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

Summary Abiotic environmental predictors and broad‐scale vegetation have been used widely to model the regional distributions of faunal species within forested regions of Australia. These models have been developed using stepwise statistical procedures but incorporate only limited expert involvement of the type sometimes advocated in distribution modelling. The objectives of this study were twofold. First, to evaluate techniques for incorporating fine‐scaled vegetation and growth‐stage mapping into models of species distribution. Secondly, to compare methods that incorporate expert opinion directly into statistical models derived using stepwise statistical procedures. Using faunal data from north‐east New South Wales, Australia, logistic regression models using fine‐scale vegetation and expert opinion were compared with models employing only abiotic and broad vegetation variables. Vegetation and growth‐stage information was incorporated into models of species distribution in two ways, both of which used expert opinion to derive new explanatory variables. The first approach amalgamated fine‐scaled vegetation classes into broader classes of ecological relevance to fauna. In the second approach, ordinal habitat indices were derived from vegetation and growth‐stage mapping using rules specified by an expert panel. These indices described habitat features thought to be relevant to the faunal groups studied (e.g. tree hollow availability, fleshy fruit production). Landscape 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 variable and two spatial context variables. 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‐stage information. At the model‐fitting stage an expert panel selected a subset of potential explanatory variables from the available set. At the post‐modelling stage expert opinion modified or refined maps of predicted species distribution generated by statistical models. For comparative purposes expert opinion was also used to develop maps of species distribution by defining rules within a geographical information system, without the aid of statistical modelling. Predictive accuracy was not improved significantly by incorporating habitat indices derived by applying expert opinion to fine‐scaled vegetation and growth‐stage mapping. Use of expert input at the pre‐modelling stage to derive and select potential explanatory variables therefore does not provide more information than that provided by remotely mapped vegetation. The incorporation of expert opinion at the model‐fitting or post‐modelling stages resulted in small but insignificant gains in predictive accuracy. The predictive accuracy of purely expert models was less than that achieved by approaches based on statistical modelling. The study, one of few available evaluations of expert opinion in models of species distribution, suggests that expert modification of fitted statistical models should be confined to species for which models are grossly in error, or for which insufficient data exist to construct solely statistical models.