Size-selective fishing leads to trade-offs between fishery productivity and reproductive capacity

ECOSPHERE(2020)

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摘要
Most fishing is inherently size-selective, in that fishers preferentially select a subset of the population for harvest based on economic incentives associated with different-sized fish. Size-selective fishing influences the targeted population and fishery performance in multiple ways, including changing the reproductive capacity of the target population and altering fishery yield. Understanding how social-ecological variability, including size selectivity, affects target species populations is critical for fisheries management to optimize the benefits of fisheries and the ecological impacts on target populations. In this study, we used yield per recruit, spawning stock biomass per recruit, and length-based spawning potential ratio models to explore how a range of size selectivity scenarios affect fishery and population productivity for Mexican chocolate clams, Megapitaria squalida, in Loreto, Baja California Sur, Mexico. We found that alternate slot limits result in trade-offs between fishery yield and reproductive productivity of the target population. A more restrictive slot limit reduced the proportion of the population available to harvest, resulting in higher reproductive capacity of the population, compared to a less restrictive slot limit, conditional on the rate of fishing mortality. In the long run, a more restrictive slot limit will likely lead to a higher number of recruits, larger stock size, and higher long-term fishery yield relative to a less restrictive scenario. Our findings highlight that how people fish matters, perhaps as much as the quantity of fish harvested; size-selective fishing that aligns with the life history of target populations and stakeholders' goals is critical to sustaining fisheries and the valuable food and livelihoods they provide.
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data-limited fisheries,Megapitaria squalida,Mexico,size selectivity,slot limits,small-scale fisheries,social-ecological variability
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