Estimating transitions between states using measurements with imperfect detection: application to serological data
Classifying the states of an individual and quantifying transitions between states are crucial while modeling animal behavior, movement, and physiologic status. When these states are hidden or imperfectly known, it is particularly convenient to relate them to appropriate quantitative measurements ta...
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ftdatacite:10.6084/m9.figshare.c.3306192.v1 2023-05-15T15:44:56+02:00 Estimating transitions between states using measurements with imperfect detection: application to serological data Choquet, Rémi Carrié, Cécile Chambert, Thierry Boulinier, Thierry 2016 https://dx.doi.org/10.6084/m9.figshare.c.3306192.v1 https://figshare.com/collections/Estimating_transitions_between_states_using_measurements_with_imperfect_detection_application_to_serological_data/3306192/1 unknown Figshare https://dx.doi.org/10.1890/12-1849.1 https://dx.doi.org/10.6084/m9.figshare.c.3306192 CC-BY http://creativecommons.org/licenses/by/3.0/us CC-BY Environmental Science Ecology FOS Biological sciences Collection article 2016 ftdatacite https://doi.org/10.6084/m9.figshare.c.3306192.v1 https://doi.org/10.1890/12-1849.1 https://doi.org/10.6084/m9.figshare.c.3306192 2021-11-05T12:55:41Z Classifying the states of an individual and quantifying transitions between states are crucial while modeling animal behavior, movement, and physiologic status. When these states are hidden or imperfectly known, it is particularly convenient to relate them to appropriate quantitative measurements taken on the individual. This task is, however, challenging when quantitative measurements are not available at each sampling occasion. For capture–recapture data, various ways of incorporating such non-discrete information have been used, but they are either ad hoc and/or use a fraction of the available information by relying on a priori thresholds to assign individual states. Here we propose assigning discrete states based on a continuous measurement, and then modeled survival and transition probabilities based on these assignments. The main advantage of this new approach is that a more informative use of the non-discrete information is done. As an illustrative working example, we applied this approach to eco-epidemiological data collected across a series of years in which individuals of a long-lived seabird, the Black-legged Kittiwake ( Rissa tridactyla ), could either be visually detected or physically recaptured and blood sampled for subsequent immunological analyses. We discuss how this approach opens many perspectives in eco-epidemiology, but also more broadly, in population ecology. Article in Journal/Newspaper Black-legged Kittiwake rissa tridactyla DataCite Metadata Store (German National Library of Science and Technology) |
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Environmental Science Ecology FOS Biological sciences |
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Environmental Science Ecology FOS Biological sciences Choquet, Rémi Carrié, Cécile Chambert, Thierry Boulinier, Thierry Estimating transitions between states using measurements with imperfect detection: application to serological data |
topic_facet |
Environmental Science Ecology FOS Biological sciences |
description |
Classifying the states of an individual and quantifying transitions between states are crucial while modeling animal behavior, movement, and physiologic status. When these states are hidden or imperfectly known, it is particularly convenient to relate them to appropriate quantitative measurements taken on the individual. This task is, however, challenging when quantitative measurements are not available at each sampling occasion. For capture–recapture data, various ways of incorporating such non-discrete information have been used, but they are either ad hoc and/or use a fraction of the available information by relying on a priori thresholds to assign individual states. Here we propose assigning discrete states based on a continuous measurement, and then modeled survival and transition probabilities based on these assignments. The main advantage of this new approach is that a more informative use of the non-discrete information is done. As an illustrative working example, we applied this approach to eco-epidemiological data collected across a series of years in which individuals of a long-lived seabird, the Black-legged Kittiwake ( Rissa tridactyla ), could either be visually detected or physically recaptured and blood sampled for subsequent immunological analyses. We discuss how this approach opens many perspectives in eco-epidemiology, but also more broadly, in population ecology. |
format |
Article in Journal/Newspaper |
author |
Choquet, Rémi Carrié, Cécile Chambert, Thierry Boulinier, Thierry |
author_facet |
Choquet, Rémi Carrié, Cécile Chambert, Thierry Boulinier, Thierry |
author_sort |
Choquet, Rémi |
title |
Estimating transitions between states using measurements with imperfect detection: application to serological data |
title_short |
Estimating transitions between states using measurements with imperfect detection: application to serological data |
title_full |
Estimating transitions between states using measurements with imperfect detection: application to serological data |
title_fullStr |
Estimating transitions between states using measurements with imperfect detection: application to serological data |
title_full_unstemmed |
Estimating transitions between states using measurements with imperfect detection: application to serological data |
title_sort |
estimating transitions between states using measurements with imperfect detection: application to serological data |
publisher |
Figshare |
publishDate |
2016 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.3306192.v1 https://figshare.com/collections/Estimating_transitions_between_states_using_measurements_with_imperfect_detection_application_to_serological_data/3306192/1 |
genre |
Black-legged Kittiwake rissa tridactyla |
genre_facet |
Black-legged Kittiwake rissa tridactyla |
op_relation |
https://dx.doi.org/10.1890/12-1849.1 https://dx.doi.org/10.6084/m9.figshare.c.3306192 |
op_rights |
CC-BY http://creativecommons.org/licenses/by/3.0/us |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.3306192.v1 https://doi.org/10.1890/12-1849.1 https://doi.org/10.6084/m9.figshare.c.3306192 |
_version_ |
1766379306205839360 |