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Long-distance migratory shorebirds travel faster towards their breeding grounds, but fly faster post-breeding

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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 differences 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 differences in wind. Arriving at the breeding grounds in good body condition is beneficial 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 reaffirm 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.
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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 dierences 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 dierences in wind. Arriving at the breeding grounds in good body
condition is benecial 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 rearm
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 opportunities13. 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 aer 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 migration1214. However, not all studies have detected seasonal dierences in migration speed1517,
and several studies have found patterns opposite to the above predictions1821. 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 inuence on the rate of migratory progress22, and because these environmental factors can
be dicult 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 inuence migration behaviour
is the risk of predation2527. 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-
tors2831. Migratory shorebirds forage in dense ocks, and because of their dependence on intertidal mudats 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 shorebirds3638. In contrast, aer 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 dierences in migratory decisions and performance is crucial for understanding the eco-
logical and evolutionary constraints that shape migratory behaviour12,41. To identify seasonal dierences in the
speed of migration, most studies have used overall migration speed (km day1) 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 dierences 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 reects the birds’ ight behaviour and the energy costs incurred by that behaviour, aer
accounting for any seasonal dierences in wind that could inuence 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-specic dierences in migration
speed44,45, we also explore potential age dierences. 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 oen and for shorter periods en route, and move more rapidly towards their
breeding grounds. Aer 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 specically, 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 TableS1 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 dierent 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 day1) prior
to the breeding season was more rapid than aer 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 eects of year and capture site. Northbound transit was almost twice as
fast across species versus the southbound transit (116 km day1 during the pre-breeding season versus 62 km day1
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 kg1), based on power curve calculations (see TableS2 for details).
Predator abundance showed a clear and simultaneous seasonal peak aer 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 aer 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. Aer 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 signicantly across species (GLMM, X2 = 1.6, p = 0.64), but
diered 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 dierences 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 dierence 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 dierence in migration speed between both seasons appeared to reect dierences 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; TableS3). 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 inuence 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 simplied 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 oers other benets upon arrival
to the breeding grounds. Conversely, post-breeding and juvenile birds during the post-breeding season face a
dierent 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 aer the breeding season, suggestive of successful breeding and greater survival, respectively41.
Maximizing airspeeds during the pre-breeding season could impose a signicant cost of reduced body stores
upon arrival but yield only a modest benet in terms of reducing the total duration of the migration.
Surprisingly, we found only minor age-related dierences 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 dierences 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 benet from social information for navigation and stopover site use
during their rst post-breeding migration.
Predation risk is a factor aecting birds’ migratory behavior in a profound way25,33. Although direct mortality
from predation can be signicant, non-lethal eects 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, aer 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 inuence 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% condence 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% condence 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 signicance
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 dierent 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 inuenced 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 specic 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 aer 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 eects
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 diculties of measuring ight speeds for small-bodied migrant birds. Until
recently, tracking technologies for small-bodied migrant birds have not oered 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 aer they leave our study areas. Possibly, northbound individuals initiate a nal sprint
to their breeding sites and counterbalance the mean dierences in airspeed between both seasons over the entire
trajectory67. Likewise, potential dierences 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 sucient 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 dierences
may have been more pronounced. Nevertheless automated telemetry results such as these, with ne temporal
resolution, oer fertile ground for explorations of the predictions of optimal migration theory. Location-specic
counts of predators, and location-specic assessments of departure behaviour, might allow for a further renement
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
TableS1 for details). All birds were banded with numbered metal bands, and standard biometric measurements were
taken. Age was determined from species-specic plumage features and classied as hatch-year (rst post-breeding
migration) or aer-hatch-year (experienced at least one previous southward migration), while sex could not be
determined at capture based on morphology69. Immediately aer 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 denitive
identication 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 specic
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 dierent site.
Ground speed (m s1) was calculated as the orthodrome distance (m) between two dierent 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 inuencing the speed calculations.
We calculated the minimum stopover duration for each individual as the sum of all time dierences (s)
between the last and rst detection per receiver station, including the time an individual remained at the capture
site aer capture. A stopover event was dened as a minimum duration of 30 min, and shorter durations were
considered migratory movements41.
Migration speed (km day1) 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°4N and 74°6W) and the southern James Bay region
for the post-breeding migration (51°4N, 80°4W). 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 diered 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., dierent 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 protable pressure level.
