, suggesting that long-term population monitoring should use a consistent method across years. Incorporation of telemetry data and the marking process can produce more accurate and precise density estimates. Our results can be used to guide future study designs to efficiently estimate carnivore densities for better understanding of population dynamics, predator-prey relationships, and community assemblages.

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Section: Discussionmentioning

confidence: 99%

Evaluating and integrating spatial capture-recapture models with data of variable individual identifiability

Ruprecht

Eriksson

Forrester

et al. 2020

Preprint

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“…SMR models have been developed and implemented for a range of sampling scenarios (Sollmann et al 2013;Efford & Hunter 2018;Whittington et al 2018). In particular, the use of camera-trap surveys is appealing as they are cost-effective and allow for sampling carnivores that are wide-ranging, nocturnal, secretive and occur at low population densities (Alonso et al 2015;Kane et al 2015;Whittington et al 2018;Jimenez et al 2019;Murphy et al 2019). When individuals are identified by natural markings (as in the case of leopards), it is appropriate to assume that marked individuals are a random subset of the entire population, thereby allowing for the robust estimation of population density Efford & Hunter 2018).…”

Section: Discussionmentioning

confidence: 99%

Population density modeling of mixed polymorphic phenotypes: an application of spatial mark-resight models

Harihar

Lahkar

Singh³

et al. 2020

Preprint

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Melanism is a form of pigmentation polymorphism where individuals have darker coloration than what is considered the “wild” phenotype. In the case of leopards, Panthera pardus, melanism occurs at higher frequencies amongst populations in tropical and subtropical moist forests of south and southeast Asia, presenting a unique challenge in estimating and monitoring these populations. Unlike the wild phenotype that are readily recognizable by their rosette patterns, melanism results in individuals being unidentifiable or ‘unmarked’ through photographic captures obtained using white flash cameras. Spatial mark-resight (SMR) models that require only a subset of the population to be ‘marked’ offer the opportunity to estimate population density. In this study, we present an application of SMR models to estimate leopard densities using camera trap survey data from three sampling sessions at Manas National Park (MNP), India. By using an SMR model that allowed us to include captures of unidentified sightings of marked individuals, we were also able to incorporate captures where identity was either not confirmed or only known from a single flank. Following 18,674 trap-days of sampling across three sessions, we obtained 728 leopard photo-captures, of which 22.6% (165) were melanistic. We estimated leopard densities of 4.33, 2.61and 3.37 individuals/100km2 across the three sessions. To our best knowledge, these represent the first known estimates of leopard densities from such populations. Finally, we highlight that SMR models present an opportunity to revisit past camera trap survey data for leopards and other species that exhibit phenotypic polymorphism towards generating valuable information on populations.

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“…Therefore, after calculating trap saturation, or the average proportion of traps that were occupied at the end of each occasion, we modeled traps as multi-catch via a multinomial observation model [ 32 , 67 , 69 ]. Recaptures of multiple individuals occurred in traps that were located on >1 sampling grid (i.e., cross-grid captures), effectively constituting a pseudo-clustered sampling design; therefore, we collapsed the grid-specific capture histories into a single study area capture history with five occasions [ 41 , 70 , 71 ].…”

Section: Methodsmentioning

confidence: 99%

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

et al. 2020

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Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

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Improving estimation of puma (Puma concolor) population density: clustered camera-trapping, telemetry data, and generalized spatial mark-resight models

Cited by 39 publications

References 66 publications

Evaluating and integrating spatial capture-recapture models with data of variable individual identifiability

Evaluating and integrating spatial capture-recapture models with data of variable individual identifiability

Population density modeling of mixed polymorphic phenotypes: an application of spatial mark-resight models

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

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