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Designing ecological climate change impact assessments to reflect key climatic drivers

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

  • Good Attention Score compared to outputs of the same age (67th percentile)

Mentioned by

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9 X users
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2 Facebook pages

Citations

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29 Dimensions

Readers on

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130 Mendeley
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Title
Designing ecological climate change impact assessments to reflect key climatic drivers
Published in
Global Change Biology, March 2017
DOI 10.1111/gcb.13653
Pubmed ID
Authors

Helen R. Sofaer, Joseph J. Barsugli, Catherine S. Jarnevich, John T. Abatzoglou, Marian K. Talbert, Brian W. Miller, Jeffrey T. Morisette

Abstract

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling. This article is protected by copyright. All rights reserved.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Canada 1 <1%
Brazil 1 <1%
Unknown 127 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 22%
Student > Ph. D. Student 28 22%
Student > Master 16 12%
Other 6 5%
Student > Doctoral Student 6 5%
Other 19 15%
Unknown 26 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 33%
Environmental Science 39 30%
Earth and Planetary Sciences 4 3%
Social Sciences 2 2%
Engineering 2 2%
Other 8 6%
Unknown 32 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 03 June 2017.
All research outputs
#6,088,378
of 22,952,268 outputs
Outputs from Global Change Biology
#4,096
of 5,731 outputs
Outputs of similar age
#99,119
of 311,177 outputs
Outputs of similar age from Global Change Biology
#72
of 105 outputs
Altmetric has tracked 22,952,268 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 5,731 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.9. This one is in the 28th percentile – i.e., 28% 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 311,177 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 67% of its contemporaries.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.