Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach
European eel is thought to be a symbol of the effects of global change on aquatic biodiversity. The species has persisted for millions of years and faced drastic environmental fluctuations thanks to its phenotypic plasticity. However, the species has recently declined to historically low levels unde...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Fishes |
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| Online Access: | https://www.mdpi.com/2410-3888/7/5/274 |
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| author | Bastien Bourillon Eric Feunteun Anthony Acou Thomas Trancart Nils Teichert Claude Belpaire Sylvie Dufour Paco Bustamante Kim Aarestrup Alan Walker David Righton |
| author_facet | Bastien Bourillon Eric Feunteun Anthony Acou Thomas Trancart Nils Teichert Claude Belpaire Sylvie Dufour Paco Bustamante Kim Aarestrup Alan Walker David Righton |
| author_sort | Bastien Bourillon |
| collection | DOAJ |
| description | European eel is thought to be a symbol of the effects of global change on aquatic biodiversity. The species has persisted for millions of years and faced drastic environmental fluctuations thanks to its phenotypic plasticity. However, the species has recently declined to historically low levels under synergistic human pressures. Sublethal chemical contamination has been shown to alter reproductive capacity, but the impacts and required actions are not fully addressed by conservation plans. This paper proposes a modelling approach to quantify the effects of sublethal contamination by anthropogenic pollutants on the expression of life history traits and related fitness of the critically endangered European eel. <b>Material and Methods</b>: We sampled female silver eels from eight different catchments across Europe previously shown to be representative of the spectrum of environmental variability and contamination. We measured 11 fitness-related life history traits within four main categories: fecundity, adaptability and plasticity, migratory readiness, and spawning potential. We used machine learning in models to explore the phenotypic reaction (expression of these life history traits) according to geographical parameters, parasite burdens (the introduced nematode <i>Anguillicoloides crassus</i>) and anthropogenic contaminants (persistent organic pollutants (POPs) in muscular tissue and trace elements (TEs) in gonads, livers and muscles). Finally, we simulated, the effects of two management scenarios—contamination reduction and contamination increase—on the fecundity and recruitment. <b>Results</b>: Contamination in our sampling was shown to have a stronger control on life history traits than do geographic and environmental factors that are currently described in the literature. We modelled different contamination scenarios to assess the benefit of mitigation: these scenarios suggest that reducing pollutants concentrations to the lowest values that occurred in our sampling design would double the fecundity of eels compared to the current situation. <b>Discussion</b>: Remediation of contamination could represent a viable management option for increasing the resilience of eel populations, with much more effects than solely reducing fishing mortality. More broadly, our work provides an innovative way for quantitative assessment of the reaction norms of species’ biological traits and related fecundity to contamination by organic and inorganic pollutions thus opening new management and conservation pathways to revert the erosion of biodiversity. |
| first_indexed | 2024-03-09T20:13:43Z |
| format | Article |
| id | doaj.art-90b61e0be0b241f78621d6b1db5e08cc |
| institution | Directory Open Access Journal |
| issn | 2410-3888 |
| language | English |
| last_indexed | 2024-03-09T20:13:43Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fishes |
| spelling | doaj.art-90b61e0be0b241f78621d6b1db5e08cc2023-11-24T00:06:37ZengMDPI AGFishes2410-38882022-10-017527410.3390/fishes7050274Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning ApproachBastien Bourillon0Eric Feunteun1Anthony Acou2Thomas Trancart3Nils Teichert4Claude Belpaire5Sylvie Dufour6Paco Bustamante7Kim Aarestrup8Alan Walker9David Righton10Laboratoire Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum National d’Histoire Naturelle, UMR 8067 CNRS, Centre de Recherche et d’Enseignement sur les Systèmes Côtiers, station de biologie marine de Dinard, Sorbonne Université, IRD 207, Université de Caen Normandie, Université des Antilles, 38 rue du Port Blanc, 35800 Dinard, FranceLaboratoire Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum National d’Histoire Naturelle, UMR 8067 CNRS, Centre de Recherche et d’Enseignement sur les Systèmes Côtiers, station de biologie marine de Dinard, Sorbonne Université, IRD 207, Université de Caen Normandie, Université des Antilles, 38 rue du Port Blanc, 35800 Dinard, FranceUAR OFB-CNRS-MNHN PatriNat, Station de Biologie Marine de Dinard, 38 rue du Port Blanc, 35800 Dinard, FranceLaboratoire Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum