Bio-optical signatures of insitu photosymbionts predict bleaching severity prior to thermal stress in the Caribbean coral species Acropora palmata
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 photophysiological metrics measured with a low-cost and open access bio-optical tool. First, a four-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 phenotypes generated via a photophysiology-based dendrogram, indicating strong concordance between fluorescence-based photophysiological metrics and future bleaching severity. 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 0.42 and 0.33 for Fv/Fm and absorbance, respectively). Strong concordance between photophysiology-based phenotypes and future bleaching severity may provide a highly scalable means for assessing reef corals.