Automated Author ProfileO'Meara, Brian
0000-0002-0337-5997
O'Meara, Brian
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: 20.0 (sum of 18 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Applications of molecular phylogenetic approaches have uncovered evidence of hybridization across numerous clades of life, yet the environmental factors responsible for driving opportunities for hybridization remain obscure. Verbal models implicating geographic range shifts that brought species together during the Pleistocene have often been invoked, but quantitative tests using paleoclimatic data are needed to validate these models. Here, we produce a phylogeny for Heuchereae, a clade of 15 genera and 83 species in Saxifragaceae, with complete sampling of recognized species, using 277 nuclear loci and nearly complete chloroplast genomes. We then employ an improved framework with a coalescent simulation approach to test and confirm previous hybridization hypotheses and identify one new intergeneric hybridization event. Focusing on the North American distribution of Heuchereae, we introduce and implement a newly developed approach to reconstruct potential past distributions for ancestral lineages across all species in the clade and across a paleoclimatic record extending from the late Pliocene. Time calibration based on both nuclear and chloroplast trees recovers a mid- to late-Pleistocene date for most inferred hybridization events, a timeframe concomitant with repeated geographic range restriction into overlapping refugia. Our results indicate an important role for past episodes of climate change, and the contrasting responses of species with differing ecological strategies, in generating novel patterns of range contact among plant communities and therefore new opportunities for hybridization. The new ancestral niche method flexibly models the shape of niche while incorporating diverse sources of uncertainty and will be an important addition to the current comparative methods toolkit.
Authors
- Folk, Ryan A. ;
- Gaynor, Michelle L. ;
- Engle-Wrye, Nicholas J. ;
- O’Meara, Brian C. ;
- Soltis, Pamela S. ;
- Soltis, Douglas E. ;
- Guralnick, Robert P. ;
- Smith, Stephen A. ;
- Grady, Charles J. ;
- Okuyama, Yudai
This file contains the data, the R statistical environment scripts and the results for the analyses of the biogeography of Tropical and Temperate conifers. The file is a compressed file in 'tar.gz' format created on a Mac computer.
Authors
- Caetano, Daniel S. ;
- O'Meara, Brian ;
- Beaulieu, Jeremy
This file contains the data, the R statistical environment scripts and the results for the analyses of the biogeography of Tropical and Temperate conifers. The file is a compressed file in 'tar.gz' format created on a Mac computer.
Authors
- Caetano, Daniel S. ;
- O'Meara, Brian ;
- Beaulieu, Jeremy
Data, results and code for the simulation study in the Supplementary Material. Data is stored in 'RData' format for the R statistical programming. Code is available as a Markdown document (both 'Rmd' and 'pdf' formats). Code and markdown was created in RStudio. Results are presented as '.zip' packages.
Plese see more information on the 'README.txt' file
Authors
- Caetano, Daniel S. ;
- O'Meara, Brian ;
- Beaulieu, Jeremy
Data, results and code for the simulation study in the Supplementary Material. Data is stored in 'RData' format for the R statistical programming. Code is available as a Markdown document (both 'Rmd' and 'pdf' formats). Code and markdown was created in RStudio. Results are presented as '.zip' packages.
Plese see more information on the 'README.txt' file
Authors
- Caetano, Daniel S. ;
- O'Meara, Brian ;
- Beaulieu, Jeremy
Data set, code and results for the simulations. Data is stored in 'RData' format for the R statistical environment. Code is in Markdown format (both 'Rmd' and the exported 'pdf' files). Results are compressed in .zip files and separated by simulation scenario.
See 'README.txt' file for more information.
Authors
- Caetano, Daniel S. ;
- O'Meara, Brian ;
- Beaulieu, Jeremy
Data set, code and results for the simulations. Data is stored in 'RData' format for the R statistical environment. Code is in Markdown format (both 'Rmd' and the exported 'pdf' files). Results are compressed in .zip files and separated by simulation scenario.
See 'README.txt' file for more information.
Authors
- Caetano, Daniel S. ;
- O'Meara, Brian ;
- Beaulieu, Jeremy
No description available
Authors
- Carstens, Bryan C. ;
- Morales, Ariadna A. ;
- Jackson, Nathan ;
- O'Meara, Brian C.
No description available
Authors
- Morales, Ariadna E. ;
- Jackson, Nathan D. ;
- Dewey, Tanya A. ;
- O'Meara, Brian C. ;
- Carstens, Bryan C.