Capture–recapture abundance estimation using a semi-complete data likelihood approach

King, Ruth; McClintock, Brett T.; Kidney, Darren; Borchers, David L.

doi:10.1214/15-aoas890

Cited by 32 publications

(72 citation statements)

References 29 publications

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“…Formulating open population SCR as a HMM incorporates CJS and JS models into a general framework and allows marginalization over life histories and movement to be achieved with computational efficiency, making more complex modeling and model selection practically feasible. Maximum likelihood estimation leads to similar inference compared with a data augmentation, Bayesian approach; furthermore, marginalization could be used in the Bayesian context with a semi‐complete data likelihood (King et al ., ). Overall, the presented framework is flexible and open to extension: alternative movement models, life histories with more states (eg, temporary emigration), and incorporation of observed states (ie, dead recoveries or recorded births).…”

Section: Resultsmentioning

confidence: 97%

“…Bayesian SCR models (Royle et al ., ) obtain inference by sampling activity centers within a Markov chain Monte Carlo (MCMC) algorithm, using the full joint likelihood of detection parameters and activity centers. Alternatively, Bayesian inference can be obtained from the semi‐complete data likelihood where integration over activity centers is achieved by quadrature (King et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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Open Population Maximum Likelihood Spatial Capture-Recapture

et al. 2019

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Open population capture-recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture-recapture (SCR), allowing for estimation of the effective area sampled and population density.Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack-Jolly-Seber and Jolly-Seber models, with and without activity center movement. The method is applied to a 12-year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root-mean-square error in predicting population density compared to closed population models. K E Y W O R D S hidden Markov model, open population, Panthera onca, population density, spatial capture-recapture, survival

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Open Population Maximum Likelihood Spatial Capture-Recapture

et al. 2019

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“…Frequentist inference tends to be faster, because the data augmentation Markov chain Monte Carlo methods implemented for Bayesian inference can be much more computationally demanding. This may change with the advent of a more efficient Bayesian method by King et al (2016). Bayesian methods are more easily able to deal with open population models in which there is temporal dependence in the state of the population, with situations in which only a fraction of the population is marked, and in which there is spatial correlation in individuals' locations.…”

Section: Discussionmentioning

confidence: 99%

From distance sampling to spatial capture–recapture

Borchers

Marques

2017

AStA Adv Stat Anal

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Distance sampling and capture-recapture are the two most widely used wildlife abundance estimation methods. capture-recapture methods have only recently incorporated models for spatial distribution and there is an increasing tendency for distance sampling methods to incorporated spatial models rather than to rely on partly design-based spatial inference. In this overview we show how spatial models are central to modern distance sampling and that spatial capture-recapture models arise as an extension of distance sampling methods. Depending on the type of data recorded, they can be viewed as particular kinds of hierarchical binary regression, Poisson regression, survival or time-to-event models, with individuals' locations as latent variables and a spatial model as the latent variable distribution. Incorporation of spatial models in these two methods provides new opportunities for drawing explicitly spatial inferences. Areas of likely future development include more sophisticated spatial and spatio-temporal modelling of individuals' locations and movements, new methods for integrating spatial capture-recapture and other kinds of ecological survey data, and methods for dealing with the recapture uncertainty that often arise when "capture" consists of detection by a remote device like a camera trap or microphone.

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“…multimark is also the first capture-recapture software to implement generalized Bayesian multimodel inference based on the RJMCMC algorithm proposed by Barker and Link (2013). Relative to previous applications using multiple marks (Bonner and Holmberg 2013;McClintock et al 2013), the relatively fast computation times of the package are attributable to its use of "semicomplete" data likelihoods (King et al 2015), parallel processing, and MCMC algorithms written in C (instead of R). Because parallel processing relies on the parallel package (R Core Team 2013), first-time Windows and OS X users can expect a firewall pop-up dialog box asking if an R process should accept incoming connections.…”

Section: Discussionmentioning

confidence: 99%

“…multimark currently includes open population Cormack-Jolly-Seber (CJS) and closed population abundance models (e.g., Williams et al 2002). These Bayesian implementations are similar in spirit to the CJS model of Royle (2008) and the abundance model of King et al (2015). Given the latent encounter histories (Y) that generated the observed encounter histories ðỸ 1 ;Ỹ 2 ;Ỹ known Þ, the likelihood for the CJS model with two mark types is…”

Section: Modelsmentioning

confidence: 99%

multimark: an R package for analysis of capture–recapture data consisting of multiple “noninvasive” marks

McClintock

2015

Ecology and Evolution

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I describe an open‐source R package, , for estimation of survival and abundance from capture–mark–recapture data consisting of multiple “noninvasive” marks. Noninvasive marks include natural pelt or skin patterns, scars, and genetic markers that enable individual identification in lieu of physical capture. provides a means for combining and jointly analyzing encounter histories from multiple noninvasive sources that otherwise cannot be reliably matched (e.g., left‐ and right‐sided photographs of bilaterally asymmetrical individuals). The package is currently capable of fitting open population Cormack–Jolly–Seber (CJS) and closed population abundance models with up to two mark types using Bayesian Markov chain Monte Carlo (MCMC) methods. can also be used for Bayesian analyses of conventional capture–recapture data consisting of a single‐mark type. Some package features include (1) general model specification using formulas already familiar to most R users, (2) ability to include temporal, behavioral, age, cohort, and individual heterogeneity effects in detection and survival probabilities, (3) improved MCMC algorithm that is computationally faster and more efficient than previously proposed methods, (4) Bayesian multimodel inference using reversible jump MCMC, and (5) data simulation capabilities for power analyses and assessing model performance. I demonstrate use of using left‐ and right‐sided encounter histories for bobcats (Lynx rufus) collected from remote single‐camera stations in southern California. In this example, there is evidence of a behavioral effect (i.e., trap “happy” response) that is otherwise indiscernible using conventional single‐sided analyses. The package will be most useful to ecologists seeking stronger inferences by combining different sources of mark–recapture data that are difficult (or impossible) to reliably reconcile, particularly with the sparse datasets typical of rare or elusive species for which noninvasive sampling techniques are most commonly employed. Addressing deficiencies in currently available software, also provides a user‐friendly interface for performing Bayesian multimodel inference using capture–recapture data consisting of a single conventional mark or multiple noninvasive marks.

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Capture–recapture abundance estimation using a semi-complete data likelihood approach

Cited by 32 publications

References 29 publications

Open Population Maximum Likelihood Spatial Capture-Recapture

Open Population Maximum Likelihood Spatial Capture-Recapture

From distance sampling to spatial capture–recapture

multimark: an R package for analysis of capture–recapture data consisting of multiple “noninvasive” marks

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