Joy and Death (2004): Predictive modelling and spatial mapping of freshwater fish and decapod assemblages.

Joy, M.K. & Death, R.G. (2004) Predictive modelling and spatial mapping of freshwater fish and decapod assemblages: an integrated GIS and neural network approach. Freshwater Biology, 49, 1036–1052.

We used stream fish and decapod spatial occurrence data extracted from the New Zealand Freshwater Fish Database combined with recent surveys and geospatial landuse data, geomorphologic, climatic, and spatial data in a geographical information system (GIS) to model fish occurrence in the Wellington Region, New Zealand. To predict the occurrence of each species at a site from a common set of predictor variables we used a multi- response, artificial neural network (ANN), to produce a single model to predict the entire fish and decapod assemblage in one procedure. The predictions from the ANN using this landscape scale data proved very accurate and four other evaluation metrics independent of species abundance or probability thresholds also confirmed the accuracy of the model. The geospatial data available for the entire regional river network were then used to create a habitat-suitability map for all 18 species over the regional river network using GIS. This prediction map has many potential uses including; monitoring and predicting temporal changes in fish communities caused by human activities and shifts in climate, identifying of areas in need of protection, biodiversity hotspots, and areas for the reintroduction of endangered or rare species.