hydrological changes could influence the structure and composition of the swamp community. Bayesian network To demonstrate how a causal model can be framed in a Bayesian network, a model was developed in the workshop on the basis of the influence diagram (Figure 4.8), but not populated. Note that the states of the variables when populated would capture a control model versus a stressor model. A Bayesian network example (Figure 4.9) was presented to allow workshop participants to gain an understanding of how a Bayesian network is developed, populated and used for running some scenario analyses. The participants were able to follow the Bayesian network example with a temporary set of chosen probabilities as an example of how the wet and dry phases could be captured within the model. For example, increased groundwater drawdown and increased temperature would stress most components of the vegetation community, under especially dry conditions, potentially leading to the elimination of some species. This example focuses on impacts of groundwater drawdown on a Melaleuca community in wet and dry phases. Note this model is for demonstration purposes only to depict how a BN would predict an outcome. The underlying probabilities are only an example. Thus the model does not portray a realistic scenario of a system. Figure 4.9 Example of a small Bayesian network The next steps for this model would be to: carefully define a specific, measurable ecological endpoint (e.g. recruitment, adult survival) define states for all nodes determine whether data and/or expert knowledge are available to populate the model or any of its component variables. The Bayesian network can consist entirely of empirical evidence or expert opinion or can be a mixture of these or other knowledge sources (e.g. output from another model). do an initial parameterisation by populating the conditional probability tables from a range of inputs, including expert knowledge and field and simulated data. Gunnedah case study: conceptual model for silver perch Silver (Bidyanus bidyanus) was chosen as a case-study example to explore the process of developing a species-specific ecological conceptual model and its accompanying narrative table for a given species at a specific location. The chosen endpoint was the persistence of a silver perch population in the section of the Mooki River at its confluence with Quirindi Creek. A desktop survey of relevant literature (Appendix A) was used to generate the control
hydrological changes could influence the structure and composition of the swamp community. Bayesian network To demonstrate how a causal model can be framed in a Bayesian network, a model was developed in the workshop on the basis of the influence diagram (Figure 4.8), but not populated. Note that the states of the variables when populated would capture a control model versus a stressor model. A Bayesian network example (Figure 4.9) was presented to allow workshop participants to gain an understanding of how a Bayesian network is developed, populated and used for running some scenario analyses. The participants were able to follow the Bayesian network example with a temporary set of chosen probabilities as an example of how the wet and dry phases could be captured within the model. For example, increased groundwater drawdown and increased temperature would stress most components of the vegetation community, under especially dry conditions, potentially leading to the elimination of some species. This example focuses on impacts of groundwater drawdown on a Melaleuca community in wet and dry phases. Note this model is for demonstration purposes only to depict how a BN would predict an outcome. The underlying probabilities are only an example. Thus the model does not portray a realistic scenario of a system. Figure 4.9 Example of a small Bayesian network The next steps for this model would be to: carefully define a specific, measurable ecological endpoint (e.g. recruitment, adult survival) define states for all nodes determine whether data and/or expert knowledge are available to populate the model or any of its component variables. The Bayesian network can consist entirely of empirical evidence or expert opinion or can be a mixture of these or other knowledge sources (e.g. output from another model). do an initial parameterisation by populating the conditional probability tables from a range of inputs, including expert knowledge and field and simulated data. Gunnedah case study: conceptual model for silver perch Silver (Bidyanus bidyanus) was chosen as a case-study example to explore the process of developing a species-specific ecological conceptual model and its accompanying narrative table for a given species at a specific location. The chosen endpoint was the persistence of a silver perch population in the section of the Mooki River at its confluence with Quirindi Creek. A desktop survey of relevant literature (Appendix A) was used to generate the control
modelling-water-related-ecological-responses-csg-extraction.docx