Data‐driven bioregionalization: A seascape‐scale study of macrobenthic communities in the Eurasian Arctic

Abstract

AbstractAimWe conduct the first model‐based assessment of the biogeographical subdivision of Eurasian Arctic seas to (1) delineate spatial distribution and boundaries of macrobenthic communities on a seascape level; (2) assess the significance of environmental drivers of macrobenthic community structures; (3) compare our modelling results to historical biogeographical classifications; and (4) couple the model to climate scenarios of environmental changes to project potential shifts in the distribution and composition of macrobenthic communities by 2100.LocationEurasian Arctic seas, in particular Barents, Kara and Laptev Seas.Taxon169 species of macrobenthic fauna; most common taxa are Polychaeta (85 species), Malacostraca (30 species), Bivalvia (26 species) and Gastropoda (10 species).MethodsWe employed the Region of Common Profile (RCP) approach to assess the bioregionalization patterns of Eurasian Arctic seafloor communities. The RCP approach allows the identification of seascape‐scale distribution patterns by simultaneously considering biotic and environmental data within one modelling step.ResultsFour RCPs were identified within the Eurasian Arctic. The results showed that water depth, sea‐ice cover, bottom‐water temperature and salinity, proportion of fine sediments, particulate organic carbon (POC) and depth of the euphotic zone were among the most important driving variables of macrobenthos communities. The projections, driven by the climate‐change scenarios, suggested a general north‐eastward shift of the RCPs over the 21st century, mainly correlated with retreating sea‐ice and increasing sea‐bottom temperature.Main conclusionsThe identified RCPs largely match the previously reported large‐scale distribution patterns of macrobenthic communities in Eurasian Arctic seas. The spatio‐temporal dynamics of RCPs are in agreement with local long‐term observation data on macrobenthic resilience/vulnerability in the studied region. The representation of the ecoregions and biotas in a probabilistic form, together with quantitative assessment of potential climate‐driven changes, will help to adequately consider macrobenthic biodiversity dynamics in the development of science‐based conservation measures.