Abstract
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.
Highlights
Natural resource management often requires a high level of evidence from applied research so that the basis for environmental decision-making can be demonstrated and defended [1, 2]
The abundance of invertebrates classified as leaf shredders (1–285 individuals) was used to illustrate the change in functional feeding groups/traits (FFGs): the change in abundance of leaf shredders was negatively correlated with the doi:10.1371/journal.pone.0120901.g003
The proportion of substrate area represented by fine sediments varied from 0 to 45% and was well correlated with community structure, while the three nutrient variables (NOx (0 to 0.82 mg/L), dissolved reactive phosphorus (DRP) (0 to 0.041 mg/L) and total nitrogen (TN) (0 to 1.3 mg/L)) were weakly correlated with the first dbRDA axis, with NOx showing a negative relationship with the first dbRDA axis and with the area proportion of grazing land-use (Fig. 3)
Summary
Natural resource management often requires a high level of evidence from applied research so that the basis for environmental decision-making can be demonstrated and defended [1, 2]. Increased nutrient inputs from agricultural land-use can interact with increased light availability (resulting from riparian vegetation removal) to stimulate algal production and increase ecosystem respiration [8], but flow reductions associated with agriculture can lead to reduced delivery of organic material and decreased ecosystem respiration [9]. Such confounding means that poor correlations between potential stressors and ecological condition do not necessarily indicate the absence of a causal relationship [1]. Researchers need to use a “weight-of-evidence” approach to identify causal pathways of impact [3, 12]
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