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Research
Cite this article: Delmore KE, Kenyon HL,
Germain RR, Irwin DE. 2015 Phenotypic
divergence during speciation is inversely
associated with differences in seasonal
migration. Proc. R. Soc. B 282: 20151921.
http://dx.doi.org/10.1098/rspb.2015.1921
Received: 9 August 2015
Accepted: 15 October 2015
Subject Areas:
behaviour, evolution, ecology
Keywords:
migratory divide, seasonal migration,
speciation, comparative analysis,
evolutionary rate, phenotypic divergence
Author for correspondence:
Kira E. Delmore
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2015.1921 or
via http://rspb.royalsocietypublishing.org.
Phenotypic divergence during speciation
is inversely associated with differences
in seasonal migration
Kira E. Delmore1, Haley L. Kenyon1, Ryan R. Germain2 and Darren E. Irwin1
1
Department of Zoology, University of British Columbia, 6270 University Boulevard, Vancouver, British Columbia,
Canada V6T1Z4
2
Department of Forest and Conservation Sciences, University of British Columbia, 2424 Main Mall, Vancouver,
British Columbia, Canada V6T1Z4
Differences in seasonal migration might promote reproductive isolation and
differentiation by causing populations in migratory divides to arrive on the
breeding grounds at different times and/or produce hybrids that take inferior
migratory routes. We examined this question by quantifying divergence
in song, colour, and morphology between sister pairs of North American
migratory birds. We predicted that apparent rates of phenotypic differentiation would differ between pairs that do and do not form migratory divides.
Consistent with this prediction, results from mixed effects models and
Ornstein–Uhlenbeck models of evolution showed different rates of divergence
between these groups; surprisingly, differentiation was greater among nondivide pairs. We interpret this finding as a result of variable rates of population
blending and fusion between partially diverged forms. Ancient pairs of populations that subsequently fused are now observed as a single form, whereas
those that did not fuse are observable as pairs and included in our study. We
propose that fusion of two populations is more likely to occur when they
have similar migratory routes and little other phenotypic differentiation that
would cause reproductive isolation. By contrast, pairs with migratory divides
are more likely to remain reproductively isolated, even when differing little
in other phenotypic traits. These findings suggest that migratory differences
may be one among several isolating barriers that prevent divergent populations from fusing and thereby increase the likelihood that they will continue
differentiating as distinct species.
1. Background
Seasonal migration has been proposed as an important driver of population
differentiation, reproductive isolation, and speciation [1–6]. One set of hypotheses on this topic is derived from cases of ‘migratory divides’, in which
closely related but somewhat differentiated populations breed adjacent to one
another but use different migratory routes. Differences in the migratory behaviour of these populations could serve as both pre- and postmating isolating
barriers (reviewed in [5]). For instance, populations at divides could arrive on
their breeding grounds at different times and mate assortatively based on arrival time [5,7]. Migratory routes are largely genetically determined in many
groups and can involve navigation around unsuitable areas (e.g. deserts and
mountain ranges). Accordingly, hybrids in divides could use intermediate or
mixed routes that are inferior to those of parental forms [5,6,8– 12].
Migratory divides are found in many taxonomic groups [13 –15], and there
is growing interest in the role that they and other behavioural differences could
play in speciation [16,17]. A few empirical studies focusing on single species
have accumulated support for migration’s role in speciation. For example,
Bearhop et al. [7] used stable isotopes to identify the wintering grounds of
European blackcap populations that form a divide in central Europe. These
authors documented differences in arrival time between the populations and
assortative mating based on location of wintering grounds. In another study,
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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2. Methods
(a) Study species
We identified 49 pairs of closely related migratory passerines and
near-passerines that breed in North America using information
from published molecular phylogenies [37], literature reviews
[6], focused studies of particular taxa [38], Clements checklist
(http://www.birds.cornell.edu/clementschecklist/), and Birds
of North America (http://bna.birds.cornell.edu/bna/). We
defined migrants as those that winter primarily south of the
United States – Mexico border. In total, 38/49 pairs were immediate sister taxa (i.e. each other’s most closely related taxonomic
group, including subspecies) and 11/49 pairs were from the
same phylogenetic clade but not the most closely related group
(e.g. if the most closely related group was not migratory, we
chose the next most closely related migratory group). The latter
set of pairs (‘clade sisters’) were never more than two branches
apart and thus will still be relevant to our understanding of phenotypic divergence in recent evolutionary history [23]. We split
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Proc. R. Soc. B 282: 20151921
rates of phenotypic differentiation or (ii) greater phenotypic
differentiation among pairs that do not form divides, suggesting
that ancient pairs of partially differentiated populations were
more likely to fuse into a single population if they lacked both
migratory differences and phenotypic divergence.
