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 suppor...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001633 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849314595129262080 |
|---|---|
| author | Loukas Katikas Sofia Reizopoulou Paraskevi Drakopoulou Vassiliki Vassilopoulou |
| author_facet | Loukas Katikas Sofia Reizopoulou Paraskevi Drakopoulou Vassiliki Vassilopoulou |
| author_sort | Loukas Katikas |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-edd8cea21f29441ea0a60b27c65f6c2a |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-edd8cea21f29441ea0a60b27c65f6c2a2025-08-20T03:52:24ZengElsevierEcological Informatics1574-95412025-09-018810315410.1016/j.ecoinf.2025.103154Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean watersLoukas Katikas0Sofia Reizopoulou1Paraskevi Drakopoulou2Vassiliki Vassilopoulou3National and Technical University of Athens, School of Rural, Surveying and Geoinformatics Engineering, Zografou Campus, 9, Iroon Polytechniou str., 15780 Zografou, Greece; Corresponding author.Hellenic Centre for Marine Research, Institute of Oceanography, 46.7km Athens-Sounio Ave., 19013 Anavyssos, GreeceHellenic Centre for Marine Research, Institute of Oceanography, 46.7km Athens-Sounio Ave., 19013 Anavyssos, GreeceHellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 576A Vouliagmenis Ave., Argyroupoli, GreeceMarine 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.http://www.sciencedirect.com/science/article/pii/S1574954125001633Local ecological knowledge dataSeabed habitat classificationMachine learningConservation planningGeographic information systems (GIS) |
| spellingShingle | Loukas Katikas Sofia Reizopoulou Paraskevi Drakopoulou Vassiliki Vassilopoulou Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters Ecological Informatics Local ecological knowledge data Seabed habitat classification Machine learning Conservation planning Geographic information systems (GIS) |
| title | Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters |
| title_full | Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters |
| title_fullStr | Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters |
| title_full_unstemmed | Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters |
| title_short | Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters |
| title_sort | using fishery related data scientific expertise and machine learning to improve marine habitat mapping in northeastern mediterranean waters |
| topic | Local ecological knowledge data Seabed habitat classification Machine learning Conservation planning Geographic information systems (GIS) |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125001633 |
| work_keys_str_mv | AT loukaskatikas usingfisheryrelateddatascientificexpertiseandmachinelearningtoimprovemarinehabitatmappinginnortheasternmediterraneanwaters AT sofiareizopoulou usingfisheryrelateddatascientificexpertiseandmachinelearningtoimprovemarinehabitatmappinginnortheasternmediterraneanwaters AT paraskevidrakopoulou usingfisheryrelateddatascientificexpertiseandmachinelearningtoimprovemarinehabitatmappinginnortheasternmediterraneanwaters AT vassilikivassilopoulou usingfisheryrelateddatascientificexpertiseandmachinelearningtoimprovemarinehabitatmappinginnortheasternmediterraneanwaters |