Delimiting oceanographic provinces to determine drivers of mesoscale patterns in benthic megafauna: A case study in the Barents Sea

• Published in: Progress in oceanography 2016-08, Vol.146, p.187-198
• Main Authors: Lacharité, Myriam, Jørgensen, Lis Lindal, Metaxas, Anna, Lien, Vidar S., Skjoldal, Hein Rune
• Format: Article
• Language: English
• Subjects: Barents Sea; Brackish; Deep water; Marine; Mathematical models; Ocean currents; Ocean floors; Ocean models; Sea currents; Water circulation
• ISSN: 0079-6611; 1873-4472
• DOI: 10.1016/j.pocean.2016.06.008

•The relationship between ocean conditions and benthic megafauna is assessed.•An ocean model is used to determine conditions over 10years in the Barents Sea.•Mean surface temperature was the best predictor of turnover in benthic megafauna.•Oceanographic provinces (i.e., regions of similar conditions) are identified.•Patterns in provinces were linked with their importance as predictor of turnover. Communities of benthic megafauna in the deep waters of continental shelves (> 100m) are important components of marine ecosystems. In high-latitude ecosystems, this fauna is increasingly impacted by human activities and climate variability. In this study, we provide baseline knowledge on the oceanographic conditions affecting its distribution in the Barents Sea in the vicinity of the Polar Front – an oceanic front occurring at the transition zone between the Atlantic and Arctic water masses. We used fields of temperature and currents from an ocean circulation model (Regional Ocean Modelling System – ROMS) to derive variables divided into 3 groups relevant to bottom fauna (temperature, water column structure and bottom currents) expressing either mean conditions or temporal variability over 10years (2001–2010). Benthic megafauna was surveyed in summer 2011 at 139sites. To analyze the relationship between spatial variability in the composition of benthic megafauna (i.e., β-diversity) and oceanographic conditions, we: (1) used generalized dissimilarity modelling (GDM) and (2) delimited oceanographic provinces (i.e., regions of similar conditions) for each group of variables using principal component analysis (PCA) followed by cluster analysis. Turnover in benthic megafauna was explained by 7 oceanographic variables (temperature: 4, water column structure: 2, bottom currents: 1), depth and geographic distance (56.7% of total deviance explained). Concurrently, patterns in oceanographic provinces among the 3 groups of variables coincided with results from the GDM, where provinces derived from temperature were sharply delimited relative to the other groups. We concluded that the spatial structure of the environment is important in the relationship between spatial variability of benthic megafauna and oceanographic conditions in shelf deep waters. Ocean models are powerful tools to study this relationship, but the way in which their inherent uncertainty affects the conclusions of ecological models should be assessed more thoroughly.