Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks

PloS One
Regina H MagierowskiPeter E Davies

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, su...Continue Reading

References

Dec 1, 1992·Toxicology Letters·N O Crossland
May 4, 2006·The American Naturalist·Ulf BergströmKjell Leonardsson
Jan 9, 2007·Ecological Applications : a Publication of the Ecological Society of America·Regina H Magierowski, Craig R Johnson
May 11, 2007·Ecology·C L BurcherE F Benfield
Jan 1, 1994·IEEE Transactions on Neural Networks·M T Hagan, M B Menhaj
Jul 9, 2008·The Quarterly Review of Biology·Julian D OldenN LeRoy Poff
Jul 22, 2009·The Science of the Total Environment·Igor LinkovTodd Bridges
Jun 12, 2013·Environmental Monitoring and Assessment·Ulrike Bende-MichlHamish P Cresswell

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AusRivAS
R
trainlm
PRIMER
ArcMap
PERMANOVA +
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