The Delphi process - an expert-based approach to ecological modelling in data-poor environments

Resource managers are involved in difficult decisions that affect rare species and habitats but often lack relevant ecological knowledge and experience. Ecological models are increasingly being looked to as a means of assisting the decision-making process, but very often the data are missing or are unsuited to empirical modelling. This paper describes the development and application of the Delphi approach to develop a decision support tool for wildlife conservation and management. The Delphi process is an expert-based approach to decision support that can be used as a means for predicting outcomes in situations where 'absolute' or 'objective' models are unavailable or compromised by lack of appropriate data. The method aims to develop consensus between experts over several rounds of deliberation on the assumption that combining the expertise of several individuals will provide more reliable results than consulting one or two individuals. In this paper the approach is used to engineer soft knowledge on the conservation requirements of capercaillie Tetrao urogallus, an endangered woodland grouse, into a model that can be used by forests managers to improve the quality of forest habitat for capercaillie over extensive commercial forest areas. This paper concludes with a discussion of the potential advantages and disadvantages of Delphi and other soft knowledge approaches to ecological modelling and conservation management

Quasi-Bayesian Analysis Using Imprecise Probability Assessments And The Generalized Bayes’ Rule

The generalized Bayes’ rule (GBR) can be used to conduct ‘quasi-Bayesian’ analyses when prior beliefs are represented by imprecise probability models. We describe a procedure for deriving coherent imprecise probability models when the event space consists of a finite set of mutually exclusive and exhaustive events. The procedure is based on Walley’s theory of upper and lower prevision and employs simple linear programming models. We then describe how these models can be updated using Cozman’s linear programming formulation of the GBR. Examples are provided to demonstrate how the GBR can be applied in practice. These examples also illustrate the effects of prior imprecision and prior-data conflict on the precision of the posterior probability distribution. Copyright Springer 2005imprecise probability, generalized Bayes’ rule, second-order probability, quasi-Bayesian analysis,