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The mean function provides robustness to linear inverse modelling flow estimation in food webs: A comparison of functions derived from statistics and ecological theories

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  • Saint-Béat, B.
  • Vézina, A.F.
  • Asmus, R.
  • Asmus, H.
  • Niquil, N.

Abstract

Quantitative estimates of carbon flows within food webs are increasingly viewed as essential to progress on a number of questions in basic and applied ecosystem science. Inverse modelling has been used for more than 20 years to estimate flow values for incomplete data sets. Monte Carlo Markov Chain linear inverse modelling calculates a probability density function for each flow. Among this distribution of possible values for each flow, the mean is generally chosen when a single solution is needed. The objective of the present study is to compare the robustness of the result when using the mean function, compared with 2 other statistical functions and 7 ecological functions derived from ecological theories on ecosystem maturity. The performance of the various functions was tested by comparing their accuracy in reconstructing a complete data set, the marine food web of Sylt–Rømø Bight, with known flows systematically removed. This was carried out on seven habitats and for 4 levels of degradation of the information. The robustness of each function was measured by comparing the estimated values of flows from inverse modelling after degradation with values from the original, complete data set. The analysis of results shows that the error of the estimated flows increases with the degradation of information, independent of the considered function. Two functions, the mean and the system omnivory index, provide more precise results than the others independent of the level of degradation of the information considered. The mean had the least impact on the reconstruction of food web flow values and on their organization described by ecological network analysis indices.

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  • Saint-Béat, B. & Vézina, A.F. & Asmus, R. & Asmus, H. & Niquil, N., 2013. "The mean function provides robustness to linear inverse modelling flow estimation in food webs: A comparison of functions derived from statistics and ecological theories," Ecological Modelling, Elsevier, vol. 258(C), pages 53-64.
    • Handle: RePEc:eee:ecomod:v:258:y:2013:i:c:p:53-64
    DOI: 10.1016/j.ecolmodel.2013.01.023
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    References listed on IDEAS

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    1. Van den Meersche, Karel & Soetaert, Karline & Van Oevelen, Dick, 2009. "xsample(): An R Function for Sampling Linear Inverse Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(c01).
    2. Johnson, Galen A. & Niquil, Nathalie & Asmus, Harald & Bacher, Cédric & Asmus, Ragnhild & Baird, Daniel, 2009. "The effects of aggregation on the performance of the inverse method and indicators of network analysis," Ecological Modelling, Elsevier, vol. 220(23), pages 3448-3464.
    3. Gascuel, Didier & Morissette, Lyne & Palomares, Maria Lourdes D. & Christensen, Villy, 2008. "Trophic flow kinetics in marine ecosystems: Toward a theoretical approach to ecosystem functioning," Ecological Modelling, Elsevier, vol. 217(1), pages 33-47.
    4. Kones, Julius K. & Soetaert, Karline & van Oevelen, Dick & Owino, John O., 2009. "Are network indices robust indicators of food web functioning? A Monte Carlo approach," Ecological Modelling, Elsevier, vol. 220(3), pages 370-382.
    5. Fath, Brian D. & Scharler, Ursula M. & Ulanowicz, Robert E. & Hannon, Bruce, 2007. "Ecological network analysis: network construction," Ecological Modelling, Elsevier, vol. 208(1), pages 49-55.
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    Cited by:

    1. van der Heijden, L.H. & Niquil, N. & Haraldsson, M. & Asmus, R.M. & Pacella, S.R. & Graeve, M. & Rzeznik-Orignac, J. & Asmus, H. & Saint-Béat, B. & Lebreton, B., 2020. "Quantitative food web modeling unravels the importance of the microphytobenthos-meiofauna pathway for a high trophic transfer by meiofauna in soft-bottom intertidal food webs," Ecological Modelling, Elsevier, vol. 430(C).
    2. 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.

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