DOI: 10.5061/dryad.m63xsj47x
title: Data from: Earlier springs increase goose breeding propensity and nesting success at Arctic but not at temperature latitudes

description: 1. Intermittent breeding is an important tactic in long-lived species that
 trade off survival and reproduction to maximize lifetime reproductive
 success. When breeding conditions are unfavourable, individuals are
 expected to skip reproduction to ensure their own survival. 2.
 Breeding propensity (i.e. the probability for a mature female to breed in
 a given year) is an essential parameter in determining reproductive output
 and population dynamics, but is not often studied in birds because it is
 difficult to obtain unbiased estimates. Breeding conditions are especially
 variable at high latitudes, potentially resulting in a large effect on
 breeding propensity of Arctic-breeding migratory birds, such as geese. 3.
 With a novel approach, we used GPS-tracking data to determine nest
 locations, breeding propensity and nesting success of barnacle geese, and
 studied how these varied with breeding latitude and timing of arrival on
 the breeding grounds relative to local onset of spring. 4. Onset of spring
 at the breeding grounds was a better predictor of breeding propensity and
 nesting success than relative timing of arrival. At Arctic latitudes
 (&gt; 66°), breeding propensity decreased from 0.89 (95% CI:
 0.65-0.97) in early springs to 0.22 (95% CI: 0.06-0.55) in late springs,
 while at temperate latitudes it varied between 0.75 (95% CI: 0.38-0.93)
 and 0.89 (95% CI:  0.41-0.99) regardless of spring phenology.
 Nesting success followed a similar pattern, and was lower in later springs
 at Arctic latitudes, but not at temperate latitudes.  In early
 springs, a larger proportion of geese started breeding despite arriving
 late relative to the onset of spring, possibly because the early spring
 enabled them to use local resources to fuel egg laying and incubation. 5.
 While earlier springs due to climate warming are considered to have mostly
 negative repercussions on reproductive success through phenological
 mismatches, our results suggest that these effects may partly be offset by
 higher breeding propensity and nesting success.
description: We
 collated tracking data (GPS and accelerometer when available) of 96 adult
 female barnacle geese. This dataset includes geese with various life
 history tactics, ranging from long-distance migrants breeding in the
 Arctic (above 66°N) to short-distance migrants and residents in the
 temperate zone (between 51°N and 66°N). Birds were caught and equipped
 with GPS-transmitters on the breeding grounds in the Arctic in 2014 and
 2018 (N=6; 68°34' N, 52°18' E), on breeding grounds in the
 temperate zone (residents) in 2015-2018 (N=7; 51°47' N, 4°08'
 E), and on the wintering grounds in the North of the Netherlands and North
 Germany in 2016-2020 (N=72). Additionally we retrieved tracking data from
 Kölzsch et al. (2015) gathered in 2008-2010 (N=11), which is published on
 movebank.org (van der Jeugd et al., 2014).  In winter, geese were caught
 using cannon-nets, while in summer geese were captured either on the nest
 using clap-nets or by rounding geese up in a catching pen during the wing
 moult when geese are flightless. Throughout the study period, geese were
 equipped with different transmitter types: Milsar (24 g, GSMRadioTag,
 Milsar Technologies S.R.L.; N=2), UvABiTS (19 g, (Bouten et al., 2013);
 N=11), Madebytheo (27 g, N=24), Ornitela (25 g; N=48) and solar Argos/GPS
 PTTs (30 g, N=11, Kölzsch et al., 2015). Transmitters were attached using
 a 16-gram Teflon harness (Lameris et al., 2017), with total mass of
 transmitter and harness being &lt; 3% of the average body mass of a
 female barnacle goose. The GPS-tags used by Kölzsch et al. (2015) were
 attached using a comparable nylon harness.  We
 selected data from the period between April 1 and July 31, during which
 barnacle geese of different breeding populations initiate nesting. IN case
 of high frequence records, we sub-sampled data to 1 GPS-fix every 15 min
 to make transmitters with different sampling regimes comparable. All
 tracks had tracking information for at least 112 days within the 122-day
 period. ACC data was not available for all tracks, due to differences in
 transmitter type as well as settings used.  When ACC data were available
 (Ornitela, UvaBiTS; 78 tracks), we defined a potential nest location as
 the median coordinates of GPS-fixes at which the goose was motionless, on
 days when the goose was mostly motionless (see Schreven et al., 2021). We
