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Structural changes in lake functioning induced from nutrient loading and climate variability

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  • Law, Tony
  • Zhang, Weitao
  • Zhao, Jingyang
  • Arhonditsis, George B.

Abstract

Climate variability is increasingly recognized as an important regulatory factor, capable of influencing the structural properties of aquatic ecosystems. Lakes appear to be particularly sensitive to the ecological impacts of climate variability, and several long time series have shown a close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food web dynamics. Thus, understanding the complex interplay among meteorological forcing, hydrological variability, and ecosystem functioning is essential for improving the credibility of model-based water resources/fisheries management. Our objective herein is to examine the relative importance of the ecological mechanisms underlying plankton seasonal variability in Lake Washington, Washington State (USA), over a 35-year period (1964–1998). Our analysis is founded upon an intermediate complexity plankton model that is used to reproduce the limiting nutrient (phosphate)–phytoplankton–zooplankton–detritus (particulate phosphorus) dynamics in the lake. Model parameterization is based on a Bayesian calibration scheme that offers insights into the degree of information the data contain about model inputs and allows obtaining predictions along with uncertainty bounds for modeled output variables. The model accurately reproduces the key seasonal planktonic patterns in Lake Washington and provides realistic estimates of predictive uncertainty for water quality variables of environmental management interest. A principal component analysis of the annual estimates of the underlying ecological processes highlighted the significant role of the phosphorus recycling stemming from the zooplankton excretion on the planktonic food web variability. We also identified a moderately significant signature of the local climatic conditions (air temperature) on phytoplankton growth (r=0.41), herbivorous grazing (r=0.38), and detritus mineralization (r=0.39). Our study seeks linkages with the conceptual food web model proposed by Hampton et al. [Hampton, S.E., Scheuerell, M.D., Schindler, D.E., 2006b. Coalescence in the Lake Washington story: interaction strengths in a planktonic food web. Limnol. Oceanogr. 51, 2042–2051.] to emphasize the “bottom-up” control of the Lake Washington plankton phenology. The posterior predictive distributions of the plankton model are also used to assess the exceedance frequency and confidence of compliance with total phosphorus (15μgL−1) and chlorophyll a (4μgL−1) threshold levels during the summer-stratified period in Lake Washington. Finally, we conclude by underscoring the importance of explicitly acknowledging the uncertainty in ecological forecasts to the management of freshwater ecosystems under a changing global environment.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:7:p:979-997
    DOI: 10.1016/j.ecolmodel.2009.01.001
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    References listed on IDEAS

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    1. 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.
    2. Ben D. MacArthur & Richard O. C. Oreffo, 2005. "Bridging the gap," Nature, Nature, vol. 433(7021), pages 19-19, January.
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    4. Arhonditsis, George B. & Qian, Song S. & Stow, Craig A. & Lamon, E. Conrad & Reckhow, Kenneth H., 2007. "Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake," Ecological Modelling, Elsevier, vol. 208(2), pages 215-229.
    5. C. D. Thomas & E. J. Bodsworth & R. J. Wilson & A. D. Simmons & Z. G. Davies & M. Musche & L. Conradt, 2001. "Ecological and evolutionary processes at expanding range margins," Nature, Nature, vol. 411(6837), pages 577-581, May.
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    Cited by:

    1. Li, Yuzhao & Liu, Yong & Zhao, Lei & Hastings, Alan & Guo, Huaicheng, 2015. "Exploring change of internal nutrients cycling in a shallow lake: A dynamic nutrient driven phytoplankton model," Ecological Modelling, Elsevier, vol. 313(C), pages 137-148.
    2. McDonald, Cory P. & Urban, Noel R., 2010. "Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models," Ecological Modelling, Elsevier, vol. 221(3), pages 428-432.
    3. Ramin, Maryam & Labencki, Tanya & Boyd, Duncan & Trolle, Dennis & Arhonditsis, George B., 2012. "A Bayesian synthesis of predictions from different models for setting water quality criteria," Ecological Modelling, Elsevier, vol. 242(C), pages 127-145.
    4. Chen, Qiuwen & Zhang, Chengcheng & Recknagel, Friedrich & Guo, Jing & Blanckaert, Koen, 2014. "Adaptation and multiple parameter optimization of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic Lake," Ecological Modelling, Elsevier, vol. 273(C), pages 109-116.
    5. McDonald, C.P. & Bennington, V. & Urban, N.R. & McKinley, G.A., 2012. "1-D test-bed calibration of a 3-D Lake Superior biogeochemical model," Ecological Modelling, Elsevier, vol. 225(C), pages 115-126.
    6. 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.
    7. Islam, Md. Nazrul & Kitazawa, Daisuke & Kokuryo, Naoki & Tabeta, Shigeru & Honma, Takamitsu & Komatsu, Nobuyuki, 2012. "Numerical modeling on transition of dominant algae in Lake Kitaura, Japan," Ecological Modelling, Elsevier, vol. 242(C), pages 146-163.

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