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A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing

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Although reconstruction of the phylogeny of living birds has progressed tremendously in the last decade, the evolutionary history of Neoaves-a clade that encompasses nearly all living bird species-remains the greatest unresolved challenge in dinosaur systematics. Here we investigate avian phylogeny with an unprecedented scale of data: >390,000 bases of genomic sequence data from each of 198 species of living birds, representing all major avian lineages, and two crocodilian outgroups. Sequence data were collected using anchored hybrid enrichment, yielding 259 nuclear loci with an average length of 1,523 bases for a total data set of over 7.8 × 10(7) bases. Bayesian and maximum likelihood analyses yielded highly supported and nearly identical phylogenetic trees for all major avian lineages. Five major clades form successive sister groups to the rest of Neoaves: (1) a clade including nightjars, other caprimulgiforms, swifts, and hummingbirds; (2) a clade uniting cuckoos, bustards, and turacos with pigeons, mesites, and sandgrouse; (3) cranes and their relatives; (4) a comprehensive waterbird clade, including all diving, wading, and shorebirds; and (5) a comprehensive landbird clade with the enigmatic hoatzin (Opisthocomus hoazin) as the sister group to the rest. Neither of the two main, recently proposed Neoavian clades-Columbea and Passerea-were supported as monophyletic. The results of our divergence time analyses are congruent with the palaeontological record, supporting a major radiation of crown birds in the wake of the Cretaceous-Palaeogene (K-Pg) mass extinction.
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LETTER
doi:10.1038/nature15697
A comprehensive phylogeny of birds (Aves) using
targeted next-generation DNA sequencing
Richard O. Prum
1,2
*, Jacob S. Berv
3
*, Alex Dornburg
1,2,4
, Daniel J. Field
2,5
, Jeffrey P. Townsend
1,6
,
Emily Moriarty Lemmon
7
& Alan R. Lemmon
8
Although reconstruction of the phylogeny of living birds has pro-
gressed tremendously in the last decade, the evolutionary history of
Neoaves—a clade that encompasses nearly all living bird species—
remains the greatest unresolved challenge in dinosaur systematics.
Here we investigate avian phylogeny with an unprecedented scale
of data: .390,000 bases of genomic sequence data from each of
198 species of living birds, representing all major avian lineages,
and two crocodilian outgroups. Sequence data were collected using
anchored hybrid enrichment, yielding 259 nuclear loci with an
average length of 1,523 bases for a total data set of over 7.8 3 10
7
bases. Bayesian and maximum likelihood analyses yielded highly
supported and nearly identical phylogenetic trees for all major
avian lineages. Five major clades form successive sister groups to
the rest of Neoaves: (1) a clade including nightjars, other caprimul-
giforms, swifts, and hummingbirds; (2) a clade uniting cuckoos,
bustards, and turacos with pigeons, mesites, and sandgrouse; (3)
cranes and their relatives; (4) a comprehensive waterbird clade,
including all diving, wading, and shorebirds; and (5) a compre-
hensive landbird clade with the enigmatic hoatzin (
Opisthocomus
hoazin
) as the sister group to the rest. Neither of the two main,
recently proposed Neoavian clades—Columbea and Passerea
1
were supported as monophyletic. The results of our divergence
time analyses are congruent with the palaeontological record, sup-
porting a major radiation of crown birds in the wake of the
Cretaceous–Palaeogene (K–Pg) mass extinction.
Birds (Aves) are the most diverse lineage of extant tetrapod verte-
brates. They comprise over 10,000 living species
2
, and exhibit an extra-
ordinary diversity in morphology, ecology, and behaviour
3
. Substantial
progress has been made in resolving the phylogenetic history of birds.
Phylogenetic analyses of both molecular and morphological data sup-
port the monophyletic Palaeognathae (the tinamous and flightless
ratites) and Galloanserae (gamebirds and waterfowl) as successive,
monophyletic sister groups to the Neoaves—a diverse clade including
all other living birds
4
. Resolving neoavian phylogeny has proven to be a
difficult challenge because this radiation was very rapid and deep in
time, resulting in very short internodes
4
.
In the last decade, phylogenetic analyses of large, multilocus data
sets have resulted in the proposal of numerous, novel neoavian rela-
tionships. For example, a clade consisting of diving and wading birds
has been consistently recovered, as well as a large landbird clade in
which falcons and parrots are successive sister groups to the perching
birds
4–8
. Recently, phylogenetic analyses of 48 whole avian genomes
resulted in the proposal of a novel phylogenetic resolution of the initial
branching sequence within Neoaves
1
. Although this genomic study
provided much needed corroboration of many neoavian clades, the
limited taxon sampling precluded further insights into the evolution-
ary history of birds.
It has long been recognized that phylogenetic confidence depends
not only on the number of characters analysed and their rate of evolu-
tion, but also on the number and relationships of the taxa sampled
relative to the nodes of interest
9–11
. Theory predicts that sampling a
single taxon that diverges close to a node of interest will have a far
greater effect on phylogenetic resolution than will adding more char-
acters
11
. Despite using an alignment of .40 million base pairs, sparse
sampling of 48 species in the recent avian genomic analysis may not
have been sufficient to confidently resolve the deep divergences among
major lineages of Neoaves. Thus, expanded taxon sampling is required
to test the monophyly of neoavian clades, and to further resolve the
phylogenetic relationships within Neoaves.
Here, we present a phylogenetic analysis of 198 bird species and
2 crocodilians (Supplementary Table 1) based on loci captured using
anchored enrichment
12
. Our sample includes species of 122 avian
families in all 40 extant avian orders
2
, with denser representation of
non-oscine birds (108 families) than of oscine songbirds (14 families).
Effort was made to include taxa that would break up long phylogenetic
branches, and provide the highest likelihood of resolving short inter-
nodes at the base of Neoaves
11
. We also sampled multiple species
within groups whose monophyly or phylogenetic interrelationships
have been controversial—that is, tinamous, nightjars, hummingbirds,
turacos, cuckoos, pigeons, sandgrouse, mesites, rails, storm petrels,
petrels, storks, herons, hawks, hornbills, mousebirds, trogons, king-
fishers, barbets, seriemas, falcons, parrots, and suboscine passerines.
We targeted 394 loci centred on conserved anchor regions of the
genome that are flanked by more variable regions
12
. We performed all
phylogenetic analyses on a data set of 259 genes with the highest
quality assemblies. The average locus was 1,524 bases in length
(361–2,316 base pairs (bp)), and the total percentage of missing data
was 1.84%. The concatenated alignment contained 394,684 sites. To
minimize overall model complexity while accurately accounting for
substitution processes, we performed a partition model sensitivity
analysis with PartitionFinder
13,14
, and compared a complex partition
model (one partition per locus) to a heuristically optimized (rclust)
partition model. Phylogenetic informativeness (PI) approaches
15,16
provided strong evidence that the phylogenetic utility of our data set
was high, with low declines in PI profiles for individual loci, data set
partitions, and the concatenated matrix (Supplementary Fig. 4). We
estimated concatenated trees in ExaBayes
17
and RAxML
18
using a 75
partition model. Coalescent species trees were estimated with the gene
tree summation methods in STAR
19
, NJst
20
, and ASTRAL
21
from gene
trees estimated with RAxML (see Methods.)
Our concatenated Bayesian analyses resulted in a completely
resolved, well supported phylogeny. All clades had a posterior prob-
ability (PP) of 1, except for a single clade including shoebill
(Balaeniceps) and pelican (PP 5 0.54) (Fig. 1). The concatenated
*These authors contributed equally to this work.
1
Department of Ecology & Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA.
2
Peabody Museum of Natural History, Yale University, New Haven, Connecticut 06520, USA.
3
Department of Ecology and Evolutionary Biology, Fuller Evolutionary Biology Program, Cornell University, and Cornell Laboratory of Ornithology, Ithaca, New York 14853, USA.
4
North Carolina Museum of
Natural Sciences, Raleigh, North Carolina 27601, USA.
5
Department of Geology & Geophysics, Yale University, New Haven, Connecticut 06520, USA.
6
Department of Biostatistics, and Program in
Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA.
7
Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA.
8
Department
of Scientific Computing, Florida State University, Tallahassee, Florida 32306, USA.
G
2015 Macmillan Publishers Limited. All rights reserved
00 MONTH 2015 | VOL 000 | NATURE | 1
Palaeognathae
Galloanserae
Neoaves
Strisores Columbaves Gruiformes Aequorlitornithes
Tinam.
Galliformes
Anseriform.
Apodiform.
Otidimorph.
Columbimorph.
Tinam.Tinaam.
quorlitornnnnnnnnnnnnnnnnnnnnnithes
Ple.
Pli.
Miocene
Oligocene
Eocene
Palaeocene
Upper
Q.
Neogene
Palaeogene
Cretaceous
Streptoprocne
Tauraco
Treron
Corythaeola
Tringa
Theristicus
Chroicocephalus
Burhinus
Ciconia
Columba
Charadrius
Topaza
Phaethornis
Leptotila
Crax
Odontophorus
Nothoprocta
Crypturellus
Tinamus
Coccyzus
Tigrisoma
Columbina
Chordeiles
Ardea
Chaetura
Nyctibius
Colinus
Anas
Anseranas
Morus
Podargus
Leipoa
Oxyura
Caprimulgus
Dromaius
Psophia
Sterna
Balaeniceps
Archilochus
Bonasa
Jacana
Ardeotis
Oceanodroma
Dendrocygna
Anser
Phoenicopterus
Aythya
Haematopus
Oceanites
Mesitornis
Sarothrura
Monias
Recurvirostra
Rollulus
Phalacrocorax
Chauna
Gallus
Phaethon
Leptoptilos
Heliornis
Anhinga
Casuarius
Fregata
Pelecanoides
Hemiprocne
Apteryx
Pelecanus
Rynchops
Aegotheles
Pterodroma
Eurypyga
Centropus
Eurostopodus
Glareola
Rostratula
Syrrhaptes
Fulmarus
Grus
Puffinus
Porphyr io
Uria
Turnix
Pterocles
Pelagodroma
Rhea
Phoebastria
Scopus
Aramus
Ixobrychus
Rollandia
Cuculus
Tapera
Micropygia
Ortalis
Arenaria
Rallus
Limosa
Eudromia
Balearica
Ptilinopus
Steatornis
Numida
Gavia
Spheniscus
Struthio
Pedionomus
70 60 50 40 30 20 10 0
Ma
1
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Aves
Figure 1
|
Phylogeny of birds. Time-calibrated phylogeny of 198 species of
birds inferred from a concatenated, Bayesian analysis of 259 anchored
phylogenomic loci using ExaBayes
17
. Figure continues on the opposite page
from green arrow at the bottom of this panel. Complete taxon data in
Supplementary Table 1. Higher taxon names appear at right. All clades are
supported with posterior probability (PP) of 1.0, except for the Balaeniceps–
Pelecanus clade (PP 5 0.54; clade 109). The five major, successive, neoavian
sister clades are: Strisores (brown), Columbaves (purple), Gruiformes (yellow),
Aequorlitornithes (blue), and Inopinaves (green). Background colours mark
geological periods. Ma, million years ago; Ple, Pleistocene; Pli, Pliocene;
Q., Quaternary. Clade numbers refer to the plot of estimated divergence
dates (Supplementary Fig. 7). Fossil age-calibrated nodes are shown in grey.
