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\begin{document}

\title{Genetic and phenotypic differentiation in a Neotropical passerine with a
disjunct distribution in the Andean and Atlantic forests (Thamnophilus
ruficapillus)}



\author[1]{Belén Bukowski}%
\author[2]{Leonardo Campagna}%
\author[1]{Gustavo Cabanne}%
\author[1]{Pablo Tubaro}%
\author[1]{Darío Lijtmaer}%
\affil[1]{Museo Argentino de Ciencias Naturales Bernardino Rivadavia}%
\affil[2]{Cornell Lab of Ornithology}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The Andean and Atlantic forests are separated by the open vegetation
corridor, which acts as a geographic barrier. However, these forests
experimented cycles of connection and isolation in the past which shaped
the phylogeographic patterns of their biotas. We analyzed the
evolutionary history of the Rufous-capped Antshrike (Thamnophilus
ruficapillus), a species with a disjunct distribution in the Atlantic
and Andean forests and therefore an appropriate model to study the
effect of the open vegetation corridor and the Andes on the
diversification of the Neotropical avifauna. We performed a
phylogenetic/phylogeographic analysis including the five subspecies,
using mitochondrial and nuclear genomic DNA, and also studied their
differences in vocalizations and plumage coloration. Both the
mitochondrial and nuclear DNA evidenced a marked phylogeographic
structure with three differentiated lineages that diverged without gene
flow in the Pleistocene (1.0-1.7 million years ago): one in the Atlantic
Forest and two in the Andean forest. However, the two Andean lineages do
not coincide with the two disjunct areas of distribution of the species
in the Andes. Vocalizations were significantly different between most
subspecies, but their pattern of differentiation was discordant with
that of the nuclear and mitochondrial DNA. In fact, there is no song
differentiation between the subspecies of the Atlantic Forest and that
of the northwestern Bolivian Andes, even though they differ genetically
and belong to different lineages. Consistently, no differences were
found in plumage coloration between the subspecies of the Atlantic
Forest and that of the southern Andes. Our results suggest a complex
evolutionary history in this species, which differentiated both due to
dispersion across the open vegetation corridor, likely during a period
of connection between the Andean and Atlantic forests, and as a
consequence of a geographic barrier in northern Bolivia. In both cases
Pleistocene climatic oscillations appear to have influenced the species
diversification.%
\end{abstract}%



\sloppy


\textbf{Genetic and phenotypic differentiation in a Neotropical
passerine with a disjunct distribution in the Andean and Atlantic
forests (\emph{Thamnophilus ruficapillus})}

\textbf{Abstract}

The Andean and Atlantic forests are separated by the open vegetation
corridor, which acts as a geographic barrier. However, these forests
experimented cycles of connection and isolation in the past which shaped
the phylogeographic patterns of their biotas. We analyzed the
evolutionary history of the Rufous-capped Antshrike (\emph{Thamnophilus
ruficapillus} ), a species with a disjunct distribution in the Atlantic
and Andean forests and therefore an appropriate model to study the
effect of the open vegetation corridor and the Andes on the
diversification of the Neotropical avifauna. We performed a
phylogenetic/phylogeographic analysis including the five subspecies,
using mitochondrial and nuclear genomic DNA, and also studied their
differences in vocalizations and plumage coloration. Both the
mitochondrial and nuclear DNA evidenced a marked phylogeographic
structure with three differentiated lineages that diverged without gene
flow in the Pleistocene (1.0-1.7 million years ago): one in the Atlantic
Forest and two in the Andean forest. However, the two Andean lineages do
not coincide with the two disjunct areas of distribution of the species
in the Andes. Vocalizations were significantly different between most
subspecies, but their pattern of differentiation was discordant with
that of the nuclear and mitochondrial DNA. In fact, there is no song
differentiation between the subspecies of the Atlantic Forest and that
of the northwestern Bolivian Andes, even though they differ genetically
and belong to different lineages. Consistently, no differences were
found in plumage coloration between the subspecies of the Atlantic
Forest and that of the southern Andes. Our results suggest a complex
evolutionary history in this species, which differentiated both due to
dispersion across the open vegetation corridor, likely during a period
of connection between the Andean and Atlantic forests, and as a
consequence of a geographic barrier in northern Bolivia. In both cases
Pleistocene climatic oscillations appear to have influenced the species
diversification.

\textbf{Keywords:} Andean forest, Atlantic Forest, mitochondrial and
genomic DNA, vocalizations, plumage coloration, dispersion, Pleistocene
climatic oscillations.



\textbf{Introduction}

The main factors that have promoted the diversification of the
Neotropical avifauna include the Andes Mountains (Weir 2006, Brumfield
and Edwards 2007, Sedano and Burns 2010, Weir and Price 2011), wide
rivers (Ribas et al. 2012, Naka and Brumfield 2018, Kopuchian et al.
2020, Thom et al. 2020) and the closure of the Isthmus of Panama (Weir
et al. 2009, Smith and Klicka 2010). Although with a more prominent role
in the Northern Hemisphere and the southern extreme of South America,
the Pleistocene glacial cycles also played a relevant role as avian
diversification drivers throughout the Neotropics (Lessa et al. 2003,
Weir and Schluter 2004, Lovette 2005, Campagna et al. 2012, Kopuchian et
al. 2016, Acosta et al. 2020, Bukowski et al. 2023). This is clearly the
case for highland Andean taxa (Weir 2006, Jetz et al. 2012), but also in
the lowlands Pleistocene climatic oscillations have driven speciation by
promoting cycles of contraction, fragmentation and expansion of forest
ranges, which generated vicariance, divergence and in some cases
secondary contact of both forest and dry habitat species (Rull et al.
2011, Trujillo-Arias et al. 2017, 2020, Silva et al. 2019,
Bol\selectlanguage{ngerman}ívar-Leguizamón et al. 2020, Thom et al. 2020).

Another relevant driver of diversification in the Neotropical region,
which until recently has been less studied than those mentioned above,
is the open vegetation corridor. This dry strip formed by the Caatinga,
Cerrado and Chaco biomes isolates the Amazon and Andean forests from the
Atlantic Forest, thus affecting the connectivity of three of the most
biodiverse rainforests in the world (Orme et al. 2005). Either by
vicariance due to the establishment of the open vegetation corridor in
the Neogene in a previously continuous forest or as a consequence of
dispersion through these dry habitats after its formation, multiple
species of birds are disjunctly codistributed in these forests, with
varying degrees of differentiation between forests (Lavinia et al. 2015,
2019, Trujillo-Arias et al. 2017, 2018, 2020, Cabanne et al. 2019,
Bocalini et al. 2023).

