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Comparing network analysis methodologies for consumer–resource relations at species and ecosystems scales

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  • Scharler, Ursula M.
  • Fath, Brian D.

Abstract

This research compares two existing methodologies, mixed trophic impact analysis and utility analysis, which use network analysis to evaluate the direct, pair-wise, and indirect, holistic, ecological relations between ecosystem compartments. The two approaches have many similarities, but differ in some key assumptions which affect both the final results and interpretations. Here, we briefly introduce both methodologies through a series of two simple examples; a 3-compartment competition model and a 3-compartment food chain model, and then apply the methodologies to a 15-compartment ecosystem model of the Chesapeake Bay. This example demonstrates how implementing the various conceptual and methodological assumptions lead to differing results. Notably, the overall number of positive relations is greatly affected by the treatment of the self-interactions and the handling of detritus compartments lead to a distinction between ecological or trophic relations. We recommend slight changes to both methodologies, not necessarily in order to bring them completely together, but because each has some points which are stronger and better defensible.

Suggested Citation

  • Scharler, Ursula M. & Fath, Brian D., 2009. "Comparing network analysis methodologies for consumer–resource relations at species and ecosystems scales," Ecological Modelling, Elsevier, vol. 220(22), pages 3210-3218.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:22:p:3210-3218
    DOI: 10.1016/j.ecolmodel.2009.02.011
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    Citations

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    Cited by:

    1. Fath, Brian D. & Scharler, Ursula M. & Baird, Dan, 2013. "Dependence of network metrics on model aggregation and throughflow calculations: Demonstration using the Sylt–Rømø Bight Ecosystem," Ecological Modelling, Elsevier, vol. 252(C), pages 214-219.
    2. Sagarese, Skyler R. & Lauretta, Matthew V. & Walter, John F., 2017. "Progress towards a next-generation fisheries ecosystem model for the northern Gulf of Mexico," Ecological Modelling, Elsevier, vol. 345(C), pages 75-98.
    3. Borrett, Stuart R. & Moody, James & Edelmann, Achim, 2014. "The rise of Network Ecology: Maps of the topic diversity and scientific collaboration," Ecological Modelling, Elsevier, vol. 293(C), pages 111-127.
    4. Liu, G.Y. & Yang, Z.F. & Chen, B. & Zhang, Y., 2011. "Ecological network determination of sectoral linkages, utility relations and structural characteristics on urban ecological economic system," Ecological Modelling, Elsevier, vol. 222(15), pages 2825-2834.
    5. Borrett, Stuart R. & Sheble, Laura & Moody, James & Anway, Evan C., 2018. "Bibliometric review of ecological network analysis: 2010–2016," Ecological Modelling, Elsevier, vol. 382(C), pages 63-82.
    6. Mao, Xufeng & Cui, Lijuan & Wang, Changhai, 2013. "Exploring the hydrologic relationships in a swamp-dominated watershed—A network-environ-analysis based approach," Ecological Modelling, Elsevier, vol. 252(C), pages 273-279.
    7. Banerjee, Arnab & Scharler, Ursula M. & Fath, Brian D. & Ray, Santanu, 2017. "Temporal variation of keystone species and their impact on system performance in a South African estuarine ecosystem," Ecological Modelling, Elsevier, vol. 363(C), pages 207-220.
    8. Upadhyay, Shashankaditya & Roy, Arijit & Ramprakash, M. & Idiculla, Jobin & Kumar, A. Senthil & Bhattacharya, Sudeepto, 2017. "A network theoretic study of ecological connectivity in Western Himalayas," Ecological Modelling, Elsevier, vol. 359(C), pages 246-257.
    9. Liu, Gengyuan & Yang, Zhifeng & Fath, Brian D. & Shi, Lei & Ulgiati, Sergio, 2017. "Time and space model of urban pollution migration: Economy-energy-environment nexus network," Applied Energy, Elsevier, vol. 186(P2), pages 96-114.

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