The first outcome described above follows from the
potential for migratory differences to lead to greater reproductive isolation and associated phenotypic divergence. The
second outcome would be explained by the geographic history of North American birds and the process of differential
fusion [29,30]. Specifically, differentiating groups have
likely experienced cycles of allopatry and secondary contact
throughout their geographic histories [31– 33]. Gene flow following secondary contact could result in blending and fusion
of population pairs that had not diverged sufficiently in traits
involved in reproductive isolation while allopatric [34]. If
differences in migration reduce gene flow between groups,
the process of fusion would be less likely to occur in pairs
with migratory divides. By contrast, pairs that do not form
divides and had not diverged sufficiently in other phenotypic
traits would have fused upon secondary contact, owing to
low levels of reproductive isolation. This selective elimination
of ancient pairs that were not sufficiently phenotypically
divergent would result in greater phenotypic differentiation
among surviving pairs that do not form divides compared
to those that do.
Geography can influence phenotypic differentiation
through either convergence or divergence (e.g. convergence
can occur if taxa learn heterospecific acoustic signals, and
divergence can occur if interspecific interactions lead to character displacement [35,36]). In pairs of migratory species or
subspecies that do not form divides, both members of the
pair are often restricted to either eastern or western North
America. This is not the case in pairs that form divides; the
range of one taxon is often restricted to the east and the other
to the west (see the electronic supplementary material, figure
S1 for examples). This difference between migratory categories
could result in greater secondary contact among pairs that do
not form divides. We examined this potentially confounding
effect of geography on phenotypic divergence by including
the extent of geographical overlap between pairs in our analyses. We also used mitochondrial sequence data to control
for differences in divergence time in our analyses.
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Helbig [12] bred first-generation hybrids between populations
of blackcaps and assayed their orientation using Emlen funnels. Hybrids oriented in directions intermediate to parental
forms that would have brought them over the Alps, the Mediterranean Sea and the Sahara Desert. More recently, Delmore &
Irwin [11] used light-level geolocators to track hybrids between
two subspecies groups of Swainson’s thrushes that form a
divide in western North America; hybrids took intermediate
routes over mountainous and arid regions in southwestern
North America.
A few studies have begun to examine the role migration
plays in speciation on a broader scale. For example, Møller
et al. [4] used data on band recoveries to identify migratory
divides between passerines in Europe. Over one-quarter of
all species they identified formed divides and many showed
evidence of local adaptation, likely reducing gene flow at
divides. Rohwer & Irwin [6] identified migratory divides
between birds in North America, and found that 12 pairs of
closely related taxa had well-studied contact zones and eight
formed migratory divides with substantial reproductive isolation (a lack of hybrids and/or narrow hybrid zone).
Likewise, north Asian migratory behaviour was reviewed by
Irwin & Irwin [5], who found that seven species are known
to form migratory divides between western and eastern subspecies that migrate to south Asia via routes going west or
east of the Tibetan Plateau, a high-elevation desert. These
studies established the plausibility that migratory divides
could promote reproductive isolation on a broad scale but
did not include a rigorous statistical analysis or a quantitative
measure of reproductive isolation.
Quantitative measures of reproductive isolation are often
collected using mate choice trials and measurements of the viability and fertility of hybrids produced in laboratory crosses
[18–20]. Studies using these measures focus primarily on
organisms that can be held in the laboratory (e.g. Drosophila,
plants, and domestic species of birds). New methods to estimate
reproductive isolation using phenotypic data from natural
populations have recently been employed in the literature
[21–23]. These methods use divergence in traits related to
mate choice and species recognition—morphology, colour
and, song—as a proxy for reproductive isolation [17,24–26].
