Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.12/9054 |
Resumo: | The establishment of high-throughput sequencing technologies and subsequent large-scale genomic datasets has flourished across fields of fundamental biological sciences. The introduction of genomic resources in fisheries management has been proposed from multiple angles, ranging from an accurate re-definition of geographical limitations of stocks and connectivity, identification of fine-scale stock structure linked to locally adapted subpopulations, or even the integration with individual-based biophysical models to explore life history strategies. While those clearly enhance our perception of patterns at the light of a spatial scale, temporal depth and consequently forecasting ability might be compromised as an analytical trade-off. Here, we present a framework to reinforce our understanding of stock dynamics by adding also a temporal point of view. We propose to integrate genomic information on temporal projections of species distributions computed by Species Distribution Models (SDMs). SDMs have the potential to project the current and future distribution ranges of a given species from relevant environmental predictors. These projections serve as tools to inform about range expansions and contractions of fish stocks and suggest either suitable locations or local extirpations that may arise in the future. However, SDMs assume that the whole population respond homogenously to the range of environmental conditions. Here, we conceptualize a framework that leverages a conventional Bayesian joint-SDM approach with the incorporation of genomic data. We propose that introducing genomic information at the basis of a joint-SDM will explore the range of suitable habitats where stocks could thrive in the future as a function of their current evolutionary potential. |
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Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modellingHigh-throughput sequencinggenomicsSpecies distribution model (SDMs)Fisheries applicationsEvolutionary ecologyThe establishment of high-throughput sequencing technologies and subsequent large-scale genomic datasets has flourished across fields of fundamental biological sciences. The introduction of genomic resources in fisheries management has been proposed from multiple angles, ranging from an accurate re-definition of geographical limitations of stocks and connectivity, identification of fine-scale stock structure linked to locally adapted subpopulations, or even the integration with individual-based biophysical models to explore life history strategies. While those clearly enhance our perception of patterns at the light of a spatial scale, temporal depth and consequently forecasting ability might be compromised as an analytical trade-off. Here, we present a framework to reinforce our understanding of stock dynamics by adding also a temporal point of view. We propose to integrate genomic information on temporal projections of species distributions computed by Species Distribution Models (SDMs). SDMs have the potential to project the current and future distribution ranges of a given species from relevant environmental predictors. These projections serve as tools to inform about range expansions and contractions of fish stocks and suggest either suitable locations or local extirpations that may arise in the future. However, SDMs assume that the whole population respond homogenously to the range of environmental conditions. Here, we conceptualize a framework that leverages a conventional Bayesian joint-SDM approach with the incorporation of genomic data. We propose that introducing genomic information at the basis of a joint-SDM will explore the range of suitable habitats where stocks could thrive in the future as a function of their current evolutionary potential.Fundação para a Ciência e Tecnollogia - FCT; ARNETFrontiers Media S.A.Repositório do ISPABaltazar-Soares, MiguelLima, André R.A.Silva, GonçaloGaget, Elie2023-02-27T20:17:10Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.12/9054engBaltazar-Soares, M., Lima, A. R. A., Silva, G., & Gaget, E. (2023). Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling. Frontiers in Marine Science, 9 https://doi.org/10.3389/fmars.2022.1014361o10.3389/fmars.2022.1014361info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-05T02:15:15Zoai:repositorio.ispa.pt:10400.12/9054Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:48:13.765879Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
title |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
spellingShingle |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling Baltazar-Soares, Miguel High-throughput sequencing genomics Species distribution model (SDMs) Fisheries applications Evolutionary ecology |
title_short |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
title_full |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
title_fullStr |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
title_full_unstemmed |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
title_sort |
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling |
author |
Baltazar-Soares, Miguel |
author_facet |
Baltazar-Soares, Miguel Lima, André R.A. Silva, Gonçalo Gaget, Elie |
author_role |
author |
author2 |
Lima, André R.A. Silva, Gonçalo Gaget, Elie |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório do ISPA |
dc.contributor.author.fl_str_mv |
Baltazar-Soares, Miguel Lima, André R.A. Silva, Gonçalo Gaget, Elie |
dc.subject.por.fl_str_mv |
High-throughput sequencing genomics Species distribution model (SDMs) Fisheries applications Evolutionary ecology |
topic |
High-throughput sequencing genomics Species distribution model (SDMs) Fisheries applications Evolutionary ecology |
description |
The establishment of high-throughput sequencing technologies and subsequent large-scale genomic datasets has flourished across fields of fundamental biological sciences. The introduction of genomic resources in fisheries management has been proposed from multiple angles, ranging from an accurate re-definition of geographical limitations of stocks and connectivity, identification of fine-scale stock structure linked to locally adapted subpopulations, or even the integration with individual-based biophysical models to explore life history strategies. While those clearly enhance our perception of patterns at the light of a spatial scale, temporal depth and consequently forecasting ability might be compromised as an analytical trade-off. Here, we present a framework to reinforce our understanding of stock dynamics by adding also a temporal point of view. We propose to integrate genomic information on temporal projections of species distributions computed by Species Distribution Models (SDMs). SDMs have the potential to project the current and future distribution ranges of a given species from relevant environmental predictors. These projections serve as tools to inform about range expansions and contractions of fish stocks and suggest either suitable locations or local extirpations that may arise in the future. However, SDMs assume that the whole population respond homogenously to the range of environmental conditions. Here, we conceptualize a framework that leverages a conventional Bayesian joint-SDM approach with the incorporation of genomic data. We propose that introducing genomic information at the basis of a joint-SDM will explore the range of suitable habitats where stocks could thrive in the future as a function of their current evolutionary potential. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-27T20:17:10Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.12/9054 |
url |
http://hdl.handle.net/10400.12/9054 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Baltazar-Soares, M., Lima, A. R. A., Silva, G., & Gaget, E. (2023). Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling. Frontiers in Marine Science, 9 https://doi.org/10.3389/fmars.2022.1014361o 10.3389/fmars.2022.1014361 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Media S.A. |
publisher.none.fl_str_mv |
Frontiers Media S.A. |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130952982593536 |