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Investigating the relationships between environmental stressors and stream condition using Bayesian belief networks

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Allan, J D and Yuan, LL and Black, P and Davies, PE and Stockton, T and Magierowski, RH and Read, S (2011) Investigating the relationships between environmental stressors and stream condition using Bayesian belief networks. Freshwater Biology, Virtual Issue: Achieving Ecological Outcomes. pp. 1365-2427. ISSN 0046-5070

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Abstract

1. Stream reaches found to be impaired by physical, chemical or biological assessment generally are associated with greater extent of urban and agricultural land uses, and lesser amount of undeveloped lands. However, because stream condition commonly is influenced by multiple stressors as well as underlying natural gradients, it can be difficult to establish mechanistic relationships between altered land use and impaired stream condition. 2. This study explores the use of Bayesian belief networks (BBNs) to model presumed causal relationships between stressors and response variables. A BBN depicts the chain of causal relationships resulting in some outcome such as environmental impairment and can make use of evidence from expert judgment as well as observational and experimental data. 3. Three case studies illustrate the flexibility of BBN models. Expert elicitation in a workshop setting was employed to model the effects of sedimentation on benthic invertebrates. A second example used empirical data to explore the influence of natural and anthropogenic gradients on stream habitat in a highly agricultural watershed. The third application drew on several forms of evidence to develop a decision support tool linking grazing and forestry practices to stream reach condition. 4. Although data limitations challenge model development and our ability to narrow the range of possible outcomes, model formulation forces participants to conceptualise causal mechanisms and consider how to resolve data shortfalls. With sufficient effort and resources, models with greater evidentiary strength from observational and experimental data may become practical tools to guide management decisions. 5. Such models may be used to explore possible outcomes associated with a range of scenarios, thus benefiting management decision-making, and to improve insight into likely causal relationships.

Item Type: Article
Keywords: Bayesian belief network, causal inference, multiple stressors, stream impairment, stream management
Journal or Publication Title: Freshwater Biology
Page Range: pp. 1365-2427
ISSN: 0046-5070
Identification Number - DOI: 10.1111/j.1365-2427.2011.02683.x
Additional Information: The definitive published version is available online at: http://www.interscience.wiley.com
Date Deposited: 08 Jan 2012 23:44
Last Modified: 25 May 2012 01:48
URI: http://eprints.utas.edu.au/id/eprint/12222
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