Decision Support Systems for Sustainable Aquaculture under Climate Uncertainty: A Critical Review of Models, Applications and Methodological Limitations
Rajan Dinesh *
Department of Fisheries Extension, Economics and Statistics, TNJFU - Dr. M.G.R. Fisheries College and Research Institute, Thalainayeru, Nagapattinam – 614 712, India.
*Author to whom correspondence should be addressed.
Abstract
Aquaculture now supplies a substantial share of global aquatic food, and its continued expansion is expected to occur in waters whose temperature, oxygen content, carbonate chemistry and hydrodynamics are being reshaped by anthropogenic climate change. Decision support systems have been promoted as the principal means of translating environmental knowledge into siting, stocking, harvesting, licensing and investment choices, yet the field remains fragmented across spatial planning, bioenergetic simulation, ecological forecasting, machine learning and economic risk analysis, and existing reviews have generally examined these strands in isolation. This review critically evaluates how decision support systems for aquaculture represent, propagate and communicate climate uncertainty, and assesses whether the resulting tools are adequate to the decisions they are intended to inform. Literature was identified through structured searching of open scholarly indexes and institutional sources, complemented by citation tracking, with critical appraisal directed at model structure, treatment of uncertainty, validation status, spatial and temporal scale, and evidence of operational use. Four findings emerge. First, the mapping between the scale at which climate information is produced and the scale at which aquaculture decisions are taken remains poorly resolved, and downscaling to the coastal margin introduces uncertainties that are rarely propagated into decision outputs. Second, the great majority of tools treat climate as a scenario-indexed boundary condition rather than as a source of deep uncertainty, which favours optimisation over robustness. Third, data-driven and digital-twin approaches have advanced rapidly in short-horizon prediction while inheriting a stationarity assumption that is inconsistent with their stated climate purpose. Fourth, formal evaluation of decision quality, as distinct from predictive skill, is almost absent, and evidence of sustained uptake is concentrated in a small number of well-resourced temperate systems. Research priorities are identified in uncertainty propagation, robustness-oriented appraisal, transferable evaluation protocols and the extension of decision support to smallholder production.
Keywords: Aquaculture, decision support systems, climate uncertainty, ecological forecasting, marine spatial planning, carrying capacity, robust decision making