Amina Hassan
Marine ecosystems are changing faster than the observation systems designed to track them. This article presents an integrative review of digital technology interventions in marine ecosystem monitoring, synthesising peer-reviewed research on sensing platforms, molecular and acoustic methods, machine learning, and ocean data infrastructures published mainly between 2014 and 2026. The review addresses three questions: which classes of digital technology now support marine ecosystem monitoring and what ecological information each supplies; which recurring technical and institutional constraints limit their contribution to sustained, decision-relevant observation; and how these classes can be combined conceptually into an architecture linking sensing to management decisions. The synthesis shows that satellite remote sensing, autonomous platforms, environmental DNA, and passive acoustics have matured within largely separate research streams, each with distinctive observational strengths and each constrained by calibration demands, energy and sensor limits, interpretive uncertainty, or fragmented data practices. Machine learning relieves an analysis bottleneck created by high-volume sensing but introduces its own validation requirements. Building on these findings, the article proposes a four-layer sensing-to-decision architecture that connects observation, analytics, data integration, and governance through explicit feedback loops, using essential ocean variables as the prioritisation logic that binds the layers together. The framework offers monitoring programme designers and ocean governance bodies a structured basis for investment and evaluation decisions, and it identifies cost-effectiveness evidence, cross-domain interoperability, and institutional data governance as priorities for future research.