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A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

The Cormack–Jolly–Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect...

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Autores principales: Jeyam, Anita, McCrea, Rachel S., Bregnballe, Thomas, Frederiksen, Morten, Pradel, Roger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954010/
https://www.ncbi.nlm.nih.gov/pubmed/31983870
http://dx.doi.org/10.1007/s13253-017-0315-4
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author Jeyam, Anita
McCrea, Rachel S.
Bregnballe, Thomas
Frederiksen, Morten
Pradel, Roger
author_facet Jeyam, Anita
McCrea, Rachel S.
Bregnballe, Thomas
Frederiksen, Morten
Pradel, Roger
author_sort Jeyam, Anita
collection PubMed
description The Cormack–Jolly–Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman–Kruskal’s gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (Thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie’s test of equal catchability and Carothers’ extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions. Supplementary materials accompanying this paper appear online. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary materials for this article are available at 10.1007/s13253-017-0315-4.
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spelling pubmed-69540102020-01-23 A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data Jeyam, Anita McCrea, Rachel S. Bregnballe, Thomas Frederiksen, Morten Pradel, Roger J Agric Biol Environ Stat Article The Cormack–Jolly–Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman–Kruskal’s gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (Thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie’s test of equal catchability and Carothers’ extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions. Supplementary materials accompanying this paper appear online. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary materials for this article are available at 10.1007/s13253-017-0315-4. Springer US 2017-12-11 2018 /pmc/articles/PMC6954010/ /pubmed/31983870 http://dx.doi.org/10.1007/s13253-017-0315-4 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Jeyam, Anita
McCrea, Rachel S.
Bregnballe, Thomas
Frederiksen, Morten
Pradel, Roger
A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data
title A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data
title_full A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data
title_fullStr A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data
title_full_unstemmed A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data
title_short A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data
title_sort test of positive association for detecting heterogeneity in capture for capture–recapture data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954010/
https://www.ncbi.nlm.nih.gov/pubmed/31983870
http://dx.doi.org/10.1007/s13253-017-0315-4
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