Faculty of Science
CITIZEN SCIENCE REVEALS RECENT SHIFTS IN MIGRATION
ROUTES AND BREEDING LATITUDE IN NORTH AMERICAN
BLUEBIRDS
2021 | JARED JOHN SONNLEITNER

B.Sc. Honours thesis – Biology

CITIZEN SCIENCE REVEALS RECENT SHIFTS IN MIGRATION ROUTES AND
BREEDING LATITUDE IN NORTH AMERICAN BLUEBIRDS

by

Jared John Sonnleitner

A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF

BACHLOR OF SCIENCE (HONS.)
in the
DEPARTMENT OF BIOLOGICAL SCIENCES
(Ecology and Environmental Biology)

This thesis has been accepted as conforming to the required standards by:
Dr. Matthew Reudink (Ph.D.), Thesis Supervisor, Dept. Biological Sciences
Dr. Nancy Flood (Ph.D.), Co-supervisor, Dept. Biological Sciences
Dr. Tom Dickinson (Ph.D.), External examiner, Dept. Biological Sciences

Dated this 14th day of April, 2021, in Kamloops, British Columbia, Canada
© Jared John Sonnleitner, 2021

ABSTRACT
Both spatial and temporal shifts to the migratory patterns of birds have become more
common as climate change and habitat alterations have continued to impact ecosystems and the
species dependent on them. Our ability to track these changes for individual species is limited by
costs associated with current tracking methods such as GPS and geolocator technology. In this
study, we used eBird citizen science data collected over ten years on Eastern (Sialia. sialis),
Western (S. mexicana) and Mountain (S. currucoides) bluebirds. Using a Generalized Additive
Model, we produced smoothed migration paths for all three species over each season from 2009 to
2018. We asked whether there were changes over this ten-year period in the timing of spring and
fall migration, longitude of migration, and maximum breeding ground latitude. We found that
Eastern Bluebirds shifted their migratory routes westward, whereas Western Bluebirds shifted their
migratory routes eastwards; there was no significant change in the migratory patterns of Mountain
Bluebirds over the same period. Based on our analysis there was no change in the migratory timing
or speed of any of the species from 2009 to 2018; however, there were interesting interspecific
differences in timing and speed of migration that may be the result of divergent migratory
strategies. Our analysis is one of the first to compare shifts in the migratory timing and distributions
of multiple closely related species of passerines. By comparing these shifts, we found that bluebirds
are in fact altering many aspects of their migration in response to local and more broadly ranging
geographical factors. Further work is needed to determine the cause of these shifts at these different
scales.

Thesis Supervisor: Professor Matthew Reudink

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ACKNOWLEDGEMENTS
I would like to thank the Thompson Rivers University Undergraduate Research Experience
Award Program (UREAP) for funding my thesis. In addition, I would like to thank all contributors
to this work including Dr. Steffi LaZerte, Dr. Ann McKellar as well as my committee members. A
special thank you to Dr. Matthew Reudink for many revisions, and his guidance throughout this
process.

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Table of Contents
ABSTRACT .......................................................................................................................................ii
INTRODUCTION ............................................................................................................................. 1
METHODS ....................................................................................................................................... 4
General Additive Models (GAMs) of migration .......................................................................... 5
Migration timing .......................................................................................................................... 6
Migration speed ............................................................................................................................ 6
Maximum latitude and median longitude..................................................................................... 7
Maps, Data and Scripts................................................................................................................. 7
Statistical analysis ........................................................................................................................ 7
RESULTS.......................................................................................................................................... 8
Migration timing .......................................................................................................................... 8
Migration speed ............................................................................................................................ 9
Maximum latitude ........................................................................................................................ 9
Median longitude........................................................................................................................ 10
DISCUSSION ................................................................................................................................. 12
LITERATURE CITED .................................................................................................................... 16

List of Figures
Figure 1 ........................................................................................................................................... 9
Figure 2 ......................................................................................................................................... 10
Figure 3 ......................................................................................................................................... 11

