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Open population maximum likelihood spatial capture-recapture

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Open population maximum likelihood spatial capture-recapture. / Glennie, Richard; Borchers, David L.; Murchie, Matthew; Harmsen, Bart J.; Foster, Rebecca J.

In: Biometrics, Vol. Early View, 25.07.2019.

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Glennie, R, Borchers, DL, Murchie, M, Harmsen, BJ & Foster, RJ 2019, 'Open population maximum likelihood spatial capture-recapture' Biometrics, vol. Early View. https://doi.org/10.1111/biom.13078

APA

Glennie, R., Borchers, D. L., Murchie, M., Harmsen, B. J., & Foster, R. J. (2019). Open population maximum likelihood spatial capture-recapture. Biometrics, Early View. https://doi.org/10.1111/biom.13078

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Glennie R, Borchers DL, Murchie M, Harmsen BJ, Foster RJ. Open population maximum likelihood spatial capture-recapture. Biometrics. 2019 Jul 25;Early View. https://doi.org/10.1111/biom.13078

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Glennie, Richard ; Borchers, David L. ; Murchie, Matthew ; Harmsen, Bart J. ; Foster, Rebecca J. / Open population maximum likelihood spatial capture-recapture. In: Biometrics. 2019 ; Vol. Early View.

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@article{7c8ad57b8fc2427bb8aa25ae8e76d5a1,
title = "Open population maximum likelihood spatial capture-recapture",
abstract = "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.",
keywords = "Hidden Markov model, Open population, Panthera onca, Population density, Spatial capture-recapture, Survival",
author = "Richard Glennie and Borchers, {David L.} and Matthew Murchie and Harmsen, {Bart J.} and Foster, {Rebecca J.}",
note = "Funding: Part-funded by UK EPSRC grant EP/K041061/1 (DB); Richard Glennie was funded by the Carnegie Trust.",
year = "2019",
month = "7",
day = "25",
doi = "10.1111/biom.13078",
language = "English",
volume = "Early View",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley",

}

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TY - JOUR

T1 - Open population maximum likelihood spatial capture-recapture

AU - Glennie, Richard

AU - Borchers, David L.

AU - Murchie, Matthew

AU - Harmsen, Bart J.

AU - Foster, Rebecca J.

N1 - Funding: Part-funded by UK EPSRC grant EP/K041061/1 (DB); Richard Glennie was funded by the Carnegie Trust.

PY - 2019/7/25

Y1 - 2019/7/25

N2 - 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.

AB - 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.

KW - Hidden Markov model

KW - Open population

KW - Panthera onca

KW - Population density

KW - Spatial capture-recapture

KW - Survival

U2 - 10.1111/biom.13078

DO - 10.1111/biom.13078

M3 - Article

VL - Early View

JO - Biometrics

T2 - Biometrics

JF - Biometrics

SN - 0006-341X

ER -

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ID: 251041104