A spatial capture–recapture model with attractions between individuals

McLaughlin, Paul; Bar, Haim

doi:10.1002/env.2653

Cited by 9 publications

(9 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For completeness, we note that a variety of other approaches exist for implementing CR models, including Dirichlet process approaches (Diana et al, 2020; Manrique‐Vallier, 2016), numerical integration (Borchers & Efford, 2008; Coull & Agresti, 1999), and transdimensional methods like reversible‐jump MCMC (King & Brooks, 2008; McLaughlin, 2019). In fact, King et al (2016).…”

Section: Discussionmentioning

confidence: 99%

Multistage hierarchical capture–recapture models

et al. 2023

View full text Add to dashboard Cite

Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual‐level data. A variety of implementations exist for such models, including integrated likelihood, parameter‐expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species.

show abstract

Section: Discussionmentioning

confidence: 99%

Multistage hierarchical capture–recapture models

et al. 2023

View full text Add to dashboard Cite

show abstract

“… 2013 c ), landscape connectivity (Royle et al. 2013 a ), territorial species (Reich and Gardner 2014 ), attractions between individuals (McLaughlin and Bar 2020 ), integrated population models (Chandler and Clark 2014 ), passive acoustics (Efford et al. 2009 , Kidney et al.…”

Section: Looking Back: How Did We Get Here?mentioning

confidence: 99%

An integrated path for spatial capture–recapture and animal movement modeling

McClintock

Abrahms

Chandler

et al. 2021

Ecology

View full text Add to dashboard Cite

Ecologists and conservation biologists increasingly rely on spatial capture-recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), while animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual-to population-level processes, while SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.

show abstract

“…For the remaining set of parameters,

\left({\mathbf{s}}^1,{\mathbf{s}}^2\right)

, we note that since we use an unconditional PP prior, the DA scheme, used for example in Reich and Gardner (2014) and McLaughlin and Bar (2021), cannot be used for inference, as the DA scheme assumes a Binomial prior on the population size independently on the point locations. Hence, we sample the latent ACs

\left({\mathbf{s}}^1,{\mathbf{s}}^2\right)

from their full conditional by generalizing the algorithm for simulating PPs of Geyer and Møller (1994), according to which, in each step, one of the following moves is proposed: changing the location of the existing points, proposing a random number

{n}_b

of new points and deleting a random number

{n}_d

of existing points.…”

Section: Spatial Capture‐recapture Modelmentioning

confidence: 99%

“…To our knowledge, the only SCR models that do not assume a Poisson process are presented in Reich and Gardner (2014) and McLaughlin and Bar (2021). Reich and Gardner (2014) used a Strauss process (Strauss, 1975) to model the ACs of a single species while accounting for interaction within the species, while McLaughlin and Bar (2021) assume no interaction between individuals but instead assume interaction between the ACs of each individual over time. As shown by Reich and Gardner (2014), it is important to account for interaction since failing to account for it can lead to biases in the estimate of the population size.…”

Section: Introductionmentioning

confidence: 99%

“…This dependence can manifest either as repulsion , where the presence of a point at a particular location decreases the probability of another point at a nearby location, or as attraction , where the opposite is true and points tend to be clustered. To our knowledge, the only SCR models that do not assume a Poisson process are presented in Reich and Gardner (2014) and McLaughlin and Bar (2021). Reich and Gardner (2014) used a Strauss process (Strauss, 1975) to model the ACs of a single species while accounting for interaction within the species, while McLaughlin and Bar (2021) assume no interaction between individuals but instead assume interaction between the ACs of each individual over time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A vector of point processes for modeling interactions between and within species using capture‐recapture data

et al. 2022

View full text Add to dashboard Cite

Capture-recapture data and corresponding models have been used extensively to estimate the size of wildlife populations when detection probability is less than 1. When the locations of traps or cameras used to capture or detect individuals are known, spatially-explicit capture-recapture models are used to infer the spatial pattern of the individual locations and population density. Individual locations, referred to as activity centres, are defined as the locations around which the individuals move. These activity centres are typically assumed to be independent, and their spatial pattern is modelled using homogeneous Poisson processes. However, this assumption is often unrealistic, since individuals can interact with each other, either within a species or between different species. In this paper, we consider a vector of point processes from the general class of interaction point processes and develop a model for capturerecapture data that can account for interactions, in particular repulsions, between and within multiple species. Interaction point processes present a challenge from an inferential perspective because of the intractability of the normalising constant of the likelihood function, and hence standard Markov Chain Monte Carlo procedures to perform Bayesian inference cannot be applied. Therefore, we adopt an inference procedure based on the Monte Carlo Metropolis Hastings algorithm of Liang and Jin (2013), which scales well when modelling more than one species. Finally, we adopt an inference method for jointly sampling the latent activity centres and the population size based on the algorithm of Geyer and Møller (1994). This approach also allows us to adaptively tune the proposal distribution of new points, which leads to better mixing especially in the case of non-uniformly distributed traps. We apply the model to a capture-recapture data-set on leopards and tigers collected at the Corbett Tiger Reserve in India. Our findings suggest that between species repulsion is stronger than within species, while tiger population density is higher than leopand population density at the park.

show abstract

A spatial capture–recapture model with attractions between individuals

Cited by 9 publications

References 56 publications

Multistage hierarchical capture–recapture models

Multistage hierarchical capture–recapture models

An integrated path for spatial capture–recapture and animal movement modeling

A vector of point processes for modeling interactions between and within species using capture‐recapture data

Contact Info

Product

Resources

About