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Unmanned aerial vehicles for surveying marine fauna: assessing detection probability

Overview of attention for article published in Ecological Applications, April 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
37 X users

Readers on

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300 Mendeley
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Article details
Title
Unmanned aerial vehicles for surveying marine fauna: assessing detection probability
Published in
Ecological Applications, April 2017
DOI 10.1002/eap.1519
Pubmed ID
Authors
Abstract

Aerial surveys are conducted for various fauna to assess abundance, distribution, and habitat use over large spatial scales. They are traditionally conducted using light-aircraft with observers recording sightings in real time. Unmanned Aerial Vehicles (UAVs) offer an alternative with many potential advantages, including eliminating human-risk. To be effective, this emerging platform needs to provide detection rates of animals comparable to traditional methods. UAVs can also acquire new types of information, and this new data requires a re-evaluation of traditional analyses used in aerial surveys; including estimating the probability of detecting animals. We conducted 17 replicate UAV surveys of humpback whales (Megaptera novaeangliae) while simultaneously obtaining a 'census' of the population from land-based observations, to assess UAV detection probability. The ScanEagle UAV, carrying a digital SLR camera, continuously captured images (with 75% overlap) along transects covering the visual range of land-based observers. We also used ScanEagle to conduct focal follows of whale pods (n = 12, mean duration = 40 min), to assess a new method of estimating availability. A comparison of the whale detections from the UAV to the land-based census provided an estimated UAV detection probability of 0.33 (CV = 0.25) (incorporating both availability and perception biases), which was not affected by environmental covariates (Beaufort sea state, glare and cloud cover). According to our focal follows, the mean availability was 0.63 (CV = 0.37), with pods including mother/calf pairs having a higher availability (0.86, CV = 0.20) than those without (0.59, CV = 0.38). The follows also revealed (and provided a potential correction for) a downward bias in group size estimates from the UAV surveys, which resulted from asynchronous diving within whale pods, and a relatively short observation window of 9 s. We have shown that UAVs are an effective alternative to traditional methods, providing a detection probability that is within the range of previous studies for our target species. We also describe a method of assessing availability bias that represents: spatial and temporal characteristics of a survey, from the same perspective as the survey platform; is benign; and provides additional data on animal behavior. This article is protected by copyright. All rights reserved.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 37 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 300 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Argentina 1 <1%
Unknown 299 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 59 20%
Student > Master 50 17%
Student > Ph. D. Student 43 14%
Student > Bachelor 26 9%
Other 16 5%
Other 27 9%
Unknown 79 26%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 94 31%
Environmental Science 69 23%
Engineering 15 5%
Earth and Planetary Sciences 9 3%
Computer Science 6 2%
Other 19 6%
Unknown 88 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 16 May 2026.
All research outputs
#1,436,514
of 32,686,267 outputs
Outputs from Ecological Applications
#346
of 3,926 outputs
Outputs of similar age
#21,778
of 345,081 outputs
Outputs of similar age from Ecological Applications
#9
of 62 outputs
Altmetric has tracked 32,686,267 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,926 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.3. This one has done particularly well, scoring higher than 91% 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 345,081 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 93% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.