 used ACC measurements which were taken simultaneous with GPS positions, or
 if ACC was not measured simultaneously, we took the nearest
 ACC-measurement with maximum 10 min time difference. We used VeDBA as
 measure of activity (Dokter et al., 2018). To deal with the differences in
 accelerometer types as well as burst length and burst frequency, the
 threshold in VeDBA below which a goose was categorized as motionless was
 set per transmitter type, at: 24 (Milsar), 16.5 (Ornitela) and 26.5
 (UvABiTS) following methods by Boom et al. (2023). For tracks where
 ACC-measurements were not available (78 out of 154 tracks), we used the
 GPS-only method (see Schreven et al., 2021, who showed that this method
 worked well in comparison with the GPS and ACC based nest detection),
 defining a potential nest location as the median coordinates of GPS-fixes
 on days when the standard deviation in latitude was below 50 m. For each
 potential nest location, we calculated the time spent per day within 50 m
 of the nest. These locations were classified as nest when at least 4
 subsequent days had &gt;75% attendance within 50 m, allowing for
 differentiation between non-breeding and early nest failure (after first 4
 days). Tracks of geese for which no nesting location could be determined
 (because no nest location was assigned which the goose attended for at
 least 4 days) were considered non-breeding tracks
 (N=81). To
 retrieve information on location (latitude), timing of arrival and spring
 phenology for non-breeding bird tracks (N=81), we needed to estimate the
 most likely potential nest location of non-breeding birds (hereafter
 referred to as “potential nest location”). To determine the potential nest
 locations, we calculated for each non-breeding bird the similarity between
 its track and the tracks of breeding birds (i.e. those with an assigned
 nest, N = 73). Tracks were subset to the period April-June and mean
 locations per day were taken. We used Dynamic Time Warping (DTW), using
 the R package “dtw” (Giorgino, 2009) to calculate trajectory similarities
 (Janoska, 2014). The potential nest location of non-breeding birds was
 determined as the nest location of the most similar breeding bird (track
 of the bird with the lowest “distance” as determined by the DTW-analysis).
 For every track, arrival date was determined as
 the first day a goose was within 35 km of its assigned nest location (for
 breeders) or potential nest location (for
 non-breeders).
 Not all non-breeding birds
 came within 35 km of the potential nest location. We concluded that for
 these birds determination of a potential nest location was not reliable
 and therefore these birds were excluded from the
  statistical analysis. 
 We
 derived information on the local onset of spring from temperature data for
 all years between 2008-2020. We used the R package RNCEP (Kemp et al.,
 2012) to retrieve temperature data from January 1 till September 30 for
 all assigned (breeders) and potential (non-breeders) nest locations. Air
 temperature (2 m above the surface) was retrieved from a 2.5°x2.5° gridded
 dataset with 6 h temporal resolution, and interpolated using planar
 interpolation. We calculated the Growing Degree Days (GDD) for every nest
 location. Due to the wide latitudinal range in our study, the threshold
 used to determine the GDD was latitude-dependent, following a linear
 relationship (Tthreshold = -0.25 x Latitude + 13; see van Wijk et al.
 2012). Subsequently, we fitted a sigmoid curve, and derived the GDD jerk
 (third derivative) following van Wijk et al. (2012). By solving the
 derivative of the GDD jerk to zero, we determined the date of peak spring
 temperature acceleration. To correct for variation in timing of onset of
 spring with latitude, local onset of spring was standardized.
       Boom, M. P., Lameris, T. K., Schreven, K.
 H., Buitendijk, N. H., Moonen, S., de Vries, P. P., Zaynagutdinova, E.,
 Nolet, B.A., van der Jeugd, H.P. & Eichhorn, G. (2023). ‘Year-round activity levels reveal diurnal
 foraging constraints in the annual cycle of migratory and non-migratory
 barnacle geese’. Oecologia, pp. 287–298.  doi:
 10.1007/s00442-023-05386-x. Bouten,
 W.,Baaij, E.W.,Shamoun-Baranes, J. & Camphuysen, K.C.J. (2013) ‘A
 flexible GPS tracking system for studying bird behaviour at multiple
 scales’, Journal of Ornithology, 154(2), pp. 571–580. doi:
 10.1007/s10336-012-0908-1 Giorgino, T. (2009) ‘Computing and visualizing
 dynamic time warping alignments in R: The dtw package’, Journal of
 Statistical Software, 31(7), pp. 1–24. doi:
