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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model
|
|---|---|
| Published in |
PLOS ONE, October 2015
|
| DOI | 10.1371/journal.pone.0138759 |
| Pubmed ID | |
| Authors |
Sarah L. Shafer, Patrick J. Bartlein, Elizabeth M. Gray, Richard T. Pelltier |
| Abstract |
Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0-58.0°N latitude by 136.6-103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070-2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 17% |
| United Kingdom | 1 | 17% |
| India | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 67% |
| Members of the public | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 71 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 23% |
| Student > Ph. D. Student | 11 | 15% |
| Student > Master | 6 | 8% |
| Other | 6 | 8% |
| Student > Bachelor | 5 | 7% |
| Other | 7 | 10% |
| Unknown | 20 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Environmental Science | 19 | 27% |
| Earth and Planetary Sciences | 12 | 17% |
| Agricultural and Biological Sciences | 10 | 14% |
| Arts and Humanities | 2 | 3% |
| Social Sciences | 2 | 3% |
| Other | 1 | 1% |
| Unknown | 25 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 November 2015.
All research outputs
#8,136,162
of 24,401,594 outputs
Outputs from PLOS ONE
#103,940
of 210,531 outputs
Outputs of similar age
#98,580
of 288,290 outputs
Outputs of similar age from PLOS ONE
#2,161
of 5,532 outputs
Altmetric has tracked 24,401,594 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 210,531 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.6. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 288,290 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 5,532 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.