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INTRODUCTION
Since the mid 1900s, global temperatures have risen by 0.6 degrees Celsius (IPCC 2001)
and the earth has experienced unprecedented anthropogenic changes, which have altered
ecosystems and disrupted biological processes (Walther et al 2002). Spring is advancing in the
northern hemisphere, both in terms of first-leaf and first-bloom dates (Buermann et al. 2013). These
advances are forcing insects--and the wildlife dependent on them--to alter their life cycles
accordingly (Schwartz et al 2006). The rapidity of the change in spring phenology can result in a
mis-match between peak resource availability and the peak resource demands of many animals,
including migratory birds, which may be constrained in their ability to alter the timing of life
history events such as migration and reproduction.
With advancing springs, migratory birds may need to arrive on their breeding grounds
earlier to take advantage of changes in high early spring productivity (Mayor et al. 2017; Saino et
al. 2011). To advance the timing of migration species may need to alter their patterns of migrations.
This may be done by advancing the start of their spring migration, so they arrive at the breeding
grounds earlier. However, by doing this, individuals may be migrating along routes in very early
spring before resources have become optimal and when weather conditions may be less favorable
(Nilsson et al. 2013). Species may also migrate quicker; many bird species already migrate faster
during spring compared to fall migration due to the time constraints on arrival; for example, to
obtain high quality territories and mates (Nilsson et al. 2013) so increasing the speed of spring
migration is possible.
In addition to temporal shifts as a result of climate change, species may also be undergoing
spatial shifts in their distribution (Huang et al. 2017; La Sorte et al. 2007; La Sorte and Graham

1

2020). For example, in response to increasing temperatures, many avian species’ ranges are moving
to higher latitudes (Curley et al. 2020; Thomas and Lennon 1999) and elevations (Tingley et al.
2012). There has also been evidence for shifts in the longitudinal distributions of many species, but
these studies often focus on species presence or absence at the range margins (Huang et al 2017),
rather than changes in the mean centroid of the species across their total distribution (Virkkala and
Lehikoinen 2014).
Historically, many studies looking at migratory birds have focused on events occurring
during their breeding period. since this allows scientists to study natality and pair dynamics (Marra
et al 2015); although events that occur during other stages in the life cycle also affect fitness, they
are far less studied. For instance, a species’ density on their breeding grounds can be heavily
influenced by winter survival (Fretwell 1972) and increased understanding of this period may
contribute significantly to the species’ conservation or management. In order to better understand
the total life history of a species we need to better appreciate the events occurring outside of their
relatively short breeding period (Faaborg 2010). Recent advances in tracking technology (including
satellite tracking, GPS tags, and geolocators) have shed light on individual-level patterns of
migration, while other studies have made use of large-scale, often citizen-science based, datasets
to examine changes in entire populations (Curley et al. 2020; Rushing et al. 2020). Here, we make
use of citizen-science data available from eBird to examine shifts in migration patterns among three
closely related migratory bird species.
Specifically, we ask whether Eastern Bluebirds (Sialis sialis), Western Bluebirds (Sialis
mexicana) and Mountain Bluebirds (Sialis currucoides) have shifted their migration patterns over
the past decade. These species are all short distant migrants that both breed and winter in North
America (Billerman et al. 2020); however, while closely related, they have distinct ecologies and

