Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

Sollmann, Rahel; Tôrres, Natália Mundim; Furtado, Mariana Malzoni; Jácomo, Anah Tereza de Almeida; Palomares, Francisço; Roques, Séverine; Silveira, Leandro

doi:10.1016/j.biocon.2013.08.003

Cited by 62 publications

(59 citation statements)

References 46 publications

(56 reference statements)

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“…, Sollman et al. , Fisher and Bradbury ). Additionally, multi‐method models serve as valuable tools for evaluating the strengths and weaknesses of different field methods, allowing practitioners to more effectively design and efficiently implement survey or monitoring efforts.…”

Section: Discussionmentioning

confidence: 99%

“…, Sollman et al. , Fisher and Bradbury ). Because multi‐method models allow simultaneous field‐method comparison, bias correction, and improved estimation of state parameters, they represent very powerful tools for a variety of applied objectives if correctly implemented.…”

Section: Introductionmentioning

confidence: 99%

“…, Sollman et al. ) increases the likelihood that population size or distribution changes over the survey duration, while staggering detection methods in space can induce additional detection variance associated with local habitat conditions or the location of individuals with respect to specific methods (Graves et al. ).…”

Section: Introductionmentioning

confidence: 99%

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Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

Clare

McKinney

DePue

et al. 2017

Ecological Applications

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It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

Clare

McKinney

DePue

et al. 2017

Ecological Applications

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“…Reliable spatial distribution estimates of population density are crucial to population conservation and habitat management of elusive endangered species (Sollmann et al, 2013;Li and Wang, 2013;Zimmermann et al, 2013;Zhang et al, 2014). Camera trapping is an effective, non-invasive technique for wildlife surveys and is currently a popular tool for estimating population sizes of elusive, rare species (Karanth, 1995).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution drivers of Amur leopard density in northeast China

Shi

Wang

et al. 2015

Biological Conservation

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“…An important property of spatial capture-recapture methods is that detection probability is directly linked to the distance between an animal's activity center (i.e., home range center) and detectors via a spatial scale parameter (r), which is related to the extent of space use (i.e., home range or territory; Royle et al 2014). Examples include combining photos from camera trapping and DNA from scat sampling (Gopalaswamy et al 2012, Sollmann et al 2013c, photos from camera trapping and telemetry data from radio-collars (Sollmann et al 2013a, b, Linden et al 2017, and telemetry data from radio-collars and DNA from hair sampling (Royle et al 2013, Tenan et al 2017. Detection data produced by a single sampling method are often sparse, however, which can erode accuracy and precision of parameter estimates or possibly prevent density estimation entirely.…”

Section: Introductionmentioning

confidence: 99%

Integrating multiple genetic detection methods to estimate population density of social and territorial carnivores

et al. 2018

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Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

Cited by 62 publications

References 46 publications

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

Spatial distribution drivers of Amur leopard density in northeast China

Integrating multiple genetic detection methods to estimate population density of social and territorial carnivores

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