• DOI:10.1186/s12862-018-1325-8
• Corpus ID: 57617416

First genomic study on Lake Tanganyika sprat Stolothrissa tanganicae: a lack of population structure calls for integrated management of this important fisheries target species

@article{DeKeyzer2018FirstGS,
  title={First genomic study on Lake Tanganyika sprat Stolothrissa tanganicae: a lack of population structure calls for integrated management of this important fisheries target species},
  author={Els L R De Keyzer and Zo{\"e} De Corte and Maarten Van Steenberge and Joost A. M. Raeymaekers and Federico C. F. Calboli and Nikol Kmentov{\'a} and Th{\'e}ophile N'Sibula Mulimbwa and Massimiliano Virgilio and Carl Vangestel and Pascal Masilya Mulungula and Filip A. M. Volckaert and Maarten P. M. Vanhove},
  journal={BMC Evolutionary Biology},
  year={2018},
  volume={19}
}

• Els L R De Keyzer, Zoë De Corte, +9 authors M. Vanhove
• Published 8 January 2019
• Biology
• BMC Evolutionary Biology

BackgroundClupeid fisheries in Lake Tanganyika (East Africa) provide food for millions of people in one of the world’s poorest regions. Due to climate change and overfishing, the clupeid stocks of Lake Tanganyika are declining. We investigate the population structure of the Lake Tanganyika sprat Stolothrissa tanganicae, using for the first time a genomic approach on this species. This is an important step towards knowing if the species should be managed separately or as a single stock. Population structure is important for fisheries management, yet understudied for many African freshwater species. We hypothesize that distinct stocks of S. tanganicae could be present due to the large size of the lake (isolation by distance), limnological variation (adaptive evolution), or past separation of the lake (historical subdivision). On the other hand, high mobility of the species and lack of obvious migration barriers might have resulted in a homogenous population.ResultsWe performed a population genetic study on wild-caught S. tanganicae through a combination of mitochondrial genotyping (96 individuals) and RAD sequencing (83 individuals). Samples were collected at five locations along a north-south axis of Lake Tanganyika. The mtDNA data had low global FST and, visualised in a haplotype network, did not show phylogeographic structure. RAD sequencing yielded a panel of 3504 SNPs, with low genetic differentiation (FST = 0.0054; 95% CI: 0.0046–0.0066). PCoA, fineRADstructure and global FST suggest a near-panmictic population. Two distinct groups are apparent in these analyses (FST = 0.1338 95% CI: 0.1239,0.1445), which do not correspond to sampling locations. Autocorrelation analysis showed a slight increase in genetic difference with increasing distance. No outlier loci were detected in the RADseq data.ConclusionOur results show at most very weak geographical structuring of the stock and do not provide evidence for genetic adaptation to historical or environmental differences over a north-south axis. Based on these results, we advise to manage the stock as one population, integrating one management strategy over the four riparian countries. These results are a first comprehensive study on the population structure of these important fisheries target species, and can guide fisheries management.