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Developing a high taxonomic resolution food web model to assess the functional role of forage fish in the California Current ecosystem

Author

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  • Koehn, Laura E.
  • Essington, Timothy E.
  • Marshall, Kristin N.
  • Kaplan, Isaac C.
  • Sydeman, William J.
  • Szoboszlai, Amber I.
  • Thayer, Julie A.

Abstract

Understanding the role of forage fish in marine food webs is an important part of ecosystem-based fisheries management. Food web models are a common tool used to account for important characteristics of forage fish and their trophodynamics. One primary limitation of many existing food web models is that the taxonomic resolution of forage fish and their predators is overly simplified. Here, we developed a food web model with high taxonomic resolution of forage fish and their predators in the California Current to more comprehensively describe trophic linkages involving forage fish and examine the ecological role of forage fish in this system. We parameterized a mass-balanced food web model (Ecopath) with 92 living functional groups, and used this to quantify diet dependency on forage fish, determine the main predators of forage fish, identify the topological position of forage fish in the food web, and calculate an index that identifies forage species or species aggregations that have key ecological roles (Supportive Role to Fishery ecosystem, SURF). Throughout, we characterized parameter uncertainty using a Monte Carlo approach. Though diets revealed some predators had high diet dependencies on individual forage fish species, most predators consumed multiple forage fish and also had notable diet overlap with forage fish. Consequently, no single forage fish appeared to act as a vital nexus species that is characteristic of “wasp-waisted” food webs in other upwelling regions. Additionally, no single forage fish was identified as “key” by the SURF index, but if predators and fisheries view certain pairs of forage fish as functionally equivalent, some plausible pairs would be identified as key assemblages. Specifically, sardine & anchovy (Sardinops sagax &Engraulis mordax) and herring & anchovy (Clupea pallasii &E. mordax) are key when predator populations depend on the aggregate availability of these species. This food web model can be used to support generalized equilibrium trade-off analysis or dynamic modeling to identify specific predators that would be of conservation concern under conditions of future forage fish depletion.

Suggested Citation

  • Koehn, Laura E. & Essington, Timothy E. & Marshall, Kristin N. & Kaplan, Isaac C. & Sydeman, William J. & Szoboszlai, Amber I. & Thayer, Julie A., 2016. "Developing a high taxonomic resolution food web model to assess the functional role of forage fish in the California Current ecosystem," Ecological Modelling, Elsevier, vol. 335(C), pages 87-100.
    • Handle: RePEc:eee:ecomod:v:335:y:2016:i:c:p:87-100
    DOI: 10.1016/j.ecolmodel.2016.05.010
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    References listed on IDEAS

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    1. S. Lewis & T. N. Sherratt & K. C. Hamer & S. Wanless, 2001. "Evidence of intra-specific competition for food in a pelagic seabird," Nature, Nature, vol. 412(6849), pages 816-819, August.
    2. B. A. Block & I. D. Jonsen & S. J. Jorgensen & A. J. Winship & S. A. Shaffer & S. J. Bograd & E. L. Hazen & D. G. Foley & G. A. Breed & A.-L. Harrison & J. E. Ganong & A. Swithenbank & M. Castleton & , 2011. "Tracking apex marine predator movements in a dynamic ocean," Nature, Nature, vol. 475(7354), pages 86-90, July.
    3. Elizabeth A Fulton & Anthony D M Smith & David C Smith & Penelope Johnson, 2014. "An Integrated Approach Is Needed for Ecosystem Based Fisheries Management: Insights from Ecosystem-Level Management Strategy Evaluation," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-16, January.
    4. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
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    Cited by:

    1. Walters, Carl & Christensen, Villy, 2019. "Effect of non-additivity in mortality rates on predictions of potential yield of forage fishes," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.
    2. Punt, André E. & MacCall, Alec D. & Essington, Timothy E. & Francis, Tessa B. & Hurtado-Ferro, Felipe & Johnson, Kelli F. & Kaplan, Isaac C. & Koehn, Laura E. & Levin, Phillip S. & Sydeman, William J., 2016. "Exploring the implications of the harvest control rule for Pacific sardine, accounting for predator dynamics: A MICE model," Ecological Modelling, Elsevier, vol. 337(C), pages 79-95.
    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. Whitehouse, George A. & Aydin, Kerim Y., 2020. "Assessing the sensitivity of three Alaska marine food webs to perturbations: an example of Ecosim simulations using Rpath," Ecological Modelling, Elsevier, vol. 429(C).
    5. Goedegebuure, Merel & Melbourne-Thomas, Jessica & Corney, Stuart P. & Hindell, Mark A. & Constable, Andrew J., 2017. "Beyond big fish: The case for more detailed representations of top predators in marine ecosystem models," Ecological Modelling, Elsevier, vol. 359(C), pages 182-192.

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