Illustrations of representative bird species
30
are depicted by their lineages. See
Supplementary Information for details and further discussion.
RESEARCH LETTER
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2015 Macmillan Publishers Limited. All rights reserved
2|NATURE|VOL000|00MONTH2015
maximum likelihood analysis recovered a single topology that was
identical to the Bayesian tree except for three clades, all of which are
far from the base of Neoaves: the relationships among pigeons; among
skimmers, gulls, and terns; and among pelicans, shoebill, and waders
(Supplementary Fig. 1). Almost all clades in the maximum likelihood
tree were maximally supported with bootstrap scores (BS) of 1.00, but
nine clades within Neoaves (including four of the most inclusive
neoavian clades) received support ,0.70 (Supplementary Fig. 1).
Coalescent species tree analyses produced substantially different
hypotheses for neoavian relationships (Supplementary Fig. 3), but
Ple.
Pli.
Miocene
Oligocene
Eocene
Palaeocene
Upper
Q.
Neogene
Palaeogene
Cretaceous
70 60 50 40 30 20 10 0
Ma
Inopinaves
Neoaves continued
Coraciimorphae
Australaves
Passeriformes
Buteo
Momotus
Trogon
Smithornis
Apaloderma
Indicator
Alcedo
Buccanodon
Corvus
Tockus
Merops
Furnarius
Cathartes
Hymenops
Hirundinea
Thamnophilus
Strix
Jynx
Sylvia
Regulus
Micrastur
Rupicola
Myiobius
Turdus
Sclerurus
Piprites
Rhynchocyclus
Neopelma
Fringilla
Upupa
Todus
Falco
Myrmornis
Cotinga
Deroptyus
Ceratopipra
Lepidocolaptes
Tyrannus
Caracara
Tityra
Picus
Terenura
Oxyruncus
Ibycter
Schiffornis
Capito
Bucco
Accipiter
Psittrichas
Chloroceryle
Galbula
Chelidoptera
Vultur
Probosciger
Coracias
Ramphastos
Sagittarius
Atelornis
Leptosomus
Opisthocomus
Psittacus
Melanopareia
Climacteris
Malurus
Barnardius
Elanus
Eurylaimus
Nestor
Phoeniculus
Megalaima
Pitta
Colius
Urocolius
Menura
Cryptopipo
Cariama
Myrmothera
Elaenia
Neodrepanis
Ptilonorhynchus
Pandion
Tyto
Chunga
Calandrella
Poecile
Lophorina
Calyptomena
Sericulus
Spizella
Pycnonotus
Bucorvus
Acanthisitta
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Accipitriformes
Figure 1
|
Continued.
LETTER RESEARCH
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2015 Macmillan Publishers Limited. All rights reserved
00 MONTH 2015 | VOL 000 | NATURE | 3
most of the discordant clades received conspicuously lower bootstrap
support values (0.07 , BS , 0.30). Quantifying the phylogenetic
informativeness of individual loci
15,16
revealed that these low support
values were not due to homoplasy driven by saturation of nucleotide
states, but rather by the low power of individual loci to resolve the
entire range of internode lengths across the depth of the tree
(Supplementary Figs 4 and 5; see Methods). This result was not unex-
pected. The low phylogenetic information content of individual genes
at deep timescales has been demonstrated to impede phylogenetic
resolution in a coalescent species tree framework
22,23
. Furthermore,
when clades with ,0.75 bootstrap support values in the species trees
are collapsed, the resulting topology is exactly congruent with the con-
catenated Bayesian tree (except for the relationships of tinamous
among palaeognaths; Supplementary Fig. 3). Although coalescent spe-
cies trees account for incomplete lineage sorting, simulations show that
species tree methods based on gene tree summation may not provide
significantly better performance over concatenation methods
22
.
Our phylogeny identifies many new clades, and supports many
phylogenetic relationships proposed in previous studies (see detailed
phylogenetic discussion in the Supplementary Information).
Congruent with all recent studies, the phylogeny places palaeognaths
as the sister group to the rest of birds, and the flying tinamous
(Tinamidae) within the flightless ratites. This tree, however, places
tinamous as the sister group to cassowary and emu alone (Fig. 1, grey).
The phylogeny of Galloanserae is exactly congruent with previous
studies
4
(Fig. 1, red).
Within the monophyletic Neoaves, we recover five major clades,
each of which is the successive sister group to the remaining clades in
the series (Fig. 1). The Strisores includes the nightjars and their noc-
turnal relatives with the diurnal swifts and hummingbirds (Fig. 1,
brown). Four nocturnal lineages—nightjars, a neotropical oilbird-
potoo clade, frogmouths, and owlet-nightjars—form successive sister
groups to the diurnal swift and hummingbird clade.
The Columbaves is a novel clade that consists of two monophyletic
groups recently identified by Jarvis et al.
1
(Fig. 1, purple). A clade
consisting of turacos, bustards, and cuckoos (Otidimorphae) is sister
to a clade consisting of pigeons as the sister group to sandgrouse and
mesites (Columbimorphae). The third neoavian clade consists of a well
recognized monophyletic group of core gruiform birds (Gruiformes;
Fig. 1, yellow), with interrelationships that are consistent with previous
phylogenies
4
.
The Aequorlitornithes is a novel, comprehensive clade of waterbirds,
including all shorebirds, diving birds, and wading birds (Fig. 1, blue).
Within this group, the flamingos and grebes
1,4–6
are the sister group to
shorebirds, and the sunbittern and tropicbirds
1,4,6
are the sister group to
the wading and diving birds (Fig. 1, blue). Other interrelationships
within these groups are extensively congruent with the results in
ref. 4 and the work of others (see Supplementary Information).
The fifth major neoavian clade, which we name Inopinaves, is a very
diverse landbird clade with the samecompositionaspreviouslyrecog-
nized (Telluraves)
1,4–6
,butwiththeenigmatic,neotropicalhoatzin
(Opisthocomus hoazin)asthesistergrouptoallotherlandbirds(Fig.1,
green). The phylogeny of the landbirds shares many points of congruence
with earlier hypotheses, including the relationships of seriemas, falcons,
parrots, and perching birds
1,4–6
,andtheinterrelationshipsamongoscine
songbirds
24
.However,wefindthathawks(Accipitriformes) are the sister
group to a new clade including the rest of the landbirds, to be called
Eutelluraves (see Supplementary Information).
Our divergence time analyses employed 19 phylogenetically and
geologically well-constrained fossil calibrations (following recently
proposed best practices
25
), documenting many deep divergences
within the avian crown group (Fig. 1, grey nodes; see Supplementary
Information). Our analysis supports an extremely rapid radiation of
the avian crown group in the wake of the K–Pg mass extinction event
(Fig. 1, Supplementary Figs. 6 and 7). Although the post-K–Pg radi-
ation hypothesis has long been strongly supported by the avian fossil
record
26,27
, it has so far received little support from molecular diver-
gence time analyses
4,28
. The tempo and mode of the extant avian radi-
ation remains contentious. For example, an alternative calibration
analysis including the fossil Vegavis did not support significantly
different dates of divergence outside of the Galloanserae (see Supple-
mentary Information and Supplementary Figs 10–12). Confident
determination of the age of crown Aves will have to await discoveries
of Mesozoic stem neognaths and palaeognaths, and detailed assess-
ments of the influence of soft maximum bound parameterization on
the age of the deepest avian divergences.
Our results indicate that the recent genome phylogeny
1
may contain
some erroneous relationships induced by long branch attraction from
sparse taxon sampling. Maximum likelihood analysis of our sequence
data pruned down to a phylogenetically equivalent subsample of
48 species produces relationships along the neoavian ‘backbone’
(Supplementary Fig. 8) that are entirely discordant with the phylogeny
based on our full data set (Fig. 1). This reduced taxon analysis recovers
some of the specific features of the recent genome phylogeny by Jarvis
et al.
1
(Supplementary Fig. 8): for example, the placement of the
pigeons, mesites, and sandgrouse (a subclade of Columbea
1
) outside
of the rest of the Neoaves. Differences in tree topology when taxa are
excluded are to be expected if early internodes in Neoaves are very
short. Adding taxa that have diverged near nodes of interest has been
theoretically demonstrated to constrain the possible historical substi-
tution patterns, and increase the accuracy of phylogenetic inference
11
.
By increasing our taxon sampling to include all major avian lineages,
we have minimized the possibility that additional taxon sampling
alone will alter the relationships in our tree.
Jarvis et al.
1
also identified a well supported clade consisting of the
hoatzin (Opisthocomus) as the sister group to a crane (Grus) and a
plover (Charadrius) (total evidence nucleotide tree, BS 5 0.91, 0.96,
respectively). However, Grus and Charadrius were the only species
sampled from two very diverse neoavian orders: Gruiformes, 185 spe-
cies; and Charadriiformes, 385 species
2
. Our results indicate that
Opisthocomus is the most ancient bird lineage (, 64 million years)
consisting of only a single, extant species. Thus, the three taxa placed
in this assemblage by Jarvis et al.
1
comprise three of the most ancient,
and under-sampled lineages within all birds, indicating the strong
possibility of long branch attraction artefacts. By contrast, these same
groups are represented by 26 species in our analysis, and they do not
form an exclusive clade (Fig. 1).