Airspeed (m s1) 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 s1, 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 identied 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 s1 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 TablesS2 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°39N, 81°10W), Hawk Mountain Sanctuary in Pennsylvania, USA
(40°38N, 75°59W) and Cape May Point, New Jersey, USA (38°55N, 74°57W; 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 hr1). Next, we used mean values of the three years and calculated
the relative frequency per day.
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Statistical analysis. Dierences in migration speed (km day1) between seasons were examined using a
generalized linear mixed model (GLMM)79. e model included migration speed (km day1) 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 simplied 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 dierences 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 aer capture and all possible interactions and we simplied the model based on a log-likelihood ratio test
(LRT). e nal model included season and species. In order to identify whether airspeeds diered prior to and
aer 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 simplied 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 simplied 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 dier 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 diered 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 ination factor
(VIF) function. All variables had a VIF < 2 and coecients 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 Scientic 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 Scientic Permit to Capture and Band Migratory
Birds (10565M), the Canadian Wildlife Service National Wildlife Area Permit (NUN-NWA-14-03), the Canadian
Wildlife Service Scientic 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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13
SCIENTIFIC REPORTS | (2019) 9:9420 | https://doi.org/10.1038/s41598-019-45862-0
www.nature.com/scientificreports
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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 andvolunteers 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 thanktheNew Jersey natural lands trust and national sh and wildlife
foundation for their support.We alsothank 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 signicant 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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... Later arrival on the breeding grounds may be the result of later departure from the wintering grounds (Bojarinova et al. 2024;Cooper, Hallworth, and Marra 2017), or may be the result of slower migration (Dossman et al. 2023;Morbey and Hedenström 2020). Variation in the pace of migration can arise because of variation in birds' flight effort, or by the wind conditions experienced during flight Duijns et al. 2019;Senner et al. 2018). However, as refuelling rather than flying occupies the majority of the time spent migrating, refuelling time may contribute more to the observed variation in the duration of migration (e.g., Battley et al. 2012;Johnson et al. 2016). ...
... Migration speed is governed by factors such as flight speed, route choice and the time needed to refuel between legs of migration. Flight speed is affected both by the flight capability of a bird, and the wind conditions under which it chooses to fly Duijns et al. 2019;Senner et al. 2018). The difference in ground speed between birds experiencing headwinds versus tailwinds has been documented at 11.4 m/s and may exceed this figure in more extreme conditions (Alerstam and Gudmundsson 1999). ...
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Understanding how and where individuals migrate between breeding and wintering areas is important for assessing threats, identifying important areas for conservation, and determining a species’ vulnerability to changing environmental conditions. Between 2017 and 2020, we tracked post-breeding movements of 72 Red Phalaropes (Phalaropus fulicarius) with satellite tags from 7 Arctic-breeding sites in the Alaskan and Central Canadian Arctic. All tracked Red Phalaropes left their Arctic breeding grounds (i.e., were obligate migrants) but then switched to a more facultative migration strategy with a fly-and-forage migration pattern once in the marine environment. We documented high variability in migration timing and routes, with birds often taking indirect, circuitous routes with numerous stops that greatly lengthened both the duration and distance of their southward migration. Across nearly 500 stopover areas, which were often associated with areas of presumed greater food availability, individuals spent an average of 6 days and traveled within an average area of 1,880 km2. Stopover areas were concentrated in onshore and nearshore habitats of the Beaufort and Chukchi seas, the western edge of the Bering Strait, along the Alaska Peninsula and Aleutian Islands, and near the Pribilof Islands in Alaska. Within the Beaufort and Chukchi seas, females frequently stopped within the marginal ice zone, whereas males tended to stay on land or in open water. Our results identified important marine areas that can aid future conservation and management decisions. However, conservation of the species will also need to address the numerous direct and indirect anthropogenic threats Red Phalaropes experience at sea, many of which are not site-specific.
