Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution

1. Abiotic environmental predictors and broad-scale vegetation have been us ed widely to model the regional distributions of faunal species within fore sted regions of Australia. These models have been developed using stepwise statistical procedures but incorporate only limited expert involvement of t he type sometimes advocated in distribution modelling. The objectives of th is study were twofold. First, to evaluate techniques for incorporating fine -scaled vegetation and growth-stage mapping into models of species distribu tion. Secondly, to compare methods that incorporate expert opinion directly into statistical models derived using stepwise statistical procedures. 2. Using faunal data from north-east New South Wales, Australia, logistic r egression models using fine-scale vegetation and expert opinion were compar ed with models employing only abiotic and broad vegetation variables. 3. Vegetation and growth-stage information was incorporated into models of species distribution in two ways, both of which used expert opinion to deri ve new explanatory variables. The first approach amalgamated fine-scaled ve getation classes into broader classes of ecological relevance to fauna. In the second approach, ordinal habitat indices were derived from vegetation a nd growth-stage mapping using rules specified by an expert panel. These ind ices described habitat features thought to be relevant to the faunal groups studied (e.g. tree hollow availability, fleshy fruit production). Landscap e composition was calculated using these new variables within a 500-m and 2 -km radius of each site. Each habitat index generated a spatially neutral v ariable and two spatial context variables. 4. Expert opinion was incorporated during the pre-modelling, model-fitting and post-modelling stages. At the pre-modelling stage experts developed new explanatory variables based on mapped fine-scale vegetation and growth-sta ge information. At the model-fitting stage an expert panel selected a subse t of potential explanatory variables from the available set. At the post-mo delling stage expert opinion modified or refined maps of predicted species distribution generated by statistical models. For comparative purposes expe rt opinion was also used to develop maps of species distribution by definin g rules within a geographical information system, without the aid of statis tical modelling. 5. Predictive accuracy was not improved significantly by incorporating habi tat indices derived by applying expert opinion to fine-scaled vegetation an d growth-stage mapping. Use of expert input at the pre-modelling stage to d erive and select potential explanatory variables therefore does not provide more information than that provided by remotely mapped vegetation. 6. The incorporation of expert opinion at the model-fitting or post-modelli ng stages resulted in small but insignificant gains in predictive accuracy. The predictive accuracy of purely expert models was less than that achieve d by approaches based on statistical modelling. 7. The study, one of few available evaluations of expert opinion in models of species distribution, suggests that expert modification of fitted statis tical models should be confined to species for which models are grossly in error, or for which insufficient data exist to construct solely statistical models.