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Long-distance migratory shorebirds
travel faster towards their breeding
grounds, but y faster post-
breeding
Sjoerd Duijns
1,2, Alexandra M. Anderson3, Yves Aubry4, Amanda Dey5, Scott A. Flemming3,
Charles M. Francis6, Christian Friis7, Cheri Gratto-Trevor8, Diana J. Hamilton9,
Rebecca Holberton10, Stephanie Koch11, Ann E. McKellar12, David Mizrahi13,
Christy A. Morrissey14, Sarah G. Neima9, David Newstead15, Larry Niles16, Erica Nol17,
Julie Paquet
18, Jennie Rausch19, Lindsay Tudor20, Yves Turcotte
21 & Paul A. Smith2
Long-distance migrants are assumed to be more time-limited during the pre-breeding season compared
to the post-breeding season. Although breeding-related time constraints may be absent post-
breeding, additional factors such as predation risk could lead to time constraints that were previously
underestimated. By using an automated radio telemetry system, we compared pre- and post-breeding
movements of long-distance migrant shorebirds on a continent-wide scale. From 2014 to 2016, we
deployed radio transmitters on 1,937 individuals of 4 shorebird species at 13 sites distributed across
North America. Following theoretical predictions, all species migrated faster during the pre-breeding
season, compared to the post-breeding season. These dierences in migration speed between seasons
were attributable primarily to longer stopover durations in the post-breeding season. In contrast,
and counter to our expectations, all species had higher airspeeds during the post-breeding season,
even after accounting for seasonal dierences in wind. Arriving at the breeding grounds in good body
condition is benecial for survival and reproductive success and this energetic constraint might explain
why airspeeds are not maximised in the pre-breeding season. We show that the higher airspeeds
in the post-breeding season precede a wave of avian predators, which could suggest that migrant
shorebirds show predation-minimizing behaviour during the post-breeding season. Our results rearm
the important role of time constraints during northward migration and suggest that both energy and
predation-risk constrain migratory behaviour during the post-breeding season.
1Department of Biology, Carleton University, Ottawa, ON, Canada. 2Environment and Climate Change Canada,
Wildlife Research Division, Ottawa, ON, Canada. 3Environmental and Life Sciences Graduate Program, Trent
University, Peterborough, ON, Canada. 4Environment and Climate Change Canada, Canadian Wildlife Service,
Quebec, QC, Canada. 5Endangered and Nongame Species, New Jersey Division of Fish and Wildlife, Trenton,
USA. 6Environment and Climate Change Canada, Canadian Wildlife Service, Ottawa, ON, Canada. 7Environment
and Climate Change Canada, Canadian Wildlife Service, Toronto, ON, Canada. 8Environment and Climate Change
Canada, Science and Technology Branch, Saskatoon, SK, Canada. 9Department of Biology, Mount Allison
University, Sackville, NB, Canada. 10Lab of Avian Biology, Department of Biology & Ecology, University of Maine,
Orono, ME, USA. 11United States Fish and Wildlife Service, Sudbury, MA, USA. 12Environment and Climate Change
Canada, Canadian Wildlife Service, Saskatoon, SK, Canada. 13New Jersey Audubon Society, Bernardsville, NJ, USA.
14Department of Biology and School of Environment and Sustainability, University of Saskatchewan, SK, Canada.
15Coastal Bend Bays and Estuaries Program (CBBEP), Corpus Christi, TX, USA. 16Wildlife Restoration Partnerships
LLC, Greenwich, NJ, USA. 17Department of Biology, Trent University, Peterborough, ON, Canada. 18Environment
and Climate Change Canada, Canadian Wildlife Service, Sackville, NB, Canada. 19Environment and Climate Change
Canada, Canadian Wildlife Service, Yellowknife, NT, Canada. 20Maine Department of Inland Fisheries and Wildlife,
Bangor, ME, USA. 21Département des sciences et techniques biologiques, Collège de La Pocatière, La Pocatière, QC,
Canada. Correspondence and requests for materials should be addressed to S.D. (email: duijns.sjoerd@gmail.com)
Received: 18 May 2018
Accepted: 14 June 2019
Published: xx xx xxxx
OPEN
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Every year, billions of animals migrate in search of improved foraging conditions, safety from predators, and
enhanced reproductive opportunities1–3. Although seasonal migration is widespread among animals, it is espe-
cially well studied in birds4. During birds’ pre-breeding migration (i.e., the boreal spring for temperate and
Neotropical migrants), it is generally assumed that individuals are more time constrained in reaching their goals
than during post-breeding (boreal autumn) migration. is is because of: (i) competition among individuals
where the timing of arrival provides advantages in acquiring the best breeding territories5, (ii) the importance of
matching temporally narrow peaks in food abundance6, and/or (iii) for Arctic breeding birds, the need to breed
early to maximize re-nesting opportunities given the compressed breeding season7.