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Highly migratory shorebirds are among the fastest declining avian guilds, so determining causes of mortality is critically important for their conservation. Most of these species depend on a specific geographic arrangement of suitable sites that reliably provide resources needed to fuel physiologically demanding life histories. Long-term mark-resight projects allow researchers to investigate specific potential sources of variation in demographic rates between populations. Red Knots (Calidris canutus) occur in three relatively distinct regions across the northern Gulf of Mexico, and two of these areas have been experiencing episodic harmful algal blooms (red tide) with increased frequency in recent decades. Since knots are mostly molluscivorous during the nonbreeding season in the Gulf, they are potentially exposed to red tide toxins at high concentrations via their filter-feeding prey. We used long-term mark-resight data from Texas, Louisiana, and Florida (USA) to estimate apparent survival, and to assess the effects of red tides on survival of Red Knots. We also assessed effects of tracking devices deployed in conjunction with the projects over the years. While overall apparent annual survival rates were similar across the three locations (0.768 – 0.819), several red tide events were associated with catastrophically low seasonal (fall) survival in Florida (as low as 0.492) and Texas (as low as 0.510). Leg-mounted geolocators, but not temporary glued-on VHF tags, were associated with a reduction in apparent survival (~8%/year). Movement of knots between the three areas was rare and site fidelity is known to be high. Harmful algal blooms are predicted to increase in frequency and severity with climate change and increased anthropogenic degradation of coastal habitats, which may further endanger these as well as other shorebird populations around the world.
Article
Full-text available
Understanding how and where individuals migrate between breeding and wintering areas is important for assessing threats, identifying important areas for conservation, and determining a species’ vulnerability to changing environmental conditions. Between 2017 and 2020, we tracked post-breeding movements of 72 red phalaropes Phalaropus fulicarius with satellite tags from 7 Arctic-breeding sites in the Alaskan and Central Canadian Arctic. All tracked red phalaropes left their Arctic breeding grounds (i.e. were obligate migrants) but then switched to a more facultative migration strategy with a fly-and-forage migration pattern once in the marine environment. We documented high variability in migration timing and routes, with birds often taking indirect, circuitous routes with numerous stops that greatly lengthened both the duration and distance of their southward migration. Across nearly 500 stopover areas, which were often associated with areas of presumed greater food availability, individuals spent an average of 6 d and traveled within an average area of 1880 km ² . Stopover areas were concentrated in onshore and nearshore habitats of the Beaufort and Chukchi seas, the western edge of the Bering Strait, along the Alaska Peninsula and Aleutian Islands, and near the Pribilof Islands in Alaska. Within the Beaufort and Chukchi seas, females frequently stopped within the marginal ice zone, whereas males tended to stay on land or in open water. Our results identified important marine areas that can aid future conservation and management decisions. However, conservation of the species will also need to address the numerous direct and indirect anthropogenic threats red phalaropes experience at sea, many of which are not site-specific.
Chapter
About half of the approximately 10,000 species of birds are classified as migrants. Ideas about the origins of avian migration are discussed in this chapter and, for present-day birds, the distances, routes, and heights that migrants fly are explained. In mountainous regions, many species of birds are altitudinal migrants and the reasons why birds exhibit such behavior are discussed. The reasons why some migrants follow loop and figure-eight migration routes are also explained. Also discussed is the importance of stopover sites for migrants. Other topics covered in this chapter include bird migration in the Neotropics and seasonal differences in the speed of migration. Many migratory species exhibit protandry, with males arriving in breeding areas before females and factors contributing to such behavior are discussed. Reasons why birds migrate during the day versus at night are explained, and the chapter closes with a discussion of the possible effects of climate change on bird migration.
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For migrating animals, realized migration routes and timing emerge from hundreds or thousands of movement decisions made along migration routes. Local weather conditions along migration routes continually influence these decisions, and even relatively small changes in en route weather may cumulatively result in major shifts in migration patterns. Here, we analysed satellite tracking data to score a discrete navigation decision by a large migratory bird as it navigated a high-latitude, 5000 m elevation mountain range to understand how those navigational decisions changed under different weather conditions. We showed that wind conditions in particular areas along the migration pathway drove a navigational decision to reroute a migration; conditions encountered predictably resulted in migrants routing either north or south of the mountain range. With abiotic conditions continuing to change globally, simple decisions, such as the one described here, might additively emerge into new, very different migration routes.
Article
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Faster migration in spring than in autumn seems to be a common pattern in birds. This has been ultimately explained by seasonally different selection pressures. Variation in migration speed is proximately caused by adjusting travel speed (distance covered during flight) and/or stopover duration (times when birds rest and refuel). Yet, it remains unclear whether individual seasonal differences in migration speed match the common pattern and what the precise role of the proximate, behavioural mechanisms for adjusting migration speed is. By reviewing 64 studies of 401 tracks, I show that in waders, gulls, swifts, and songbirds speeds were significantly higher in spring, while the opposite was the case in waterfowl and owls. Thus, the ultimate mechanisms selecting for a faster migration in spring might not consistently act across bird groups. Breeding latitude, migration strategy, migration distance, flight style, body mass, and sex did not explain seasonal differences in speed. The ratio between spring and autumn total stopover duration of 257 bird tracks significantly negatively affected the seasonal migration speed ratio of the same individuals in a comparative analysis accounting for shared ancestry. Seasonal variation in stopover duration appears thus to be the main biological mechanism regulating seasonal differences in migration speed.
