Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ...
Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., is...
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Online Access: | https://dx.doi.org/10.5061/dryad.b8gtht7gr https://datadryad.org/stash/dataset/doi:10.5061/dryad.b8gtht7gr |
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ftdatacite:10.5061/dryad.b8gtht7gr 2024-02-04T10:03:19+01:00 Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... Arai, Kohma Castonguay, Martin Lyubchich, Vyacheslav Secor, David 2022 https://dx.doi.org/10.5061/dryad.b8gtht7gr https://datadryad.org/stash/dataset/doi:10.5061/dryad.b8gtht7gr en eng Dryad https://dx.doi.org/10.1371/journal.pone.0285702 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 FOS Agriculture, forestry, and fisheries Dataset dataset 2022 ftdatacite https://doi.org/10.5061/dryad.b8gtht7gr10.1371/journal.pone.0285702 2024-01-05T04:39:59Z Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this integrated framework to predict the geographic origin of the Northwest Atlantic mackerel (Scomber scombrus), a migratory pelagic fish comprised of northern and southern components that have distinct spawning sites off Canada ... Dataset Northwest Atlantic DataCite Metadata Store (German National Library of Science and Technology) Canada |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
FOS Agriculture, forestry, and fisheries |
spellingShingle |
FOS Agriculture, forestry, and fisheries Arai, Kohma Castonguay, Martin Lyubchich, Vyacheslav Secor, David Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
topic_facet |
FOS Agriculture, forestry, and fisheries |
description |
Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this integrated framework to predict the geographic origin of the Northwest Atlantic mackerel (Scomber scombrus), a migratory pelagic fish comprised of northern and southern components that have distinct spawning sites off Canada ... |
format |
Dataset |
author |
Arai, Kohma Castonguay, Martin Lyubchich, Vyacheslav Secor, David |
author_facet |
Arai, Kohma Castonguay, Martin Lyubchich, Vyacheslav Secor, David |
author_sort |
Arai, Kohma |
title |
Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
title_short |
Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
title_full |
Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
title_fullStr |
Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
title_full_unstemmed |
Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
title_sort |
data from: integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades ... |
publisher |
Dryad |
publishDate |
2022 |
url |
https://dx.doi.org/10.5061/dryad.b8gtht7gr https://datadryad.org/stash/dataset/doi:10.5061/dryad.b8gtht7gr |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Northwest Atlantic |
genre_facet |
Northwest Atlantic |
op_relation |
https://dx.doi.org/10.1371/journal.pone.0285702 |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.b8gtht7gr10.1371/journal.pone.0285702 |
_version_ |
1789970662037651456 |