DataSheet_1_Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies.pdf

• Main Authors: Oisín Callery, Anthony Grehan
• Format: Dataset
• Language: unknown
• Published: 2023
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; habitat modelling; benthic habitats; random forest; ecosystem-based management; marine spatial planning; North Atlantic
• Online Access: https://doi.org/10.3389/fmars.2023.1139425.s001; https://figshare.com/articles/dataset/DataSheet_1_Extending_regional_habitat_classification_systems_to_ocean_basin_scale_using_predicted_species_distributions_as_proxies_pdf/22559293

The patchy nature and overall scarcity of available scientific data poses a challenge to holistic ecosystem-based management that considers the whole range of ecological, social, and economic aspects that affect ecosystem health and productivity in the deep sea. In particular, the evaluation of, for instance, the impact of human activities/climate change, the adequacy and representativity of MPA networks, and the valuation of ecosystem goods and services is hampered by the lack of detailed seafloor habitat maps and a univocal classification system. To maximize the use of current evidence-based management decision tools, this paper investigates the potential application of a supervised machine learning methodology to expand a well-established habitat classification system throughout an entire ocean basin. A multi-class Random Forest habitat classification model was built using the predicted distributions of 6 deep-sea fish and 6 cold-water corals as predictor variables (proxies). This model, found to correctly classify the area covered by an existing European seabed habitat classification system with ~90% accuracy, was used to provide a univocal deep-sea habitat classification for the North Atlantic. Until such time as global seabed mapping projects are complete, supervised machine learning approaches, as described here, can provide the full coverage classified maps and preliminary habitat inventories needed to underpin marine management decision making.