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A probabilistic algorithm to process geolocation data

Overview of attention for article published in Movement Ecology, November 2016
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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 (76th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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11 tweeters
facebook
1 Facebook page

Citations

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9 Dimensions

Readers on

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92 Mendeley
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Title
A probabilistic algorithm to process geolocation data
Published in
Movement Ecology, November 2016
DOI 10.1186/s40462-016-0091-8
Pubmed ID
Authors

Benjamin Merkel, Richard A. Phillips, Sébastien Descamps, Nigel G. Yoccoz, Børge Moe, Hallvard Strøm

Abstract

The use of light level loggers (geolocators) to understand movements and distributions in terrestrial and marine vertebrates, particularly during the non-breeding period, has increased dramatically in recent years. However, inferring positions from light data is not straightforward, often relies on assumptions that are difficult to test, or includes an element of subjectivity. We present an intuitive framework to compute locations from twilight events collected by geolocators from different manufacturers. The procedure uses an iterative forward step selection, weighting each possible position using a set of parameters that can be specifically selected for each analysis. The approach was tested on data from two wide-ranging seabird species - black-browed albatross Thalassarche melanophris and wandering albatross Diomedea exulans - tracked at Bird Island, South Georgia, during the two most contrasting periods of the year in terms of light regimes (solstice and equinox). Using additional information on travel speed, sea surface temperature and land avoidance, our approach was considerably more accurate than the traditional threshold method (errors reduced to medians of 185 km and 145 km for solstice and equinox periods, respectively). The algorithm computes stable results with uncertainty estimates, including around the equinoxes, and does not require calibration of solar angles. Accuracy can be increased by assimilating information on travel speed and behaviour, as well as environmental data. This framework is available through the open source R package probGLS, and can be applied in a wide range of biologging studies.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Portugal 1 1%
Unknown 90 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 28%
Researcher 20 22%
Student > Master 19 21%
Student > Bachelor 9 10%
Other 4 4%
Other 10 11%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 54%
Environmental Science 19 21%
Computer Science 4 4%
Earth and Planetary Sciences 4 4%
Engineering 2 2%
Other 6 7%
Unknown 7 8%

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 06 June 2019.
All research outputs
#2,880,958
of 15,184,149 outputs
Outputs from Movement Ecology
#85
of 190 outputs
Outputs of similar age
#91,406
of 384,666 outputs
Outputs of similar age from Movement Ecology
#13
of 22 outputs
Altmetric has tracked 15,184,149 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 190 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has gotten more attention than average, scoring higher than 55% 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 384,666 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 22 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.