Geospatial Decision-Making for Aquaculture Site Selection
Summary
Geospatial decision-making for aquaculture site selection brings together spatial datasets, analytical models and stakeholder requirements to identify locations that maximise productivity while minimising environmental impacts and user conflicts. Central to this approach is the use of Geographic Information Systems (GIS) and Remote Sensing (RS) to assemble layers of information on water quality, hydrodynamics, bathymetry, habitat sensitivity and socio-economic constraints. Multi-Criteria Evaluation (MCE) frameworks then weight and integrate these diverse factors to generate suitability indices under alternative development scenarios. Advances in eco-physiological modelling, uncertainty analysis and spatial autocorrelation methods enhance the robustness of outcomes and support adaptive management. This integrative methodology underpins sustainable aquaculture expansion—from inland pond systems to offshore cage farms—and informs maritime spatial planning, licence allocation and risk mitigation at local, regional and global scales.
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Geospatial Decision-Making for Aquaculture Site Selection publication trend
The graph below shows the total number of articles in geospatial decision-making for aquaculture site selection across all publications each year (not limited to Nature Index journals).
Technical terms
Geographic Information System (GIS): A computer-based tool for capturing, storing, analysing and visualising spatial data across multiple thematic layers.
Remote Sensing (RS): The acquisition of information about Earth’s surface and water bodies via satellite or aerial sensors, providing consistent spatial coverage.
Multi-Criteria Evaluation (MCE): A decision-support methodology that integrates and weights diverse criteria—environmental, technical and socio-economic—to rank site suitability.
Weighted Linear Combination (WLC): A common MCE technique that overlays criteria maps by applying user-defined weights to calculate a composite suitability score.
Spatial Multi-Criteria Evaluation (SMCE): The extension of MCE within a GIS environment, enabling the spatially explicit assessment of aquaculture site options.
Analytical Hierarchy Process (AHP): A structured method for deriving weights in MCE by pair-wise comparison of criteria based on expert judgement.
Spatial Autocorrelation: A statistical measure of the degree to which similar values of a variable cluster in geographic space, used to refine and validate suitability analyses.
Uncertainty Analysis: Techniques to quantify the sensitivity of model outputs to input data variability and expert-driven assumptions, enhancing decision confidence.
References
- Checklist and reporting framework to support documentation and communication of GIS-based Multi-Criteria Evaluation (MCE) models for aquaculture site selection. PLOS Sustainability and Transformation (2025).
- Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting. Frontiers in Marine Science (2020).
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