<![CDATA[Escalating environmental pollution and climate-related stressors necessitate sensitive and mechanistically grounded tools for assessing ecosystem health. Traditional ecological indicators often detect degradation only after substantial biological damage has occurred, limiting early intervention capacity. Molecular signatures derived from multi-omics technologies offer high-resolution insight into sublethal biological responses to environmental exposure. This study aims to identify and validate integrated molecular signatures associated with contaminant gradients and to evaluate their predictive capacity for ecosystem health assessment across aquatic environments. A multi-site cross-sectional design was implemented involving 180 sentinel organisms collected along defined pollution gradients. Transcriptomic, proteomic, and metabolomic profiling was conducted using high-throughput sequencing and mass spectrometry platforms. Multivariate statistical modeling, including principal component analysis and structural equation modeling, was applied to link molecular perturbations with contaminant concentrations and ecological indices. Significant increases in differentially expressed genes, altered protein abundance, and metabolite perturbation indices were observed in high-exposure sites (p < 0.001). Molecular signatures accurately classified exposure categories with 91% predictive accuracy and significantly predicted biodiversity decline (? = –0.68, p < 0.001). Integrated multi-omics molecular signatures provide sensitive, early-warning indicators of ecosystem impairment, enabling mechanistic linkage between environmental exposure and ecological degradation.]]>
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