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Diet composition uncertainty determines impacts on fisheries following an oil spill


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


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.

Suggested Citation

  • 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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    References listed on IDEAS

    1. Garza-Gil, M. Dolores & Prada-Blanco, Albino & Vazquez-Rodriguez, M. Xose, 2006. "Estimating the short-term economic damages from the Prestige oil spill in the Galician fisheries and tourism," Ecological Economics, Elsevier, vol. 58(4), pages 842-849, July.
    2. Ronald L. Iman & Jon C. Helton, 1988. "An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models," Risk Analysis, John Wiley & Sons, vol. 8(1), pages 71-90, March.
    3. Morris, David J. & Speirs, Douglas C. & Cameron, Angus I. & Heath, Michael R., 2014. "Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: Factors affecting the biomass of fish and benthos," Ecological Modelling, Elsevier, vol. 273(C), pages 251-263.
    4. Storlie, Curtis B. & Swiler, Laura P. & Helton, Jon C. & Sallaberry, Cedric J., 2009. "Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1735-1763.
    5. Lassalle, Géraldine & Bourdaud, Pierre & Saint-Béat, Blanche & Rochette, Sébastien & Niquil, Nathalie, 2014. "A toolbox to evaluate data reliability for whole-ecosystem models: Application on the Bay of Biscay continental shelf food-web model," Ecological Modelling, Elsevier, vol. 285(C), pages 13-21.
    6. Larsen, Lars-Henrik & Sagerup, Kjetil & Ramsvatn, Silje, 2016. "The mussel path – Using the contaminant tracer, Ecotracer, in Ecopath to model the spread of pollutants in an Arctic marine food web," Ecological Modelling, Elsevier, vol. 331(C), pages 77-85.
    7. Masi, M.D. & Ainsworth, C.H. & Chagaris, D., 2014. "A probabilistic representation of fish diet compositions from multiple data sources: A Gulf of Mexico case study," Ecological Modelling, Elsevier, vol. 284(C), pages 60-74.
    8. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    9. Thomas De Graaff & Raymond J.C.M. Florax & Peter Nijkamp & Aura Reggiani, 2001. "A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity," Journal of Regional Science, Wiley Blackwell, vol. 41(2), pages 255-276, May.
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    Cited by:

    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. 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.
    3. 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).
    4. 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.


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