The practical value of modelling relative abundance of species for regional conservation planning: a case study Original Research Article

Statistical modelling of species presence/absence data in relation to mapped environmental predictors has been widely used to predict distributions of species for use in regional conservation planning. This paper evaluates the extent to which predictive mapping of habitat suitability might be refined by modelling relative abundance or density of a species instead of presence/absence. We use data collected at field survey sites in north-east New South Wales to develop models predicting the abundance of vascular plant and vertebrate fauna species as a function of regional-scale environmental variables. The predictive accuracy of these models is then evaluated using survey data collected at independent evaluation sites. A number of ‘direct’ abundance modelling techniques were evaluated including generalised linear and generalised additive Poisson regression, and zero-inflated negative binomial regression. We also evaluated the performance of predicted probability of occurrence generated by logistic regression modelling as an ‘indirect’ index of abundance. Both the direct and indirect modelling techniques generally failed to provide consistently reliable predictions of abundance. Reasonably accurate models were produced for only 12 of the 44 species evaluated. A further key finding was that, for all 12 of these species, predictions from direct abundance models performed no better as a relative index of abundance than predicted probabilities of occurrence generated by logistic regression modelling. Implications of these results for the use of predictive modelling in regional conservation planning are discussed.