Hierarchical computing for hierarchical models in ecology

McCaslin, Hanna M.; Feuka, Abigail B.; Hooten, Mevin B.

doi:10.1111/2041-210x.13513

Cited by 12 publications

(12 citation statements)

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“…Gelfand and Ghosh (2015) note that BHMs have been a great benefit to science, but the current rate of data acquisition and model complexity are "beginning to stretch our computational capabilities" and future inference will have to be the result of "an artful mixture of model specification and approximation." Hierarchical conditioning provides a beneficial way to build large complex models; thus, it is sensible that inferential computing for these models may also be best accomplished in a hierarchical fashion (McCaslin et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

Johnson

Brost

Hooten

2022

JABES

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We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.

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Section: Discussionmentioning

confidence: 99%

Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

Johnson

Brost

Hooten

2022

JABES

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“…We used the two-stage model-fitting technique described by McCaslin et al [ 55 ], which allowed us to fit individual first-stage models to each snake in parallel using package “parallel” in R version 3.6.1 [ 56 ] and then use those samples as proposals in the second stage to fit the full hierarchical model [ 57 , 58 ].…”

Section: Methodsmentioning

confidence: 99%

“…This was to create first-stage MCMC parameter updates that match the support of each parameter and did not require tuning or supervision, because we used the prior as a proposal for the Metropolis-Hastings update on and and a Gibbs update for in the first stage. We accounted for this difference in model specification by using a change of variables correction in the second stage Metropolis-Hastings updates [ 55 ].…”

Section: Methodsmentioning

confidence: 99%

Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile

et al. 2022

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Background Invasive reptiles pose a serious threat to global biodiversity, but early detection of individuals in an incipient population is often hindered by their cryptic nature, sporadic movements, and variation among individuals. Little is known about the mechanisms that affect the movement of these species, which limits our understanding of their dispersal. Our aim was to determine whether translocation or small-scale landscape features affect movement patterns of brown treesnakes (Boiga irregularis), a destructive invasive predator on the island of Guam. Methods We conducted a field experiment to compare the movements of resident (control) snakes to those of snakes translocated from forests and urban areas into new urban habitats. We developed a Bayesian hierarchical model to analyze snake movement mechanisms and account for attributes unique to invasive reptiles by incorporating multiple behavioral states and individual heterogeneity in movement parameters. Results We did not observe strong differences in mechanistic movement parameters (turning angle or step length) among experimental treatment groups. We found some evidence that translocated snakes from both forests and urban areas made longer movements than resident snakes, but variation among individuals within treatment groups weakened this effect. Snakes translocated from forests moved more frequently from pavement than those translocated from urban areas. Snakes translocated from urban areas moved less frequently from buildings than resident snakes. Resident snakes had high individual heterogeneity in movement probability. Conclusions Our approach to modeling movement improved our understanding of invasive reptile dispersal by allowing us to examine the mechanisms that influence their movement. We also demonstrated the importance of accounting for individual heterogeneity in population-level analyses, especially when management goals involve eradication of an invasive species.

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“…Whether used for frequentist or Bayesian inference, this is another area where HMMs and sparse matrix methods may be able to provide substantial gains in computational feasibility and efficiency. Other strategies that can be useful for large data sets and/or complex hierarchical structures include multi-stage model fitting techniques such as multiple imputation (e.g., McClintock, 2017;Scharf et al, 2017) and recursive Bayesian computing (e.g., Hooten et al, 2019aHooten et al, , 2020McCaslin et al, 2020). Movement models formulated in continuous time also involve integrals, but, instead of being over space, the integration is with respect to time.…”

Section: Model Fittingmentioning

confidence: 99%

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

McClintock

Abrahms

Chandler

et al. 2021

Ecology

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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.

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Hierarchical computing for hierarchical models in ecology

Cited by 12 publications

References 50 publications

Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile

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

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