Because of these different time constraints, theory predicts that migratory birds should adopt a
time-minimization strategy in the pre-breeding season8. Birds employing this strategy should devote as much
energy as possible to fuel deposition and consequently avoid other time and energy consuming activities such
as moult on their stopover site, leading to a faster migration8,9. During the post-breeding season however, when
time constraints are less, migrants should shi towards an energy-minimizing strategy, i.e., to reduce their energy
expenditure as much as possible10, and to recover their energy reserves aer an energetically demanding breeding
season. Birds following this strategy are predicted to stop more en route to refuel10 and because they are less time
constrained11, they may be more inclined to wait for favorable migration conditions11.
Many empirical studies have supported elements of these general predictions relating to pre-breeding versus
post-breeding migration12–14. However, not all studies have detected seasonal dierences in migration speed15–17,
and several studies have found patterns opposite to the above predictions18–21. ese reverse patterns suggest that
there may be some advantage for a slower migration in the pre-breeding season, such as to minimise energy expend-
iture prior to breeding, or it could result from atypical environmental conditions such as cold temperatures or high
ice cover at breeding areas that slow down this pre-breeding migration. ese environmental conditions are poten-
tially unpredictable in space and time, possibly confounding general patterns. For example, the optimal speed to
achieve time- or energy minimization depends on the rate of energy acquisition at current and future stopover sites,
and the rate of migratory progress depends on the head- or tailwinds encountered8. Environmental variables can
therefore have a strong inuence on the rate of migratory progress22, and because these environmental factors can
be dicult or impossible to predict23, birds’ migration strategies must also incorporate elements of risk avoidance24.
In addition to these weather related risks, another important risk that could inuence migration behaviour
is the risk of predation25–27. High departure fuel loads and changes in organ size required by time-minimizing
shorebirds impair take-o performance and manoeuverability, compromising their ability to escape from preda-
tors28–31. Migratory shorebirds forage in dense ocks, and because of their dependence on intertidal mudats to
forage, they congregate in large numbers on only a few sites along their migratory route to refuel for migration
north and south32, thereby attracting a variety of avian predators33,34.
Raptors, especially peregrine falcon Falco peregrinus, merlin Falco columbarius and Cooper’s hawk Accipiter
cooperii are key predators of migrant shorebirds in the Atlantic flyway of North America33,35. During the
pre-breeding season, these raptor species migrate north over a protracted period and initiate migration before
most shorebirds, as they travel to their breeding grounds in the vast tundra and boreal regions of North America;
breeding areas which are at lower latitudes than for arctic-breeding shorebirds36–38. In contrast, aer the breeding
season, these raptors migrate during a narrower time window and concentrate in space and time as they head
south, due to the inverted triangular shape of the continent38,39. erefore, migratory shorebirds face a greater
risk from migratory raptors during the post-breeding season versus the pre-breeding season40. Most migrant
shorebirds begin their southbound migration ahead of the wave of migrating raptors; thus, a slow post-breeding
migration for shorebirds could increase the overlap with these avian predators27.
Identifying seasonal dierences in migratory decisions and performance is crucial for understanding the eco-
logical and evolutionary constraints that shape migratory behaviour12,41. To identify seasonal dierences in the
speed of migration, most studies have used overall migration speed (km day−1) as the behavioral metric12,14; a
metric which describes the northward or southward progress including stopover time en route, and given the
environmental conditions experienced. However, seasonal dierences in these environmental conditions, specif-
ically head- or tailwinds, can profoundly impact migration speed even without any change in birds’ behaviour;
therefore, it is essential to incorporate other behavioural and environmental (wind) metrics in assessments of
birds’ migration behaviour. For example airspeed, a bird’s ight speed relative to the surrounding air, might better
indicate whether birds are more time constrained in the pre-breeding season versus the post-breeding season.
Airspeed more directly reects the birds’ ight behaviour and the energy costs incurred by that behaviour, aer
accounting for any seasonal dierences in wind that could inuence total migration duration42,43.
Here, we test the hypothesis that on the North American continent, Arctic-breeding shorebirds (extreme
long-distance migrants) display relatively more time-minimizing migration behaviour pre-breeding, and
energy-minimizing behaviour post-breeding. Since there is evidence for age-specic dierences in migration
speed44,45, we also explore potential age dierences. We predict that migration speed and the airspeed of shore-
birds is faster during the pre-breeding season compared to the post-breeding season. Because the rate of energy
expenditure during migratory ights is higher than the refuelling rate, most of the migration period is spent at
stopover sites8,10, suggesting that stopover duration is the main driver of migration speed14. Pre-breeding migrants
are therefore expected to stop less oen and for shorter periods en route, and move more rapidly towards their
breeding grounds. Aer the breeding season, because energy minimizers are inclined to wait for favorable migra-
tion conditions11, we predict that birds will be more wind selective, less goal oriented, and have a longer migration
period. However, this protracted migration could expose birds to a “wave” of migrant predators39. erefore, we
also explore the evidence for predation-minimizing behaviours27. More specically, we evaluate shorebird migra-
tion speed in relation to the timing and abundance of avian predators at key raptor migration sites. To evaluate
these predictions, we used automated VHF telemetry on a continental scale46 and followed the seasonal migration
of Arctic-breeding red knots Calidris canutus rufa, sanderlings C. alba, semipalmated sandpipers C. pusilla and
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ruddy turnstones Arenaria interpres over multiple years. e ne temporal resolution of these automated telem-
etry data allow us to explore the migration speed, airspeed and timing of departure in a way that was not previ-
ously possible on this temporal and geographic scale. e results provide new insights into the factors underlying
migratory strategies for these extreme long-distance migrants.
