DOI: 10.5061/dryad.w0vt4b93d
title: Data from: Migratory birds advance spring arrival and egg-laying in the Arctic, mostly by travelling faster

description: In the current, warming climate many organisms in seasonal environments
 advance their timing of reproduction to benefit from resource peaks
 earlier in spring. For migrants, the potential to advance reproduction may
 be constrained by their migration strategies, notably their ability to
 advance arrival on the breeding grounds. Recent studies show various
 changes in migration strategies, including wintering closer to the
 breeding grounds, earlier departure from the wintering grounds or faster
 travels by spending less time on stopover sites. However, whether such
 changes lead to earlier arrival or earlier breeding remain open questions.
 We studied changes in migration and reproduction timing in 12 populations
 of nine migratory birds, including seabirds, shorebirds, birds of prey and
 waterfowl breeding on Arctic sites bordering the Greenland and Barents
 Sea, a region undergoing rapid climate warming. Timing of migration and
 reproduction was derived from tracking and field data, and analysed to
 study (1) how timing has changed in response to changing moment of
 snowmelt at the breeding grounds, and (2) what adjustments in migration
 strategies this involved. We found that in years with early snowmelt
 egg-laying in multiple populations advanced, but only two waterfowl
 populations also advanced arrival in the Arctic. In contrast, arrival in
 the Arctic generally advanced with time, even when snowmelt or egg-laying
 dates did not advance. Earlier arrival with time was mostly explained by
 populations travelling to the Arctic faster, likely spending less time at
 stopover sites. Inability to forecast conditions in the Arctic may limit
 birds to adjust migration timing to annually varying snowmelt, but we show
 that several species, particularly waterfowl, are able to travel faster
 and advance timing of migration over the years. The question remains
 whether this reflects adaptations to Arctic climate change, or other
 factors, for example environmental changes along the migratory route.
description: We gathered data on the timing of spring migration and egg-laying
 to local snowmelt timing for a suite of arctic-breeding bird species,
 using a large set of tracking data supplemented by field data on
 egg-laying dates. Rather than using tracking data of individuals tracked
 from the non-breeding grounds and breeding across the entire breeding
 range of a species, the focus was placed on tracking data of individuals
 breeding at specific Arctic breeding sites. In this way, we aimed to
 reduce variation in the timing of arrival in the Arctic caused by
 variation in breeding location (e.g., Conklin et al., 2010).
 <em>Study species and sites</em>
 Our dataset contained tracking data (519 spring migrations of 274
 individual birds) from 12 populations and nine migratory bird species,
 breeding at seven Arctic study sites in the Barents and Greenland Sea
 region from the period 2007 - 2023. For this paper, we treat each species
 at a separate study site as a separate population. The species and study
 sites included greater white-fronted goose <em>Anser albifrons
 </em>(Kolguyev Island, north-western Russia)<em>,
 </em>pink-footed goose <em>Anser brachyrhynchus
 </em>(Adventdalen, Svalbard), barnacle goose <em>Branta
 leucopsis </em>(Kolguyev Island, north-western Russia and
 Kongsfjorden, Svalbard), tundra swan <em>Cygnus colombianus
 </em>(Malozemelskaya tundra, north-western Russia), red-necked
 phalarope <em>Phalaropus lobatus</em> (Ammarnäs, northern
 Sweden and Slettnes, northern Norway), sanderling <em>Calidris alba
 </em>(Zackenberg, north-eastern Greenland)<em>,
 </em>Arctic skua <em>Stercorarius parasiticus
 </em>(Slettnes, northern Norway and Kongsfjorden, Svalbard),
 long-tailed skua <em>Stercorarius longicaudus </em>(Ammarnäs,
 northern Sweden) and rough-legged buzzard (Kolguyev Island, north-western
 Russia)<em>,</em>. Birds were tracked using GPS-loggers,
 GPS-GSM-transmitters, satellite transmitters (GPS-PTT) or geolocators
 (Global Location Sensing, GLS). We tracked only females of the three goose
 species and both females and males of the other species. Most birds were
 captured and fitted with tracking devices on their breeding grounds, and
 the study sites of these populations, defined as the area in which birds
 were captured, were relatively small (8 – 56 km<sup>2</sup>).
