Automated Author ProfileHamlin, Jennafer A. P.
University of Georgia
Hamlin, Jennafer A. P.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Identifying processes that promote or limit gene flow can help define the ecological and evolutionary history of a species. Furthermore, defining those factors that make up “species boundaries” can provide a definition of the independent evolutionary trajectories of related taxa. For many species, the historic processes that account for their distribution of genetic variation remain unresolved. In this study, we examine the geographic distribution of genetic diversity for two species of Louisiana Irises, Iris brevicaulis and Iris fulva. Specifically, we asked how populations are structured and if population structure coincides with potential barriers to gene flow. We also asked whether there is evidence of hybridization between these two species outside Louisiana hybrid zones. We used a genotyping-by-sequencing approach and sampled a large number of single nucleotide polymorphisms across these species' genomes. Two different population assignment methods were used to resolve population structure in I. brevicaulis; however, there was considerably less population structure in I. fulva. We used a species tree approach to infer phylogenies both within and between populations and species. For I. brevicaulis, the geography of the collection locality was reflected in the phylogeny. The I. fulva phylogeny reflected much less structure than detected for I. brevicaulis. Lastly, combining both species into a phylogenetic analysis resolved two of six populations of I. brevicaulis that shared alleles with I. fulva. Taken together, our results suggest major differences in the level and pattern of connectivity among populations of these two Louisiana Iris species.
Authors
- Hamlin, Jennafer A. P. ;
- Arnold, Michael L.