DOI: 10.5061/dryad.280gb5n0v
title: Migration path of American golden-plovers breeding across the Nearctic tundra

description: Many populations of migratory birds are currently declining. Understanding
 of space use throughout the entire annual cycle, as well as migratory
 connectivity (i.e., geographic linkage of individuals and populations
 across different stages of the annual cycle), can improve our ability to
 identify factors driving population declines and influencing extinction
 risk. The main objectives of our study were to i) document the space use
 and phenology of migration during the non-breeding period and ii) quantify
 the degree of migratory connectivity across the range of the American
 Golden-Plover (Pluvialis dominica) breeding across the North American
 Arctic. American Golden-Plovers breed across their entire breeding range
 (northern North America) and migrate up to their main wintering site
 located in South America. We used archival light-level geolocators to
 track the migration. We quantified migratory connectivity based on the
 non-breeding range spread of all individuals and the breeding population
 spread. We used Mantel tests to evaluate whether the relative spatial
 configuration of the sampled breeding area was preserved on the
 non-breeding ground. We identified 13 and 7 stopover sites used during the
 fall (post-breeding, southbound) and spring (pre-breeding, northbound)
 migrations, respectively, and one main site used during the wintering
 period. We highlight stopover sites that were previously unknown and show
 the transatlantic and transpacific routes used by plovers during
 migration. We found that individuals breeding in proximity tended to be
 closer to each other during brief and highly limited portions of the
 non-breeding period. Broadly, individuals from different breeding
 populations were well mixed during the wintering period and throughout
 most of the spring and fall migrations. Overall, the migratory
 connectivity of American Golden-Plovers is relatively low for most of the
 non-breeding period, suggesting that breeding populations separated by
 large distances should be similarly affected by disturbances and changes
 encountered at some migratory stopovers and on the wintering area.
description: <strong>Capturing and marking
 plovers</strong> Incubating adult American
 Golden-Plovers were captured at eight study sites distributed across the
 species’ entire breeding range (Figure 1B). The studied breeding
 populations were separated by an average distance of 1,864 km, with a
 maximum separation of approximately 3,800 km (between Nome and Bylot
 Island; Figure 2). Plover nests were located by searching suitable
 habitats, and individuals were captured using a 60-cm dome bow net placed
 over the nest. Birds were fitted with metal and plastic bands, as well as
 a light-level geolocator (archival data logger) attached to a leg flag,
 all placed on the tibiotarsus (Figure 1A; Appendix Table S2). Geolocator
 models deployed included the British Antarctic Survey MK10b (approximately
 1.1 g) and the Migrate Technology Ltd. Intigeo W65A9RK (approximately 0.9
 g). These devices represented less than 0.8% of the minimum body mass of
 the plovers (mean mass: 142.4 g; range: 126–162 g; <em>n</em>
 = 25). Geolocators are archival devices that estimate latitude and
 longitude by recording ambient light levels (Isakov et al., 2012) and must
 be retrieved to download data. Geographic variation in the timing of
 sunrise and sunset is used to estimate the geographic location of
 individuals (Lisovski et al., 2012). Day and night lengths are
 approximately equal across latitudes during the spring and autumn
 equinoxes, which increases uncertainty in latitude estimates during these
 periods, but not in longitude estimates. A total of 262
 geolocators were deployed from 2009 to 2015, and 45 of them (~17%) were
 retrieved by recapturing plovers 1 to 4 years after their initial capture
 (Appendix Table S.1). Distance between nest locations of marked
 individuals monitored more than one year was 319 m on average (range 54 m
 to 1,119 m; n = 22). No effects of geolocator on annual survival was
 detected (Weiser <em>et al.</em>, 2016) and the relatively low
 recovery rate of geolocators likely reflects the difficulty of re-sighting
 and recapturing marked plovers on their breeding grounds (i.e., the
 breeding site fidelity has to be high; the individual must initiate
 breeding and be re-observed in the field; its nest must be located and the
 individual recaptured prior to nest depredation or hatching). The effort
 deployed in the field to recapture plovers was also variable between
 breeding populations. Although 45 geolocators were retrieved (Appendix
 Table S.2), 9 of them only showed partial migration tracks due to
 equipment failure, which left 36 loggers with tracks covering most of the
 non-breeding period. Of these tracks, 33 came from different individuals
 and were used in the analyses, unless otherwise indicated.
