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Genetic structure of Daphnia galeata populations in Eastern China.

Wei W, Gießler S, Wolinska J, Ma X, Yang Z, Hu W, Yin M - PLoS ONE (2015)

Bottom Line: Clonal diversity was high in all D. galeata populations, and most samples showed no deviation from Hardy-Weinberg equilibrium, indicating that clonal selection had little effect on the genetic diversity.Overall, populations did not cluster by geographical origin.Further studies will show if the observed pattern can be explained by natural colonization processes or by recent anthropogenic impact on predominantly artificial lakes.

View Article: PubMed Central - PubMed

Affiliation: Yangzhou University, College of Animal Science and Technology, Yangzhou, China.

ABSTRACT
This study presents the first examination of the genetic structure of Daphnia longispina complex populations in Eastern China. Only one species, D. galeata, was present across the eight investigated lakes; as identified by taxon assignment using allelic variation at 15 microsatellite loci. Three genetically differentiated D. galeata subgroups emerged independent of the type of statistical analysis applied. Thus, Bayesian clustering, discriminant analysis based on results from factorial correspondence analysis, and UPGMA clustering consistently showed that populations from two neighbouring lakes were genetically separated from a mixture of genotypes found in other lakes, which formed another two subgroups. Clonal diversity was high in all D. galeata populations, and most samples showed no deviation from Hardy-Weinberg equilibrium, indicating that clonal selection had little effect on the genetic diversity. Overall, populations did not cluster by geographical origin. Further studies will show if the observed pattern can be explained by natural colonization processes or by recent anthropogenic impact on predominantly artificial lakes.

No MeSH data available.


Relatedness among eight D. galeata populations from Eastern China (based on up to 15 microsatellite loci).(a) Discriminant analysis on FCA scores (four factorial axes) was used to discriminate among groups of individuals from eight lakes. Shown are values from the first two discriminant functions per individual and eight group centroids (full symbols) representing the eight lakes. The predicted lake membership of individuals in open symbols. (b) UPGMA clustering of Nei’s genetic distances.
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pone.0120168.g002: Relatedness among eight D. galeata populations from Eastern China (based on up to 15 microsatellite loci).(a) Discriminant analysis on FCA scores (four factorial axes) was used to discriminate among groups of individuals from eight lakes. Shown are values from the first two discriminant functions per individual and eight group centroids (full symbols) representing the eight lakes. The predicted lake membership of individuals in open symbols. (b) UPGMA clustering of Nei’s genetic distances.

Mentions: In the FCA run on 250 unique MLGs (allowing missing data at up to four loci) originating from the eight Chinese populations, the first two factorial axes explained 10.46% of the variance in the data (Fig. 2a). Discriminant analysis on all extracted factorial axis scores (eight lakes as grouping factor) resulted in four functions. The first function with an eigenvalue of 2.97 already explained 86.4% of the variance, while the second function explained additional 11.2%. Group centroids based on the first two functions revealed that BYH and HZH were separated from the other six populations by scores on function 1 (Fig. 2a). The close neighbourhood of group centroids from the latter six lakes with respect to function 1 revealed that the likelihood of misclassification of individuals by MLG to a wrong population is high. Accordingly, only 48.0% of individuals are correctly reclassified to their lake of origin. The separation of BYH and HZH from other populations was further confirmed by the pattern in the UPGMA tree (Fig. 2b) and the clustering in STRUCTURE (Fig. 3). Additionally, the other six populations were split into two groups, by both methods (Figs. 2b and 3), a slight differentiation was also visible regarding the group centroids of function 2 in DFCA (Fig. 2a). Accordingly, in terms of STRUCTURE, K = 3 was the best fit (Fig. 3a). A single cluster, containing BYH and HZH, emerged by all methods, but because the resolution among the remaining six populations was low, STRUCTURE identified one additional cluster of two lakes and another cluster of four lakes while UPGMA and DFCA weakly resolved among two clusters of three lakes each. Interestingly, the second highest peak of ΔK (i.e. K = 7, Fig. 3a), implies that further substructure can be found among seven units only, instead of among all eight distinct lakes (lakes BYH and HZH could not be separated at this level; data not shown). This result suggests, that lake-specific substructure exists but is only of minor importance, while the major differentiation is among three groups of lakes supported by all analyses. Notably, the results from STRUCTURE, DFCA and UPGMA suggest that besides populations from the two neighbouring lakes HZH and BYH, most populations did not cluster by geographic region (S1 Fig.).


