Evolution of an approach to integrated adaptive management: The Coastal Lake Assessment and Management (CLAM) tool

The coastal zone provides highly valuable resources for its residents and visitors. Increasing pressure for development and conservation of the natural environment can lead to conflict in the management of these areas. The Coastal Lake Assessment and Management (CLAM) tool is an approach to the development of an iterative decision support tool for management within the coastal zone, including coastal lakes, inlets, lagoons and estuaries. The CLAM tool assists in identifying trade-offs between social, cultural, economic and ecological values within a coastal lake and its catchment for various future management scenarios. This paper describes the lessons learnt from applying the CLAM tool approach many times in New South Wales (Australia) and how this has been used to adapt and improve the process. Learning indicates that not only does a decision support tool need to be adaptive in order to best use available resources, but the approach to developing such tools also needs to be adaptive and improve in response to the successes and shortfalls of each application

Build collaborative models or capacity? Reflections from two years on

In 2007 we undertook 'capacity building' with six Natural Resource Management (NRM) bodies within Australia, where the aim was to train staff in how to develop Bayesian networks (Bns). Previously, the NRM staff had expressed interest in developing Bns themselves to assist with their target setting, planning and reporting needs, so that investments in on-ground activities can be better targeted to outcomes in resource condition. Concurrently, we were developing generic Bns 'collaboratively' with the same groups. Six months after completing the initial training, none of the NRM regions had made any significant progress in the development of their own Bns. Follow-up surveys, two and a half years later, found that the development of Bns by the NRM regions themselves had largely been limited to conceptual diagrams and influence diagrams. The NRM regions who had made the most progress were those that had staff complete other external training, and those who had committed additional funding to external projects, rather than just internal management and target setting. The time commitment required to develop the Bns and lack of data resources remained the major limitations. Since the 2007 Bn training exercise, fewer participants believe that it is valuable and feasible for the NRM regions to develop their own Bns, but a greater proportion can see the usefulness of the Bn approach to their work. If completed Bns are the measure of success, then it is best to build collaborative models, but detailed capacity building in the initial stages of this project aided the depth of the collaborative feedback, the building of a working relationship between the researchers and stakeholders, and provided a systems approach to environmental management for the stakeholders. Consequently, we would recommend building both capacity and collaborative models to improve NRM decision making processes and to increase adoption of decision making tools

Can existing practices expected to lead to improved on-farm water use efficiency enable irrigators to effectively respond to reduced water entitlements in the Murray-Darling Basin?

Australia is the driest continent and there is increasing competition for scarce fresh water resources between agriculture and the environment. In the Murray-Darling Basin (MDB) that conflict has largely been resolved by reallocating water from agriculture to the environment. As part of the water reform process both governments and industry are focussed on improving on-property water use efficiency (WUE), particularly of irrigated agriculture. This paper examines the potential for WUE to enable MDB irrigators to adapt to cuts in their irrigation entitlements. The paper draws on data from a case study in the Namoi Valley of New South Wales. The distinctive contribution of this paper is that we draw on survey data of the existing and intended adoption of a limited suite of currently available WUE practices. That is, we have not simply assumed that all irrigators, or a specific proportion of irrigators, will adopt each WUE option. Given survey respondents' intended level of adoption, we calculated the potential water savings for each property and then the catchment, without extrapolating beyond the survey respondents. Those calculations suggest that water savings of up to 100.9. GL could be achieved across the Namoi catchment if those interested in doing so were to convert to existing improved WUE practices. Those savings represented 82% of the reduction in irrigator entitlements under the draft MDB Plan, and exceed the 10. GL/yr reductions required under the revised MDB Plan. These results suggest that those adopting existing WUE practices will have additional water for irrigation. To the extent that this is the case, there seems to be less justification for government support for irrigators during the adjustment process

Analysing social data on adoption of conservation practices: Exploring Bayesian networks

