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Implications of network particle tracking (NPT) for ecological model interpretation

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  • Tollner, E.W.
  • Schramski, J.R.
  • Kazanci, C.
  • Patten, B.C.

Abstract

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the definition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughflow- and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughflow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirect effects and Finn cycling. Analysis of ecological orientors associated with NEA showed NPT to fully support conventional NEA results when the common conditions of donor control and steady state are satisfied. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.

Suggested Citation

  • Tollner, E.W. & Schramski, J.R. & Kazanci, C. & Patten, B.C., 2009. "Implications of network particle tracking (NPT) for ecological model interpretation," Ecological Modelling, Elsevier, vol. 220(16), pages 1904-1912.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:16:p:1904-1912
    DOI: 10.1016/j.ecolmodel.2009.04.018
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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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    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. 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.
    3. Coskun, Huseyin, 2018. "Dynamic Ecological System Analysis," OSF Preprints 35xkb, Center for Open Science.
    4. Li, Y. & Chen, B. & Yang, Z.F., 2009. "Ecological network analysis for water use systems—A case study of the Yellow River Basin," Ecological Modelling, Elsevier, vol. 220(22), pages 3163-3173.
    5. 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.
    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. 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.
    8. Patten, Bernard C., 2016. "The cardinal hypotheses of Holoecology," Ecological Modelling, Elsevier, vol. 319(C), pages 63-111.

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