The dataset consists of results from two stream mesocosm experiments that were conducted in the summer-autumn of 1996 and 1997 to distinguish the influence of fine sediment loads and nutrient concentrations on benthic macro-invertebrate and algal communities. 11 biological variables were extracted from the results of this experiment and were standardized for the purpose of training neural networks that could be used to diagnose nutrient and fine sediment impacts in field surveys. The 11 variables were selected according to how well they correlated with the experimental treatment levels (high and low values of both nutrients and fine sediments). The 11 variables were: chlorophyll a (mg/m2), macro-invertebrate familial richness, total abundance, and the abundance of <em>Oligochaeta, Leptoperla varia (Gripopterygidae), Nousia spp. (Leptophlebiidae), Austrophlebioides spp. (Leptophlebiidae), Orthocladiinae, Tanypodinae, Tipulidae</em> and larval <em>Scirtidae</em>. These taxa were abundant within and among the stream mesocosm communities and are common in a wide range of Tasmanian rivers. Values for each of 11 biological response variables were standardized by dividing by their average value observed in the experimental controls mesocosm samples from that year. See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC, et al. (2015) <i>Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks.</i> PLoS ONE 10(3): e0120901. https://doi.org/10.1371/journal.pone.0120901 https://doi.org/10.1371/journal.pone.0120901. This data was collected for the purpose of training artificial neural networks that could diagnose nutrient and sediment impacts in Tasmanian rivers. Each of the 11 variables were standardized by their average value observed in the experimental control samples from that year and some experimental treatment effects (Light) were ignored to simplify the neural network training process. Therefore, these data should not be used to make conclusions about the impacts of fine sediments and nutrients in Tasmanian rivers.

The data set contains information on: sediment characteristics, univariate indices of the macrofauna community and ecosystem functions (net primary production, sediment oxygen consumption and nutrient cycling).

River sites were sampled during the summers of 2008/09 and 2009/10 in a survey designed to identify correlations between commonly used river condition variables and grazing land-use. Potential stream sites in northern Tasmania were screened by catchment size, northing and slope, and according to attributes aimed at minimising confounding variables, maintaining broad consistency in landscape and geomorphological context, and promoting independence among sites. A set of 27 survey sites was selected across a gradient from low to high proportion of land under grazing in their upstream catchments. Catchment sizes varied from 20-120 km2 and proportion grazing from 0-80%. Macroinvertebrates were sampled using Surber sampler. All macroinvertebrates within a 20% sub-sample identified to family and counted, with individuals from the insect orders Ephemeroptera, Plecoptera and Trichoptera identified to genus/species (by Laurie Cook, UTAS). Algal abundance was estimated at each site as the proportion of algal cover and as areal density of benthic chlorophyll a. Physical data variables collected were: water temperature, conductivity, turbidity, pH, total alkalinity, nitrate+nitrate, dissolved reactive phosphorus, total nitrogen, total phosphorus, overhead shading, the proportion of fine sediments within the sampled riffle zone, accumulated abstraction index and accumulated regulation index. For more information see: See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC and Davies PE. Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks. PLOS ONE.