 Only greater white-fronted geese, barnacle geese (north-western Russian
 population) and tundra swans were captured and tracked from their
 non-breeding grounds in the Netherlands and Germany. For these
 populations, we selected larger areas as study sites (6,061 – 15,132
 km<sup>2</sup>) known to host major concentrations of these
 species (Glazov et al., 2021; Kondratyev et al., 2013; Rees, 2010), namely
 Kolguyev Island (greater white-fronted geese, barnacle geese) and the
 Malozemelskaya tundra region, west of the Pechora Sea (tundra swans). Only
 individuals that initiated a nest in these study sites were included (see
 methods section ‘Date of egg laying’). Whether or not tundra swans showed
 nesting behaviour was more difficult to detect from tracking data, and we
 developed a more comprehensive method to evaluate this, making additional
 use of acceleration data (supplemental materials). To increase the sample
 size for this species, we also included male individual swans that stayed
 in the Malozemelskaya tundra study site for the entire month of June.
 Rough-legged buzzards were captured and tracked from various locations on
 Kolguyev Island (Pokrovsky et al. 2024), and we therefore used the study
 area of Kolguyev Island also for this population. Detailed information on
 tracking methods can be found in the species-specific studies.
 <em>Tracking data preprocessing</em>
 Tracking data were acquired from Movebank (Kays et al. 2022) and
 datasets from the authors. For Arctic skuas, long-tailed skuas, red-necked
 phalaropes, and sanderlings, we used previously obtained tracks from GLS
 data as described in (van Bemmelen et al., 2017; Reneerkens et al., 2019;
 van Bemmelen et al., 2019; van Bemmelen et al., 2024). All studies used a
 light intensity threshold of 2.0 to determine twilight events. To avoid
 large errors around the equinox, latitude data were deleted from 14 days
 before 18 days after the spring equinox. For greater white-fronted geese
 (Kölzsch et al., 2016), barnacle geese (Boom, Lameris, et al., 2023;
 Kölzsch et al., 2015), pink-footed geese (Schreven et al., 2021), tundra
 swans (Linssen et al., 2023; R. Nuijten & Nolet, 2020) and
 rough-legged buzzards (Pokrovsky et al., 2021), previously published
 GPS-tracks were downloaded from Movebank. GPS-tracks were filtered to
 exclude outliers, identified as positions where the speed required to
 travel from that to the next positions was larger than 120 km/h (which is
 10 km/h faster than recorded flight speeds for these species, (Alerstam et
 al., 2007; Miller et al., 2005)). We only included GPS- and
 geolocator-tracks that contained at least one position per day from
 January until arrival at the Arctic Circle.
 <em>Migration timing, migration distance, and travel
 speed</em> From tracking data, we extracted
 individual data on spring migration timing, including departure from,
 non-breeding area and arrival in the breeding area, as well as total
 migration distance and travel speed. First, individual non-breeding sites
 were calculated as mid-winter geographical centroids: the median position
 during January and February, a period during which all populations are
 considered to be present in their non-breeding regions. The calculation of
 departure date from the non-breeding site differed between geolocation-
 and GPS-based tracks. For GPS tracks, we defined the departure date as the
 date with the last position in spring within 200 km of the mid-winter
 centroid. For GLS-tracks, which suffer from a larger positional error,
 especially in latitude during the period around the equinox (Lisovski et
 al., 2012), we closely inspected raw position estimates to find the first
 date with consistent directional movement away from the non-breeding
 site. Arrival date in the Arctic was determined as the
 date at which birds crossed the Arctic Circle (latitude 66.33°N). For GPS
 data, this was defined as the first date above 66.33°N. GLS-tracks are
 calculated based on sunrise and sunset events, and therefore cannot be
 calculated for positions above the Arctic Circle during the polar day with
 24 hours of continuous light (Lisovski, 2018). For these tracks, we
 defined crossing of the Arctic Circle as the first day after the date of
 the last night (which usually occurred at c. 60˚ N latitude). An earlier
 study found that the crossing from these last positions to the Arctic is
 generally rapid without stops (van Bemmelen et al. 2024).
