Drivers influencing adaptive management: a retrospective evaluation of water quality decisions in South East Queensland (Australia).

This article analyzes interviews with natural resource managers in South East Queensland (SEQ), Australia. The objectives of the research are (i) to apply and test deductive/inductive text analysis methods for constructing a conceptual model of water quality decision-making in SEQ, (ii) to understand the role of information in the decision-making process, and (iii) to understand how to improve adaptive management in SEQ. Our methodology provided the means to quickly and objectively explore interview data and also reduce potential subjective bias normally associated with deductive text analysis methods. At a more practical level, our methodology indicates potential intervention points if one is to influence the decision-making process in the region. Results indicate that relevant information is often ignored in SEQ, with significant consequences for adaptive management. Contextual factors (political, social, and environmental) together with effective communication or lobbying strategies often prevent evidence-based decisions. We propose that in addition to generating information to support decisions, adaptive management also requires an appraisal of the true character of the decision-making process, which includes how stakeholders interact, what information is relevant and salient to management, and how the available information should be communicated to stakeholders and decision-making bodies.

An empirical model of water quality for use in rapid management strategy evaluation in Southeast Queensland, Australia.

Simulation models have been widely adopted in fisheries for management strategy evaluation (MSE). However, in catchment management of water quality, MSE is hampered by the complexity of both decision space and the hydrological process models. Empirical models based on monitoring data provide a feasible alternative to process models; they run much faster and, by conditioning on data, they can simulate realistic responses to management actions. Using 10 years of water quality indicators from Queensland, Australia, we built an empirical model suitable for rapid MSE that reproduces the water quality variables' mean and covariance structure, adjusts the expected indicators through local management effects, and propagates effects downstream by capturing inter-site regression relationships. Empirical models enable managers to search the space of possible strategies using rapid assessment. They provide not only realistic responses in water quality indicators but also variability in those indicators, allowing managers to assess strategies in an uncertain world.