In addition to providing a new backbone for comprehensive avian
supertrees and comparative evolutionary analyses
28
, this new avian
phylogeny supports many interesting hypotheses about avian evolu-
tion. This phylogeny upholds the hypothesis that the ancestor of the
diurnal swifts and hummingbirds evolved from a clade that had been
predominantly nocturnal for ,10 million years. Although humming-
birds have acute near-ultraviolet vision
29
, the effect of extended ances-
tral nocturnality on the evolution of the visual system in this group of
birds is unknown. Our findings also support the emerging pattern that
landbirds evolved from a raptorial grade
1
. The sister group relation-
ships of hawks to the rest of the landbirds, of owls to the diverse
coraciimorph clade, and of seriemas and falcons to the parrots and
passerines indicate the persistence of a raptorial ecology among ances-
tral landbirds. Lastly, the identification of a new, broadly comprehens-
ive waterbird–shorebird clade indicates a striking, and previously
unappreciated, level of evolutionary constraint on the ecological diver-
sification of birds that will be exciting to investigate in the future.
Online Content Methods, along with any additional Extended Data display items
and Source Data,are available in the online version of the paper; references unique
to these sections appear only in the online paper.
Received 3 May; accepted 9 September 2015.
Published online 7 October 2015.
1. Jarvis, E. D. et al. Whole-genomeanalyses resolve early branches in the treeof life of
modern birds. Science 346, 1320–1331 (2014).
RESEARCH LETTER
G
2015 Macmillan Publishers Limited. All rights reserved
4|NATURE|VOL000|00MONTH2015
2. Gill, F. & Donsker, D. IOC World Bird List (v5.1) http://dx.doi.org/10.14344/
IOC.ML.5.1 (2015).
3. Gill, F. B. Ornithology 2nd edn (W. H. Freeman and Co., 1995).
4. Hackett, S. J. et al. A phylogenomic study of birds reveals their evolutionary history.
Science 320, 1763–1768 (2008).
5. Ericson, P. G. P. et al. Diversification of Neoaves: integration of molecular sequence
data and fossils. Biol. Lett. 2, 543–547 (2006).
6. McCormack, J. E. et al. A phylogeny of birds based on over 1,500 loci collected by
target enrichment and high-throughput sequencing. PLoS ONE 8, e54848 (2013).
7. Mayr, G. Paleogene Fossil Birds (Springer, 2009).
8. Mayr, G. Metaves, Mirandornithes, Strisores and other novelties a critical review
of the higher-level phylogeny of neornithine birds. J. Zoological Syst. Evol. Res. 49,
58–76 (2011).
9. Graybeal, A. Is it better to add taxa or characters to a difficult phylogenetic
problem? Syst. Biol. 47, 9–17 (1998).
10. Heath, T. A., Hedtke, S. M. & Hillis, D. M. Taxon sampling and the accuracy of
phylogenetic analyses. Journal of Systematics and Evolution 46, 239–257 (2008).
11. Townsend, J. P. & Lopez-Giraldez, F. Optimal selection of gene and ingroup taxon
sampling for resolving phylogenetic relationships. Syst. Biol. 59, 446–457 (2010).
12. Lemmon, A. R., Emme, S. A. & Lemmon, E. M. Anchored hybrid enrichment for
massively high-throughput phylogenomics. Syst. Biol. 61, 727–744 (2012).
13. Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: combined selection
of partitioning schemes and substitution models for phylogenetic analyses. Mol.
Biol. Evol. 29, 1695–1701 (2012).
14. Berv, J. S. & Prum, R. O. A comprehensive multilocus phylogeny of the neotropical
cotingas (Cotingidae, Aves) with a comparative evolutionary analysis of breeding
system and plumage dimorphism and a revised phylogenetic classification. Mol.
Phylogenet. Evol. 81, 120–136 (2014).
15. Townsend, J. P. Profiling phylogenetic informativeness. Syst. Biol. 56, 222–231
(2007).
16. Townsend, J. P., Su, Z. & Tekle, Y. I. Phylogenetic signal and noise: predicting the
power of a data set to resolve phylogeny. Syst. Biol. 61, 835–849 (2012).
17. Aberer, A. J., Kobert, K. & Stamatakis, A. ExaBayes: massively parallel Bayesian tree
inference for the whole-genome era. Mol. Biol. Evol. 31, 2553–2556 (2014).
18. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis
of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
19. Liu, L., Yu, L., Pearl, D. K. & Edwards, S. V. Estimating species phylogenies using
coalescence times among sequences. Syst. Biol. 58, 468–477 (2009).
20. Liu, L. & Yu, L. Estimating species trees from unrooted gene trees. Syst. Biol. 60,
661–667 (2011).
21. Mirarab, S. et al. ASTRAL: genome-scale coalescent-based species tree estimation.
Bioinformatics 30, i541–i548 (2014).
22. Tonini, J., Moore, A., Stern, D., Shcheglovitova, M. & Or
´
, G. Concatenation and
species tree methods exhibit statistically indistinguishable accuracy under a
range of simulated conditions. PLOS Currents Tree of Life 1, http://dx.doi.org/
10.1371/currents.tol.34260cc27551a527b124ec5f6334b6be (2015).
23. Mirarab, S., Bayzid, M. S. & Warnow, T. Evaluating summary methods for multi-
locus species tree estimation in the presence of incomplete lineage sorting. Syst.
Biol. http://dx.doi.org/10.1093/sysbio/syu063 (2014).
24. Barker, F. K., Cibois, A., Schikler, P., Felsenstein, J. & Cracraft, J. Phylogeny and
diversification of the largest avian radiation. Proc. Natl Acad. Sci. USA 101,
11040–11045 (2004).
25. Parham, J. F. et al. Best practices for justifying fossil calibrations. Syst. Biol. 61,
346–359 (2012).
26. Longrich, N. R., Tokaryk, T. & Field, D. J. Mass extinction of birds at the Cretaceous–
Paleogene (K–Pg) boundary. Proc. Natl Acad. Sci. USA 108, 15253–15257 (2011).
27. Feduccia, A. The Origin and Evolution of Birds 2nd edn (Yale Univ. Press, 1999).
28. Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity
of birds in space and time. Nature 491, 444–448 (2012).
29. Goldsmith, T. H. Hummingbirds see near ultraviolet light. Science 207, 786–788
(1980).
30. del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds
of the World Alive (Lynx Edicions, 2015).
Supplementary Information is available in the online version of the paper.
Acknowledgements The research was supported by W. R. Coe Funds from Yale
University to R.O.P., and by NSF grants to A.R.L. and E.M.L. We thank the ornithology
curators and staff of the following collec tions for granting research access to the
invaluable avian tissue collections that made this work possible: American Museum
of Natural History, Field Museum of Natural History, Royal Ontario Museum,
University of Kansas Museum of Natural History and Biodiversity Research Center,
University of Washington Burke Museum of Natural History, and Yale Peabody
Museum of Natural Hist ory. We thank M. Kortyna and H. Ralicki for contributions to
laboratory work, S. Gullapalli for computational assistance, and N. J. Carriero and
R. D. Bjornson at the Yale University Biomedical High Performance Computing
Center, which is supported by the NIH. Bird illustrations reproduced with
permission from the Handbook of the Birds of the World Alive Online, Lynx Edicions,
Barcelona
30
. The research was aided by discussions wi th R. Bow ie, S. Edwards,
I. Lovette, J. Musser, T. Near, and K. Zyskowski.
Author Contributions R.O.P., J.S.B., A.R.L., and E.M.L. conceived of and designed the
study. R.O.P. selected the taxa studied. A.R.L. selected the loci and designed the probes.
J.S.B., A.R.L., and E.M.L. collected the data. J.S.B. and A.R.L. performed the phylogenetic
analyses. A.D. and J.P.T. performed the phylogenetic informativeness, and signal and
noise analyses. D.J.F. selected fossil taxa for calibration, and J.S.B., D.J.F., and A.D.
designed and performed the dating analyses. R.O.P. wrote the paper with contributions
from all other authors.
Author Information Electronic data files and software are permanently archived at
http://dx.doi.org/10.5281/zenodo.28343. Reprints and permissions information is
available at www.nature.com/reprints. The authors declare no competing financial
interests. Readers are welcome to comment on the online version of the paper.
Correspondence and requests for materials should be addressed to R.O.P.
(richard.prum@yale.edu) or J.S.B. (jsb439@cornell.edu).
LETTER RESEARCH
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2015 Macmillan Publishers Limited. All rights reserved
00 MONTH 2015 | VOL 000 | NATURE | 5
METHODS
Locus selection and probe design. Anchor loci described in ref. 12 were extended
such that each contained approximately 1,350 bp. In some cases neighbouring loci
were joined to form a single locus. Also, loci that performed poorly in ref. 12 were
removed from the locus set. This process produced 394 loci (referred to as the
version 2 vertebrate loci). Genome coordinates corresponding to these regions in
the Gallus gallus genome (galGal3, UCSC genome browser) were identified and
sequences corresponding to this region were extracted (coordinates are available
in the Zenodo archive (http://dx.doi.org/10.5281/zenodo.28343)). In order to
improve the capture efficiency for passerines, we also obtained homologous
sequences for Taeniopygia guttata. After aligning the Gallus and Taeniopygia
sequences using MAFFT
31
, alignments were trimmed to produce the final probe
region alignments (alignments available in the Zenodo archive), and probes were
tiled at approximately 1.5 3 tiling density (probe specification will be made avail-
able upon publication).
Data collection. Data were collected following the general methods of ref. 12
through the Center for Anchored Phylogenomics at Florida State University
(http://www.anchoredphylogeny.com). Briefly, each genomic DNA sample was
sonicated to a fragment size of ,150–350 bp using a Covaris E220 focused-
ultrasonicator with Covaris microTUBES. Subsequently, library preparation and
indexing were performed on a Beckman-Coulter Biomek FXp liquid-handling
robot following a protocol modified from ref. 32. One important modification is
a size-selection step after blunt-end repair using SPRIselect beads (Beckman-
Coulter; 0.9 3 ratio of bead to sample volume). Indexed samples were then pooled
at equal quantities (typically 12–16 samples per pool), and enrichments were
performed on each multi-sample pool using an Agilent Custom SureSelect kit
(Agilent Technologies), designed as specified above. After enrichment, the 12
enrichment pools were pooled in groups of three in equal quantities for sequencing
on four PE150 Illumina HiSeq2000 lanes (three enrichment pools per lane).
Sequencing was performed in the Translational Science Laboratory in the
College of Medicine at Florida State University.
Data processing. Paired-read merging (Merge.java). Typically, between 50% and
75% of sequenced library fragments had an insert size between 150 bp and 300 bp.
