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Cited by 50Avian ecology and behaviorWildlife Ecology and ConservationMarine animal studies overview

The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.

Short-term behavioural impact contrasts with long-term fitness consequences of biologging in a long-lived seabird

Open access
Gillies, Natasha, Fayet, Annette L., Padget, Oliver, Syposz, Martyna, Wynn, Joe, Bond, Sarah, Evry, James, Kirk, Holly, Shoji, Akiko, Dean, Ben, Freeman, Robin, Guilford, Tim·2020·Scientific Reports
DOI 10.1038/s41598-020-72199-wPMC PMC7490266
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Abstract

Biologging has emerged as one of the most powerful and widely used technologies in ethology and ecology, providing unprecedented insight into animal behaviour. However, attaching loggers to animals may alter their behaviour, leading to the collection of data that fails to represent natural activity accurately. This is of particular concern in free-ranging animals, where tagged individuals can rarely be monitored directly. One of the most commonly reported measures of impact is breeding success, but this ignores potential short-term alterations to individual behaviour. When collecting ecological or behavioural data, such changes can have important consequences for the inference of results. Here, we take a multifaceted approach to investigate whether tagging leads to short-term behavioural changes, and whether these are later reflected in breeding performance, in a pelagic seabird. We analyse a long-term dataset of tracking data from Manx shearwaters (Puffinus puffinus), comparing the effects of carrying no device, small geolocator (GLS) devices (0.6% body mass), large Global Positioning System (GPS) devices (4.2% body mass) and a combination of the two (4.8% body mass). Despite exhibiting normal breeding success in both the year of tagging and the following year, incubating birds carrying GPS devices altered their foraging behaviour compared to untagged birds. During their foraging trips, GPS-tagged birds doubled their time away from the nest, experienced reduced foraging gains (64% reduction in mass gained per day) and reduced flight time by 14%. These findings demonstrate that the perceived impacts of device deployment depends on the scale over which they are sought: long-term measures, such as breeding success, can obscure finer-scale behavioural change, potentially limiting the validity of using GPS to infer at-sea behaviour when answering behavioural or ecological questions.

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Crossref · 10.1038/s41598-020-72199-w

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Figure 1 Foraging trip duration in days grouped by deployment type within island. Black horizontal lines indicate the mean for each group; black vertical lines indicate the standard error. Data are plotted with horizontal ‘jitter’.
Figure 1 Foraging trip duration in days grouped by deployment type within island. Black horizontal lines indicate the mean for each group; black vertical lines indicate the standard error. Data are plotted with horizontal ‘jitter’.CC BY · PubMed Central
Figure 2 Proportion of each 24 hour period (00:00-23:59) spent in rest, flight, or foraging behaviour according to deployment type, where dark blue = combined GPS, and lilac = GLS only. Black horizontal lines indicate the mean for each group; black vertical lines indicate standard error. Data are plotted with horizontal ‘jitter’.
Figure 2 Proportion of each 24 hour period (00:00-23:59) spent in rest, flight, or foraging behaviour according to deployment type, where dark blue = combined GPS, and lilac = GLS only. Black horizontal lines indicate the mean for each group; black vertical lines indicate standard error. Data are plotted with horizontal ‘jitter’.CC BY · PubMed Central
Figure 3 Mass gain per day (g) of the foraging trip during incubation according to deployment type. Black horizontal lines indicate the mean for each group, black vertical lines indicate standard error. Data are plotted with horizontal ‘jitter’.
Figure 3 Mass gain per day (g) of the foraging trip during incubation according to deployment type. Black horizontal lines indicate the mean for each group, black vertical lines indicate standard error. Data are plotted with horizontal ‘jitter’.CC BY · PubMed Central

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Metadata

Authors
Gillies, N., Fayet, A. L., Padget, O., Syposz, M., Wynn, J., Bond, S., Evry, J., Kirk, H., Shoji, A., Dean, B., Freeman, R., & Guilford, T.
Affiliations
  • University of Oxford (GB)
  • Bangor University (GB)
  • RMIT University (AU)
  • University of Tsukuba (JP)
  • Zoological Society of London (GB)
Corresponding author
  • Gillies, Natasha
  • Guilford, Tim
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2020-09-14
Volume
10
Issue
1
Pages
15056-15056
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-72199-w
PMC
PMC7490266
OpenAlex
W3086484288
Cited by
50
References
69
Citations by year
2026: 9 · 2025: 13 · 2024: 8 · 2023: 4 · 2022: 6 · 2021: 10
FWCI
2.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RMIT Research Repository (RMIT University Library) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Figshare (submittedVersion)
Funders
Biotechnology and Biological Sciences Research Council (BB/M011224/1)
Topics
  • Physical Sciences › Environmental Science › Ecology › Avian ecology and behavior
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
Keywords
biologging, behavior change, Manx shearwater, Puffinus puffinus, GPS tracking, geolocators, foraging behavior, breeding success, seabirds, incubation, flight time, mass gain
MeSH terms
Animals, Behavior, Animal, Birds, Oceans and Seas, Remote Sensing Technology

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