This project has described data, methods, analyses, and results to inform management procedures, and to explain the respective roles of fishing and environmental influences on saucer scallops. It has also highlighted new models for setting sustainable harvest and fishing effort. In this regard, the results demonstrated the importance of using and regularly conducting surveys to help calibrate information for fishery stock assessment and management. This has produced results to help understand and rebuild the scallop fishery. The work contained in this report has national relevance to other scallop fisheries.
6.1 Associations between saucer scallops and environmental variables
Analyses focused on correlations between scallop catch rates and SST and Chl-a. Above average winter SST was negatively associated with scallop catch rates over the months that followed. Associations with Chl-a were inconsistent, compared to earlier analyses by Courtney et al. (2015), which found a significantly high correlation of 0.85 between November standardised commercial catch rates and Chl-a in June (five months earlier). This initial correlation was based on relatively few years of observations (2002–2013; n=12) due to the relatively short time series for the available Chl-a data, which were from the MODIS Aqua satellite that was launched in May 2002. The correlation was confirmed using updated catch rates and Chl-a data from 2014, but the relationship was much weaker when additional data from 2015 and 2016 were eventually included.
The designed population models tested the long-time series of winter SSTs. The scale of increase in sea surface temperatures (SST anomalies) over years was not immense, and the Queensland scallop fishery does not appear to have endured high SST anomalies like the 2010–2012 marine heat wave event in Western Australia (Caputi et al., 2014). Results were most sensitive to the assumption of natural mortality, and related SST effects.
The SST data were confounded with abundance, with SST rising at the same time that abundance was falling. As a result, any change in abundance maybe overly ascribed to SST, rather than to other elements of the model. However, the falling abundance could well be due to another undocumented environmental effect, or a greater effect of fishing than the model estimated. The latter could be revealed by a lower natural
mortality rate than Dredge (1985).
The modelled consequence of increased SST was for reduced scallop survival, abundance and fishery yield. This result, in the context of future fishery management and harvest strategies, suggested harvest/effort control rules might need allowance for high natural mortalities, in order of 8% per month suggested by the range change in M. This is an important consideration, given that many years in the last two decades experienced above average winter SSTs.
The stock assessment can suitably describe scallops with and without the SST data. The simpler non-spatial and non-temperature model herein, together with suitable target reference points and harvest/effort control rules, could effectively manage the fishery. Verification of any management procedures is best through Management Strategy Evaluation (Punt et al., 2001; Punt et al., 2016).
In addition to this advice, estimates of natural mortality are soon to be revised, and potentially lowered, by FRDC project 2017-048. This may suggest saucer scallops are longer-lived and that measures of fishing mortality are higher than estimated previously and herein, therefore better explaining the declines in catch rates pre-1988. Lower estimates of natural mortality will change the research assumptions. Irrespective, scallops have a strong spawner-recruitment relationship, and historical fishing has played a key role in the stock’s depletion. Clearly, many years of harvest were greater than MSY reference points, and at times the data suggested extreme harvest rates. Management procedures need to ensure a sufficient winter spawning stock remains each year to support the scallop population and fishery.
6.2 Saucer scallop population size and results for management
procedures.
The results from the non-spatial model analysis suggested the 2018 region 3 spawning biomass was at 22% (95% confidence interval 17–32%) of virgin level in 1956 (Figure 33). Broader uncertainty was revealed by comparing model analyses, having different structural setups and assumptions. The greater uncertainty in results suggested the 2018 region 3 spawning biomass was below 40% of virgin levels in 1956 (Figure 34) (considering 95% confidence intervals around predictions were generally -5% and +10% on estimates). The results indicate that the scallop spawning population size was less than spawning levels for MSY at 45%.
The population results provide support for the new improved Queensland Government management procedures. For 2018, the precautionary result suggested up to 160 t of scallop meat was a sustainable harvest from region 3. After heavy fishing, minimal stock appeared to be present in 2018 off K’gari. From the reference points and model projections, around 84 000 effort units will lead to higher biomass to achieve the Sustainable Fisheries Strategy ecological objectives. The effort units equate to about 1500 boat-days, scaled to the potential fishing power of the fleet.
To improve management procedures, settings of recommended biological catches (RBC) and fishing efforts need to weigh up the uncertainty and risk levels in results, confirm a target reference point, and ensure from model projections that any RBC will produce increasing spawning biomass for the short term. This is critical given scallop spawning biomass is depleted below the MSY biomass level.
Currently, no Queensland Government policy defines a harvest control rule for rebuilding/increasing fished population biomass. The Australian Government (2007) and Sainsbury (2008) provide example guidance, and suggest rebuild times based on considering the life history characters of the fished species. The Australian Government (2007) summarises:
Typically, recovery times are the minimum of 1) the mean generation time plus ten years, or 2) three times the mean generation time. Note that the mean generation time is the average age of a
reproductively mature animal in an unexploited population. For saucer scallops, the mean generation time is about 1.5 years.
Further, for stocks above BLIM (20% spawning biomass ratio), but below the level that will produce
maximum sustainable yield (BMSY), it is necessary first to rebuild stocks to BMSY. Once stocks are
above BMSY, rebuilding shall continue toward the target biomass BTARG. However, the rate of
rebuilding may be slower and shall be determined in a way that considers the appropriate balance between short-term losses and longer term economic gains.
Reference points for MSY indicated catch rates below 25 baskets per boat-day, as a threshold, gauged poor abundance of scallops on the ground. This measure can allow fishers to judge the cost/benefit of fishing in different areas and times, as well as for management to monitor within year trends of the fishery to tailor appropriate harvest control rules.
The project, methods and results feed directly into the design of the Queensland Government’s proposed model-based harvest strategy for scallops. Use of the non-spatial age-based assessment herein, provides the best rapid and repeatable assessment methodology to produce a harvest control rule (e.g. like the rules by the Australian Government (2007)). The estimated recommended biological catches and fishing effort are the best available advice on what sustainable fishing pressure is for the scallop population.