Results
A total of 1,937 shorebirds were tted with a transmitter between 2014 and 2016 (see Fig.1 and TableS1 for
details); 647 during the pre-breeding and 1,290 during the post-breeding season. Of these, we were able to calcu-
late the total migration speed of 516 individuals and airspeed for 389 individuals that were detected at least twice
>50 km apart within the same season. Among the remainder, 88 (<5%) individuals were not detected anywhere
in the network indicating the tags may have malfunctioned, fallen o, or otherwise escaped detection; 804 were
only ever detected at a single receiver station, usually near the tagging location; 362 individuals were detected at
multiple towers but <50 km apart, which we considered local movements and did not include in further analyses;
and 255 individuals were detected at greater distances in dierent seasons, but only at one station within each
season, thus preventing use in analyses.
Migration speed, stopover and predator avoidance. As predicted, migration speed (km day−1) prior
to the breeding season was more rapid than aer the breeding season for all four species considered (GLMM,
p < 0.001; Fig.2a). The final model for migration speed included season (X² = 17.4, p < 0.001) and species
(X² = 75.2, p < 0.001), along with random eects of year and capture site. Northbound transit was almost twice as
fast across species versus the southbound transit (116 km day−1 during the pre-breeding season versus 62 km day−1
post breeding). In accordance with predictions, based on our nal model which included season, all species
stopped for longer periods during post-breeding migration (GLMM, X² = 73.0, p < 0.001; Fig.2b). e mean min-
imum stopover duration (i.e., sum of all stopover duration per season), for pre-breeding migrants was 14.7 ± 0.4
d (SE) across species, whereas for post-breeding individuals the mean stopover duration was 32.6 ± 1.3 d (SE).
In contrast to these patterns, the airspeeds were ~10% higher during the post-breeding season for all species
(GLMM, X² = 6.1, p = 0.01; Fig.2c), suggesting that birds had higher levels of energy expenditure during the
southbound migration ights. ese higher post-breeding airspeeds correspond to ~10% increase in instantane-
ous ight cost (W kg−1), based on power curve calculations (see TableS2 for details).
Predator abundance showed a clear and simultaneous seasonal peak aer the breeding season at three key
raptor migration sites (GAMHawk Cli, F = 7.9, R2 = 0.32; GAMHawk Mountain, F = 28.8, R2 = 0.65 and GAMCape May,
F = 26.8, R2 = 0.69; Fig.3a). All shorebird species showed similarities in their seasonal patterns of airspeed
(GAMred knot, F = 19.2, R2 = 0.33, Fig.3b; GAMruddy turnstone, F = 2.9, R2 = 0.18, Fig.3c; LMsanderling, F1,105 = 8.1,
R2 = 0.08, Fig.3d; GAMsemipalmated sandpiper, F = 4.8, R2 = 0.05; Fig.3e), with peaks in airspeeds prior to the peak of
raptors and reduced airspeeds aer the peak occurred (GLMM, X² = 3.7, p = 0.024).
Timing of migration. Pre-breeding departure timing was highly synchronized across years and showed a
narrow time window, whereas in the post-breeding season the departures were distributed over a longer period
(Fig.4). Large numbers of shorebirds stage in Delaware Bay (New Jersey and Delaware, USA) before making
their nal non-stop ight into their Arctic breeding grounds47. Aer the breeding season, the Southern James
Bay region (Canada, ON), is one of the key stopover sites on the Southern edge of the Arctic breeding sites48.
e mean date of departure from Delaware Bay was (day of year) 148.4 ± 3.5 days (mean ± SD; 148 = 28 May),
and all species departed close to this date (day of year 148.2 ± 2.9 days for red knots; 148.9 ± 3.8 days for ruddy
turnstones; 150.9 ± 3.8 days for sanderlings and 146.6 ± 9.8 days for semipalmated sandpipers). e mean date of
departure from Southern James Bay during the post-breeding season for adults was 231.9 ± 15.6 days (mean ± SD,
232 = 20 Aug). ese departure dates did not vary signicantly across species (GLMM, X2 = 1.6, p = 0.64), but
diered between adults and juveniles (GLMM, X2 = 52.2, p < 0.004), with juveniles leaving about 19 days later.