Australia's Natural Resource Management (NRM) regions are required to report to the Australian Government on the impact their investments have on natural resource condition. Reporting to date has typically been limited to describing the nature of investment and on-ground activities that have taken place. In some cases activities are linked by crude assumptions to an expected change in condition. With the Australian Government pushing for regional reporting that focuses on outcomes (change in condition) rather than outputs (activity and dollars spent), the regions are seeking ways to improve their current investment planning and resource condition reporting. This requires an improved understanding of biophysical systems to establish causal links between management actions and change in resource condition, and social research to better understand who in the community is likely to respond to which type of environmental programs and why. Techniques exist to analyse large complex social data sets including statistical and social-psychology models. Bayesian Networks (BNs), which are increasingly being used to model environmental systems, have not often been used in the analysis of social data. A BN (Figure 1) is a dynamic way to analyse complex cause and effect relationships. This paper explores the utility of BNs for analysis of social data sets and compares this approach to other more commonly applied techniques. Survey data from the Wimmera Catchment Management Authority was used to develop a BN model of the social drivers of conservation activity adopted by landholders to protect native vegetation. The resulting BN shows relationships between a landholder's likelihood of fencing native vegetation and their values, knowledge, attitudes, income and access to government support. It clearly illustrated the importance of government funding in the uptake of conservation, but also showed that a significant amount of fencing was carried out in the absence of government programs. In the absence of government funding, on-farm income was found to be critical to the uptake this activity, illustrating that whilst landholders may have been willing to adopt recommended practice, the behaviour depended on their 'capacity to change' (in this case, financial capacity). This paper suggests that BNs based on social research could be used by managers to support their decisionmaking and reporting, and has value for researchers as a tool for analysing, interpreting and communicating social data. (Figure Presented)

Using Bayesian Networks to complement conventional analyses to explore landholder management of native vegetation

Influencing the management of private landholders is a key element of improving the condition of Australia's natural resources. Despite substantial investment by governments, effecting behavioural change on a scale likely to stem biodiversity losses has proven difficult. Understanding landholder decision-making is now acknowledged as fundamental to achieving better policy outcomes. There is a large body of research examining landholder adoption of conservation practices. Social researchers are able to employ a suite of conventional techniques to analyse their survey data and assist in identifying significant and causal relationships between variables. However, these techniques can be limited by the type of data available, the breadth of issues that can be represented and the extent that causality can be explored. In this paper we discuss the findings of a unique study exploring the benefits of combining Bayesian Networks (BNs) with conventional statistical analysis to examine landholder adoption. Our research examined the landholder fencing of native bushland in the Wimmera region in south east Australia. Findings from this study suggest that BNs provided enhanced understanding of the presence and strength of causal relationships. There was also the additional benefit that a BN could be quickly developed and that this process helped the research team clarify and understand relationships between variables. However, a key finding was that the interpretation of the results of the BNs was complemented by the conventional data analysis and expert review. An additional benefit of the BNs is their capacity to present key findings in a format that is more easily interpreted by researchers and enables researchers to more easily communicate their findings to natural resource practitioners and policy makers

Integration modelling and decision support: A case study of the Coastal Lake Assessment and Management (CLAM) Tool

Decision Support Tools (DSTs) are designed to assist in making more informed management decisions, through prediction of the outcomes from various future scenarios and as an education resource. The many coastal lakes in New South Wales, Australia are areas where DSTs are able to assist in making management and planning decisions. A variety of economic, ecological and social demands on the lakes and their catchment's finite resources are increasing conflict over their use and sustainable management. The issues are intricately linked, so that understanding trade-offs and making management decisions about coastal lakes and their catchments requires knowledge of the processes and interactions between all key components of the system. This is a complex problem requiring the integration of, often minimal, information, from various disciplines. This paper describes an approach for developing a DST to provide information about the potential impacts of management decisions on key components of a coastal lake system. Integration of the catchment components was completed using a Bayesian Decision Network (BDN). This paper uses a case study of a DST for Merimbula Lake on the east coast of Australia to illustrate the strengths of the BDN approach, and to show how the design of the DST helps to address some of the limitations inherent in the integrative method

Model design for the hydrology of tree belt plantations on hillslopes

When selecting or developing a model to use for research it is important that the model structure and complexity meet the objectives of the research while avoiding problems from overparameterisation. In this paper a procedure is outlined for the selection or development of a model to be used to assist in locating and designing tree belt plantations on hillslopes. Sensitivity analysis and field data interpretation are used to define the important hillslope properties and processes occurring at a field site in southern New South Wales. These are combined with the research objectives to identify the model requirements for further study on tree belt plantations. A brief review of potentially suitable models available reveals that no single model meets all of the requirements. It is concluded that field data should be used to develop a simple cascading bucket model for hillslope hydrology using a top-down approach