 Total migration distance was calculated as the cumulative sum of
 great circle distances between daily averaged positions, from departure
 from the non-breeding site to arrival in the Arctic (at the Arctic
 Circle). Travel speed was calculated as the total migration distance
 divided by the time between departure and arrival in the Arctic in km/day.
 Due to high flight speeds, the actual time spent flying is relatively
 short, therefore, variation in migration speed mostly represents variation
 in the amount of time spent on stopover sites along the migratory routes
 (Alerstam & Bäckman, 2018). We emphasise that our measure of
 travel speed is not the same as migration speed, which includes the time
 spent fuelling at non-breeding sites before departure (Alerstam,
 2003). <em>Date of
 egg-laying</em> Date of egg-laying, defined as
 the date at which the first egg was laid, was determined from (i) field
 observations, (ii) patterns in light measurements, and (iii) from GPS
 tracking data. (i) In field observations, date of egg-laying for
 individuals with and without tracking devices were either observed
 directly, back-calculated from incomplete clutches in geese (van der Jeugd
 et al., 2009) and shorebirds (Liebezeit et al., 2014; Reneerkens et al.,
 2016), back-calculated from observed hatching dates (by subtracting the
 period required for incubation, based on (Cramp & Perrins, 1988)
 or assessed by egg floatation (Liebezeit et al., 2007). (ii) For GLS-data,
 laying dates were determined based on regular periods of darkness of at
 least 1h while the bird was in an area with continuous daylight (Verhoeven
 et al., 2020). (iii) For GPS-data of geese (of which only females were
 tagged), timing and position of nesting was determined from GPS-locations
 after arrival on the breeding site, where the nest location was determined
 as a position where the daily standard deviation in latitude was less than
 25.4 m (Schreven et al. 2021) for at least three consecutive days. The
 first of these three days was then determined as the date of egg-laying.
 For greater white-fronted geese and barnacle geese from Kolguyev Island,
 for which both population-average laying dates from field data as well as
 extracted from tracking data were available, yearly averaged laying date
 correlated strongly between both methods (Pearson’s correlation = 0.83,
 t<sub> </sub> = 4.38, p = 0.002, n = 9 years). In contrast to
 geese, where only the female incubates the eggs, in tundra swans, females
 and males take turns to sit on the nest. This makes it more difficult to
 distinguish nest positions, and we therefore used a modified method that
 also used accelerometer data were also used (for details, see
 Supplementary methods). For GPS data of rough-legged buzzards, we
 determined the start of nesting as the first day in a period for which a
 bird stayed within one position, i.e., a difference between GPS positions
 of less than 3 m, for more than 1 day. This method was verified using
 accelerometer data (for details, see Curk et al., 2022). For long-tailed
 skuas, red-necked phalaropes, and sanderlings, laying dates could not
 (with a few exceptions) be determined for tracked individuals, as
 geolocators stopped functioning before breeding, or birds did not breed.
 To analyse trends in laying dates for these populations, we supplemented
 our dataset with laying dates determined from field data for the same
 study sites and years. <em>Date of
 snowmelt</em> Snow cover for all study sites was
 estimated using satellite images of the 500m resolution MODIS Terra
 Surface Reflectance Daily Global product (MOD09GA, v6.1, Vermote &
 Wolfe, 2021). The analysis was conducted in Google Earth Engine (Gorelick
 et al., 2017), using the R package RGEE (Aybar et al., 2020) and the
 automated workflows for the quantification of snowmelt by Versluijs
 (2024). We manually mapped spatial overlays of study
 sites. Although sites differed strongly in size, all sites represented
 areas of mostly homogeneous elevation with little expected variation in
 snowmelt, thus giving an average across each of the study sites. We
