Despite hundreds of hours in the water and dedicated efforts by 20+ scientists, we canvassed less than 1% of Palau’s coral reefs; is it possible to use knowledge on these reefs to predict what the 99% we did NOT survey may look like? And specifically, could we leverage the data to project where those reefs with the highest coral cover may be? This is the subject of a recent article based primarily on these data.
By having trained a machine-learning algorithm in JMP Pro, we can at least provide you with some clues. The model below is associated with a validation data R2 of around 0.7. By plugging in known coordinates and reef conditions, you can get an estimate of what the coral cover may be. *If there is an error message, it means I need to log in again, so please contact me.
We have also shown the results of a desirability analysis, in which the AI was commanded to simulate the conditions associated with the highest coral cover. Those looking for the most coral-rich habitats, then, should consider reefs with the following properties:
And here is another neural network modeling the characteristics of resilient pocilloporid corals in Palau (Mayfield & Dempsey, under review). Enter in data for your sites to receive an estimate of the local corals’ “coral health index” (CHI) scores, which range from 0 (dead) to 5 (immortal).
A relatively “simple” neural network for predicting whether a coral would demonstrate resilience or not (“sick”) based on assessment of 22 environmental and ecological (namely benthic survey) parameters, followed by a desirability analysis showing conditions predicted to be associated with a resilient coral at 99% certainty.
Coral health index (CHI) plotted across the entirety of Palau’s western (leeward) side, as well as plots depicting absence of correlation between CHI and coral abundance (from Mayfield & Dempsey, under review).