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Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test

Overview of attention for article published in Scientific Reports, June 2015
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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2 X users
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1 Wikipedia page

Citations

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4 Dimensions

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27 Mendeley
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Title
Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test
Published in
Scientific Reports, June 2015
DOI 10.1038/srep10702
Pubmed ID
Authors

J. R. Matchett, Philip B. Stark, Steven M. Ostoja, Roland A. Knapp, Heather C. McKenny, Matthew L. Brooks, William T. Langford, Lucas N. Joppa, Eric L. Berlow

Abstract

Statistical models often use observational data to predict phenomena; however, interpreting model terms to understand their influence can be problematic. This issue poses a challenge in species conservation where setting priorities requires estimating influences of potential stressors using observational data. We present a novel approach for inferring influence of a rare stressor on a rare species by blending predictive models with nonparametric permutation tests. We illustrate the approach with two case studies involving rare amphibians in Yosemite National Park, USA. The endangered frog, Rana sierrae, is known to be negatively impacted by non-native fish, while the threatened toad, Anaxyrus canorus, is potentially affected by packstock. Both stressors and amphibians are rare, occurring in ~10% of potential habitat patches. We first predict amphibian occupancy with a statistical model that includes all predictors but the stressor to stratify potential habitat by predicted suitability. A stratified permutation test then evaluates the association between stressor and amphibian, all else equal. Our approach confirms the known negative relationship between fish and R. sierrae, but finds no evidence of a negative relationship between current packstock use and A. canorus breeding. Our statistical approach has potential broad application for deriving understanding (not just prediction) from observational data.

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X Demographics

The data shown below were collected from the profiles of 2 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 26%
Student > Ph. D. Student 6 22%
Student > Master 4 15%
Professor 3 11%
Student > Bachelor 1 4%
Other 2 7%
Unknown 4 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 30%
Environmental Science 7 26%
Mathematics 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Computer Science 1 4%
Other 1 4%
Unknown 7 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 March 2019.
All research outputs
#6,589,683
of 23,312,088 outputs
Outputs from Scientific Reports
#45,202
of 126,005 outputs
Outputs of similar age
#77,570
of 268,905 outputs
Outputs of similar age from Scientific Reports
#599
of 1,760 outputs
Altmetric has tracked 23,312,088 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 126,005 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. 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 268,905 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 70% of its contemporaries.
We're also able to compare this research output to 1,760 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.