There are two ways reproductive isolating barriers like seasonal
migration could drive divergence in these traits: (i) selection
against maladaptive hybridization and competition (i.e. character displacement [16,17]) and/or (ii) a reduction in gene flow
caused by pre- and/or postmating isolation which allows
populations to follow different evolutionary trajectories.
In this study, we used divergence in phenotypic traits (morphology, colour, and song) as a proxy for reproductive isolation
to test the hypothesis that differences in migratory routes promote speciation in taxonomic groups that form migratory
divides. We focused on birds because divides are common in
this group [4–6] and could be especially important for their speciation, as intrinsic postmating isolation is believed to play a
limited role in avian speciation [19,27,28]. We identified pairs
of closely related bird species or subspecies that breed in
North America and compared phenotypic divergence between
pairs that form divides and pairs that do not. If migration plays a
role in generating reproductive isolation, we predicted that rates
of phenotypic divergence would differ between pairs that do
and pairs that do not form divides, with two possible outcomes:
(i) greater phenotypic differentiation among pairs that form
divides, suggesting that differences in migration cause faster
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(b) Measuring phenotypic traits
(i) Morphology and colour
We measured morphology and colour from specimens at the Beaty
Biodiversity Museum in Vancouver, British Columbia, and the
Field Museum of Chicago in Chicago, Illinois. We measured six morphological variables: distance between the longest primary and
secondary flight feathers, wing chord, tail, tarsus, and bill length
(all measured in millimetres [39]). We used an Ocean Optics
USB2000 spectrometer (Dunedin, FL) to gather reflectance data
from the feathers of seven body regions: crown, back, rump, tail,
throat, belly, and wing coverts. We took five measurements from
each region and plotted these data in tetrahedral colour space [40]
using the average avian UV visual model and the ‘pavo’ package
[41] in R v. 3.0.3 [42] to model the relative stimulation of each of the
four cones in the avian visual system. In total, we collected measures
of the maximum stimulation of each cone for all seven body regions,
resulting in 28 plumage variables for each individual specimen.
(ii) Song
We focused our analysis on those vocalizations that play a major
role in mate selection: songs of songbirds and territorial or
pair-bonding calls in non-songbirds [43]. We obtained vocalizations from the Macaulay Library (http://macaulaylibrary.org),
Xeno-canto (http://www.xeno-canto.org), and field recordings
(K.E.D., H.L.K. and D.E.I.). We used Syrinx v. 2.6 [44] to analyse
these songs, measuring the following five variables from at least
three songs for each individual: duration (s), bandwidth (kHz),
maximum frequency (kHz), total number of syllables, and
number of syllable types. We defined a syllable as a unit within a
vocalization which is made up of components that are always
repeated together in the same sequence [43].
(c) Quantifying phenotypic divergence
We used Hedges’ g to quantify divergence [45]. This parameter is
calculated by taking the difference between mean values for a
given trait in two separate taxonomic groups and dividing it by
the pooled standard deviation of the trait. Higher values of
Hedges’ g suggest that mean trait values are farther apart and/
or that variability within each group is lower. We calculated
Hedges’ g for all variables and averaged these estimates for each
set of traits separately (i.e. song, colour, and morphology). We
then averaged these summary values (i.e. averages for each set of
traits) to obtain an overall estimate of divergence for each pair.
(d) Measuring additional predictors of phenotypic
divergence
(i) Genetic distance
It can be expected that phenotypic divergence would tend
to increase with the time that populations have evolved in
(ii) Geographical distribution, breeding latitude, and migration
distance
Secondary contact can result in phenotypic divergence or convergence. We accounted for this potentially confounding effect
by including the extent of geographical overlap between pairs
in our models. We downloaded range maps from Bird Life
International and Nature Serve [50] and used ArcGIS (Esri) to calculate geographical overlap, as the proportion of the smaller range
that occurred within the larger range [21]. Separate range maps
were not available for subspecies pairs; for these pairs, we split
the ranges and estimated the width and length of hybrid zones
using Birds of North America and the literature (see the electronic
supplementary material, table S1 for sources). We estimated the
range of overlap as half of the hybrid zones’ area (square kilometres (km2)), since in most hybrid zones the parental forms are
both reasonably common in roughly the central 50% of the
hybrid zone. Results are robust to changes in this assumption,
including the use of 0% overlap for these pairs. Migratory divides
are also more common at higher latitudes [6] and selection related
to differences in migration may be higher for taxa that travel longer
distances. We controlled for these effects by including estimates of
breeding range latitude and migratory distance (km).
