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Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities

Abundance estimation methods that combine several types of data are becoming increasingly common because they yield more accurate and precise parameter estimates and predictions than are possible from a single data source. These beneficial effects result from increasing sample size (through data poo...

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Autores principales: Jarrett, Crinan, Haydon, Daniel T., Morales, Juan M., Ferreira, Diogo F., Forzi, Francis Alemanji, Welch, Andreanna J., Powell, Luke L., Matthiopoulos, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787363/
https://www.ncbi.nlm.nih.gov/pubmed/35620844
http://dx.doi.org/10.1002/ecy.3769
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author Jarrett, Crinan
Haydon, Daniel T.
Morales, Juan M.
Ferreira, Diogo F.
Forzi, Francis Alemanji
Welch, Andreanna J.
Powell, Luke L.
Matthiopoulos, Jason
author_facet Jarrett, Crinan
Haydon, Daniel T.
Morales, Juan M.
Ferreira, Diogo F.
Forzi, Francis Alemanji
Welch, Andreanna J.
Powell, Luke L.
Matthiopoulos, Jason
author_sort Jarrett, Crinan
collection PubMed
description Abundance estimation methods that combine several types of data are becoming increasingly common because they yield more accurate and precise parameter estimates and predictions than are possible from a single data source. These beneficial effects result from increasing sample size (through data pooling) and complementarity between different data types. Here, we test whether integrating mark–recapture data with passive acoustic detections into a joint likelihood improves estimates of population size in a multi‐guild community. We compared the integrated model to a mark–recapture‐only model using simulated data first and then using a data set of mist‐net captures and acoustic recordings from an Afrotropical agroforest bird community. The integrated model with simulated data improved accuracy and precision of estimated population size and detection parameters. When applied to field data, the integrated model was able to produce, for each bird guild, ecologically plausible estimates of population size and detection parameters, with more precision compared with the mark–recapture model. Overall, our results show that adding acoustic data to mark–recapture analyses improves estimates of population size. With the increasing availability of acoustic recording devices, this data collection technique could readily be added to routine field protocols, leading to a cost‐efficient improvement of traditional mark–recapture population estimation.
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spelling pubmed-97873632022-12-27 Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities Jarrett, Crinan Haydon, Daniel T. Morales, Juan M. Ferreira, Diogo F. Forzi, Francis Alemanji Welch, Andreanna J. Powell, Luke L. Matthiopoulos, Jason Ecology Statistical Report Abundance estimation methods that combine several types of data are becoming increasingly common because they yield more accurate and precise parameter estimates and predictions than are possible from a single data source. These beneficial effects result from increasing sample size (through data pooling) and complementarity between different data types. Here, we test whether integrating mark–recapture data with passive acoustic detections into a joint likelihood improves estimates of population size in a multi‐guild community. We compared the integrated model to a mark–recapture‐only model using simulated data first and then using a data set of mist‐net captures and acoustic recordings from an Afrotropical agroforest bird community. The integrated model with simulated data improved accuracy and precision of estimated population size and detection parameters. When applied to field data, the integrated model was able to produce, for each bird guild, ecologically plausible estimates of population size and detection parameters, with more precision compared with the mark–recapture model. Overall, our results show that adding acoustic data to mark–recapture analyses improves estimates of population size. With the increasing availability of acoustic recording devices, this data collection technique could readily be added to routine field protocols, leading to a cost‐efficient improvement of traditional mark–recapture population estimation. John Wiley & Sons, Inc. 2022-07-15 2022-10 /pmc/articles/PMC9787363/ /pubmed/35620844 http://dx.doi.org/10.1002/ecy.3769 Text en © 2022 The Authors. Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Statistical Report
Jarrett, Crinan
Haydon, Daniel T.
Morales, Juan M.
Ferreira, Diogo F.
Forzi, Francis Alemanji
Welch, Andreanna J.
Powell, Luke L.
Matthiopoulos, Jason
Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
title Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
title_full Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
title_fullStr Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
title_full_unstemmed Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
title_short Integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
title_sort integration of mark–recapture and acoustic detections for unbiased population estimation in animal communities
topic Statistical Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787363/
https://www.ncbi.nlm.nih.gov/pubmed/35620844
http://dx.doi.org/10.1002/ecy.3769
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