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A central question in evolutionary developmental biology is how highly conserved developmental systems can generate the remarkable phenotypic diversity observed among distantly related species. In part, this paradox reflects our limited knowledge about the potential for species to both respond to selection and generate novel variation. Consequently, the developmental links between small-scale microevolutionary variations within populations to larger macroevolutionary patterns among species remains unbridged. Domesticated species such as the pigeon are unique resources for addressing this question because a history of strong artificial selection has significantly increased morphological diversity, offering a direct comparison of the developmental potential of a single species to broader evolutionary patterns. Here we demonstrate that patterns of variation and covariation within and between the face and braincase in domesticated breeds of the pigeon are predictive of avian cranial evolution. These results indicate that selection on variation generated by a conserved developmental system is sufficient to explain the evolution of crania as different in shape as the albatross or eagle, parakeet or hummingbird. These “rules” of craniofacial variation are a common pattern in the evolution of a broad diversity of vertebrate species, and may ultimately reflect structural limitations of a shared embryonic bauplan on functional variation.
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NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 | 1
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Craniofacial diversification in the domestic pigeon
and the evolution of the avian skull
Nathan M. Young1*, Marta Linde-Medina1, John W. Fondon III2, Benedikt Hallgrímsson3 and
Ralph S. Marcucio1
A central question in evolutionary developmental biology is how highly conserved developmental systems can generate the
remarkable phenotypic diversity observed among distantly related species. In part, this paradox reflects our limited knowledge
about the potential for species to both respond to selection and generate novel variation. Consequently, the developmental
links between small-scale microevolutionary variations within populations to larger macroevolutionary patterns among spe-
cies remain unbridged. Domesticated species, such as the pigeon, are unique resources for addressing this question, because
a history of strong artificial selection has significantly increased morphological diversity, offering a direct comparison of the
developmental potential of a single species to broader evolutionary patterns. Here, we demonstrate that patterns of variation
and covariation within and between the face and braincase in domesticated breeds of the pigeon are predictive of avian cranial
evolution. These results indicate that selection on variation generated by a conserved developmental system is sufficient to
explain the evolution of crania as different in shape as the albatross or eagle, parakeet or hummingbird. These ‘rules’ of cranio-
facial variation are a common pattern in the evolution of a broad diversity of vertebrate species and may ultimately reflect
structural limitations of a shared embryonic bauplan on functional variation.
Darwin wrote: “We may look in vain through the 288 known
species [of pigeons and doves] for a beak so small and conical
as that of the short-faced tumbler; for one so broad and short
as that of the barb; for one so long, straight, and narrow as that of
the English carrier1
Domesticated animals have long played an important role in our
understanding of the evolution and diversification of natural spe-
cies. One of Darwin’s2 key insights was that domesticates provided
an analogous and accelerated window into the vastly slower process
of natural selection, accomplishing in generations what may nor-
mally take millions of years. One species in particular, the common
pigeon, was central to his understanding of selection and its role in
evolution. Among other traits3,4, Darwin noted how the shape of
the pigeons head and beak has been subjected to extensive selection
by humans5, resulting in breeds that qualitatively not only exceed
variation seen within a single wild bird species, but also converge on
phylogenetically distant avians (Fig.1 and Supplementary Fig. 1).
Darwin believed selection was the primary force driving breed
and species diversification, but he also observed how the “correla-
tion of growth among body parts” could confound the breeder’s
goals1,5. That said, he was largely unaware of how these rules were
determined or how they might ultimately impact phenotypic diver-
sification. In the modern view, genetic pleiotropy drives covaria-
tion6,7 through the correlated effects of variation in developmental
processes8, in turn biasing the long-term direction and magnitude of
evolutionary responses to selection9,10. However, there is continued
debate about the relative importance of development and selection
to the patterning of macroevolutionary diversity, since evolution-
ary changes can occur in both the distribution of variation across
such processes and in the developmental basis for the correlations
themselves8. Consequently, selection not only impacts observed
phenotypic distributions, but also can alter the rules for how varia-
tion is generated within the ‘space’ in which phenotypes exist11,12.
In this context, the dramatic variation among domesticated
pigeon breeds provides critical evidence of not only selection, but
also a unique window into how the developmental potential of a
single species corresponds to evolutionary diversification. Avian
diversity is driven by a myriad of functional factors that are particu-
lar to lineages and contingent within the history of each group. If the
diversity of morphology generated within a single bird species were
to resemble that produced by evolution among all birds, then this
would provide powerful evidence for the existence of key develop-
mental drivers of correlations among parts that persist across broad
ranges of phylogenetic diversity. Despite the potential importance of
domestic breeds to these questions, there have been few attempts to
either characterize skeletal variation in the pigeon13, or relate them
to broader avian radiations14. Here, we test whether differences
in morphological variation among avians and domestic and feral
pigeon populations reflect changes in the organization of cranial
covariation, measured in terms of pattern and magnitude of integra-
tion and modularity, or if instead variation within and among these
groups reflects the potential of a common developmental system to
generate phenotypic diversity.
We first compared feral and domestic pigeons to proxies of the wild-
type ancestral populations, the rock dove. Linear data for rock doves
and feral and domestic pigeons (Supplementary Table 1) were signif-
icantly correlated with overall body size. All groups overlapped and
there were no significant differences in slope, with the exception of
premaxilla and cranial width in domestics (Supplementary Fig. 2).
In all dimensions, domestics exhibited increased mean and variance
1University of California San Francisco, Department of Orthopaedic Surgery, 2550 23rd Street, San Francisco, California 94115, USA. 2University of Texas
at Arlington, Department of Biological Sciences, Texas 76019, USA. 3University of Calgary, Department of Cell Biology and Anatomy and the Alberta
Children’s Hospital Research Institute, Alberta T2N 4N1, Canada. *e-mail:
2 NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 |
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
(Levene’s test, P< 0.0001), with the average coefficient of variation
(c.v.) across traits threefold greater than estimated for feral popu-
lations (c.v.dom=13.3%, c.v.fer= 4.5%) and nearly fourfold that of
the wild rock dove (c.v.rd= 3.6%) (Supplementary Table 2). Ferals
were more variable in some traits (premaxilla and skull width, over-
all size) relative to the rock dove (Levenes test, P< 0.05), but not
others (premaxilla and mandible length; Levenes test, P> 0.05), in
part due to geographic population heterogeneity. Increased varia-
tion in domestics was generally consistent with predictions of the
rock dove within-group regression, with the exception of width.
The first axis of the principal component analysis (PCA) (PC1,
81.5% variation) was significantly associated with size (geometric
mean) (r2=0.958, P< 0.0001), with no significant difference in
slope among groups (P= 0.522) (Fig.2). The pattern of trait cor-
relations among pigeons was highly significant when adjusted for
repeatability (matrix correlation (rm) > 1 between rock dove and
domestic (P< 0.0001); rm= 0.798 for the feral–domestic compari-
son (P< 0.0001); Supplementary Table 3). The unadjusted variance
of the eigenvalues (v.e.) of the domestics was significantly higher
than wild doves and pigeons, but was statistically indistinguishable
from the rock dove at comparable c.v. values (for example, when
c.v.= 3, v.e.rd= 0.56 and v.e.dom= 0.62) and converged with all wild-
types when c.v.= 6. Together, these results suggest that domestics
have increased magnitude of variation produced by a trait correla-
tion structure shared with wild pigeons.