Even though the Andean and Atlantic forests are currently isolated, they
have experienced cycles of connection and isolation associated with the
geotectonic processes and climatic fluctuations of the Neogene and the
Quaternary (Nores 1992, Silva 1994), adding more complexity to this
system and its effect on the avifauna of the region (Trujillo-Arias et
al. 2017, 2018, 2020, Cabanne et al. 2019, Lavinia et al. 2019). The
cyclic connection between these rainforests could have occurred during
glacial maxima through the expansions of forests into the Cerrado (Silva
1994), a possibility supported by palynological studies (Ledru 1991,
1993, Oliveira-Filho and Ratter 1995) and avian data (Carnaval and
Moritz 2008, Cabanne et al. 2016, 2019, Trujillo-Arias et al. 2017). The
contact could have also occurred during interglacial periods through
gallery forests in the Chaco region (Olrog 1963, Nores 1992), a
hypothesis based mainly on forest bird distribution patterns but without
clear evidence (Zurita et al. 2014, Trujillo-Arias et al. 2017).
Irrespective of their differences in timing and location, these proposed
connections could have acted in combination (Trujillo-Arias et al. 2017,
2018, 2020).

As shown with other geographic barriers or habitat mosaics in the
Neotropics (Smith et al. 2014, Naka and Brumfield 2018, van Els et al.
2021), the effect of this dynamic rainforest history also depends on
species-specific ecological characteristics and dispersal abilities
(Trujillo-Arias et al. 2018, 2020, Lavinia et al. 2019). Several studies
have focused on the evolutionary history of species that inhabit
exclusively the Amazon forest or the Atlantic Forest, providing insights
into the historical diversification processes operating within these
regions (e.g. Ribas et al. 2012, Maldonado-Coelho et al. 2013, Cabanne
et al. 2016, Silva et al. 2019). Fewer comprehensive evolutionary
studies have been performed on species that inhabit both the Atlantic
and the Andean or Amazonian forests (Lavinia et al. 2015, 2019,
Trujillo-Arias et al. 2017, 2018, 2020, Cabanne et al. 2019,
Bolívar-Leguizamón et al. 2020).

The Rufous-capped Antshrike (\emph{Thamnophilus ruficapillus} ) has
three disjunct areas of distribution, one of them in the Atlantic Forest
and the other two in the Andean forest (Figure 1a). In the latter, it
inhabits humid and semi-humid forests (with different degrees of human
alteration) and patches of dense shrubs bordering watercourses in
predominantly open areas east of the Andes (Brumfield and Edwards 2007,
del Hoyo et al. 2020). In the Atlantic Forest it occurs in low elevation
areas of northern Argentina and southern Brazil (Brumfield and Edwards
2007, del Hoyo et al. 2020). \emph{T. ruficapillus} belongs to the group
of suboscine passerines and it is an insectivorous, monogamous and
sexually dichromatic species (del Hoyo et al. 2020). Currently, five
subspecies are recognized (del Hoyo et al. 2020, Clements et al.
2022):\emph{T. r. jaczewskii} (in the Andean forest of northern
Peru),\emph{T. r. marcapatae} (in southeastern Peru, particularly Puno
and Cusco departments), \emph{T. r. subfasciatus} (in the Yungas of
northwestern Bolivia), \emph{T. r. cochabambae} (in southern Bolivia to
northwestern Argentina) and \emph{T. r. ruficapillus} (in the Atlantic
Forest of southeastern Brazil and northeastern Argentina) (see Figure
1a). Consistent with these designations, previous analyses of
mitochondrial DNA have shown intraspecific divergences of around 4\% in
the cytochrome c oxidase subunit I (COI) gene between \emph{T. r.
cochabambae} in the Andean forest and \emph{T. r. ruficapillus} in the
Atlantic Forest (Kerr et al. 2009).

Here we leverage the distribution of \emph{T. ruficapillus} to use this
species as a model for the study of the effect of the open vegetation
corridor and the Andes on the diversification of the Neotropical
avifauna. For doing this, we analyzed the evolutionary history of this
species using a comprehensive approach that includes both genetic and
genomic analyses (mitochondrial and nuclear genomic DNA) as well as
phenotypic analyses (study of vocalizations and plumage coloration) to
better understand its phylogeographic patterns and the role played by
the aforementioned Neotropical diversification drivers.

\textbf{Materials and methods}

\emph{Taxon sampling for genetic and genomic analyses}

We analyzed 30 specimens of \emph{T. ruficapillus} from 18 collection
sites covering the main distribution range of the species and including
representatives of the five subspecies that have been described to date
(Figure 1a, see also Table S1 in the Supporting information Appendix).
We also included one specimen of \emph{T. doliatus} and one specimen
of\emph{T. caerulescens} to be used as outgroups (we could not
include\emph{T. torquatus} , which is the sister species of \emph{T.
ruficapillus} ; Brumfield and Edwards 2007). Subspecies were determined
according to the distribution range described by del Hoyo et al. (2020)
and Clements et al. (2022).

\emph{Laboratory protocols for mitochondrial markers}

We extracted DNA from fresh tissue (pectoral muscle or blood) following
the silica-based protocol described by Ivanova et al. (2006), using
individual spin columns (Lijtmaer et al. 2012). We amplified two
mitochondrial genes: cytochrome c oxidase I (COI) and cytochrome\emph{b}
(cyt \emph{b} ). Primers used for the amplification of the 695 base
pairs (bp) of the COI were BirdF1 (Hebert et al. 2004) and COIbirdR2
(Kerr et al. 2009) and to amplify 1007 bp of the cyt \emph{b}we used
L14841 (Kocher et al. 1989) and H16065 (Lougheed et al. 2000).

PCR amplification reaction cocktails and the thermocycling profile for
COI from fresh tissue followed Lijtmaer et al. (2012). To amplify the
cyt \emph{b} we followed the protocol in Arrieta et al. (2013) and the
PCR cocktail was prepared in 20 \selectlanguage{greek}μ\selectlanguage{english}l volume with the following reagents: 3
\selectlanguage{greek}μ\selectlanguage{english}l of genomic DNA, 1x PCR buffer, 0.2 mM dNTPs, 3 mM
Cl\textsubscript{2}Mg/SO\textsubscript{4}Mg, 0.5 \selectlanguage{greek}μ\selectlanguage{english}M primer forward, 0.5
\selectlanguage{greek}μ\selectlanguage{english}M primer reverse and 1U Taq DNA polymerase (Invitrogen). The PCR
thermocycling profile was as follows: 3 min at 94 \selectlanguage{ngerman}ºC; 40 cycles of 45
sec at 94 ºC, 30 sec at 53 ºC and 1 min at 72 ºC; and finally 10 min at
72 ºC.

Sequencing of both mitochondrial markers was conducted at Macrogen Korea
(Seoul, Korea) and performed bidirectionally with the same primers used
for amplification. GenBank accession numbers for all sequences generated
in this study are included in Table S1.

