Abstract
Fishing reduces stock size and shifts demographics, and selective mortality may also lead to evolutionary changes. Previous studies suggest that traits may change evolutionarily because of fishing on decadal time scales. Here we examine the potential bioeconomic impacts of fishing-induced evolutionary change. We used a life-history model with stock dynamics based on evolving maturation age, which has consequences for size-at-age, coupled with a fishing module that describes costs and economic yield. Size-dependent natural mortality and trawl-like fishing mortality are drivers of selection, and in the analysis we varied fishing mortality and size-selectivity of the fishing gear to determine trait evolution as well as economic yield. Comparison of two scenarios — allowing for evolution and assuming no evolution — shows that under current size selectivity, the fishing regimes generating maximum economic yield are not different when evolution is accounted for. However, ignoring evolution overestimates long-term yield under optimal fishing regimes and underestimates resilience to overfishing. Whether fishing-induced evolution matters for management strategies depends on size selectivity, stock state, how it acts on specific traits, and its sensitivity to the assumed discount rate, calling for a cautious use of net present value as sole criterion for management of evolving resources.
Highlights
Management of living resources relies heavily on models that predict future consequences of natural dynamics and management actions
Our model predicts that evolution of age at maturation will affect yield and profit and has bioeconomic consequences, but the quantitative bias introduced by ignoring evolution depends on the stock’s initial state and fishing strategy and is small for most parameter values even at low discount rates
Models that ignore evolution tended to overestimate yield and profit for stocks managed near their maximum economic yield (MEY, defined as maximum net present value (NPV))
Summary
Management of living resources relies heavily on models that predict future consequences of natural dynamics and management actions. These models range from being conceptual and simple to being complex and data-hungry, but all depend on assumptions about how the real world works. The predictive power of model projections is often overestimated (Brander et al 2013) This applies for uncertainty arising from the use of data or the lack of data, as well as from the properties of the mechanisms included (or ignored) in a model. A systematic bias may be introduced, in longterm reference points like maximum sustainable yield (MSY) or maximum economic yield (MEY)
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