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Plankton community patterns across a trophic gradient: The role of zooplankton functional groups

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  • Zhao, Jingyang
  • Ramin, Maryam
  • Cheng, Vincent
  • Arhonditsis, George B.

Abstract

We use a complex aquatic biogeochemical model to examine competition patterns and structural shifts in plankton communities under nutrient enrichment conditions. Our model simulates multiple elemental cycles (organic C, N, P, Si, O), multiple functional phytoplankton (diatoms, green algae and cyanobacteria) and zooplankton (copepods and cladocerans) groups. The model provided a realistic platform to examine the functional properties (e.g., grazing strategies, food quality, predation rates, stoichiometry, basal metabolism, and temperature requirements) and the abiotic conditions (temperature, nutrient loading) under which the different plankton groups can dominate or can be competitively excluded in oligo-, meso- and eutrophic environments. Our analysis shows that the group-specific maximum grazing rates, the predation rates from planktivorous fish, along with the temperature requirements to attain optimal growth can be particularly influential on the structure of plankton communities. The model also takes into account recent advances in stoichiometric nutrient recycling theory, which allowed examining the effects of the cyanobacteria food quality, the critical threshold for mineral P limitation, and the half saturation constant for assimilation efficiency on the zooplankton functional group biomass across a range of nutrient loading conditions. Our study highlights the adverse effects that the cyanobacteria food quality can have on the two functional zooplankton groups in productive systems, despite the differences in their feeding selectivity strategies, i.e., cladocerans are filter-feeders with equal preference among the different types of food, whereas copepods are assumed to be capable of selecting on the basis of food quality. Finally, we conclude that the articulate representation of the producer–grazer interactions using stoichiometrically/biochemically realistic terms will offer insights into the patterns of nutrient and energy flow transferred to the higher trophic levels.

Suggested Citation

  • Zhao, Jingyang & Ramin, Maryam & Cheng, Vincent & Arhonditsis, George B., 2008. "Plankton community patterns across a trophic gradient: The role of zooplankton functional groups," Ecological Modelling, Elsevier, vol. 213(3), pages 417-436.
  • Handle: RePEc:eee:ecomod:v:213:y:2008:i:3:p:417-436
    DOI: 10.1016/j.ecolmodel.2008.01.016
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    References listed on IDEAS

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    1. Dörthe C. Müller-Navarra & Michael T. Brett & Sangkyu Park & Sudeep Chandra & Ashley P. Ballantyne & Eduardo Zorita & Charles R. Goldman, 2004. "Unsaturated fatty acid content in seston and tropho-dynamic coupling in lakes," Nature, Nature, vol. 427(6969), pages 69-72, January.
    2. Mulder, Kenneth & Bowden, William Breck, 2007. "Organismal stoichiometry and the adaptive advantage of variable nutrient use and production efficiency in Daphnia," Ecological Modelling, Elsevier, vol. 202(3), pages 427-440.
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    Cited by:

    1. Law, Tony & Zhang, Weitao & Zhao, Jingyang & Arhonditsis, George B., 2009. "Structural changes in lake functioning induced from nutrient loading and climate variability," Ecological Modelling, Elsevier, vol. 220(7), pages 979-997.
    2. Strauss, Tido & Gabsi, Faten & Hammers-Wirtz, Monika & Thorbek, Pernille & Preuss, Thomas G., 2017. "The power of hybrid modelling: An example from aquatic ecosystems," Ecological Modelling, Elsevier, vol. 364(C), pages 77-88.
    3. Eisenhauer, L. & Carlotti, F. & Baklouti, M. & Diaz, F., 2009. "Zooplankton population model coupled to a biogeochemical model of the North Western Mediterranean Sea ecosystem," Ecological Modelling, Elsevier, vol. 220(21), pages 2865-2876.
    4. Jørgensen, Sven Erik, 2010. "A review of recent developments in lake modelling," Ecological Modelling, Elsevier, vol. 221(4), pages 689-692.
    5. Tanioka, Tatsuro & Matsumoto, Katsumi, 2018. "Effects of incorporating age-specific traits of zooplankton into a marine ecosystem model," Ecological Modelling, Elsevier, vol. 368(C), pages 257-264.
    6. Li-kun, Yang & Sen, Peng & Xin-hua, Zhao & Xia, Li, 2017. "Development of a two-dimensional eutrophication model in an urban lake (China) and the application of uncertainty analysis," Ecological Modelling, Elsevier, vol. 345(C), pages 63-74.
    7. Perhar, Gurbir & Arhonditsis, George B., 2009. "The effects of seston food quality on planktonic food web patterns," Ecological Modelling, Elsevier, vol. 220(6), pages 805-820.
    8. Perhar, Gurbir & Arhonditsis, George B. & Brett, Michael T., 2013. "Modeling zooplankton growth in Lake Washington: A mechanistic approach to physiology in a eutrophication model," Ecological Modelling, Elsevier, vol. 258(C), pages 101-121.
    9. Ramin, Maryam & Perhar, Gurbir & Shimoda, Yuko & Arhonditsis, George B., 2012. "Examination of the effects of nutrient regeneration mechanisms on plankton dynamics using aquatic biogeochemical modeling," Ecological Modelling, Elsevier, vol. 240(C), pages 139-155.

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