\emph{Laboratory protocols for nuclear genomic markers}

We extracted genomic DNA from fresh tissue samples using DNeasy tissue
extraction kit (Qiagen, CA, USA). To generate the genomic data we used
double-digest restriction site-associated DNA sequencing (ddRADseq)
following the protocol of Peterson et al. (2012) with modifications
described in Thrasher et al. (2018). Briefly, for each sample we
isolated \textasciitilde{}500 ng of DNA, at a standardized concentration
of 20 ng/\selectlanguage{greek}µ\selectlanguage{english}l, and digested it with the restriction enzymes SbfI High
Fidelity (8 base bp recognition site; 5'-CCTGCAGG-3') and MspI (4 base
bp recognition site; 5'-CCGG-3') (New England BioLabs, MA, USA). The
digested DNA was ligated to P1 and P2 adapters on both ends (sequences
are available in Peterson et al. 2012). We size-selected groups of 20
uniquely barcoded samples (index groups), retaining fragments between
400 and 700 bp using Blue Pippin (Sage Science, MA, USA). To incorporate
the full Illumina TruSeq primer sequences and unique indexing primers
into each library, we performed low cycle number PCR with Phusion
High-Fidelity DNA Polymerase (New England BioLabs), with the following
thermocycling profile: 98\selectlanguage{ngerman}°C for 30 sec followed by 11 cycles at 98°C for
5 sec, 60°C for 25 sec, and 72°C for 10 sec with a final extension at
72°C for 5 min. We visualized the product of this amplification on a 1\%
agarose gel and performed a final 0.73 AMPure cleanup to eliminate DNA
fragments smaller than 200 bp. Finally, sequencing was performed on an
Illumina HiSeq 2500 lane at the Cornell University Biotechnology
Resource Center as part of a larger sequencing batch, obtaining
single-end 150-bp sequences, with an average mean sequencing depth of
151 reads per locus per individual (range: 104-220).

\emph{Genetic diversity and population structure based on mitochondrial
markers}

To perform the genetic analyses, we edited and aligned COI and
cyt\emph{b} sequences using CodonCode Aligner 4.0.4 (CodonCode
Corporation, Dedham, MA) and carefully checked the chromatograms for
ambiguities and sequences to detect the presence of any stop codons, as
well as alignment gaps.

Because mitochondrial sequences are linked within the same genome, COI
and cyt \emph{b} sequences were concatenated for the analyses. First, to
analyze the genetic variation within the species and understand the
relationship among haplotypes, we calculated the average p distances
using MEGA 5.2 (Tamura et al. 2011) and generated haplotype networks
using the median joining algorithm implemented in PopART 1.0
(\textless{} http://popart.otago.ac.nz \textgreater{}), respectively. We
conducted an analysis of molecular variance (AMOVA) in Arlequin 3.5
(Excoffier and Lischer 2010) to explore the distribution of genetic
variation within \emph{T. ruficapillus} and specifically test whether
there are differences in the frequency of mitochondrial haplotypes among
subspecies. The \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values between pairs of subspecies
were computed using uncorrected genetic distance matrices between
haplotypes and significance was tested through 2,000 random
permutations.

\emph{Phylogenetic analyses and divergence time estimations based on
mitochondrial DNA}

We inferred gene trees with Bayesian and maximum parsimony (MP)
methodologies using MrBayes 3.2.2 (Ronquist et al. 2012) and TNT 1.1
(Goloboff et al. 2003), respectively. The best-fit model of nucleotide
substitution for each locus for the Bayesian analysis was selected using
the Bayesian information criterion (BIC) implemented in jModelTest 2.1.1
(Darriba et al. 2012). HKY+I was chosen for COI and KHY for cyt
\emph{b}(Hasegawa et al. 1985). Both loci were placed in unlinked
partitions allowing parameters to vary and to be estimated independently
(except for topology and branch lengths). We conducted two independent
runs of 10 million generations sampling trees every 100 generations
under default priors for all parameters. We discarded the first 25\% of
the sampled trees as burn-in and the remaining 75,000 trees of each run
were combined to generate a majority rule consensus tree. The standard
deviation of split frequencies (SDSF) between runs was always
\textless{} 0.01 indicating convergence. Using Tracer 1.6 (Rambaut et
al. 2014), we assured that both runs reached the stationary phase and
that we had a good sample of the posterior probability distributions.
For the MP analysis we ran heuristic searches based on 1,000 random
addition sequences (RAS) coupled with the tree bisection reconnection
(TBR) branch-swapping algorithm, saving 10 trees per replication. A
strict consensus tree was estimated from all the most parsimonious
trees. To estimate node support we conducted a bootstrap analysis
(Felsenstein 1985) that consisted of 1,000 pseudoreplicates of 100 RAS +
TBR saving 10 trees per replicate.

The age of the most recent common ancestor between \emph{T.
ruficapillus} and \emph{T. doliatus} and the time of separation of the
mitochondrial lineages within \emph{T. ruficapillus} were estimated by
generating a time-calibrated ultrametric tree with the Bayesian approach
implemented in BEAUTi/BEAST 1.8 (Drummond et al. 2012). Both
mitochondrial markers were placed in separate partitions with unlinked
substitution and clock models selected with jModelTest: HKY+I for COI
and HKY for cyt \emph{b} . The tree models were linked since the
mitochondria is a single unit of inheritance. We specified a Yule
speciation tree prior, assuming a constant population size and a relaxed
uncorrelated lognormal clock. We used a calibration of 2.1\% per million
years (My) for cyt \emph{b}
(1.05x10\textsuperscript{-2}substitutions/site/lineage/My; Weir and
Schluter 2008) and 1.17x10\textsuperscript{-2}
substitutions/site/lineage/My for COI (Lavinia et al. 2016). The
analysis was run for 100 million generations sampling every 1,000
generations. We checked for stationarity in the estimation of the
parameters and adequate ESS values using Tracer. We discarded the first
10\% of the sampled trees (burn-in = 10,000) and then estimated the 95\%
highest posterior density (HPD) intervals of divergence dates using
TreeAnnotator 1.8 (Drummond et al. 2012).

\emph{Population structure and phylogenetic analysis based on genomic
DNA}

We demultiplexed raw reads and applied standard quality filters in
Ipyrad 0.7.28 (Eaton and Overcast 2020). We discarded sequences when a
single base had a Phred quality score below 10 or more than 5\% of bases
had a Phred quality score below 20. Additional filtering was applied to
only retain reads that did not have enzyme cleavage sites, adaptor
sequences or index sequences and included up to 3 ambiguous sites.
Finally, we only kept loci that were present in at least 80\% of the
samples. The ddRAD data is available in Dryad (doi: xxx).

We performed a principal component analysis (PCA), using the gdsfmt and
SNPRelate packages (Zheng et al. 2012) in R 3.5.1 (R Core Team, 2018),
to visualize possible clusters in the data. We used all the SNPs of the
dataset (including multiple SNPs per locus), but because the PCA is
sensitive to missing data, SNPs missing from at least one individual
were removed. We also assigned individuals to genetic clusters (K) using
Structure 2.3.4 (Pritchard et al. 2000). For this analysis we used a
single random SNP from each RAD locus. We implemented the admixture
ancestry model with correlated allele frequencies and an allele
frequency prior of \selectlanguage{greek}λ \selectlanguage{english}= 1. We conducted 10 runs for each value of K =
1-4, and each run consisted in 500,000 generations following a burn-in
of 100,000. The most likely value of K was determined following the \selectlanguage{greek}Δ\selectlanguage{english}K
method described by Evanno et al. (2005) and implemented in Structure
Harvester 0.6.94 (Earl and vonHoldt 2012). We averaged results across
the 10 runs using the greedy algorithm in the program CLUMPP 1.1.2
(Jakobsson and Rosenberg 2007) and visualized results using the
conStruct package (Bradburd et al. 2018) in R.

