↓ Skip to main content

Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation

Overview of attention for article published in PLOS ONE, February 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
5 news outlets
policy
2 policy sources
twitter
25 X users

Citations

dimensions_citation
44 Dimensions

Readers on

mendeley
153 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
Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation
Published in
PLOS ONE, February 2018
DOI 10.1371/journal.pone.0192538
Pubmed ID
Authors

Benjamin J. Dittbrenner, Michael M. Pollock, Jason W. Schilling, Julian D. Olden, Joshua J. Lawler, Christian E. Torgersen

Abstract

Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors-information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are on large tracts of privately-owned timber land. Thus, although there are a large number of areas that could be suitable for relocation and restoration using beavers, current land use patterns may substantially limit feasibility in these areas.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 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 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 153 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 18%
Student > Master 22 14%
Student > Ph. D. Student 17 11%
Student > Bachelor 14 9%
Other 14 9%
Other 9 6%
Unknown 49 32%
Readers by discipline Count As %
Environmental Science 47 31%
Agricultural and Biological Sciences 26 17%
Earth and Planetary Sciences 8 5%
Social Sciences 4 3%
Engineering 3 2%
Other 9 6%
Unknown 56 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 66. 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 2021.
All research outputs
#596,044
of 23,957,285 outputs
Outputs from PLOS ONE
#8,248
of 205,564 outputs
Outputs of similar age
#14,489
of 333,823 outputs
Outputs of similar age from PLOS ONE
#184
of 3,572 outputs
Altmetric has tracked 23,957,285 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 205,564 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.5. This one has done particularly well, scoring higher than 95% 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 333,823 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 95% of its contemporaries.
We're also able to compare this research output to 3,572 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.