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Managing more than the mean: using quantile regression to identify factors related to large elk groups

Overview of attention for article published in Journal of Applied Ecology, August 2015
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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 (91st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

blogs
1 blog
twitter
13 tweeters
googleplus
1 Google+ user

Citations

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11 Dimensions

Readers on

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65 Mendeley
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Title
Managing more than the mean: using quantile regression to identify factors related to large elk groups
Published in
Journal of Applied Ecology, August 2015
DOI 10.1111/1365-2664.12514
Pubmed ID
Authors

Angela Brennan, Paul C. Cross, Scott Creel

Abstract

Animal group size distributions are often right-skewed, whereby most groups are small, but most individuals occur in larger groups that may also disproportionately affect ecology and policy. In this case, examining covariates associated with upper quantiles of the group size distribution could facilitate better understanding and management of large animal groups.We studied wintering elk groups in Wyoming, where group sizes span several orders of magnitude, and issues of disease, predation and property damage are affected by larger group sizes. We used quantile regression to evaluate relationships between the group size distribution and variables of land use, habitat, elk density and wolf abundance to identify conditions important to larger elk groups.We recorded 1263 groups ranging from 1 to 1952 elk and found that across all quantiles of group size, group sizes were larger in open habitat and on private land, but the largest effect occurred between irrigated and non-irrigated land [e.g. the 90th quantile group size increased by 135 elk (95% CI = 42, 227) on irrigation].Only upper quantile group sizes were positively related to broad-scale measures of elk density and wolf abundance. For wolf abundance, this effect was greater on elk groups found in open habitats and private land than those in closed habitats or public land. If we had limited our analysis to mean or median group sizes, we would not have detected these effects. Synthesis and applications. Our analysis of elk group size distributions using quantile regression suggests that private land, irrigation, open habitat, elk density and wolf abundance can affect large elk group sizes. Thus, to manage larger groups by removal or dispersal of individuals, we recommend incentivizing hunting on private land (particularly if irrigated) during the regular and late hunting seasons, promoting tolerance of wolves on private land (if elk aggregate in these areas to avoid wolves) and creating more winter range and varied habitats. Relationships to the variables of interest also differed by quantile, highlighting the importance of using quantile regression to examine response variables more completely to uncover relationships important to conservation and management.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
Australia 2 3%
United Kingdom 1 2%
Mexico 1 2%
Canada 1 2%
Unknown 56 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 25%
Researcher 15 23%
Student > Master 8 12%
Student > Bachelor 7 11%
Lecturer 4 6%
Other 9 14%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 60%
Environmental Science 13 20%
Mathematics 1 2%
Computer Science 1 2%
Social Sciences 1 2%
Other 1 2%
Unknown 9 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 18 December 2015.
All research outputs
#884,184
of 13,182,016 outputs
Outputs from Journal of Applied Ecology
#726
of 2,476 outputs
Outputs of similar age
#20,831
of 233,954 outputs
Outputs of similar age from Journal of Applied Ecology
#14
of 34 outputs
Altmetric has tracked 13,182,016 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,476 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.4. This one has gotten more attention than average, scoring higher than 70% 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 233,954 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 34 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 58% of its contemporaries.