We used RAxML version 8.2.4 (Stamatakis 2014) to infer a maximum
likelihood phylogenetic tree. We implemented the ASC\_GTRGAMMA model and
the Lewis correction for ascertainment bias and we conducted 200
bootstrap replicates to assess node support. We included one specimen
of\emph{T. caerulescens} as outgroup (we obtained ddRADseq data for this
specimen from a previous study; Kopuchian et al. 2020; see Table S1).

\emph{Demographic history modeling}

We used the Generalized Phylogenetic Coalescent Sampler (G-PhoCS)
version 1.2.3 (Gronau et al. 2011) to estimate divergence times,
effective population sizes and gene flow. Because of the computationally
intensive nature of this analysis, we restricted our data set to a
maximum of 10 individuals per subspecies, selecting one sample per
location. We ran G-PhoCS using the standard MCMC settings described in
Gronau et al. (2011) and default parameters with 75,000 burn-in
generations and 750,000 additional sampling generations. We assessed
adequate mixing and convergence using Tracer. Divergence times and
effective population sizes were converted from mutation scale to
generations (T) and individuals (Ne) respectively, by assuming an
average mutation rate of 10\textsuperscript{-9} mutations per bp per
generation (Kumar and Subramanian 2002). The model implemented in
G-PhoCS is conditioned upon a given phylogenetic topology. Thus, we ran
G-PhoCS using the topology of the nuclear genomic tree (which is
concordant with the Bayesian and MP mitochondrial trees, see the Results
section below).

\emph{Song analyses}

We analyzed 50 recordings belonging to the five subspecies of \emph{T.
ruficapillus} and covering the entire geographic distribution range of
the species (see Table S2). Recordings were in `wav' format and were
obtained from the Macaulay Library of Natural Sounds (Cornell Lab of
Ornithology) and Xeno-Canto.

We generated and analyzed the spectrograms using Raven Pro 1.5
(http://www.birds.cornell.edu/raven). The conditions for the analyses
were a 512 fast Fourier transform length with a 50\% overlap, a Hamming
window type, and a grayscale color scheme. We analyzed one song per
individual choosing the one with the best signal-to-noise ratio. Songs
in this species consist of a repetition of similar notes. The first note
is the longest, followed by successively shorter notes, until the last
note, which is longer and similar to the first one. We measured five
variables: mean duration of the note, mean duration of the interval
between notes, fundamental frequency (calculated as the difference in
frequency between the harmonics and measured in the center of each
harmonic), bandwidth of the note (difference between the maximum and
minimum frequencies of the note) and the number of notes in each song
(Figure S1). Variables were measured on the first note, then
additionally averaged between the second, fourth and sixth note to
obtain a measurement of the intermediate notes, and finally again on the
last note.

To assess vocal variation we performed a PCA and analyzed the data with
a one-way analysis of variance (ANOVA) followed by Bonferroni's
contrasts to assess differences among subspecies in their PC scores (all
the principal component scores met the assumptions of homoscedasticity
and normality). All variables were standardized previous to the analysis
and we considered that variables were significantly correlated with a PC
only when the module of their factor loading value was greater than 0.7
(Tabachnick and Fidell 2001).

Finally, we also performed an ANOVA using the original song variables,
followed by Bonferroni's contrasts, because this can provide a more
detailed analysis of specific differences among subspecies.

\emph{Plumage coloration analyses}

To objectively describe plumage colour variation within \emph{T.
ruficapillus} , we obtained reflectance spectra measurements from museum
skins deposited at the MACN (Table S3). We measured five males and four
females of \emph{T. r. cochabambae} and 30 males and 18 females
of\emph{T. r. ruficapillus} , which are the only two subspecies present
in Argentina and available in the MACN collection. We used only adult
specimens that were in excellent preservation condition for reflectance
measurements.

Male plumage in this species is mostly brown (\emph{T. r.
cochabambae}and \emph{T. r. ruficapillus} ) or dark gray (\emph{T. r.
jaczewskii} ,\emph{T. r. marcapatae} and \emph{T. r. subfasciatus} ),
with rufous wings, a rufous or chestnut cap and barred underparts and
tail. The species is sexually dichromatic, with brown females in all
subspecies (i.e., no gray plumage) and either lack or very faint barring
in their underparts and no white marking in their tales (Figure S2).
Taking into account this colour patterning, we measured reflectance in
six plumage patches on each specimen: throat, breast, belly, crown, nape
and back. All reflectance measurements were performed with an Ocean
Optics USB 2000 spectrometer (Ocean Optics Inc., Dunedin, Florida) with
a PX-2 pulsed xenon light source (effective range of emission from 220
to 750 nm), calibrated against a WS-1 diffuse reflectance white standard
(Ocean Optics, Inc., Dunedin, Florida, USA). Plumage was illuminated and
reflectance data were collected with a bifurcated probe housed in a
prismatic holder that was held against the chosen region on the study
skin. The probe was held with an angle of 90\selectlanguage{ngerman}º to the surface of the
plumage patch. The diameter of the circular measured area was
approximately 6 mm, and the distance between the probe and the plumage
was 23 mm.

We performed analyses using the pavo package in R (Maia et al. 2013).
Because this species is sexually dichromatic, males and females were
analyzed separately. We evaluated colour differentiation in the
chromatic component between subspecies for each plumage patch by
estimating a perceptual distance (\selectlanguage{english}[?]S) using the Vorobyev and Osorio
(1998) colour discrimination model. Colour perceptual distances are
expressed in terms of just noticeable differences (jnd) and a value of
1.0 jnd represents the theoretical threshold for discrimination of two
colours, meaning that it is the distance in the perceptual colour space
at which two colours would be visually discernible (Barreira et al.
2021). Thus, \selectlanguage{english}[?]S \textgreater{} 1.0 jnd indicates significant colour
discrimination by the birds in all light conditions (Vorobyev et al.
1998, Siddiqi et al. 2004). In addition, we determined the statistical
significance of the estimated \selectlanguage{english}[?]S values for each plumage patch
comparison between subspecies (Maia and White 2018). For this, we
estimated the geometric average of \selectlanguage{english}[?]S values for all comparisons
performed between subspecies and calculated the 95\% confidence interval
(CI) through a bootstrap analysis with the bootcoldist function of pavo
in R. We also tested the significance of these comparisons through a
PERMANOVA analysis with the adonis function of the vegan package in R
(Oksanen et al. 2008).