Next, we turned to the pigeon shape data. Here, brain–shape
allometry accounted for 20.2% of total variation (P< 0.001), with
smaller individuals having proportionately shorter, straighter beaks
and wider crania (Supplementary Fig. 3). The first three princi-
pal components of non-allometric pigeon cranial shape variation
described facial length (PC1, 64.2% variation), beak and cranial
flexion (PC2, 16.7% variation) and facial width (PC3, 5.4% varia-
tion), together accounting for 86.4% of total shape variation (Fig.3).
The matrix correlation between feral and domestic shape data was
significant (rm= 0.549, P< 0.0001), suggesting a similar covari-
ance structure. This result was further confirmed by a significant
non-random alignment between PC1–4 eigenvectors (P< 0.001)
(Supplementary Table 4). Feral and domestic pigeons overlap
in shapespace, although Procrustes configurations of domestics
account for a larger proportion of landmark variance compared with
ferals (Levene’s test, P< 0.001) (Supplementary Fig. 4). Domestic
pigeon faces exhibited approximately eightfold higher landmark
variance compared with ferals, while braincase configurations were
approximately fourfold more variable. Partial least squares (PLS)
analysis between face and braincase shape indicated that almost all
their covariation can be explained in the first axis (94.4%, P< 0.001)
(Supplementary Table 5), which describes a narrowing of the brain-
case with increasing facial length (Fig.4).
Finally, we compared pigeon shape data to the avian radiation.
Here, brain-shape allometry accounted for 10.0% of avian shape vari-
ation and was weakly non-significant (P= 0.091) (Supplementary
Fig. 3). Allometric variation was associated with curvature of the
beak and skull base but not with beak length, in contrast to pigeons.
PCA of the non-allometric avian data decomposed cranial shape
into premaxilla length (PC1, 44.4%), skull flexion (PC2, 27.4%)
and facial width (PC3: 7.5%) (Fig. 5 and Supplementary Fig. 5).
There was evidence of significant phylogenetic signal within this
shapespace (Κ= 0.895, P= 0.042) (Supplementary Fig. 6), with
feral pigeons most closely aligned with the fruit dove and domes-
tic breeds distributed in a space overlapping ferals, the fruit dove
and the short-beaked parakeet. The avian shapespace exhibited a
strong correspondence to the pigeon shape covariance structure as a
whole (rm= 0.851, P< 0.001) and individually to ferals (rm= 0.510,
P< 0.001) and domestics (rm= 0.777, P< 0.001). Pigeon and avian
shapespaces exhibited non-random alignment (angle (°) and vec-
tor correlation (rv) between individual principal components: PC1:
25.2°, rv= 0.91, P< 0.0001; PC2: 55.9°, rv= 0.56, P< 0.0001; PC3:
56.7°, rv=0.55, P< 0.0001; Supplementary Table 4). Avians exhibited
higher disparity (~1.5× ) and landmark variance (~3× ) compared
with domestic pigeons (Supplementary Fig. 4). RV and covariance
ratio (CR) coefficients for hypothesized face–braincase modules in
avians (RV= 0.76, P= 0.098; CR= 0.939, P= 0.005; r-PLS= 0.924,
P= 0.001), ferals (RV=0.46, P<0.001; CR = 0.868, P= 0.010;
r-PLS = 0.845, P= 0.030), and domestic pigeons (RV = 0.91,
P= 0.006; CR=1.029, P= 0.010; r-PLS = 0.978, P= 0.001) were
all significant, suggesting strong shape integration (Fig. 4 and
Wild rock pigeon
Columba livia
English carrier
clear leg
Feral pigeon
English carrier
show flight
long face
Figure 1 | Variation in the pigeon craniofacial skeleton compared with avian diversity. a, Original examples of domesticated pigeon breed diversity from
Darwin1. b, Left images: modern examples illustrate notable breed variants examined in this study (see Supplementary Fig. 1 for additional examples
and Supplementary Table 6 for a complete list of breeds used). Right images: examples of pigeon breeds with extreme craniofacial shapes qualitatively
converge on distantly related avians. c, Examples of avian crania with shape characteristics similar to domesticated pigeon breeds (individual crania
shown scaled to similar braincase length).
NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 | 3
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Supplementary Fig. 7). However, both measures were generally
less than or equal to random module partitions of the dataset,
suggesting greater covariation within hypothesized modules than
between, consistent with modularity. PLS analysis of all avian
data (RV=0.820, P< 0.001) further confirmed that 88.8% of the
covariation between face and braincase shape was associated with
neurocranial width and facial length (PLS1, r= 0.938, P< 0.001)
(Supplementary Table 5) and was similar to the pattern of integra-
tion found within pigeons (PLS1, 23.4°, rv= 0.918, P< 0.0001).
These results demonstrate that under a regime of strong artificial
selection, domestic pigeons recapitulate the principal axes of avian
craniofacial shape variation, but not the magnitude. Importantly,
domestic pigeons have increased the relative contributions of the same
determinants of covariation that influence evolutionary diversifica-
tion among avians, even though there is no reason to predict a priori
that the direction of selection on functional demands in different spe-
cies of birds should be similar to those of human breeders. This result
indicates that both pigeon breeds and avians have diversified utilizing
a common pattern of integration and modularity between the face
and braincase, resulting in allometric and integrated non-allometric
variation associated with length, curvature and width. Although face
and braincase shape are strongly integrated, changes in facial shape
are accompanied by predictable but relatively small corresponding
changes in braincase shape, which accounts for the greater diversity of
beak shapes compared with brains. Together, these results imply that
avian craniofacial diversity is a product of selection on primitive pat-
terns of variation and covariation between the face and braincase that
are defined by a developmental system common to all birds and have
undergone little change during avian evolutionary diversification.
This analysis samples a broad diversity of living birds, with
species distributed across shapespace. That said, examination of
predicted shapes from relatively unoccupied regions suggests that
some potential outcomes may be less likely, due to a mixture of
developmental and functional limitations. For example, the ‘short-
face’ pigeon breeds have highly reduced premaxilla or beak size
(see Supplementary Fig. 1) and are often incapable of feeding their
hatchlings, requiring human intervention or surrogate parents to
reach maturity. Similarly, the generation of short and curved beaks
in domesticated breeds like the African owl creates functional lim-
itations to jaw closure, which would help explain why birds with
the greatest cranial or beak curvature more frequently have longer
beaks. At the other extreme, avians with long beaks and unflexed
crania are unsampled. This result may reflect the integrated effects
of a longer braincase and narrower frontal bones, suggesting either a
requirement of additional neck musculature as a balancing mecha-
nism or a limitation on how narrow the frontal may become before
affecting structural integrity. Given the relatively small changes
of braincase shape compared with the face, this combination may
be impacted by functional variation available in the braincase to
match facial length.
