## 1 Introduction  
Models seeking to predict the fates of coral reefs generally rely on temperature [1, 2] and coral cover [3, 4, 5] alone. At most, coral diversity, algal cover, and fish biomass may also be included [6]. Although this simplistic approach underestimates the complexity of these fragile ecosystems, the fact that extended periods of abnormally high temperatures elicit bleaching in many, if not most, of the world's corals [7] means that, at km to multi-km scales, models based on temperature alone, such as NOAA's \"Coral Reef Watch\" [8], actually have quite high predictive power in terms of forecasting the onset of bleaching in certain areas. However, at smaller spatial scales (cm-km), more heterogeneity may be seen [9], particularly when markedly different reef assemblages exist within close proximity to one another. For instance, an acroporid-dominated reef would likely bleach before and/or more severely than a nearby reef dominated by massive corals [10], though the actual genera of the coral present are excluded from current predictive models. Furthermore, the physiological responses of individual coral colonies do not feature in current models of coral health and resilience. This is despite the fact that even clonemates in close proximity, and presumably exposed to similar seawater conditions, may show distinct responses to environmental change due to, for instance, differences in dinoflagellate communities within the host antozoans' tissues [11; 12]. Microbial activity, host autofluorescence, and a diverse array of other physiological properties displayed by these multicompartmental holobionts can influence their capacity for acclimatization [13; 14] and certainly must account for at least some of the variability in resilience observed during bleaching and disease surveys [15]. It follows, then, that environmental data (e.g., temperature), ecological data (i.e., benthic survey), and physiological data from the corals themselves could be used to devise superior models (vs. temperature alone) that would allow us to predict where resilient corals may be located (Figure 1), yet to date no such comprehensive model has been constructed. The current approach for doing so is generally post-hoc; resilient corals are found in situ during post-bleaching surveys or in the laboratory (inevitably entailing a large amount of research effort to deduce). Given that only a minuscule fraction of Earth's coral reefs has been surveyed, these reactionary approaches for identifying resilient corals do not yield conservation-oriented data within a timeframe that is commensurate with the dramatic rate at which reefs are being lost. In other words, by the time we find the resilient reefs, many others will have perished. Could we expedite our search for resilient corals by leveraging the immense quantity of data the marine biology field has acquired over these past few decades? As a step towards this goal of \"coral reef triage\" [16; 17], we explored herein a subset of environmental+ecological+coral physiological data collected during the Solomon Islands leg (Figure 2) of the world's largest-ever coral reef survey, the Khaled bin Sultan Living Oceans Foundation's (LOF) \"_Global Reef Expedition_\" (GE; [18; 19]). We hypothesized that we could probe this \"molecules-to-satellites\" dataset with an artificial intelligence (AI) developed in JMP(r) Pro (ver. 16-17; Cary, NC, USA) that would create machine learning (ML) and other predictive models for forecasting the locations and/or environmental conditions in which resilient corals will be found. This involved, for the first time ever, integrating seawater quality, satellite imagery, reef benthic survey, and physiological data (molecular biomarker-derived \"stress tests\") from biopsies of the model reef coral _Pocillopora acuta_ sampled from diverse habitats in this vastly under-studied Coral Triangle nation [20]. Figure 1: A schematic of the approach.