Wind support and ight directions. Most individuals showed a high degree of wind selectivity in both
seasons. Wind support on the observed departure date was higher than the wind support on the preceeding 10
days (GLM, X² = 100.6, p < 0.001; see Supplementary Fig.S1 for details). e relative wind support at the time of
departure was greater for the post-breeding versus pre-breeding migration (GLM, X² = 445.0, p < 0.001); birds
experienced more supportive tailwinds during the post-breeding season. However this greater wind support does
not account for the dierences in birds’ airspeeds between seasons, as airspeed accounts for head- or tailwinds
(i.e., represents the ight speed in still air). A wider range of ight directions were utilized post-breeding (as
indicated by a lower vector length (r); Fig.5), suggesting a less clear orientation towards a destination, which is
consistent with an energy minimizing strategy. No dierence in orientation between the age classes was observed
(p < 0.10, Watson’s U2 test).
Discussion
Our result of a faster migratory progress prior to breeding is consistent with the basic predictions of optimal
migration. e dierence in migration speed between both seasons appeared to reect dierences in stopover
duration. Our results indicate that the airspeed of shorebirds during the pre-breeding season is not at its upper
limit, as the estimated pre-breeding airspeeds were below those post-breeding, and also below the theoretical
maximum-range speed (Vmr; TableS3). is is surprising given the wide acceptance that birds respond to the
time constraints imposed by the breeding season by minimizing the duration of the pre-breeding migration.
Our ndings suggest that other considerations such as body condition upon departure41,49 or upon arrival at the
breeding sites50, may inuence a bird’s migratory decisions during the pre-breeding season to a greater degree
than previously believed.
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An increase in airspeed comes at a cost of increased ight power, which necessitates increased fueling. e
optimal airspeed for maximizing total migration speed (or equivalently, minimizing the total time of migration)
is 5–15% higher than the optimal airspeed for minimizing energy costs per unit distance8,51, leading to an increase
Figure 1. Locations of active receiver stations (yellow dots with black outlines), and the migratory trajectories
of the birds used in analyses. e lines (great circle routes) connect detections for individuals of each of
the four shorebird species (tracks are coloured per species; Red Knot = pink, Ruddy Turnstone = green,
Sanderling = turquoise and Semipalmated Sandpiper = purple), with panels separating year and season.
ese tracks represent simplied ight trajectories; birds may have deviated from these great circle routes.
Maps created using R 3.4.3 using packages ggplot284 and ggmap85 (image data providers: US Dept. of State
Geographer© 2018 Google).
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in total migration speed of only 0.2–2%42,52. erefore, increasing airspeed to increase the speed of migration
might be disadvantageous during the pre-breeding season, when body condition oers other benets upon arrival
to the breeding grounds. Conversely, post-breeding and juvenile birds during the post-breeding season face a
dierent suite of temporal and energetic constraints, and importantly, are faced with increased predation pressure
due to potential co-occurrence with migrating raptors.
Shorebirds are primarily income breeders (i.e., they acquire all the necessary resources on the breeding
grounds)53; however, body condition upon arrival to the breeding grounds can nevertheless play an important
role in the survival and reproduction of Arctic-breeding shorebirds. Several studies have demonstrated that
individuals arriving to the breeding grounds with greater body stores are able to withstand harsh early-season
weather50, and high body condition during the pre-breeding period has been linked to higher quality eggs54. Red
knots with greater energetic reserves during spring migration remained in the Arctic longer and were more likely
to be detected aer the breeding season, suggestive of successful breeding and greater survival, respectively41.
Maximizing airspeeds during the pre-breeding season could impose a signicant cost of reduced body stores
upon arrival but yield only a modest benet in terms of reducing the total duration of the migration.
Surprisingly, we found only minor age-related dierences in migratory behaviour, such as timing of migra-
tion for red knots, sanderling and semipalmated sandpiper (Fig.3b,d,e; we did not have juvenile ruddy turn-
stone in the data). Our post-breeding migration data were collected at Arctic, subarctic and north-temperate
latitudes close to the breeding sites (in some cases, departing from the breeding sites). Age-related dierences in
migration behaviour might occur later in the post-breeding season, farther south. Although some shorebirds are
known to migrate in groups of variable age classes55, others show more distinct waves of adults and juveniles56.
Nevertheless, inexperienced juveniles may benet from social information for navigation and stopover site use
during their rst post-breeding migration.
Predation risk is a factor aecting birds’ migratory behavior in a profound way25,33. Although direct mortality
from predation can be signicant, non-lethal eects can illicit variable behavioural responses in a wide range of
taxa57,58, including in shorebirds25,59. Among shorebirds, responses to increased predation danger include behav-
ioural changes such as variable stopover duration33, habitat use9, body mass changes60 and even morphological
changes48. Here we showed that post-breeding birds that migrated in advance of the “predator front”, had higher
Figure 2. Model estimates of (a) total migration speed per day, (b) minimum stopover duration and (c)
airspeed of four species of Arctic-breeding shorebirds during pre- and post-breeding migration. Estimates are
derived from linear mixed models (see text) and the box-and-whisker plots give median (horizontal line within
box), interquartile range (box), range (bars) and the transparent dots show estimates for individual birds.
