The expansion of the Tasmanian Salmonid Industry in new growing areas, such as Storm bay, is contingent on demonstrating that further development is done in a responsible and sustainable way. This is central to maintaining public confidence in the salmon industry. Demonstrating best practice in environmental sustainability requires that the environmental footprint of the industry is well understood and contained within acceptable levels. An environmental monitoring program that assesses the environmental performance of farming at both local and system wide scales will provide this understanding, enabling appropriate regulatory responses. The development and validation of a biogeochemical model that can estimate the natural systems capacity to assimilate salmonid derived nutrient inputs at both local and broader system scales provides the capacity to both understand current environmental conditions and forecast the environmental responses under alternate management responses. This combination of a reliable and “fit for purpose” environmental monitoring and modelling program will help meet the needs and expectations of a science based adaptive management framework necessary for the proposed development of salmonid farming in Storm Bay.
Project number: 2018-131
Budget expenditure: $3,683,627.70
Principal Investigator: Jeff Ross
Organisation: University of Tasmania (UTAS)
Project start/end date: 15 Nov 2019 - 29 Sep 2022
1. Develop a robust monitoring program
2. Provide a comprehensive map of benthic habitats and bathymetry of the Storm Bay region and assessment of change at key focus areas
3. Develop and apply a lease scale model for assessing the environmental footprint of dissolved and particulate farm inputs
4. Assess the interactions between farming and the receiving environment
5. Evaluate and review the monitoring program
Authors: Elisabeth Strain Camille White and Jeff Ross
Final Report • 2020-07-01 • 3.55 MB
2018-131 IMAS Environmental Monitoring Review_Storm Bay.pdf
In Tasmania, farming of Atlantic salmon (Salmo salar L) has developed rapidly since the first trials in 1985 and has grown progressively to the current 60,000 tonnes produced in 2020.. Salmon farming in open sea cages produces organic and inorganic wastes which have the potential to impact the receiving environment. The waste products consist of faecal material, uneaten feed pellets and metabolic waste products in dissolved inorganic forms. Dissolved wastes may enhance ambient nutrient levels (Price, Black et al. 2015), influencing primary and secondary production (Price, Black et al. 2015), and when the particulate matter sinks to the seabed it has the potential to change the structure and function of the surrounding benthic communities (Bannister, Valdemarsen et al. 2014, Oh, Edgar et al. 2015). Hence, the expansion of the Tasmania salmon industry into new growing areas, is contingent on developing a robust science-based environmental monitoring program. This monitoring is central to environmental management, good farm health and maintaining public confidence in the industry. The program must be able to provide the information required to detect ecosystems change and the influence of salmon farming at multiple spatial and temporal scales. Specifically, the program must identify and monitor the relevant ecosystems components that could be affected by salmon farming using an appropriate sampling design. This report will describe the current methods being employed to understand the effects of salmon farming inputs into Storm Bay, and where sufficient information is available, conduct a review of the ecological and statistical sensitivity of the sampling design to inform a future monitoring program. The report is an initial review synopsis that will be updated as the project progresses. The information will culminate in a full review of the project outputs to inform the future monitoring program, including recommendations for potential refinement in work package four in the last phase of this project. This review will also be informed by the biogeochemical model as it becomes available; and model simulations of biomass scenarios will identify hot spots for change and the optimal time and space scales on which to collect observations (e.g. Wild-Allen et al., 2011).