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).