Population genomics of the introduced and cultivated Pacific kelp Undaria pinnatifida: Marinas-not farms-drive regional connectivity and establishment in natural rocky reefs

Abstract

Ports and farms are well-known primary introduction hot spots for marine non-indigenous species (NIS). The extent to which these anthropogenic habitats are sustainable sources of propagules and influence the evolution of NIS in natural habitats was examined in the edible seaweed Undaria pinnatifida, native to Asia and introduced to Europe in the 1970s. Following its deliberate introduction 40 years ago along the French coast of the English Channel, this kelp is now found in three contrasting habitat types: farms, marinas and natural rocky reefs. In the light of the continuous spread of this NIS, it is imperative to better understand the processes behind its sustainable establishment in the wild. In addition, developing effective management plans to curtail the spread of U. pinnatifida requires determining how the three types of populations interact with one another. In addition to an analysis using microsatellite markers, we developed, for the first time in a kelp, a ddRAD-sequencing technique to genotype 738 individuals sampled in 11 rocky reefs, 12 marinas, and two farms located along ca. 1,000 km of coastline. As expected, the RAD-seq panel showed more power than the microsatellite panel for identifying fine-grained patterns. However, both panels demonstrated habitat-specific properties of the study populations. In particular, farms displayed very low genetic diversity and no inbreeding conversely to populations in marinas and natural rocky reefs. In addition, strong, but chaotic regional genetic structure, was revealed, consistent with human-mediated dispersal (e.g., leisure boating). We also uncovered a tight relationship between populations in rocky reefs and those in nearby marinas, but not with nearby farms, suggesting spillover from marinas into the wild. At last, a temporal survey spanning 20 generations showed that wild populations are now self-sustaining, albeit there was no evidence for local adaptation to any of the three habitats. These findings highlight that limiting the spread of U. pinnatifida requires efficient management policies that also target marinas.

Keywords: adaptation; artificial habitats; gene flow; genotyping by sequencing; invasive species; rocky reefs.

Figure 1

Study area (Brittany, France) and sampling localities for each habitat type. Triangles, circles and squares represent marinas, natural habitats and farms, respectively. Each site's code indicates the habitat type (M, marina; R, natural rocky reef; F, farm), the bay (no. 1–12) and the year of sampling (2005, 2009 and 2015) (e.g., M8‐15 indicates that this site is a marina within bay no. 8 sampled in 2015). The colour code refers to the colours used in the DAPC analysis shown in Supporting Information Figure S2. The geographic name of each locality and bay are detailed in Table 1

Figure 2

Comparison of genetic characteristics amongst habitat categories (marinas, farms, natural sites) in the 2015 data set for (a) RAD‐seq and (b) microsatellite markers. Boxplots indicating the average expected heterozygosity (H_E), fixation index (F_IS) and selfing rate estimate(s), with standard errors, for marinas (N = 11 and 12 for a and b, respectively), cultivated populations (N = 2), and natural rocky habitat sites (N = 11). The box shows the interquartile range (25–75th percentiles, with horizontal black line as median and red cross as mean). The upper and lower whiskers extend from the hinge to the largest and smallest value no further than 1.5 times the interquartile range. Data beyond this range (outliers) are plotted individually

Figure 3

Bayesian clustering analyses (fastSTRUCTURE software) using the RAD‐seq panel (a) over the whole data set collected in 2015 and (b) in the Bay of St. Malo only (bay no. 12 in Figure 1). Each individual is represented by a vertical line divided into K coloured segments, the length of which indicates the individual's membership fraction to each of K clusters. Individuals are grouped according to their sampling locality (ordered along a south to north gradient) for the regional‐scale analysis, and according to locality and year of sampling for the analysis at the bay scale. Locality codes correspond to those given in Table 1

Figure 4

Spatial versus temporal genetic structure computed on (a) the RAD‐seq panel and (b) the microsatellite panel. Each bar represents the within‐group F _ST, with the type of individuals comprising each group indicated on the x‐axis. *p‐value < 0.01, **p‐value < 0.001