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Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea

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  • Taffi, Marianna
  • Paoletti, Nicola
  • Liò, Pietro
  • Pucciarelli, Sandra
  • Marini, Mauro

Abstract

Modelling bioaccumulation processes at the food web level is the main step to analyse the effects of pollutants at the global ecosystem level. A crucial question is understanding which species play a key role in the trophic transfer of contaminants to disclose the contribution of feeding linkages and the importance of trophic dependencies in bioaccumulation dynamics. In this work we present a computational framework to model the bioaccumulation of organic chemicals in aquatic food webs, and to discover key species in polluted ecosystems. As a result, we reconstruct the first PCBs bioaccumulation model of the Adriatic food web, estimated after an extensive review of published concentration data. We define a novel index aimed to identify the key species in contaminated networks, sensitivity centrality, and based on sensitivity analysis. The index is computed from a dynamic ODE model parametrised from the estimated PCBs bioaccumulation model and compared with a set of established trophic indices of centrality. Results evidence the occurrence of PCBs biomagnification in the Adriatic food web, and highlight the dependence of bioaccumulation on trophic dynamics and external factors like fishing activity. We demonstrate the effectiveness of the introduced sensitivity centrality in identifying the set of species with the highest impact on the total contaminant flows and on the efficiency of contaminant transport within the food web.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:306:y:2015:i:c:p:205-215
    DOI: 10.1016/j.ecolmodel.2014.11.030
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    References listed on IDEAS

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    1. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).
    2. 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.
    3. 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.
    4. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
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    Cited by:

    1. McGill, Lillian M. & Gerig, Brandon S. & Chaloner, Dominic T. & Lamberti, Gary A., 2017. "An ecosystem model for evaluating the effects of introduced Pacific salmon on contaminant burdens of stream-resident fish," Ecological Modelling, Elsevier, vol. 355(C), pages 39-48.
    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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