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Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics

Current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. Sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of i...

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Autores principales: Bled, Florent, Belant, Jerrold L., Van Daele, Lawrence J., Svoboda, Nathan, Gustine, David, Hilderbrand, Grant, Barnes, Victor G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5696435/
https://www.ncbi.nlm.nih.gov/pubmed/29187987
http://dx.doi.org/10.1002/ece3.3469
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author Bled, Florent
Belant, Jerrold L.
Van Daele, Lawrence J.
Svoboda, Nathan
Gustine, David
Hilderbrand, Grant
Barnes, Victor G.
author_facet Bled, Florent
Belant, Jerrold L.
Van Daele, Lawrence J.
Svoboda, Nathan
Gustine, David
Hilderbrand, Grant
Barnes, Victor G.
author_sort Bled, Florent
collection PubMed
description Current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. Sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of individual and global parameters. We present a general integrated population model (IPM), specifically designed for brown bears (Ursus arctos), using three common data types for bear (U. spp.) populations: repeated counts, capture–mark–recapture, and litter size. We considered factors affecting ecological and observation processes for these data. We assessed the practicality of this approach on a simulated population and compared estimates from our model to values used for simulation and results from count data only. We then present a practical application of this general approach adapted to the constraints of a case study using historical data available for brown bears on Kodiak Island, Alaska, USA. The IPM provided more accurate and precise estimates than models accounting for repeated count data only, with credible intervals including the true population 94% and 5% of the time, respectively. For the Kodiak population, we estimated annual average litter size (within one year after birth) to vary between 0.45 [95% credible interval: 0.43; 0.55] and 1.59 [1.55; 1.82]. We detected a positive relationship between salmon availability and adult survival, with survival probabilities greater for females than males. Survival probabilities increased from cubs to yearlings to dependent young ≥2 years old and decreased with litter size. Linking multiple information sources based on ecological and observation mechanisms can provide more accurate and precise estimates, to better inform management. IPMs can also reduce data collection efforts by sharing information among agencies and management units. Our approach responds to an increasing need in bear populations’ management and can be readily adapted to other large carnivores.
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spelling pubmed-56964352017-11-29 Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics Bled, Florent Belant, Jerrold L. Van Daele, Lawrence J. Svoboda, Nathan Gustine, David Hilderbrand, Grant Barnes, Victor G. Ecol Evol Original Research Current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. Sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of individual and global parameters. We present a general integrated population model (IPM), specifically designed for brown bears (Ursus arctos), using three common data types for bear (U. spp.) populations: repeated counts, capture–mark–recapture, and litter size. We considered factors affecting ecological and observation processes for these data. We assessed the practicality of this approach on a simulated population and compared estimates from our model to values used for simulation and results from count data only. We then present a practical application of this general approach adapted to the constraints of a case study using historical data available for brown bears on Kodiak Island, Alaska, USA. The IPM provided more accurate and precise estimates than models accounting for repeated count data only, with credible intervals including the true population 94% and 5% of the time, respectively. For the Kodiak population, we estimated annual average litter size (within one year after birth) to vary between 0.45 [95% credible interval: 0.43; 0.55] and 1.59 [1.55; 1.82]. We detected a positive relationship between salmon availability and adult survival, with survival probabilities greater for females than males. Survival probabilities increased from cubs to yearlings to dependent young ≥2 years old and decreased with litter size. Linking multiple information sources based on ecological and observation mechanisms can provide more accurate and precise estimates, to better inform management. IPMs can also reduce data collection efforts by sharing information among agencies and management units. Our approach responds to an increasing need in bear populations’ management and can be readily adapted to other large carnivores. John Wiley and Sons Inc. 2017-10-11 /pmc/articles/PMC5696435/ /pubmed/29187987 http://dx.doi.org/10.1002/ece3.3469 Text en © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Bled, Florent
Belant, Jerrold L.
Van Daele, Lawrence J.
Svoboda, Nathan
Gustine, David
Hilderbrand, Grant
Barnes, Victor G.
Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
title Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
title_full Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
title_fullStr Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
title_full_unstemmed Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
title_short Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
title_sort using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5696435/
https://www.ncbi.nlm.nih.gov/pubmed/29187987
http://dx.doi.org/10.1002/ece3.3469
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