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Mark-Recapture and Mark-Resight Methods for Estimating Abundance with Remote Cameras: A Carnivore Case Study

Overview of attention for article published in PLoS ONE, March 2015
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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 (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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12 tweeters
reddit
1 Redditor

Citations

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20 Dimensions

Readers on

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209 Mendeley
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Title
Mark-Recapture and Mark-Resight Methods for Estimating Abundance with Remote Cameras: A Carnivore Case Study
Published in
PLoS ONE, March 2015
DOI 10.1371/journal.pone.0123032
Pubmed ID
Authors

Robert S. Alonso, Brett T. McClintock, Lisa M. Lyren, Erin E. Boydston, Kevin R. Crooks

Abstract

Abundance estimation of carnivore populations is difficult and has prompted the use of non-invasive detection methods, such as remotely-triggered cameras, to collect data. To analyze photo data, studies focusing on carnivores with unique pelage patterns have utilized a mark-recapture framework and studies of carnivores without unique pelage patterns have used a mark-resight framework. We compared mark-resight and mark-recapture estimation methods to estimate bobcat (Lynx rufus) population sizes, which motivated the development of a new "hybrid" mark-resight model as an alternative to traditional methods. We deployed a sampling grid of 30 cameras throughout the urban southern California study area. Additionally, we physically captured and marked a subset of the bobcat population with GPS telemetry collars. Since we could identify individual bobcats with photos of unique pelage patterns and a subset of the population was physically marked, we were able to use traditional mark-recapture and mark-resight methods, as well as the new "hybrid" mark-resight model we developed to estimate bobcat abundance. We recorded 109 bobcat photos during 4,669 camera nights and physically marked 27 bobcats with GPS telemetry collars. Abundance estimates produced by the traditional mark-recapture, traditional mark-resight, and "hybrid" mark-resight methods were similar, however precision differed depending on the models used. Traditional mark-recapture and mark-resight estimates were relatively imprecise with percent confidence interval lengths exceeding 100% of point estimates. Hybrid mark-resight models produced better precision with percent confidence intervals not exceeding 57%. The increased precision of the hybrid mark-resight method stems from utilizing the complete encounter histories of physically marked individuals (including those never detected by a camera trap) and the encounter histories of naturally marked individuals detected at camera traps. This new estimator may be particularly useful for estimating abundance of uniquely identifiable species that are difficult to sample using camera traps alone.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 209 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
United Kingdom 2 <1%
Austria 1 <1%
Australia 1 <1%
Bulgaria 1 <1%
France 1 <1%
Unknown 200 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 21%
Student > Master 41 20%
Researcher 37 18%
Student > Bachelor 24 11%
Other 14 7%
Other 25 12%
Unknown 25 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 102 49%
Environmental Science 58 28%
Veterinary Science and Veterinary Medicine 5 2%
Computer Science 3 1%
Biochemistry, Genetics and Molecular Biology 3 1%
Other 9 4%
Unknown 29 14%

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 13 December 2017.
All research outputs
#2,623,371
of 14,528,192 outputs
Outputs from PLoS ONE
#35,765
of 149,993 outputs
Outputs of similar age
#46,348
of 224,424 outputs
Outputs of similar age from PLoS ONE
#1,168
of 4,956 outputs
Altmetric has tracked 14,528,192 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 149,993 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has done well, scoring higher than 76% 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 224,424 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 79% of its contemporaries.
We're also able to compare this research output to 4,956 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.