To define whether the beta diversity of stream invertebrate communities in New Zealand exhibits geographical variation unexplained by variation in gamma diversity and, if so, what mechanisms (productivity, habitat heterogeneity, dispersal limitation, disturbance) best explain the observed broad-scale beta diversity patterns. We sampled 120 streams across eight regions (stream catchments), spanning a north–south gradient of 12° of latitude, and calculated beta diversity (with both species richness and abundance data) for each region. We explored through a null model if beta diversity deviates from the expectation of stochastic assembly processes and whether the magnitude of the deviation varies geographically. We then performed multimodel inference analysis on the key environmental drivers of beta diversity, using Akaike's information criterion and model and predictor weights to select the best model(s) explaining beta diversity. Beta diversity was, unexpectedly, highest in the South Island. The null model analysis revealed that beta diversity was greater than expected by chance in all eight regions, but the magnitude of beta deviation was higher in the South Island, suggesting differences in environmental filtering and/or dispersal limitation between North and South Island. Habitat heterogeneity was the predominant driver of beta diversity of stream macroinvertebrates, with productivity having a secondary, and negative, contribution. This is one of the first studies accounting for stochastic effects while examining the ecological drivers of beta diversity. Our results suggest that local environmental heterogeneity may be the strongest determinant of beta diversity of stream invertebrates, more so than regional- or landscape-scale variables.

SUMMARY1. The ecological health of rivers worldwide continues to decline despite increasing effort and investment in river science and management. Bayesian belief networks (BBNs) are increasingly being used as a mechanism for decision-making in river management because they provide a simple visual framework to explore different management scenarios for the multiple stressors that impact rivers. However, most applications of BBN modelling to resource management use expert knowledge and/or limited real data, and fail to accurately assess the ability of the model to make predictions.2. We developed a BBN to model ecological condition in a New Zealand river using field/GIS data (from multiple rivers), rather than expert opinion, and assessed its predictive ability on an independent dataset. The developed BBN performed moderately better than a number of other modelling techniques (e.g., artificial neural networks, classification trees, random forest, logistic regression), although model construction was more time-consuming. Thus the predictive ability of BBNs is (in this case at least) on a par with other modelling methods but the approach is distinctly better for its ability to visually present the data linkages, issues and potential outcomes of management options in real time. We have demonstrated this for a BBN of ecological condition in a New Zealand river, shown that model fit is better than that for other modelling techniques, and that improving habitat would be equally effective to reducing nutrients to improve ecological condition.

The management of nutrient inputs into New Zealand waterways is a key focus for environmental agencies, but few North Island studies have examined whether nitrates, phosphates or a combination of nutrients limit periphyton proliferations. The underlying volcanic geology that the Rangitikei River, a large central North Island river, drains would suggest nitrogen (N) as a likely limiting nutrient for periphyton growth. However, examination of nutrient ratios in water samples indicated N-limitation consistently at only 7 of 11 sites sampled on the mainstem and tributaries of the Rangitikei River. The actual effect of nutrient limitation on periphyton growth was investigated at those same sites using in situ nutrient diffusing substrates. Nutrient limitation was also examined upstream and downstream of sewage discharges from the townships of Taihape, Hunterville, and Bulls. The nutrient diffusing substrates indicated N was the limiting nutrient for periphyton growth at most sites in the mainstem and several tributaries of the Rangitikei River. Sewage discharges at both Taihape and Bulls seemed to alter the nutrient balance in the receiving waterways to the extent that the nutrient diffusing substrates did not indicate any nutrient limitation.