Reduced Single Nucleotide Polymorphism Panels for Assigning Atlantic Albacore and Bay of Biscay Anchovy Individuals to Their Geographic Origin: Toward Sustainable Fishery Management

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

BACKGROUND

With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F_ST) to select the most informative SNPs for ancestry inference.

RESULTS

Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof.

CONCLUSIONS

The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

Genetic analyses reveal complex dynamics within a marine fish management area

Genetic data have great potential for improving fisheries management by identifying the fundamental management units-that is, the biological populations-and their mixing. However, so far, the number of practical cases of marine fisheries management using genetics has been limited. Here, we used Atlantic cod in the Baltic Sea to demonstrate the applicability of genetics to a complex management scenario involving mixing of two genetically divergent populations. Specifically, we addressed several assumptions used in the current assessment of the two populations. Through analysis of 483 single nucleotide polymorphisms (SNPs) distributed across the Atlantic cod genome, we confirmed that a model of mechanical mixing, rather than hybridization and introgression, best explained the pattern of genetic differentiation. Thus, the fishery is best monitored as a mixed-stock fishery. Next, we developed a targeted panel of 39 SNPs with high statistical power for identifying population of origin and analyzed more than 2,000 tissue samples collected between 2011 and 2015 as well as 260 otoliths collected in 2003/2004. These data provided high spatial resolution and allowed us to investigate geographical trends in mixing, to compare patterns for different life stages and to investigate temporal trends in mixing. We found similar geographical trends for the two time points represented by tissue and otolith samples and that a recently implemented geographical management separation of the two populations provided a relatively close match to their distributions. In contrast to the current assumption, we found that patterns of mixing differed between juveniles and adults, a signal likely linked to the different reproductive dynamics of the two populations. Collectively, our data confirm that genetics is an operational tool for complex fisheries management applications. We recommend focussing on developing population assessment models and fisheries management frameworks to capitalize fully on the additional information offered by genetically assisted fisheries monitoring.