Microsatellites play an important role when investigating population and ecological genetics, although high effort in development and genotyping constitute a technical constraint and remains a major bottleneck. Here we use a microsatellite genotyping approach utilizing sequences of amplicons for allele calling (SSR-GBS) based on Illumina that requires less effort and time. The approach consist of development of highly polymorphic loci, sequencing of multiplexed PCR amplified microsatellites on an Illumina Miseq PE 300 platform and bioinformatic treatment of the sequenced data using custom scripts. The procedure allows automation in allele calling, which can be more reliably replicated and thereby removes biases that might prevent concatenation of datasets from different analyses. Additionally, the methodology enhances information content in the sequenced data beyond the traditional amplicon length (AL) approaches. Using 26 newly developed microsatellite markers and SSR-GBS we investigate the population genetic assessment of anthropogenically altered populations of East African Nile tilapia to show the potential of this genotyping approach. More precisely, we compare genotypic data generated considering AL and whole amplicon information (WAI). We found that genotypes based on WAI are not only able to recover a higher number of alleles but also a more detailed genetic structure pattern. We discuss the capability and importance of WAI allele calling and show perspectives for implementation in the future conservation genetic studies. More specifically, we demonstrate how the current markers and techniques might contribute useful information for studies concerning resources sustainable exploitation and conservation using the East African Nile tilapia.

Background: The need for enhancing the productivity of fisheries in Africa triggered the introduction of non-native fish, causing dramatic changes to local species. In East Africa, the extensive translocation of Nile tilapia (Oreochromis niloticus) is one of the major factors in this respect. Using 40 microsatellite loci with SSR-GBS techniques, we amplified a total of 664 individuals to investigate the genetic structure of O. niloticus from East Africa in comparison to Ethiopian and Burkina Faso populations.Results: All three African regions were characterized by independent gene-pools, however, the Ethiopian population from Lake Tana was genetically more divergent (F st = 2.1) than expected suggesting that it might be a different sub-species. In East Africa, the genetic structure was congruent with both geographical location and anthropogenic activities (Isolation By Distance for East Africa, R 2 = 0.67 and Uganda, R 2 = 0.24). O. niloticus from Lake Turkana (Kenya) was isolated, while in Uganda, despite populations being rather similar to each other, two main natural catchments were able to be defined. We show that these two groups contributed to the gene-pool of different non-native populations. Moreover, admixture and possible hybridization with other tilapiine species may have contributed to the genetic divergence found in some populations such as Lake Victoria. We detected other factors that might be affecting Nile tilapia genetic variation. For example, most of the populations have gone through a reduction in genetic diversity, which can be a consequence of bottleneck (G-W, < 0.5) caused by overfishing, genetic erosion due to fragmentation or founder effect resulting from stocking activities. Conclusions: The anthropogenic activities particularly in the East African O. niloticus translocations, promoted artificial admixture among Nile Tilapia populations. Translocations may also have triggered hybridization with the native congenerics, which needs to be further studied. These events may contribute to outbreeding depression and hence compromising the sustainability of the species in the region.

Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images. Assigning landmarks is tedious and error prone. Predefined landmarks may in addition miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are interpretable and hence to mitigate the black-box nature of ML methods. To overcome the black-box nature of ML we apply complementary methods and investigate internal representations with saliency maps to reliably identify location specific characteristics in images of Nile tilapia populations. Analyzing fish images which were sampled from six Ethiopian lakes reveals that deep learning improves on a conventional morphometric analysis in predictive performance. A critical assessment of established saliency maps with a novel significance test reveals however that the improvement is aided by artifacts which have no biological interpretation. More interpretable results are obtained by a Bayesian approach which allows us to identify genuine Nile tilapia body features which differ in dependence of the animals habitat. We find that automatically inferred Nile tilapia body features corroborate and expand the results of a landmark based analysis that the anterior dorsum, the fish belly, the posterior dorsal region and the caudal fin show signs of adaptation to the fish habitat. We may thus conclude that Nile tilapia show habitat specific morphotypes and that a ML analysis allows inferring novel biological knowledge in a reproducible manner.