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Envisioning the future of coral reefs with machine-learning

Anderson B. Mayfield, Ph.D.

Coral reef fate is currently modeled on temperature and coral abundance alone. I hypothesize that we can make more robust predictions by not only considering these benchmarks, but by also factoring in data from the framework-building corals themselves. After all, actuaries do not estimate our own lifespans by looking exclusively at the sizes of the cities in which we live, but instead consider our respective physiologies, as well. To this end, I have spent the better part of 20 years in both the Pacific Rim and Caribbean regions growing the datasets needed to devise more holistic models for coral reef ecosystem forecasting by conducting 1) field surveys, 2) laboratory tank experiments (microcosms & coral reef mesocosms), 3) molecular and cellular biology benchwork (“multi-‘Omics”), and 4) bioinformatic pipeline development. I then used the resulting “molecules-to-ecosystems” datasets to train incredibly sophisticated machine-learning models capable of accurately predicting coral bleaching susceptibility and reef resilience. With these same datasets, I have been formulating a different series of machine-learning-based decision-making schematics that instead dictate the optimal solution for preventing coral extinction (be it via stress-hardening, cross-breeding, transplantation, or other means). If conditions in the ocean are no longer amenable to target species survival regardless of the intervention measure(s) taken, then we can instead employ the coral biopreservation tools (e.g., ex situ husbandry & cryopreservation) my colleagues and I have developed over the years.

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