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Impacts of Human Recreation on Brown Bears (Ursus arctos): A Review and New Management Tool

Overview of attention for article published in PLoS ONE, January 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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
1 news outlet
twitter
8 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
115 Mendeley
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Title
Impacts of Human Recreation on Brown Bears (Ursus arctos): A Review and New Management Tool
Published in
PLoS ONE, January 2016
DOI 10.1371/journal.pone.0141983
Pubmed ID
Authors

Jennifer K. Fortin, Karyn D. Rode, Grant V. Hilderbrand, James Wilder, Sean Farley, Carole Jorgensen, Bruce G. Marcot

Abstract

Increased popularity of recreational activities in natural areas has led to the need to better understand their impacts on wildlife. The majority of research conducted to date has focused on behavioral effects from individual recreations, thus there is a limited understanding of the potential for population-level or cumulative effects. Brown bears (Ursus arctos) are the focus of a growing wildlife viewing industry and are found in habitats frequented by recreationists. Managers face difficult decisions in balancing recreational opportunities with habitat protection for wildlife. Here, we integrate results from empirical studies with expert knowledge to better understand the potential population-level effects of recreational activities on brown bears. We conducted a literature review and Delphi survey of brown bear experts to better understand the frequencies and types of recreations occurring in bear habitats and their potential effects, and to identify management solutions and research needs. We then developed a Bayesian network model that allows managers to estimate the potential effects of recreational management decisions in bear habitats. A higher proportion of individual brown bears in coastal habitats were exposed to recreation, including photography and bear-viewing than bears in interior habitats where camping and hiking were more common. Our results suggest that the primary mechanism by which recreation may impact brown bears is through temporal and spatial displacement with associated increases in energetic costs and declines in nutritional intake. Killings in defense of life and property were found to be minimally associated with recreation in Alaska, but are important considerations in population management. Regulating recreation to occur predictably in space and time and limiting recreation in habitats with concentrated food resources reduces impacts on food intake and may thereby, reduce impacts on reproduction and survival. Our results suggest that decisions managers make about regulating recreational activities in time and space have important consequences for bear populations. The Bayesian network model developed here provides a new tool for managers to balance demands of multiple recreational activities while supporting healthy bear populations.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 114 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 37 32%
Student > Ph. D. Student 19 17%
Researcher 15 13%
Student > Bachelor 15 13%
Unspecified 12 10%
Other 17 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 43%
Environmental Science 32 28%
Unspecified 16 14%
Social Sciences 4 3%
Computer Science 3 3%
Other 10 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 June 2018.
All research outputs
#919,851
of 13,040,510 outputs
Outputs from PLoS ONE
#15,694
of 140,553 outputs
Outputs of similar age
#29,512
of 335,779 outputs
Outputs of similar age from PLoS ONE
#582
of 5,232 outputs
Altmetric has tracked 13,040,510 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 140,553 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.9. This one has done well, scoring higher than 88% 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 335,779 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 5,232 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.