Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters

Marine habitat mapping is an essential tool for planning conservation efforts and sustainable management of marine activities. High spatial resolution in marine habitat maps is of utmost importance, as it may encompass more detail in imagery and reveal important biotopes. This level of detail supports directing monitoring and analysis efforts for effective implementation of European Union (EU) environmental policies and provides more relevant advice for robust decision-making under sectorial policies (e.g., the Common Fisheries Policy) and more integrated policies (e.g., marine spatial planning). In this study, sea bottom type data recorded during national monitoring of commercial fishing vessel operations and fishery surveys in the Greek Seas were used. These data were then assigned to the EU EMODnet seabed habitats using local ecological knowledge. Two machine-learning algorithms, i.e., random forest classifier (RFC) and gradient boosting classifier, were trained on the entire national-scale dataset and subsequently applied to assess their performance in predicting habitat types in the Saronikos Gulf (regional scale) using various environmental factors as predictors. The borderline synthetic minority oversampling technique was applied to manage inherent data class imbalances. A validation dataset and georeferenced data from previous studies were used to compare the accuracy and predictive performance of the models. Using this approach, the Saronikos Gulf was enriched with five more habitat types than visualised in the EMODnet portal, which also filled habitat gaps in areas where no data existed. Results from application of the RFC-Borderline Smote (BS) model (62 % accuracy, 0.51 kappa score) were then used to address conservation planning commitments recently made by the Greek government. The vast majority of marine seabed priority habitats in the study area appeared to fall outside the borders of the current Natura 2000 sites, which served as the baseline for the declared trawl bans in Greek waters, following the provisions of the EU Marine Action Plan. • Local ecological knowledge from sea users as a supplementary data source adds significant value to marine habitat mapping • The Saronikos Gulf was enriched by 5 more habitat types than those visualised in the EMODnet portal . • RFC-BS classifier outperforms GBC-BS, however, with small differences in all accuracy metrics (62 % and 61 % accuracy). • Mud-muddy, sand, rock-rocky and seagrasses were better predicted than maerl, coarse and mixed sediment types . • Most locations with priority habitats, as extracted from the RFC-BS model, seem to fall outside the current Natura area boundaries .