↓ Skip to main content

Translating statistical species-habitat models to interactive decision support tools

Overview of attention for article published in PLOS ONE, December 2017
Altmetric Badge

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 (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
13 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
43 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Translating statistical species-habitat models to interactive decision support tools
Published in
PLOS ONE, December 2017
DOI 10.1371/journal.pone.0188244
Pubmed ID
Authors

Lyndsie S. Wszola, Victoria L. Simonsen, Erica F. Stuber, Caitlyn R. Gillespie, Lindsey N. Messinger, Karie L. Decker, Jeffrey J. Lusk, Christopher F. Jorgensen, Andrew A. Bishop, Joseph J. Fontaine

Abstract

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.

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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 23%
Researcher 7 16%
Professor > Associate Professor 4 9%
Other 3 7%
Student > Doctoral Student 3 7%
Other 8 19%
Unknown 8 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 26%
Environmental Science 10 23%
Computer Science 3 7%
Business, Management and Accounting 2 5%
Engineering 2 5%
Other 2 5%
Unknown 13 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 04 September 2019.
All research outputs
#3,240,472
of 17,966,891 outputs
Outputs from PLOS ONE
#40,611
of 167,699 outputs
Outputs of similar age
#93,424
of 419,436 outputs
Outputs of similar age from PLOS ONE
#1,089
of 4,661 outputs
Altmetric has tracked 17,966,891 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 167,699 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one has done well, scoring higher than 75% 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 419,436 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 76% of its contemporaries.
We're also able to compare this research output to 4,661 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.