As 150 bp paired-end sequencing was performed, this means that the majority of
the paired reads overlap and thus should be merged before assembly. The over-
lapping reads were identified and merged following the methods of ref. 33. In
short, for each degree of overlap for each read we computed the probability of
obtaining the observed number of matches by chance, and selected degree of
overlap that produced the lowest probability, with a P value less than 10
210
required to merge reads. When reads are merged, mismatches are reconciled using
base-specific quality scores, which were combined to form the new quality scores
for the merged read (see ref. 33 for details). Reads failing to meet the probability
criterion were kept separate but still used in the assembly. The merging process
produces three files: one containing merged reads and two containing the
unmerged reads.
Assembly (Assembler.java). The reads were assembled into contigs using an
assembler that makes use of both a divergent reference assembly approach to
map reads to the probe regions and a de novo assembly approach to extend the
assembly into the flanks. The reference assembler uses a library of spaced 20-mers
derived from the conserved sites of the alignments used during probe design. A
preliminary match was called if at least 17 of 20 matches exist between a spaced
kmer and the corresponding positions in a read. Reads obtaining a preliminary
match were then compared to an appropriate reference sequence used for probe
design to determine the maximum number of matches out of 100 consecutive
bases (all possible gap-free alignments between the read and the reference ware
considered). The read was considered mapped to the given locus if at least
55 matches were found. Once a read is mapped, an approximate alignment posi-
tion was estimated using the position of the spaced 20-mer, and all 60-mers
existing in the read were stored in a hash table used by the de novo assembler.
The de novo assembler identifies exact matches between a read and one of the 60-
mers found in the hash table. Simultaneously using the two levels of assembly
described above, the three read files were traversed repeatedly until an entire pass
through the reads produced no additional mapped reads.
For each locus, mapped reads were then clustered into clusters using 60-mer
pairs observed in the reads mapped to that locus. In short, a list of all 60-mers
found in the mapped reads was compiled, and the 60-mers were clustered if found
together in at least two reads. The 60-mer clusters were then used to separate the
reads into clusters for contig estimation. Relative alignment positions of reads
within each cluster were then refined in order to increase the agreement across
the reads. Up to one gap was also inserted per read if needed to improve the
alignment. Note that given sufficient coverage and an absence of contamination,
each single-copy locus should produce a single assembly cluster. Low coverage
(leading to a break in the assembly), contamination, and gene duplication, can all
lead to an increased number of assembly clusters. A whole-genome duplication,
for example, would increase the number of clusters to two per locus.
Consensus bases were called from assembly clusters as follows. For each site an
unambiguous base was called if the bases present were identical or if the poly-
morphism of that site could be explained as sequencing error, assuming a binomial
probability model with the probability of error equal to 0.1 and alpha equal to 0.05.
If the polymorphism could not be explained as sequencing error, the ambiguous
base was called that corresponded to all of the observed bases at that site (for
example, ‘R’ was used if ‘A’ and ‘G’ were observed). Called bases were soft-masked
(made lowercase) for sites with coverage lower than five. A summary of the
assembly resu lts is presen ted in a spreadsheet in the electronic data archive (http://
dx.doi.org/10.5281/zenodo.28343; Prum_AssemblySummary_Summary.xlsx).
Contamination filtering (IdentifyGoodSeqsViaReadsMapped.r, GatherALL
ConSeqsWithOKCoverage.java). In order to filter out possible low-level contami-
nants, consensus sequences derived from very low coverage assembly clusters
(,10 reads) were removed from further analysis. After filtering, consensus
sequences were grouped by locus (across individuals) in order to produce sets
of homologues.
Orthology (GetPairwiseDistanceMeasures.java, plotMDS5.r). Orthology was
then determined for each locus as follows. First, a pairwise distance measure
was computed for pairs of homologues. To compute the pairwise distance between
two sequences, we computed the percent of 20-mers observed in the two sequences
that were found in both sequences. Note that the list of 20-mers was constructed
from consecutive 20-mers as well as spaced 20-mers (every third base), in order to
allow increased levels of sequence divergence. Using the distance matrix, we
clustered the sequences using a neighbour-joining algorithm, but allowing at most
one sequence per species to be in a given cluster. Clusters containing fewer than
50% of the species were removed from downstream processing.
Alignment (MAFFT). Sequences in each orthologous set were aligned using
MAFFT v7.023b
31
with “–genafpair” and “–maxiterate 1000” flags.
Alignment Trimming (TrimAndMaskRawAlignments3). The alignment for
each locus was then trimmed/masked using the following procedure. First, each
alignment site was identified as ‘good if the most common character observed was
present in .40% of the sequences. Second, 20 bp regions of each sequence that
contained ,10 good sites were masked. Third, sites with fewer than 12 unmasked
bases were removed from the alignment. Lastly, entire loci were removed if both
outgroups or more than 40 taxa were missing. This filter yielded 259 trimmed loci
containing fewer than 2.5% missing characters overall.
Model selection and phylogenetic inference. To minimize the overall model
complexity while accurately accounting for substitution processes, we performed
a partition-model sensitivity analysis with the development version of
PartitionFinder v2.0 (ref. 13), sensu
14
, and compared a complex partition-model
(one partition per gene) to a heuristically optimized (relaxed clustering with the
RAxML option for accelerated model selection) partition-model using BIC. Based
on a candidate pool of potential partitioning strategies that spanned a single
partition for the entire data set to a model allowing each locus to represent a
unique partition, the latter approach suggested that 75 partitions of our data set
represented the best-fitting partitioning scheme, which reduced the number of
necessary model parameters by 71%, and hugely decreased computation time.
We analysed each individual locus in RAxML v8.0.20 (ref. 18), and then the
concatenated alignment, using the two partitioning strategies identified above
with both maximum likelihood and Bayesian based approaches in RAxML
v8.0.20, and ExaBayes v1.4.2 9 (ref. 34). For each RAxML analysis, we executed
100 rapid bootstrap inferences and thereafter a thorough ML search using a
GTR1C
4
model of nucleotide substitution for each data set partition. Although
this may potentially over-parameterize a partition with respect to substitution
model, the influence of this form of model over-parameterization has been found
to be negligible in phylogenetic inference
35
. For the Bayesian analyses, we ran four
Metropolis-coupled ExaBayes replicates for 10 million generations, each with
three heated chains, and sampling every 1,000 generations (default tuning and
branch swap parameters; branch lengths among partitions were linked).
Convergence and proper sampling of the posterior distribution of parameter
values were assessed by checking that the effective sample sizes of all estimated
parameters and branch lengths were greater than 200 in the Tracer v1.6 software
36
(most were greater than 1,000), and by using the ‘sdsf and ‘postProcParam’ tools
included with the ExaBayes package to ensure the average standard deviation of
split frequencies and potential scale reduction factors across runs were close to
zero and one, respectively. Finally, to check for convergence in topology and clade
posterior probabilities, we summarized a greedily refined majority-rule consensus
tree (default) from 10,000 post burn-in trees using the ExaBayes ‘consense’ tool for
each run independently and then together. Analyses of the reduced data set refer-
enced in the main text were conducted using the same partition-model as the full
data set.
RESEARCH LETTER
G
2015 Macmillan Publishers Limited. All rights reserved
To explore variation in gene tree topology and to look for outliers that might
influence combined analysis, we calculated pairwise Robinson-Foulds
37
(RF) and
Matching Splits (MS) tree distances implemented in TreeCmp
38
. We then visua-
lized histograms of tree distances and multidimensional scaling plots in R,
and estimated neighbour-joining ‘trees-of-trees’ in the Phangorn R package
sensu lato
39,40
. Using RF and MS distances, outlier loci were identified as those
that occurred in the top 10% of pairwise distances for .30 comparisons to other
loci (,10%) in the data set. We also identified putative outlier loci using the
kdetrees.complete function of the kdetrees R package
41
. All three methods iden-
tified 13 of the same loci as potential outliers; however removal of these loci from
the analysis had no effect on estimating topology or branch lengths.
Coalescent species tree analyses. Although fully parametric estimation (for
example, *BEAST, see ref. 42) of a coalescent species tree with hundreds of genes
and hundreds of taxa is not currently possible, we estimated species trees using
three gene-tree summation methods that have been shown to be statistically
consistent under the multispecies coalescent model
43
. First, we used the
STRAW web server
44
to estimate bootstrapped species trees using the STAR
19
and NJ-ST
20
algorithms (also available through STRAW). The popular MP-
EST
45
method cannot currently work for more than ,50 taxa. STAR takes rooted
gene trees and uses the average ranks of coalescence times
19
to build a distance
matrix from which a species tree is computed with the neighbour-joining
method
46
. By contrast, NJst applies the neighbour-joining method to a distance
matrix computed from average gene-tree internode distances, and relaxes the
requirement for input gene trees to be rooted
20
.
We also summarized a species tree with the ASTRAL 4.7.6 algorithm. With
simulated data, ASTRAL has been shown to outperform concatenation or other
summary methods under certain amounts of incomplete lineage sorting
21
. For
very large numbers of taxa and genes, ASTRAL uses a heuristic search to find the
species tree that agrees with the largest number of quartet trees induced by the set
of input gene trees. For analysis with ASTRAL, we also attempted to increase the
resolution of individual gene trees (Supplementary Fig. 2) by generating supergene
alignments using the weighted statistical binning pipeline of refs 47, 48 with a
bootstrap score of 0.75 as a bin threshold.
STAR, NJst (not shown), and the binned ASTRAL (Supplementary Fig. 3)
analysis produced virtually identical inferences when low support branches
(,0.75) were collapsed, and differed only with respect to the resolution of a few
branches. NJst resolved the Passeroidea (Fringilla plus Spizella) as the sister group
to a paraphyletic sample of Sylvioidea (Calandrella, Pycnonotus, and Sylvia), while
STAR does not resolve this branch. Comparing STAR/NJst to ASTRAL, we find
five additional differences: (1) within tinamous, STAR/NJst resolves Crypturellus
as sister to the rest of the tinamous, whereas ASTRAL resolves Crypturellus as
sister to Tinamus (similar to ExaBayes/RAxML); (2) STAR/NJst resolves pigeons
as sister to a clade containing Mesitornithiformes and Pteroclidiformes, while
ASTRAL does not resolve these relationships; (3), STAR/NJst fails to resolve
Oxyruncus and Myiobius as sister genera, while ASTRAL does (similar to
RAxML/ExaBayes); (4), in STAR/NJst, bee-eaters (Merops) are resolved as the
sister group to coraciiforms (congruent with ref. 4), while ASTRAL resolves
bee-eaters as sister to the rollers (Coracias) (similar to RAxML/ExaBayes);
(5) lastly, in STAR/NJst, buttonquail (Turnix) is resolved as sister to the most
inclusive clade of Charadriiformes not including Burhinus, Charadrius,
Haematopus, and Recurvirostra, while in ASTRAL, buttonquail is resolved as sister
to a clade containing Glareola, Uria, Rynchops, Sterna, and Chroicocephalus (sim-
ilar to RAxML/ExaBayes).
