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Informing the management of multiple stressors on estuarine ecosystems using an expert-based Bayesian Network model
R H Bulmer1, F Stephenson1, A M Lohrer1
1National Institute of Water & Atmospheric Research, New Zealand.
Estuarine management needs to consider cumulative stressor effects. A Bayesian network model shows reducing suspended sediment and mud/metal content improves ecosystem health, but nutrient loads must also be managed to prevent algal blooms.
Area of Science:
- Environmental Science
- Ecosystem Management
- Ecological Modeling
Background:
- Estuarine management strategies increasingly use stressor load limits.
- Uncertainty exists regarding cumulative impacts of multiple stressors on estuarine ecosystems.
- Data limitations hinder detailed modeling of estuarine responses to multiple stressors.
Purpose of the Study:
- To develop and validate an expert-driven Bayesian network (BN) model for estuarine ecosystem response to multiple stressors.
- To assess the utility of the BN framework for informing estuarine management decisions.
- To investigate the cumulative effects of suspended sediment, sediment composition, and nutrient inputs on estuarine condition.
Main Methods:
- Development and validation of an expert-driven Bayesian network (BN).
- Application of the BN framework to a case study estuary.
- Comparison of model predictions with field observations.
Main Results:
- BN model predictions showed good concordance with field observations (71-84%).
- Reductions in suspended sediment, mud, and metal content predicted improvements in estuarine condition.
- Reducing suspended sediment without managing nutrient loads increased the risk of nuisance algal blooms.
Conclusions:
- Bayesian network models can effectively predict estuarine condition trends.
- Stressor limit setting must account for cumulative effects and potential unexpected outcomes.
- Integrated management of multiple stressors, including nutrients, is crucial for effective estuarine health improvement.
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