2

have shown different population trends. They may thus differ in the degree to which they are
impacted by climate change, and in the strategies, they use to respond.
Eastern, Western and Mountain bluebirds are all secondary cavity nesters that nest in
artificial nest boxes or in cavities created by other species such as woodpeckers (Cornell Lab of
Ornithology 2020). For breeding, Western Bluebirds prefer forested areas, such as stands of
Ponderosa pine (Pinus ponderosa) and areas that have been disturbed by fire. Threats to this species
mainly come from habitat destruction as a result of fire suppression and logging. Mountain
Bluebirds prefer high elevation habitats that contain a mix of open fields and forested areas. Eastern
Bluebirds prefer open fields with some forested areas that contain underbrush. Their habitat is most
common in open agricultural fields and areas that historically have been opened as a result of fire
(Cornell Lab of Ornithology 2020).
The North American Breeding Bird Survey shows that the three species have experienced
differing population trends over the last fifty years (Smith A.C. et al. unpublished, an update of
Environment Canada 2017). Eastern Bluebird populations increased from the 1970s through the
early 2000s, at an average of 1.22% per year (95% CI 1.07, 1.37). but since 2009 they have declined
by 1.14% per year (95% CI -1.61, -0.65). Western Bluebirds have shown a steady increase of
around 0.89% from the 1970’s until 2019 (95% CI 0.097, 1.54). Finally, Mountain Bluebird
populations have shown a long-term decline of around 0.38% from the 1970s until 2019, however
the credible intervals overlap zero (95% CI -0.94, 0.122) and therefore there has most likely been
no significant change.
To examine potential changes in the migratory patterns of North American bluebirds we
used 10 years of citizen-science data from eBird (eBird 2020). The eBird app and website provide
unique opportunities to study questions related to species-level changes in distribution patterns
during the breeding and migration of migratory birds. Large-scale citizen science datasets such as
3

those available from eBird to harness millions of data points and allow for comparisons of year to
year shifts in migratory patterns (Supp et al. 2015; Sullivan et al. 2014). In response to
environmental changes, we expected that bluebirds in North America may have altered aspects of
their migratory behavior such as the speed, timing and routes used, to remain in synchrony with
the events occurring within their habitats (Mayor et al. 2017; Visser and Both 2005). In line with
data from the Breeding Bird Survey and previous work (Duckworth and Badyaev 2007; Duckworth
2009), we predicted that Western Bluebirds would demonstrate an eastward shift in distribution.
We also predicted a shift in the range for Eastern bluebirds as their survival has been tied to late
winter conditions (Sauer and Droege 1990) which through climatic drivers may have changed.
Finally, for Mountain bluebirds due to their preference for high elevation habitat they may be less
inclined to shift longitudinally when compared to Mountain or Western bluebirds. Consistent with
studies conducted on other migratory birds, we predicted that as a result of increasing temperatures,
all three species of bluebirds would arrive earlier on their breeding grounds (Parmesan and Yohe
2003; Jonzen 2006) and/or migratory speed (Nilsson et al. 2013), and on average a higher breeding
latitude over time (Rushing et al 2020).

METHODS
We obtained presence data for Western, Mountain and Eastern Bluebirds for each day of
the year from 2009 through 2018 from eBird (eBird Basic Dataset 2019; Sullivan et al. 2009) and
processed these data using the auk package (v0.4.0, Strimas-Mackey et al. 2018) for R (v3.6.2 R
Core Team 2020). Following recommended best practices (Strimas-Mackey et al. 2020), this
processing involved filtering the eBird checklists to include only those that were “Stationary” or
“Traveling” (leaving out those that were “Incidental” or “Historical”), had a duration of 0 to 5

4

hours, a distance of between 0 and 5 km, and that were “complete” (i.e., all species observed were
recorded). Finally, we ‘zero-filled’ the data, a process that adds counts of 0 for each checklist that
did not include any bluebird observations.
We then followed the methodology of Supp et al. (2015) to summarize presence by
geographic location using equal-area icosahedron hex grids. Hex grids were created at a resolution
of 23,323 km2 using the dggridR R package (v2.0.3 Barnes 2018). Presence was summarized into
daily measures by binning checklists into hexes by date and then calculating the proportion of
checklists that included a bluebird of a given species (number of checklists with a bluebird
observed/total number of checklists).

For each species we calculated daily weighted mean

longitude and latitude using coordinates of the hex centroids. These means were weighted by the
proportion of checklists that included observations of a bluebird species, as a method of controlling
for effort. This resulted in a measure of mean species presence per day, per hex. Hexes without
checklists were omitted from analysis.

