DOI: 10.5061/dryad.j0zpc86ss
title: Data from: Diel rhythmicity of activity and corticosterone metabolites in Arctic barnacle geese during breeding

description: Birds that migrate from temperate areas to the Arctic to breed lose their
 strongest Zeitgeber of circadian organization when they cross the Arctic
 Circle in spring – the 24h light-dark cycle. Under continuous daylight,
 diverse behavioral and physiological patterns have been detected in both
 free-ranging and laboratory animals. To better understand the evolution of
 plasticity in circadian clocks, it is essential to study behavioral and
 physiological rhythmicity in the context of a species' ecology.
 Employing a multifaceted approach, which included wildlife cameras,
 accelerometers, and non-invasive sampling of hormone metabolites, we
 investigated activity patterns and corticosterone rhythmicity in a
 migratory herbivore, the barnacle goose (Branta leucopsis), during its
 Arctic breeding season on Svalbard. We found that females showed a
 combination of both ultradian and diel rhythmicity in nest recesses and
 sleep during incubation. In both parents, these rhythms in activity
 continued also during the gosling rearing phase. During molt, many geese
 aligned activity with the prevailing tidal rhythm. Barnacle geese showed
 weak diel rhythmicity in excreted corticosterone metabolites (CORTm). This
 suggests that while Arctic geese may adopt an alternative Zeitgeber during
 the Arctic summer to maintain a diel rhythm, ultradian rhythmicity remains
 essential, allowing the geese to flexibly adjust their rhythms to
 environmental conditions.
description: # Data from: Diel rhythmicity of activity and corticosterone metabolites
 in Arctic barnacle geese during breeding Dataset DOI:
 [10.5061/dryad.j0zpc86ss](10.5061/dryad.j0zpc86ss) ## Description of the
 data and file structure Employing a multifaceted approach, which included
 wildlife cameras, accelerometers, and non-invasive sampling of hormone
 metabolites, we investigated activity patterns and corticosterone
 rhythmicity in a migratory herbivore, the barnacle goose (*Branta
 leucopsis*), during its Arctic breeding season on Svalbard. ### Files and
 variables #### File:
 deJongetal2025_Diel-rhythmicity-of-activity-and-corticosterone-metabolites-in-Arctic-barnacle-geese-during-breeding.zip **Description:**  ReadMe for the article: **“*Diel rhythmicity of activity and corticosterone metabolites in Arctic barnacle geese during breeding*”** published in *Behavioral Ecology* 2025-06-10 This readme file describes the data files associated with the above manuscript. The R files contain the R-code used to investigate rhythmicity in activity and sleep during incubation (Code wildlife camera analyses.R), activity during the entire breeding season (Code accelerometer analyses.R), and in corticosterone metabolite concentration (Code for corticosterone analyses.R), by performing periodicity analyses and linear mixed-effects models as described in the manuscript. In addition, a post hoc analysis on locations of geese during the gosling and moult phase is included (Code location analyses.R). The scripts use the files described below: (A) For the analyses of **wildlife camera data**, the following .Rds files are included: 1. “allgeesebehavr2.Rds” This dataset contains goose incubation behaviour as scored from the wildlife camera photos using the Timelapse2 Image Analyser (version 2.2.4.3), and has been converted to a *behavr* dataset as described by the *rethomics* framework ([https://rethomics.github.io/#](https://rethomics.github.io/)). The dataset contains the following columns: * **“id"** gives the individual metal ring code of each goose in the dataset * **“datetime"** gives the date and time of the photo from which the behaviour was scored * **“starttime"** gives the date and time of the first photo * **“t"** is the time in seconds after the start date of the first photo * **“behaviour"** provides the behaviour scored from the photo: (i) sitting on the nest, (ii) in ‘sleep posture’, *i.e.