A General Approach to Predicting Ecological Responses to Environmental Flows: Making Best Use of the Literature, Expert Knowledge, and Monitoring Data

Around the world, governments are making huge investments in environmental flows. However, much of the rationale for these releases is based on expert opinion and is thus open to challenge. Empirical studies that relate ecological responses to flow restoration are mostly case studies of limited generality. Radically, different approaches are required to inform the development of general models that will allow us to predict the effects of environmental flows. Here, we describe the modelling framework being used in a major study of environmental flows in the Australian state of Victoria. The framework attempts to make best use of all the information available from the literature, experts, and monitoring data, to inform the development of general quantitative response models. It uses systematic review of the literature to develop evidence-based conceptual models, formal expert elicitation to provide an initial quantification of model links, and data derived from purpose-designed monitoring programs over large spatial scales. These elements come together in a Bayesian hierarchical model that quantifies the relationship between flow variation and ecological response and hence can be used to predict ecological responses to flow restoration. We illustrate the framework using the example of terrestrial vegetation encroachment into regulated river channels. Our modelling framework aims to develop general flowresponse models and can immediately be used to demonstrate the ecological return on investment from environmental flow programs. However, the framework also has the potential to be incorporated into planning and decision-making processes, helping to drive a transformation in evidence-based practice for environmental flow management. © 2014 The Authors. River Research and Applications published by John Wiley & Sons, Ltd. key words: flow-response modelling; monitoring and assessment; hierarchical Bayes; Eco Evidence Received 26 September 2013; Revised 3 July 2014; Accepted 1 August 2014