National d’Histoire Naturelle, UMR 8067 CNRS, Centre de Recherche et d’Enseignement sur les Systèmes Côtiers, station de biologie marine de Dinard, Sorbonne Université, IRD 207, Université de Caen Normandie, Université des Antilles, 38 rue du Port Blanc, 35800 Dinard, FranceLaboratoire Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum National d’Histoire Naturelle, UMR 8067 CNRS, Centre de Recherche et d’Enseignement sur les Systèmes Côtiers, station de biologie marine de Dinard, Sorbonne Université, IRD 207, Université de Caen Normandie, Université des Antilles, 38 rue du Port Blanc, 35800 Dinard, FranceResearch Institute for Nature and Forest (INBO), Dwersbos 28, 1630 Linkebeek, BelgiumLaboratoire Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum National d’Histoire Naturelle, CNRS UMR 8067, Sorbonne Université, IRD 207, Université de Caen Normandie, Université des Antilles, 43 rue Cuvier, CEDEX 5, 75231 Paris, FranceLittoral, Environnement et Sociétés (LIENSs), UMR 7266 CNRS, La Rochelle Université, 2 rue Olympe de Gouges, 17000 La Rochelle, FranceDTU AQUA, National Institute of Aquatic Resources, Section for Freshwater Fisheries Ecology, Technical University of Denmark, Vejlsøvej 39, 8600 Silkeborg, DenmarkCentre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UKCentre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UKEuropean eel is thought to be a symbol of the effects of global change on aquatic biodiversity. The species has persisted for millions of years and faced drastic environmental fluctuations thanks to its phenotypic plasticity. However, the species has recently declined to historically low levels under synergistic human pressures. Sublethal chemical contamination has been shown to alter reproductive capacity, but the impacts and required actions are not fully addressed by conservation plans. This paper proposes a modelling approach to quantify the effects of sublethal contamination by anthropogenic pollutants on the expression of life history traits and related fitness of the critically endangered European eel. <b>Material and Methods</b>: We sampled female silver eels from eight different catchments across Europe previously shown to be representative of the spectrum of environmental variability and contamination. We measured 11 fitness-related life history traits within four main categories: fecundity, adaptability and plasticity, migratory readiness, and spawning potential. We used machine learning in models to explore the phenotypic reaction (expression of these life history traits) according to geographical parameters, parasite burdens (the introduced nematode <i>Anguillicoloides crassus</i>) and anthropogenic contaminants (persistent organic pollutants (POPs) in muscular tissue and trace elements (TEs) in gonads, livers and muscles). Finally, we simulated, the effects of two management scenarios—contamination reduction and contamination increase—on the fecundity and recruitment. <b>Results</b>: Contamination in our sampling was shown to have a stronger control on life history traits than do geographic and environmental factors that are currently described in the literature. We modelled different contamination scenarios to assess the benefit of mitigation: these scenarios suggest that reducing pollutants concentrations to the lowest values that occurred in our sampling design would double the fecundity of eels compared to the current situation. <b>Discussion</b>: Remediation of contamination could represent a viable management option for increasing the resilience of eel populations, with much more effects than solely reducing fishing mortality. More broadly, our work provides an innovative way for quantitative assessment of the reaction norms of species’ biological traits and related fecundity to contamination by organic and inorganic pollutions thus opening new management and conservation pathways to revert the erosion of biodiversity.https://www.mdpi.com/2410-3888/7/5/274life history traitsglobal changebiogeographyAnguilla |
| spellingShingle | Bastien Bourillon Eric Feunteun Anthony Acou Thomas Trancart Nils Teichert Claude Belpaire Sylvie Dufour Paco Bustamante Kim Aarestrup Alan Walker David Righton Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach Fishes life history traits global change biogeography Anguilla |
| title | Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach |
| title_full | Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach |
| title_fullStr | Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach |
| title_full_unstemmed | Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach |
| title_short | Anthropogenic Contaminants Shape the Fitness of the Endangered European Eel: A Machine Learning Approach |
| title_sort | anthropogenic contaminants shape the fitness of the endangered european eel a machine learning approach |
| topic | life history traits global change biogeography Anguilla |
| url | https://www.mdpi.com/2410-3888/7/5/274 |
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