Spatio-temporal scaling of environmental selection in pelagic predators
Over the last couple decades, scientists have gathered many tracking datasets of marine pelagic predators. These organisms perform large scale, ocean wide movements and migrations. These datasets vary greatly, in terms of size, physiology or trophic level of the animals tracked. I will use these datasets to understand the impact of observational grain size on organisms location predictions, and the effect that different biological scales (animal size, trophic level...) can have on these predictions. In addition to tracking datasets, I will use Eulerian NASA satellite products of the environment (e.g. temperature, light attenuation coefficient, chlorophyll a...) and computed Lagrangian features (Lagrangian Coherent Structures, a measure of aggregation in the oceans) from the HYCOM model. I will investigate whether considering Eulerian and/or Lagrangian variables increases our understanding of organisms movements.
Diel Vertical Migrations
Diel Vertical Migration (DVM) is the daily movement of organisms up and down the water column. It is believed to be the biggest migration of the planet in terms of biomass. Simply put, organisms hide in darkness during daytime to avoid being eaten, and migrate to the surface at night, where more food is available. The predators of these migrating organisms follow them as well, to maximize their encounter rates. As a consequence, the optimal DVM pattern of each organism depends on its prey, predators and conspecifics in the system but also on their migrating strategy. I use game theoretic principles to explore the optimal position of all organisms of a food-web at once.
This is not only interesting in itself, but it has also consequences on the world's biogeochemical cycles. Organisms feeding at the surface and migrating down actively bring carbon to depths of 100m or more, where it is respired and excreted by the migrator itself, or consumed by their predators. This represents the active part of the biological pump, which has been estimated to represent between 16 and 30% of the total export flux associated with the biological pump.
Long-distance migrations
For animals migrating across entire ocean basins (sea turtles, whales...), a good management of arrival time and energy expenditure can be crucial parameters affecting their fitness. A good optimization of these parameters is key to reproductive success or good feeding opportunities. As these animals navigate in an environment subject to currents, the most efficient migration route is not necessarily a straight line but a path making a smart use of these currents. The most efficient path varies depending on the importance of an early arrival or the necessity to save energy. I developed a theoretical model that reproduces migration routes when provided with a current field, a time/energy trade-off to optimize and a risk-sensitivity parameter accounting for the level of risk that an individual is willing to take to accomplish a potentially successful migration. Then, I used GPS tag data to reconstruct the trade-offs that animals are optimizing for based on their migration routes.
Among other results, we saw that different humpback whales were optimizing for very similar trade-offs, whereas green turtles displayed a huge diversity of strategies between individuals. This might be explained by a difference in maternal care, where migration routes are taught during several years in whale pods, whereas sea turtles need to learn their migration routes by themselves.
Over the last couple decades, scientists have gathered many tracking datasets of marine pelagic predators. These organisms perform large scale, ocean wide movements and migrations. These datasets vary greatly, in terms of size, physiology or trophic level of the animals tracked. I will use these datasets to understand the impact of observational grain size on organisms location predictions, and the effect that different biological scales (animal size, trophic level...) can have on these predictions. In addition to tracking datasets, I will use Eulerian NASA satellite products of the environment (e.g. temperature, light attenuation coefficient, chlorophyll a...) and computed Lagrangian features (Lagrangian Coherent Structures, a measure of aggregation in the oceans) from the HYCOM model. I will investigate whether considering Eulerian and/or Lagrangian variables increases our understanding of organisms movements.
Diel Vertical Migrations
Diel Vertical Migration (DVM) is the daily movement of organisms up and down the water column. It is believed to be the biggest migration of the planet in terms of biomass. Simply put, organisms hide in darkness during daytime to avoid being eaten, and migrate to the surface at night, where more food is available. The predators of these migrating organisms follow them as well, to maximize their encounter rates. As a consequence, the optimal DVM pattern of each organism depends on its prey, predators and conspecifics in the system but also on their migrating strategy. I use game theoretic principles to explore the optimal position of all organisms of a food-web at once.
This is not only interesting in itself, but it has also consequences on the world's biogeochemical cycles. Organisms feeding at the surface and migrating down actively bring carbon to depths of 100m or more, where it is respired and excreted by the migrator itself, or consumed by their predators. This represents the active part of the biological pump, which has been estimated to represent between 16 and 30% of the total export flux associated with the biological pump.
Long-distance migrations
For animals migrating across entire ocean basins (sea turtles, whales...), a good management of arrival time and energy expenditure can be crucial parameters affecting their fitness. A good optimization of these parameters is key to reproductive success or good feeding opportunities. As these animals navigate in an environment subject to currents, the most efficient migration route is not necessarily a straight line but a path making a smart use of these currents. The most efficient path varies depending on the importance of an early arrival or the necessity to save energy. I developed a theoretical model that reproduces migration routes when provided with a current field, a time/energy trade-off to optimize and a risk-sensitivity parameter accounting for the level of risk that an individual is willing to take to accomplish a potentially successful migration. Then, I used GPS tag data to reconstruct the trade-offs that animals are optimizing for based on their migration routes.
Among other results, we saw that different humpback whales were optimizing for very similar trade-offs, whereas green turtles displayed a huge diversity of strategies between individuals. This might be explained by a difference in maternal care, where migration routes are taught during several years in whale pods, whereas sea turtles need to learn their migration routes by themselves.
