Machine Learning Analysis of Coral Complexity and Reef Fish Resilience

Abstract

Introduction: Structural rugosity and physical spatial architecture in biogenic coral ecosystems govern animal shelter, prey evasion, and community carrying capacities. As climate-induced bleaching and environmental pressures degrade physical reef frameworks, determining how ecological architecture stabilizes teleost communities is crucial for ecosystem management. Materials & Methods: In situ ecological assessments were conducted across 48 reef habitats spanning varying degrees of physical structural decay. Habitat structural attributes—including functional canopy height, surface rugosity, interstitial cavity distributions, and live benthic cover—were quantified alongside multi-guild fish density, functional richness, and standing biomass. Machine learning analytical formulations were employed strictly as non-linear ecological evaluators to capture complex ecological dependencies and identify community tipping points without imposing artificial linear constraints. Results: Environmental feature rankings identified three-dimensional rugosity and interstitial cavity volumes as primary determinants of fish survival and guild richness. When structural rugosity dropped below an empirical threshold ( ), teleost biomass underwent a non-linear contraction of 64%, severely dislodging specialized corallivores and juvenile grazers irrespective of short-term live tissue cover. Conclusion: Physical habitat complexity acts as an ecological buffering mechanism against disturbance, providing thermal micro-refugia and shelter from predation. Preserving architectural framework geometry is foundational to sustaining reef animal populations, demanding conservation strategies centered on structural habitat integrity alongside biological species protection.