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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ftdoajarticles:oai:doaj.org/article:7b1b934e937c4a87b61377c82b4366d3 2023-07-02T03:33:16+02:00 Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. Kohma Arai Martin Castonguay Vyacheslav Lyubchich David H Secor 2023-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0285702 https://doaj.org/article/7b1b934e937c4a87b61377c82b4366d3 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pone.0285702 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0285702 https://doaj.org/article/7b1b934e937c4a87b61377c82b4366d3 PLoS ONE, Vol 18, Iss 5, p e0285702 (2023) Medicine R Science Q article 2023 ftdoajarticles https://doi.org/10.1371/journal.pone.0285702 2023-06-11T00:37:10Z 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 (northern contingent) and the US (southern contingent), and seasonally overlap in the US fished regions. The nominal approach based on otolith carbon and oxygen stable isotopes (δ13C/δ18O) yielded high contingent classification accuracy (84.9%). Contingent assignment of unknown-origin samples revealed prevalent, yet highly varied contingent mixing levels (12.5-83.7%) within the US waters over four decades (1975-2019). Nominal predictions were integrated into mackerel-specific otolith oxygen isoscapes developed independently for Canadian and US waters. The combined approach identified geographic nursery hotspots in known spawning sites, but also detected geographic shifts over multi-decadal time scales. This framework can be applied to other marine species to understand migration and connectivity at a high spatial resolution, relevant to management of unit stocks in fisheries and other conservation assessments. Article in Journal/Newspaper Northwest Atlantic Directory of Open Access Journals: DOAJ Articles Canada PLOS ONE 18 5 e0285702 |
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Open Polar |
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ftdoajarticles |
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English |
topic |
Medicine R Science Q |
spellingShingle |
Medicine R Science Q Kohma Arai Martin Castonguay Vyacheslav Lyubchich David H Secor Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. |
topic_facet |
Medicine R Science Q |
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 (northern contingent) and the US (southern contingent), and seasonally overlap in the US fished regions. The nominal approach based on otolith carbon and oxygen stable isotopes (δ13C/δ18O) yielded high contingent classification accuracy (84.9%). Contingent assignment of unknown-origin samples revealed prevalent, yet highly varied contingent mixing levels (12.5-83.7%) within the US waters over four decades (1975-2019). Nominal predictions were integrated into mackerel-specific otolith oxygen isoscapes developed independently for Canadian and US waters. The combined approach identified geographic nursery hotspots in known spawning sites, but also detected geographic shifts over multi-decadal time scales. This framework can be applied to other marine species to understand migration and connectivity at a high spatial resolution, relevant to management of unit stocks in fisheries and other conservation assessments. |
format |
Article in Journal/Newspaper |
author |
Kohma Arai Martin Castonguay Vyacheslav Lyubchich David H Secor |
author_facet |
Kohma Arai Martin Castonguay Vyacheslav Lyubchich David H Secor |
author_sort |
Kohma Arai |
title |
Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. |
title_short |
Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. |
title_full |
Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. |
title_fullStr |
Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. |
title_full_unstemmed |
Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades. |
title_sort |
integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2023 |
url |
https://doi.org/10.1371/journal.pone.0285702 https://doaj.org/article/7b1b934e937c4a87b61377c82b4366d3 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Northwest Atlantic |
genre_facet |
Northwest Atlantic |
op_source |
PLoS ONE, Vol 18, Iss 5, p e0285702 (2023) |
op_relation |
https://doi.org/10.1371/journal.pone.0285702 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0285702 https://doaj.org/article/7b1b934e937c4a87b61377c82b4366d3 |
op_doi |
https://doi.org/10.1371/journal.pone.0285702 |
container_title |
PLOS ONE |
container_volume |
18 |
container_issue |
5 |
container_start_page |
e0285702 |
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