Climate and fisheries

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Integrated modeling

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Animal movement

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Life-history

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Climate and Fisheries

  • Climate is a major driver of the productivity and distribution of fishery resources. If the mechanisms by which climate affects living marine resources are correctly identified and quantified, then models incorporting climate effects should have a higher predictive capacity than statistical models with no climate considerations. However, mis-identification of these mechanisms can lead to even more erroneous predictions underlining the need for caution when using climate variable in fishery projections. Building upon life-history and macro-ecology theories, we have been able to mechanistically show that temperature drives growth, maturity, mortality and recruitment in the American lobster (Le Bris et al. In Prep).
  • Using these relationships into a population model, we have successfully reconstructed the dynamics of the two US lobster stocks (Gulf of Maine and Southern New England) and demonstrated that temperature, along with management, is a major driver of the productivity of American lobster stocks (Le Bris et al. In Prep).
  • Integrated modeling

  • The combined effects of climate and fishing are rarely considered when projecting the future productivity of living marine resources. In this work, we integrate climate projections, population dynamics and fleet dynamics into a simulation model to identify the management measures that can promote the resiliency of the American lobster fishery to climate change. This work is in progress but should soon lead to interesting results!

  • Animal movement

  • High resolution fishery (semi) independent data recorded by electronic archival tags (data-storage tags or pop-up satellite tags) can provide unique insights into the annual migratory behavior of fish. Often however, positions are not directly available (e.g. gps signals are quickly attenuated in the water) and need to be inferred from recorded environmental variables (depth, temperature). During my Ph.D. thesis I developed a geolocation model to track demersal fish species tagged with electronic tags in the Gulf of St. Lawrence. With my collaborators, we have successfully used this model to show that migratory behavior of Atlantic cod (Le Bris et al. 2013a) and of Atlantic halibut (Le Bris et al. In Prep) is more diverse than previously recognised. Geolocation results have also helped us in identify putative spawning areas for Atlantic halibut in the Gulf of St. Lawrence (Le Bris et al. In Prep). I am currently improving this geolocation model and I plan to make it open-source for other researchers using electronic tags with demersal species in the Gulf of St. Lawrence.

  • Fish daily geolocation can also be used to improve the spatiotemporal design of fishery closures. For instance, post-analysis of geolocation estimates of Atlantic cod showed that the spring spawning closure in the Gulf of St. Lawrence would be more efficient if it was moved south and if the closure start and end dates were set earlier in the spring (Le Bris et al. 2013b).

  • Life-history

  • Variation in life-history traits affects population productivity and has implications for the sustainable management of fisheries. Using a simple population dynamics model, we showed that high individual fecundity is more important for population resilience than maternal effects or batch spawning. The model also demonstrates that a slot fishery increases population resistance to overfishing but that a minimum size limit only benefits more the population recovery (Le Bris et al. 2015).

  • Life-history traits respond to environmental changes and fishing pressure and these responses vary across scales. Using data from sea-sampling conducted with harvesters, we demonstrated that both increase in temperature and fishing reduced the size at maturity in the American lobster. These responses in size at maturity varied across spatiotemporal scales, potentially reflecting scale dependent processes such as phenotypic plasticity and local adaptation (Le Bris et al. 2016).