The phylogenetic reconstructions within the shapespace repre-
sent testable hypotheses about both ancestral states and the devel-
opmental transformations underlying evolutionary trajectories
between ancestral and descendant taxa. In the case of ancestral states,
the predictions of phylogenetic hypotheses are directly testable with
the inclusion of plausible ancestral taxa from the fossil record into
the morphospace. Considering the developmental transformations,
the trajectories are plausibly linked to previously documented devel-
opmental determinants of beak shape in avians, providing potential
experimental routes for testing mechanistic explanations for inter-
specific variation. For example, regulation of Bmp4 expression dur-
ing upper beak morphogenesis of phenotypically diverse species of
the genus Geospiza impacts depth and width15,16, while the expres-
sion of calmodulin impacts length17. Variation in beak curvature
may be responsive to differential regulation of local growth zones in
the frontonasal mass by Bmp418,19; alternatively, it may be a product
v.e.dom = 1.57 × ln(c.v.) – 1.16
r2 = 0.618
v.e. = 1.26 × ln(c.v.) – 0.76
r2 = 0.247
v.e.fer = 2.58 × ln(c.v.) – 3.03
r2 = 0.388
Variance of eigenvalues (v.e.)
Average coecient of variation (c.v.)
–2.0 –1.00.0
Size (geometric mean)
21 24 27 30 33 36 39
a b
Rock dove
Figure 2 | Results of the PCA from linear distance data. ac, Feral and domestic pigeon breeds reflect a nested pattern of covariation with the ancestral
rock dove but have increased variation along these commonly held axes. Rock dove, orange; feral, green; domestic, blue. 90% equal frequency ellipses
shown. d, Overall magnitude of integration is similar in rock doves and ferals. Domestic breed integration is comparable after controlling for variation.
Curves illustrate log-linear regressions for resampled populations (10,000 replicates).
4 NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 |
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
of how the inferior growth of the frontonasal mass interacts with the
anterior extension of the maxillaries during primary palatal fusion20.
These determinants are also thought to exhibit modularity21,
in that they have independent effects on beak shape, consistent with
our results. Future studies could directly test these hypotheses by
experimentally modulating the underlying developmental determi-
nants associated with each axis in model species and comparing the
phenotypic outcomes to the predictions of the avian shapespace.
The results support the hypothesis that a conserved developmen-
tal system is sufficient to explain the majority of avian craniofacial
diversification. A recent analysis reached a similar conclusion in rap-
tors, arguing that most observed variation in this group was due to
either allometric scaling or non-allometric integration between the
braincase and face; this suggested that developmental constraints
have played an important role in the evolution of the raptor skull22.
There was further speculation that increasing modularity of the
passerine skull might have enabled a greater relative diversification
in songbirds22. Although changes in clade-specific modularity may
help explain relative differences in diversification, our results sug-
gest that they are not necessary to explain diversification at the scale
of all avians. Although the avian cranium is highly integrated, the
magnitudes of correlated changes we identified in the braincase are
small relative to those in the face (~1:3). Thus, although integration
of the face and braincase influences craniofacial diversification as
a whole, it does not appear to be a strict limit on the ability of the
face to vary or evolve. Increased phylogenetic coverage of popula-
tion-level variation of individual clades that vary widely in diversity
would help to answer this question.
There are notable parallels between the patterns of variation and
covariation described here and those described in the mamma-
lian literature that suggest they are widely shared among amniotes.
For example, analyses of craniofacial shape in domesticated dog
breeds, wolves and carnivores found that the canine skull not only
exhibits a similar pattern of integration and modularity between
the face and brain, but also varies primarily in facial length, the
positioning of the face relative to braincase (that is, cranial flexion)
0. 05
0. 05
–0. 05
0. 04
–0. 04
English carrier
American fantail
Figure 3 | Pigeon craniofacial shape morphospace. Results of the PCA showing the first three axes. The mean sample cranium is shown warped to
maximum positive and negative values along each axis. PC1 (top, lateral view; bottom, oblique view) distinguishes between short (brachycephalic) and long
(dolichocephalic) crania, largely due to extension of the beak skeleton (that is, premaxilla) (64.2% total variation). PC2 (left, oblique view; right, lateral
view) discriminates curved beaks located higher on the braincase with a flexed basicranium from crania in which the beak is straighter and located lower on
the braincase with a flat basicranium (16.7% total variation). PC3 (bottom right, frontal view) discriminates between the width of the frontal bone and the
space separating the orbits (5.4% total variation). Feral pigeons, green; domestic breeds, blue. ASR, American show racer; BLF, Berlin long-faced tumbler;
BP, Brunner pouter; BSF, Budapest short-faced; DSF, Domestic show flight; ESF, English short-faced; GBH, German beauty homer; BR, Birmingham roller;
HS, Hamburg sticken; LFCL, Long-face clear leg; NYF, New York flight; PS, Polish srebrniak; VMF, Vienna medium face; WET, West of England tumbler.
NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 | 5
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
and the width of the skull23. A similar distribution of shape
variation was observed in human craniofacial datasets24,25. More
broadly, a comparative analysis of genetic and phenotypic covaria-
tion in the skull of 15 mammalian orders found that “both remained
remarkably stable for a long evolutionary period, despite extensive
morphological change and environmental shifts”26. This stabil-
ity in the pattern of variation and covariation between avians and
mammals suggests that amniote craniofacial diversity varies and
evolves within a shared space utilizing similar mechanisms and
under similar constraints.
Why do amniote crania share a common pattern of variation
and covariation? Covariation of traits within species occurs because
of the correlated effects of variation in developmental processes8
and may result from directional selection operating on coordi-
nated functional variation among traits12. One possibility is that the
embryonic bauplan of the head limits how structural components
can be organized and vary relative to each other, while maintaining
functional relationships. In particular, the amniote brain is derived
from the neural tube, while the face has its origin in prominences
arranged around the developing brain (that is, the frontonasal from
nasal placodes and the maxillary from the first branchial arch,
respectively), which grow together and fuse to form a continuous
upper jaw27. These relationships suggest the brain acts as a ‘platform
on which the face grows, with both structures influencing the other’s
development through molecular signals, structural interactions and
later somatic growth28. Consequently, the width of the face is both
dependent on brain size and is limited in where it is located (for
example, higher or lower), impacting overall curvature or flexion of
the cranium. The increased integration of brain and face variation
among the domestic pigeon breeds supports this argument. Similar
integration of these features across amniotes therefore suggests a
fundamental limitation on the generation of functional variation
imposed by the structural design of the face–braincase complex,
presumably primitive in the earliest vertebrates. Similar analyses of
species with extreme craniofacial shapes that violate these rules may
help to determine when and how these patterns are generated.
Darwin highlighted the ability of humans to select, in a short
span of time, pigeon breeds that mimicked or exceeded variation
observed in wild doves. When considering the role of selection in
domestication, he argued that “in the construction of a building,
mere stones or bricks are of little avail without the builder’s art, so,
in the production of new [breeds], selection has been the presiding
power”1. What Darwin could not appreciate at the time was that
although breeders have wide latitude to generate variation, these
variants mirror patterns of avian diversity at large. In the case of the
avian cranium, and perhaps across vertebrates, we speculate that it
is the embryonic architecture of the brain and face that shapes the
space in which phenotypic diversity is possible, ultimately limiting
the axes of functional variation that can be generated.
