Collaborative decision-making processes for maintaining biodiversity: Two Australian case studies

There have been many models developed by scientists to assist decision-makers in making socio-economic and environmental decisions. It is now recognised that there is a shift in the dominant paradigm to making decisions with stakeholders, rather than making decisions for stakeholders. Our paper investigates two case studies where group model building has been undertaken for maintaining biodiversity in Australia. The first case study focuses on preservation and management of green spaces and biodiversity in metropolitan Melbourne under the umbrella of the Melbourne 2030 planning strategy. A geographical information system is used to collate a number of spatial datasets encompassing a range of cultural and natural assets data layers including: existing open spaces, waterways, threatened fauna and flora, ecological vegetation covers, registered cultural heritage sites, and existing land parcel zoning. Group model building is incorporated into the study through eliciting weightings and ratings of importance for each datasets from urban planners to formulate different urban green system scenarios. The second case study focuses on modelling ecoregions from spatial datasets for the state of Queensland. The modelling combines collaborative expert knowledge and a vast amount of environmental data to build biogeographical classifications of regions. An information elicitation process is used to capture expert knowledge of ecoregions as geographical descriptions, and to transform this into prior probability distributions that characterise regions in terms of environmental variables. This prior information is combined with measured data on the environmental variables within a Bayesian modelling technique to produce the final classified regions. We describe how linked views between descriptive information, mapping and statistical plots are used to decide upon representative regions that satisfy a number of criteria for biodiversity and conservation. This paper discusses the advantages and problems encountered when undertaking group model building. Future research will extend the group model building approach to include interested individuals and community groups.

Comparison of ASAS improvement criteria for ankylosing spondylitis with clinically relevant improvement according to an expert panel

Objective: To investigate whether the recently developed (statistically derived) "ASsessment in Ankylosing Spondylitis Working Group" improvement criteria (ASAS-IC) for ankylosing spondylitis (AS) reflect clinically relevant improvement according to the opinion of an expert panel. Methods: The ASAS-IC consist of four domains: physical function, spinal pain, patient global assessment, and inflammation. Scores on these four domains of 55 patients with AS, who had participated in a non-steroidal anti-inflammatory drug efficacy trial, were presented to an international expert panel (consisting of patients with AS and members of the ASAS Working Group) in a three round Delphi exercise. The number of (non-) responders according to the ASAS-IC was compared with the final-consensus of the experts. The most important domains in the opinion of the experts were identified, and also selected with discriminant analysis. A number of provisional criteria sets that best represented the consensus of the experts were defined. Using other datasets, these clinically derived criteria sets as well as the statistically derived ASAS-IC were then tested for discriminative properties and for agreement with the end of trial efficacy by patient and doctor. Results: Forty experts completed the three Delphi rounds. The experts considered twice as many patients to be responders than the ASAS-IC (42 v 21). Overall agreement between experts and ASAS-IC was 62%. Spinal pain was considered the most important domain by most experts and was also selected as such by discriminant analysis. Provisional criteria sets with an agreement of greater than or equal to 80% compared with the consensus of the experts showed high placebo response rates (27-42%), in contrast with the ASAS-IC with a predefined placebo response rate of 25%. All criteria sets and the ASAS-IC discriminated well between active and placebo treatment (chi(2) = 36-45; p < 0.001). Compared with the end of trial efficacy assessment, the provisional criteria sets showed an agreement of 71-82%, sensitivity of 67-83%, and specificity of 81-88%. The ASAS-IC showed an agreement of 70%, sensitivity of 62%, and specificity of 89%. Conclusion: The ASAS-IC are strict in defining response, are highly specific, and consequently show lower sensitivity than the clinically derived criteria sets. However, those patients who are considered as responders by applying the ASAS-IC are acknowledged as such by the expert panel as well as by. patients' and doctors' judgments, and are therefore likely to be true responders.