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Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method

Overview of attention for article published in Ecology and Evolution, March 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

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10 tweeters
facebook
1 Facebook page

Citations

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13 Dimensions

Readers on

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41 Mendeley
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Title
Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method
Published in
Ecology and Evolution, March 2017
DOI 10.1002/ece3.2884
Pubmed ID
Authors

Kendra Spence Cheruvelil, Shuai Yuan, Katherine E. Webster, Pang‐Ning Tan, Jean‐François Lapierre, Sarah M. Collins, C. Emi Fergus, Caren E. Scott, Emily Norton Henry, Patricia A. Soranno, Christopher T. Filstrup, Tyler Wagner

Abstract

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question-How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km(2)); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation-approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 44%
Student > Ph. D. Student 9 22%
Student > Doctoral Student 3 7%
Student > Master 3 7%
Student > Bachelor 1 2%
Other 2 5%
Unknown 5 12%
Readers by discipline Count As %
Environmental Science 14 34%
Agricultural and Biological Sciences 11 27%
Earth and Planetary Sciences 3 7%
Computer Science 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 2 5%
Unknown 9 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 April 2017.
All research outputs
#4,249,331
of 17,902,988 outputs
Outputs from Ecology and Evolution
#2,093
of 5,768 outputs
Outputs of similar age
#75,066
of 273,793 outputs
Outputs of similar age from Ecology and Evolution
#65
of 185 outputs
Altmetric has tracked 17,902,988 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,768 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one has gotten more attention than average, scoring higher than 63% 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 273,793 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 72% of its contemporaries.
We're also able to compare this research output to 185 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 65% of its contemporaries.