 extracted satellite images between March 15<sup>th</sup> and
 September 15<sup>th</sup> for each study site, covering the
 years 2000 - 2023. Within each extracted image, we masked pixels that were
 classified as clouds by the 1000m MOD35 cloud mask product (MODIS
 Atmosphere Science Team, 2012), and as waterbodies (i.e. oceans, rivers,
 lakes and large ponds) by the 250m Terra Land Water Mask dataset (MOD44W,
 v6.0, (Carroll et al., 2017). We subsequently calculated the normalized
 difference snow index (i.e., NDSI; Dietz et al., 2012; Dozier, 1989) value
 per pixel, as the normalized difference between the green and the
 short-wave infrared band: For each satellite image and
 within each study site, we then calculated the percentage of snow cover as
 the fraction of pixels with NDSI values larger than 0.4 (Dozier, 1989;
 Hall et al., 1995). We fit a general additive model (GAM; Wood, S., 2023)
 to these annual time series of snow cover and extracted the moment the GAM
 first dropped below 50% snow cover, which we used in subsequent analyses
 as the date of snowmelt. <em>Statistical
 analyses</em> For analyses, we used generalized
 linear mixed models (GLMMs, using the R-package ‘lme4’(Bates et al.,
 2012)) were used including a random intercept for individual birds in
 models with year as an independent variable, and random intercepts for
 individual birds and year in all other models. The year and date of
 snowmelt were centered by subtracting population-specific means. As strong
 interannual variation in environmental conditions (incl. date of snowmelt)
 could distort temporal trends, we excluded short time series (less than 5
 years) from analyses of trends over time (i.e., over the years). Below,
 the steps of the full analysis are described in detail:
 First, we analysed trends in date of snowmelt over time (using
 the specific time period for which tracking data for the population was
 available) using linear models (LMs) including date of snowmelt as
 dependent variable and year, population, and their interaction as
 independent variables (i).<br>(i) date of snowmelt
 (D<sub>sm</sub>) = intercept (intrcpt) + year (Y) + population
 (P) + P x Y Thereafter, we analysed the relationships
 of arrival date and laying date with date of snowmelt (ii, iv) and trends
 over time (iii, v). We used GLMMs for all species combined, including
 arrival date or laying date as a dependent variable and either date of
 snowmelt, population, and their interaction, or year, population, and
 their interaction as independent variables. From these LMMs,
 population-specific trends were inspected post-hoc using confidence
 intervals in the ‘emtrends’ function in the package ‘emmeans’ (Lenth
 2017).<br>(ii) arrival date (Da) = D<sub>sm</sub>  + P +
 D<sub>sm</sub> x P + (1|ID) + (1|Y)<br>(iii) Da = Y + P
 + Y x P + (1|ID)<br>(iv) laying date (Dl) =
 D<sub>sm</sub> + P + D<sub>sm</sub>  x P + (1|ID)
 + (1|Y)<br>(v) Dl = Y + P + Y x P + (1|ID)
 Subsequently, we analysed whether population-level relationships
 between arrival date and snowmelt, as well as population-level trends over
 time, could be explained by similar changes in departure date, migration
 distance, and travel speed about snowmelt or over time. To do so, we
 extracted slopes from population-specific GLMMs (vi) with arrival date,
 departure date, migration distance, or travel speed as dependent variables
 and date of snowmelt as an independent variable, including random
 intercepts as described above. In these GLMMs, arrival date, departure
 date, migration distance, and travel speed were standardized by
 subtracting the overall mean and dividing by the standard deviation to
 allow for later comparison of slopes. We then fitted LMs (vii) for all
 populations combined with arrival date slope as dependent variable and
 departure date, migration distance, and travel speed slopes as independent
 variables. We followed the same procedure for population-level trends with
 time, extracting slopes from population-specific GLMMs with time as an
 independent variable (viii) and fitting LMs for all populations combined
