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Space can substitute for time in predicting climate-change effects on biodiversity

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, May 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

blogs
1 blog
policy
2 policy sources
twitter
53 tweeters
reddit
1 Redditor

Citations

dimensions_citation
313 Dimensions

Readers on

mendeley
777 Mendeley
citeulike
3 CiteULike
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Title
Space can substitute for time in predicting climate-change effects on biodiversity
Published in
Proceedings of the National Academy of Sciences of the United States of America, May 2013
DOI 10.1073/pnas.1220228110
Pubmed ID
Authors

Jessica L. Blois, John W. Williams, Matthew C. Fitzpatrick, Stephen T. Jackson, Simon Ferrier

Abstract

"Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

Twitter Demographics

The data shown below were collected from the profiles of 53 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 777 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 2%
Brazil 10 1%
United Kingdom 9 1%
Germany 7 <1%
Switzerland 3 <1%
France 3 <1%
Mexico 2 <1%
Spain 2 <1%
Sweden 2 <1%
Other 16 2%
Unknown 706 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 207 27%
Researcher 180 23%
Student > Master 99 13%
Student > Bachelor 49 6%
Student > Doctoral Student 41 5%
Other 130 17%
Unknown 71 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 324 42%
Environmental Science 215 28%
Earth and Planetary Sciences 63 8%
Biochemistry, Genetics and Molecular Biology 13 2%
Engineering 12 2%
Other 41 5%
Unknown 109 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 44. 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 01 January 2020.
All research outputs
#572,469
of 17,406,316 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#10,709
of 89,393 outputs
Outputs of similar age
#5,178
of 164,360 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#159
of 977 outputs
Altmetric has tracked 17,406,316 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 89,393 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.8. This one has done well, scoring higher than 88% of its peers.
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 164,360 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 977 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.