Patterns of spatial genetic structure and diversity at the onset of a rapid range expansion: colonisation of the UK by the small red-eyed damselfly Erythromma viridulum

2010 ◽  
Vol 12 (11) ◽  
pp. 3887-3903 ◽  
Author(s):  
Phillip C. Watts ◽  
Simon Keat ◽  
David J. Thompson
2019 ◽  
Author(s):  
Pierre De Wit ◽  
Per R. Jonsson ◽  
Ricardo T. Pereyra ◽  
Marina Panova ◽  
Carl André ◽  
...  

AbstractIn the Baltic Sea, recent range expansions following the opening of the Danish straits have resulted in a low-diversity ecosystem, both among and within species. However, relatively little is known about population genetic patterns within the basin, except for in a few commercially caught species and some primary producers thought to be ecosystem engineers. Here, we investigate the population genetic structure of the ecologically important crustaceanIdotea balthicathroughout the Baltic Sea using an array of 33,774 genome-wide SNP markers derived from 2b-RAD sequencing. We also generate a biophysical connectivity matrix, with which we compare the genomic data. We find strong population structure on small scales across the Baltic Sea, and that genomic patterns in most cases closely match biophysical connectivity, suggesting that current patterns are important for dispersal of this species. We also find a strong signal of multiple bottlenecks during the initial range expansion, in the form of reduced heterozygosity along the historical expansion front. The lack of gene flow among sampling sites in the Baltic Sea environmental gradient potentiates local adaptation, while at the same time also increasing genetic drift in low-diversity areas.


2015 ◽  
Vol 34 (3) ◽  
pp. 1065-1078 ◽  
Author(s):  
M. Olalla Lorenzo-Carballa ◽  
Sónia Ferreira ◽  
Angela M. Sims ◽  
David J. Thompson ◽  
Phillip C. Watts ◽  
...  

2018 ◽  
Vol 45 (12) ◽  
pp. 2645-2657 ◽  
Author(s):  
Jean-Baptiste Ledoux ◽  
Maša Frleta-Valić ◽  
Silvija Kipson ◽  
Agostinho Antunes ◽  
Emma Cebrian ◽  
...  

2020 ◽  
Vol 153 (1) ◽  
pp. 82-100
Author(s):  
Jean-Pierre Labouisse ◽  
Philippe Cubry ◽  
Frédéric Austerlitz ◽  
Ronan Rivallan ◽  
Hong Anh Nguyen

Backgrounds and aims – Previous studies showed that robusta coffee (Coffea canephora Pierre ex A.Froehner), one of the two cultivated coffee species worldwide, can be classified in two genetic groups: the Guinean group originating in Upper Guinea and the Congolese group in Lower Guinea and Congolia. Although C. canephora of the Guinean group is an important resource for genetic improvement of robusta coffee, its germplasm is under-represented in ex situ gene banks and its genetic diversity and population structure have not yet been investigated. Methods – To overcome the limitations of living collections, we explored old herbarium specimens collected in Guinea and Côte d’Ivoire and conserved at the Muséum National d’Histoire Naturelle, Paris. First, we reviewed the history of collection missions in both countries and how the C. canephora herbaria from the Muséum were assembled. Then, using 23 nuclear microsatellite markers, factorial and model-based Bayesian analyses, we investigated the genetic diversity of 126 specimens and 36 controls, analysed their distribution among the Congolese and Guinean groups, and estimated admixture proportions for each individual.Key results – For the first time, we detected population genetic structure within the Guinean group of C. canephora. The Guinean genotypes can be assigned to five sub-groups with distinct geographic distribution, especially in Guinea where two sub-groups (Maclaudii and Gamé) are characterized by a low level of admixture due to geographical isolation.Conclusions – We showed how combining a literature review and genetic data from old herbarium specimens can shed light on previous observations made by botanists and guide further actions to better preserve native coffee plants in forest remnants of West Africa.


PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0226225
Author(s):  
Łukasz Walas ◽  
Petros Ganatsas ◽  
Grzegorz Iszkuło ◽  
Peter A. Thomas ◽  
Monika Dering

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