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airspeeds. Later in the season, aer the peak of predation risk had passed, airspeeds were lower and similar to
those observed during pre-breeding. is observation is consistent with the mortality-minimizing hypothesis,
which predicts that when the predation front is approaching, individuals should increase the fuel-loading rate to
increase migration speed27.
Wind is another factor with a crucial inuence on the speed of migration; headwinds or tailwinds directly
reduce or increase the speed of migration for a given bird’s airspeed. However, wind can also alter the decisions
Figure 3. Mean relative frequency of avian predators at three sites in North America (see text for details) and
airspeeds of 4 shorebird species separated by age class, throughout the protracted post-breeding migration
period. e airspeeds are higher prior to the wave of predators. e solid line represents the regression (linear
model or GAM; see text) and the grey area indicates the 95% condence intervals of the regression.
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Figure 4. Timing of migration for the four species combined, as determined from individual departure dates
from Delaware Bay (pre-breeding) and the southern James Bay region (post-breeding), separated by year. e
white dots represent the relative frequency of individuals departing the sites and the lines and shaded areas
represent estimated model means and 95% condence intervals. e timing is highly synchronized during the
pre-breeding season, but not during post-breeding.
Figure 5. Circular distributions of migratory ight bearings (in degrees) for the four shorebird species
and three years combined, for pre- and post-breeding migrations. Each line shows the direction of tracked
individuals and the length refers to the number of movements in a given direction (scale 0–15). Each line is
a measurement of a migration segment, which may represent only a part of the detected migratory route and
therefore includes some non-independent measurements across individuals. e inset shows (α) the mean track
direction, (r) vector length, (sd) angular deviation and (n) sample size per season. e statistical signicance
refers to a Rayleigh test (***p < 0.001).
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made by migrants. An individual that is maximising the migration speed should increase its airspeed in head-
winds and decrease its airspeed with tailwinds61. As we have shown, the dierent shorebird species are highly
wind selective and predominantly initiate migration on days with supportive tailwinds. However, our results
show that birds migrate with higher tailwinds in the post-breeding season (Fig.S2), suggesting that they should
lower their airspeed to maximise ight range. erefore, the higher airspeeds in the post-breeding season are not
the result of migrating into headwinds.
During the post-breeding season, birds also exhibited a high exibility in their departure direction to capture
these tailwinds, as shown by the wider range of post-breeding departure bearings. Together, these results of high
airspeed, longer stopover durations and exible departure directions suggest the possibility that post-breeding
migration is inuenced by the constraints of predation risk, despite the fact that post-breeding migration speed
is lower compared to the pre-breeding season. Increased predation risk post-breeding could potentially reduce
fueling rates due to increased vigilance62, which could in turn lead to a lower departure mass from a stopover
site. is low departure mass coupled with the high airspeed observed in this study could increase the required
stopover duration(s) further south in the migration. Alternatively, the urge to depart from sites where predators
were encountered is greater than the drive to y in a specic direction. Competition, deteriorating weather63 and
declining food availability might also drive shorebirds to move out of the area64 and temporarily speed up their
pace, forcing them to stop more and/or longer subsequently en route.
For most Arctic-breeding shorebirds, the post-breeding season also brings on moult; species show a great
variability in timing of moult as they may moult prior to, during, or aer southward migration26. Missing feathers
will cause a reduction of wing area and/or wing span, and ight costs are altered during active moult. However,
few species undertake active moult during migration (e.g., dunlin Calidris alpina65), and the energetic eects
of moult on ight performance are generally small66. Although variation in moult across the species and indi-
viduals studied here could increase the observed variation in airspeed, it cannot explain faster airspeeds in the
post-breeding season.
Our nding of a higher airspeed for migratory shorebirds during post-breeding has not previously been doc-
umented, likely in part because of the diculties of measuring ight speeds for small-bodied migrant birds. Until
recently, tracking technologies for small-bodied migrant birds have not oered both large-spatial coverage for
tracking long-distance migrations and ne temporal resolution for understanding these subtle changes in behav-
iour. However, our automated telemetry array does not cover the full length of the yway. Birds in our dataset
might alter their behaviour aer they leave our study areas. Possibly, northbound individuals initiate a nal sprint
to their breeding sites and counterbalance the mean dierences in airspeed between both seasons over the entire
trajectory67. Likewise, potential dierences in migratory behaviour such as protandry (males arriving to breeding
areas before females), which is common in many bird species68, cannot be excluded, as unfortunately, we did not
have sucient data concerning the sexes of the species involved. Although we tracked some individuals to the
southern edge of the breeding grounds, most species migrated further north, where these intersexual dierences
may have been more pronounced. Nevertheless automated telemetry results such as these, with ne temporal
resolution, oer fertile ground for explorations of the predictions of optimal migration theory. Location-specic
counts of predators, and location-specic assessments of departure behaviour, might allow for a further renement
of the understanding of the role of predators in shaping the migration behaviour of long-distance migrant birds.
