Use of a Bayesian network for Red Listing under uncertainty

  • Authors:

  • Adrian C. Newton

  • Affiliations:

  • Centre for Conservation Ecology and Environmental Change, School of Conservation Sciences, Bournemouth University, Talbot Campus, Poole, Dorset BH12 5BB, United Kingdom

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The IUCN (International Union for Conservation of Nature) Red List is widely recognised as an authoritative assessment of the conservation status of species. However, the data available for Red Listing are often lacking or uncertain. This paper presents a Bayesian network that may be used to perform a Red List assessment of a taxon using uncertain data. In such cases, input variables can be entered as likelihoods, and the appropriate Red List category is identified by the network using Bayesian inference. Relative performance of the Bayesian network was evaluated by comparison with an alternative method (RAMAS^(R) Red List), based on the use of fuzzy numbers. While results were generally comparable, some differences were noted for species with uncertain input data. Contrasting results may be attributed to differences in how uncertain data are analysed by the two approaches. The Bayesian network has the advantage of being more transparent, facilitating sensitivity analysis. The method consequently has potential for facilitating Red List assessments, particularly for poorly known taxa.