Open Access Repository

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


Downloads per month over past year

Allan, J D, Yuan, LL, Black, P, Davies, PE ORCID: 0000-0003-2651-4061, Stockton, T, Magierowski, RH and Read, S 2011 , 'Investigating the relationships between environmental stressors and stream condition using Bayesian belief networks' , Freshwater Biology, vol. Virtual Issue: Achieving Ecological Outcomes , pp. 1365-2427 , doi: 10.1111/j.1365-2427.2011.02683.x.

[img] PDF
davies.pdf | Document not available for request/download
Full text restricted
Available under University of Tasmania Standard License.


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
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

Item Type: Article
Authors/Creators:Allan, J D and Yuan, LL and Black, P and Davies, PE and Stockton, T and Magierowski, RH and Read, S
Keywords: Bayesian belief network, causal inference, multiple stressors, stream impairment, stream management
Journal or Publication Title: Freshwater Biology
ISSN: 0046-5070
DOI / ID Number: 10.1111/j.1365-2427.2011.02683.x
Additional Information:

The definitive published version is available online at:

Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page