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Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model

We use a spatially explicit biogeochemical end-to-end ecosystem model, Atlantis, to simulate impacts from the Deepwater Horizon oil spill and subsequent recovery of fish guilds. Dose-response relationships with expected oil concentrations were utilized to estimate the impact on fish growth and morta...

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Autores principales: Ainsworth, Cameron H., Paris, Claire B., Perlin, Natalie, Dornberger, Lindsey N., Patterson, William F., Chancellor, Emily, Murawski, Steve, Hollander, David, Daly, Kendra, Romero, Isabel C., Coleman, Felicia, Perryman, Holly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784916/
https://www.ncbi.nlm.nih.gov/pubmed/29370187
http://dx.doi.org/10.1371/journal.pone.0190840
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author Ainsworth, Cameron H.
Paris, Claire B.
Perlin, Natalie
Dornberger, Lindsey N.
Patterson, William F.
Chancellor, Emily
Murawski, Steve
Hollander, David
Daly, Kendra
Romero, Isabel C.
Coleman, Felicia
Perryman, Holly
author_facet Ainsworth, Cameron H.
Paris, Claire B.
Perlin, Natalie
Dornberger, Lindsey N.
Patterson, William F.
Chancellor, Emily
Murawski, Steve
Hollander, David
Daly, Kendra
Romero, Isabel C.
Coleman, Felicia
Perryman, Holly
author_sort Ainsworth, Cameron H.
collection PubMed
description We use a spatially explicit biogeochemical end-to-end ecosystem model, Atlantis, to simulate impacts from the Deepwater Horizon oil spill and subsequent recovery of fish guilds. Dose-response relationships with expected oil concentrations were utilized to estimate the impact on fish growth and mortality rates. We also examine the effects of fisheries closures and impacts on recruitment. We validate predictions of the model by comparing population trends and age structure before and after the oil spill with fisheries independent data. The model suggests that recruitment effects and fishery closures had little influence on biomass dynamics. However, at the assumed level of oil concentrations and toxicity, impacts on fish mortality and growth rates were large and commensurate with observations. Sensitivity analysis suggests the biomass of large reef fish decreased by 25% to 50% in areas most affected by the spill, and biomass of large demersal fish decreased even more, by 40% to 70%. Impacts on reef and demersal forage caused starvation mortality in predators and increased reliance on pelagic forage. Impacts on the food web translated effects of the spill far away from the oiled area. Effects on age structure suggest possible delayed impacts on fishery yields. Recovery of high-turnover populations generally is predicted to occur within 10 years, but some slower-growing populations may take 30+ years to fully recover.
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spelling pubmed-57849162018-02-09 Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model Ainsworth, Cameron H. Paris, Claire B. Perlin, Natalie Dornberger, Lindsey N. Patterson, William F. Chancellor, Emily Murawski, Steve Hollander, David Daly, Kendra Romero, Isabel C. Coleman, Felicia Perryman, Holly PLoS One Research Article We use a spatially explicit biogeochemical end-to-end ecosystem model, Atlantis, to simulate impacts from the Deepwater Horizon oil spill and subsequent recovery of fish guilds. Dose-response relationships with expected oil concentrations were utilized to estimate the impact on fish growth and mortality rates. We also examine the effects of fisheries closures and impacts on recruitment. We validate predictions of the model by comparing population trends and age structure before and after the oil spill with fisheries independent data. The model suggests that recruitment effects and fishery closures had little influence on biomass dynamics. However, at the assumed level of oil concentrations and toxicity, impacts on fish mortality and growth rates were large and commensurate with observations. Sensitivity analysis suggests the biomass of large reef fish decreased by 25% to 50% in areas most affected by the spill, and biomass of large demersal fish decreased even more, by 40% to 70%. Impacts on reef and demersal forage caused starvation mortality in predators and increased reliance on pelagic forage. Impacts on the food web translated effects of the spill far away from the oiled area. Effects on age structure suggest possible delayed impacts on fishery yields. Recovery of high-turnover populations generally is predicted to occur within 10 years, but some slower-growing populations may take 30+ years to fully recover. Public Library of Science 2018-01-25 /pmc/articles/PMC5784916/ /pubmed/29370187 http://dx.doi.org/10.1371/journal.pone.0190840 Text en © 2018 Ainsworth et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ainsworth, Cameron H.
Paris, Claire B.
Perlin, Natalie
Dornberger, Lindsey N.
Patterson, William F.
Chancellor, Emily
Murawski, Steve
Hollander, David
Daly, Kendra
Romero, Isabel C.
Coleman, Felicia
Perryman, Holly
Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model
title Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model
title_full Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model
title_fullStr Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model
title_full_unstemmed Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model
title_short Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model
title_sort impacts of the deepwater horizon oil spill evaluated using an end-to-end ecosystem model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784916/
https://www.ncbi.nlm.nih.gov/pubmed/29370187
http://dx.doi.org/10.1371/journal.pone.0190840
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