Local adaptation and oceanographic connectivity patterns explain genetic differentiation of a marine diatom across the North Sea-Baltic Sea salinity gradient

Abstract

Drivers of population genetic structure are still poorly understood in marine micro-organisms. We exploited the North Sea-Baltic Sea transition for investigating the seascape genetics of a marine diatom, Skeletonema marinoi. Eight polymorphic microsatellite loci were analysed in 354 individuals from ten locations to analyse population structure of the species along a 1500-km-long salinity gradient ranging from 3 to 30 psu. To test for salinity adaptation, salinity reaction norms were determined for sets of strains originating from three different salinity regimes of the gradient. Modelled oceanographic connectivity was compared to directional relative migration by correlation analyses to examine oceanographic drivers. Population genetic analyses showed distinct genetic divergence of a low-salinity Baltic Sea population and a high-salinity North Sea population, coinciding with the most evident physical dispersal barrier in the area, the Danish Straits. Baltic Sea populations displayed reduced genetic diversity compared to North Sea populations. Growth optima of low salinity isolates were significantly lower than those of strains from higher native salinities, indicating local salinity adaptation. Although the North Sea-Baltic Sea transition was identified as a barrier to gene flow, migration between Baltic Sea and North Sea populations occurred. However, the presence of differentiated neutral markers on each side of the transition zone suggests that migrants are maladapted. It is concluded that local salinity adaptation, supported by oceanographic connectivity patterns creating an asymmetric migration pattern between the Baltic Sea and the North Sea, determines genetic differentiation patterns in the transition zone.

Keywords: local adaptation; marine phytoplankton; oceanographic connectivity; population genetics.

Fig. 1

Baltic Sea ecoregion as defined by the ICES, which divides the study area into the Baltic Sea and the North Sea with a boundary (thick line) at the NE and SE parts of the Danish Belts. The salinity gradient is visualized by sea surface salinity at respective areas divided by dashed lines. Black circles indicating locations for sediment samples. Five of them were located in the North Sea (LY = Lysekil, VI = Vinga, BA = Båstad, AR = Arild and RO = Öresund) and five in the Baltic Sea (YS = Ystad, GK = Gdansk, GD = Gotland, SF = Storfjärden and BS = Bothnian Sea).

Fig. 2

Bayesian probability assignment (structure) displaying genetic differentiation in the data set. The green colour represents individuals that were assigned to the Baltic Sea, and the red colour represents individuals that were assigned to North Sea populations. The structure analysis included admixture; therefore, most of the individuals are a mix of red and green. A split in genetic resemblance was observed at the entrance of the Baltic Sea.

Fig. 3

(a) IBD analyses showed significant correlation when all samples were included. (b) Genetic versus geographic distance of the Baltic Sea. F_ST of Baltic Sea populations were not isolated by geographic distance. (c) The North Sea stations were not significantly isolated by distance. Note the different scale on x-axes.

Fig. 4

Experimental assessments of the reaction norm of three local populations along the salinity gradient. (a) The Bothnian Sea (BS) population had its maximum growth rate at the native salinity of 5 psu. Growth was significantly reduced in salinities exceeding 10 psu. (b) The Gotland (GD) population had its maximum growth rate close to the native salinity of 7 psu. Growth rates were reduced with increasing salinity. (c) The Båstad (BA) population had reduced growth rates at lower salinities (<12 psu). The highest growth rate was observed at 35 psu. Dashed line represents the reaction norm and grey-shaded box the observed salinity range at each location. Vertical lines represent standard error (SE) of ten replicates.

Fig. 5

(a) The directional relative migration network including all relative migrations values indicates stronger gene flow within the subareas than between. (b) Directional relative migration network displaying relative migrations above 0.5. The direction of the relative migration between RO and YS was significantly asymmetric (*CI 95%).