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Cycling in ecosystems: An individual based approach

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  • Kazanci, C.
  • Matamba, L.
  • Tollner, E.W.

Abstract

Cycling index is an important ecological indicator used in ecosystem analysis. The higher the cycling in an ecosystem, the higher the utilization of mass and energy within the system before it is lost due to respiration and other factors. For a stock-flow type ecosystem model at steady state, Finn’s cycling index (FCI) can be computed using simple matrix algebra. However, it is difficult to measure how well this index represents the actual cycling occurring in the system. In this paper, we study cycling in ecological networks using an individual based approach (particle tracking algorithm). This new simulation method provides access to the pathway data of individual particles that flow in the system, therefore one can quantify cycling using this pathway data quite literally. We used particle tracking simulations (PTS) to compute a cycling index using Finn’s idea of flux based cycling. Our simulation based results (using no matrix algebra) agree with Finn’s cycling index, verifying the accuracy of both the PTS, and the original linear algebraic formulation of FCI.

Suggested Citation

  • Kazanci, C. & Matamba, L. & Tollner, E.W., 2009. "Cycling in ecosystems: An individual based approach," Ecological Modelling, Elsevier, vol. 220(21), pages 2908-2914.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:21:p:2908-2914
    DOI: 10.1016/j.ecolmodel.2008.09.013
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    References listed on IDEAS

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    1. Tollner, Ernest W. & Kazanci, Caner, 2007. "Defining an ecological thermodynamics using discrete simulation approaches," Ecological Modelling, Elsevier, vol. 208(1), pages 68-79.
    2. 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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    Citations

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    Cited by:

    1. Patten, Bernard C. & Straškraba, Milan & Jørgensen, Sven E., 2011. "Ecosystems emerging. 5: Constraints," Ecological Modelling, Elsevier, vol. 222(16), pages 2945-2972.
    2. 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.
    3. Whipple, Stuart J. & Patten, Bernard C. & Borrett, Stuart R., 2014. "Indirect effects and distributed control in ecosystems," Ecological Modelling, Elsevier, vol. 293(C), pages 161-186.
    4. Rodríguez, Ricardo A. & Herrera, Ada Ma. & Riera, Rodrigo & Delgado, Juan D. & Quirós, Ángel & Perdomo, María E. & Santander, Jacobo & Miranda, Jezahel V. & Fernández-Rodríguez, María J. & Jiménez-Rod, 2015. "Thermostatistical distribution of a trophic energy proxy with analytical consequences for evolutionary ecology, species coexistence and the maximum entropy formalism," Ecological Modelling, Elsevier, vol. 296(C), pages 24-35.
    5. Coskun, Huseyin, 2018. "Dynamic Ecological System Analysis," OSF Preprints 35xkb, Center for Open Science.
    6. Livi, Carmen Maria & Jordán, Ferenc & Lecca, Paola & Okey, Thomas A., 2011. "Identifying key species in ecosystems with stochastic sensitivity analysis," Ecological Modelling, Elsevier, vol. 222(14), pages 2542-2551.
    7. Mateusz Iskrzyński & Freek Janssen & Francesco Picciolo & Brian Fath & Franco Ruzzenenti, 2022. "Cycling and reciprocity in weighted food webs and economic networks," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 838-849, June.
    8. Jørgensen, S.E. & Nielsen, S.N., 2015. "Hierarchical networks," Ecological Modelling, Elsevier, vol. 295(C), pages 59-65.
    9. Kazanci, C. & Ma, Q., 2012. "Extending ecological network analysis measures to dynamic ecosystem models," Ecological Modelling, Elsevier, vol. 242(C), pages 180-188.
    10. Buzhdygan, Oksana Y. & Patten, Bernard C. & Kazanci, Caner & Ma, Qianqian & Rudenko, Svitlana S., 2012. "Dynamical and system-wide properties of linear flow-quantified food webs," Ecological Modelling, Elsevier, vol. 245(C), pages 176-184.
    11. 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.
    12. Patten, Bernard C., 2016. "The cardinal hypotheses of Holoecology," Ecological Modelling, Elsevier, vol. 319(C), pages 63-111.

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