\textbf{Results}

\emph{Genetic diversity and population structure based on mitochondrial
markers}

We analyzed the concatenated mitochondrial DNA sequences (COI + cyt b =
1,130 bp) of 24 individuals of \emph{T. ruficapillus} belonging to the
five subspecies (Table S1). The specimens were collected in 16
localities from Peru, Bolivia, Argentina and Uruguay, covering a wide
range of the species distribution (Figure 1a). Our results showed
considerable variation within \emph{T. ruficapillus} and, in particular,
the subspecies that inhabits the Atlantic Forest, \emph{T. r.
ruficapillus} , clearly differed from the four Andean subspecies, with a
mean genetic differentiation between 2.6\% and 3.6\% (Table 1). There
was also considerable variation among the Andean subspecies,
with\emph{T. r. cochabambae} showing the highest genetic divergence both
with respect to \emph{T. r. ruficapillus} and with the remaining Andean
subspecies (Table 1). Lower levels of differentiation were observed
between \emph{T. r. subfasciatus} , \emph{T. r. marcapatae} and \emph{T.
r. jaczewskii} and in fact we found almost no differentiation (0.2\%)
between \emph{T. r. subfasciatus} and \emph{T. r. marcapatae} .

Both the haplotype network (Figure 1b) and the AMOVA (Table 1) show a
marked phylogeographic structure within \emph{T. ruficapillus} , with
three clearly differentiated lineages: one formed by the subspecies
present in the Atlantic Forest (\emph{T. r. ruficapillus} ), another one
by the subspecies distributed in the Andes of southern Bolivia and
northwestern Argentina (\emph{T. r. cochabambae} ), and the last one
containing the remaining Andean subspecies from northern Bolivia to Peru
(see Figure 1b). Consistently, \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values from the AMOVA
were high and significant between the subspecies of these three
different lineages (\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} 0.7-0.83), but were not
significant between the three subspecies of the northern lineage
(\emph{T. r. subfasciatus} , T\emph{. r. marcapatae} and \emph{T. r.
jaczewskii} ; see Table 1). The haplotype network confirmed a lack of
differentiation and shared haplotypes between \emph{T. r.
subfasciatus}and \emph{T. r. marcapatae} , and low genetic
differentiation between these two subspecies and \emph{T. r. jaczewskii}
, despite its geographically isolated and disjunct distribution.

\emph{Phylogenetic analyses and diversification dating} \emph{based on
mitochondrial DNA}

Bayesian and maximum parsimony (MP) reconstructions recovered \emph{T.
ruficapillus} as monophyletic in relation to \emph{T. doliatus} with
maximum statistical support (Figure S3). The time-calibrated ultrametric
phylogeny (Figure 1c) shows that the split between them occurred 1.65
million years ago (Mya) (95\% HDP: 0.82-2.27). These reconstructions
show three clades that correspond to the lineages described above: one
includes the subspecies from the Atlantic Forest (\emph{T. r.
ruficapillus} ), another one is formed by \emph{T. r. cochabambae} and
the third one by \emph{T. r. subfasciatus} , \emph{T. r. jaczewskii}
and\emph{T. r. marcapatae} (see Figure 1c and Figure S3). In the case of
the Bayesian and MP reconstructions the oldest split was between the
subspecies from the Atlantic Forest and those from the Andean forest
(Figure S3), with low values of both posterior probability and bootstrap
support between the two Andean clades (0.63 and 63\%, respectively).
However, the time-calibrated ultrametric phylogeny indicated that the
first split within the species, which occurred in the Pleistocene
approximately 1.29 Mya (95\% HPD: 0.71-2.10 Mya), was between the
southernmost Andean subspecies (\emph{T. r. cochabambae} ) and the rest
of the subspecies (Figure 1c). This analysis suggested a more recent
divergence between the subspecies from the Atlantic Forest (\emph{T. r.
ruficapillus} ) and the clade that includes the rest of the Andean
subspecies (\emph{T. r. subfasciatus} , \emph{T. r. jaczewskii}
and\emph{T. r. marcapatae} ), approximately 0.97 Mya (95\% HPD:
0.52-1.57 Mya). This split, however, also had low support (posterior
probability: 0.58; Figure 1c).

\emph{Population structure, phylogenetic tree and demographic history
modelling based on ddRAD markers}

We obtained reduced-representation genomic data for 18 specimens
of\emph{T. ruficapillus} collected in 11 localities from Bolivia,
Argentina and Uruguay that belong to the subspecies \emph{T. r.
ruficapillus} , \emph{T. r. cochabambae} and \emph{T. r.
subfasciatus}(Figure 1a, Table S1) and represent the three clades
obtained with mitochondrial DNA. The \emph{de novo} assembly produced a
total of 6,165 RADseq loci (13,485 SNPs) present in at least 80\% of the
individuals. This data set was used to perform the analyses described
below.

Nuclear genomic variation was congruent with the results described for
mitochondrial DNA, showing a clear differentiation between the three
lineages. First, individuals clustered in the PCA in three clearly
differentiated groups which corresponded to each of the subspecies
(Figure 2a). The Structure analysis also supported the three main
groups, as K = 3 was the model with the highest likelihood (Figure S4)
and the three genomic clusters correspond to the mitochondrial lineages
(Figure 2b). Only one individual of \emph{T. r. ruficapillus} from
Uruguay presented mixed genomic content, but the proportion of its
genome belonging to the \emph{subfasciatus} cluster was very low (less
than 5\%). The rest of the individuals did not show admixture in their
genome, suggesting a lack of gene flow among these three subspecies.

The maximum likelihood reconstruction recovered a topology with the same
three clades obtained in the Structure and PCA analyses, which also
coincide with the mitochondrial lineages (Figure 2c). All nodes had
maximum bootstrap support. The first split within the species occurred
between \emph{T. r. ruficapillus} (from the Atlantic Forest) and the
subspecies from the Andean forest (\emph{T. r. cochabambae} and \emph{T.
r. subfasciatus} ), with a subsequent separation between these Andean
subspecies. This topology is congruent with those obtained in the MP and
Bayesian analysis with mitochondrial data (but differ from the topology
of the calibrated ultrametric tree; see Figure 1c and Figure S3).

Our demographic analysis in G-PhoCS was based on the topology of the
nuclear phylogeny, which indicated that the initial split occurred
between the eastern T. r. ruficapillus and the Andean subspecies
(\emph{T. r. cochabambae} and \emph{T. r. subfasciatus} ), with a
subsequent separation between the latter. The result, however, indicated
that the three lineages split almost at the same time (Figure
3):\emph{T. r. ruficapillus} diverged from the other two subspecies
almost 1,7 million generations ago (Mga) (95\% confidence interval:
1,5-2,0 Mga) and the two Andean subspecies diverged from each other only
440 generations after this first split. The effective population size of
each of the subspecies is between one third and half of the ancestral
effective population size estimated for the species
(\textasciitilde{}1.5M individuals). However, the most noticeable result
in this regard is the very small estimated ancestral effective
population size of the lineage leading to \emph{T. r. subfasciatus}
and\emph{T. r. cochabambae} , which was estimated to be only 313
individuals. This result suggests that this population could have
experienced a bottleneck after (or during) its split from \emph{T. r.
ruficapillus} , expanding afterwards to reach the current effective
population size of the Andean subspecies (\textasciitilde{}0.4M and
\textasciitilde{}0.55M individuals; Figure 3). This is consistent with
the fact that Andean populations could have been isolated in refugia
during the glacial periods after their split from \emph{T. r.
ruficapillus} (Chaves et al. 2011). Finally, this analysis suggests very
low gene flow between subspecies (Figure 3), a result consistent with
the PCA and Structure analyses.