(iii) Body mass
We included body mass in our analyses to control for allometric
effects on the traits measured. We obtained estimates of body
mass from the CRC Handbook of Avian Body Masses [51].
Where available, mean male mass was used, otherwise mass estimates come from both sexes or unknown sexes. For some pairs,
mass estimates were not available at the subspecies level—for
those the mean mass for the species was recorded for both
members of the pair (for a difference of zero).
(e) Statistical analysis
We used two complimentary approaches to evaluate the prediction that phenotypic divergence will differ between taxa that do
and do not form divides. First, we used an information theoretic
approach with linear mixed effects models and model averaging
[52]. We ran these analyses using the ‘lmer’, ‘MuMin’, and
‘visreg’ packages in R and included six fixed effects: migration
category (divide or no divide), geographical overlap, genetic distance, breeding range latitude, migration distance, and body
mass. We controlled for phylogenetic relationships in our dataset
by including genus nested within family as a random effect in all
models [23]. In total, we ran 64 models with average divergence
as the response variable (all possible models from n ¼ 6 predictor
variables [2n models, where n ¼ number of predictors]). We
selected models with DAICc (Akaike information criterion, corrected for sample size) less than 2 from the top model [52] and
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Proc. R. Soc. B 282: 20151921
We measured divergence in three sets of phenotypic traits: morphology, plumage colour, and song. To the best of our
knowledge, all of the birds included in our study were breeding
adult males (based on breeding plumage, location, and date of capture (May– July, with a few from August for taxa with low sample
sizes) for museum specimens and location and date of recording
(May– July) for recordings). When available, we included 10 individuals per taxon (mean for colour and morphology ¼ 9.89,
range ¼ 6–10; mean for song ¼ 9.27, range ¼ 4 –10).
separation. To control for this variation in divergence times, we estimated genetic distance between each pair using mitochondrial
sequences from GenBank (https://www.ncbi.nlm.nih.gov/genbank). When available, we obtained sequence data from
cytochrome b (available for 37 pairs). When data from this region
were not available, we used other mitochondrial sequences: cytochrome c oxidase subunit I (COI) (nine pairs), control region
(two pairs), nicotinamide adenine dinucleotide plus hydrogen
(NADH) (one pair) [46,47]). We aligned sequence data using
MEGA 6.06 and estimated genetic distances as p-distance, the proportion of nucleotide sites that differ between clades [48]. Note that
we also ran analyses removing pairs with data from NADH and the
control region and adjusting estimates from COI to account for
slower divergence times with this locus compared to cytochrome
b ( p-distance 7.8% smaller [49]) and found similar results (electronic
supplementary material, tables S2 and S3).
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these pairs into two categories based on whether they formed a
migratory divide or not. Most pairs that form divides come into
contact along the Rocky Mountains or the Great Plains (electronic
supplementary material, figure S1).
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The entire dataset used in our analysis can be found in the
electronic supplementary material (electronic supplementary
material, table S1). Phenotypic divergence between pairs
ranged from 0.44 to 3.93 (Hedges’ g-values; mean ¼ 1.37).
To examine the contribution of each trait set (i.e. song,
colour, morphology) to total divergence, we identified the
trait set with the highest Hedges’ g-value in each pair. The
number of pairs maximally diverged in the song, colour,
and morphological datasets was not different than expected
by chance (18 morphology, 18 plumage, 16 song; expected
values ¼ 16.33; x 2 ¼ 0.124, d.f. ¼ 2, p ¼ 0.93).
Using the model averaging approach to investigate how
different combinations of the fixed effects influenced overall
phenotypic divergence, we found that migration category
(divide or no divide), time since divergence, and body mass
were significant predictors of overall phenotypic divergence
(i.e. confidence intervals on estimates did not overlap zero,
table 1a). The modelled relationship between migration
category and phenotypic divergence is shown in figure 1
(relationships for all significant predictors in the final averaged
model are shown in the electronic supplementary material,
figure S2). In support of our second predicted outcome, phenotypic divergence was greater among pairs that do not form divides
than among those that do. Time since divergence and body mass
were positively associated with phenotypic divergence (table 1a).
