Automated Author ProfileMcGill, Brian J.
McGill, Brian J.
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: 7.6 (sum of 18 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
<b>Abstract</b><br/>Humans have elevated global extinction rates and thus lowered global-scale species richness. However, there is no a priori reason to expect that losses of global species richness should always, or even often, trickle down to losses of species richness at regional and local scales, even though this relationship is often assumed. Here, we show that scale can modulate our estimates of species richness change through time in the face of anthropogenic pressures, but not in a unidirectional way. Instead, the magnitude of species richness change through time can increase, decrease, reverse, or be unimodal across spatial scales. Using several case studies, we show different forms of scale-dependent richness change through time in the face of anthropogenic pressures. For example, Central American corals show a homogenization pattern, where small scale richness is largely unchanged through time, while larger scale richness change is highly negative. Alternatively, birds in North America showed a differentiation effect, where species richness was again largely unchanged through time at small scales, but was more positive at larger scales. Finally, we collated data from a heterogeneous set of studies of different taxa measured through time from sites ranging from small plots to entire continents, and found highly variable patterns that nevertheless imply complex scale-dependence in several taxa. In summary, understanding how biodiversity is changing in the Anthropocene requires an explicit recognition of the influence of spatial scale, and we conclude with some recommendations for how to better incorporate scale into our estimates of change.
Authors
- Chase, Jonathan M. ;
- McGill, Brian J. ;
- Thompson, Patrick L. ;
- Antão, Laura H. ;
- Bates, Amanda E. ;
- Blowes, Shane A. ;
- Dornelas, Maria ;
- Gonzalez, Andrew ;
- Magurran, Anne E. ;
- Supp, Sarah R. ;
- Winter, Marten ;
- Bjorkmann, Anne D. ;
- Bruelheide, Helge ;
- Byrnes, Jarrett E.K. ;
- Cabral, Juliano Sarmento ;
- Ehali, Robin ;
- Gomez, Catalina ;
- Guzman, Hector M. ;
- Isbell, Forest ;
- Myers-Smith, Isla H. ;
- Jones, Holly P. ;
- Hines, Jessica ;
- Vellend, Mark ;
- Waldock, Conor ;
- O'Connor, Mary
No description available
Authors
- Chase, Jonathan ;
- McGill, Brian J. ;
- Thompson, Patrick ;
- Antão, Laura ;
- Bates, Amanda ;
- Blowes, Shane ;
- Dornelas, Maria ;
- Gonzalez, Andrew ;
- Magurran, Anne ;
- Supp, Sarah ;
- Winter, Marten ;
- Bjorkmann, Anne ;
- Bruelheide, Helge ;
- Byrnes, Jarrett ;
- Cabral, Juliano Sarmento ;
- Ehali, Robin ;
- Gomez, Catalina ;
- Guzman, Hector ;
- Isbell, Forest ;
- Myers-Smith, Isla ;
- Jones, Holly ;
- Hines, Jessica ;
- Vellend, Mark ;
- Waldock, Conor ;
- O'Connor, Mary
A description of the algorithms and usage of code for fitting analytical ZSM.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
A description of the algorithms and usage of code for fitting analytical ZSM.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
Details of statistical methods for comparing SAD fits.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
Details of statistical methods for comparing SAD fits.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
Summaries of empirical tests.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
Summaries of empirical tests.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
Detailed comparison of neutral models.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.
File List Individual Matlab files estimate_anal_ZSM.m
estimate_anal_zsm_theta.m
expphi.m
getphi.m
isvector.m
myhistc.m
philocal.m
plotexpphi.m
sadhist.m
solvetheta.m
All files at once anzsm.zip - Contains all Matlab files Description This paper will calculate Eq. 7 in Volkov et al. (2003. Nature 424:1035–1037) and estimate the parameters θ and m that make the ZSM best fit a particular data set. Details are found at Appendix D.
Authors
- McGill, Brian J. ;
- Maurer, Brian A. ;
- Weiser, Michael D.