 10.18637/jss.v031.i07. Janoska,
 Z. (2014) Trajectory similarity calculation using Dynamic Time Warping.
 https://rpubs.com/janoskaz/10351. Accesed on
 2022-01-24 Kemp,
 M.U.,Emiel van Loon, E.,Shamoun-Baranes, J. & Bouten, W. (2012)
 ‘RNCEP: Global weather and climate data at your fingertips’, Methods in
 Ecology and Evolution, 3(1), pp. 65–70. doi:
 10.1111/j.2041-210X.2011.00138.x. Kölzsch, A.,Bauer, S.,de Boer, R.,Griffin,
 L.,Cabot, D.,Exo, K.-M.,van der Jeugd, H.P. & Nolet, B.A. (2015)
 ‘Forecasting spring from afar? Timing
 of migration and predictability of phenology along different migration
 routes of an avian herbivore.’, The Journal of animal ecology. Edited by
 S. Bearhop, 84(1), pp. 272–83. doi:
 10.1111/1365-2656.12281. Lameris,
 T.K.,Kölzsch, A.,Dokter, A.M.,Nolet, B.A. & Müskens, G.J.D.M.
 (2017) ‘A novel harness for attaching tracking devices to migratory
 geese’, Goose Bulletin, (22), pp. 25–30. Schreven, K.H.T.,Stolz, C.,Madsen, J. &
 Nolet, B.A. (2021) ‘Nesting attempts and success of Arctic-breeding geese
 can be derived with high precision from accelerometry and GPS-tracking’,
 Animal Biotelemetry, 9(1), pp. 1–13. doi:
 10.1186/s40317-021-00249-9. van der
 Jeugd, H.P.,Oosterbeek, K.,Ens, B.J.,Shamoun-Baranes, J. & Exo, K.
 (2014) ‘Data from: Forecasting spring from afar? Timing of migration and
 predictability of phenology along different migration routes of an avian
 herbivore [Barents Sea data].’, Movebank Data Repository. doi:
 10.5441/001/1.ps244r11 van
 Wijk, R.E.,Kölzsch, A.,Kruckenberg, H.,Ebbinge, B.S.,Müskens, G.J.D.M.
 & Nolet, B. a. (2012) ‘Individually tracked geese follow peaks of
 temperature acceleration during spring migration’, Oikos, 121(5), pp.
 655–664. doi: 10.1111/j.1600-0706.2011.20083.x.
description: # Data from: Earlier springs increase goose breeding propensity and
 nesting success at Arctic but not at temperature latitudes
 [https://doi.org/10.5061/dryad.m63xsj47x](https://doi.org/10.5061/dryad.m63xsj47x) This dataset contains nest locations of 96 female Barnacle geese (some tracked over multiple years) with various life history tactics, ranging from long-distance migrants breeding in the Arctic (above 66°N) to short-distance migrants and residents in the temperate zone (between 51°N and 66°N). Nest locations were derived from tracking data between 2008 and 2020. For birds that did not breed, the potential nest location (location where the bird could have bred) is determined. Birds were caught and equipped with GPS-transmitters on the breeding grounds in the Arctic in 2014 and 2018, on breeding grounds in the temperate zone (residents) in 2015-2018, and on the wintering grounds in the North of the Netherlands and North Germany in 2016-2020. Additionally, we retrieved tracking data from Kölzsch et al. (2015) gathered in 2008-2010, which is published on movebank.org (van der Jeugd *et al.*, 2014). The majority of the raw tracking data is stored on Movebank (see links below). Raw tracking data not stored in Movebank is provided with this dataset in separate files. Kölzsch, A.,Bauer, S.,de Boer, R.,Griffin, L.,Cabot, D.,Exo, K.-M.,van der Jeugd, H.P. & Nolet, B.A. (2015) ‘Forecasting spring from afar? Timing of migration and predictability of phenology along different migration routes of an avian herbivore.’, *The Journal of animal ecology*. Edited by S. Bearhop, 84(1), pp. 272–83. doi: 10.1111/1365-2656.12281. van der Jeugd, H.P.,Oosterbeek, K.,Ens, B.J.,Shamoun-Baranes, J. & Exo, K. (2014) ‘Data from: Forecasting spring from afar? Timing of migration and predictability of phenology along different migration routes of an avian herbivore [Barents Sea data].’, *Movebank Data Repository*. doi: 10.5441/001/1.ps244r11 ## Description of the data and file structure This dataset consists of 4 separate files: 1\. NestLocationData 2\. RawTrackingData\_GPSACC 3\. RawTrackingData\_GPSonly 4\. ID\_list Data file 1 (NestLocationData) contains the nest location data, including the following columns: **Bird\_ID** = a unique identifier for each individual bird. **Year** = the year in which the bird was tracked (some birds are tracked for multiple years). **Track\_ID** = A unique identifier for each track on which the nest location is based, consists of a combination of Bird\_ID and Year. **attend75.duration** = Number of days on which more than 75% of the GPS-locations