General Additive Models (GAMs) of migration
A Generalized Additive model was used following methodologies from Supp et al. (2015),
to demonstrate the relationship between daily latitude/longitude and time. The GAM was created
with the mgcv R package (v1.8-31, Wood 2011) using a penalized regression spline-based
smoothing parameter with a basis dimension of 40 (i.e. k = 40) and a gamma of 1.5 (the degree of
smoothing). This model was used to smooth the centroids from the weighted daily mean locations
which were calculated using coordinates of the hex centroids. This allowed us to create smoothed
migration paths for species over the ten years. These smoothed paths were then used to predict the
mean daily latitude and longitude for each species.

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Migration timing
Migration timing was calculated in two steps. First, a coarse migration timing for each
species in each year was defined as the date the daily latitudes predicted from the GAMs crossed
southern or northern latitudinal thresholds calculated for each season using specific ordinal date
ranges. These latitudinal thresholds were defined as the most northerly latitudinal extent during the
non-breeding period and the most southerly latitudinal extent during the breeding period for each
species. Specifically, migrations that started or ended in the south (i.e., the start of northward and
end of southward migration) used the minimum latitude of the upper limit of the 99% confidence
band of predicted daily locations calculated over ordinal dates 1-80 (northward) and 285-345
(southward) (as in Supp et al 2015). Migrations that started or ended in the north (i.e., the end of
northward and the start of southward migration) used the maximum latitude of the lower limit of
the 99% confidence band of predicted daily locations calculated over ordinal dates 80-175
(northward) and 225-285 (southward).
Migration timing was then fine-tuned with segmented regressions (segmented R package
v1.0-0, Muggeo 2008). The date calculated by using the thresholds in the previous step was used
as the starting point, to determine the break-point where there was no longer a relationship between
predicted latitude and date. This resulted in a more precise calculation of the start and end of
migration (as in Supp et al 2015).

Migration speed
Maximum daily migration speed for each species in each year was calculated for both
northward and southward migration as the median distance traveled (km) over the 5 fastest days in
each period and is expressed as km/day (northward ordinal dates 1-175; southward ordinal dates
225-340; as in Supp et al. 2015). Date ranges extending into the non-breeding period were used
6

because there was little movement in the non-breeding period and to ensure that as much of the
migration period as possible was included.

Maximum latitude and median longitude
The maximum latitude during the breeding season for each species in each year was defined
as the maximum predicted daily latitude between the end of northward migration and the start of
southward migration. The median longitude of both northward and southward migrations for each
species and each year was defined as the median predicted daily longitude between the calculated
start and end dates of each migration period.

Maps, Data and Scripts
Maps were created using the ggplot2 R package (Wickham 2016), with data obtained from
Natural Earth (https://naturalearthdata.com) via the rnaturalearth R package (v0.1.0, South 2017).
Raw data are available from eBird (Sullivan et al. 2019). Scripts for data summarization, analysis
and figures are available at/from here (GitHub).

Statistical analysis
We ran a series of a linear models with species and year as fixed effects and a species*year
interaction term, with the following response variables: start/end of spring/fall migration,
spring/fall latitude and longitude, maximum and minimum spring/fall latitude as well as spring/
fall migratory speed. If we detected a significant interaction, and interaction plots revealed
differences in the slopes, we ran a separate linear regression for each of the three species to examine
the relationship between year and the response variable independently. Significance was evaluated

7

using an F test with an alpha value of 0.05 and analyses were completed using R (v3.6.2 R Core
Team 2020).