The results described above considered all traits together
(i.e. average divergence in song, colour, and morphological
traits). We also used model averaging to examine the relationship between our predictor variables and divergence in each
4. Discussion
In this study, we used a quantitative and comparative approach
to examine a prediction based on a prominent hypothesis in the
speciation literature, that differences in the migratory routes of
populations that form migratory divides promote reproductive
isolation [4–12]. Our broad statistical analysis of divergence
between migratory taxon pairs supports the hypothesis that differentiated migratory routes promote reproductive isolation:
the extent and rate of phenotypic differentiation differed
among sister pairs that do or do not form migratory divides,
with divergence being less for pairs that form divides. This
result suggests that a portion of the reproductive isolation
between pairs that form divides results from migratory differences. We explain the role the geographical history of North
American birds and the process of differential fusion likely
played in generating this pattern below.
Owing to climatic cycles throughout the Pleistocene, bird
species in North America have had complex histories that
involved periods of population contraction and fragmentation followed by periods of population expansion and
contact between refugial populations [31–33]. Upon contact,
many partially differentiated populations simply fused back
together, blending back into a single form [54]. Examples in
which population fusion is thought to have occurred include
ravens [55], black-capped chickadees [56], and snow geese
[57] of North America, and blue tits [58] of Europe. Cases
in which blending appears to be presently occurring include
the Pacific-slope and cordilleran flycatchers, which have similar migratory behaviour and are blending together over a
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Proc. R. Soc. B 282: 20151921
3. Results
set of traits separately. Migration category and time since
divergence emerged as significant predictors in the average
best model for divergence in song and colour (table 1b,c).
Time since divergence was the only significant predictor of
morphological divergence (table 1d). Similar to results
using divergence in all trait sets, pairs that formed migratory
divides had lower values of song and colour divergence, and
time since divergence had a positive effect on divergence in
both traits. Geographical overlap did not emerge as a significant predictor in the average best model of any trait set
(table 1).
Figure 2 shows the relationship between phenotypic
divergence and time since divergence for all trait sets. In
each case, the evolution of phenotypic divergence appears
to be bounded, with divergence occurring rapidly at first
and slowing down with increased time since divergence.
DAICc values from a comparison of BM and OU models support this observation, with OU models providing the best fit
to the data in all comparisons (table 2). The best-fit model in
all cases (when all traits were combined and when each trait
set was examined separately) included separate rates for each
migration category and allowed these rates to vary linearly
with geographical overlap. Similar to results from model
averaging and in support of our second predicted outcome,
rates of evolution (b) were lower for pairs that form divides
(table 2 and figure 2). Note, that the sign of b slope (which
describes how evolutionary rates change with geographical
overlap) was not consistent between comparisons (i.e. was
positive for some and negative for others) and 95% confidence intervals on these slopes included 0, suggesting that
they are non-significant.
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ran these selected models again to recalculate relative AICc
weights. Parameter estimates for each predictor were then
averaged across the selected top models and weighted by the
summed relative AICc weight of each model in which they
were included [52]. This model averaging approach allows us
to investigate how different combinations of the fixed effects
influence phenotypic divergence, select the most parsimonious
combination, and understand how influential different migratory
strategies are in relation to other predictors.
We also used Brownian motion (BM) and Ornstein –
Uhlenbeck (OU) models to compare phenotypic divergence
between different migratory categories. These models estimate
rates of evolutionary divergence. BM models include a single
parameter for evolutionary rate (b) and assume that phenotypic
divergence is unbounded. OU models extend BM models by
including an additional parameter (a) that constrains phenotypic
divergence to an intermediate value, constraining phenotypic
divergence. The constraint parameter (a) varies from 0 to 1,
with higher values making divergence more difficult. We ran
these analyses using the ‘EvoRAG’ package [53] in R and compared
four models: (i) a single evolutionary rate; (ii) separate evolutionary
rates for the two migratory categories; (iii) a single evolutionary rate
that varied linearly with geographical overlap; and (iv) separate
evolutionary rates for each migratory category that each varied linearly with geographical overlap. Note that models including
geographical overlap allowed b but not a to vary with geographical
overlap (OUb-linear). We multiplied p-distance by 100 to run these
models, divided phenotypic divergence by 10 and considered the
model with the lowest AICc value the most parsimonious fit to
the data [52]. These models allow us to examine rates of phenotypic
divergence in more detail than our model averaging approach,
including a comparison between models with and without
evolutionary constraint.