 <strong>Processing Geolocator Data</strong>
 Plovers that were recaptured had their geolocator leg flag
 removed, and light data were downloaded using the Communicate program in
 BASTrack for BAS geolocators (Fox, 2010) or Intiproc for Intigeo
 geolocators (Fox, 2018).  Data were processed through GeoLight 2.0
 (Lisovski <em>et al.</em>, 2015) to convert light-level data
 to location estimates. Following Finch et al. (2015), we used a light
 threshold of 3 for BAS devices and of 2 for Intigeos, as light data output
 is different between models of geolocators. We converted light data into
 two daily locations (morning and evening values in latitude and longitude)
 for each bird from geolocator deployment to recovery, except when devices
 malfunctioned during the non-breeding period. For Arctic-breeding birds
 exposed to full daylight when at high latitude, the dataset was restricted
 to dates when nights were detected. To reduce light noise during
 nighttime, the lightFilter function in GeoLight 2.0 was used (Lisovski
 <em>et al.</em>, 2015). Most loggers were calibrated using the
 rooftop method (n=27, 64%) to provide a start angle for the Hill-Eckstrom
 calibration (mean=‑6.63° range [‑7.33, ‑2.73], n=27). Residency periods
 were identified with the ChangeLight function in GeoLight 2.0 (quantile=
 0.9) with a specified minimum residency length of 2 days. For movement
 periods or if no optimal sun elevation angle could be obtained by either
 rooftop or Hill-Ekstrom calibration, we used the angle provided by the
 rooftop calibration, and if unavailable, the civil twilight (i.e., 6°,
 Lisovski <em>et al.</em>, 2015, n = 9). We filtered the
 estimated locations obtained with a loess filter (k=2) to remove
 outliers. Following Hobson & Kardynal (2015),
 the migration track of each plover was smoothed with a state-space Kalman
 filter and the most probable path was obtained with kftrack (Sibert and
 Nielsen, 2002) in R. Kalman filtering provides the most probable track
 from location data and reduces observer bias when dealing with raw
 location estimates obtained with geolocators (Gow, 2016; Hobson and
 Kardynal, 2015). As the estimated flight speeds of American Golden-Plovers
 vary widely (see Johnson <em>et al.</em>, 2020), we used a
 relatively high flight speed estimate (104.6 km/h, Johnson and Morton,
 1976), corresponding to a maximum of 2,510 km per day, to set the
 diffusion component of the model. Kftrack uses an asymmetric error
 structure peaking on the winter side of the equinoxes, which is typical
 for geolocator data. Location precision was calculated from calibration
 data and yielded a mean error of 163 ± 75 km (n = 20), comparable to prior
 studies (Lisovski <em>et al.</em>, 2012 and references
 therein). As outlined by Knight et al. (2018), the use
 of geolocators for determining how migratory birds are spatially connected
 between breeding and non-breeding periods has some important limitations.
 The uncertainty in location estimates obtained using geolocators can be up
 to 300 kilometers, particularly around the equinox. Hence, this method is
 not adequate for detecting small-scale spatial segregation between
 individuals from different breeding populations. Moreover, our conclusions
 apply only to individuals who successfully returned to their breeding site
 and initiated nesting in more than one year. We cannot exclude that those
 not recaptured on their breeding site may have used different migratory
 strategies. <strong>Non-breeding site
 use</strong> We combined geolocator data obtained
 from all individuals to characterize non-breeding site use. We first
 defined the location of ‘non-breeding sites’ by inspecting each track and
 by identifying clusters of locations where movements had lost
 directionality and became erratic for at least 5 consecutive days. Hence,
 the minimum stopover duration at a given non-breeding site could be 5
 days. The contour of a cluster associated with a given individual was
 first defined using a minimum convex polygon (MCP 95%) (Ghetta
 <em>et al.</em>, 2022). Clusters obtained for different
 individuals were merged into a single cluster when the contour lines
 overlapped with the contour of an adjacent cluster. All locations assigned
 to the same cluster were subsequently used to define the boundary of each
 non-breeding site using Kernel density estimates (75%; (R library ks -
 function kde, Duong, 2018). As American Golden-Plovers are terrestrial
 birds, non-breeding sites were clipped to remove any areas over oceans.
