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Analyzing the effects of estuarine freshwater fluxes on fish abundance using artificial neural network ensembles

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  • Zhang, Hua
  • Zimba, Paul V.

Abstract

Decreased estuarine freshwater inflow can adversely impact commercially and recreationally important fisheries as many fish species utilize estuaries during a portion of their life. To ameliorate effect on estuarine fisheries, regression models using fish catch and freshwater inflow have been implemented to determine minimum flow necessary to sustain these populations. These models typically use streamflow data, with no correction for evaporation and precipitation. Our models including evaporation and precipitation developed using artificial neural network (ANN) ensembles had nearly 50% better classification accuracy compared to regression model using flow. This ANN ensemble method was successfully applied to the Nueces Estuary in the United States. It can improve the decision-making processes of freshwater regulation and fishery management in many coastal regions.

Suggested Citation

  • Zhang, Hua & Zimba, Paul V., 2017. "Analyzing the effects of estuarine freshwater fluxes on fish abundance using artificial neural network ensembles," Ecological Modelling, Elsevier, vol. 359(C), pages 103-116.
    • Handle: RePEc:eee:ecomod:v:359:y:2017:i:c:p:103-116
    DOI: 10.1016/j.ecolmodel.2017.05.010
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    References listed on IDEAS

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    1. Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    2. Kim, Hae-Cheol & Montagna, Paul A., 2012. "Effects of climate-driven freshwater inflow variability on macrobenthic secondary production in Texas lagoonal estuaries: A modeling study," Ecological Modelling, Elsevier, vol. 235, pages 67-80.
    3. repec:eee:ecomod:v:313:y:2015:i:c:p:1-12 is not listed on IDEAS
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