Data. To compare feral and domestic pigeons to proxies of the wildtype
ancestral populations, we rst obtained raw two-dimensional linear distance
data for the wild rock dove (Columba livia, Sardinia locality; n= 35), feral
pigeons (combined North American and European populations; n= 91) and
domesticated pigeon breeds (n= 53) (see Supplementary Table 1)13. We next
collected a sample of both feral (n= 15) and domestic (n= 58) pigeon skulls
(see Supplementary Table 6), which were deeshed using dermestid beetles
then scanned at a resolution of 40 μ m using micro-computed tomography
(μ CT) (Scanco Viva40). μ CT scans were thresholded and converted into
three-dimensional objects in Amira 5.6.0 soware (Visage Imaging). Ferals
were collected at a single locality (Arlington, Texas) and domestics were from
breeders located in both California and Texas. Domestic pigeons included
the following breeds (n= 36): Aachen langer, African owl, American fantail,
American show racer, archangel, Berlin long-faced tumbler, Birmingham
‘wholly’ roller, blondinette, brunner pouter, Budapest short-faced, Chinese owl,
damascene, dewlap, domestic show ight, Egyptian Baghdad, English carrier,
English short-faced, German beauty homer, Hamburg sticken, helmet, Indian
fantail, Italian owl, Lahore, long-face clear leg, magpie, Maltese, Modena,
mookee, New York ight, Polish ice tumbler, Polish srebrniak, roller, scandaroon,
swallow, Vienna medium face and West of England tumbler.
–0.3 0.2
–0.1 0.1–0.2 0.0
Brain PLS axis 1
Face PLS axis 1
0.2–0.2 0.0–0.1 0.1
Brain PLS axis 2
Face PLS axis 2
0.4 0.6
RV coecient
0.8 1.0
Replicate frequency
= 0.46
= 0.76
= 0.91
r = 0.938
P < 0.001
Total squared covariance (%)
0PLS axis
–0.1 r = 0.905
P < 0.001
Figure 4 | Results of the PLS analysis of face and braincase modules. a, RV estimates relative to resampled distributions for ferals (green), domestics
(blue) and avians (purple) indicate face and braincase modularity. bc, PLS axis 1 and PLS axis 2 are consistent with strong integration associated with
craniofacial length and width. d, The majority of covariation between the avian and pigeon face and brain is explained by the first PLS axis.
6 NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 |
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
To compare pigeon shape to the larger avian radiation, we next sampled
avian crania (n= 20) representing 17 orders and encompassing a range of beak
shapes, sizes and functions (Supplementary Fig. 8 and Supplementary Table 6):
black-naped fruit dove (Ptilinopus melanospilus, Order: Columbiformes),
kiwi (Apteryx australis, Order: Tinamiformes), ostrich (Struthio camelus,
Order: Struthioniformes), chicken (Gallus gallus, Order: Galliformes),
white pekin duck (Anas platyrhynchos, Order: Anseriformes), black vulture
(Coragy ps atratus, Order: Acciptriformes), bald eagle (Haliaeetus leucocephalus,
Order: Accipitriformes), little penguin (Eudyptula minor, Order: Sphenisciformes),
emperor penguin (Aptenodytes forsteri, Order: Sphenisciformes), Brandt’s
cormorant (Phalacrocorax penicillatus, Order: Pelecaniformes), frigatebird
(Fregata magnificens, Order: Pelecaniformes), common loon (Gavia immer, Order:
Gaviiformes), giant hummingbird (Patagona gigas, Order: Caprimulgiformes),
white-tipped sicklebill (Eutoxeres aquila, Order: Apodiformes), golden-winged
parakeet (Brotogeris chrysoptera, Order: Psittaciformes), grebe (Podilymbus
podiceps, Podicipediformes), albatross (Diomedea exulans,
Order: Procellariiformes), auk (Alca torda, Order: Charadriiformes), red-tailed
tropicbird (Phaethon rubricauda, Order: Phaethontiformes) and manakin
(Lepidothrix coronata, Order: Passeriformes). Avian computed tomography
(CT) data were obtained courtesy of the University of Texas High Resolution
X-ray CT Facility (UTCT;
Linear analysis. We first performed a reduced major axis linear regression of
cranial measurements on size (estimated as the geometric mean from both cranial
and postcranial measurements). We tested for significant differences in elevation
in the smatR module29, implemented in the software R (ref. 30). We next compared
trait correlation structure via the matrix correlation (rm), adjusted for repeatability
(that is, radjusted= ( robserved / [ta×tb]0.5), where t is the matrix repeatability of matrices
a and b) and resampled each population with replacement (10,000 replicates)
to estimate autocorrelation, before testing for significance using Mantel’s test31.
To estimate and compare the magnitude of integration we calculated the variance
of the eigenvalues (v.e.) for each correlation matrix32,33. To control for the effect of
sampled variance on correlation and eigenvalue estimates, we followed ref. 34 and
resampled each population with replacement and recalculated v.e. and the average
c.v. of all cranial measurements for each replicate (10,000). We next calculated the
log-linear relationship of c.v. to v.e. in those samples where rm> 0.95 with the
original matrix and compared populations at similar c.v. values.
Shape analysis. We next used geometric morphometric methods to quantify
and compare shape variation and covariation among feral and domestic pigeons
and the avian radiation. For each cranium, we identified a series of 32 midline
and bilaterally symmetric three-dimensional landmarks evenly divided between
the face and braincase (see Supplementary Fig. 9 and Supplementary Table 7).
16. Parakeet
1. Pigeon (feral & domestic)
6. Dove
19. Sicklebill
11. Hummingbird
12. Kiwi
9. Frigatebird
2. Albatross
5. Cormorant
13. Loon
17. Penguin (emperor)
18. Penguin (little)
10. Grebe
21. Vulture
4. Chicken
14. Manakin
8. Eagle
20. Tropicbird
15. Ostrich
3. Auk
7. Duck
–0.2 –0.1 00.1 0.2 0.3
21 4
11 19
17 18
20 15
Figure 5 | Comparison of craniofacial diversification in domesticated pigeon breeds with the avian radiation. The PCA results illustrated as a
‘chronophylomorphospace’46 with reconstructed phylogenetic branching pattern and ancestral states (grey) shown relative to divergence time
(vertical axis). Domesticated pigeons (blue) overlap the fruit dove and the short-beaked parakeet. Domestics have diversified relative to ferals (green)
over 10,000 years of artificial selection in a pattern mirroring avians (purple) as a whole, but not as extensively. Warped specimens on each axis illustrate
associated shape transformations: PC1 describes beak length and PC2 is associated with cranial flexion/curvature and brain width. The morphospace
exhibits significant phylogenetic structure (Κ= 0.895, P= 0.042) (Supplementary Fig. 6).
NATURE ECOLOGY & EVOLUTION 1, 0095 (2017) | DOI: 10.1038/s41559-017-0095 | 7
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Individual specimen coordinates (x, y, z) were located in Landmark Editor (v.3.5; using a semi-automated
process that was repeated on two separate occasions. We compared between
landmarking trials and noted low error, so we averaged coordinates between
trials. We next performed a Procrustes superimposition on all the landmark data
to remove the effects of scale, translation/location and orientation. Data were
averaged across the plane of bilateral symmetry to reduce dimensonality relative
to sample size. We further averaged Procrustes data and centroid sizes within each
breed and/or species to account for unequal sample sizes.
