# Variable Responses to a Marine Heat Wave in Five Fringing Reefs of Southern Taiwan
## 2 Materials and Methods
### Statistical Analyses  
All statistical analyses were undertaken with the JMP\({}^{\circledR}\) Pro 16-17 suite (Cary, NC, USA). We used two-way ANOVA to assess the effects of survey time (month; df = 4), site (df = 4), time \(\times\) site, and transect(site \(\times\) month) on coral cover and bleaching percentage (Table S1); the latter was treated as a random effect in a mixed model. Note that since the 2020 bleaching event was so much more severe than the 2021 one, we typically parsed these univariate ANOVAs by survey year except for when directly testing recovery-associated processes. For certain analyses, we considered depth as a factor: four shallow sites (3 m) vs. one deeper site (7 m). We also tested for the effects of upwelling (df = 1: upwelling [\(n=4\)] vs. non- upwelling [\(n=1\)]) and reef type (df = 1): natural (\(n=3\)) vs. artificial (\(n=2\); ILI & OL; see Table 2.). Figure 2: Sea surface temperature (SST; \({}^{\circ}\)C; left \(y\)-axis), degree-heating weeks (DHWs; right \(y\)-axis), and coral bleaching alert benchmarks in Southern Taiwan from January 2019 to December 2020, as calculated by the United States’ National Oceanic and Atmospheric Administration’s Coral Reef Watch (CRW; https:// coralreefwatch.noaa.gov/; accessed on 31 January 2021). The horizontal orange lines at four and eight DHWs reflect the window within which bleaching typically begins to occur based on CRW’s historical predictions. Figure 3: Annual temperature profiles of the fives study sites _in situ_ and overall benthic composition. Both raw temperature (left y-axes of (**A–E**)) and degree-heating weeks (DHWs; right y-axes of (**A–E**)) have been shown. In the stacked bar charts (**F–J**), the two non-sclercatinian reef-builders (_Millepora_ spp. & _Heliopora_ sp.) have been grouped with the hard corals: “non-bleached corals” (light red with hatches) and/or “bleached corals” (solid red). Hard coral cover (left y-axes of (**K–O**)) and the coral/algae ratio (right y-axes of (**K–O**)) are shown as box plots and black lines, respectively; the shading about the latter corresponds to SEM, and the upper- and lowercase letters correspond to [PERSON]’s _post-hoc_ differences for hard coral cover and the coral/algae ratio, respectively (\(p<0.02\)). With the exception of Inlet-outside (ILO; (**J.N**)), all benthic communities changed in structure over time (Table 3). For the coral community composition (to genus level), a similarity matrix was constructed using Euclidean distances derived from raw percent abundance values. After determining the minimum number of multi-dimensional scaling (MDS) dimensions with a stress <0.1, a non-parametric multivariate ANOVA (NP-MANOVA) was undertaken with the 2-6 dimensions (stress = 0.04-0.1) as Y and the same model as described for coral cover as the predictors. JMP's \"predictor screen,\" which is based on random tree bootstrap forests (\(n\) = 100 trees), was used to identify the univariate trend that contributed the most variation to the multivariate effects documented by NP-MANOVA. This analysis was repeated for each site in isolation, albeit with only month as a fixed effect. In these analyses, we also tested for the effects of transect and transect(month). We also considered the effects of survey month, year, site, upwelling, and reef type on overall percentages of bleached tissues to determine which most strongly affected the bleaching response via univariate NP-ANOVAs, student's \(t\)-tests, or Wilcoxon tests (depending on the number of factors & the distribution + variance of the data); in general, this analysis was carried out separately for each of the two survey years since the degree of bleaching in 2020 was far greater than in 2021 (when few DHWs were documented; Figure S1). To construct a more comprehensive, holistic model of the environmental drivers of the coral community assemblage changes documented by NP-MANOVA, we used partial least squares (PLS) with the environmental predictors as X's and the 83 benthic categories as Y's.