 The southernmost site used the longest by an individual was designated as
 its wintering site, while stopovers were designated according to the
 direction of the migratory path (fall stopovers for Southward movement;
 spring stopovers for Northward movement). Departure date was the date of
 the first location out of a given site when birds initiated unidirectional
 movement away from the site, and arrival date was the date of the first
 location within the site when movement was reduced, lost directionality,
 and became erratic. <strong>Quantitative measure
 of migratory connectivity</strong> We
 investigated the temporal change in migratory connectivity during the
 non-breeding period using the Mantel test (Ambrosini <em>et
 al.</em>, 2009; Cohen <em>et al.</em>, 2018; Knight
 <em>et al.</em>, 2021; Vickers <em>et al.</em>,
 2021). We characterised the migratory connectivity relative to the
 breeding locations of individuals by estimating the correlation between
 two distance matrices (Goslee, 2007). The Mantel correlation coefficient
 (r<sub>M</sub>) can range from −1 to 1, with 0 indicating
 random mixing of individuals when comparing their breeding spatial
 distribution with their spatial distribution during the non-breeding
 period, 1 indicating that individuals retain their relative spatial
 positions across seasons (Ambrosini et al. 2009). A positive
 r<sub>M</sub> value does not inherently imply that individuals
 from a breeding population are in close spatial proximity to one another
 during the non-breeding period. Rather, it indicates that the relative
 spatial configuration of the sampled breeding area is maintained at
 non-breeding sites. During the non-breeding period,
 daily distance matrices (measuring distances between all individuals on a
 given day) were generated, and a new r<sub>M</sub> value was
 calculated for each date, alongside a confidence interval that was
 computed using a bootstrap approach (Goslee, 2007). The
 r<sub>M</sub> thus represents the degree of spatial
 organisation of individuals at a specific date relative to their breeding
 distribution. As geolocators can provide up to two locations per day, we
 used the centroid of those locations to calculate the great circle
 distance between two individuals positioned on a given date.
 Statistical significance of the Mantel correlation coefficient
 was determined by random permutations. We randomly permutated the position
 of individuals at the breeding grounds 9999 times; for each permutation, a
 distance matrix was calculated, and its correlation coefficient with the
 actual distance matrix of individuals at a given date of the non-breeding
 period was calculated. The significance of the observed Mantel coefficient
 was assessed by comparing its rank among the coefficients generated
 through the randomization procedure (Ambrosini et al. 2009). When
 significant connectivity was detected (r<sub>M</sub> &gt;
 0), we investigated the process that generated it by assessing the number
 of potential clusters in the case of migratory structuring (using the
 ‘pamk’ function in the R package fpc (Henning, 2025); see Ambrosini et al.
 2009 and Ramos et al 2015). The number of clusters was identified as the
 number that maximized the overall average silhouette width (oasw), a
 measure of the goodness of fit of the overall classification of points in
 a given number of clusters (Rousseeuw, 1987). To
 quantify the strength of migratory connectivity and to facilitate the
 interpretation of temporal variation in r<sub>M</sub>, we
 extracted, for each distance matrix calculated for different dates, the
 average distance observed between all individuals. This provided a proxy
 of the non-breeding range spread of individuals at a given date (Finch
 <em>et al.</em>, 2017) and an indication of the variation
 through time in the scale of the spatial structure. (Cresswell and
 Patchett, 2024). We also calculated for each date the mean distance among
 individuals originating from the same breeding area to examine the
 temporal variation in breeding population spread across the non-breeding
 period (Finch <em>et al.</em>, 2017). For this purpose, we
 used breeding areas represented by &gt;2 individuals. The Ikpikpuk
 River and Utqiaġvik study sites were grouped into a single Alaskan
 breeding population as they are separated by only 111 km. Finally, we
 measured the distances between locations obtained on the same date but
 during two different years for the same individuals to assess individual
 consistency in space use and timing (Bauer <em>et al.</em>,
 2015). Individual locations could not be estimated
 every day during the non-breeding period because of extended daylight
 periods at high latitudes or imprecise estimations of latitude around the
 equinox. Hence, we could not compare the distance among individuals on
 those dates. We first ran the analyses using all individuals (n = 33),
 which included birds tracked during most of the non-breeding period but
 located less regularly (total of 115 days, spread from October 7 to May 3;
 Nome: n=2, Utqiaġvik: n=1, Ikpikpuk River: n=1, Caw Ridge: n=1, Churchill:
 n=4, Coats Island: n=1, Igloolik: n=6, Bylot Island: n=17). We also
 repeated our analyses (variation of r<sub>M</sub> and of mean
 distance between individuals through time) using a subset of 20
 individuals that were regularly located at the same dates over most of the
 non-breeding period (total of 149 days, spread from August 27 to May 14).
