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Completed Project

Modeling the Ventilatory Behavior of the Embryos of Little Skates, Leucoraja erinacea

Connor McShaffrey ’21 and Professor John Long (Biology and Cognitive Science)

The embryos of little skates face a daunting task: develop in their egg case at the bottom of the ocean for up to a year. As they develop, they must exchange respiratory gases with the world outside, and they do so by actively ventilating their egg with a specialized embryonic appendage at the tip of their tail that pumps water in through any one of the egg capsule’s four tubular tendrils. This mechanical work takes energy, and the only nutrition that they have while in the egg is their yolk, which must also power metabolism and growth. Thus, ventilation presents the embryo with a mission-critical cost-benefit trade-off between the energy required and the functional benefit of the behavior. What are behavioral algorithms that the embryos employ to ventilate successfully? In embryos at different stages of development, we observed changes in their behavior-environment relationships that may correlate with the gradual expansion of their Umwelt, the subjective sensory information that they integrate from their environment. One challenge is that the egg capsule is initially closed to the environment: no ventilation is possible. Each tendril has a small slit that is blocked by a jelly-like substance; only after the jelly is dissolved by enzymes from the embryo can that tendril be used for ventilation. In the intermediate case, where some but not all of the tendrils are open, the embryo must decide which to use. Pumping in a closed tendril expends energy without a functional benefit of gas exchange. How might an embryo, with an incomplete Umwelt, decide? We propose a simple stochastic model. Specifically, the on-going decision of which tendril to use is randomized. We show how this stochastic model may allow for sufficient long-term gas exchange with little extra cost compared to the deterministic model that assumes a complete Umwelt and perfect knowledge of the system.