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Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks
Citation
Magierowski, RH and Read, SM and Carter, SJB and Warfe, DM and Cook, LS and Lefroy, EC and Davies, PE, Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks, PLoS ONE, 10, (3) Article e0120901. ISSN 1932-6203 (2015) [Refereed Article]
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Copyright Statement
Copyright 2015 Magierowski et al. Licenced under the terms of the Creative Commons Attribution 4.0 Internatioanl License (CC BY 4.0)
DOI: doi:10.1371/journal.pone.0120901
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.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | artificial neural networks, sediment, grazing, spatial and landscape ecology, rivers, freshwater ecology |
| Research Division: | Environmental Sciences |
| Research Group: | Environmental management |
| Research Field: | Environmental assessment and monitoring |
| Objective Division: | Environmental Management |
| Objective Group: | Fresh, ground and surface water systems and management |
| Objective Field: | Assessment and management of freshwater ecosystems |
| UTAS Author: | Magierowski, RH (Dr Regina Magierowski) |
| UTAS Author: | Carter, SJB (Dr Steven Carter) |
| UTAS Author: | Warfe, DM (Dr Danielle Warfe) |
| UTAS Author: | Cook, LS (Mr Laurence Cook) |
| UTAS Author: | Lefroy, EC (Professor Ted Lefroy) |
| UTAS Author: | Davies, PE (Professor Peter Davies) |
| ID Code: | 99173 |
| Year Published: | 2015 |
| Web of Science® Times Cited: | 5 |
| Deposited By: | Zoology |
| Deposited On: | 2015-03-17 |
| Last Modified: | 2017-10-31 |
| Downloads: | 306 View Download Statistics |
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