To test and control for significant size–shape allometry, we first calculated
disparity and landmark variance for the entire craniofacial skeleton as well as
within the braincase and face modules35. We noted that facial shape variation
was significantly increased whereas braincase shape was more conservative
(Supplementary Fig. 10). We also collected published measures of body mass
brain volume for all species in our analysis (see Supplementary Table 6).
As previously demonstrated36, brain–body allometry is significant in avians
(r2= 0.888, P< 0.0001) (Supplementary Fig. 10), indicating brain volume
is a good proxy for body mass. For the pigeon data, information on individual
body mass was not collected, so we instead used braincase centroid size as the
measure of size. For comparisons among avians, we used log(brain volume)
as within cranium proxy to remain consistent with the pigeon analyses and to
test for size–shape allometry in our cranial data. We performed a within-group
multivariate regression of shape on braincase centroid size in pigeons (feral and
domestic; see ‘Data’ above). To compare between pigeon and avian brain–shape
allometry, we centred average pigeon log(brain volume) on the mean feral
pigeon braincase centroid size.
To compare the distribution of shape variation and covariation in the
non-allometric shape data, we performed PCA on residuals of brain–shape
allometry. To compare overall covariation structure similarity we calculated rm and
tested the hypothesis of no similarity using a permutation test (10,000 replicates).
We further compared covariance structure by calculating the angle (°) and vector
correlation (rv) between individual principal components and tested whether
they were significantly better aligned than predicted by chance alone using a
permutation test (1,000 iterations). To investigate integration and modularity
of the face and braincase in the individual shape datasets, we performed a PLS
analysis, estimated both the RV (ref. 37) and CR coefficients38, and tested for
significance using resampling procedures (200 replicates), utilizing the integration.
test and modularity.test functions in the R package geomorph (ref. 39).
We also tested for global integration following ref. 40.
To estimate phylogenetic structure in our avian shape data, we generated
1,000 trees with molecular-derived branch lengths scaled to divergence times
from (ref. 41), calculated a majority-rule consensus tree in
Mesquite v.3.10 (ref. 42; see Supplementary Fig. 8), mapped the rooted tree in
the PCA shapespace weighted by branch lengths scaled to divergence time and
tested the hypothesis of no phylogenetic structure via a permutation test43 and
the Κ statistic44,45. Occupation of shapespace over time was visualized by plotting
PC1 and PC2 against divergence time46 as implemented in geomorph. Unless
otherwise noted, all analyses were performed in MorphoJ v.1.06d (ref. 47)
or the geomorph v.3.0.3 package39 as implemented in R.
Data availability. Complete raw linear data is provided in Supplementary Table 1.
Avian CT data are publicly available at Phylogenetic data are
publicly available at Additional pigeon landmark and CT data
that support the findings of this study are available from the corresponding author
on reasonable request.
Received 30 September 2016; accepted 17 January 2017;
published 13 March 2017
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We thank J. DeCarlo for kindly donating specimens of domestic pigeon breeds,
A. Goode for preparing their skeletons and R. Johnston for generously sharing
his original pigeon data. Non-pigeon avian CT data were provided courtesy of the
University of Texas High Resolution X-ray CT Facility (UTCT) (National Science
Foundation grant number IIS-0208675). Research reported in this publication was
supported by the National Institute of Dental and Craniofacial Research of the National
Institutes of Health under Award Numbers F32DE018596 (to N.M.Y.), R01DE019638
(to R.S.M. and B.H.) and R01DE021708 (to R.S.M. and B.H.). The content is solely the
responsibility of the authors and does not necessarily represent the official views
of the National Institutes of Health.
Author contributions
N.M.Y. designed the research. N.M.Y. and J.W.F. collected pigeon specimens.
N.M.Y. and M.L.-M. performed the analyses. N.M.Y., M.L.-M., B.H., and
R.S.M. contributed to the interpretation of the results. N.M.Y. drafted the paper.
All authors contributed to the final version of the paper.
Additional information
Supplementary information is available for this paper.
Reprints and permissions information is available at
Correspondence and requests for materials should be addressed to N.M.Y.
How to cite this article: Young, N. M., Linde-Medina, M., Fondon, J. W. III,
Hallgrímsson, B. & Marcucio, R. S. Craniofacial diversification in the domestic pigeon
and the evolution of the avian skull. Nat. Ecol. Evol. 1, 0095 (2017).
Competing interests
The authors declare no competing financial interests.
... The massive diversity of craniofacial morphology among birds has inspired excellent comparative morphometric analyses of shape variation across species (recent examples include, but are not limited to, (Wu et al. 2006;Foster et al. 2008;Campàs et al. 2010;Mallarino et al. 2012;Fritz et al. 2014;Shao et al. 2016;Bright et al. 2016;Cooney et al. 2017;Young et al. 2017a;Felice and Goswami 2018;Yamasaki et al. 2018;Navalón et al. 2019;Bright et al. 2019;Navalón et al. 2020)). In contrast, there are relatively few examples of pairing geometric morphometric shape analysis with genome-wide scans to identify the genetic architecture of avian craniofacial variation (but see (Yusuf et al. 2020)). ...
... Pigeons have spectacular craniofacial variation among hundreds of breeds within a single species; the magnitude of their intraspecific diversity is more typical of interspecific diversity (Baptista et al. 2009). Recently, Young et al. (Young et al. 2017a) used geometric morphometrics to compare craniofacial shape among breeds of domestic pigeon and diverse wild bird species and concluded that the shape changes that differentiate pigeon breeds recapitulate the major axes of variation in distantly related wild bird species. However, unlike most distantly related species, domestic pigeon breeds are interfertile, so we can establish laboratory crosses between anatomically divergent forms and map the genetic architecture of variable traits. ...
... Surface meshes were converted to the Polygon (Stanford) ASCII file format (*.ply) using i3D Converter v3.80 and imported into IDAV Landmark Editor v3.0 (UC Davis) for landmarking. A set of midline and bilateral Type 1 (defined by anatomy) and Type 3 (defined mathematically) reference landmarks on the braincase (29 landmarks), upper beak (20 landmarks), and mandible (24 landmarks) was developed using the pigeon reference landmarks described in (Young et al. 2017a) as a foundation. After landmarks were applied to 116 F 2 individuals and the cross founders, the coordinates were exported as a NTsys landmark point dataset (*.dta) for geometric morphometric analysis. ...
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Deciphering the genetic basis of vertebrate craniofacial variation is a longstanding biological problem with broad implications in evolution, development, and human pathology. One of the most stunning examples of craniofacial diversification is the adaptive radiation of birds, in which the beak serves essential roles in virtually every aspect of their life histories. The domestic pigeon (Columba livia) provides an exceptional opportunity to study the genetic underpinnings of craniofacial variation because of its unique balance of experimental accessibility and extraordinary phenotypic diversity within a single species. We used traditional and geometric morphometrics to quantify craniofacial variation in an F2 laboratory cross derived from the straight-beaked Pomeranian Pouter and curved-beak Scandaroon pigeon breeds. Using a combination of genome-wide quantitative trait locus scans and multi-locus modeling, we identified a set of genetic loci associated with complex shape variation in the craniofacial skeleton, including beak shape, braincase shape, and mandible shape. Some of these loci control coordinated changes between different structures, while others explain variation in the size and shape of specific skull and jaw regions. We find that in domestic pigeons, a complex blend of both independent and coupled genetic effects underlie three-dimensional craniofacial morphology.
