A Review of Statistical and Machine Learning Approaches for Coral Bleaching Assessment

TL;DR

This review summarizes statistical and machine learning methods used to assess coral bleaching, highlighting their roles in understanding environmental impacts and guiding reef management strategies.

Contribution

It provides a comprehensive overview of existing models and discusses future research directions in applying data-driven approaches to coral bleaching assessment.

Findings

Statistical models like regression and Bayesian methods are used to analyze environmental factors.

Machine learning techniques such as random forests and SVMs effectively detect nonlinear relationships.

The review identifies gaps and proposes future research directions in coral bleaching modeling.

Abstract

Coral bleaching is a major concern for marine ecosystems; more than half of the world's coral reefs have either bleached or died over the past three decades. Increasing sea surface temperatures, along with various spatiotemporal environmental factors, are considered the primary reasons behind coral bleaching. The statistical and machine learning communities have focused on multiple aspects of the environment in detail. However, the literature on various stochastic modeling approaches for assessing coral bleaching is extremely scarce. Data-driven strategies are crucial for effective reef management, and this review article provides an overview of existing statistical and machine learning methods for assessing coral bleaching. Statistical frameworks, including simple regression models, generalized linear models, generalized additive models, Bayesian regression models, spatiotemporal models, and resilience indicators, such as Fisher's Information and Variance Index, are commonly used to explore how different environmental stressors influence coral bleaching. On the other hand, machine learning methods, including random forests, decision trees, support vector machines, and spatial operators, are more popular for detecting nonlinear relationships, analyzing high-dimensional data, and allowing integration of heterogeneous data from diverse sources. In addition to summarizing these models, we also discuss potential data-driven future research directions, with a focus on constructing statistical and machine learning models in specific contexts related to coral bleaching.