 (ix).<br>(vi) Da/departure date (Dd) / migration distance (MD) /
 travel speed (V) = D<sub>sm</sub> + (1|ID) +
 (1|Y)<br>(vii) slope arrival date ~ date of snowmelt
 (Da<sub>sm</sub>) = intrcpt + slope departure date
 (Dd<sub>sm</sub>) + slope migration distance
 (MD<sub>sm</sub>) + slope travel speed
 (V<sub>sm</sub>)<br>(viii) Da / Dd / MD / V = Y + (1|ID)
 <br>(ix) slope arrival date ~ year (Da<sub>y</sub>) =
 intrcpt + Dd<sub>y</sub> + MD<sub>y</sub> +
 V<sub>y</sub> Finally, we analysed whether
 variation in laying dates between individuals and among years could be
 explained either by local date of snowmelt or individual arrival date,
 using GLMMs (x) including date of egg laying as dependent variable and
 date of snowmelt, arrival date and population, as well as interactions
 date of snowmelt x population and arrival date x population, as
 independent variables, along with random intercepts described above. In
 these analyses, laying and arrival dates were centred by subtracting
 population-specific means.<br>(x) Dl = Dsm + Da + P + Dsm x P + Da x
 P + (1|Y) + (1|P) We used AICc values to compare the
 performance of models, including all possible combinations of independent
 variables, including an intercept-only model. For most analyses, this
 meant comparing the performance of models including either date of
 snowmelt or year as independent variables with intercept-only models. We
 selected the model with the lowest AICc, but models within 2 ΔAICc of the
 top supported model were also considered informative, except when
 containing additional parameters (Arnold, 2010). All analyses were
 conducted using R (version 4.3.1). <em>Data from
 literature</em> We collated papers that reported
 on arrival and laying date relative to the date of snowmelt in the Web of
 Science database, conducted on 7 February 2024. We used the search term:
 bird AND Arctic AND (snowmelt OR snow cover OR snow) AND (migration timing
 OR arrival OR laying date OR nest initiation OR egg) using the option ‘all
 fields’. This resulted in 213 papers, which were scanned for statistics
 and data on the timing of migratory arrival (on northern stopover sites or
 breeding sites), measured for at least three years, concerning any metric
 of snow cover. We omitted studies where it was not possible to extract
 slopes, arrival, laying, or snowmelt dates from either the text, tables,
 or graphs. Data were found in six papers (Table S2). Three papers (which
 were not identified in the search) known to contain similar data were
 added (Ely et al., 2018; Hupp et al., 2018; Lameris et al., 2018).
 Unpublished data for three additional species were supplemented by S.
 Volkov, with the data provided in the dataset linked to this paper
 (Lameris et al. 2025) and details on data collection in the supplemental
 material. Among all considered studies, the snow cover fraction used or
 extracted as a measure of ‘date of snowmelt’ varied between 60% and 0%. In
 general, different fractions of snow cover will be highly correlated
 (e.g., 0.99 Pearson’s correlation between 0.25 and 0.75 fraction of snow
 cover in our dataset), although the moment of 0% snow cover may show less
 of a correlation with mid-point snow cover. In total, the dataset from
 literature contained 18 species (five species of waterfowl, 10 shorebirds,
 two passerines, and one gull) from six different locations in the
 Holarctic. We extracted slopes of the change in arrival dates, laying
 dates, and date of snowmelt over time, and slopes of the change in arrival
 dates and laying dates about date of snowmelt. We then used t-tests to
 analyse whether the slope in change in arrival date and laying date, with
 time as well as concerning date of snowmelt, differed from zero. In
 addition, we used LMs to analyse whether changes in arrival and laying
 date over time correlated with changes in date of snowmelt over time,
 comparing models with either slopes of arrival or laying date as dependent
 variables and the slope of date of snowmelt as independent variables, and
 performing model selection as described above.
