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Forecasting changes in population genetic structure of alpine plants in response to global warming

Overview of attention for article published in Molecular Ecology, April 2012
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386 Mendeley
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Article details
Title
Forecasting changes in population genetic structure of alpine plants in response to global warming
Published in
Molecular Ecology, April 2012
DOI 10.1111/j.1365-294x.2012.05541.x
Pubmed ID
Authors
Abstract

Species range shifts in response to climate and land use change are commonly forecasted with species distribution models based on species occurrence or abundance data. Although appealing, these models ignore the genetic structure of species, and the fact that different populations might respond in different ways because of adaptation to their environment. Here, we introduced ancestry distribution models, that is, statistical models of the spatial distribution of ancestry proportions, for forecasting intra-specific changes based on genetic admixture instead of species occurrence data. Using multi-locus genotypes and extensive geographic coverage of distribution data across the European Alps, we applied this approach to 20 alpine plant species considering a global increase in temperature from 0.25 to 4 °C. We forecasted the magnitudes of displacement of contact zones between plant populations potentially adapted to warmer environments and other populations. While a global trend of movement in a north-east direction was predicted, the magnitude of displacement was species-specific. For a temperature increase of 2 °C, contact zones were predicted to move by 92 km on average (minimum of 5 km, maximum of 212 km) and by 188 km for an increase of 4 °C (minimum of 11 km, maximum of 393 km). Intra-specific turnover-measuring the extent of change in global population genetic structure-was generally found to be moderate for 2 °C of temperature warming. For 4 °C of warming, however, the models indicated substantial intra-specific turnover for ten species. These results illustrate that, in spite of unavoidable simplifications, ancestry distribution models open new perspectives to forecast population genetic changes within species and complement more traditional distribution-based approaches.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 386 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 9 2%
Germany 4 1%
United Kingdom 3 <1%
Canada 2 <1%
South Africa 1 <1%
Uganda 1 <1%
Sweden 1 <1%
Portugal 1 <1%
Poland 1 <1%
Other 7 2%
Unknown 356 92%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 106 27%
Student > Ph. D. Student 95 25%
Student > Master 45 12%
Professor > Associate Professor 27 7%
Student > Doctoral Student 23 6%
Other 54 14%
Unknown 36 9%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 238 62%
Environmental Science 63 16%
Biochemistry, Genetics and Molecular Biology 21 5%
Medicine and Dentistry 4 1%
Social Sciences 3 <1%
Other 7 2%
Unknown 50 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 January 2017.
All research outputs
#15,593,749
of 24,712,008 outputs
Outputs from Molecular Ecology
#5,341
of 6,623 outputs
Outputs of similar age
#100,013
of 165,954 outputs
Outputs of similar age from Molecular Ecology
#53
of 79 outputs
Altmetric has tracked 24,712,008 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,623 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one is in the 17th percentile – i.e., 17% 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 165,954 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.