\emph{Vocalization analyses}

The PCA evidenced differences in vocalizations among subspecies. The
first three PCs explained 65\% of song variation. PC1 correlated with
the fundamental frequency of the first and the last note, PC2 correlated
with the mean duration of the first and intermediate notes and PC3 did
not strongly correlate with any song variable (Table S4). Subspecies
statistically differed in PC1 (F = 11.32, p < \selectlanguage{english}0.01) and PC2 (F = 18.43,
p < \selectlanguage{english}0.01), whereas no significant differences were found for PC3 (F =
2.87, p = 0.09). In particular, pairwise comparisons showed that\emph{T.
r. jaczewskii} significantly differed from \emph{T. r. ruficapillus} and
\emph{T. r. cochabambae} in PC1 and from \emph{T. r. marcapatae} in PC2
(Table S5). In turn, \emph{T. r. cochabambae}differed from all
subspecies in PC2 (Table S5). These differences can be observed in the
space delimited by the first two PCs (Figure 4). No differences were
found in the rest of the pairwise comparisons between subspecies for
PC1, PC2 or PC3 (Table S5).

The results of the individual song variables provided more detail about
song variation and showed substantial differentiation among subspecies.
In fact, the ANOVA indicated significant differences for most song
variables (Tables S6 and S7). Moreover, the Bonferroni contrasts showed
significant differences in at least one variable for almost all
subspecific comparisons, with the only exceptions of \emph{T. r.
subfasciatus} vs. \emph{T. r. ruficapillus} and \emph{T. r.
subfasciatus} vs. \emph{T. r. marcapatae} (Table S8). Therefore, and
consistently with the PCA result, both \emph{T. r. jaczewskii}
and\emph{T. r. cochabambae} significantly differed in their songs from
all other subspecies, being the subspecies with the most differentiated
vocalizations. In addition. \emph{T. r. jaczewskii} was the subspecies
that differed in more song variables from the rest (and particularly so
from \emph{T. r. cochabambae} and \emph{T. r. ruficapillus} ) (Table
S8).

\emph{Coloration analyses}

The quantification of plumage chromatic distances between females
of\emph{T. r. ruficapillus} (from the Atlantic Forest) and \emph{T. r.
cochabambae} (from the Andean forest) using the Vorobyev and Osorio
(1998) model indicated that five plumage patches had differences in
colour above the discrimination threshold ([?]S \textgreater{} 1.00 jnd):
throat, breast, belly, crown and nape (Table 2). In turn, the comparison
between males of these same two subspecies showed differences higher
than 1.00 jnd for three plumage patches: throat, belly and nape (Table
2). The PERMANOVA, however, was not significant for any of the
comparisons (Table 2), suggesting that the variation between subspecies
was not significantly higher than the variation within each of them
neither for females nor for males. This relatively subtle,
non-significant colour differentiation between subspecies can also be
observed in the reflectance spectra of the patches (Figure S5).

\textbf{Discussion}

The combination of mitochondrial and nuclear DNA, complemented with data
on vocalizations and plumage coloration, allowed us to study the
evolutionary history of \emph{T. ruficapillus} . Our findings provide a
good starting point towards understanding the history of diversification
of this species, the relative patterns of genotypic and phenotypic
variation, and the role played by the open vegetation corridor and the
Andes Mountains in this process.

First, the mitochondrial DNA results suggest that the split
between\emph{T. doliatus} and the lineage that includes \emph{T.
ruficapillus}and \emph{T. torquatus} (which are sister species;
Brumfield and Edwards 2007) occurred 1.65 Mya, within the Pleistocene.
This indicates that the origin of \emph{T. ruficapillus} took place even
more recently within the Pleistocene. The subsequent diversification
within the species generated a phylogeographic structure with three main
genetic lineages: one in the Atlantic Forest (\emph{T. r. ruficapillus}
), another one including the southernmost Andean subspecies (\emph{T. r.
cochabambae} ), and finally one lineage formed by the three remaining
Andean subspecies (\emph{T. r. subfasciatus} , \emph{T. r. marcapatae}
and \emph{T. r. jaczewskii} ). The various analyses based on both the
mitochondrial DNA and the nuclear genome recovered these three genetic
clusters (although we note that for genomic analyses we were only able
to include \emph{T. r. subfasciatus} as a representative of the northern
Andean lineage).

The relationship between these three main lineages is not clear and it
can actually be considered a polytomy. The Bayesian and MP mitochondrial
reconstructions, as well as the nuclear genomic phylogeny, suggested
that the first split within the species took place between the lineage
from the Atlantic Forest and an Andean lineage, with a subsequent
separation within the latter. However, the time-calibrated ultrametric
phylogeny inferred that the first split occurred between the two Andean
lineages. Surprisingly, the topology of this ultrametric phylogeny
indicates that shortly after the Andean split, the colonization of the
lowland areas of the Atlantic Forest occurred from the northern Andean
lineage and not from the lineage that inhabits northwestern Argentina
and southern Bolivia, which is much geographically closer to the
Atlantic Forest. This discordance in the relationship among linages
could be due to the temporal proximity of both splits, a result common
to all analyses. In particular, the time-calibrated ultrametric
phylogeny based on mitochondrial DNA suggested that the first split
within \emph{T. ruficapillus} (in this reconstruction the split between
the two Andean lineages) occurred approximately 1.3 Mya, and the
colonization of the Atlantic Forest approximately 1 Mya. The G-PhoCS
analysis, which was based on nuclear DNA, suggested that the two splits
between lineages took place approximately 1.7 million generations ago.
If we consider that the generation time for passerines could be
estimated to be around 1 year on average (Wang 2004, Cabanne et al.
2008), these two results (from the analyses of mitochondrial and nuclear
genomic DNA) are relatively concordant. Consistent with the temporal
proximity of these two deepest splits and their different sequence of
occurrence depending on the reconstruction methodology, one should note
that the support of the nodes that define the relationship of these
three lineages was very low in all mitochondrial reconstructions (63\%
bootstrap support in the MP analysis, 0.63 posterior probability in the
Bayesian phylogeny and 0.58 posterior probability in the time-calibrated
phylogeny; see Figure 1c and Figure S3). Regardless of the subtle
difference in the timing of the splits and the uncertainty in their
order, our results are congruent in suggesting that the main events of
diversification of \emph{T. ruficapillus} that generated its current
patterns of population diversity emerged in a short time period during
the Pleistocene, shortly after the origin of the species.