* head resting on back with beak often tucked under a wing, (iii) standing right next to the nest or (iv) absent from the nest * **“activity"** gives the pooled behaviours (iii) and (iv) as TRUE and behaviours (i) and (ii) as FALSE * **“reverseactivity"** gives the reverse of **“activity"** * **“sleep"** is TRUE when geese are in the sleep posture and FALSE when they are not * **“sleeprev”** is the reverse of **“sleep"** (B) For the analyses of **accelerometer data**, the following .Rds files are included: 1. “ACC_data_2021.Rds” 2. “ACC_data_2022.Rds” 3. “gpsinfo21.Rds” 4. “gpsinfo22.Rds” 5. “goosemetadata2021.Rds” 6. “goosemetadata2022.Rds” 7. “allgeese_act_data.Rds” Datasets 1) and 2) provide the accelerometer bursts and GPS data of the geese for the years 2021 and 2022 respectively. Datasets 3) and 4) give information on the geese with transmitters as observed in the field for both years. Datasets 5) and 6) contain the simplified metadata for both years, containing no missing values. In the R script I give examples for two individual geese in estimating the nesting period. Showing the calculations for all individual geese would make the R script very long and cluttered. Therefore, dataset 7) is added as the finished full dataset with the estimated nesting, gosling and moult periods for all geese. The datasets 1) and 2) contain the following columns: * **“device_id"** is the individual transmitter number * **“metal"** gives the individual metal ring code of each goose in the dataset * **“UTC_datetime"** gives the date time of the measurement in UTC * **“UTC_date"** gives the date of the measurement * **“UTC_time"** gives the time of the measurement * **“datatype"** indicates whether the measurement is a GPS measurement of a sensor measurement * **“Latitude"** gives the latitude * **“Longitude"** gives the longitude * **“DT"** gives the “UTC_datetime” in POSIXct format * **“acc_x"** is the accelerometer measurement in g/1000 in the x-axis: surge axis * **“acc_y"** is the accelerometer measurement in g/1000 in the y-axis: sway axis * **“acc_z"** is the accelerometer measurement in g/1000 in the y-axis: heave axis * **“Position.Burst.ID"** gives an ascending number per burst per individual The datasets 3) and 4) contain the following columns: * **“animal.ring.id"** gives the individual metal ring code of each goose in the dataset * **“tag.id"** is the individual transmitter number * **“animal.sex"** gives the sex of the goose with f indicating females and m males * **“Breeding"** gives if incubation is observed in the field * **“hatch"** gives the approximate date of hatch based on field observations * **“loss"** gives the approximate date of nest loss based on field observations * **“nest_location"** gives the nest location in Kongsfjorden: sh is Storholmen, ph is Prins Heinrichøya, jutta is Juttaholmen, midt is Midtholmen and obs is Observasjonsholmen. * **“latitude"** is the latitude of the nest location as taken by handheld GPS * **“longitude"** is the longitude of the nest location as taken by handheld GPS * **“First_obs_goslings"** is the date of the first observation of goslings from a goose pair in the field * **“last_obs_goslings”** gives the last observation of goslings of a transmitter goose in the field * **“first_obs_moult"** is the first observation of moult for a transmitter goose * **“last_obs_moult"** is the last observation of moult for a transmitter goose * **“moult_accuracy"** the number of days between subsequent moult observations * **“Year"** gives the year of the observation; 2021 or 2022 The datasets 5) and 6) contain the following columns: * **“tag.id"** is the individual transmitter number * **“animal.id"** gives the colour ring code * **“animal.ring.id"** gives the individual metal ring code of each goose in the dataset * **“animal.sex"** gives the sex of the goose with f indicating females and m males The dataset 7) contains the following columns: * **“metal"** gives the individual metal ring code of each goose in the dataset * **“Position.Burst.ID"** gives an ascending number per burst per individual * **“device_id"** is the individual transmitter number * **“vedba"** is the vectorial sum of dynamic body acceleration * **“activity"** gives activity (1) and inactivity (0) based on VeDBA * **“DT_EST"** is the date time in European Summer Time * **“Day"** gives the date * **“Year"** gives the year of the observation; 2021 or 2022 * **“gps_outcome"** gives estimated periods ‘nesting’, ‘goslings’ and ‘moult’ during which the measurements were taken (C) For the analyses of **corticosterone metabolite concentration data**, the following .csv files are included: 1. “CORT2020+indiv_info.csv” 2. “CORT2021+indiv_info.csv” Datasets 1) and 2) provide the corticosterone metabolite concentration data with additional information about the geese.  