Article
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Many north American shorebird populations are declining. it is therefore urgent to identify major sites used during their annual cycle to achieve effective conservation measures. our objective was to expand some aspects of the knowledge base needed to assess the ecological value of the St. Lawrence River Estuary for shorebird conservation. Here, we present the results of the most intensive shorebird survey ever conducted in the St. Lawrence River Estuary during fall migration. Surveys were conducted between St-Jean-Port-Joli and St-Simon-sur-Mer, Quebec, Canada, in 2011 and 2012, from late June/early July through late november, corresponding to the migration period of all species potentially present in the study area. The Semipalmated Sandpiper (Calidris pusilla) was one of the two most abundant species during both years of our study (most abundant species, followed by Dunlin [Calidris alpina] and Black-bellied Plover [Pluvialis squatarola] in 2011; second to Blackbellied Plover in 2012). Considering the entire shorebird community, abundance of individuals peaked in early September. Peak abundance occurred earlier for adults than for juveniles. For most species, juveniles largely outnumbered adults. Juveniles were relatively less abundant in 2012 than in 2011. This reflected a general trend observed in northeastern north America between those years, suggesting a lower breeding success in 2012. Given its importance as a staging site for juvenile birds (study area used annually by up to a few hundred thousand shorebirds) and therein, its conservation value, we recommend that the St. Lawrence River Estuary should be included within the Western Hemisphere Shorebird Reserve network.
Article
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Populations of migratory birds present unique conservation challenges given the often vast distances separating critical resources throughout the annual cycle. Migration areas close to the breeding grounds represent a link between two key stages of the annual cycle, and understanding migration ecology as birds exit the breeding grounds may be particularly informative for successful conservation. We studied migration phenology and stopover ecology of an endangered subspecies of the Red Knot Calidris canutus rufa at a migration area relatively close to its breeding range. Using mark-recapture/resight data and a Jolly-Seber model for open populations, we described the arrival and departure schedules, stopover duration, and passage population size at the Mingan Archipelago, Quebec, Canada. Red Knots arrived at the study area in two distinct waves of birds separated by approximately 22 days. Nearly 30% of the passage population arrived in the first wave of arrivals during 15-18 July, and approximately 22% arrived in a second wave during 8-11 August. The sex-ratio in the stopover population at the time of the first wave was slightly skewed toward females, whereas the second wave was heavily skewed toward males. Because males remain on the breeding grounds to care for young, this may reflect successful breeding in the year of our study. The estimated stopover duration (population mean) was 11 days (95% credible interval: 10.3-11.7 days), but stopover persistence was variable throughout the season. We estimated a passage population size of 9,450 birds (8,355-10,710), a minimum estimate for reasons related to the duration of our sampling. Mingan Archipelago is thus an important migration area for this endangered subspecies and could be a priority in conservation planning. Our results also emphasize the advantages of mark-recapture/resight approaches for estimating migration phenology and stopover persistence.
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Body condition (i.e. relative mass after correcting for structural size) affects the behaviour of migrating birds, but how body condition affects migratory performance, timing and fitness is still largely unknown. Here, we studied the effects of relative body condition on individual departure decisions, wind selectivity, flight speed and timing of migration for a long-distance migratory shorebird, the red knot Calidris canutus rufa. By using automated VHF telemetry on a continental scale, we studied knots’ migratory movements with unprecedented temporal resolution over a 3-year period. Knots with a higher relative body condition left the staging site later than birds in lower condition, yet still arrived earlier to their Arctic breeding grounds compared to knots in lower relative body condition. They accomplished this by selecting more favourable winds at departure, thereby flying faster and making shorter stops en route. Individuals with a higher relative body condition in spring migrated south up to a month later than individuals in lower condition, suggesting that individuals in better condition were more likely to have bred successfully. Moreover, individuals with a lower relative body condition in spring had a lower probability of being detected in autumn, suggestive of increased mortality. The pressure to arrive early to the breeding grounds is considered to be an important constraint of migratory behaviour and this study highlights the important influence of body condition on migratory decisions, performance and potentially fitness of migrant birds.