... The phenotypic diversification of domestic species provides a unique and accelerated perspective on evolutionary processes. Artificial selection has proven able to strongly impact the phenotype of domestic taxa over short time frames, producing great amount of morphological disparity often exceeding that of wild counterparts [1][2][3][4][5][6]. Indeed, sustained selection by breeders (e.g. for specific morphological, functional or behavioral features) can generate novel shape variation and contribute to large-scale phenotypic diversification in a few generations [3]. ...
... Conversely, the magnitude of morphological integration (i.e. intensity of the association between traits) has been shown to vary considerably across taxa, which could have consequences on evolvability [26,27] and thus facilitate cranial diversification in domestic taxa [1,3,6,28]. Whether modularity constrains or facilitates evolution is still a subject of debate, and no clear relationship between degree of modularity and shape disparity has yet been demonstrated [1,22,28]. ...
... Crosses represent the centroid value for each group. AON anterior oral-nasal, ORB orbital, MOL molar, CB basicranium, ZP zygomatic-pterygoid, CV cranial vault revert to wild-type features has been observed in several taxa [6,[40][41][42]83]. Further research including a larger number of feral populations is now needed to assess the response of domestic captive-bred horses to natural selection and free-roaming lifestyle. ...
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The potential of artificial selection to dramatically impact phenotypic diversity is well known. Large-scale morphological changes in domestic species, emerging over short timescales, offer an accelerated perspective on evolutionary processes. The domestic horse ( Equus caballus ) provides a striking example of rapid evolution, with major changes in morphology and size likely stemming from artificial selection. However, the microevolutionary mechanisms allowing to generate this variation in a short time interval remain little known. Here, we use 3D geometric morphometrics to quantify skull morphological diversity in the horse, and investigate modularity and integration patterns to understand how morphological associations contribute to cranial evolvability in this taxon. We find that changes in the magnitude of cranial integration contribute to the diversification of the skull morphology in horse breeds. Our results demonstrate that a conserved pattern of modularity does not constrain large-scale morphological variations in horses and that artificial selection has impacted mechanisms underlying phenotypic diversity to facilitate rapid shape changes. More broadly, this study demonstrates that studying microevolutionary processes in domestic species produces important insights into extant phenotypic diversity.
... does and comparisons across species can then reveal commonalities and differences that generate hypotheses of mechanisms behind them. The skull has been a preferred and rich subject of investigation (e.g., Drake & Klingenberg, 2010;G e i g e r et al., Heck et al., 2018;Young et al., 2017), as it is complex and correlated with diverse kinds of sensory, developmental and phylogenetic variables. ...
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Domestication leads to phenotypic characteristics that have been described to be similar across species. However, this "domestication syndrome" has been subject to debate, related to a lack of evidence for certain characteristics in many species. Here we review diverse literature and provide new data on cranial shape changes due to domestication in the European rabbit (Oryctolagus cuniculus) as a preliminary case study, thus contributing novel evidence to the debate. We quantified cranial shape of 30 wild and domestic rabbits using micro-computed tomography scans and three-dimensional geometric morphometrics. The goal was to test (1) if the domesticates exhibit shorter and broader snouts, smaller teeth, and smaller braincases than their wild counterparts; (2) to what extent allometric scaling is responsible for cranial shape variation; (3) if there is evidence for more variation in the neural crest-derived parts of the cranium compared with those derived of the mesoderm, in accordance with the "neural crest hypothesis." Our own data are consistent with older literature records, suggesting that although there is evidence for some cranial characteristics of the "domestication syndrome" in rabbits, facial length is not reduced. In accordance with the "neural crest hypothesis," we found more shape variation in neural crest versus mesoderm-derived parts of the cranium. Within the domestic group, allometric scaling relationships of the snout, the braincase, and the teeth shed new light on ubiquitous patterns among related taxa. This study-albeit preliminary due to the limited sample size-adds to the growing evidence concerning nonuniform patterns associated with domestication.
... Shared developmental pathways lead to correlated morphological variation, or morphological integration [51][52][53][54][55][56][57] . To enable analyses of integration, we added landmark configurations and segmentations to different regions of the adult skull atlas. ...
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Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen ( N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( , 10.25550/3-HXMC ) and GitHub ( ).
... Domesticated animals exhibit phenotypic changes compared to their wild counterparts (Clutton-Brock, 1999). This is the case in domestic birds, as shown in recent works on the skulls of pigeons (Columba livia) and chickens (Gallus gallus) (Young et al., 2017;Stange et al., 2018) and the integument in chickens (Núñez León et al., 2019). In contrast, postcranial anatomy, including limb bones, has rarely been dealt with analytically and globally in domesticated species (see Wayne, 1986 for dogs;Van Grouw, 2018 for an overview for many species), despite having been widely studied in bird evolutionary biology (Middleton & Gatesy, 2000;Dyke & Nudds, 2009;Nudds et al., 2013). ...
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Background Domestication, including selective breeding, can lead to morphological changes of biomechanical relevance. In birds, limb proportions and sternum characteristics are of great importance and have been studied in the past for their relation with flight, terrestrial locomotion and animal welfare. In this work we studied the effects of domestication and breed formation in limb proportions and sternum characteristics in chicken ( Gallus gallus ), mallard ducks ( Anas plathyrhynchos ) and Muscovy ducks ( Cairina moschata ). Methods First, we quantified the proportional length of three long bones of the forelimb (humerus, radius, and carpometacarpus) and the hind limb (femur, tibiotarsus, and tarsometatarsus) in domestic chickens, mallard ducks, and Muscovy ducks and their wild counterparts. For this, we took linear measurements of these bones and compared their proportions in the wild vs. the domestic group in each species. In chicken, these comparisons could also be conducted among different breeds. We then evaluated the proportional differences in the context of static and ontogenetic allometry. Further, we compared discrete sternum characteristics in red jungle fowl and chicken breeds. In total, we examined limb bones of 287 specimens and keel bones of 63 specimens. Results We found a lack of significant change in the proportions of limb bones of chicken and Muscovy duck due to domestication, but significant differences in the case of mallard ducks. Variation of evolvability, allometric scaling, and heterochrony may serve to describe some of the patterns of change we report. Flight capacity loss in mallard ducks resulting from domestication may have a relation with the difference in limb proportions. The lack of variation in proportions that could distinguish domestic from wild forms of chicken and Muscovy ducks may reflect no selection for flight capacity during the domestication process in these groups. In chicken, some of the differences identified in the traits discussed are breed-dependent. The study of the sternum revealed that the condition of crooked keel was not unique to domestic chicken, that some sternal characteristics were more frequent in certain chicken breeds than in others, and that overall there were no keel characteristics that are unique for certain chicken breeds. Despite some similar morphological changes identified across species, this study highlights the lack of universal patterns in domestication and breed formation.