RESULTS
Migration timing
When looking at migratory timing, we found no significant year*species interaction when
examining the start (F = 0.23; p = 0.8) and end (F = 2.24; p = 0.13) of spring migration or the
beginning of fall migration (F = 0.15; p = 0.86) and the interaction terms were therefore removed
from subsequent models. We found no effect of year on the start (F = 0.08; p = 0.78) or end (F =
1.01; p = 0.32) of spring migration or the start of fall migration (F = 1.1; p = 0.3). There was a
significant effect of species on the end of spring migration (F = 108.8; p < 0.0001) and the start of
fall migration (F = 11.7; p = 0.0002), but not the start of spring migration (F = 0.25; p = 0.78). On
average, Eastern Bluebirds ended their spring migration 57 days later than Western Bluebirds (p <
0.0001) and 60 days later than Mountain Bluebirds (p < 0.0001) and began their fall migration 13
days later than Western Bluebirds (p = 0.0007) and 14 days later than Mountain Bluebirds (p =
0.0029).
We found a significant year*species interaction for the end of fall migration (F = 3.9; p
=0.03). We subsequently examined each species separately and found that Eastern Bluebirds
advanced the end of fall migration (r2 = 0.4, p = 0.03) by 1.78 days per year from 2008 to 2019,
while Western (r2 = 0.14, p = 0.16) and Mountain Bluebirds (r2 = 0.09, p = 0.20) showed no change.
In summary, none of the three species of bluebirds altered their migratory timing for the start of
spring and fall migration or end of spring migration. There was however a shift in the end of fall
migration for Eastern bluebirds, which ended their fall migration earlier over the ten-year period.

8

Migration speed
When examining annual migration speed, the interaction between year and species was not
significant in spring (F = 0.75; p = 0.48) or fall (F = 1.02; p = 0.38) and was subsequently removed
from the models. After removing the interaction term from the model, there remained no effect of
year for spring (F = 0.94; p = 0.34) or fall (F = 2.98; p = 0.09), but there was an effect of species
in both spring (F = 108.4; p < 0.0001) and fall (F = 58.3; p < 0.0001). In other words, we detected
a difference in the speed at which the three-bluebird species migrated (Figure 1), with Mountain
Bluebirds migrating 11.5 km/day faster in the fall and 15 km/day faster in the spring than Eastern
Bluebirds (p < 0.0001) and 11.8 km/day faster in the fall and 14 km per day faster in the winter
than Western bluebirds (p < 0.0001).

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

Figure 1. Differences in the spring (A) and fall (B) migratory speed of Eastern, Western and
Eastern bluebirds. Speed of migration was calculated as the mean distance travelled (km) over the
fastest 5 days. Boxplots represent the median value for each species interquartile range (Q1 below
as 25th percentile of data, Q3 above as 75th percentile of data).

9

Maximum latitude
When we examined whether the maximum breeding latitude changed over time, the
interaction between year and species was not significant (F = 0.68, p = 0.51) and was subsequently
removed from the model. We found that the maximum breeding latitude of bluebirds as a group
changed over time (F = 4.69, p = 0.04) with all 3 species reaching a lower maximum breeding
latitude between 2009 and 2018. There was a difference in the change in maximum breeding
latitude among species with Mountain bluebirds migrating 6.5 units of latitude further north than
Eastern bluebirds and 5.7 units of latitude further north than Western bluebirds.

Median longitude
When we examined median annual longitude during migration, we found a significant year
by species interaction for both spring (F = 11.04, p = 0.0003) and fall migration (F = 15.19, p =
0.0006). Because the interaction plots displayed clear differences in the slopes for each species
(Figure 2), we examined the relationship between median longitude and year separately for each
species.
A

B

B
F
i
g
u
r
e
2
.
C
h
a
n
Figure
3. Changes in the spring (A) and fall (B) median longitude of Eastern, Mountain and
g
Western
bluebirds from daily longitudes and latitudes predicted using a GAM. Blue line shows
e
Western
bluebirds shifting eastward whereas red line shows Eastern bluebirds shifting westward.
s
i
n
10
t
h
e