Although lower level relationships detected with concatenation are generally
recapitulated in the species trees, few of the higher level, or interordinal, relation-
ships are resolved. This lack of resolution of the gene-tree species-tree based
inferences relative to the inferences based on concatenation are not surprising,
as it is increasingly recognized that the phylogenetic information content
required to resolve the gene-tree histories of individual loci becomes scant at
deep timescales
47
. Despite our extensive taxon sampling and the slow rate of
nucleotide substitution that characterizes loci captured using anchored enrich-
ment
12
, no single locus was able to fully resolve a topology, and this lack of
information will challenge the accuracy of any coalescent-based summary
approach relative to concatenation
49–54
. Finally, all summation methods tested
here assume a priori that the only source of discordance among gene trees is deep
coalescence, and violations of this assumption may introduce systematic error in
phylogeny estimation
54
.
Phylogenetic informativeness. Site-specific evolutionary rates, l
i…j
, were calcu-
lated for each locus using the program HyPhy
55
in the PhyDesign web interface
56
in conjunction with a guide chronogram generated by a nonparametric rate
smoothing algorithm
57
applied to our concatenated RAxML tree. Using these rates
to predict whether an alignment will yield correct, incorrect, or no resolution of a
given node, we quantified the probability of phylogenetically informative changes
(y)
16
contributing to the resolution of the earliest divergences in Neoaves.
Estimates generated under a three character state model
58
reveal that the majority
of loci have a strong probability of y, and suggest a high potential for most loci and
partitions containing multiple loci (assigned by PartitionFinder) to correctly
resolve this internode. The potential for resolution as a consequence of phylogen-
etic signal is therefore high relative to the potential for saturation and misleading
inference induced by stochastic changes along the subtending lineages (Supple-
mentary Fig. 4a).
To assess the information content of the loci across the entire topology, we
profiled their phylogenetic informativeness (PI)
15
, (Supplementary Fig. 4b). There
was considerable variation in PI across loci (Supplementary Fig. 4). In all cases, the
loci with the lowest values of y are categorized by substantially lower (60–90%)
values of PI, rather than sharp declines in their PI profiles. The absence of a sharp
decline in the PI profile suggests that a lack of phylogenetic information, rather
than rapid increasesin homoplasious sites, underlie low values of the probability of
signal y
59
.
Because declines in PI can be attributed to increases in homoplasious site
patterns
59
, we further assessed the phylogenetic utility of data set partitions by
quantifying the ratio of PI at the most recent common ancestor of Neoaves to the
PI at the most recent common ancestor of Aves (Supplementary Fig. 4c). Values of
this ratio that are less than 1 correspond to a rise in PI towards the root. Values
close to 1 correspond to fairly uniform PI. Values greater than 1 correspond to a
decline in PI towards the root. Sixty-six out of 75 partitions demonstrated less than
a 50% percent decline in PI, and only six partitions demonstrated a decline of PI
greater than 75% (Supplementary Fig. 4c). As all but a few nodes in this study
represent divergences younger than the crown of Neoaves, these ratios of PI
suggest that the predicted impact of homoplasy on our topological inferences
should be minimal.
As PI profiles do not directly predict the impact of homoplasious site patterns
on topological resolution
16,60
, we evaluated probabilities of y for focal nodes using
both the concatenated data set as well as individual loci that span the variance in
locus lengths. Concordant with expectations from the PI profiles, all quantifica-
tions strongly support the prediction that homoplasy will have a minimal impact
on topological resolution for the concatenated data set across a range of tree depths
and internode distances (y 5 1.0 for all nodes), while individual loci vary in their
predicted utility (Supplementary Fig. 4d). As the guide tree does not represent a
true known tree, we additionally quantified y across a range of tree depths and
internode distances to test if our predictions of utility are in line with general
trends in the data. Concordant with our results above, the concatenated data set
is predicted to be of high phylogenetic utility at all timescales (y 5 1.0 for all
nodes), while the utility of individual loci begins to decline for small internodes
at deep tree depths (Supplementary Fig. 5).
Estimating a time-calibrated phylogeny. We estimated a time-calibrated tree
with a node dating approach in BEAST 1.8.1 (ref. 42) that used 19 well justified
fossil calibrations phylogenetically placed by rigorous, apomorphy-based dia-
gnoses (see the descriptions of avian calibration fossils in the Supplementary
Information). We used a starting tree topology based on the ExaBayes inference
(Fig. 1), and prior node age calibrations that followed a lognormal parametric
distribution based on occurrences of fossil taxa. To prevent BEAST from exploring
topology space and only allow estimation of branch lengths, we turned off the
subtree-slide, Wilson–Balding, and narrow and wide exchange operators
61,62
.
Finally, we applied a birth–death speciation model with default priors.
As rates of molecular evolution are significantly variable across certain bird
lineages
63–65
, we applied an uncorrelated relaxed clock (UCLN) to each partition
of the data set where rates among branches are distributed according to a lognor-
mal distribution
66
. All dating analyses were performed without crocodilian out-
groups to reduce the potential of extreme substitution rate heterogeneity to bias
rate and consequent divergence time estimates of the UCLN model
67
.
All calibrations were modelled using soft maximum age bounds to allow for the
potential of our data to overwhelm our user-specified priors
68
. Soft maximum
bounds are the preferred method for assigning upper limits on the age of phylo-
genetic divergences
69
. As effective priors necessarily reflect interactions between
user specified priors, topology, and the branching-model, they may not precisely
reflect the user-specified priors
70
. To correct for this potential source of error, we
carefully examined the effective calibration priors by first running the prepared
BEAST XML without any nucleotide data (until all ESS values were above 200).
We then iteratively adjusted our user-defined priors until all of the effective priors
(as examined in the Tracer software) reflected the intended calibration densities.
Finally, using the compare.phylo function in the Phyloch R package, we examined
how the inclusion of molecular data influenced the divergence time estimates
relative to the effective prior (Supplementary Fig. 9; see below).
Defining priors. Our initial approach was to set a prior’s offset to the age of its
associated fossil; the mean was then manually adjusted such that 95% of the
LETTER RESEARCH
G
2015 Macmillan Publishers Limited. All rights reserved
calibration density fell more recently than the K–Pg boundary at 65 Ma (million
years ago) (the standard deviation was fixed at 1 Ma). In general, priors con-
structed this way generated calibrationdensitiesthat specified their highest density
peak (their mode) about 3–5 million years older than the age of the offset.
We applied a loose gamma prior to the node reflecting the most recent common
ancestor of crown birds—we used an offset of 60.5 Ma (the age of the oldest known
definitive, uncontroversial crown bird fossil; the stem penguin Waimanu), and
adjusted the scale and shape of the prior such that 97.5% of the calibration density
fell more recently than 86.5 Ma
71
(see below and Supplementary Information for
discussion of the .65 Ma putative crown avian Vegavis). This date (86.5 Ma)
reflects the upper bound age estimate of the Niobrara Formation—one of many
richly fossiliferous Mesozoic deposits exhibiting many crownward Mesozoic stem
birds, without any trace of avian crown group representatives. The Niobrara, in
particular, has produced hundreds of stem birds and other fragile skeletons, with-
out yielding a single crown bird fossil, and therefore represents a robust choice for
a soft upper bound for the root divergence of the avian crown
71–73
. Previous soft
maxima employed for this divergence have arbitrarily selected the age of other
Mesozoic stem avians (that is, Gansus yumenensis, 110 Ma) that are phylogeneti-
cally stemward of the Niobrara taxa
28
. Although the implementation of very
ancient soft maxima such as the age of Gansus are often done in the name of
conservatism, the extremely ancient divergence dates yielded by such analyses
illustrate the misleading influence of assigning soft maxima that are vastly too
old to be of relevance to the divergence of crown group birds
74
. However, this
problem has been eliminated in some more recent analyses
75
.
All of the fossil calibrations employed in our analysis represent neognaths;
rootward divergences within Aves (for example the divergence between
Palaeognathae and Neognathae, and Galloanserae and Neoaves) cannot be con-
fidently calibrated due to a present lack of fossils representing the palaeognath,
neognath, galloanserine, and neoavian stem groups. As such, the K–Pg soft bound
was only applied to comparatively apical divergences within neognaths. Although
the question of whether major neognath divergences occurred during the
Mesozoic has been the source of controversy
76–78
, renewed surveys of Mesozoic
sediments for definitive crown avians or even possible crown neoavians have been
unsuccessful (with the possible exception of Vegavis; see Supplementary
Information), and together with recent divergence dating analyses have cast doubt
on the presence of neoavian subclades before the K–Pg mass extinction
1,74,79
.
Further, recent work has demonstrated the tendency of avian divergence estimates
to greatly exceed uninformative priors, resulting in spuriously ancient divergence
dating results (for example, refs 28, 75, 76, 80). These results motivated our
implementation of the 65 Ma soft bound for our neoavian calibrations.
Contrary to expectation, when we compared the effective prior on the entire tree
to the final summary derived from the posterior distribution of divergence times
(Supplementary Fig. 9), we found no overall trend of posterior estimated ages post-
dating prior calibrations. In fact, the inclusion of our molecular data decreases the
inferred ages of almost all of the deepest nodes in our tree. A similar result has been
obtained for mammals by using large amounts of nuclear DNA sequences
81
.
Future work investigating the interplay of the density of genomic sampling and
the application of various calibration age priors will be indispensible for sensitivity
analyses to help us further develop a robust timescale of avian evolution. However,
the pattern of posterior versus prior age estimates observed in our study raises the
prospect that the new class of data used in this study (that is, semi-conserved
anchor regions) may exhibit some immunity to longstanding problems associated
with inferring avian divergence times, such as systematically over-estimating the
antiquity of extant avian clades.
