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Predicting the Occurrence of Cave-Inhabiting Fauna Based on Features of the Earth Surface Environment

Overview of attention for article published in PLoS ONE, August 2016
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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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
twitter
11 tweeters

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
59 Mendeley
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Title
Predicting the Occurrence of Cave-Inhabiting Fauna Based on Features of the Earth Surface Environment
Published in
PLoS ONE, August 2016
DOI 10.1371/journal.pone.0160408
Pubmed ID
Authors

Mary C. Christman, Daniel H. Doctor, Matthew L. Niemiller, David J. Weary, John A. Young, Kirk S. Zigler, David C. Culver

Abstract

One of the most challenging fauna to study in situ is the obligate cave fauna because of the difficulty of sampling. Cave-limited species display patchy and restricted distributions, but it is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management is more easily obtained. We examined the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative in the eastern United States (Illinois to Virginia and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. Models successfully predicted the presence of a group greater than 65% of the time (mean = 88%) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the presence of species in understudied areas.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 2%
Spain 1 2%
United States 1 2%
Unknown 56 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 34%
Student > Master 9 15%
Student > Ph. D. Student 7 12%
Student > Bachelor 6 10%
Other 4 7%
Other 8 14%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 61%
Environmental Science 9 15%
Biochemistry, Genetics and Molecular Biology 4 7%
Psychology 1 2%
Earth and Planetary Sciences 1 2%
Other 1 2%
Unknown 7 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 06 August 2020.
All research outputs
#1,526,526
of 15,792,083 outputs
Outputs from PLoS ONE
#22,157
of 156,768 outputs
Outputs of similar age
#34,776
of 266,740 outputs
Outputs of similar age from PLoS ONE
#542
of 4,313 outputs
Altmetric has tracked 15,792,083 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 156,768 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one has done well, scoring higher than 85% 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 266,740 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 4,313 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.