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A Bayesian Nonparametric Approach to Ecological Risk Assessment

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Bayesian Statistics in Action (BAYSM 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 194))

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Abstract

We revisit a classical method for ecological risk assessment, the Species Sensitivity Distribution (SSD) approach, in a Bayesian nonparametric framework. SSD is a mandatory diagnostic required by environmental regulatory bodies from the European Union, the United States, Australia, China etc. Yet, it is subject to much scientific criticism, notably concerning a historically debated parametric assumption for modelling species variability. Tackling the problem using nonparametric mixture models, it is possible to shed this parametric assumption and build a statistically sounder basis for SSD. We use Normalized Random Measures with Independent Increments (NRMI) as the mixing measure because they offer a greater flexibility than the Dirichlet process. Indeed, NRMI can induce a prior on the number of components in the mixture model that is less informative than the Dirichlet process. This feature is consistent with the fact that SSD practitioners do not usually have a strong prior belief on the number of components. In this short paper, we illustrate the advantage of the nonparametric SSD over the classical normal SSD and a kernel density estimate SSD on several real datasets. We summarise the results of the complete study in [18], where the method is generalised to censored data and a systematic comparison on simulated data is also presented, along with a study of the clustering induced by the mixture model to examine patterns in species sensitivity.

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Acknowledgements

The authors would like to thank two anonymous reviewers for helpful comments and suggestions. J. Arbel was a postdoc at Bocconi University and Collegio Carlo Alberto, Italy, when this article was submitted. J. Arbel and I. Prünster are supported by the European Research Council (ERC) through StG “N-BNP” 306406.

Author information

Authors and Affiliations

  1. University of Torino, Italy and Collegio Carlo Alberto, Via Real Collegio, 30, 10024, Moncalieri, TO, Italy

    Guillaume Kon Kam King

  2. Inria Grenoble - Rhône-Alpes & Université Grenoble Alpes, 655 Avenue de l’Europe, 38330, Montbonnot-Saint-Martin, France

    Julyan Arbel

  3. Department of Decision Sciences, BIDSA and IGIER, Bocconi University, Via Röntgen 1, 20136, Milan, Italy

    Igor Prünster

Authors
  1. Guillaume Kon Kam King
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  2. Julyan Arbel
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  3. Igor Prünster
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Corresponding author

Correspondence to Guillaume Kon Kam King .

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Editors and Affiliations

  1. CNR–IMATI, Milano, Italy

    Raffaele Argiento

  2. CNR-IMATI, Milan, Italy

    Ettore Lanzarone

  3. Decision Sciences, Bocconi University, Milan, Italy

    Isadora Antoniano Villalobos

  4. Dipartimento di Statistica "G. Parenti", Università di Firenze, Firenze, Italy

    Alessandra Mattei

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Kon Kam King, G., Arbel, J., Prünster, I. (2017). A Bayesian Nonparametric Approach to Ecological Risk Assessment. In: Argiento, R., Lanzarone, E., Antoniano Villalobos, I., Mattei, A. (eds) Bayesian Statistics in Action. BAYSM 2016. Springer Proceedings in Mathematics & Statistics, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-54084-9_14

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