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Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data

Overview of attention for article published in Oecologia, March 2016
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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 (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

policy
1 policy source
twitter
5 tweeters
facebook
1 Facebook page

Citations

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19 Dimensions

Readers on

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91 Mendeley
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Title
Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data
Published in
Oecologia, March 2016
DOI 10.1007/s00442-016-3594-5
Pubmed ID
Authors

Michael R. Ebinger, Mark A. Haroldson, Frank T. van Manen, Cecily M. Costello, Daniel D. Bjornlie, Daniel J. Thompson, Kerry A. Gunther, Jennifer K. Fortin, Justin E. Teisberg, Shannon R. Pils, P. J. White, Steven L. Cain, Paul C. Cross

Abstract

Global positioning system (GPS) wildlife collars have revolutionized wildlife research. Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears (Ursus arctos), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations (n = 732 site investigations; 2004-2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods-leaving out individual clusters, or leaving out individual bears-showed that correct prediction of bear visitation to large-biomass carcasses was 78-88 %, whereas the false-positive rate was 18-24 %. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002-2011) and examined trends in carcass visitation during fall hyperphagia (September-October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 2%
Spain 1 1%
Unknown 88 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 21%
Researcher 18 20%
Student > Ph. D. Student 13 14%
Student > Bachelor 6 7%
Student > Doctoral Student 5 5%
Other 10 11%
Unknown 20 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 54%
Environmental Science 15 16%
Veterinary Science and Veterinary Medicine 2 2%
Computer Science 2 2%
Earth and Planetary Sciences 1 1%
Other 3 3%
Unknown 19 21%

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 01 March 2019.
All research outputs
#4,667,766
of 19,298,685 outputs
Outputs from Oecologia
#988
of 3,917 outputs
Outputs of similar age
#69,166
of 273,340 outputs
Outputs of similar age from Oecologia
#20
of 64 outputs
Altmetric has tracked 19,298,685 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,917 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has gotten more attention than average, scoring higher than 74% 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 273,340 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 74% of its contemporaries.
We're also able to compare this research output to 64 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 70% of its contemporaries.