Spatio‐temporal turnover and drivers of bentho‐demersal community and food web structure in a high‐latitude marine ecosystem

Abstract

AbstractAimAssess the spatial and temporal turnover of bentho‐demersal marine fauna by integrating ecological metrics at the community and food web levels and evaluate their main environmental and anthropogenic drivers.LocationBarents Sea.MethodWe analysed data of benthic and bentho‐pelagic fish and megabenthic invertebrates caught in the Barents Sea ecosystem survey in August–September 2009–2017 to characterize the spatial and temporal variability of bentho‐demersal communities and food webs. We used a trait dataset and highly resolved bentho‐demersal food web to calculate community and food web metrics in space and time. We spatially clustered the community and food web based on their properties using archetypal analysis and investigated their co‐variation with environmental and fishing pressure using (hierarchical) redundancy analysis.ResultThe community and food web metrics partitioned the Barents Sea into four sub‐regions where different pressures act on the bentho‐demersal fauna, such as sea ice loss and fisheries. Multiple community metrics (e.g. mean body length and trophic level) varied along an environmental gradient of annual mean sea bottom temperature, trawling intensity and ice‐cover, whereas multiple food web metrics (e.g. nestedness and connectance) varied along an environmental gradient of depth and sediment composition. Communities had higher biomass‐weighted variability in body size and omnivory values in areas where the Atlantic and Arctic water masses mix. Several food web and community metrics co‐varied (e.g. food chain length and mean trophic level). We found no clear temporal trends in the ecological metrics in any of the four sub‐regions, but the metrics had large inter‐annual variability with some local minima or maxima coinciding with high sea temperature and ice‐cover anomalies.ConclusionAnalyses at the community and food web level are seldom integrated in ecological studies, while this integration gives complementary information to assess patterns and drivers of ecosystem state and to better prioritize conservation efforts.