... We know of several examples of rapid evolution suggesting that geneticdevelopmental constraints may be readily broken given the right selection pressures. For instance, under artificial selection domesticated pigeons have evolved a striking diversity of beak size and shape (Young et al. 2017). The island radiations of Darwin's finches, Hawaiian honeycreepers, and Madagascan vangas are further examples of rapid adaptive evolution in response to selection (Lovette et al. 2002;Reddy et al. 2012;Navalón et al. 2020). ...
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Evolution can involve periods of rapid divergent adaptation and expansion in the range of diversity, but evolution can also be relatively conservative over certain timescales due to functional, genetic-developmental, and ecological constraints. One way in which evolution may be conservative is in terms of allometry, the scaling relationship between the traits of organisms and body size. Here, we investigate patterns of allometric conservatism in the evolution of bird beaks with beak size and body size data for a representative sample of over 5000 extant bird species within a phylogenetic framework. We identify clades in which the allometric relationship between beak size and body size has remained relatively conserved across species over millions to tens of millions of years. We find that allometric conservatism is nonetheless punctuated by occasional shifts in the slopes and intercepts of allometric relationships. A steady accumulation of such shifts through time has given rise to the tremendous diversity of beak size relative to body size across birds today. Our findings are consistent with the Simpsonian vision of macroevolution, with evolutionary conservatism being the rule but with occasional shifts to new adaptive zones.
... Various studies have investigated the links between patterns of skeletal integration and modularity at different hierarchical scales (for example, within-species patterns compared with among-species patterns 18,41,42 ). Our findings extend this investigation, providing evidence for variation between patterns of integration in size and shape data, even at the same hierarchical level. ...
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Birds show tremendous ecological disparity in spite of strong biomechanical constraints imposed by flight. Modular skeletal evolution is generally accepted to have facilitated this, with distinct body regions showing semi-independent evolutionary trajectories. However, this hypothesis has received little scrutiny. We analyse evolutionary modularity and ecomorphology using three-dimensional data from across the entire skeleton in a phylogenetically broad sample of extant birds. We find strongly modular evolution of skeletal element sizes within body regions (head, trunk, forelimb and hindlimb). However, element shapes show substantially less modularity, have stronger relationships to ecology, and provide evidence that ecological adaptation involves coordinated evolution of elements across different body regions. This complicates the straightforward paradigm in which modular evolution facilitated the ecological diversification of birds. Our findings suggest the potential for undetected patterns of morphological evolution in even well-studied groups, and advance the understanding of the interface between evolutionary integration and ecomorphology.
Avian skull shape diversity is classically thought to result from selection for structures that are well adapted for distinct ecological functions, but recent work has suggested that allometry is the dominant contributor to avian morphological diversity. If true, this hypothesis would overturn much conventional wisdom regarding the importance of form-function relationships in adaptive radiations, but it is possible that these results are biased by the low taxonomic levels of the clades that have been studied. Using 3D morphometric data from the skulls of a relatively old and ecologically diverse order of birds, the Charadriiformes (shorebirds and relatives), we found that foraging ecology explains more than two-thirds of the variation in skull shape across the clade. However, we also found support for the hypothesis that skull allometry evolves, contributing more to shape variation at the level of the family than the order. Allometry may provide an important source of shape variation on which selection can act over short timescales, but its potential to evolve complicates generalizations between clades. Foraging ecology remains a better predictor of avian skull shape over macroevolutionary timescales.
Vertebrate craniofacial morphogenesis is a highly orchestrated process that is directed by evolutionarily conserved developmental pathways.1,2 Within species, canalized development typically produces modest morphological variation. However, as a result of millennia of artificial selection, the domestic pigeon displays radical craniofacial variation within a single species. One of the most striking cases of pigeon craniofacial variation is the short-beak phenotype, which has been selected in numerous breeds. Classical genetic experiments suggest that pigeon beak length is regulated by a small number of genetic factors, one of which is sex linked (Ku2 locus).3-5 However, the genetic underpinnings of pigeon craniofacial variation remain unknown. Using geometric morphometrics and quantitative trait locus (QTL) mapping on an F2 intercross between a short-beaked Old German Owl (OGO) and a medium-beaked Racing Homer (RH), we identified a single Z chromosome locus that explains a majority of the variation in beak morphology in the F2 population. Complementary comparative genomic analyses revealed that the same locus is strongly differentiated between breeds with short and medium beaks. Within the Ku2 locus, we identified an amino acid substitution in the non-canonical Wnt receptor ROR2 as a putative regulator of pigeon beak length. The non-canonical Wnt pathway serves critical roles in vertebrate neural crest cell migration and craniofacial morphogenesis.6,7 In humans, ROR2 mutations cause Robinow syndrome, a congenital disorder characterized by skeletal abnormalities, including a widened and shortened facial skeleton.8,9 Our results illustrate how the extraordinary craniofacial variation among pigeons can reveal genetic regulators of vertebrate craniofacial diversity.
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Synopsis “Brachycephaly” is generally considered a phenotype in which the facial part of the head is pronouncedly shortened. While brachycephaly is characteristic for some domestic varieties and breeds (e.g., Bulldog, Persian cat, Niata cattle, Anglo-Nubian goat, Middle White pig), this phenotype can also be considered pathological. Despite the superficially similar appearance of “brachycephaly” in such varieties and breeds, closer examination reveals that “brachycephaly” includes a variety of different cranial modifications with likely different genetic and developmental underpinnings and related with specific breed histories. We review the various definitions and characteristics associated with brachycephaly in different domesticated species. We discern different types of brachycephaly (“bulldog-type,” “katantognathic,” and “allometric” brachycephaly) and discuss morphological conditions related to brachycephaly, including diseases (e.g., brachycephalic airway obstructive syndrome). Further, we examine the complex underlying genetic and developmental processes and the culturally and developmentally related reasons why brachycephalic varieties may or may not be prevalent in certain domesticated species. Knowledge on patterns and mechanisms associated with brachycephaly is relevant for domestication research, veterinary and human medicine, as well as evolutionary biology, and highlights the profound influence of artificial selection by humans on animal morphology, evolution, and welfare.
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Intensive artificial selection over thousands of years has produced hundreds of varieties of domestic pigeon. As Charles Darwin observed, the morphological differences among breeds can rise to the magnitude of variation typically observed among different species. Nevertheless, different pigeon varieties are interfertile, thereby enabling forward genetic and genomic approaches to identify genes that underlie derived traits. Building on classical genetic studies of pigeon variation, recent molecular investigations find a spectrum of coding and regulatory alleles controlling derived traits, including plumage color, feather growth polarity, and limb identity. Developmental and genetic analyses of pigeons are revealing the molecular basis of variation in a classic example of extreme intraspecific diversity, and have the potential to nominate genes that control variation among other birds and vertebrates in general.
