Appendix B. A figure showing Bray-Curtis similarity between the surrogate set identified by SIMPER (PRIMER5) for each deployment date and the surrogate set identified by SIMPER for holdfasts deployed in December 1997, for each community age.

A figure showing Bray-Curtis similarity between the surrogate set identified by SIMPER (PRIMER5) for each deployment date and the surrogate set identified by SIMPER for holdfasts deployed in December 1997, for each community age

Appendix A. A figure showing Bray-Curtis similarity between the surrogate set identified by SIMPER (PRIMER5) for each community age and the surrogate set identified by SIMPER for one month holdfasts, for each deployment date.

A figure showing Bray-Curtis similarity between the surrogate set identified by SIMPER (PRIMER5) for each community age and the surrogate set identified by SIMPER for one month holdfasts, for each deployment date

Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks

<div><p>Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.</p></div

Validation results for ANNs trained to predict nutrient or fine sediment condition.

<p>Predictions from nutrient ANNs were generated from training (a) and validation (b) datasets. Predictions from sediment ANNs were generated from training (c) and validation (d) datasets. Each pair of points represents one mesocosm (labelled ‘Record’): the white circle is plotted at the mean (+/- standard deviation) of the condition scores predicted by 50 ANNs, while the black diamond plots the actual condition (nutrient or sediment level) of each mesocosm as 1 (low) or 2 (high). Diamonds coloured red indicate an incorrect prediction.</p

Ordination plot of sites separated by relative abundance of functional feeding groups.

<p>The distance-based redundancy analysis was constrained by the environmental data. The lengths of the overlaying vectors are proportional to the multiple partial correlations of each environmental variable with dbRDA1 (the circle represents the maximum vector length, a correlation of |1|) and the direction of the vectors illustrates both the direction of the correlation with dbRDA1 and the degree of correlation with dbRDA2. Only correlations >|0.2| with dbRDA1 are displayed. Symbol size represents the abundance of invertebrates classified as ‘leaf shredders’.</p

Map of study area and survey sites.

<p>Twenty-seven sites (●) were surveyed across northern Tasmania over two years. Site catchments were independent (black lines) and covered a gradient of grazing intensity (0 to 80% grazing of total catchment areas; grey shading shows land used for grazing livestock). Land-use data were supplied by DPIPWE (2009), and other spatial data were extracted from the Conservation of Freshwater Ecosystem Values database (DPIWE 2005) and the Land Information System Tasmania (theLIST) © Tasmania.</p

Relationship between %EPT and proportion of upstream catchment subject to grazing.

<p>Relationship between %EPT and proportion of upstream catchment subject to grazing.</p