Make the Most of the Data You’ve Got: Bayesian Models and a Surrogate Species Approach to Assessing Benefits of Upstream Migration Flows for the Endangered Australian Grayling

Environmental water managers must make best use of allocations, and adaptive management is one means of improving effectiveness of environmental water delivery. Adaptive management relies on generation of new knowledge from monitoring and evaluation, but it is often difficult to make clear inferences from available monitoring data. Alternative approaches to assessment of flow benefits may offer an improved pathway to adaptive management. We developed Bayesian statistical models to inform adaptive management of the threatened Australian grayling (Prototroctes maraena) in the coastal Thomson River, South-East Victoria Australia. The models assessed the importance of flows in spring and early summer (migration flows) for upstream dispersal and colonization of juveniles of this diadromous species. However, Australian grayling young-of-year were recorded in low numbers, and models provided no indication of the benefit of migration flows. To overcome this limitation, we applied the same models to young-of-year of a surrogate species (tupong—Pseudaphritis urvilli)—a more common diadromous species expected to respond to flow similarly to Australian grayling—and found strong positive responses to migration flows. Our results suggest two complementary approaches to supporting adaptive management of Australian grayling. First, refine monitoring approaches to allow direct measurement of effects of migration flows, a process currently under way. Second, while waiting for improved data, further investigate the use of tupong as a surrogate species. More generally, alternative approaches to assessment can improve knowledge to inform adaptive management, and this can occur while monitoring is being revised to directly target environmental responses of interest.