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Bird beaks are textbook examples of ecological adaptation to diet, but their shapes are also controlled by genetic and developmental histories. To test the effects of these factors on the avian craniofacial skeleton, we conducted morphometric analyses on raptors, a polyphyletic group at the base of the landbird radiation. Despite common perception, we find that the beak is not an independently targeted module for selection. Instead, the beak and skull are highly integrated structures strongly regulated by size, with axes of shape change linked to the actions of recently identified regulatory genes. Together, size and integration account for almost 80% of the shape variation seen between different species to the exclusion of morphological dietary adaptation. Instead, birds of prey use size as a mechanism to modify their feeding ecology. The extent to which shape variation is confined to a few major axes may provide an advantage in that it facilitates rapid morphological evolution via changes in body size, but may also make raptors especially vulnerable when selection pressures act against these axes. The phylogenetic position of raptors suggests that this constraint is prevalent in all landbirds and that breaking the developmental correspondence between beak and braincase may be the key novelty in classic passerine adaptive radiations.
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Introduction: The regular collection of 3-dimensional (3D) imaging data is critical to the development and implementation of accurate predictive models of facial skeletal growth. However, repeated exposure to x-ray-based modalities such as cone-beam computed tomography has unknown risks that outweigh many potential benefits, especially in pediatric patients. One solution is to make inferences about the facial skeleton from external 3D surface morphology captured using safe nonionizing imaging modalities alone. However, the degree to which external 3D facial shape is an accurate proxy of skeletal morphology has not been previously quantified. As a first step in validating this approach, we tested the hypothesis that population-level variation in the 3D shape of the face and skeleton significantly covaries. Methods: We retrospectively analyzed 3D surface and skeletal morphology from a previously collected cross-sectional cone-beam computed tomography database of nonsurgical orthodontics patients and used geometric morphometrics and multivariate statistics to test the hypothesis that shape variation in external face and internal skeleton covaries. Results: External facial morphology is highly predictive of variation in internal skeletal shape ([Rv] = 0.56, P <0.0001; partial least squares [PLS] 1-13 = 98.7% covariance, P <0.001) and asymmetry (Rv = 0.34, P <0.0001; PLS 1-5 = 90.2% covariance, P <0.001), whereas age-related (r(2) = 0.84, P <0.001) and size-related (r(2) = 0.67, P <0.001) shape variation was also highly correlated. Conclusions: Surface morphology is a reliable source of proxy data for the characterization of skeletal shape variation and thus is particularly valuable in research designs where reducing potential long-term risks associated with radiologic imaging methods is warranted. We propose that longitudinal surface morphology from early childhood through late adolescence can be a valuable source of data that will facilitate the development of personalized craniodental and treatment plans and reduce exposure levels to as low as reasonably achievable.
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Modularity describes the case where patterns of trait covariation are unevenly dispersed across traits. Specifically, trait correlations are high and concentrated within subsets of variables (modules), but the correlations between traits across modules are relatively weaker. For morphometric data sets, hypotheses of modularity are commonly evaluated using the RV coefficient, an association statistic used in a wide variety of fields. In this article, I explore the properties of the RV coefficient using simulated data sets. Using data drawn from a normal distribution where the data were neither modular nor integrated in structure, I show that the RV coefficient is adversely affected by attributes of the data (sample size and the number of variables) that do not characterize the covariance structure between sets of variables. Thus, with the RV coefficient, patterns of modularity or integration in data are confounded with trends generated by sample size and the number of variables, which limits biological interpretations and renders comparisons of RV coefficients across data sets uninformative. As an alternative, I propose the covariance ratio (CR) for quantifying modular structure and show that it is unaffected by sample size or the number of variables. Further, statistical tests based on the CR exhibit appropriate type I error rates and display higher statistical power relative to the RV coefficient when evaluating modular data. Overall, these findings demonstrate that the RV coefficient does not display statistical characteristics suitable for reliable assessment of hypotheses of modular or integrated structure and therefore should not be used to evaluate these patterns in morphological data sets. By contrast, the covariance ratio meets these criteria and provides a useful alternative method for assessing the degree of modular structure in morphological data.
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The biologist examining samples of multicellular organisms in anatomical detail must already have an intuitive concept of morphological integration. But quantifying that intuition has always been fraught with difficulties and paradoxes, especially for the anatomically labelled Cartesian coordinate data that drive today’s toolkits of geometric morphometrics. Covariance analyses of interpoint distances, such as the Olson–Miller factor approach of the 1950’s, cannot validly be extended to handle the spatial structure of complete morphometric descriptions; neither can analyses of shape coordinates that ignore the mean form. This paper introduces a formal parametric quantification of integration by analogy with how time series are approached in modern paleobiology. Over there, a finding of trend falls under one tail of a distribution for which stasis comprises the other tail. The null hypothesis separating these two classes of finding is the random walks, which are self-similar, meaning that they show no interpretable structure at any temporal scale. Trend and stasis are the two contrasting ways of deviating from this null. The present manuscript introduces an analogous maneuver for the spatial aspects of ontogenetic or phylogenetic organismal studies: a subspace within the space of shape covariance structures for which the standard isotropic (Procrustes) model lies at one extreme of a characteristic parameter and the strongest growth-gradient models at the other. In-between lies the suggested new construct, the spatially self-similar processes that can be generated within the standard morphometric toolkit by a startlingly simple algebraic manipulation of partial warp scores. In this view, integration and “disintegration” as in the Procrustes model are two modes of organismal variation according to which morphometric data can deviate from this common null, which, as in the temporal domain, is formally featureless, incapable of supporting any summary beyond a single parameter for amplitude. In practice the classification can proceed by examining the regression coefficient for log partial warp variance against log bending energy in the standard thin-plate spline setup. The self-similarity model, for which the regression slope is precisely \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-1,$$\end{document}-1, corresponds well to the background against which the evolutionist’s or systematist’s a-priori notion of “local shape features” can be delineated. Integration as detected by the regression slope can be visualized by the first relative intrinsic warp (first relative eigenvector of the nonaffine part of a shape coordinate configuration with respect to bending energy) and may be summarized by the corresponding quadratic growth gradient. The paper begins with a seemingly innocent toy example, uncovers an unexpected invariance as an example of the general manipulation proposed, then applies the new modeling tactic to three data sets from the existing morphometric literature. Conclusions follow regarding findings and methodology alike.
Scaling of avian brain:body mass throughout the diversification of the class was investigated by analysis of a large collection of adult brain and body masses. Linear regression model analysis of whole-class brain:body scaling resulted in scaling exponents ranging from 0.574 to 0.609, values which exclude several prior empirical and theoretical estimates. Taxonomic level-specific analysis of brain:body scaling was performed by major-axis regression of trait variances partitioned among levels of taxonomic distinction. Brain:body scaling exponents varied markedly among avian orders, but were not easily related to ecological differences among taxa. Avian brain:body scaling exhibited a partial taxon-level effect, in that scaling exponents vary with the taxonomic level of investigation. However, scaling exponents were greatest at the family level, a pattern not consistent with prior ontogenetic or genetic covariance models of trait diversification. Instead, it is suggested that initial diversification among birds was largely through body size diversification, while later diversification of families within orders contained a relatively greater degree of brain size diversification. Avian developmental mode, known to influence avian brain size at hatching, was associated with relatively little variance in adult brain mass. Avian brain:body diversification has occurred relatively uniformly in precocial taxa, while diversification within altricial taxa is marked by a relatively high degree of brain mass diversification among families within orders.