A
D
A
A
A
A
A
A
A
D
A
A
A
A
E

B

A
A

B

A

B

E

B

A

B
C

A

B
C

A

Latitude

B

Latitude

B
A

A

F

F
C
C
C
C
C

Longitude

Date

C

Longitude

Date

Figure 5. Average GAM location of mean weighted centroids over ten years smoothed into
Date
migration paths (Left) forLongitude
(A) Eastern, (B) Mountain and (C) Western bluebirds.
Each line
represents a single year with each color representing a different grouping of months with dark blue
Date
as January and dark red as
December. Both Eastern and Western Bluebirds
are shifting their
Longitude
migrations towards the center of the continent. Predicted migratory latitude (Right) based on GAM
Date
over ten years for (D) Eastern, (E) Mountain and (F) Western bluebirds. Timing based on
migratory latitude shows no change over ten years in the start of spring migration
Date (purple), end of
spring migration (blue), start of fall migration (dark green) but an advancement in the end of fall
migration (light green) for Eastern Bluebirds.
Date
Date

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The median longitude during spring migration for both Eastern (r2 = 0.66, p = 0.003) and Western
bluebirds (r2 = 0.70, p = 0.001) changed from 2009 to 2018, with Eastern Bluebirds shifting their
distribution westward by 0.22 degrees of longitude per year and Western Bluebirds shifting
eastward by 0.26 degrees of longitude per year. In contrast, for Mountain Bluebirds, the median
longitude during spring migration (r2 = 0.09, p = 0.38) did not change over that time period (Figure
3). Similar to spring migration, the median longitude during fall migration in Eastern Bluebirds
shifted westward by 0.12 degrees of longitude per year (r2 = 0.59, p = 0.006) while the median
longitude during fall migration in Western Bluebirds (r2 = 0.69, p = 0.003) shifted eastward by 0.3
degrees of longitude per year from 2009 to 2018 (Figure 3). As with spring migration, for Mountain
Bluebirds, the median longitude during fall migration (r2 = 0.16, p = 0.14) did not change over
time.

DISCUSSION
Using a 10-year citizen science dataset extracted from eBird, we demonstrated consistent
and rapid species-level changes in migration patterns, including shifts to distributions, occurring
across two of the three species of bluebirds in North America. While other studies have shown
trends in migratory birds this analysis differs due to the close relationship between all bluebird
species, allowing for an examination of how the divergence of these three species has altered their
migratory patterns in response to different selective pressures across their entire ranges. We
detected longitudinal shifts during migration which partly supported our predictions, with Western
bluebirds continuing to shift eastward and Eastern bluebirds shifting their distribution which we
found to be in the westward direction. This shift appears as a convergent trend towards the center
of the continent which may be in response to them to track ecological productivity along latitudinal

12

and elevational gradients during spring migration (La Sorte et al 2014). Another possibility is that
this trend may be explained by the Pacific and Atlantic Oceans limiting coastward range expansion,
and the movement of human populations towards coastal areas (Seto et al. 2011; Neumann et al.
2015) reducing available habitat these regions (Isaksson 2018). Thus, range expansion could only
occur toward the east for Western bluebirds and west for Eastern bluebirds. Population trends of
all bluebird species support our findings of longitudinal shifts as species’ ranges may often be
shifted in response to conditions causing increasing or decreasing population trends (Virkkala and
Lehikoinen 2017). BBS data for Eastern and Western bluebirds have been increasing and
decreasing respectively and therefore changes to their distributions are therefore expected. Western
Bluebirds are secondary cavity nesters and their success is dependent on having sufficient nest sites
at their breeding grounds. Duckworth (2009) and Hejl (1994) proposed that changes in fire
suppression regimes, along with nest box programs, have led to improved nesting opportunities for
Western Bluebirds, facilitating an increase in population size and their eastward range expansion.
Poleward shifts have been observed across many taxa (Parmesan and Yohe 2003; Parmesan
2006) and are thought to be an ecological response to warmer conditions in both winter and early
spring at higher latitudes (Rushing et al. 2020; Beuermann et al. 2013). Our results were contrary
to the predictions as we found that all species appear to be lowering their maximum breeding
latitude year over year at very similar rates (figure 3). This may indicate that conditions influencing
the maximum breeding latitude most likely involve some broad change that has similar effects on
all species of bluebirds. This southward shift over 10 years demonstrates that bluebirds are capable
of altering their migrations in a relatively short period of time. Even though this shift is small, it
represents a complication for conservation efforts in the future (Méndez et al. 2018; Gill et al.
2019), as protected areas and nest boxes, which have been largely successful, particularly for