Methods
As part of several ongoing studies of shorebirds in North America, red knots, sanderlings, semipalmated sandpipers
and ruddy turnstones were captured between 2014 and 2016 and measured using standard protocols at 13 loca-
tions distributed widely across the Atlantic yway from Texas, United States, to Nunavut, Canada (see Fig.1 and
TableS1 for details). All birds were banded with numbered metal bands, and standard biometric measurements were
taken. Age was determined from species-specic plumage features and classied as hatch-year (rst post-breeding
migration) or aer-hatch-year (experienced at least one previous southward migration), while sex could not be
determined at capture based on morphology69. Immediately aer these measurements were taken, a sample of birds
were tted with digitally coded radio-transmitters (Avian Nanotags, Lotek Wireless Inc., Newmarket, ON, Canada),
which have an estimated life span of 33–150 days, depending on type and burst rate (see below). e radio tags were
glued to the skin and clipped feather stubble of the synsacral region with a cyanoacrylate gel adhesive. Tag trans-
missions were tracked using a network of automated radio telemetry receiving stations, the Motus Wildlife Tracking
System46, with towers distributed across North America and to a lesser extent in South America.
Receiver stations were equipped with a digital telemetry receiver (Lotek SRX600, Lotek SRX800, or a
SensorGnome receiver; www.sensorgnome.org), and most receiver stations had multiple directional antennas ori-
ented at xed angles (typically three to six 9-element Yagi antennas distributed evenly around 360°) that scanned
continuously for tags. All tags operated on a single frequency (166.380 MHz) and were distinguished by a unique
series of pulses contained within each burst of radio transmission (burst rate: 5.3–14.9 s), allowing for denitive
identication of individuals. Tagged birds can be detected at distances up to 20 km away from a receiver station,
although topography, weather, and vegetation can all decrease detection range46,70. Tag detections were recorded
by the automated receivers and time stamped by an internal GPS clock, allowing us to track individual move-
ments at a continental scale with a temporal resolution of ±15 s.
Analysis of automated telemetry data. We processed the data as previously described41,71. In summary,
all tag, station, project, and user metadata are submitted by users, archived in the database, and linked and man-
aged through the Motus research platform46. e tag detection data collected at receiving stations are joined with
the master tag and station metadata to produce a complete database of unique detections from each station. e
radio signals captured by the receivers are cross-referenced against the tag recordings submitted to Motus during
tag registration. Due to local interference and the high gain on the receivers, spurious signals (i.e., false positives)
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were recorded. From the raw detection data, detections that are outside of the deployment period for a specic
tag were removed. We removed detection data where the standard deviation in signal strength was >0.1 and the
run length <2 (number of continuous detections of a unique code by a receiver). Detections in this ltered data-
base (containing tag-id, tower location, GPS time stamp, antenna number, and signal strength), were considered
valid if there were three detections from a tag at multiples of the tag’s burst rate (±4 ms, with the allowed error
in the burst rate increasing by 1.5 ms for each missed burst and allowing for up to 20 consecutive missed bursts).
Although this ltering procedure might remove some valid detections, it ensured that the resulting data set con-
tained only valid detections of the tagged birds.
Departure time (UTC) from each site was determined as the last of at least three consecutive detections prior
to no further detections at a site. Arrival times (UTC) were determined as the rst detection in a series of at least
three consecutive detections at a receiver station within a dierent site.
Ground speed (m s−1) was calculated as the orthodrome distance (m) between two dierent receiver stations,
divided by the total time (s) between departure and arrival. As the objective was to determine migration speed,
we excluded local movements (<50 km). e mean distance between all detections was 550 km and therefore the
bias from the uncertainty of an individuals’ actual position (e.g., a 20 km detection distance), is likely not a large
factor inuencing the speed calculations.
We calculated the minimum stopover duration for each individual as the sum of all time dierences (s)
between the last and rst detection per receiver station, including the time an individual remained at the capture
site aer capture. A stopover event was dened as a minimum duration of 30 min, and shorter durations were
considered migratory movements41.
Migration speed (km day−1) was calculated as the sum of the orthodrome distance (km) between all receiver
stations per season at which an individual was detected, divided by the total time an individual was detected,
including the minimal stopover time.
When an individual had been detected between two receiving stations, we calculated the bearing in degrees
using the package fossil72 in the program R73. We only used the bearings describing departures from one key site
per season; Delaware Bay for pre-breeding migration (39°4′N and 74°6′W) and the southern James Bay region
for the post-breeding migration (51°4′N, 80°4′W). A majority of birds were detected at these sites, and they are of
known importance for these species47,48. Analysing bearings in this way minimizes the potential bias arising from
site-dependent departure directions, and the fact that northbound migrants in spring are heading towards higher
latitudes where there are fewer towers (see Fig.1). Based on these bearings, we calculated the mean vector length
r (which can vary from 0 to 1) as a measure of directional concentration, and the Watson U2-test was used to test
whether the age classes diered from each other.
