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An evaluation of semi‐automated methods for collecting ecosystem‐level data in temperate marine systems

Overview of attention for article published in Ecology and Evolution, May 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
30 tweeters

Readers on

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23 Mendeley
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Title
An evaluation of semi‐automated methods for collecting ecosystem‐level data in temperate marine systems
Published in
Ecology and Evolution, May 2017
DOI 10.1002/ece3.3041
Pubmed ID
Authors

Griffin, Kingsley J., Hedge, Luke H., González‐Rivero, Manuel, Hoegh‐Guldberg, Ove I., Johnston, Emma L., Kingsley J. Griffin, Luke H. Hedge, Manuel González‐Rivero, Ove I. Hoegh‐Guldberg, Emma L. Johnston

Abstract

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high-resolution imaging and associated machine-learning image-scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free-to-use machine-learning software to semi-automatically generate dense and widespread abundance records of a habitat-forming algae over ~5,000 m(2) of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver-based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10-20 transects (50 × 1 m) were required to obtain reliable results. This represents 2-20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine-resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi-automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.

Twitter Demographics

The data shown below were collected from the profiles of 30 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 35%
Student > Master 5 22%
Student > Ph. D. Student 3 13%
Student > Bachelor 2 9%
Student > Doctoral Student 2 9%
Other 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 48%
Environmental Science 6 26%
Unspecified 3 13%
Earth and Planetary Sciences 2 9%
Business, Management and Accounting 1 4%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 2018.
All research outputs
#712,990
of 11,667,520 outputs
Outputs from Ecology and Evolution
#326
of 3,053 outputs
Outputs of similar age
#29,987
of 268,434 outputs
Outputs of similar age from Ecology and Evolution
#26
of 201 outputs
Altmetric has tracked 11,667,520 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,053 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has done well, scoring higher than 89% 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 268,434 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 88% of its contemporaries.
We're also able to compare this research output to 201 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.