PredictaMAR: An integrated Copernicus-based decision-support platform for sustainable artisanal fisheries

PredictaMAR is an integrated decision-support platform aimed at improving the sustainability, efficiency, and resilience of artisanal fisheries by reducing spatial, operational, and economic uncertainty in fishing activities. The platform leverages satellite-derived and modelled oceanographic data from the Copernicus Marine Service and combines them with a species-specific weighting framework developed through an extensive scientific literature review. This review draws on peer-reviewed studies, technical reports, and public scientific information, including criteria and oceanographic knowledge aligned with research produced by the Instituto del Mar del Perú (IMARPE).

The methodological core of PredictaMAR integrates key environmental variables—chlorophyll concentration, sea surface temperature, bathymetry, salinity, and ocean circulation—into a multi-criteria weighting table that reflects the relative ecological relevance of each variable for different target species and types of fish aggregations. Rather than producing deterministic predictions, the system estimates a probability of fish presence, translating scientific knowledge into an operational layer that supports risk-aware decision-making for small-scale fishers.

PredictaMAR is being iteratively refined through ongoing field validations conducted in collaboration with artisanal fishers along the Peruvian coast. These validations compare predicted fishing zones with observed fishing outcomes, enabling continuous calibration of weighting parameters and improving model robustness under real operational conditions. Preliminary field tests indicate that the use of targeted predictive zones can significantly reduce exploratory navigation time at sea, which traditionally represents a major source of fuel consumption and economic risk for artisanal fleets.

From an environmental and economic perspective, initial simulations and field observations suggest that optimized route selection enabled by PredictaMAR could reduce navigation distances by approximately 15–30%, depending on species and seasonal conditions. This reduction translates into an estimated decrease in fuel consumption of 20–25% per fishing trip for small vessels using outboard engines, directly lowering operational costs for fishers. Considering average fuel usage patterns in artisanal fisheries, such reductions may correspond to a decrease of several kilograms of CO₂ emissions per trip, contributing cumulatively to meaningful reductions in greenhouse gas emissions at the coastal community scale.

By lowering fuel consumption and time spent at sea, the platform also reduces pressure on marine ecosystems, minimizes unnecessary disturbance, and supports safer fishing practices. These benefits directly contribute to food security by stabilizing fishers’ incomes, improving catch efficiency, and reducing vulnerability to fuel price volatility, which is a critical factor for small-scale fisheries in developing coastal regions.

Conceptually, PredictaMAR aligns with the objectives of the European Digital Twin of the Ocean by demonstrating how Copernicus data, scientific literature, and field validation can be integrated into a practical, scalable tool for sustainable ocean use. The platform illustrates a pathway for translating large-scale Earth observation data into actionable insights that support environmentally responsible fisheries management, climate mitigation efforts, and the long-term resilience of artisanal fishing communities.