Genetic structure of Daphnia galeata populations in Eastern China.

Wei W, Gießler S, Wolinska J, Ma X, Yang Z, Hu W, Yin M - PLoS ONE (2015)

Relatedness among eight D. galeata populations from Eastern China (based on up to 15 microsatellite loci).(a) Discriminant analysis on FCA scores (four factorial axes) was used to discriminate among groups of individuals from eight lakes. Shown are values from the first two discriminant functions per individual and eight group centroids (full symbols) representing the eight lakes. The predicted lake membership of individuals in open symbols. (b) UPGMA clustering of Nei’s genetic distances.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4358959&req=5

pone.0120168.g002: Relatedness among eight D. galeata populations from Eastern China (based on up to 15 microsatellite loci).(a) Discriminant analysis on FCA scores (four factorial axes) was used to discriminate among groups of individuals from eight lakes. Shown are values from the first two discriminant functions per individual and eight group centroids (full symbols) representing the eight lakes. The predicted lake membership of individuals in open symbols. (b) UPGMA clustering of Nei’s genetic distances.
Mentions: In the FCA run on 250 unique MLGs (allowing missing data at up to four loci) originating from the eight Chinese populations, the first two factorial axes explained 10.46% of the variance in the data (Fig. 2a). Discriminant analysis on all extracted factorial axis scores (eight lakes as grouping factor) resulted in four functions. The first function with an eigenvalue of 2.97 already explained 86.4% of the variance, while the second function explained additional 11.2%. Group centroids based on the first two functions revealed that BYH and HZH were separated from the other six populations by scores on function 1 (Fig. 2a). The close neighbourhood of group centroids from the latter six lakes with respect to function 1 revealed that the likelihood of misclassification of individuals by MLG to a wrong population is high. Accordingly, only 48.0% of individuals are correctly reclassified to their lake of origin. The separation of BYH and HZH from other populations was further confirmed by the pattern in the UPGMA tree (Fig. 2b) and the clustering in STRUCTURE (Fig. 3). Additionally, the other six populations were split into two groups, by both methods (Figs. 2b and 3), a slight differentiation was also visible regarding the group centroids of function 2 in DFCA (Fig. 2a). Accordingly, in terms of STRUCTURE, K = 3 was the best fit (Fig. 3a). A single cluster, containing BYH and HZH, emerged by all methods, but because the resolution among the remaining six populations was low, STRUCTURE identified one additional cluster of two lakes and another cluster of four lakes while UPGMA and DFCA weakly resolved among two clusters of three lakes each. Interestingly, the second highest peak of ΔK (i.e. K = 7, Fig. 3a), implies that further substructure can be found among seven units only, instead of among all eight distinct lakes (lakes BYH and HZH could not be separated at this level; data not shown). This result suggests, that lake-specific substructure exists but is only of minor importance, while the major differentiation is among three groups of lakes supported by all analyses. Notably, the results from STRUCTURE, DFCA and UPGMA suggest that besides populations from the two neighbouring lakes HZH and BYH, most populations did not cluster by geographic region (S1 Fig.).

Bottom Line: Clonal diversity was high in all D. galeata populations, and most samples showed no deviation from Hardy-Weinberg equilibrium, indicating that clonal selection had little effect on the genetic diversity.Overall, populations did not cluster by geographical origin.Further studies will show if the observed pattern can be explained by natural colonization processes or by recent anthropogenic impact on predominantly artificial lakes.

View Article: PubMed Central - PubMed

Affiliation: Yangzhou University, College of Animal Science and Technology, Yangzhou, China.

ABSTRACT
This study presents the first examination of the genetic structure of Daphnia longispina complex populations in Eastern China. Only one species, D. galeata, was present across the eight investigated lakes; as identified by taxon assignment using allelic variation at 15 microsatellite loci. Three genetically differentiated D. galeata subgroups emerged independent of the type of statistical analysis applied. Thus, Bayesian clustering, discriminant analysis based on results from factorial correspondence analysis, and UPGMA clustering consistently showed that populations from two neighbouring lakes were genetically separated from a mixture of genotypes found in other lakes, which formed another two subgroups. Clonal diversity was high in all D. galeata populations, and most samples showed no deviation from Hardy-Weinberg equilibrium, indicating that clonal selection had little effect on the genetic diversity. Overall, populations did not cluster by geographical origin. Further studies will show if the observed pattern can be explained by natural colonization processes or by recent anthropogenic impact on predominantly artificial lakes.

No MeSH data available.