Taking together our genetic and genomic results, including the lack of
gene flow between the Atlantic Forest and Andean forest populations, we
can confirm the role of the open vegetation corridor as a geographic
barrier for \emph{T. ruficapillus} . Nevertheless, the origin of the
allopatric populations of this species should be explained as a result
of dispersal through the open vegetation corridor and not as a
vicariance event due to its establishment. This is because the Atlantic
Forest and the Andean forest became isolated by this barrier before the
Pleistocene (Costa 2003, Trujillo-Arias et al. 2017, Cabanne et al.
2019, Lavinia et al. 2019) and therefore before the origin of \emph{T.
ruficapillus} and the split between its Atlantic and Andean lineages
(which is dated to approximately 1-1.7 Mya by our analyses). This
suggests a relevant role of the Pleistocene climatic cycles on the
diversification of \emph{T. ruficapillus} , since the dispersion through
the open vegetation corridor could have occurred during one of the
periods of connection between the Atlantic and Andean forests. If the
first split within the species occurred between the lineage from the
Atlantic Forest and the ancestral lineage from the Andean forest (as
suggested by most of our analyses, including the phylogeny based on
genomic DNA), it is difficult to establish the direction of the
dispersion and colonization. On the other hand, if the first split
occurred within the Andes (as suggested by the time-calibrated Bayesian
analysis), it could be considered that the species originated in the
mountain range and after its first split between the Andean lineages the
dispersion occurred to the east, allowing the colonization of the
Atlantic Forest. In this second scenario, the Bayesian phylogeny
indicates that the colonization occurred from the northern lineage of
the Andean forest, suggesting that it could have taken place across the
Cerrado as proposed by Silva (1994), and not across the Chaco as
hypothesized by Nores (1992). We note that this is only a possibility
since our lack of sampling on the northern portion of the species
distribution in the Atlantic Forest prevents us from drawing robust
conclusions about the route/s of connection. Moreover, the fact
that\emph{T. doliatus} (the sister species of \emph{T. ruficapillus} )
is widely distributed to the north of \emph{T. ruficapillus} and also
has populations in the east and west of the Neotropics, precludes us
from assuming which is the area in which these two species may have
split, which could have helped distinguishing among hypothesized
colonization scenarios. Further studies with a more geographically
comprehensive sampling of \emph{T. ruficapillus} and the inclusion of
\emph{T. doliatus} are needed to fully elucidate this matter.

The Andes Mountains have also played a relevant role in the
diversification of \emph{T. ruficapillus} , as indicated by the
existence of two distinct Andean lineages without evidence of gene flow.
Surprisingly, these two lineages do not coincide with the two disjunct
areas of distribution of this species in the Andes (at least from a
mitochondrial perspective; see Fig. 1): one lineage is formed by the
populations of \emph{T. r. cochabambae} (from northwestern Argentina and
southern Bolivia) and the other one is constituted by the three
remaining Andean subspecies (\emph{T. r. subfasciatus} , \emph{T. r.
marcapatae} and \emph{T. r. jaczewskii} from northern Bolivia and Peru,
which span both areas of disjunct distribution in the mountain range and
only exhibit a shallow differentiation in the case of \emph{T. r.
jaczewskii} , the northern subspecies that is geographically isolated).
This pattern suggests that there is a geographic barrier in northern
Bolivia, which in fact coincides with phylogeographic breaks found in
the same area in various other montane bird species (Cadena et al. 2007,
Weir 2009, Chaves et al. 2011, Valderrama et al. 2014, Guti\selectlanguage{ngerman}érrez-Pinto
et al. 2019). Given that our analyses indicated that the divergence
between these two Andean lineages occurred around 1.3-1.7 Mya, during
the Pleistocene, one could hypothesize that Pleistocene climatic
oscillations have also played a role in the diversification of this
species in the Andes (Weir 2006, 2009, Chaves et al. 2011, Valderrama et
al. 2014).

The vocal analyses showed significant differences in one or various song
variables between most of the subspecies, which at first glance seems to
be consistent with the presence of a marked phylogeographic structure.
In fact, only two of the subspecies comparisons did not show vocal
differentiation. One of them was between \emph{T. r. subfasciatus}
and\emph{T. r. marcapatae} , which were also the pair of subspecies with
the lowest genetic divergence and shared haplotypes. The second pair of
subspecies without song differences was \emph{T. r. ruficapillus}
and\emph{T. r. subfasciatus} , an unexpected result given that they
belong to different mitochondrial lineages and their areas of
distribution are disjunct, with the former present in the Atlantic
Forest and the latter in the Andean forest of northwestern Bolivia. On
the other extreme of our results, \emph{T. r. jaczewskii} was one of the
most vocally differentiated subspecies, significantly differing in its
song from all other subspecies. This result could be expected given that
\emph{T. r. jaczewskii} is geographically isolated from the other
populations of the species, but contrasts with the relatively shallow
mitochondrial DNA differentiation in this subspecies (nuclear DNA could
not be analysed), particularly when compared with \emph{T. r.
subfasciatus} and \emph{T. r. marcapatae} , which in fact are part of
its same lineage but significantly differed in their songs.

The finding of these discordances between the genetic/genomic patterns
of divergence and vocal differentiation in \emph{T. ruficapillus} is
surprising given that this species is a suboscine, and therefore its
song is likely developed without learning (Kroodsma and Konishi 1991,
Isler et al. 1998, Touchton et al. 2014), being free from the influence
of cultural evolution and possessing a more direct connection with
genetic divergence than in oscines (Seddon 2005). In fact, previous
intraspecific studies of suboscine song variation have shown more
concordant patterns of vocal and genetic structuring (Isler et al. 2005,
García et al. 2018, Acero-Murcia et al. 2021, Bukowski et al. 2024),
although with some cases of divergent lineages lacking song
differentiation in suboscines (García et al. 2018) and other birds with
innate songs (Nwanko et al. 2018), as was the case for \emph{T. r.
ruficapillus} and \emph{T. r. subfasciatus} in this study. Future vocal
studies of this species should analyse the putative effect of natural
and sexual selection, which could cause different patterns of song
variation compared to neutral genetic markers even in species with
innate songs (García et al. 2018, 2023). On the other hand, the vocal
pattern of variation in this species can also be affected by the
adaptation to habitat characteristics and their effect on sound
transmission, which could delay or accelerate song differentiation in
relation to genetic phylogeographic patterns, depending on the
similarity or disparity of the various forest types along the species
distribution (Morton 1975, Wiley 1991, Seddon 2005, Tubaro and Lijtmaer
2006).

