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12 results for “recent evolutionary history”
Data from: Can recent social evolutionary history promote resilience to environmental change?
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Figure 3 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 3. Chronogram obtained with BEAST using two independent calibrations: COI substitution rate (scale above) and the opening of the Gibraltar Strait (scale below). The x-axis scale is in million years. Bars indicate 95% HPD intervals.
Figure 2 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 2. Bayesian tree reconstruction based on the two mitochondrial genes analysed. Stars on the branches indicate well supported clades, one star for each method: Bayesian inference, maximum-parsimony and maximum-likelihood.
Figure 4 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 4. Network of P. littoralis haplotypes. I, Iberian Peninsula; K, Turkey; G, Greece; M, Morocco; T, Tunisia; F, France.
Figure 4. Individual multilocus genotype clustering analysis for Podarcis carbonelli. A in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 4. Individual multilocus genotype clustering analysis for Podarcis carbonelli. A, inferred population structure from the number of clusters (K) = 2 to 5. These plots were obtained from the runs producing the highest values of Ln probability for each value of K, assuming correlated allele frequencies. In these plots, each individual is represented by a column divided into K segments, the size of each corresponding to the individual's estimated membership fraction in each of the K clusters. See Table 1 for locality name abbreviations. B, variation of the value of DK with the number of clusters, following Evanno et al. (2005). C, pie charts representing the mean proportion of membership for K = 4 (chosen by the previous method) for each locality.
Figure 3. Individual multilocus genotype clustering analysis for Podarcis bocagei. A in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 3. Individual multilocus genotype clustering analysis for Podarcis bocagei. A, inferred population structure from the number of clusters (K) = 2 to 5. These plots were obtained from the runs producing the highest values of Ln probability for each value of K, assuming correlated allele frequencies. In these plots, each individual is represented by a column divided into K segments, the size of each corresponding to the individual's estimated membership fraction in each of the K clusters. See Table 1 for locality name abbreviations. B, variation of the value of DK with the number of clusters, following Evanno et al. (2005). C, pie charts representing the mean proportion of membership for K = 3 and 5 (chosen by the previous method) for each locality.
Figure 2 in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 2. Synthetic maps showing patterns of geographical variation in allele frequencies across species distribution ranges based on the inversed distance weighted interpolation of the values calculated for each locality of Podarcis bocagei (top) and Podarcis carbonelli (bottom) for the first (A, D), second (B, E), and third (C, F) axes of factorial correspondence analyses. White represents higher and black lower factor score values.
Figure 1 in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 1. Distribution and localities analysed in this study for Podarcis bocagei (dark grey, circles) and Podarcis carbonelli (light grey, squares) (A), and median-joining networks of mtDNA haplotypes for P. bocagei (B) and P. carbonelli (C) reproduced from Pinho et al., 2007a. Dashed lines indicate the mtDNA groups and corresponding geographical distributions in P. carbonelli. Locality codes and sampling details are given in Table 1. Notice that the distribution shown for P. carbonelli is based on a 20 km buffer of occupied 10 ¥ 10 km UTM (Universal Transverse Mercator) squares and is therefore amplified compared to that in Pinho et al., 2007a. This was necessary in order to visualize geographical patterns of genetic variation (see Material and methods).
Figure 1 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 1. Distribution of Potomida littoralis. Numbered points show sampling localities.
Drivers of genetic differentiation and recent evolutionary history of an Eurasian wild pea
<p class="17"><strong><span>Aim:</span></strong> Genetic diversity is a major determinant for the capacity of species to persist and adapt to their environments. Unraveling the factors affecting genetic differentiation is crucial to understand how genetic diversity is shaped and species may react to changing environments. We investigated the drivers of genetic differentiation and their interplay with the evolutionary history in a wild pea to test how those may have affected the distribution of genetic diversity.</p> <p class="17"><strong><span>Location: </span></strong>Mediterranean basin, western Asia</p> <p class="17"><strong><span>Taxon: </span></strong><em><span>Pisum sativum </span></em>(Fabaceae)</p> <p class="17"><strong><span>Methods:</span></strong> We employed RAD-seqencing to test the influence of environmental factors on genetic differentiation in a collection of 81 wild pea samples. Demographic history and past expansion patterns were analyzed to test their effect on the current distribution of genetic diversity. Association of SNPs with environmental variables were analyses to find signatures of local adaptation.</p> <p class="17"><strong><span>Results:</span></strong> Genetic variation was geographically structured into six distinct genetic clusters. The effect of the tested factors influencing genetic differentiation was variable among genetic clusters. Climate predictors were most important in all clusters. Land use was more important in clusters from areas strongly influenced by human land use, especially by agriculture. We found statistically significant associations of 3,623 SNPs with environmental variables. Most of them were correlated with latitude followed by temperature. Wild peas went through a genetic bottleneck during the last glacial period followed by population recovery. The detected range expansion patterns suggested an eastward range expansion of the European cluster to Turkey and thereof southwards and eastwards.</p> <p class="17"><strong><span>Main conclusion:</span></strong> Our results suggest that it is insufficient to consider the present distribution of genetic diversity alone but rather consider it in conjunction with the evolutionary history of the respective species. Moreover, the distribution of genetic variation has to be viewed in the context of its hierarchical structure and the environment of its genetic entities to understand how this variation was shaped and may change in the future.</p>
Drivers of genetic differentiation and recent evolutionary history of an Eurasian wild pea
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Recent evolutionary history predicts population but not ecosystem level patterns
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