Article
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Background: Regional scale movement patterns of songbirds are poorly known largely due to difficulties tracking small organisms at broad scales. Using an array of over 100 automated radio telemetry towers, we followed Blackpoll Warblers (Setophaga striata) during fall migration in the Gulf of Maine region, and assessed how their regional scale movement pathways varied with age, distance to natal origin, and capture date. Results: Many individuals had movement paths that were not oriented towards their migratory goal ('indirect movement patterns'), regardless of age, distance to natal origin, or time of season. The probability of moving in indirect patterns, and the total tracking duration, decreased with capture date. The extent of indirect movement patterns varied considerably between individuals. Excluding direct flight patterns consistent with traditional migratory movements, adults tended to make more flights and moved in more tortuous patterns than hatch-years. Adults and individuals from more westerly natal origins were more likely to move south-west through time. Conclusions: A greater proportion of individuals made movements that were not oriented towards the migratory than expected. A decrease in tracking duration with capture date indicates that individuals prioritize time as the season progresses. The shorter, indirect movement patterns may be a more complete representation of 'reverse migration' at a barrier or 'landscape-scale stopovers movements'. The longer distances travelled are inconsistent with expected behaviour, even in front of a barrier. The extent of movement we observed indirectly suggests that flight is not as costly to individuals in a migratory state as is commonly assumed. Since adults were observed to move more than hatch-years, we suggest that the indirect movement patterns we observed are not accidental, and may provide some advantage to the individuals that undertake them.
Article
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We compiled a >50-year record of morphometrics for semipalmated sandpipers (Calidris pusilla), a shorebird species with a Nearctic breeding distribution and intercontinental migration to South America. Our data included >57,000 individuals captured 1972–2015 at five breeding locations and three major stopover sites, plus 139 museum specimens collected in earlier decades. Wing length increased by ca. 1.5 mm (>1%) prior to 1980, followed by a decrease of 3.85 mm (nearly 4%) over the subsequent 35 years. This can account for previously reported changes in metrics at a migratory stopover site from 1985 to 2006. Wing length decreased at a rate of 1,098 darwins, or 0.176 haldanes, within the ranges of other field studies of phenotypic change. Bill length, in contrast, showed no consistent change over the full period of our study. Decreased body size as a universal response of animal populations to climate warming, and several other potential mechanisms, are unable to account for the increasing and decreasing wing length pattern observed. We propose that the post-WWII near-extirpation of falcon populations and their post-1973 recovery driven by the widespread use and subsequent limitation on DDT in North America selected initially for greater flight efficiency and latterly for greater agility. This predation danger hypothesis accounts for many features of the morphometric data and deserves further investigation in this and other species.
Article
Migration is fundamental in the life of many birds and entails significant energetic and time investments. Given the importance of arrival time in the breeding area and the relatively short period available to reproduce (particularly at high latitudes), it is expected that birds reduce spring migration duration to a greater extent than autumn migration, assuming that pressure to arrive into the wintering area might be relaxed. This has previously been shown for several avian groups, but recent evidence from four tracked Icelandic Whimbrels (Numenius phaeopus islandicus), a long distance migratory wader, suggests that this subspecies tends to migrate faster in autumn than in spring. Here, we (1) investigate differences in seasonal migration duration, migration speed and ground speed of Whimbrels using 56 migrations from 19 individuals tracked with geolocators and (2) map the migration routes, wintering and stopover areas for this population. Tracking methods only provide temporal information on the migration period between departure and arrival. However, migration starts with the fuelling that takes place ahead of departure. Here we estimate the period of first fuelling using published fuel deposition rates and thus explore migration speed using tracking data. We found that migration duration was shorter in autumn than in spring. Migration speed was higher in autumn, with all individuals undertaking a direct flight to the wintering areas, while in spring most made a stopover. Wind patterns could drive Whimbrels to stop in spring, but be more favourable during autumn migration and allow a direct flight. Additionally, the stopover might allow the appraisal of weather conditions closer to the breeding areas and/or improve body condition in order to arrive at the breeding sites with reserves. This article is protected by copyright. All rights reserved.
Book
This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: • produce handsome, publication-quality plots with automatic legends created from the plot specification • superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales • add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression • save any ggplot2 plot (or part thereof) for later modification or reuse • create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots • approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page. New to this edition:< • Brings the book up-to-date with ggplot2 1.0, including major updates to the theme system • New scales, stats and geoms added throughout • Additional practice exercises • A revised introduction that focuses on ggplot() instead of qplot() • Updated chapters on data and modeling using tidyr, dplyr and broom