Calculations and statistics analyses

Stratification in the upper 100 m was inferred using two methods (Supplementary Table S2). First the surface mixed layer depth was determined by the depth60 at which the gradient in density between two successive depths was > 0.01 kg m−4. A second method relied on the Brunt-Väisälä (or buoyancy – N2) frequency in the upper water column, defined as the depth when N2 was the greatest in the upper waters11. The Nitracline (∆NO3) was defined as depth where the vertical gradient in NO3 concentration (dNO3/dz) was greatest11. Day-length was calculated from and based on latitude 76.36°N and longitude − 74.01°W. The pairwise Multinomial Species Classification Method (CLAM)61 test was used to classify each OTU into a category: generalist, specialist of one habitat or of the second habitat or too rare. To separate samples we used clamtest() function in R (v.3.4.2) with a p-value of 0.01 and a coverage limit of 10 sequences as a rarity threshold. We applied the CLAM test between 4 different groups of samples: rDNA or rRNA; West (Canadian side) or East (Greenland side); surface or SCM and summer (July and August) or autumn (October).

NMDS was computed using the metaMDS() function to compare similarity between samples. Distance-Based Redundancy Analysis (db-RDA) and Constrained Correspondence Analysis (CCA) were computed using dbrda() and cca() functions, to discriminate the different sampling stations according to the environmental variables. The independent parameters that best explained variability in the db-RDA and CCA were selected using ordiR2step() and envfit() functions, by automatic forward selection, which selects variables to build optimal model with the highest adjusted coefficient determination. Non-Parametric Multivariate Analysis of Variance (NP-MANOVA) and Analysis of Similarity (ANOSIM) were used to test differences in composition between seasons, sides and depths. Permutational Multivariate Analyses of Variance (PerMANOVA) was conducted to analyze the correlations between environmental factors and community changes, using adonis() function. To test differences in environmental parameters between stations and depths, Welsh two-sample t-tests, verified with a non-parametric Kruskal–Wallis One-Way Analysis of Variance on ranks were used.

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