IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v258y2013icp53-64.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013000896
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.01.023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pacella, Stephen R. & Lebreton, Benoit & Richard, Pierre & Phillips, Donald & DeWitt, Theodore H. & Niquil, Nathalie, 2013. "Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oléron Estuary, France," Ecological Modelling, Elsevier, vol. 267(C), pages 127-137.
    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.
    3. Guesnet, Vanessa & Lassalle, Géraldine & Chaalali, Aurélie & Kearney, Kelly & Saint-Béat, Blanche & Karimi, Battle & Grami, Boutheina & Tecchio, Samuele & Niquil, Nathalie & Lobry, Jérémy, 2015. "Incorporating food-web parameter uncertainty into Ecopath-derived ecological network indicators," Ecological Modelling, Elsevier, vol. 313(C), pages 29-40.
    4. 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).
    5. 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.
    6. Salas, Andria K. & Borrett, Stuart R., 2011. "Evidence for the dominance of indirect effects in 50 trophic ecosystem networks," Ecological Modelling, Elsevier, vol. 222(5), pages 1192-1204.
    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. Hosack, Geoffrey R. & Eldridge, Peter M., 2009. "Do microbial processes regulate the stability of a coral atoll's enclosed pelagic ecosystem?," Ecological Modelling, Elsevier, vol. 220(20), pages 2665-2682.
    9. Baird, Dan & Fath, Brian D. & Ulanowicz, Robert E. & Asmus, Harald & Asmus, Ragnhild, 2009. "On the consequences of aggregation and balancing of networks on system properties derived from ecological network analysis," Ecological Modelling, Elsevier, vol. 220(23), pages 3465-3471.
    10. Dunlap, J. & Schramski, J.R., 2024. "Energy-systems accounting in industrial-natural systems; An energy analysis of a managed forest ecosystem including food web biomass dynamics," Ecological Modelling, Elsevier, vol. 488(C).
    11. Panyam, Varuneswara & Huang, Hao & Davis, Katherine & Layton, Astrid, 2019. "Bio-inspired design for robust power grid networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. De Montis, Andrea & Ganciu, Amedeo & Cabras, Matteo & Bardi, Antonietta & Mulas, Maurizio, 2019. "Comparative ecological network analysis: An application to Italy," Land Use Policy, Elsevier, vol. 81(C), pages 714-724.
    13. Chen, G.Q. & Chen, Z.M., 2011. "Greenhouse gas emissions and natural resources use by the world economy: Ecological input–output modeling," Ecological Modelling, Elsevier, vol. 222(14), pages 2362-2376.
    14. Brunnermeier, M. & Clerc, L. & Scheicher, M., 2013. "Assessing contagion risks in the CDS market," Financial Stability Review, Banque de France, issue 17, pages 123-134, April.
    15. Brigolin, D. & Savenkoff, C. & Zucchetta, M. & Pranovi, F. & Franzoi, P. & Torricelli, P. & Pastres, R., 2011. "An inverse model for the analysis of the Venice lagoon food web," Ecological Modelling, Elsevier, vol. 222(14), pages 2404-2413.
    16. Chen, Shaoqing & Chen, Bin, 2017. "Coupling of carbon and energy flows in cities: A meta-analysis and nexus modelling," Applied Energy, Elsevier, vol. 194(C), pages 774-783.
    17. Zhijun Luo & Xiaofang Yang & Songkai Luo, 2024. "Land Use Simulation and Ecological Network Construction around Poyang Lake Area in China under the Goal of Sustainable Development," Sustainability, MDPI, vol. 16(18), pages 1-24, September.
    18. Taffi, Marianna & Paoletti, Nicola & Liò, Pietro & Pucciarelli, Sandra & Marini, Mauro, 2015. "Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea," Ecological Modelling, Elsevier, vol. 306(C), pages 205-215.
    19. Li, Lianwei & Li, Wendy & Zou, Quan & Ma, Zhanshan (Sam), 2020. "Network analysis of the hot spring microbiome sketches out possible niche differentiations among ecological guilds," Ecological Modelling, Elsevier, vol. 431(C).
    20. Link, Jason S. & Pranovi, Fabio & Libralato, Simone, 2022. "Simulations and interpretations of cumulative trophic theory," Ecological Modelling, Elsevier, vol. 463(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:258:y:2013:i:c:p:53-64. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.