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predictor variables
parameter estimates
standard errors
lower CI
Upper CI
(a) overall phenotypic divergence (R 2 ¼ 0.56, n ¼ 4)
1.73
20.44
1.27
0.11
20.79
20.65
4.25
20.22
time since divergence
breeding latitude
0.22
20.51
0.047
0.35
0.13
21.22
0.32
0.20
0.23
0.098
0.03
0.43
(intercept)
20.0057
1.59
23.17
3.16
migration category
time since divergence
20.58
0.19
0.17
0.073
20.92
0.039
geographical overlap
breeding latitude
20.48
0.43
0.55
0.53
21.58
20.63
0.62
1.50
0.30
0.17
20.036
0.63
body mass
(b) song divergence (R 2 ¼ 0.37, n ¼ 6)
migration distance
20.25
0.33
2
(c) colour divergence (R ¼ 0.38, n ¼ 9)
(intercept)
1.18
1.69
22.19
4.55
migration category
time since divergence
20.45
0.24
0.18
0.085
20.81
0.072
20.084
0.41
geographical overlap
breeding latitude
1.10
20.52
0.61
0.61
20.13
21.75
2.32
0.71
0.30
0.17
20.041
0.64
2.21
0.84
9.72
0.076
0.47
0.028
20.23
0.33
1.65
0.56
22.41
0.14
body mass
2
(d ) morphological divergence (R ¼ 0.29, n ¼ 4)
(intercept)
5.28
time since divergence
geographical overlap
breeding latitude
0.18
0.71
21.28
large section of southern British Columbia and Alberta,
Canada [59]; the Townsend’s and hermit warblers, in which
a hybrid zone is spreading rapidly southward and may
lead to the eventual extinction of hermit warblers as a plumage form while preserving much of its genomic variation
in the resulting Townsend’s warblers [60]; and the bluewinged and golden-winged warblers, in which a hybrid
zone is moving northward [61].
Whether population fusion occurs upon population contact
is determined largely by the level of reproductive isolation
between two forms [34]. There are a wide variety of traits that
could play a role in reproductive isolation, including migratory
differences as well as the other phenotypic traits examined here.
If a particular level of total reproductive isolation is required to
avoid fusion, and if migratory divides cause some reproductive
isolation, then populations with differences in migration do not
require as much differentiation in other traits to remain distinct
forms. In other words, for a given level of differentiation in other
phenotypic traits, ancient fusion of populations would have
been less likely to occur when those populations had migratory
divides. Populations that differed insufficiently in migration
and other traits would have been more likely to fuse, such that
they would no longer exist as a pair of forms available to a
study such as this one. Altogether, this process of differential
fusion would lead to the observed pattern, of greater observed
phenotypic difference in pairs without migratory divides.
The potentially greater rates of ancient fusion between
groups with similar migratory divides would in principal be
detectable with very detailed genomic analyses. These are
beyond the scope of this study, but we comment here on
approaches one might take to detect such ancient fusion. If a
current species resulted from an ancient species splitting for a
period of time into two refugial populations that subsequently
fused into a single population, there would tend to be distinct
peaks in the distribution of inferred coalescence times across
small segments of the genome. These peaks would correspond
to those genomic fragments with coalescence times in the
ancient single population (old coalescence times) and those
Proc. R. Soc. B 282: 20151921
(intercept)
migration category
5
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Table 1. Results from averaged linear mixed models examining the relationship between phenotypic divergence and a series of predictor variables, including
migration category (divide or no divide). Models were run with family and genus as nested random variables and phenotypic divergence as a dependent
variable (for all traits combined (a) and each set of traits separately (b– d )). Fixed effects included migration category, time since divergence (p-distance, the
proportion of nucleotide sites that differ between clades); geographical overlap, migration distance, breeding latitude, and body mass. Only fixed effects
included in the final model are shown; those that are significant predictors of phenotypic divergence (where confidence intervals (CI) do not overlap zero) are
shown in bold. Negative parameter estimates for migration category indicate that groups with migratory divides tend to have lower phenotypic divergence than
groups without divides. n ¼ number of models in final averaged model (i.e. within DAICc of 2), R 2 ¼ goodness-of-fit of the global model.