occurred within 50 m of the nest location. **Method** = Describes the method which was used to try to determine the nest location (GPS.ACC or GPS.only represent a combination of GPS and Acceleration data or GPS data only respectively). **Breeding** = Binary variable indicating whether a bird attempted to breed (1) or not (0), based on attend75.duration &gt;3 **Nesting\_success** = Binary variable indicating whether the nest of the bird hatched (1) or not (0), based on attend75.duration &gt;25 **lat** = Latitude of the (potential) nest location **lon** = longitude of the (potential) nest location **GDD\_jerk (DOY)** = Julian day on which the acceleration in spring temperature was highest in the year of tracking. Measure of onset of spring. **arrival\_date** = date at which a tracked bird was within 35 km of the nesting location for the first time **Mean\_GDD\_jerk** = mean Julian day on which the acceleration in spring temperature was highest over the period (2008-2020) **sd\_jerk** = Standard deviation of the Mean\_GDD\_jerk **GDD\_jerk\_stdz** = Standardized value of GDD\_jerk (DOY), obtained by: (GDD\_jerk (DOY) - Mean\_GDD\_jerk) / sd\_jerk **Tracking data source** = Source of the raw tracking data from which the breeding location is derived (either stored on Movebank or in this dataset) Data file 2 (RawTrackingData\_GPSACC) contains the raw tracking used in this study (April - July) for which data is not available on Movebank. This file only contains the tracking data of birds for which GPS and Accelerometer data were available. The following columns are included: **Device.ID** = Unique identifier for each bird **latitude** = latitude of the GPS position (WGS 84) **longitude** = longitude of the GPS position (WGS 84) **dtime** = Date and time of the GPS fix (UTC) **trackID** = Unique identifier for each track (combination of Device.ID and year of tracking) **VeDBA.match** = Vectorial Dynamic Body Acceleration measurement (VeDBA) taken within 10 minutes of the GPS fix. **type** = Type of transmitter used Data file 3 (RawTrackingData\_GPSonly) contains the raw tracking used in this study (April- July) for which data is not available on Movebank. This file only contains the tracking data of birds for which only GPS data was available. The following columns are included: **Device.ID** = Unique identifier for each bird **latitude** = latitude of the GPS position (WGS 84) **longitude** = longitude of the GPS position (WGS 84) **dtime** = Date and time of the GPS fix (UTC) **trackID** = Unique identifier for each track (combination of Device.ID and year of tracking) **type** = Type of transmitter used Data file 4 (ID\_list) contains a list of the IDs (based on Device.ID) and years of tracking which are included in data file 1. Additionally, the source of the raw tracking data is provided. **Bird.ID** = Unique identifier for each bird **Year** = the year in which the bird was tracked (some birds are tracked for multiple years). **Tracking data source** = Source of the raw tracking data from which the breeding location is derived (either stored on Movebank or in this dataset) ## Sharing/Access information The majority of the raw tracking data is stored on Movebank (see links below). Raw tracking data not stored in Movebank is provided with this dataset in separate files. **Movebank Datasets:** Barnacle goose from Netherlands to Russia *Link*: [https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1114583459](https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1114583459) *Contact person*: Nelleke Buitendijk *Principal investigator*: Bart Nolet Disturbance of BG by IFV and IWWR *Link*: [https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study137654491](https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study137654491) *Contact person*: Sander Moonen *Principal investigator*: Sander Moonen Data from: Forecasting spring from afar? Timing of migration and predictability of phenology along different migration routes of an avian herbivore [Barents Sea data] *Link*: [https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study29799425](https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study29799425) *Doi*: [https://www.doi.org/10.5441/001/1.ps244r11](https://www.doi.org/10.5441/001/1.ps244r11) *Contact person*: Michael Exo       *Principal investigator*: Michael Exo