The datasets contain the following columns: * **“Dropping_ID"** is the individual dropping number * **“Well_ID"** gives the well number * **“Concentration"** gives the corticosterone metabolite concentration in ng/g dropping * **“Ring"** gives the individual colour ring code of the goose of which the dropping was collected * **“Date"** is the date of dropping collection * **“Time"** gives the time of dropping collection * **“Neckband_nr"** gives the individual transmitter number * **“Metal_ID"** gives the individual metal ring code of each goose in the dataset * **“Neckband_pair"** is ‘yes’ when the male or female of the pair has a GPS transmitter * **“Sex"** gives the sex of the goose with f indicating females and m males (D) For the **post hoc analysis on locations of geese during the gosling and moult phase**, and a graph on the observed water level at Ny-Ålesund, we used the following data: 1. “S100_Land_f.shp” in map NP_S100_SHP 2. “GPS_data.Rds” 3. “gps_sf3.Rds” 4. “2021 observed_water_level.csv” 5. “2022 observed_water_level.csv” The dataset 1) cannot be shared on Dryad, because it is licensed under Creative Commons Attribution 4.0 International License, but can be found here: * A shapefile retrieved from [https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3](https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3), Kartdata © Norsk Polarinstitutt. Licensed under the Creative Commons Attribution 4.0 International License. The dataset 2) contains the following columns: * **“animal.ring.id"** gives the individual metal ring code of each goose in the dataset  * **“Position.Burst.ID"** gives an ascending number per burst per individual * **“device_id"** is the individual transmitter number * **“Latitude"** gives the latitude * **“Longitude"** gives the longitude * **“day"** gives the date * **“year”** gives the year of the observation; 2021 or 2022 * **“gps_outcome"** gives estimated periods ‘nesting’, ‘goslings’ and ‘moult’ during which the measurements were taken * **“CEST_OK"** is the date time in European Summer Time The dataset 3) contains all the columns of 2) above and in addition: * **“geometry"** contains all the spatial coordinates of the GPS data, stored in an sf-compliant format (as point geometries) * **“buffer"** indicates whether locations fall inside or outside a 25 m inward buffer inside the land polygon. Points inside the polygon are on land, points outside the polygon are in the intertidal area or at sea. The datasets 4) and 5) cannot be shared on Dryad, because they are licensed under Creative Commons Attribution 4.0 International License, but data can be downloaded as specified and the dataset contained the following columns: * **“datetime"** is the date time in European Summer Time * **“observed_water_level"** gives the tides and observed water level from Ny-Ålesund in cm every 10 minutes from the 1st of May 2021 or 2022 until 31st of August 2021 or 2022. Data are provided by Statens kartverk sjø (Norwegian Hydrographic Service, [https://vannstand.kartverket.no/tideapi_en.html](https://vannstand.kartverket.no/tideapi_en.html)); use of the data is licensed through Creative Commons Attribution 4.0 International (CC BY 4.0). ## Code/software We used the following software: * We analyzed wildlife camera pictures with Timelapse2 Image Analyser (version 2.2.4.3, Greenberg S, Godin T, Whittington J. 2019. Design patterns for wildlife-related camera trap image analysis. Ecol Evol 9(24):13706-13730. [https://doi.org/10.1002/ece3.5767](https://doi.org/10.1002/ece3.5767)). * We performed all analyses in R version 4.4.0 (The R Foundation for Statistical Computing 2024). All packages needed for the analyses are described in the R scripts that are included with this submission. The following R scripts are attached to analyze the different datasets: * Wildlife camera data: Code wildlife camera analyses.R * Accelerometer data: Code accelerometer analyses.R * Corticosterone metabolite data: Code for corticosterone analyses.R * Locations of geese during the gosling and moult phase: Code location analyses.R ## Access information Additional data was derived from the following sources: * We retrieved a shapefile from [https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3](https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3), Kartdata © Norsk Polarinstitutt. Licensed under the Creative Commons Attribution 4.0 International License. * Observed water level from Ny-Ålesund (1st of May 2021 or 2022 until 31st of August 2021 or 2022) was derived from Statens kartverk sjø (Norwegian Hydrographic Service, [https://vannstand.kartverket.no/tideapi_en.html](https://vannstand.kartverket.no/tideapi_en.html)); use of the data is licensed through Creative Commons Attribution 4.0 International (CC BY 4.0).