–0.5
divide
no divide
migration category
that coalesce in the more recent single population (recent
coalescence times). There would also be some genomic fragments that coalesce within one of the refugial populations,
but these would tend to occur at a lower frequency ( per unit
of past time), because such a pattern of coalescence would
result from the improbable extinction of all those alleles that
emerged from just one of the refugial populations, with multiple alleles from the other population surviving. Approaches
to inferring such complex population history from genomic
data are in their infancy (e.g. [62,63]), but further development
of this field may allow a test of our hypothesis that there
has been more fusion between groups with similar migratory
behaviour. In addition to the complexities of inferring history
from genomic patterns, one would also need to infer past
migratory behaviour, a task that would involve careful
inference of past migratory behaviour from current patterns.
Geography had the potential to influence our results, by
causing either convergence or divergence in the phenotypic
traits we measured [35,36]. Recall that members of sister
pairs that do not form divides are often found either both in
western or both in eastern North America while pairs that
form divides often include one taxon in the west and the
other in the east. Accordingly, pairs that do not form divides
are more proximate and may, as a result, have come into secondary contact more often than those that do form divides.
This potential increase in secondary contact among pairs that
do not form divides might account at least partially for
increased rates of divergence in this group. Nevertheless, the
bulk of evidence presented in our study does not support
this suggestion: geographical overlap was not a significant predictor in any of the final linear mixed effects models and while
models of trait evolution that included both migratory category
and geographical overlap provided the best fit to our data,
slopes describing the rate of increase between phenotypic
divergence and geographical overlap were neither significant
nor consistent in sign. In addition, models including only
geographical overlap did not provide a better fit to the data
than those with separate rates for each migration category
(table 2). This finding is in accordance with other comparative
analyses that have failed to find an effect of sympatry in
explaining phenotypic divergence ([23,46], but see [21,53]).
Instead, several additional anecdotes support the first
interpretation of our results, that secondary contact and differences in the main reproductive isolating barriers between
0
0.50
0.25
Proc. R. Soc. B 282: 20151921
Figure 1. Modelled relationship between overall phenotypic divergence and
migration category (no divide, divide). Model averaging with linear mixed
effects models were run with a random variable of genus nested within
family to control for phylogenetic relationships. Significant predictors in
the final model included migration category, time since divergence, and
body mass. The effect of migration category is plotted with these additional
variables held at their medians. (Online version in colour.)
0.25
(b)
0
(c)
colour
divergence
0
0.5
0
(d)
morphological
divergence
0.5
6
0.50
song
divergence
overall phenotypic
divergence
(a)
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overall phenotypic
divergence
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1.0
0.5
0
0
2
4
6
8
time since divergence
10
Figure 2. Relationship between phenotypic divergence and time since divergence. Results are shown for all traits combined (a) and each set of traits
separately (b – d). Time since divergence was measured using genetic distance; phenotypic divergence was measured using average Hedges’ g.
Curves show results from OU (Ornstein – Uhlenbeck) models of evolutionary
change, fitting separate models for taxa that do (dotted line and open
points) and do not (black line and filled points) form migratory divides.
migratory categories account for increased phenotypic divergence in pairs that do not form divides. To begin with,
following this interpretation we would expect to find an
excess of pairs with low values of trait divergence in the
divide category. This is exactly what we see in figure 2,
where there are more young pairs with low differentiation in
the divide group (open circles, bottom left hand corner).
Sister pairs that form divides today also provide support for
this expectation, as many of the migratory divides described
to date occur between taxa that exhibit low levels of phenotypic
divergence, including subspecies of willow warblers [9], populations of European blackcaps [64] and subspecies groups of
the Swainson’s thrush. For example, two groups of Swainson’s
thrushes form a migratory divide in western North America,
where they differ in song, morphology, and colour [65,66].
