#DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring.

Corals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress events, leading to corals losing their color due to the disruption of a symbiotic relationship with photosynthetic endosymbionts. Given the importance of corals to the marine environment, monitoring coral reefs is critical to understanding their response to anthropogenic impacts. Most coral monitoring activities involve underwater photographs, which can be costly to generate on large spatial scales and require processing and analysis that may be time-consuming. The Marine Ecology Laboratory (LECOM) at the Federal University of Rio Grande do Norte (UFRN) developed the project "#DeOlhoNosCorais" which encourages users to post photos of coral reefs on their social media (Instagram) using this hashtag, enabling people without previous scientific training to contribute to coral monitoring. The laboratory team identifies the species and gathers information on coral health along the Brazilian coast by analyzing each picture posted on social media. To optimize this process, we conducted baseline experiments for image classification and semantic segmentation. We analyzed the classification results of three different machine learning models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The best results were achieved by combining EfficientNet for feature extraction and Logistic Regression for classification. Regarding semantic segmentation, the U-Net Pix2Pix model produced a pixel-level accuracy of 86%. Our results indicate that this tool can enhance image selection for coral monitoring purposes and open several perspectives for improving classification performance. Furthermore, our findings can be expanded by incorporating other datasets to create a tool that streamlines the time and cost associated with analyzing coral reef images across various regions.

Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming.

Global climate change is a major threat to reefs by increasing the frequency and severity of coral bleaching events over time, reducing coral cover and diversity. Ocean warming may cause shifts in coral communities by increasing temperatures above coral's upper thermal limits in tropical regions, and by making extratropical regions (marginal reefs) more suitable and potential refugia. We used Bayesian models to project coral occurrence, cover and bleaching probabilities in Southwestern Atlantic and predicted how these probabilities will change under a high-emission scenario (RCP8.5). By overlapping these projections, we categorized areas that combine high probabilities of coral occurrence, cover and bleaching as vulnerability-hotspots. Current coral occurrence and cover probabilities were higher in the tropics (1°S-20°S) but both will decrease and shift to new suitable extratropical reefs (20°S-27°S; tropicalization) with ocean warming. Over 90% of the area present low and mild vulnerability, while the vulnerability-hotspots represent ~ 3% under current and future scenarios, but include the most biodiverse reef complex in South Atlantic (13°S-18°S; Abrolhos Bank). As bleaching probabilities increase with warming, the least vulnerable areas that could act as potential refugia are predicted to reduce by 50%. Predicting potential refugia and highly vulnerable areas can inform conservation actions to face climate change.