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Extending ecological network analysis measures to dynamic ecosystem models

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  • Kazanci, C.
  • Ma, Q.

Abstract

Ecological network analysis measures such as cycling index, indirect effects, and storage analysis provide insightful information on ecosystem organization and function, which can be extremely useful for environmental management and control. These system-wide measures focus on indirect relations among system compartments, providing a holistic approach. Unfortunately, the application of these useful measures are restricted to steady state models. Seasonal changes, environmental impacts, and climate shifts are not accommodated by the current methodology, which greatly limits their application. The novel methodology introduced in this paper extends the application of these useful but limited measures to dynamic compartmental models. This method relies on network particle tracking simulation, which is an agent based algorithm, whereas the current methods utilize steady-state flow rates and compartment storage values. We apply this new methodology to storage analysis, which quantifies how much storage is generated at any compartment within the system by a unit external input into another compartment. Also called compartmental mean residence time, this measure is widely used in environmental sciences, pharmacokinetics and nutrition, to assess the interaction between system boundary (e.g. drug intake, pollution, feeding) and internal compartments (e.g. tissues, crops, species). Storage analysis is chosen for demonstration because it is applicable to a limited class of dynamic models (linear and donor-controlled), which gives us an opportunity to verify our new method. The methodology introduced here is also applicable to Finn's cycling index, indirect effects index, throughflow analysis, and possibly other network analysis based indicators as well.

Suggested Citation

  • Kazanci, C. & Ma, Q., 2012. "Extending ecological network analysis measures to dynamic ecosystem models," Ecological Modelling, Elsevier, vol. 242(C), pages 180-188.
    • Handle: RePEc:eee:ecomod:v:242:y:2012:i:c:p:180-188
    DOI: 10.1016/j.ecolmodel.2012.05.021
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    References listed on IDEAS

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    1. Matamba, L. & Kazanci, C. & Schramski, J.R. & Blessing, M. & Alexander, P. & Patten, B.C., 2009. "Throughflow analysis: A stochastic approach," Ecological Modelling, Elsevier, vol. 220(22), pages 3174-3181.
    2. Shevtsov, Jane & Kazanci, Caner & Patten, Bernard C., 2009. "Dynamic environ analysis of compartmental systems: A computational approach," Ecological Modelling, Elsevier, vol. 220(22), pages 3219-3224.
    3. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    4. Tollner, Ernest W. & Kazanci, Caner, 2007. "Defining an ecological thermodynamics using discrete simulation approaches," Ecological Modelling, Elsevier, vol. 208(1), pages 68-79.
    5. Kazanci, C. & Matamba, L. & Tollner, E.W., 2009. "Cycling in ecosystems: An individual based approach," Ecological Modelling, Elsevier, vol. 220(21), pages 2908-2914.
    6. Kazancı, Caner, 2007. "EcoNet: A new software for ecological modeling, simulation and network analysis," Ecological Modelling, Elsevier, vol. 208(1), pages 3-8.
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    Cited by:

    1. Ma, Q. & Kazanci, C., 2013. "Analysis of indirect effects within ecosystem models using pathway-based methodology," Ecological Modelling, Elsevier, vol. 252(C), pages 238-245.
    2. Shibao Lu & Xiaoling Zhang & Yao Tang, 2020. "Evolutionary analysis on structural characteristics of water resource system in basins of Northern China," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 800-812, July.
    3. Coskun, Huseyin, 2018. "Dynamic Ecological System Analysis," OSF Preprints 35xkb, Center for Open Science.
    4. Burns, Thomas P. & Rose, Kenneth A. & Brenkert, Antoinette L., 2014. "Quantifying direct and indirect effects of perturbations using model ecosystems," Ecological Modelling, Elsevier, vol. 293(C), pages 69-80.
    5. Varga, M. & Csukas, B., 2017. "Generation of extensible ecosystem models from a network structure and from locally executable programs," Ecological Modelling, Elsevier, vol. 364(C), pages 25-41.
    6. Tuominen, Lindsey K. & Whipple, Stuart J. & Patten, Bernard C. & Karatas, Zekeriya Y. & Kazanci, Caner, 2014. "Contribution of throughflows to the ecological interpretation of integral network utility," Ecological Modelling, Elsevier, vol. 293(C), pages 187-201.
    7. Jørgensen, Sven E. & Nielsen, Søren Nors & Fath, Brian D., 2016. "Recent progress in systems ecology," Ecological Modelling, Elsevier, vol. 319(C), pages 112-118.
    8. Patten, Bernard C., 2016. "The cardinal hypotheses of Holoecology," Ecological Modelling, Elsevier, vol. 319(C), pages 63-111.

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