Differences in migratory routes distinguish these groups
more clearly than the other traits (Cohen’s D of 9.01 for
migration, 3.61 for song, 2.15 for morphology, and 0.28 for
colour [11]). Finally, a comparison between migratory and
non-migratory taxa supports the suggestion that migration
0.0066
0.014/0.0035
0.0053
0.0092/0.0028
0.0061
0.013/0.0029
overlap
no divide/divide and overlap
(b) song divergence
one rate
no divide/divide
overlap
no divide/divide and overlap
0.0079
0.017/0.0052
overlap
no divide/divide and overlap
0.0078
0.011/0.0056
0.0086
0.015/0.0047
one rate
no divide/divide
overlap
no divide/divide and overlap
(d ) morphological divergence
0.0087
0.014/0.0053
one rate
no divide/divide
(c) colour divergence
0.0061
0.0097/0.0038
one rate
no divide/divide
(a) overall phenotypic divergence
22.36
12.59
20.92
17.97
16.22
8.14
14.36
8.59
21.44
7.96
20.94
12.44
7.88
17.95
16.09
10.79
0.17
0.14/0.012
0.021
0.14/0.0059
0.037
0.063/0.0091
0.020
0.037/0.0092
0.013
0.015/0.0073
0.013
0.021/0.0085
0.042/0.0081
0.039
0.018
0.040/0.0061
b
DAICc
b
0.98 (20.0095 – 5.31)
20.018 (20.097– 0.0013)
n.a.
n.a.
0.20 (0.0061 – 5.59)
0.49 (20.020– 13.57)
n.a.
n.a.
0.0051 (20.016 – 1.87)
20.01 (20.033 – 0.81)
n.a.
n.a.
0.0041 (20.043 – 5.80)
0.17 (0.0040 – 3.86)
n.a.
n.a.
b slope no divide
0.16 (20.036 – 6.25)
0.0091 (20.014 – 4.29)
0.095 (0.0088 – 2.62)
0.078 (20.0065 – 2.43)
b slope divide
Proc. R. Soc. B 282: 20151921
Ornstein – Uhlenbeck
DAICc
7.82
3.73
5.79
0
13.55
3.81
15.74
0
8.32
3.36
4.03
0
14.57
4.88
10.34
0
a
0.62
1.18/0.18
2.90
1.29/0.68
0.48
0.42/0.69
0.01
0.20/1.13
0.43
0.57/0.24
2.01
4.36/0.35
0.56
6.43/0.018
11.29
5.71/0.76
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Brownian motion
Table 2. Results from BM (Brownian Motion) and OU (Ornstein– Uhlenbeck) models of evolutionary change. Models including a single evolutionary rate are compared with models fitting separate rates for each migration category (no
divide/divide), allowing phenotypic divergence to vary linearly with geographical overlap (overlap) and allowing rates of evolution to vary with both migration category and geographical overlap (no divide/divide and overlap). Models
were run with all traits combined (a) and each set of traits separately (b – d ). OU models include an additional parameter (a) that constrains the rate (b) of divergence to an intermediate value. b slope describes how the evolutionary
rate changes with geographical overlap (a was not permitted to vary with geographical overlap); 95% confidence intervals generated using 10 000 bootstraps are shown in parentheses.
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7
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Data accessibility. The entire dataset used in our analysis can be found in
the electronic supplementary material.
Authors’ contributions. K.E.D. designed the research with help from D.E.I.
and H.L.K. K.E.D., H.L.K., and R.R.G. collected and analysed the
data. K.E.D. wrote the paper with input from all authors.
Competing interests. We have no competing interests.
Funding. This work was supported by grants from the Natural Sciences
and Engineering Research Council of Canada (Discovery Grant
311931 to D.E.I. and CGS-D to K.E.D. and R.R.G.), the Field
Museum of Chicago (Visiting Scholarship to K.E.D.), and Environment Canada (Science Horizons Youth Internship Program for
H.L.K. and Leslie Poulson).
Acknowledgements. We thank the Field Museum of Chicago (especially
Dr John Bates and Dr Ben Marks) and the Beaty Biodiversity
Museum (especially Ildiko Szabo and Chris Stinson) for access to
specimens. We are grateful to Jason Weir, Chris Muir, and Dolph
Schluter for advice on the analysis and Leslie Poulson for assistance
in data collection. The manuscript also benefitted from comments
from Oscar Gaggiotti and Kevin Winker.
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rspb.royalsocietypublishing.org
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