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1. We used stream fish and decapod spatial occurrence data extracted from a national database and recent surveys with geospatial landuse data, geomorphologic, climatic, and spatial data in a geographical information system (GIS) to model fish and decapod occurrence in the Wellington Region, New Zealand. 2. 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 that predicted the entire fish and decapod assemblage in one procedure. 3. The predictions from the ANN using this landscape scale data proved very accurate based on evaluation metrics that are independent of species abundance or probability thresholds. The important variables contributing to the predictions included the latitudinal and elevational position of the site reach, catchment area, average air temperature, the vegetation type, landuse proportions of the catchment, and catchment geology. 4. Geospatial data available for the entire regional river network were then used to create a habitat-suitability map for all 14 species over the regional river network using a 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 areas in need of protection, biodiversity hotspots, and areas suitable for the reintroduction of endangered or rare species.

Over the past two decades there have been major increases in dairy production in New Zealand. This increase in intensity has required increased use of external inputs, in particular fertilizer, feed, and water. Intensified dairy farming thus incurs considerable environmental externalities: impacts that are not paid for directly by the dairy farmer. These externalities are left for the wider New Zealand populace to deal with, both economically and environmentally. This is counter-intuitive given the dairy industry itself relies on a 'clean green' image to maximize returns. This is the first nationwide assessment of some of the environmental costs of the recent increase of dairy intensification in New Zealand. Significant costs arise from nitrate contamination of drinking water, nutrient pollution to lakes, soil compaction, and greenhouse gas emissions. At the higher end, the estimated cost of some environmental externalities surpasses the 2012 dairy export revenue of NZ$11.6 billion and almost reaches the combined export revenue and dairy's contribution to Gross Domestic Product in 2010 of NZ$5 billion. For the dairy industry to accurately report on its profitability and maintain its sustainable marketing label, these external costs should be reported. This assessment is in fact extremely conservative as many impacts have not been valued, thus, the total negative external impact of intensified dairying is probably grossly underestimated.

SUMMARY 1. A challenge has been issued to ecologists to find quantitative ecological relationships that have predictive power. A predictive approach has been successful when applied to biomonitoring using stream invertebrates with the River Invertebrate Prediction and Classification System (RIVPACS). This approach, to our knowledge, has not been applied to freshwater fish assemblages. 2. This paper describes the initial results of the application of a regional predictive model of freshwater fish occurrence using 200 reference sites sampled in the Manawatu–Wanganui region of New Zealand over late summer/autumn 2000. In brief (i) sites were classified into biotic groups (ii) the physical and chemical characteristics that best describe variation among these groups were determined and (iii) the relationship between these environmental variables and fish communities was used to predict the fauna expected at a site. 3. Reference sites clustered into six groups based on fish density and community composition. Using 14 physical variables least influenced by human activities, a discriminant model allocated 70% of sites to the correct biological classification group. The variables that best separated the site groups were mainly large‐scale variables including altitude, distance from the coast, lotic ecoregion and map co‐ordinates. 4. The model was further validated by randomly removing 20% of the sites, rebuilding the model and then determining the number of removed sites correctly allocated to their original biotic groups using environmental variables. Using this process 67% of the removed sites were correctly reassigned to the six predetermined groups. 5. A further 30 sites were used to determine the ability of the model to detect anthropogenic impact. The observed over expected taxa (O/E) ratios were significantly lower than the reference site O/E ratios, indicating a response of the fish assemblages to the known stressors.

Broadening the scope of conservation efforts to protect entire communities provides several advantages over the current species-specific focus, yet ecologists have been hampered by the fact that predictive modeling of multiple species is not directly amenable to traditional statistical approaches. Perhaps the greatest hurdle in community-wide modeling is that communities are composed of both co-occurring groups of species and species arranged independently along environmental gradients. Therefore, commonly used "short-cut" methods such as the modeling of so-called "assemblage types" are problematic. Our study demonstrates the utility of a multiresponse artificial neural network (MANN) to model entire community membership in an integrative yet species-specific manner. We compare MANN to two traditional approaches used to predict community composition: (1) a species-by-species approach using logistic regression analysis (LOG) and (2) a "classification-then-modeling" approach in which sites are classified into assemblage "types" (here we used two-way indicator species analysis and multiple discriminant analysis [MDA]). For freshwater fish assemblages of the North Island, New Zealand, we found that the MANN outperformed all other methods for predicting community composition based on multiscaled descriptors of the environment. The simple-matching coefficient comparing predicted and actual species composition was, on average, greatest for the MANN (91%), followed by MDA (85%), and LOG (83%). Mean Jaccard's similarity (emphasizing model performance for predicting species' presence) for the MANN (66%) exceeded both LOG (47%) and MDA (46%). The MANN also correctly predicted community composition (i.e., a significant proportion of the species membership based on a randomization procedure) for 82% of the study sites compared to 54% (MDA) and 49% (LOG), resulting in the MANN correctly predicting community composition in a total of 311 sites and an additional 117 sites (n = 379), on average, compared to LOG and MDA. The MANN also provided valuable explanatory power by simultaneously quantifying the nature of the relationships between the environment and both individual species and the entire community (composition and richness), which is not readily available from traditional approaches. We discuss how the MANN approach provides a powerful quantitative tool for conservation planning and highlight its potential for biomonitoring programs that currently depend on modeling discrete assemblage types to assess aquatic ecosystem health.