## 3 Results and Discussion
### Coral Physiological Data
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**X’s (\#)** & \begin{tabular}{l} **Validation** \\ **Type** \\ \end{tabular} & **Model Type** & **R\({}^{2}\)** & **Model Details** & \begin{tabular}{l} **Most Important** \\ **Predictor** \\ \end{tabular} \\ \hline ENV(11) 1 & NA & Predictor screen & NA & 100 trees & Site (45\%) \\ \hline ENV(14) 1 & NA & Predictor screen & NA & 100 trees 1 & Site (37\%) \\ ENV(14) 1 & Val w test & NN GUI-HL(1) & 0.83 & TanH(1)-Linear(1)-Gaussian(3)-Boost(6) 2 & Longitude \\ ENV(14) 1 & 20\% HB & NN GUI-HL(1) & 0.86 & TanH(3)-Linear(1)-Gaussian(1)-Boost(11) 3 & Date \\ \hline ECO(50) 1 & NA & Predictor screen & NA & 100 trees & Turf-sediment (19\%) \\ \hline ENV(11)+ECO(50) & 10\% HB & NN GUI-HL(1) & 0.98 & \
\begin{tabular}{l} HL1: \\ TanH(4)-Linear(2)-Gaussian(3)-Boost(8) 3 \\ TanH(4)-Linear(1)-Boost(11) 3 \\ TanH(4)-Linear(1)-Boost(11) 3 \\ \end{tabular} &
\begin{tabular}{l} Reef emergence \\ Site \\ Site \\ \end{tabular} \\ ENV(14)+ECO(50) & 20\% HB & NN GUI-HL(1) & 0.92 & TanH(4)-Linear(1)-Boost(11) 3 & Site \\ \hline \hline \end{tabular}  
* See Figure 4c. 2 See Figure S1a. 3 4c: Covariates transformed. 4 Robust fit. 5 See Python script in the online supplemental data file (OSDF; Supplementary File S2). 6 See Figure S2a.  
\end{table} 
Table 3: Environmental and ecological (i.e., benthic) predictors of the coral health index (CHI; sinh-arcsinEH [SHASH]-transformed). In all models, CHI was the Y, and first, second, and third-order factorial combinations of the 11-14 environmental parameters and/ or 50 ecological predictors were considered. “ENV(14)” = those of Table 2. “ENV(11)” = the former 14 minus site, date, and time of day. The neural network (NN) GUI, models (\(n\) > 1500) were run with either one (“HL(1)”) or two (“HL(2)”) hidden layers (HL); with the former, up to 20 boosts were allowed (learning rate: 0.05-0.3). The number of activation nodes ranged from 0 to 4, the weight decay penalty was used, and up to 100 tours were considered. When test samples were used in addition to validation (Val) samples (Val w/test), the test R\({}^{2}\) is shown in the “R\({}^{2}\)” column; all other values represent validation R\({}^{2}\) (from 10–20% holdback [HB] or validation column [Val col]-based validation). Models with R\({}^{2}\) < 0.8 are instead shown in Table S2. For the predictor screening analysis, the percent variation explained by the most important predictor has been shown in parentheses. Superscripts following text in the “X’s (\#)” column correspond to factorial degree (i.e., first-order). NA = not applicable. areas with low abundance of _Millepora_ and _Alveopora_. Perhaps, like _Stylophora_ (mentioned above), corals of these genera actively compete with _P. acuta_ for space on the reef. Indeed, both fire coral and _P. acuta_ occupy similar shallow reef areas in Southern Taiwan (authors' observations). The models of Figure S1 and Figure 5 also predict that more resilient corals will be found on submerged vs. emergent reefs; whether this is simply because emergent reefs are more likely to subject corals to stress-inducing periods of aerial exposure periodically remains to be determined. A scientist or conservation practitioner planning a trip to the country, and with limited time and a need for identifying climate-resilient picollopopids, should therefore seek out deeper fringing submergent fore reefs in the eastern half of the country. In a companion work [21], the theoretical highest live coral cover was also predicted to occur on submerged fringing reefs, albeit at shallower depths (8-12 m). Although seemingly similar to those conditions projected herein for corals with the highest climate resilience, it is important to note that CHI vs. live coral cover, picolloprid coral cover, and _P. acuta_ cover were associated with R\({}^{2}\) values of 0.01, 0.02, and 0.001, respectively (all correlations being slightly negative).