A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia

Ticehurst, Jenifer Lyn; Newham, Lachlan; Rissik, David; Letcher, Rebecca; Jakeman, Anthony J.

doi:10.1016/j.envsoft.2006.03.003

Cited by 146 publications

(73 citation statements)

References 12 publications

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“…For example, high fish abundance will increase the likelihood of catch per unit effort being high. These causal relationships influence the likelihood of outcome states of the variables of interest depicted in the BBN (Ticehurst et al 2007). Conditional probabilities were generated mostly from the data obtained from the household surveys.…”

Section: Bayesian Belief Network Modelmentioning

confidence: 99%

A Bayesian belief network model for community-based coastal resource management in the Kei Islands, Indonesia

Hoshino¹,

Putten²,

Girsang³

et al. 2016

E&S

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ABSTRACT. Understanding the specific relationships between ecological and socioeconomic conditions and marine tenure is likely to contribute to successful functioning of self-governance institutions for common-pool resources. Complex interrelationships of factors influencing fishing activities of coastal communities and implementation of customary marine tenure over their waters can be represented in a Bayesian belief network model. We developed a Bayesian belief network model that includes the links between factors for fishing communities in the Kei Islands in Indonesia, based on indepth local surveys. Our results showed that the cumulative impacts of multiple factors on key social, economic, and environmental outcomes can be much larger than the impact from a single source, which implies that management or policy intervention could be more effective when addressing multiple factors simultaneously. The local community's perception of fish stock abundance trends was the single most important factor influencing social, economic, and environmental outcomes of their community-based management system. The frequency of which outsiders were sighted in territorial waters was strongly (negatively) linked to weak or strong implementation of a customary tenure (Sasi) and the occurrence of intervillage and intravillage conflict. Ecological variables also drive these conflicts, which illustrates the close connection between ecological and social outcomes, and the importance of considering social-ecological systems as a whole.

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Section: Bayesian Belief Network Modelmentioning

confidence: 99%

A Bayesian belief network model for community-based coastal resource management in the Kei Islands, Indonesia

Hoshino¹,

Putten²,

Girsang³

et al. 2016

E&S

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“…The causal dependence is described probabilistically and can be defined on the basis of statistical correlations, expert judgement, process knowledge or a combination of input depending on the information available. BNs are being increasingly used to model ecological systems (Borsuk et al 2003;McCann et al 2006;Ticehurst et al 2007;Allan et al 2012) as well as being used to assist decision making within water resource management (Castelletti and Soncini-Sessa 2007;Molina et al 2010;Aguilera et al 2011). It has been proposed that BNs can be used for surface water quality assessment and prediction (Reckhow 1999) and there are some emerging applications for groundwater quality studies (Aguilera et al 2013).…”

Section: Water Quality Predictionsmentioning

confidence: 99%

The effects of climate change on ecologically-relevant flow regime and water quality attributes

Dyer

Elsawah

Croke

et al. 2013

Stoch Environ Res Risk Assess

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The management of freshwater ecosystems is usually targeted through the regulation of water quantity (limiting diversions and providing environmental flows) and regulation of water quality (setting limits or targets for constituent concentrations). Climate change is likely to affect water quantity and quality in multiple ways and the future management of freshwater ecosystems requires predictions of plausible future conditions. We use a suite of ecologically-relevant hydrological indicators to determine the significance of projected climate-driven hydrological changes in the Upper Murrumbidgee River Catchment in south eastern Australia in relation to river regulation. We also determine the possible water quality changes (in relation to guidelines for aquatic ecosystem protection) associated with the climate change projections to identify the combined effects of hydrological and water quality changes. The results of this study suggest that river regulation has resulted in greater changes to ecologically-relevant streamflow characteristics than climate change scenarios that involve a 1 and 2°C temperature rise in the Upper Murrumbidgee River Catchment. In contrast to the projected hydrological changes, Bayesian Network modelling suggests very small changes to violations of water quality thresholds designed to protect aquatic ecosystems as a result of climate change. By identifying key components of the flow and water quality regimes that may be affected by climate change, we are able to provide managers with information that assists in developing adaptation initiatives.

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“…The strength in BNs lies in the fact that quantitative and qualitative data from biophysical and social science can be combined, and uncertainty is explicitly considered in the models (e.g. Ticehurst et al 2007;Thomas et al 2009). …”

Section: Macro- Meso-and Micro-scale Economic Modelsmentioning

confidence: 99%

Modelling climate-change effects on Australian and Pacific aquatic ecosystems: a review of analytical tools and management implications

Plagányi

Bell

Bustamante

et al. 2011

Mar. Freshwater Res.

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Climate change presents significant challenges to modelling and managing aquatic resources. Equilibrium assumptions common in many modelling approaches need to be replaced by formulations that allow for changing baselines and integration of ongoing changes and adaptations by species, ecosystems and humans. As ecosystems change, so will the ways humans use, monitor and manage them. Consequently, adaptive management loops and supporting tools deserve more prominence in the management toolbox. Models are critical tools for providing an early understanding of the challenges to be faced by integrating observations and examining possible solutions. We review modelling tools currently available to incorporate the effect of climate change on marine and freshwater ecosystems, and the implications for management of natural resources. System non-linearity can confound interpretations and hence adaptive management responses are needed that are robust to unexpected outcomes. An improvement in the ability to model the effects of climate change from a social and economic perspective is necessary. The outputs from ‘end-to-end’ and socio-ecological models can potentially inform planning, in both Australia and the Pacific region, about how best to build resilience to climate change. In this context, the importance of well directed data-collection programs is also emphasised. Lessons from this region, which is advanced with regard to modelling approaches, can guide increased use of models to test options for managing aquatic resources worldwide.

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A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia

Cited by 146 publications

References 12 publications

A Bayesian belief network model for community-based coastal resource management in the Kei Islands, Indonesia

A Bayesian belief network model for community-based coastal resource management in the Kei Islands, Indonesia

The effects of climate change on ecologically-relevant flow regime and water quality attributes

Modelling climate-change effects on Australian and Pacific aquatic ecosystems: a review of analytical tools and management implications

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