Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models.
Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in v...
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2023
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ftcdlib:oai:escholarship.org:ark:/13030/qt17t1x6w5 2024-04-28T08:22:49+00:00 Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. Milleret, Cyril Dey, Soumen Dupont, Pierre Brøseth, Henrik Turek, Daniel de Valpine, Perry Bischof, Richard 2023-02-01 application/pdf https://escholarship.org/uc/item/17t1x6w5 unknown eScholarship, University of California qt17t1x6w5 https://escholarship.org/uc/item/17t1x6w5 public Ecology, vol 104, iss 2 mortality nimbleSCR population dynamics population-level inferences wolverines (Gulo gulo) Female Humans Population Density Bayes Theorem Norway Sweden article 2023 ftcdlib 2024-04-03T14:14:44Z Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics. Article in Journal/Newspaper Gulo gulo University of California: eScholarship |
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Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
unknown |
topic |
mortality nimbleSCR population dynamics population-level inferences wolverines (Gulo gulo) Female Humans Population Density Bayes Theorem Norway Sweden |
spellingShingle |
mortality nimbleSCR population dynamics population-level inferences wolverines (Gulo gulo) Female Humans Population Density Bayes Theorem Norway Sweden Milleret, Cyril Dey, Soumen Dupont, Pierre Brøseth, Henrik Turek, Daniel de Valpine, Perry Bischof, Richard Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
topic_facet |
mortality nimbleSCR population dynamics population-level inferences wolverines (Gulo gulo) Female Humans Population Density Bayes Theorem Norway Sweden |
description |
Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics. |
format |
Article in Journal/Newspaper |
author |
Milleret, Cyril Dey, Soumen Dupont, Pierre Brøseth, Henrik Turek, Daniel de Valpine, Perry Bischof, Richard |
author_facet |
Milleret, Cyril Dey, Soumen Dupont, Pierre Brøseth, Henrik Turek, Daniel de Valpine, Perry Bischof, Richard |
author_sort |
Milleret, Cyril |
title |
Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
title_short |
Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
title_full |
Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
title_fullStr |
Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
title_full_unstemmed |
Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
title_sort |
estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. |
publisher |
eScholarship, University of California |
publishDate |
2023 |
url |
https://escholarship.org/uc/item/17t1x6w5 |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_source |
Ecology, vol 104, iss 2 |
op_relation |
qt17t1x6w5 https://escholarship.org/uc/item/17t1x6w5 |
op_rights |
public |
_version_ |
1797584187469856768 |