Identifying Effective Water-Management Strategies in Variable Climates Using Population Dynamics Models

Monday, September 9, 2013: 3:40 PM
Harris Brake (The Marriott Little Rock)
Nick Bond , Australian Rivers Institute, Griffith University, Brisbane, Australia
Jian Yen , School of Biological Sciences, Monash University, Clayton, Australia
Daniel Spring , School of Biological Sciences, Monash University, Clayton, Australia
Ralph Mac Nally , School of Biological Sciences, Monash University, Clayton, Australia
Predicting the response of fish populations to temporal patterns of flow variability is an important goal for environmental flow releases in heavily regulated and managed river systems. The most common approaches rely on the use of habitat suitability models as a proxy for population response to alternative flow scenarios. However, such approaches ignore the influence of antecedent population size on future trajectories, and hence are unable to capture temporal dynamics of populations, and how these differ among species. They typically also ignore uncertainty in model parameters. In contrast, stochastic population models are at the heart of fisheries science and other areas of ecology, but are rarely used in relation to environmental flows. Such models explicitly account for the effects of antecedent population status on future trends, but are also much more data intensive and therefore often restricted to examining only single species. Here we present an approach that combines the conceptual strengths of stochastic demographic models with simple expert derived rules to inform the scaling of demographic rates. We apply the models to four species with differing life-history strategies, and contrast the effects of simulated regulated and unregulated flow regimes. While initially of heuristic value, these models better represent the influence of temporal sequencing of flow variability on populations, and better represent uncertainty and stochasticity. The conceptual framework is also amendable to inclusion of improved parameter estimates over time. In broad terms we strongly advocate stronger links between environmental flows modelling and other areas of fisheries science and population biology.