Bio-optical signatures of in situ photosymbionts predict bleaching severity prior to thermal stress in the Caribbean coral species Acropora palmata

Abstract

The identification of bleaching tolerant traits among individual corals is a major focus for many restoration and conservation initiatives but often relies on large scale or high-throughput experimental manipulations which may not be accessible to many front-line restoration practitioners. Here, we evaluate a machine learning technique to generate a predictive model which estimates bleaching severity using non-destructive chlorophyll-a fluorescence photo-physiological metrics measured with a low-cost and open access bio-optical tool. First, a 4-week long thermal bleaching experiment was performed on 156 genotypes of Acropora palmata at a land-based restoration facility. Resulting bleaching responses (percent change in Fv/Fm or Absorbance) significantly differed across the four distinct light-response phenotypes (clusters) generated via a photo-physiology-based dendrogram, indicating strong concordance between fluorescence-based photo-physiological metrics and future bleaching severity. The proportion of thermally tolerant Clade D symbionts also differed significantly across photo-physiology-based dendrogram clusters, linking light-response phenotypes and bleaching response with underlying symbiont species. Next, these correlations were used to train and then test a Random Forest algorithm-based model using a bootstrap resampling technique. Correlation between predicted and actual bleaching responses in test corals was significant (p < 0.0001) and increased with the number of corals used in model training (Peak average R² values of 0.45 and 0.35 for Fv/Fm and absorbance, respectively). Strong concordance between photo-physiology-based phenotypes and future bleaching severity may provide a highly scalable means for assessing reef corals.