## 4 Conclusions  
Herein we devised a series of ML models known as neural networks to predict an expensive- and difficult-to-derive resilience metric, the CHI, from cheaper, easier-to-measure survey parameters. Although models featuring only very rudimentary environmental parameters that do not necessitate in-water surveys or SCUBA training (e.g., type of reef) failed to yield accuracies that were deemed high enough to have utility in environmental management and bioprospecting for resilient corals, neural networks featuring both environmental data and survey-derived benthic data were of sufficient predictive power to be useful in proactively identifying areas with high densities of climate-resilient _P. acuta_ colonies. This first approach to integrating coral reef data across multiple scales of biological organization within an AI framework for the purpose of predicting shifts in coral health consequently shows promise, though it is important to emphasize that this article describes a single species in an isolated, poorly characterized region of the Coral Triangle. A concerted effort by coral scientists elsewhere (and with additional coral species) will be needed to grow this technological and computational capacity to where it can more robustly inform reef-scale decision making. Figure 6: JMP® Pro’s ”simulator,” which allows users to input data from their study reefs and then calculate a theoretical coral health index (CHI) score. In this example, the depth was increased to 40 m, causing the predicted CHI value to increase from the local mean of 2.5 to 2.67. A manager in the Solomon Islands can modify the input parameters of reef areas of interest, and the AI will update the theoretical CHI accordingly. This GUI is hosted at [[https://coralreeddiagnostics.com/environmental-consultation-and-evaluation](https://coralreeddiagnostics.com/environmental-consultation-and-evaluation)]([https://coralreeddiagnostics.com/environmental-consultation-and-evaluation](https://coralreeddiagnostics.com/environmental-consultation-and-evaluation)). **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app122412955/sl](https://www.mdpi.com/article/10.3390/app122412955/sl)]([https://www.mdpi.com/article/10.3390/app122412955/sl](https://www.mdpi.com/article/10.3390/app122412955/sl)): a Supplementary File S1 including Figures S1 and S2 and Tables S1 and S2, and a tab-delineated file featuring all data presented in the article (Supplementary File S2, i.e., the online supplemental data file [OSDF]). Conceptualization, A.B.M. and A.C.D. ; methodology, A.B.M. and A.C.D. ; software, A.B.M. and A.C.D. ; validation, A.B.M. and A.C.D. ; formal analysis, A.B.M. and A.C.D. ; investigation, A.B.M. and A.C.D. ; resources, A.B.M., A.C.D., C.L. and C.-S.C.; data curation, A.B.M. and A.C.D. ; writing--original draft preparation, A.B.M. ; writing--review and editing, A.B.M., A.C.D., C.L. and C.-S.C.; visualization, A.B.M. and A.C.D. ; supervision, A.B.M., A.C.D., C.L. and C.-S.C.; project administration, A.B.M., A.C.D., C.L. and C.-S.C.; funding acquisition, A.B.M., A.C.D., C.L. and C.-S.C. All authors have read and agreed to the published version of the manuscript. This work was funded by the Khaled bin Sultan Living Oceans Foundation (A.C.D.). Funds from the Friendly Bear Editorial Service were used for purchasing some laboratory reagents/chemicals. Funds from Taiwan's Ministry of Science and Technology (MOST) covered the article processing charges (MOST 110-2313-B-291-001-MY3 to C.L.). Corals were collected under a CITES permit to A.B.M. (EX2014/123) and legally exported from the Solomon Islands to Taiwan under this same permit. Not applicable.