Using a Population Model to Inform the Management of River Flows and Invasive Carp (Cyprinus carpio).

Carp are a highly successful invasive fish species, now widespread, abundant and considered a pest in south-eastern Australia. To date, most management effort has been directed at reducing abundances of adult fish, with little consideration of population growth through reproduction. Environmental water allocations are now an important option for the rehabilitation of aquatic ecosystems, particularly in the Murray-Darling Basin. As carp respond to flows, there is concern that environmental watering may cause floodplain inundation and provide access to spawning habitats subsequently causing unwanted population increase. This is a management conundrum that needs to be carefully considered within the context of contemporary river flow management (natural, environmental, irrigation). This paper uses a population model to investigate flow-related carp population dynamics for three case studies in the Murray-Darling Basin: (1) river and terminal lakes; (2) wetlands and floodplain lakes; and (3) complex river channel and floodplain system. Results highlight distinctive outcomes depending on site characteristics. In particular, the terminal lakes maintain a significant source carp population regardless of river flow; hence any additional within-channel environmental flows are likely to have little impact on carp populations. In contrast, large-scale removal of carp from the lakes may be beneficial, especially in times of extended low river flows. Case studies 2 and 3 show how wetlands, floodplain lakes and the floodplain itself can now often be inundated for several months over the carp spawning season by high volume flows provided for irrigation or water transfers. Such inundations can be a major driver of carp populations, compared to within channel flows that have relatively little effecton recruitment. The use of a population model that incorporates river flows and different habitats for this flow-responsive species, allows for the comparison of likely population outcomes for differing hydrological scenarios to improve the management of risks relating to carp reproduction and flows.

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.