Loggers affect the foraging behaviour and fitness of European shags
Data files
Oct 21, 2025 version files 9.10 MB
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Adult_survival.rds
69.75 KB
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Adult_weight_diff.csv
71.56 KB
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Chick_growth_rate.csv
48.55 KB
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Chick_survival.csv
4.84 KB
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Dive_behaviour.csv
8.58 MB
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Foraging_trip.csv
326.38 KB
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README.md
5.95 KB
Abstract
Biologgers enable real-time collection of detailed behavioural and physiological data from wide-ranging animals, including seabirds inhabiting remote regions. However, the number of studies using tracking devices has not been matched by research exploring the behavioural and fitness costs of deployment, and the effects on data reliability. We assessed how GPS loggers, video loggers, and time depth recorders (TDRs) affect the behaviour, physiology, and reproductive performance of European shags (Gulosus aristotelis) breeding on Sklinna, Norway. The loggers varied in mass, attachment location, and deployment duration, allowing comparison of their relative effects. Birds without loggers served as controls to assess logger-related changes in adult body mass, chick growth, reproductive success, and survival. Birds with long-term tail-mounted GPS loggers and leg-mounted TDRs showed altered foraging behaviour, including shorter trips and dives, compared to birds with tail-mounted GPS and TDRs of the same weight, instrumented for only two days. A mean loss in body mass was experienced by adult birds regardless of the logger-type used, while chick growth rate dropped to 20% of that observed in control nests when video, TDR and GPS loggers (~4% of body mass) were deployed together. Logger attachments did not impact reproductive success and overall logger birds showed higher survival than controls. However female survival was lower than males among birds fitted with video and long-term GPS loggers. Our results demonstrate the importance of measuring behavioural and physiological effects that can scale over time. The advances in our understanding of animal ecology and behaviour generated by biologging have been impressive, but there is a need to consider the impacts on animal welfare and data quality. Consistent reporting of logger deployment details is essential to assess biologging impacts across species and refine protocols that account for device weight, drag and attachment location.
Dataset DOI: 10.5061/dryad.2z34tmq07
Description of the data and file structure
Below are the definitions of each variable included in the six datasets used for the analysis in our paper “Loggers affect the foraging behaviour and fitness of European shags.” These descriptions are provided to aid correct interpretation and reuse of the data.
1. Foraging behaviour (dataset: Foraging_trip.csv)
| Variable | Definition |
|---|---|
| trip_id | Unique ID for each recorded foraging trip |
| sex | Male or Female |
| year | Year |
| TripComp | Trip completed |
| N | Number of GPS locations |
| maxdist | Maximum distance from the colony in metres |
| start | Start date and time (format: DD.MM.YYYY HH:MM) |
| end | End date and time (format: DD.MM.YYYY HH:MM) |
| length | Length of trip in decimal hours |
| tag | Type of logger used (IgotU, pathT or video) |
| duration | Time since the logger was deployed in days |
2. Dive behaviour (dataset: Dive_behaviour.csv)
| Variable | Definition |
|---|---|
| botttim | Bottom time in seconds |
| divetim | Total dive time in seconds |
| maxdep | Maximum depth in metres |
| observation | Observation number |
| year | Year |
| tag | Type of logger used (IgotU, pathT or video) |
| duration | Time since the logger was deployed in days |
| sex | M or F |
| begdesc_original | Date and time that a dive started (format: DD.MM.YYYY HH:MM) |
3. Adult body mass (dataset: Adult_weight_diff.csv)
| Variable | Definition |
|---|---|
| sex | Male or Female |
| weight_diff | Change in weight (g) over duration of season (control) or deployment (logger) |
| tagged | Tagged or untagged birds |
| logger | Control (no logger), IgotU, video or pathT |
4. Chick growth rate (dataset: Chick_growth_rate.csv)
| Variable | Definition |
|---|---|
| UniqueChickID | Unique ID number for each chick |
| growth_rate | Growth rate (g/day) |
| logger | Control (no logger), IgotU, video or pathT |
5. Chick survival (dataset: Chick_survival.csv)
| Variable | Definition |
|---|---|
| nest_no | Nest number |
| year | Year |
| start_clutch | Number of eggs or chicks in the clutch at first observation |
| end_clutch | Number of eggs or chicks in the clutch at the final observation |
| prop_surv | Proportion of chicks that survived |
| adj_prop_surv | Adjusted proportion accounting for small sample size (see methodsin the paper) |
| data | Control (no logger) or PathTrack |
6. Adult survival (dataset: Adult_survival.rds)
An rds file containing capture history, logger assignments, and sex. The file Adult_survival.rds can be loaded into R using data <- readRDS("Adult_survival.rds"). This creates a list containing the capture history matrix (CH), logger assignment matrix (Logger_mat), and sex vector (Sex). Users can access these components (e.g., data$CH) to explore the data or use the capture histories directly in mark–recapture or survival analyses with packages such as** RMark or marked.**
| Object | Variable | Definition |
|---|---|---|
| Capture history (CH) | – | Record of capture occasions for each individual (mark–recapture format). |
| Logger matrix | – | Matrix showing which individuals carried which logger type in each year. |
| Logger codes | 2 | IgotU, Pathtrack_short |
| 3 | Video, Video_dummy | |
| 4 | Pathtrack_4weeks, Pathtrack, Ornitela | |
| Sex | 0 | Female |
| 1 | Male |