Wind data. We used the wind data of the National Centers for Environmental Prediction (NCEP). e
ow-assistance experienced by each individual was calculated using the R-Package RNCEP74. Since we recorded
when and where an individual is present and the destination (i.e., dierent receiver station) is known, we used
the latitudes and longitudes of the receiver stations as the start- and end-point for each trajectory, with the cor-
responding departure times. Using this information, we downloaded the –u (west-east) and –v (south-north)
wind components, which were combined in a single wind vector incorporating the strength and the direction of
the wind, from which we obtained a tailwind component. e calculations were based on wind components for
the following pressure levels: ‘surface level’, 925, 850 and 700 hPa, corresponding to between 0 and 3000 m alti-
tude. e RNCEP tailwind model was set to calculate the heading and tailwind component at the most optimal
pressure level upon departure, and this pressure level was reassessed every 3 hr, and the heading and tailwind
component recalculated by the model. In this way, we obtained an estimate of the wind assistance experienced by
birds every 3 hr, assuming that they travelled at the most protable pressure level.
Airspeed (m s−1) was calculated by subtraction of the wind vector (wind assistance) from the track vector
(ground speed, see above) to estimate individual airspeed75. Some track segments (<3% of total sample) had
airspeeds >25 m s−1, which were the result of occasional false detections of tags (e.g., near simultaneous apparent
detections of a bird at towers hundreds of kilometers apart). ese false detection patterns were identied by
examining plots of detections for each bird by latitude and time and longitude and time, and values associated
with unrealistic movement patterns were removed76. Airspeeds below a threshold of <5 m s−1 were considered
as possible undetected stopovers or detours through areas with no tower coverage77, and were excluded from
airspeed analyses. Our power curve calculations (see below) indicate that the power required to y below these
speeds increases exponentially making these speeds unlikely. In order to identify the costs associated with air-
speeds, we calculated the power curves for the four species using the program Flight 1.24, which is available (free)
from http://books.elsevier.com/companions/9780123742995 (see TablesS2 and S3 for details).
In order to evaluate the hypothesis that long-distance migratory shorebirds would be more wind selective in
the post-breeding season, we calculated wind support for each individual at the actual departure time and loca-
tion, and calculated the wind support for the same departure time for the period up to 10 days earlier.
Predator density data. We accessed the citizen science data of the Hawk Migration Association of North
America78, and selected three important raptor post-breeding migration count sites in North America; Hawk
Cli Hawkwatch site in Ontario, Canada (42°39′N, 81°10′W), Hawk Mountain Sanctuary in Pennsylvania, USA
(40°38′N, 75°59′W) and Cape May Point, New Jersey, USA (38°55′N, 74°57′W; see Fig.1). Experienced volunteers
enter counts of migrating raptors on a daily basis. We obtained daily observations from 2014–2016, for the period
between early July and late December. Peregrine falcon, merlin and Cooper’s hawk were selected as relevant pred-
ators for the four shorebird species considered here. We corrected the counts for the daily observation duration,
generating a daily mean predator rate (number hr−1). Next, we used mean values of the three years and calculated
the relative frequency per day.
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Statistical analysis. Dierences in migration speed (km day−1) between seasons were examined using a
generalized linear mixed model (GLMM)79. e model included migration speed (km day−1) as the predicted
variable with year and capture site as random factors. We started with a full model including season, average
tailwind along all segments, age, species and all possible interactions, and then simplied the model using a
backwards elimination process based on a log-likelihood ratio test (LRT) with P < 0.05 as the selection criterion
(“drop1” in R) until reaching the minimal adequate model. Unexpectedly, age and tailwind were not included in
the nal model.
To determine dierences in airspeed between seasons, airspeed was calculated only for the period when indi-
viduals were ying (i.e., stopover time was not included). Because some individuals were detected along several
segments of the journey, they reoccur in the analysis. erefore, we used a GLMM with airspeed as the predicted
variable, and year, capture site and bird-id as random factors. e predictor variables were season, species, age,
days aer capture and all possible interactions and we simplied the model based on a log-likelihood ratio test
(LRT). e nal model included season and species. In order to identify whether airspeeds diered prior to and
aer the predator front, we selected two periods in the post-breeding season based on a visual inspection of the
predator front (Fig.3a); early (>29 July & <1 Sept) and late (>31 Aug). We used a GLMM with airspeed as
the predicted variable, and year, capture site and bird-id as random factors. e predictor variables were season,
species, age and all possible interactions and simplied the model based on a LRT. e nal model included
season, species and the interaction term. In order to visualize these patterns, we used generalized additive mod-
els (GAM) to describe patterns of predator abundance and airspeed during the post-breeding season and used
separate models for each species. e response variable for these models was either relative predator frequency or
airspeed. For both models we used day of year as an explanatory variable. e models were tted using the ‘mgcv’
package80 in R ver. 3.4.1, and compared with linear models (LM) based on the Akaike information criterion, and
the nal model was considered to be substantially better when its value was at least two AIC units lower than the
next best model81.
