## 3 Results and Discussion  
### Overview of the Field Coral Proteomic Dataset  
Sixteen proteins were quantified in all 36 field samples; for models in which only the July and August data were incorporated, 28 proteins could instead be used for model-building since the degree of overlap was higher. When viewing a PLS-derived correlation loading plot of data from the 16 common proteins (Figure 3), there is some degree of partitioning among corals of the three colony health designations. However, one bleaching susceptible colony fell within the bleaching-resistant data space, and the first two PLS factors encompassed <50% of the variation; this approach is useful for exploring patterns, then, but _not_ for making predictions. There was a significant effect of the fragment health designation on the host coral protein profile (Table S1 and Figure 4c); this was driven in part by the 50% higher concentration of protein OFAVBQ_DN223604_cl_gl_i2 in healthy control corals vs. all other phenotypes. The sequenced peptide's identity could not be resolved bioinformatically, though it features multiple BRCA2 repeats. Given that it contributed >40% of the variation across fragment health designation and was down-regulated in all but healthy corals, its identity should be uncovered in the near future.  
Figure 3: Partial least squares correlation loading plot. The 16 proteins measured in all field samples were the model Y’s (blue dots) while the following were considered as putative drivers of variation (green dots, of which several have been labeled): reef site (Cheeca Rocks, The Rocks, Little Conch, & Crocker Reef), shelf (inshore vs. offshore), date (Jul. & Dec. only), host genotype (black(a), black(f), black(g), black(i), black(r), skyblue, darkred, lightgreen, or unknown), fragment health designation  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Model Abbreviation**} & \multicolumn{2}{c}{**CHD-Lab\textgreater{}**} & \multicolumn{1}{c}{**CHD-Lab\textgreater{}CHD-Field(a)**} & \multicolumn{1}{c}{**CHD-Lab\textgreater{}CHD-Field(c)**} \\ \hline Model prediction goal & CHD-lab & CHD-field & CHD-field \\ \hline Training sample size (lab corals) & 14 & 19 & 19 \\ \hline Validation data type (\#samples) & Lab corals (5) & Field corals (12) & Field corals (24) \\ \hline Validation data months & Not applicable & Jul. & Jul. and Aug. \\ \hline Training proteins (\#) & 86 & 5 & 4 \\ \hline Model details & TanH(3)-Boost(7) \({}^{\text{a,b}}\) & TanH(1)-Linear(1)-Gaussian(3) \({}^{\text{c}}\) & TanH(2)-Linear(2)-Gaussian(4) \\ \hline Accuracy (\%) \(\pm\) std. dev. & 100\(\pm\)1 (n = 20) & 86 \(\pm\) 10 (n = 40) & 82 \(\pm\) 7 (n = 40) \\ \hline \hline \end{tabular} \({}^{\text{a}}\) A simpler generalized regression model (ridge) yielded comparable accuracy. \({}^{\text{b}}\) See Figure 4b for the unboosted TanH(3) diagram. \({}^{\text{c}}\) See Figure 4d.  
\end{table}
Table 3: Neural networks trained exclusively with lab data with accuracies > 80%. The model prediction goal was either lab colony health designation (CHD; i.e., “CHD-lab”) or field coral CHD. A subset of all 86 proteins was used in all but the first model because different proteins were sequenced in the lab and field samples (5 proteins found in all lab & pre-bleaching corals sampled in situ [4 when factoring in additional sampling times]). See Supplementary File S2 for models with accuracies < 80%. Sample sizes in the bottom row represent number of simulations (necessary for neural networks featuring multiple tours).  
(healthy control, high-temperature-acclimating [HTA], sub-lethally stressed, & actively bleaching), and colony health designation (CHD; bleaching-susceptible [BLS], bleaching-resistant [BLR], & intermediate [INT]). The samples themselves are labeled by their CHD (red, blue, & green for BLS, INT, & BLR, respectively) and include only 13 biopsies since only training data are shown.