Implementing BEAST and summarizing a final calibrated tree. In addition to
making predictions about the phylogenetic utility of a locus or partition towards
topological resolution, PI profiles have recently also been used to mitigate the
influence of substitution saturation on divergence time estimates
82
. Given the
variance in PI profile shapes for captured loci and their subsequent partition
assignments (Supplementary Fig. 4c), and observations that alignments and sub-
sets of data alignments characterized by high levels of homoplasy can mislead
branch length estimation
83,84
, we limited our divergence time estimates to 36
partitions that did not exhibit a decline in informativeness towards the root of
the tree. We ran BEAST on each partition separately until parameter ESS values
were greater than 200 (most were greater than 1,000) to ensure adequate posterior
sampling of each parameter value. After concatenating 10,000 randomly sampled
post burn-in trees from each of these completed analyses, we summarized a final
MCC tree with median node heights in TreeAnnotator v1.8.1 (ref. 42).
Supplementary Fig. 6 shows the full, calibrated Bayesian tree (Fig. 1) with 95%
HPD confidence intervals on the node ages, and Supplementary Fig. 7 shows the
distribution of estimated branching times, ranked by median age (using clade
numbers from Fig. 1). All computations were carried out on 64-core PowerEdge
M915 nodes on the Louise Linux cluster at the Yale University Biomedical High
Performance Computing Center.
Data reporting. No statistical methods were used to predetermine sample size.
31. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software
version 7: improvements in performance and usability. Mol. Biol. Evol. 30,
772–780 (2013).
32. Meyer, M. & Kircher M. Illumina sequencing library preparation for highly
multiplexed target capture and sequencing. Cold Spring Harb Protoc. http://
dx.doi.org/10.1101/pdb.prot5448 (2010).
33. Rokyta, D. R., Lemmon, A. R., Margres, M. J. & Arnow, K. The venom-gland
transcriptome of the eastern diamondback rattlesnake (Crotalus adamanteus).
BMC Genomics 13, 312 (2012).
34. Misof, B. et al. Phylogenomics resolves the timing and pattern of insect evolution.
Science 346, 763–767 (2014).
35. Dornburg, A., Santini, F. & Alfaro, M. E. The influence of model averaging on clade
posteriors: an example using the triggerfishes (Family Balistidae). Syst. Biol. 57,
905–919 (2008).
36. Tracer. v1.6 http://beast.bio.ed.ac.uk/Tracer (2014).
37. Robinson, D. F. & Foulds, L. R. in Combinatorial Mathematics VI in Lecture Notes in
Mathematics, Vol. 748 (eds Horadam A. F. & Wallis W. D.) Ch. 12 119–126
(Springer, 1979).
38. Bogdanowicz, D., Giaro, K. & Wro
´
bel, B. TreeCmp: comparison of trees in
polynomial time. Evol. Bioinform. 8, 475–487 (2012).
39. Nye, T. M. W. Trees of Trees: an approach to comparing multiple alternative
phylogenies. Syst. Biol. 57, 785–794 (2008).
40. Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593
(2011).
41. Weyenberg, G., Huggins, P. M., Schardl, C. L., Howe, D. K. & Yoshida, R. KDETREES:
non-parametric estimation of phylogenetic tree distributions. Bioinformatics 30,
2280–2287 (2014).
42. Drummond, A. J., Suchard,M. A., Xie, D. & Rambaut, A. Bayesianphylogenetics with
BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
43. Rannala, B. & Yang, Z. Bayes estimation of species divergence times and ancestral
population sizes using DNA sequences from multiple loci. Genetics 164,
1645–1656 (2003).
44. Shaw, T. I., Ruan, Z., Glenn, T. C. & Liu, L. STRAW: species tree analysis web server.
Nucleic Acids Res. 41, W238–W241 (2013).
45. Liu, L., Yu, L. & Edwards, S. A maximum pseudo-likelihood approach for estimating
species trees under the coalescent model. BMC Evol. Biol. 10, 302 (2010).
46. Saitou, N. & Nei, M. The neighbor-joining method: a new method for
reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).
47. Mirarab, S., Bayzid, M. S., Boussau, B. & Warnow, T. Statistical binning enables an
accurate coalescent-based estimation of the avian tree. Science 346 (2014).
48. Mirarab, S., Bayzid, M. S. & Warnow, T. Evaluating summary methods for
multilocus species tree estimation in the presence of incomplete lineage sorting.
Syst. Biol., (2014).
49. Bayzid, M. S. & Warnow, T. Naive binning improves phylogenomic analyses.
Bioinformatics 29, 2277–2284 (2013).
50. DeGiorgio, M. & Degnan, J. H. Fast and consistent estimation of species trees using
supermatrix rooted triples. Mol. Biol. Evol. 27, 552–569 (2010).
51. Kimball, R. T., Wang, N., Heimer-McGinn, V., Ferguson, C. & Braun, E. L. Identifying
localized biases in large datasets: a case study using the avian tree of life. Mol.
Phylogenet. Evol. 69, 1021–1032 (2013).
52. McCormack, J. E. et al. A phylogeny of birds based on over 1,500 loci collected by
target enrichment and high-throughput sequencing. PLoS ONE 8, e54848 (2013).
53. Springer, M. S. & Gatesy, J. Land plant origins and coalescence confusion. Trends
Plant Sci. 19, 267–269 (2014).
54. Tonini J., Moore A., Stearn D., Shcheglovitova M. & Ortı
´
, G. Concatenation and
species tree methods exhibit statistically indistinguishable accuracy under a
range of simulated conditions. PLOS Currents Tree of Life 1 (2015).
55. Pond, S. L. K. & Muse, S. V. in Statistical Methods in Molecular Evolution (ed. Nielsen,
R.) 125–181 (Springer, 2005).
56. Lo
´
pez-Gira
´
ldez, F. & Townsend, J. P. PhyDesign: an online application for profiling
phylogenetic informativeness. BMC Evol. Biol. 11, 152 (2011).
57. Sanderson, M. A nonparametric approach to estimating divergence times in the
absence of rate constancy. Mol. Biol. Evol. 14, 1218 (1997).
58. Simmons, M. P., Carr, T. G. & O’Neill, K. Relative character-state space, amount of
potential phylogenetic information, and heterogeneity of nucleotide and amino
acid characters. Mol. Phylogenet. Evol. 32, 913–926 (2004).
59. Townsend, J. P. & Leuenberger, C. Taxon sampling and the optimal rates of
evolution for phylogenetic inference. Syst. Biol. 60, 358–365 (2011).
60. Klopfstein, S., Kropf, C. & Quicke, D. L. J. An evaluation of phylogenetic
informativeness profiles and the molecular phylogeny of Diplazontinae
(Hymenoptera, Ichneumonidae). Syst. Biol. 59, 226–241 (2010).
61. Drummond, A. J. & Bouckaret, R. R. Bayesian Evolutionary Analysis With BEAST
(Cambridge Univ. Press, 2015).
62. Hsiang, A. Y. et al. The origin of snakes: revealing the ecology, behavior, and
evolutionary history of early snakes using genomics, phenomics, and the fossil
record. BMC Evol. Biol. 15, 87 (2015).
63. Phillips, M. J., Gibb, G. C., Crimp, E. A. & Penny, D. Tinamous and moa flock
together: mitochondrial genome sequence analysis reveals independent losses of
flight among ratites. Syst. Biol. 59, 90–107 (2010).
64. Pereira, S. L. & Baker, A. J. A mitogenomic timescale for birds detects variable
phylogenetic rates of molecular evolution and refutes the standard molecular
clock. Mol. Biol. Evol. 23, 1731–1740 (2006).
RESEARCH LETTER
G
2015 Macmillan Publishers Limited. All rights reserved
65. Nam,K. et al. Molecular evolution of genesin avian genomes. Genome Biol. 11, R68
(2010).
66. Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics
and dating with confidence. PLoS Biol. 4, e88 (2006).
67. Dornburg, A., et al. Relaxed clocks and inferences of heterogeneous patterns of
nucleotide substitution and divergence time estimates across whales and
dolphins (Mammalia: Cetacea). Mol. Biol. Evol. 29, 721–736 (2012).
68. Yang, Z. & Rannala, B. Bayesian estimation of species divergence times under a
molecular clock using multiple fossil calibrations with soft bounds. Mol. Biol. Evol.
23, 212–226 (2006).
69. Ho, S. Y. W. Calibrating molecular estimates of substitution rates and divergence
times in birds. J. Avian Biol. 38, 409–414 (2007).
70. Heled, J. & Drummond, A. J. Calibrated tree priors for relaxed phylogenetics and
divergence time estimation. Syst. Biol. 61, 138–149 (2012).
71. Benton, M. J. & Donoghue, P. C. J. Paleontological evidence to date the tree of life.
Mol. Biol. Evol. 24, 26 (2007).
72. Clarke, J. A. Morphology, phylogenetic taxonomy, and systematics of Ichthyornis
and Apatornis (Avialae: Ornithurae). Bull. Am. Mus. Nat. Hist. 286, 1–179 (2004).
73. Field, D. J., LeBlanc, A., Gau, A. & Behlke, A. D. B. Pelagic neonatal fossils support
viviparity and precocial life history of Cretaceous mosasaurs. Palaeontology 58,
401–407 (2015).
74. Mayr, G. The age of the crown group of passerine birds and its evolutionary
significance molecular calibrations versus the fossil record. Syst. Biodivers. 11,
7–13 (2013).
75. Jetz, W. et al. Global distribution and conservation of evolutionary distinctness in
birds. Curr. Biol. 24, 919–930 (2014).
76. Hedges, S. B., Parker, P. H., Sibley, C. G. & Kumar, S. Continental breakup and the
ordinal diversification of birds and mammals. Nature 381, 226–229 (1996).
77. Benton, M. J. Early origins of modern birds and mammals: molecules vs.
morphology. Bioessays 21, 1043–1051 (1999).
78. Hope, S. in Mesozoic Birds: Above the Heads of Dinosaurs (eds Chiappe L. M. &
Witmer L. M.) 339–388 (Univ. of California Press, 2002).
79. Longrich, N. R., Tokaryk, T. & Field, D. J. Mass extinction of birds at the Cretaceous–
Paleogene (K–Pg) boundary. Proc. Natl Acad. Sci. USA 108, 15253–15257 (2011).
80. Baker, A. J., Pereira, S. L. & Paton, T. A. Phylogenetic relationships and divergence
times of Charadriiformes genera: multigene evidence for the Cretaceous origin of
at least 14 clades of shorebirds. Biol. Lett. 3, 205–209 (2007).
81. dos Reis, M. et al. Phylogenomic datasets provide both precision and accuracy in
estimating the timescale of placental mammal phylogeny. Proc. R. Soc. B 279,
3491–3500 (2012).
82. Dornburg, A., Townsend, J. P., Friedman, M. & Near, T. J. Phylogenetic
informativeness reconciles ray-finned fish molecular divergence times. BMC Evol.
