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Reconciling seascape genetics and fisheries science in three codistributed flatfishes

Uncertainty hampers innovative mixed‐fisheries management by the scales at which connectivity dynamics are relevant to management objectives. The spatial scale of sustainable stock management is species‐specific and depends on ecology, life history and population connectivity. One valuable approach...

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Autores principales: Vandamme, Sara, Raeymaekers, Joost A. M., Maes, Gregory E., Cottenie, Karl, Calboli, Federico C. F., Diopere, Eveline, Volckaert, Filip A. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896710/
https://www.ncbi.nlm.nih.gov/pubmed/33664793
http://dx.doi.org/10.1111/eva.13139
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author Vandamme, Sara
Raeymaekers, Joost A. M.
Maes, Gregory E.
Cottenie, Karl
Calboli, Federico C. F.
Diopere, Eveline
Volckaert, Filip A. M.
author_facet Vandamme, Sara
Raeymaekers, Joost A. M.
Maes, Gregory E.
Cottenie, Karl
Calboli, Federico C. F.
Diopere, Eveline
Volckaert, Filip A. M.
author_sort Vandamme, Sara
collection PubMed
description Uncertainty hampers innovative mixed‐fisheries management by the scales at which connectivity dynamics are relevant to management objectives. The spatial scale of sustainable stock management is species‐specific and depends on ecology, life history and population connectivity. One valuable approach to understand these spatial scales is to determine to what extent population genetic structure correlates with the oceanographic environment. Here, we compare the level of genetic connectivity in three codistributed and commercially exploited demersal flatfish species living in the North East Atlantic Ocean. Population genetic structure was analysed based on 14, 14 and 10 neutral DNA microsatellite markers for turbot, brill and sole, respectively. We then used redundancy analysis (RDA) to attribute the genetic variation to spatial (geographical location), temporal (sampling year) and oceanographic (water column characteristics) components. The genetic structure of turbot was composed of three clusters and correlated with variation in the depth of the pycnocline, in addition to spatial factors. The genetic structure of brill was homogenous, but correlated with average annual stratification and spatial factors. In sole, the genetic structure was composed of three clusters, but was only linked to a temporal factor. We explored whether the management of data poor commercial fisheries, such as in brill and turbot, might benefit from population‐specific information. We conclude that the management of fish stocks has to consider species‐specific genetic structures and may benefit from the documentation of the genetic seascape and life‐history traits.
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spelling pubmed-78967102021-03-03 Reconciling seascape genetics and fisheries science in three codistributed flatfishes Vandamme, Sara Raeymaekers, Joost A. M. Maes, Gregory E. Cottenie, Karl Calboli, Federico C. F. Diopere, Eveline Volckaert, Filip A. M. Evol Appl Original Articles Uncertainty hampers innovative mixed‐fisheries management by the scales at which connectivity dynamics are relevant to management objectives. The spatial scale of sustainable stock management is species‐specific and depends on ecology, life history and population connectivity. One valuable approach to understand these spatial scales is to determine to what extent population genetic structure correlates with the oceanographic environment. Here, we compare the level of genetic connectivity in three codistributed and commercially exploited demersal flatfish species living in the North East Atlantic Ocean. Population genetic structure was analysed based on 14, 14 and 10 neutral DNA microsatellite markers for turbot, brill and sole, respectively. We then used redundancy analysis (RDA) to attribute the genetic variation to spatial (geographical location), temporal (sampling year) and oceanographic (water column characteristics) components. The genetic structure of turbot was composed of three clusters and correlated with variation in the depth of the pycnocline, in addition to spatial factors. The genetic structure of brill was homogenous, but correlated with average annual stratification and spatial factors. In sole, the genetic structure was composed of three clusters, but was only linked to a temporal factor. We explored whether the management of data poor commercial fisheries, such as in brill and turbot, might benefit from population‐specific information. We conclude that the management of fish stocks has to consider species‐specific genetic structures and may benefit from the documentation of the genetic seascape and life‐history traits. John Wiley and Sons Inc. 2020-11-02 /pmc/articles/PMC7896710/ /pubmed/33664793 http://dx.doi.org/10.1111/eva.13139 Text en © 2020 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Vandamme, Sara
Raeymaekers, Joost A. M.
Maes, Gregory E.
Cottenie, Karl
Calboli, Federico C. F.
Diopere, Eveline
Volckaert, Filip A. M.
Reconciling seascape genetics and fisheries science in three codistributed flatfishes
title Reconciling seascape genetics and fisheries science in three codistributed flatfishes
title_full Reconciling seascape genetics and fisheries science in three codistributed flatfishes
title_fullStr Reconciling seascape genetics and fisheries science in three codistributed flatfishes
title_full_unstemmed Reconciling seascape genetics and fisheries science in three codistributed flatfishes
title_short Reconciling seascape genetics and fisheries science in three codistributed flatfishes
title_sort reconciling seascape genetics and fisheries science in three codistributed flatfishes
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896710/
https://www.ncbi.nlm.nih.gov/pubmed/33664793
http://dx.doi.org/10.1111/eva.13139
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