From data rich to data-limited harvest strategies—does more data mean better management?
Harvest strategies (HSs) have been applied to many data-rich fisheries, and are now increasingly being applied in data-limited situations. These have been evaluated using simulation frameworks, including management strategy evaluation (MSE), but few studies have considered the full spectrum from data-rich to data-limited strategies, in the context of the risk-cost-catch trade-off. This involves evaluating whether the cost of implementing a HS, the risk to the resource and catch taken from the resource have been appropriately balanced, given the value of the resource. HSs implemented for Australian Commonwealth fisheries were placed in eight tiers, ranging from data-rich to data-limited, and their performance evaluated using an MSE based on a full end-to-end ecosystem model. Generally, the risk to the resource increased as fewer data were available, due to biases in the assessments and slow response times to unexpected declines in resource status. The most data-rich tiers maximize discounted catches and profits over a 45-year projection period. However, the opportunity costs response is variable, and shows that the benefit of short-term high catches have to be compensated by resource recovery in the long term. On average, more data leads to improved management in terms of risk of being overfished and not reaching a target, but this requires lower initial catches to recover the resources and lower short-term discounted profits.