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Diet composition uncertainty determines impacts on fisheries following an oil spill

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  • Morzaria-Luna, Hem Nalini
  • Ainsworth, Cameron H.
  • Tarnecki, Joseph H.
  • Grüss, Arnaud

Abstract

Oil spills can disrupt marine and coastal ecosystem services leading to reduced employment opportunities and income. Ecosystem models can be used to estimate the effects of oil pollution; however, uncertainty in model predictions may influence damage assessment. We performed an uncertainty analysis for the Atlantis ecosystem model of the Gulf of Mexico (Atlantis-GOM), under a scenario simulating the effects of the Deepwater Horizon oil spill. Atlantis-GOM simulates major biophysical processes, including the effects of oil hydrocarbons on fish growth and mortality. We used all available fish stomach content data to inform parameter distribution for the Atlantis-GOM availability matrix, which represents predator total consumption potential and diet preference. We sampled the fish diet composition distribution and analyzed the variability of functional group biomass and catch predicted by Atlantis-GOM simulations to changes in the availability matrix. Resulting biomass and catch were then used to fit statistical emulators of the ecosystem model and predict biomass and catch given the complete diet parameter space. We used simulated and emulated data to assess changes in recovery time to oil spill effects. Uncertainty in diet composition had large effects on model outputs and may, therefore, influence damage assessment of oil exposure on economically important species.

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  • Morzaria-Luna, Hem Nalini & Ainsworth, Cameron H. & Tarnecki, Joseph H. & Grüss, Arnaud, 2018. "Diet composition uncertainty determines impacts on fisheries following an oil spill," Ecosystem Services, Elsevier, vol. 33(PB), pages 187-198.
    • Handle: RePEc:eee:ecoser:v:33:y:2018:i:pb:p:187-198
    DOI: 10.1016/j.ecoser.2018.05.002
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    1. Baustert, Paul & Othoniel, Benoit & Rugani, Benedetto & Leopold, Ulrich, 2018. "Uncertainty analysis in integrated environmental models for ecosystem service assessments: Frameworks, challenges and gaps," Ecosystem Services, Elsevier, vol. 33(PB), pages 110-123.
    2. Perryman, Holly A. & Kaplan, Isaac C. & Blanchard, Julia L. & Fay, Gavin & Gaichas, Sarah K. & McGregor, Vidette L. & Morzaria-Luna, Hem Nalini & Porobic, Javier & Townsend, Howard & Fulton, Elizabeth, 2023. "Atlantis Ecosystem Model Summit 2022: Report from a workshop," Ecological Modelling, Elsevier, vol. 483(C).
    3. Bryant, Benjamin P. & Borsuk, Mark E. & Hamel, Perrine & Oleson, Kirsten L.L. & Schulp, C.J.E. & Willcock, Simon, 2018. "Transparent and feasible uncertainty assessment adds value to applied ecosystem services modeling," Ecosystem Services, Elsevier, vol. 33(PB), pages 103-109.
    4. Perryman, Holly A. & Tarnecki, Joseph H. & Grüss, Arnaud & Babcock, Elizabeth A. & Sagarese, Skyler R. & Ainsworth, Cameron H. & Gray DiLeone, Alisha M., 2020. "A revised diet matrix to improve the parameterization of a West Florida Shelf Ecopath model for understanding harmful algal bloom impacts," Ecological Modelling, Elsevier, vol. 416(C).
    5. Bracis, Chloe & Lehuta, Sigrid & Savina-Rolland, Marie & Travers-Trolet, Morgane & Girardin, Raphaël, 2020. "Improving confidence in complex ecosystem models: The sensitivity analysis of an Atlantis ecosystem model," Ecological Modelling, Elsevier, vol. 431(C).
    6. Grüss, Arnaud & Palomares, Maria L.D. & Poelen, Jorrit H. & Barile, Josephine R. & Aldemita, Casey D. & Ortiz, Shelumiel R. & Barrier, Nicolas & Shin, Yunne-Jai & Simons, James & Pauly, Daniel, 2019. "Building bridges between global information systems on marine organisms and ecosystem models," Ecological Modelling, Elsevier, vol. 398(C), pages 1-19.
    7. Lopez de Gamiz-Zearra, A. & Hansen, C. & Corrales, X. & Andonegi, E., 2024. "Increasing the reliability of the Bay of Biscay Atlantis model: A sensitivity analysis to parameters perturbations using a Morris screening approach," Ecological Modelling, Elsevier, vol. 488(C).

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