13

Western and Mountain bluebirds, may need to be moved or changed in order to keep pace with
their changing migratory patterns.
Advancements in the timing of spring arrival have been attributed to competition for mates
and territories (Kokko 1999) or as a response to changing ecological conditions such as first leaf
and bloom dates (Beuermann et al. 2013). Contrary to our predictions, we found no alteration in
the timing of spring migration or the arrival of any bluebird species, but we did find that Eastern
Bluebirds ended their fall migration earlier over the ten years of our study. Mayor et al (2017)
examined 48 species of passerines (none of which included bluebirds) and found that advancements
in the onset of spring at the breeding grounds were associated with earlier arrival dates. In addition
to changes in breeding ground conditions, non-breeding grounds may also affect the migratory
timing of birds (Robson and Barriocanal 2011; Paxton et al 2014). Since our analysis did not
include any environmental data, we cannot tease apart whether bluebirds are minimally impacted
by changing environmental conditions or are simply unable to respond appropriately. Most likely
it is the former, as Western and Mountain bluebirds in particular tend to arrive at their breeding
grounds earlier in the season when compared to other passerine species. Both species are recorded
as having mean arrival as early as March in some years and therefore although they may directly
benefit from advancements in spring phenology, but have not needed to advance their arrival. There
were some differences in average end of spring migration for all three species. Eastern Bluebirds
tended to end their spring migration on day 161, significantly later than Mountain Bluebirds, which
ended their spring migration on day 101, or Western bluebirds which ended on day 102. This is
supported by the changes migration speed as seen in Figure 1a: Eastern Bluebirds migrate the
slowest of the three species in spring migration. While there were interspecific differences in
migration speed, we found that there was no change in the migratory speed of bluebirds over ten
years. These interspecific differences in speed could be the result of divergent migratory strategies
14

resulting from differences in the routes and stopover sites the three species utilize along their
migration paths. However, work done on Swanson’s Thrushes, in which where coastal and inland
populations differed significantly in many aspects of their migration such as routes and stopover
sites were found to have no difference in their migratory speed (Delmore et al. 2012). Therefore,
the connection between the migratory routes, stopover sites and speed may need to be explored
further. While we lack information on the migration patterns of individuals, it is possible that
species specific migratory behaviours explain the differences in migration speed and potentially
timing among the three species. As mentioned previously, Mountain Bluebirds and Western
Bluebirds arrive early in spring, therefore migrating more quickly rather than leaving their nonbreeding grounds earlier may have been selected for in these species.
Despite the rapid (10-year) changes we saw in some migration parameters, there are several
limitations to analyses that use population-level summaries, such as we did in this study. One
limitation of utilizing eBird is that low observation effort prior to 2009 reduced the scope of our
findings to a relatively short period, which may be too narrow to fully understand the long-term
trends occurring in bluebird species. It must also be noted that due to our use of surveillance data
through eBird, variables such as migration speed or maximum breeding latitude represent species
level distribution shifts and do not represent the true values for many individuals of that species.
For instance, we now know that Western bluebirds as a species are moving eastward, however,
some individuals or populations may be experiencing local conditions altering the magnitude or
even direction of this shift. Even with these limitations, the amount of data obtained through eBird
allows researchers to ask fundamental questions related to a species’ ecology and conservation that
would otherwise not be possible (Hurlbert and Liang 2012).

15

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Cecilia Nilsson, Raymond H. G. Klaassen, Thomas Alerstam. 2013. Differences in speed and
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