Predicting the distribution of deep-sea vulnerable marine ecosystems using high-resolution data: Considerations and novel approaches

• High-resolution hydrodynamic models improve deep-sea species distribution models.
• Future applications may include species’ larval stages, fore-casting and 3D processes.
• Proportion data retain information on both prevalence and sampling effort per grid cell.
• Robust data are needed to account for spatial auto-correlation and pseudo-replication.

Little is known about species distribution patterns in deep-sea environments, primarily because sampling surveys in the high seas are expensive and time consuming. The increasing need to manage and protect vulnerable marine ecosystems, such as cold-water corals, has motivated the use of predictive modelling tools, which produce continuous maps of potential species or habitat distribution from limited point observations and full coverage environmental data. Rapid advances in acoustic remote sensing, oceanographic modelling and sampling technology now provide high quality datasets, facilitating model development with high spatial detail. This paper provides a short overview of existing methodologies for predicting deep-sea benthic species distribution, and illustrates emerging issues related to spatial and thematic data resolution, and the use of transect-derived species distribution data. In order to enhance the ecological relevance and reliability of deep-sea species distribution models, novel techniques are presented based on a case study predicting the distribution of the cold-water coral Lophelia pertusa in three carbonate mound provinces in Irish waters. Specifically, the study evaluates (1) the capacity of newly developed high-resolution (250 m grid cell size) hydrodynamic variables to explain local scale cold-water coral distribution patterns, (2) the potential value of species occurrence proportion data to maintain semi-quantitative information of coral prevalence (i.e. coverage) and sampling effort per grid cell within the response variable, and (3) mixed effect modelling to deal with spatially grouped transect data. The study shows that predictive models using vertical and horizontal flow parameters perform significantly better than models based on terrain parameters only. Semi-quantitative proportion data may decrease model uncertainty and increase model reliability, and provide a fruitful avenue of research for analysing large quantities of video data in a detailed yet time-efficient manner. The study concludes with an outlook of how species distribution models could improve our understanding of vulnerable marine ecosystem functioning and processes in the deep sea.