Predictive habitat suitability modeling of deep-sea framework-forming scleractinian corals in the Gulf of Mexico

Abstract

Framework-forming scleractinian (FFS) corals provide structurally complex habitats to support abundant and diverse benthic communities but are vulnerable to environmental changes and anthropogenic disturbances. Scientific modeling of suitable habitat provides important insights into the impact of the environmental conditions and fills the gap in the knowledge on habitat suitability. This study presents predictive habitat suitability modeling for deep-sea (depth > 50 m) FFS corals in the GoM. We first conducted a nonparametric estimate of the observed coral point process intensity as a function of each numeric environmental variable. Next, we performed species distribution modeling (SDM) using an assemble of four machine learning models - maximum entropy (ME), support vector machine (SVM), random forest (RF), and deep neural network (DNN). We found that most important variables controlling the coral distribution are super-dominant gravel and rock substrata, SW and SE aspects, slope steepness, salinity, depth, temperature, acidity, dissolved oxygen, and chlorophyll-a. Highly suitable habitats are predicted to be on the continental slope off Texas, Louisiana, and Mississippi and the shelf and slope of the West Florida Escarpment. All the four models have outstanding prediction performances with AUC values over 0.95. DNN model performs best (AUC = 0.987). The study contributes to coral habitat modeling research by presenting unique methods including nonparametric function of coral point process intensity, DNN and SVM models that have not been used in coral SDM, post-classification model assembling, and percentile approach to determine a threshold value for classifying a suitability score map into a binary map. Our findings would help support conservation prioritization, management and planning, and guide new field exploration.