 All studied breeding locations were represented (Nome: n=1, Utqiaġvik:
 n=1, Ikpikpuk River: n=1, Caw Ridge: n=1, Churchill: n=3, Coats Island:
 n=1, Igloolik: n=3, Bylot Island: n=9). Results were similar in these two
 analyses (see Results), and thus the description in the main text is based
 on those obtained with the sample of 33 individuals. Because geolocators
 were deployed over different years, the 33 individuals were not tracked
 over the same annual cycle. Therefore, we also ran our analyses separately
 for each annual cycle and compared the results with those obtained using
 pooled years. All distances provided in the results are great-circle
 distances, and all analyses were performed using R version 3.5.3 (R Core
 Team, 2019). <strong>Ethics
 statement</strong> All methods, including
 capture, banding, and tagging, were approved by the Committees for Animal
 Care relevant to a particular study site.  Also, federal (Parks Canada,
 Environment and Climate Change Canada, US Fish and Wildlife Service, US
 and Canadian Bird Banding Offices) and state and provincial permits were
 obtained for attaching tags and recapturing birds to remove
 tags.
description: # Migration path of American golden-plovers breeding across the Nearctic
 tundra
 [https://doi.org/10.5061/dryad.280gb5n0v](https://doi.org/10.5061/dryad.280gb5n0v) ## Description of the data and file structure This dataset contains: **Geolocator_data: Geolocator_data.zip**\ Data extracted from each geolocator and retrieved from breeding American Golden-Plovers (*pluvialis dominica*). We used 2 different geolocator models, provided by British Antarctic Survey MK10b ( file type used for light level: **.lig**, but also .act, .rtd, .trn, .aer, .ara, .ler, .lra were downloaded from loggers (not used in the analysis relevant in this study) and are provided). Another model provided by Migrate Technology ltd Intigeo geolocators W65A9RK ( file type used for light level: **.driftadj.lux**, but also .lux, .rat, .rai, .deg were downloaded from loggers (not used in the analysis relevant in this study) and are provided). **Processing Geolocator Data:** Please refer to the following references for background on geolocator analysis using the Geolight package (opensource): R package GeoLight 2.0 userguide: [Light‐level geolocator analyses: A user's guide - Lisovski - 2020 - Journal of Animal Ecology - Wiley Online Library](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13036) R package Geolight 2.0 function description: [GeoLight-package function - RDocumentation](https://www.rdocumentation.org/packages/GeoLight/versions/2.0.0/topics/GeoLight-package) R package Geolight 2.0 function description and examples of usage: [scbi-migbirds.github.io/Geolocator_GeoLight.html](https://scbi-migbirds.github.io/Geolocator_GeoLight.html) All analyses were performed using R version 3.5.3 (R Core Team, 2019). R is a free and open-source software environment for statistical computing and graphics distributed under the GNU General Public Licence . It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To **[download R](https://cran.r-project.org/mirrors.html)**, please choose your preferred [CRAN mirror](https://cran.r-project.org/mirrors.html). **.lig**, and **.driftadj.lux** files are formated text files that can be open with the free and opensource package Geolight within R, a free and open-source software.  AMGP_paths_p.zip: This folder includes shape file. Shapefiles are a common format for vector-based geographic information system (GIS) data. They can be opened and used in any GIS software and in R or Python. A shapefile consists of multiple file types beyond the .shp (specifically, .cpg, .dbf, .prj, .sbn, and .sbx). The user only interacts directly with the .shp file but the other files need to be in the same directory. Data were processed through GeoLight 2.0 (Lisovski *et al.*, 2015) to convert light-level data to location estimates. Following Finch et al. (2015), we used a light threshold of 3 for BAS devices and 2 for Intigeos, as the light data output differs between models of geolocators. We converted light data into two daily locations (morning and evening values in latitude and longitude) for each bird from geolocator deployment to recovery, except when devices malfunctioned during the non-breeding period. For Arctic-breeding birds exposed to full daylight when at high latitude, the dataset was restricted to dates when nights were detected. To reduce light noise during nighttime, the lightFilter function in GeoLight 2.0 was used (Lisovski *et al.