Biol. 14, 169 (2014).
83. Brandley, M. C. et al. Accommodating heterogenous rates of evolution in molecular
divergence dating methods: an example using intercontinental dispersal of
Plestiodon (Eumeces) lizards. Syst. Biol. 60, 3–15 (2011).
84. Phillips, M. J. Branch-length estimation bias misleads molecular dating for a
vertebrate mitochondrial phylogeny. Gene 441, 132–140 (2009).
LETTER RESEARCH
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... The former includes seriemas (Cariamiformes), falcons (Falconiformes), parrots (Psittaciformes), and passerines (Passeriformes), whereas the latter comprises accipitriform diurnal birds of prey, owls (Strigiformes), coraciiform and piciform birds (rollers, woodpeckers, and allies), as well as the enigmatic courol or cuckooroller, Leptosomus discolor (Leptosomiformes). This latter species was long allied with true rollers (Coraciiformes), but in current sequence-based analyses L. discolor is the sister group of a clade including trogoniform, coraciiform, and piciform birds (Ericson et al. 2006;Hackett et al. 2008;Prum et al. 2015;Kuhl et al. 2021), and close affinities to coraciiform birds were also rejected on the basis of morphological data (Mayr 1998). ...
... With regard to tarsometatarsus proportions and the morphology of the distal end of the bone, Lutavis closely resembles Plesiocathartes, but a similar tarsometatarsus morphology also occurs in the accipitriform Cathartidae (Fig. 2hh-kk). Cathartidae and Leptosomiformes are only separated by the Strigiformes and Coliiformes in current sequence-based phylogenies ( Fig. 15; Prum et al. 2015, Kuhl et al. 2021, both of which exhibit autapomorphic tarsometatarsus and hypotarsus morphologies. Therefore, similarities in the tarsometatarsi of Plesiocathartes and the Cathartidae may well be plesiomorphic for the Afroaves. ...
... Ancestral state reconstruction of basipterygoid processes in taxa of the Telluraves; extinct taxa are indicated by a dagger (concerning the extant taxa, the tree topology is based on the analyses ofPrum et al. 2015 andKuhl et al. 2021). Black lines indicate the occurrence of basipterygoid processes, white lines their absence. ...
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We report the first records of the leptosomiform taxon Plesiocathartes from the early Eocene London Clay of Walton-on-the-Naze (Essex, UK). Two species could be distinguished, one of which is described as Plesiocathartes insolitipes , sp. nov. (the other is classified as Plesiocathartes sp.). We furthermore describe two small leptosomiform-like birds from Walton-on-the-Naze, Waltonavis paraleptosomus , gen. et sp. nov. and W. danielsi , gen. et sp. nov., which are represented by partial skeletons of multiple individuals. In plesiomorphic characters of the coracoid and furcula, the new taxon Waltonavis is distinguished from Plesiocathartes and the extant Leptosomus ; the skull of Waltonavis furthermore exhibits basipterygoid processes, which are absent in Leptosomus . Another new species, Lutavis platypelvis , gen. et sp. nov., is based on a partial skeleton from Walton-on-the-Naze and closely resembles Plesiocathartes in the overall shape of the tarsometatarsus. However, L. platypelvis is clearly distinguished from leptosomiform birds in the morphology of the hypotarsus, the more massive coracoid, and other skeletal features. Although we consider close affinities to Plesiocathartes and, hence, the Leptosomiformes to be possible, we could not identify derived characters that enable an unambiguous phylogenetic placement of L. platypelvis . In any case, the new species document an unexpected diversity of Plesiocathartes -like fossil in the London Clay and may open a new perspective for a reconstruction of the ancestral morphology of the avian clade Afroaves, which includes the Leptosomiformes and other arboreal landbirds.
... In addition to their cultural importance, birds of the genus Falco (henceforth referred to as falcons) are interesting from a biological perspective. They are a well-known raptors; yet, their phylogenetically closest relatives are Australaves that include passerines and parrots (25). ...
... Here, we generate and characterize the gut microbiomes of 29 falcons from four Falco species and hybrids using amplicon sequencing of the V4 region of the 16S rDNA gene. We integrate this dataset with a previously published dataset consisting of 636 birds (15) that includes representative species from each of the 9 clades spanning the entire avian phylogeny (25). ...
... Next, all non-avian samples were removed to create an "avian reference dataset" (ARD) which consisted of 61 469 984 reads and 10 603 ASVs from 675 birds spanning 9 phylogenetic clades (Supplementary File 1) (25). This dataset was used to assess the falcon microbiome in the context of broader avian evolution. ...
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The gut microbiome is increasingly being appreciated as a master regulator of animal health. However, most avian gut microbiome studies have focused on birds of economic importance while the gut microbiomes of raptors remain underexplored. Here we examine the gut microbiota of 29 samples from four Falco species including hybrid birds— raptors of historic importance —in the context of avian evolution by sequencing the 16S rDNA V4 region. Our results reveal that evolutionary histories and diet are significantly associated with avian gut microbiota in general, whereas diet plays a major role in shaping the falcon gut microbiota. Multiple analyses revealed that gut microbial diversity, composition, and abundance of key diet-discriminating bacterial genera in the falcon gut closely resemble those of carnivorous raptors rather than those of their closest phylogenetic relatives. Furthermore, the falcon microbiota is dominated by Firmicutes and consists of Salmonella at appreciable levels. Salmonella presence may potentially alter the functional capacity of the falcon gut microbiota as its abundance is associated with depletion of multiple predicted metabolic pathways involved in protein mass buildup, muscle maintenance, and enrichment of antimicrobial compound degradation, thus increasing the pathogenic potential of the falcon gut and presents a potential risk to human health. Author Summary in Arabic
... Recent phylogenetic analyses based on molecular data (e.g. Prum et al. 2015) show that Neoaves comprises at least five large groups: (1) the Strisores, that includes the insectivorous nightjars, hyper-aerial birds such as swifts, and birds with extreme feeding adaptations to nectarivory such as hummingbirds; (2) Columbaves that includes arboreal birds such as turacos, large flying migrant birds such as bustards, and doves, (3) Gruiformes, that includes cranes, rails and trumpeters; (4) Aequorlitornithes, a group that includes birds mainly associated with aquatic environments, including extreme divers such as penguins, soaring sustained flyers such as petrels and albatrosses and the diverse Charadriiformes, among others; and (5) Inopinaves. This last group is the most diverse of all birds, including raptors, toucans, woodpeckers, seriemas, parrots, and the songbirds (i.e. ...
... Using principal component and cluster analyses with 67 bird species, Iwaniuk and Hurd (2005) recognized five cerebrotypes associated with different environments. However, the cerebrotypes do not group species 2 Neornithes brain disparity (endocasts displayed in lateral view) mapped onto a simplified phylogeny taken from Prum et al. (2015). Not to scale with similar development (e.g. ...
Chapter
Brain morphology has become a key element to predict a wide array of cognitive and behavioral, sensory and motor abilities, and to determine evolutionary rates of phenotypic transformation. Our information on early bird brain morphology comes of natural endocasts or studies of the intracranial cavity. Although the first studies of fossil bird brains were published almost two centuries ago, there is still relatively little known about the avian brain and its evolution compared with other groups such as mammals. This is due primarily to the fact that few three-dimensionally preserved skulls of early birds are recognized. The avian brain occupies the entire intracranial cavity, so that it is possible to reconstruct high-quality 3D virtual endocast models that can be used as excellent proxies for both volume and morphology of the brain. This technique has driven advances in avian paleoneurology from 2000 onwards. In this chapter, we provide a holistic view of the main features of the avian brain and senses, its disparity and potential use in paleobiological inferences, and discuss the main changes across the transition from non-avian theropods to derived Neornithes.
... Variance-covariance matrix, which is used for estimating pGLS, was created based on assumptions of phylogenetic relationship among studied species and an evolutionary model. We followed the literatures (Lerner & Mindell 2005;Jønsson & Fjeldsa 2006;Baker et al. 2006Baker et al. , 2007Pereira et al. 2007;Johansson et al. 2008;Gonzalez et al. 2009;Krajewski et al. 2010;Zuccon et al. 2012;Schirtzinger et al. 2012;Alström et al. 2013;Ksepca et al. 2013;Gonzalez et al. 2013;Ramirez et al. 2013;Wang et al. 2013;Garcia-R et al. 2014;Fuchs & Pons 2015;Prum et al. 2015;Jønsson et al. 2016;Wink 2018;Kimball et al. 2019) for the phylogenetic relationships among the studied species ( Fig. S4-1: Appendix S2). There are some studies on the phylogenetic relationship among birds that have regarded the timing of divergence (e.g., Jetz et al., 2012;Prum et al., 2015). ...
... We followed the literatures (Lerner & Mindell 2005;Jønsson & Fjeldsa 2006;Baker et al. 2006Baker et al. , 2007Pereira et al. 2007;Johansson et al. 2008;Gonzalez et al. 2009;Krajewski et al. 2010;Zuccon et al. 2012;Schirtzinger et al. 2012;Alström et al. 2013;Ksepca et al. 2013;Gonzalez et al. 2013;Ramirez et al. 2013;Wang et al. 2013;Garcia-R et al. 2014;Fuchs & Pons 2015;Prum et al. 2015;Jønsson et al. 2016;Wink 2018;Kimball et al. 2019) for the phylogenetic relationships among the studied species ( Fig. S4-1: Appendix S2). There are some studies on the phylogenetic relationship among birds that have regarded the timing of divergence (e.g., Jetz et al., 2012;Prum et al., 2015). However, the branch lengths are not available for all the studied taxa in these studies. ...
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Birds generate a propulsive force by flapping their wings. They use this propulsive force for various locomotion styles, such as aerodynamic flight, wing‐paddle swimming and wing‐assisted incline running. It is therefore important to reveal the origin of flapping ability in the evolution from theropod dinosaurs to birds. However, there are no quantitative indices to reconstruct the flapping abilities of extinct forms based on their skeletal morphology. This study compares the section modulus of the coracoid relative to body mass among various extant birds to test whether the index is correlated with flapping ability. According to a survey of 220 historical bird specimens representing 209 species, 180 genera, 83 families and 30 orders, the section modulus of the coracoid relative to body mass in non‐flapping birds was significantly smaller than that of flapping birds. This indicates that coracoid strength in non‐flapping birds is deemphasised, whereas in flapping birds the strength is emphasised to withstand the contractile force produced by powerful flapping muscles, such as the m. pectoralis and m. supracoracoideus. Therefore, the section modulus of the coracoid is expected to be a powerful tool to reveal the origin of powered flight in birds. The relationship between the body mass (M [g]) and the section modulus of the coracoid normalised by the beam length (Z/Cl [mm2]), revealed that the coracoid strength is deemphasised in non‐flapping birds, but is emphasised in flapping birds, especially in soaring birds, to withstand the contractile force produced by flapping muscles. Therefore, the coracoid strength is expected to be an indicator for discriminating the styles of wing‐propelled locomotion.
