## 1 _Editorial_
We sought to leverage breakthroughs in both coral reef diagnostics [13; 14; 15] with the thoroughly characterized nature of many reefs of the Florida Keys [16; 17; 18] whereby we could use the power of hindsight (i.e., \"hindcasting\") to ensure that the artificial intelligence (AI) trained in JMP(r) Pro (USA) could accurately predict the likelihood of future bleaching in select coral colonies from reefs of varying oceanographic conditions that were tracked over space and time. Concentration levels of a subset of only a handful of proteins involved in lipid trafficking, immunity, and other key cellular pathways could be used to predict the likelihood of bleaching in over 90% of the date-tracked colonies based on having trained with AI with a mix of both laboratory [15] and field coral protein biomarker data. Although this proteomic and AI approach for proactively identifying resilient Floridian corals was highly accurate, the endeavor was costly, both in terms of human hours and research funding [19]. Such a method would be poor for developing countries, where the vast majority of coral reefs are located. To this end, [PERSON] et al. [20] built upon a series of prior research on profiling a molecular-physiological response metric known as the \"coral health index\" (CHI) in a wide range of coral reef habitats in the grossly understudied nation of the Solomon Islands. Although the aforementioned _O. faveolatta_ project in Florida was mainly looking to model temperature effects, the Solomon Islands project factored a far wider region of environmental variables that were predicted to play a role in driving variation in the bleaching response, not limited to light, salinity, exposure, type of reef, dinoflagellate community composition, benthic community structure, and others [21; 22; 23]. Based on running a large number of simulations by the AI, in which portions of the dataset were randomly \"hidden\" from the AI, a number of predicted models for the CHI were constructed that were of sufficient power to accurately predict the whereabouts of other polliloporld corals of the region (i.e., those whose CHI data were not incorporated in initial AI training). This means that scientists can use the resulting GUI (web link cited in the article) to plan their research trips to the country, particularly in instances in which they seek to identify highly resilient corals (i.e., those with high CHI values) for use in restoration initiatives or even ad hoc experimental work. Profiling coral health across space and time, as outlined in the prior sections, is an important step towards adaptive management in the face of climate change and our growing anthropogenic footprint. However, perhaps we should take a step back and ask: \"Are we even aware of all the places where corals and other marine invertebrates are common?\" Only relatively recently have mesophotic coral ecosystems (MCEs) been identified (typically those at depths ranging from 30 to 150 m, where corals still depend on light [24]), and, to date, no such characterization of these ecosystems has been undertaken in Taiwan's islands within the Kuroshio current, namely Lanyu (Orchid Island in English). We sought to close this knowledge gap in our published work herein [25], using remotely operated vehicles (ROVs) off Lanyu's coastline. We observed abundant soft corals (families Nephtheidae, Ellisellidae, and Nidaliidae), as well as select scleractinian families in the upper zone. The lower zone featured these same soft coral families, with the addition of Octocorallia and Plexauridae; some hexacorallians were also observed. A lipid analysis found that MCE corals possess lower lipid content (specifically neutral lipids and triacylglycerols) compared to their shallow-water counterparts. Additionally, the mesophotic scleractinian samples had higher polyunsaturated fatty acid ratios, suggesting that, unsurprisingly, they rely more on heterotrophy.