Finally, and in spite of their allopatry and marked mitochondrial and
nuclear DNA differentiation, no significant differences were found
between the Atlantic Forest subspecies \emph{T. r. ruficapillus} and the
Andean subspecies \emph{T. r. cochabambae} in plumage coloration
(neither for males nor for females). The lack of correlation between
colour differentiation and genetic divergence among intraspecific
lineages has been found in previous studies of Neotropical birds (e.g.
Trujillo-Arias et al. 2020, Paulo et al. 2023), but analyses including
the remaining three subspecies, as well as a larger geographic sampling,
are needed to better assess the pattern of colour variation in this
species.

\textbf{Final remarks}

Our analyses of the evolutionary history of \emph{T. ruficapillus} shows
that this species originated in the Late Pleistocene, within the last
1.7. million years, and diversified into three clearly differentiated
lineages: an Atlantic Forest lineage and two different lineages in the
Andes, with a split in western Bolivia. This pattern of genetic
variation highlights the relevance of the open vegetation corridor and
the Andes Mountains as diversification drivers for this species, likely
in combination with the effect of the Pleistocene climatic cycles.
Levels of phenotypic variation, however, are partially discordant with
this genetic structuring. Songs are significantly differentiated among
subspecies, but their geographic pattern of variation does not coincide
with that shown by mitochondrial or nuclear DNA. We found clearly
differentiated songs between subspecies that are genetically similar
and, on the contrary, lack of detectable song differentiation between
the Atlantic Forest subspecies and that of northwestern Bolivia in the
Andes, which present a substantial genetic differentiation and belong to
different lineages. Similarly, plumage colouration between the Atlantic
Forest subspecies and that of the southern Andes did not differ
significantly. Future studies should address these discordances with an
increased sampling and the analysis of the effect of evolutionary and
ecological pressures on song and colour. Finally, our results are in
accordance with those of other birds with disjunct populations in the
Atlantic and Andean forests for which the open vegetation corridor has
promoted diversification (e.g. Lavinia et al. 2015, 2019, Trujillo Arias
et al. 2018, 2020, Cabanne et al. 2019). However, these studies show
that the level of differentiation between the populations of these
forests is notoriously heterogeneous, suggesting an idiosyncratic effect
of this barrier and the cycles of connection and isolation of these
forests and highlighting the importance of comparative analyses to
better understand the role of the main drivers of avian Neotropical
diversification and speciation.

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Table 1. Genetic pairwise comparisons among the five subspecies
of\emph{Thamnophilus ruficapillus} based on mitochondrial DNA (COI +
cyt\emph{b} ). Above the diagonal: pairwise \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values;
significant values (\emph{p} \textless{} 0.05) in bold. Diagonal: mean
uncorrected genetic distances within each subspecies. Below the
diagonal: mean uncorrected genetic distances between pairs of
subspecies. Genetic distances are expressed in percentage values.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Subspecies\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\emph{T. r.} \emph{jaczewskii}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\emph{T. r.} \emph{marcapatae}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\emph{T. r.} \emph{subfasciatus}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\emph{T. r.} \emph{cochabambae}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\emph{T. r.} \emph{ruficapillus}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{T. r. jaczewskii}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.92\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.59\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.83}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.73}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{T. r. marcapatae}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.82}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.70}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{T. r. subfasciatus}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.8\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.83}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.73}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{T. r. cochabambae}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3.5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3.3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3.4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.74}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{T. r. ruficapillus}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3.6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.3\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 2. Colour differentiation ([?]S) among females of \emph{T. r.
ruficapillus} and \emph{T. r. cochabambae} , and among males of \emph{T.
r. ruficapillus} and \emph{T. r. cochabambae} . [?]S represents relative
chromatic perceptual distance between females of the two subspecies
analyzed and between males of the two subspecies analyzed for each
plumage patch. Values are geometric means (and 95\% confidence
intervals) for all comparisons. The unit of [?]S is jnd (just noticeable
differences). Values above 1.0 jnd, the theoretical threshold for colour
discrimination, are underlined. The PERMANOVA results were statistically
not significant in all comparisons.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Plumage patch\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Females\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Females\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Females\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Males\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Males\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Males\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
N \emph{T. r. ruficapillus}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
N \emph{T. r. cochabambae}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
[?]S\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
N \emph{T. r. ruficapillus}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
N \emph{T. r. cochabambae}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
[?]S\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Throat\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.39 (0.22-3.24)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.12 (0.39-2.32)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Breast\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.63 (0.48-2.77)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.91 (0.24-1.89)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Belly\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.53 (0.54-2.59)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.61 (0.67-2.57)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Crown\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.31 (0.59-4.45)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.71 (0.41-2.61)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nape\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.34 (0.89-3.75)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.20 (0.42-6.95)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Back\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.99 (0.24-2.72)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.46 (0.24-1.99)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

Figure 1. Distribution of \emph{T. ruficapillus} , sampling information
and analyses of mitochondrial DNA. (a) Distribution map (shaded in
violet) showing sampling localities, subspecies identity and the type of
sequence data obtained for each sample. The distribution map is based on
BirdLife International and NatureServe (2014). (b) Median-joining
haplotype network of concatenated mDNA (COI + cyt \emph{b} ; 1,130 bp).
Circles represent haplotypes and their size is proportional to haplotype
frequency. The length of the branches connecting haplotypes is
proportional to the number of nucleotide differences between them, which
are indicated by the number of line marks on each branch. Colors
represent the subspecies according to the scheme incorporated in the
figure. Black circles represent unsampled hypothetical haplotypes. Bird
illustrations are from del Hoyo (2020). (c) Bayesian phylogenetic tree
with divergence times obtained from the analysis of the concatenated
mitochondrial dataset (1,130 bp). The numbers near the nodes indicate
the mean divergence time estimates and the numbers between square
brackets correspond to the 95\% highest posterior density intervals of
those estimates. Divergence times and the numbers between square
brackets are in millions of years ago. Posterior probability values are
shown below the divergence times. The colors to the right of the tree
indicate subspecies information.

Figure 2. Genomic analyses of \emph{T. ruficapillus} . (a) Principal
Component Analysis (PCA) based on 2,306 SNPs (SNPs present in all
individuals). (b) Structure plot for K=3 based on 5,073 SNPs (one random
SNP per RAD locus). (c) Phylogeny inferred with maximum likelihood; the
colours to the right of each individual indicate the subspecies. The
tree was rooted with \emph{Thamnophilus caerulescens} . Bootstrap
support values are indicated in the nodes.

Figure 3. Demographic reconstruction of \emph{T. ruficapillus} based on
6,165 RADseq loci (13,485 SNPs). Estimates of divergence times (T; in
millions of generations), migration rates (mg; in migrants per
generation) and current and ancestral effective population sizes (Ne; in
millions of individuals) are reported with their 95\% confidence
intervals.

Figure 4. Analysis of the vocalizations of \emph{T. ruficapillus} . Song
differentiation among subspecies is shown based on a PCA performed with
the song variables (mean duration of the note, mean duration of the
interval between notes, fundamental frequency, bandwidth of the note and
the number of notes in each song; the first four variables were measured
for the first note).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

