Genetic population structure of turbot (Scophthalmus maximus L.) in the Northeast Atlantic was investigated using eight highly variable microsatellite loci. In total 706 individuals from eight locations with temporal replicates were assayed, covering an area from the French Bay of Biscay to the Aaland archipelago in the Baltic Sea. In contrast to previous genetic studies of turbot, we found significant genetic differentiation among samples with a maximum pairwise FST of 0.032. Limited or no genetic differentiation was found among samples within the Atlantic/North Sea area and within the Baltic Sea, suggesting high gene flow among populations in these areas. In contrast, there was a sharp cline in genetic differentiation going from the low saline Baltic Sea to the high saline North Sea. The data were explained best by two divergent populations connected by a hybrid zone; however, a mechanical mixing model could not be ruled out. A significant part of the genetic variance could be ascribed to variation among years within locality. Nevertheless, the population structure was relatively stable over time, suggesting that the observed pattern of genetic differentiation is biologically significant. This study suggests that hybrid zones are a common phenomenon for marine fishes in the transition area between the North Sea and the Baltic Sea and highlights the importance of using interspecific comparisons for inferring population structure in high gene flow species such as most marine fishes.

No abstract

A total of 143 prokaryotic genomes were scored with an updated version of the prokaryotic genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to approximately 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms that too many short genes are annotated in numerous organisms. Furthermore, genes might be missing in the annotation of some of the genomes. We predict 41 of 143 genomes to be over-annotated by >5%, meaning that too many ORFs are annotated as genes. We also predict that 12 of 143 genomes are under-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank. We argue that the average performance of our standardized and fully automated method is slightly better than the annotation.

Measurements of the water clarification capacity of a nutrient extractive mussel farm in a eutrophic fjord in Denmark were used to optimize eutrophication mitigation capacity. Graphic: Camille Saurel, DTU Aqua