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

2025-11-15

AI Summary

Coral bleaching has emerged as the primary driver of global coral reef degradation, with over half of all reefs experiencing bleaching or mortality in the past three decades—largely attributable to rising sea surface temperatures and multidimensional environmental stressors. This paper presents the first systematic review of statistical and machine learning methods applied to coral bleaching assessment, encompassing regression models, generalized linear, Bayesian, and spatiotemporal models, random forests, support vector machines, and spatial operators. We critically evaluate each method’s capabilities and limitations in nonlinear modeling, high-dimensional variable handling, and integration of heterogeneous, multi-source data, thereby elucidating the spatially and temporally heterogeneous impacts of environmental stressors. The study bridges a critical gap in data-driven coral bleaching modeling by proposing an ecologically informed, scalable modeling framework. This framework advances methodological foundations for accurate predictive analytics and adaptive conservation strategies. (149 words)

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Reviewing statistical and machine learning methods for coral bleaching assessment
• Addressing scarce literature on stochastic modeling approaches for bleaching
• Exploring environmental stressors' influence through data-driven reef management strategies

Innovation

• Statistical models assess coral bleaching environmental stressors
• Machine learning methods detect nonlinear relationships in data
• Spatiotemporal models integrate heterogeneous environmental data sources