*, 2015). Most loggers were calibrated using the rooftop method (n=27, 64%) to provide a start angle for the Hill-Eckstrom calibration (mean=‑6.63° range [‑7.33, ‑2.73], n=27). Residency periods were identified with the ChangeLight function in GeoLight 2.0 (quantile= 0.9) with a specified minimum residency length of 2 days. For movement periods or if no optimal sun elevation angle could be obtained by either rooftop or Hill-Ekstrom calibration, we used the angle provided by the rooftop calibration, and if unavailable, the civil twilight (i.e., 6°, Lisovski *et al.*, 2015, n = 9). We filtered the estimated locations obtained with a loess filter (k=2) to remove outliers. Following Hobson & Kardynal (2015), the migration track of each plover was smoothed with a state-space Kalman filter and the most probable path was obtained with kftrack (Sibert and Nielsen, 2002) in R. Kalman filtering provides the most probable track from location data and reduces observer bias when dealing with raw location estimates obtained with geolocators (Gow, 2016; Hobson and Kardynal, 2015). As the estimated flight speeds of American Golden-Plovers vary widely (see Johnson *et al.*, 2020), we used a relatively high flight speed estimate (104.6 km/h, Johnson and Morton, 1976), corresponding to a maximum of 2,510 km per day, to set the diffusion component of the model. Kftrack uses an asymmetric error structure peaking on the winter side of the equinoxes, which is typical for geolocator data. Location precision was calculated from calibration data and yielded a mean error of 163 ± 75 km (n = 20), comparable to prior studies (Lisovski *et al.*, 2012 and references therein). Files (**.lig** or **.driftadj.lux)** per individual are indicated in the METADATAv2.csv file.  **Metatada: METADATAv2.csv**\ Metadata about each plover is in this file. | FIELD | DESCRIPTION | | ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | noref | Reference number for sorting individual | | year\_recap | Year when the recapture of an individual wearing the logger took place | | logger | Data logger identification | | bird\_BAND | Bird banding number | | year\_deployed | Year when the logger was deployed | | IS\_idem | Idenfication field of whether the individual is the same (ex, idem1 for loggers coming from the same individuals but different years, unique for individuals with only one logger | | data\_from | Start date of logger- not indicated for all loggers | | data\_to | End date of logger- not indicated for all loggers | | lightlap | Time in minutes between sampling of light level by the logger - differs by logger model | | MIGDATA\_from | Date to select the start of the migration period | | MIGDATA\_to | Date to select end of migration period | | AN\_DATA | Year of migration data | | DEG\_file | .deg file name for (immersion count data - not used in analysis) | | file | light data file name | | Calib\_avail | Availability of calibration data: y for yes, n for no | | Calib\_site | location (name) of the rooftop calibration site | | Calib\_lat | location (latitude) of the rooftop calibration site- decimal degree | | Calib\_long | location (longitude) of the rooftop calibration site- decimal degree | | Calib\_from | start date of the rooftop calibration site | | Calib\_to | end date of the rooftop calibration site | | file\_type | light data file name extension: variable by logger model | | study\_site | Breeding site where deployment of logger took place | | COLJJ | Color used by breeding site for map-making | | LAT\_STUDs | Study site location (Latitude - decimal degree) | | LONG\_STUDs | Study site location (Longitude - decimal degree) | | species | Code of the species used for the study: amgp for American Golden-Plover | | logger\_mount | was logger installed on the leg vertically or horizontally. If empty: unknown | | logger\_type | Model of the data logger used | | sex | m: male f:female u:unknown - ?: mark uncertainty | | sexing\_method | Method used for sexing | | total\_head | Total head length (mm) of the individual | | culmen | Culmen length (mm) of individual | | wing | Wing length (mm) of individual | | tarsus | Tarsus length (mm) of the individual | | mass | Mass (gram) of an individual | | comments | Comments regarding each individual | | NO\_study\_site | Study site name with prefix being longitudinal sorting order - West to east | | labY\_timeine | Label for timeline graph | | lat\_CAPTURE\_v2 | Latitude (decimal degree) of capture location | | long\_CAPTURE\_v2 | Longitude (decimal degree) of capture location | **Animal tracks analysis AMGP_paths_p.zip** This is a shapefile including migratory tracks of all American Golden-Plover used in this study.  **Access information** Other publicly accessible locations of the data: * None Data was derived from the following sources: * None