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Throughflow analysis: A stochastic approach

Author

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  • Matamba, L.
  • Kazanci, C.
  • Schramski, J.R.
  • Blessing, M.
  • Alexander, P.
  • Patten, B.C.

Abstract

Ecological network analysis (ENA), predicated on systems theory and Leontiev input–output analysis, is a method widely used in ecology to reveal ecosystem properties. An important ecosystem property computed in ENA is throughflows, the amount of matter/energy leaving each compartment of the ecosystem. Throughflows are analyzed via a matrix N representing their relationships to the driving input at the boundary. Network particle tracking (NPT) builds on ENA to offer a Lagrangian particle method that describes the activity of the ecosystem at the microscopic level. This paper introduces a Lagrangian throughflow analysis methodology using NPT and shows that the NPT throughflow matrix, N, agrees with the conventional ENA throughflow matrix, N, for ecosystems at steady-state with donor-controlled flows. The matrix N is computed solely from the pathways (particles’ histories) generated by NPT simulations and its average over multiple runs of the algorithm with longer simulation time agrees with the Eulerian N matrix (Law of Large Numbers). While the traditional NEA throughflow analysis is mostly used with steady-state ecosystem models, the Lagrangian throughflow analysis that we propose can be used with non-steady-state models and paves the way for the development of dynamic throughflow analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:22:p:3174-3181
    DOI: 10.1016/j.ecolmodel.2009.07.001
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    References listed on IDEAS

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    1. Schramski, J.R. & Gattie, D.K. & Patten, B.C. & Borrett, S.R. & Fath, B.D. & Whipple, S.J., 2007. "Indirect effects and distributed control in ecosystems: Distributed control in the environ networks of a seven-compartment model of nitrogen flow in the Neuse River Estuary, USA—Time series analysis," Ecological Modelling, Elsevier, vol. 206(1), pages 18-30.
    2. Tollner, Ernest W. & Kazanci, Caner, 2007. "Defining an ecological thermodynamics using discrete simulation approaches," Ecological Modelling, Elsevier, vol. 208(1), pages 68-79.
    3. Whipple, Stuart J. & Borrett, Stuart R. & Patten, Bernard C. & Gattie, David K. & Schramski, John R. & Bata, Seth A., 2007. "Indirect effects and distributed control in ecosystems: Comparative network environ analysis of a seven-compartment model of nitrogen flow in the Neuse River estuary, USA—Time series analysis," Ecological Modelling, Elsevier, vol. 206(1), pages 1-17.
    4. 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. Schramski, J.R. & Patten, B.C. & Kazanci, C. & Gattie, D.K. & Kellam, N.N., 2009. "The Reynolds transport theorem: Application to ecological compartment modeling and case study of ecosystem energetics," Ecological Modelling, Elsevier, vol. 220(22), pages 3225-3232.
    5. Patten, Bernard C., 2015. "Link tracking: Quantifying network flows from qualitative node–link digraphs," Ecological Modelling, Elsevier, vol. 295(C), pages 47-58.
    6. Borrett, S.R. & Freeze, M.A. & Salas, A.K., 2011. "Equivalence of the realized input and output oriented indirect effects metrics in Ecological Network Analysis," Ecological Modelling, Elsevier, vol. 222(13), pages 2142-2148.
    7. Schaubroeck, Thomas & Staelens, Jeroen & Verheyen, Kris & Muys, Bart & Dewulf, Jo, 2012. "Improved ecological network analysis for environmental sustainability assessment; a case study on a forest ecosystem," Ecological Modelling, Elsevier, vol. 247(C), pages 144-156.
    8. Kazanci, C. & Ma, Q., 2012. "Extending ecological network analysis measures to dynamic ecosystem models," Ecological Modelling, Elsevier, vol. 242(C), pages 180-188.
    9. Patten, Bernard C., 2016. "The cardinal hypotheses of Holoecology," Ecological Modelling, Elsevier, vol. 319(C), pages 63-111.

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