... Such massive datasets pose a computational burden to parameter-rich molecular dating methods that rely on Bayesian Markov chain Monte Carlo (MCMC) sampling, slowing the testing and proposition of evolutionary hypotheses [9][10][11][12]. Because of this, phylogenomic studies have frequently devised alternative strategies to compute biological timescales, including the use of reduced datasets [13][14][15][16][17][18][19] and the summarization of time estimates based on data partitioning schemes [20,21]. ...
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Advances in genome sequencing techniques produced a significant growth of phylogenomic datasets. This massive amount of data represents a computational challenge for molecular dating with Bayesian approaches. Rapid molecular dating methods have been proposed over the last few decades to overcome these issues. However, a comparative evaluation of their relative performance on empirical data sets is lacking. We analyzed 23 empirical phylogenomic datasets to investigate the performance of two commonly employed fast dating methodologies: penalized likelihood (PL), implemented in treePL, and the relative rate framework (RRF), implemented in RelTime. They were compared to Bayesian analyses using the closest possible substitution models and calibration settings. We found that RRF was computationally faster and generally provided node age estimates statistically equivalent to Bayesian divergence times. PL time estimates consistently exhibited low levels of uncertainty. Overall, to approximate Bayesian approaches, RelTime is an efficient method with significantly lower computational demand, being more than 100 times faster than treePL. Thus, to alleviate the computational burden of Bayesian divergence time inference in the era of massive genomic data, molecular dating can be facilitated using the RRF, allowing evolutionary hypotheses to be tested more quickly and efficiently.
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The bony palate diagnoses the two deepest clades of extant birds: Neognathae and Palaeognathae1–5. Neognaths exhibit unfused palate bones and generally kinetic skulls, whereas palaeognaths possess comparatively rigid skulls with the pterygoid and palatine fused into a single element, a condition long considered ancestral for crown birds (Neornithes)3,5–8. However, fossil evidence of palatal remains from taxa close to the origin of Neornithes is scarce, hindering strong inferences regarding the ancestral condition of the neornithine palate. Here we report a new taxon of toothed Late Cretaceous ornithurine bearing a pterygoid that is remarkably similar to those of the extant neognath clade Galloanserae (waterfowl + landfowl). Janavis finalidens, gen. et sp. nov., is generally similar to the well-known Mesozoic ornithurine Ichthyornis in its overall morphology, although Janavis is much larger and exhibits a substantially greater degree of postcranial pneumaticity. We recovered Janavis as the first-known well-represented member of Ichthyornithes other than Ichthyornis, clearly substantiating the persistence of the clade into the latest Cretaceous9. Janavis confirms the presence of an anatomically neognathous palate in at least some Mesozoic non-crown ornithurines10–12, suggesting that pterygoids similar to those of extant Galloanserae may be plesiomorphic for crown birds. Our results, combined with recent evidence on the ichthyornithine palatine12, overturn longstanding assumptions about the ancestral crown bird palate, and should prompt reevaluation of the purported galloanseran affinities of several bizarre early Cenozoic groups such as the ‘pseudotoothed birds’ (Pelagornithidae)13–15.
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Introduction Podocarpaceae are a diverse, primarily tropical conifer family that commonly produce large leaves and highly reduced, fleshy seed cones bearing large seeds. These features may result from relatively recent adaptation to closed-canopy angiosperm forests and bird-mediated seed dispersal, although determining precisely when shifts in leaf and seed cone morphology occurred is difficult due to a sparse fossil record and relatively few surviving deep lineages. Methods We compare the fossil record of Podocarpaceae with results from ancestral state reconstruction methods and correlated character models using neontological data and a previously published molecular time-tree. Results Ancestral state reconstructions suggest that small leaves, small seeds, and multi-seeded cones are ancestral in crown Podocarpaceae, with reduced cones bearing few seeds appearing in the Early Cretaceous and the correlated evolution of large leaves and large seeds occurring from the Late Cretaceous onwards. The exact timing of these shifts based on neontological data alone are poorly constrained, however, and estimates of leaf and seed size are imprecise. Discussion The fossil record is largely congruent with results based on the molecular time-tree, but provide important constraints on the range of leaf and seed sizes that were present in Cretaceous Podocarpaceae and the time by which changes in cone morphology and seed size likely occurred. We suggest in particular that reduced seed cones appeared in the Early Cretaceous and are linked to the contemporaneous diversification of small bodied avialans (birds), with shifts to larger seed sizes occurring after the Cretaceous in association with the spread of closed-canopy angiosperm forests.
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The ability of feathers to perform many functions either simultaneously or at different times throughout the year or life of a bird is integral to the evolutionary history of birds. Many studies focus on single functions of feathers, but any given feather performs many functions over its lifetime. These functions necessarily interact with each other throughout the evolution and development of birds, so our knowledge of avian evolution is incomplete without understanding the multifunctionality of feathers, and how different functions may act synergistically or antagonistically during natural selection. Here, we review how feather functions interact with avian evolution, with a focus on recent technological and discovery‐based advances. By synthesising research into feather functions over hierarchical scales (pattern, arrangement, macrostructure, microstructure, nanostructure, molecules), we aim to provide a broad context for how the adaptability and multifunctionality of feathers have allowed birds to diversify into an astounding array of environments and life‐history strategies. We suggest that future research into avian evolution involving feather function should consider multiple aspects of a feather, including multiple functions, seasonal wear and renewal, and ecological or mechanical interactions. With this more holistic view, processes such as the evolution of avian coloration and flight can be understood in a broader and more nuanced context.
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Casey Albert Wood’s The fundus oculi of birds, published in 1917, was the culmination of his decade-long study of the eyes of about a hundred bird species across the avian phylogeny. Wood was a professional ophthalmologist who, during his spare time, was drawn to the study of birds’ eyes because of their exceptional visual acuity. His stated goal was to inform taxonomy but that turned out to be pointless. With the assistance of an artist, Arthur William Head, Wood described and illustrated in colour the retina in these birds, with particular attention to the number, size, structure, and position of the pecten. Since the function and evolution of the avian pecten is still poorly known, Wood’s book continues to provide a useful point of departure for the study of birds’ eyes.
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Subjects: Evolutionary Biology Contributor: Michael N. Romanov Interchromosomal rearrangements involving microchromosomes are rare events in birds. To date, they have been found mostly in Neognathae and Neoaves (e.g., Psittaciformes, Falconiformes, and Cuculiformes), although only a few orders have been analyzed. Hence, cytogenomic studies focusing on microchromosomes in species belonging to different bird orders are essential to shed more light on the avian chromosome and karyotype evolution. Relevant hypothetical Neognathae, Neoaves and other ancestral karyotypes can be reconstructed to trace these rearrangements. In a more recent study, a comparative chromosome mapping for chicken microchromosomes 10 to 28 was performed using interspecies BAC-based FISH hybridization in five species, representing four Neoaves orders (Caprimulgiformes, Piciformes, Suliformes, and Trogoniformes). These results suggest that the ancestral microchromosomal syntenies are conserved in Pteroglossus inscriptus (Piciformes), Ramphastos tucanus tucanus (Piciformes), and Trogon surrucura surrucura (Trogoniformes). On the other hand, chromosome reorganization in Phalacrocorax brasilianus (Suliformes) and Hydropsalis torquata (Caprimulgiformes) included fusions involving both macro-and microchromosomes. Fissions in macrochromosomes were observed in P. brasilianus and H. torquata. No interchromosomal rearrangement involving microchromosomes were found to be shared between avian orders where rearrangements were detected. These findings suggest that convergent evolution involving microchromosomal change is a rare event in birds and may be appropriate in cytotaxonomic inferences in orders where these rearrangements occurred. Keywords: avian cytogenomics, microchromosomes, evolution, genome organization, FISH, chromosomal rearrangements
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Background The highly derived morphology and astounding diversity of snakes has long inspired debate regarding the ecological and evolutionary origin of both the snake total-group (Pan-Serpentes) and crown snakes (Serpentes). Although speculation abounds on the ecology, behavior, and provenance of the earliest snakes, a rigorous, clade-wide analysis of snake origins has yet to be attempted, in part due to a dearth of adequate paleontological data on early stem snakes. Here, we present the first comprehensive analytical reconstruction of the ancestor of crown snakes and the ancestor of the snake total-group, as inferred using multiple methods of ancestral state reconstruction. We use a combined-data approach that includes new information from the fossil record on extinct crown snakes, new data on the anatomy of the stem snakes Najash rionegrina, Dinilysia patagonica, and Coniophis precedens, and a deeper understanding of the distribution of phenotypic apomorphies among the major clades of fossil and Recent snakes. Additionally, we infer time-calibrated phylogenies using both new ‘tip-dating’ and traditional node-based approaches, providing new insights on temporal patterns in the early evolutionary history of snakes. Results Comprehensive ancestral state reconstructions reveal that both the ancestor of crown snakes and the ancestor of total-group snakes were nocturnal, widely foraging, non-constricting stealth hunters. They likely consumed soft-bodied vertebrate and invertebrate prey that was subequal to head size, and occupied terrestrial settings in warm, well-watered, and well-vegetated environments. The snake total-group – approximated by the Coniophis node – is inferred to have originated on land during the middle Early Cretaceous (~128.5 Ma), with the crown-group following about 20 million years later, during the Albian stage. Our inferred divergence dates provide strong evidence for a major radiation of henophidian snake diversity in the wake of the Cretaceous-Paleogene (K-Pg) mass extinction, clarifying the pattern and timing of the extant snake radiation. Although the snake crown-group most likely arose on the supercontinent of Gondwana, our results suggest the possibility that the snake total-group originated on Laurasia. Conclusions Our study provides new insights into when, where, and how snakes originated, and presents the most complete picture of the early evolution of snakes to date. More broadly, we demonstrate the striking influence of including fossils and phenotypic data in combined analyses aimed at both phylogenetic topology inference and ancestral state reconstruction. Electronic supplementary material The online version of this article (doi:10.1186/s12862-015-0358-5) contains supplementary material, which is available to authorized users.
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