description: # Migratory birds advance spring arrival and egg-laying in the Arctic,
 mostly by travelling faster
 [https://doi.org/10.5061/dryad.w0vt4b93d](https://doi.org/10.5061/dryad.w0vt4b93d) ## Description of the data and file structure Data on migration and reproduction phenology were extracted from bird tracking data and compared with the date of 50% snowmelt at breeding sites. From this, we analysed how migration and reproduction phenology changed over time and with the rate of 50% snowmelt. From tracking data also travel speed and distance between non-breeding and breeding sites were also calculated to analyse whether changes in arrival date in the Arctic could be explained by earlier departure dates, faster travel or shorter distances between wintering and breeding sites. ### Files and variables **1.snowmelt.csv** 1. date of 50% snowmelt in spring as extracted from 500m resolution MODIS Terra Surface Reflectance Daily Global product (MOD09GA) 1) NDSI_threshold Normalized difference snow index treshold used 2) Snowfraction_threshold the fraction of snowcover used as treshold 3) doy day of the year when treshold of snowcover was reached 4) location name of the study site 5) year year for which snowcover was measured **2.migration_data.csv** Migration timing data as extracted from tracks 1. species Name of species 2. individual_id ID of individual bird 3. site Name of study site 4. year year in which data was recorded 5. depart date of departure from non-breeding site in day of the year 6. AC date of arrival at the arctic circle in day of the year 7. arrival date of arrival at the breeding site in day of the year 8. nest.initiation date of nest initiation in day of the year 9. winter.lat mid-winter centroid latitude 10. winter.lon mid-winter centroid longitude 11. depart.lat latitude of departure moment 12. depart.lon longitude of departure moment 13. AC.lat latitude of arctic circle crossing 14. AC.lon longitude of arctic circle crossing 15. arrival.lat latitude of breeding site arrival 16. arrival.lon longitude of breeding site arrival 17. nest.lat latitude of nest site 18. nest.lon longitude of nest site 19. travel_speed travel speed in km/day 20. stopovers.n number of stopover sites between non-breeding and arctic circle 21. stopovers.f fraction of time spend on stopover sites 22. distance distance between departure and arctic circle 23. species_site name of species and site = population 24. species_site_year name of population and year 25. AC.date date of arctic arrival 26. arrival.date date of breeding site arrival 27. depart.date date of departure 28. nest.initiation.date date of nest initiation **3.laying_data.csv** seperate laying dates as collected from field data 1. site name of study site 2. year year of recorded laying date 3. laydate laying date 4. species species name **4.Kolguev_laying_dates.csv** a set of laying dates on barnacle geese and greater white-fronted geese as collected on Kolguyev Island and used to validate laying dates extracted from GPS-tracking data 1. Species name of study species 2. Year year of recorded laying dates 3. Date laying date 4. laydate laying date in day of the year 5. freq number of nests with this laying date **5.literature_data.csv** data on migration phenology extracted from literature. Missing data (in case either laying date, arrival date or trends over time / with snowmelt were not measured) are marked by 'NA' 1. Reference name of the study 2. species group species group studied 3. Species species name 4. Breeding site name of study site 5. Study period years during which study was undertaken 6. length study period number of years 7. years with data (snowmelt) number of years for studies with snowmelt data 8. years with data (time) number of years with trends over time 9. method_spring_arrival method used to measure bird arrival (observed, tracked) 10. phenology_type arrival or laying date 11. trend_snowmelt trend in bird phenology ~ date of snowmelt 12. trend_time trend in bird phenology ~ time (years) 13. snowmelt_percentage snowcover fraction used as measure of date of snowmelt 14. snowmelt_trend trend in date of snowmelt **6.locations.csv** coordinates of the study sites 1. site abbreviation of the site name 2. lat latitude of the site 3. lon longtitude of the site 4. name name of the study site **7.non-breeding&stopover.csv** coordinates of mid-winter centroids, stopover sites and arctic circle arrival of individual birds 1. site_2 type of site (dep = mid-winter centroid, stopover, or AC = arctic arrival) 2. location.lat location of the site in latitude 3. location.lon location of the site in longitude 4. id ID of the bird 5. year year of tre track 6. species study species **8.Volkov_unpublished_data_snowmelt** date of 99% snowmelt for a study site in the Lena Delta, Russia, for the years 1982 - 2013. Details on data collection can be found in Volkov & Podznyakov 2021. 1. year year when data was collected 2. date_snowmelt date of 99% snow disappearance, in day number since January 1 **9.Volkov_unpublished_data_species** Data on first observed arriving bird and first egg date of the earliest nest found for a study site in the Lena Delta, Russia, for the years 1982 - 2013. Details on data collection can be found in Volkov & Podznyakov 2021. Missing data, due to years where no laying data was collected, is marked by 'NA' 1. year year when data was collected 2. species species for which data was collected 3. arrival date of the first bird observed in the study area in spring, in day number since January 1 4. 1st egg date of first egg laid, of the earliest nest found for this species in the study area, in day number since January 1 ## Code/software One r-script is attached ("data_analyses.r"), which can be used to construct the data sets used in the analyses, run the analyses and make the figures. Figures have been edited in illustrator to add migration routes (Fig. 1), trend significance (Fig. 2, 3) and illustrations. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * Movebank (see manuscript for Movebank IDs) * MODIS Snowcover products