## 2 Materials and Methods  
### Approach and Terminology  
We sought to predict the coral response to elevated temperature exposure (Figure 2a) as defined by a response metric called the \"colony health designation\" (Figure 2b-e); this parameter could be bleaching-susceptible, bleaching-resistant, or intermediate based on colony color score [14] decreases of \(\geq\)2, 0, and 1, respectively. The colony health designation is distinct from the \"fragment health designation\" defined previously [11], which was instead the health status of a biopsy at the time of sampling. A bleaching-susceptible coral (Figure 2c,e) could be characterized by three of the four fragment health designations: (1) healthy control (no color change while at a temperature below the mean monthly maximum [MMM]), (2) sub-lethally stressed (color score decrease of 1 while at a temperature \(\geq\) MMM + 1 \({}^{\circ}\)C), or (3) actively bleaching (color score change \(\geq\)2 at a temperature \(\geq\) MMM + 1 \({}^{\circ}\)C). A bleaching-resistant coral (Figure 2b,d) could be a healthy control, present a sub-lethally stressed phenotype, or be deemed \"high-temperature-acclimating\" (no color change while at a temperature \(\geq\) MMM + 1 \({}^{\circ}\)C; pooled with healthy controls for most analyses); it would never be associated with an actively bleaching phenotype. Although not depicted in Figure 2, an intermediately bleaching-susceptible coral could generate a biopsy of any of the four fragment health designations.  
The capacity to delineate coral resilience in situ was grown through the following steps. First, we used partial least squares-discriminant analysis (PLS-DA) to differentiate lab coral biopsies of three fragment health designations (healthy controls & high-temperature-acclimating samples were combined for this analysis). Predicting the bleaching susceptibility of a perceivably healthy coral colony (step 2) was more challenging and involved training an AI with data from \"healthy\" biopsies from corals that might later respond differently to high temperatures. To this end, an AI was first trained with proteomic data from samples harvested earlier in the temperature challenge study (days 0-5; described below) to make predictions on their bleaching susceptibility (typically manifested between days 10 & 30) based on their proteomic signatures. We next used PLS-DA to differentiate actively bleaching, sub-lethally stressed, and healthy control biopsies collected across a 2019 bleaching event (described below) via their proteomic profiles. These models ensured that the AI could distinguish corals of the various fragment health designations.  
For step 4 (the primary goal), we took three approaches to predict the fates of field corals based on their protein biomarker levels. First, data exclusively from the lab study [11] (Table 1) were used to train predictive models (\"CHD-lab-CHD-field\"), which were then tested with data from field corals sampled across a bleaching event (Figure 1 and Table 2). Secondly, field proteomic data exclusively were used to train predictive models of colony bleaching susceptibility (\"CHD-field-CHD-field\"). Thirdly, we combined both lab and field sample data into an ensemble model for predicting field coral behavior.