Minimum stopover duration was analysed in the following way: the full model included minimum stopover
duration (d) as the response variable with year and capture site as random factors, and season, average tailwind,
age, species and all possible interactions as covariates. We then simplied the model based on a LRT until we
reached a minimal adequate model.
We calculated the relative proportion of birds departing daily from Delaware Bay during the pre-breeding sea-
son and from the southern James Bay region in the post-breeding season to assess the total time of the migration
as a measure of time constraints. We used adults only, as age classes are known to dier in timing at our study
sites82, and these sites were chosen because most birds were detected at these sites.
In order to investigate whether wind selectivity diered between the seasons, wind support for each segment
was analysed using LRT of GLMM’s with year and bird-id as random factors and species and season as xed fac-
tors. We used the ow-assistance from NCEP, (see above) to calculate wind support for each migratory trajectory
up to 10 days before actual departure.
Prior to all analyses, explanatory variables were assessed for collinearity using the variance ination factor
(VIF) function. All variables had a VIF < 2 and coecients did not switch between positive and negative values,
indicating low multicollinearity83.
Animal handling and ethics. e research was conducted in accordance with the Animal Welfare Act of
1970 and the most recent revision of the Ornithological Council’s guidelines in the use of wild birds in research,
and by the Institutional Animal Care and Use Committee (IACUC). Birds captured in Delaware Bay fell under
the federal banding permit issued to the Endangered and Nongame Species Program, Division of Fish and
Wildlife, NJ DEP (22803). Captures in New Jersey were done under federal banding permit (21241US), issued
to New Jersey Audubon by the United States Geological Service/Bird Banding Laboratory. Individuals captured
in Quebec fell under the protocol approved by the “Comité institutionnel de protection des animaux” under
permit 2011-M29-2 and 2015-M29-1, and the capture of individuals in the Mingan Archipelago was approved
by the Animal Care Committee of Environment and Climate Change Canada (SCF2018-01-YA, SCFQ2017-06,
SCFQ2016-07). For Sanderling and Red knots captured in Chaplin, Canada, all animal handling and research
protocols were approved by the University of Saskatchewan Animal Care Committee (AUP 20120021) and con-
ducted under Canadian Wildlife Service Scientic Permit (12SKS009). Birds trapped in Texas, fell under the
Texas Parks & Wildlife Department Permit (SPR-0911-341), and trapping on the Padre Island National Seashore
was approved by the National Park Service under permits 2015-SCI-0006, 2015-SCI-0007, and 2016-SCI-0005.
All activities related to bird capture and tagging in Maine were reviewed and approved by University of Maine
IACUC (A2013-01-02) and were performed under federal and state permits to the U.S. Fish & Wildlife Service
and the Maine Department of Inland Fisheries and Wildlife. At James Bay, birds were banded under a per-
mit from Environment and Climate Change Canada (Permit 10884), and banding and tagging was approved
by Environment and Climate Change Canada’s Wildlife Eastern Animal Care Committee (protocol 14CF01,
15CF01, 16CF01 and 17CF01), as well as by Trent University’s Animal Care Committee (23904). Birds captured
in Nunavut were approved under the Canadian Wildlife Service Scientic Permit to Capture and Band Migratory
Birds (10565M), the Canadian Wildlife Service National Wildlife Area Permit (NUN-NWA-14-03), the Canadian
Wildlife Service Scientic Research Permit (NWT-SCI-13-01), the Government of Nunavut Wildlife Research
Permit (WL2015-032), and by Environment and Climate Change Canada Western and Northern Animal Care
Committee Approval (15JR01). Birds captured and tagged at the Bay of Fundy were approved under Mount
Allison University Animal Care protocol (12–10).
Data Availability
All data are available by request through the Motus Wildlife Tracking System (www.motus.org).
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Acknowledgements
We thank Bird Studies Canada and the Motus Wildlife Tracking System for aiding in the processing of radio
tracking data. We thank the many students andvolunteers involved in the capture and tagging of shorebirds
in North America, as well as the numerous scientists, land owners and naturalists involved in developing and
maintaining the Motus network. We thanktheNew Jersey natural lands trust and national sh and wildlife
foundation for their support.We alsothank Ron Ydenberg and two anonymous referees for their helpful
comments.
Author Contributions
S.D. designed the study and A.M.A., Y.A., A.D., S.A.F., C.F., C.G.-T., D.H., R.H., S.K., A.E.M., D.M., C.A.M.,
S.N., D.N., L.N., E.N., J.P., J.R., L.T. and Y.T. carried it out. P.A.S. provided feedback to S.D. during data analysis.
S.D. wrote the manuscript with signicant input from P.A.S., A.M.A., E.N., C.A.M., C.M.F. & D.H. all authors
reviewed it and gave approval for publication.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-45862-0.
Competing Interests: e authors declare no competing interests.
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