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Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

Overview of attention for article published in Global Change Biology, October 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

blogs
2 blogs
twitter
14 X users
facebook
2 Facebook pages

Citations

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43 Dimensions

Readers on

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70 Mendeley
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Title
Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub
Published in
Global Change Biology, October 2017
DOI 10.1111/gcb.13900
Pubmed ID
Authors

Katherine M. Renwick, Caroline Curtis, Andrew R. Kleinhesselink, Daniel Schlaepfer, Bethany A. Bradley, Cameron L. Aldridge, Benjamin Poulter, Peter B. Adler

Abstract

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically-derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties, and should help land managers to maximize the value of conservation investments. This article is protected by copyright. All rights reserved.

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X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 30%
Researcher 15 21%
Student > Master 8 11%
Student > Doctoral Student 5 7%
Other 4 6%
Other 7 10%
Unknown 10 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 39%
Environmental Science 15 21%
Earth and Planetary Sciences 4 6%
Business, Management and Accounting 1 1%
Computer Science 1 1%
Other 6 9%
Unknown 16 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 27 December 2017.
All research outputs
#1,292,559
of 23,001,641 outputs
Outputs from Global Change Biology
#1,638
of 5,742 outputs
Outputs of similar age
#28,587
of 324,683 outputs
Outputs of similar age from Global Change Biology
#46
of 138 outputs
Altmetric has tracked 23,001,641 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,742 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.9. This one has gotten more attention than average, scoring higher than 71% 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 324,683 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 91% of its contemporaries.
We're also able to compare this research output to 138 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 66% of its contemporaries.