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Comparing performance of five nutrient phytoplankton zooplankton (NPZ) models in coastal lagoons

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  • Turner, Evan L.
  • Bruesewitz, Denise A.
  • Mooney, Rae F.
  • Montagna, Paul A.
  • McClelland, James W.
  • Sadovski, Alexey
  • Buskey, Edward J.

Abstract

Modeling nutrient cycle dynamics in the open ocean based on the well-known interactions among nitrogen, phytoplankton, and zooplankton (NPZ) is well-established. Difficulty arises in applying this methodology to multiple coastal systems because of differences among geography, water chemistry, microbial interactions, weather patterns, and sedimentary nutrient cycling. Current trends in ecological modeling are toward more complex modeling relationships and mathematical functions. Four published NPZ models and a new model are compared based on their varying number of equations, mathematical complexity, and required parameters. The new model adapts NPZ interactions to shallow estuary systems by adding a benthic consumption component as a nutrient producer consumer (NPC) system. Each model is calibrated and validated for two bays in the western Gulf of Mexico: San Antonio Bay, TX, USA, and Copano Bay, TX, USA. Daily riverine nutrient inputs are used as the model driver while historical measurements are used for calibration and validation. The five models are compared for their ability to simulate the observed bay response of primary production in both locations. The new model simulates primary production closer to measured observations than other models because of the inclusion of benthic consumption dynamics. However, differences of equation complexity between NPZ models had no relationship to overall goodness of fit in study area. Models produced nearly identical results regardless of different relationships and mathematical formulas. Although every location is unique, this study shows that adding mathematical complexity may only provide marginal gains in practice while decreasing portability when extending NPZ models for the coastal zone.

Suggested Citation

  • Turner, Evan L. & Bruesewitz, Denise A. & Mooney, Rae F. & Montagna, Paul A. & McClelland, James W. & Sadovski, Alexey & Buskey, Edward J., 2014. "Comparing performance of five nutrient phytoplankton zooplankton (NPZ) models in coastal lagoons," Ecological Modelling, Elsevier, vol. 277(C), pages 13-26.
  • Handle: RePEc:eee:ecomod:v:277:y:2014:i:c:p:13-26
    DOI: 10.1016/j.ecolmodel.2014.01.007
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    References listed on IDEAS

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    1. Nicolas Gruber & James N. Galloway, 2008. "An Earth-system perspective of the global nitrogen cycle," Nature, Nature, vol. 451(7176), pages 293-296, January.
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    1. Liu, Zhi-bin & Liu, Shu-tang & Tian, Da-dong & Wang, Da, 2021. "Stability analysis of the plankton community with advection," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    2. Guo, Qing & Wang, Yi & Dai, Chuanjun & Wang, Lijun & Liu, He & Li, Jianbing & Tiwari, Pankaj Kumar & Zhao, Min, 2023. "Dynamics of a stochastic nutrient–plankton model with regime switching," Ecological Modelling, Elsevier, vol. 477(C).
    3. Kumar, Vijay & Kumari, Beena, 2015. "Mathematical modelling of the seasonal variability of plankton and forage fish in the Gulf of Kachchh," Ecological Modelling, Elsevier, vol. 313(C), pages 237-250.
    4. Sk Golam Mortoja & Prabir Panja & Shyamal Kumar Mondal, 2023. "Stability Analysis of Plankton–Fish Dynamics with Cannibalism Effect and Proportionate Harvesting on Fish," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    5. Ertürk, Ali & Sakurova, Ilona & Zilius, Mindaugas & Zemlys, Petras & Umgiesser, Georg & Kaynaroglu, Burak & Pilkaitytė, Renata & Razinkovas-Baziukas, Artūras, 2023. "Development of a pelagic biogeochemical model with enhanced computational performance by optimizing ecological complexity and spatial resolution," Ecological Modelling, Elsevier, vol. 486(C).

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