Elevational Differentiation Increases Rates of Trait Evolution but Not Diversification in Neotropical Passerine Birds

Elevational Differentiation Increases Rates of Trait Evolution but not Diversification in Neotropical Passerine Birds

Vanessa Estefanía Luzuriaga-Aveiga

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Ecology and Evolutionary Biology University of Toronto

Elevational Differentiation Increases Rates of Trait Evolution but not Diversification in Neotropical Passerine Birds

Vanessa Estefanía Luzuriaga-Aveiga

Master of Science

Department of Ecology and Evolutionary Biology University of Toronto

2018 Abstract

The importance of ecologically-mediated divergent selection in elevating rates of trait evolution has been poorly studied in the most species-rich biome of the planet, the continental tropics. I performed a macroevolutionary analysis of trait divergence and diversification rates across closely-related pairs of passerine birds, belonging to the Amazon basin and adjacent Andean slopes, to assess whether the difference in elevational range separating species pairs influences the speed of trait evolution and diversification rates. Difference in elevation was used as a proxy for the degree of ecological divergence. I found that the amount of elevational separation is associated with faster differentiation of song frequency, a trait important for premating isolation, and several morphological traits, which may contribute to extrinsic postmating isolation. However, ecological differentiation does not primarily drive bird diversification and, thus, may have limited influence on patterns of species richness along the eastern slope of the tropical Andes.

Acknowledgements

I will like to especially thank my supervisor, Jason Weir, for his extraordinary guidance and support during the development of this research project. I thank Santiago Claramunt and Nick

Mandrak, for their constructive feedback during committee meetings. I also thank my labbies:

Maya Faccio, Ashley Bramwell and Sean Anderson for their helpful advice. I thank Michael Swift too, for his help with the data collection of song files.

I thank the National Secretariat of Science, Technology and Innovation (SENESCYT) for awarding me with a full graduate fellowship, from the Ecuadorian government, to pursue my graduate studies at the University of Toronto.

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Table of Contents

Acknowledgements ...... iii

Table of Contents ...... iv

List of Tables ...... vi

List of Figures ...... vii

List of Appendices ...... ix

Chapter 1: Elevational Differentiation Increseses Rates of Trait Evolution but not Diversification in Neotropical Passerine Birds ...... 1

Introduction ...... 1

Methods ...... 5

2.1 Data collection ...... 5

2.2 Time estimation ...... 6

2.3 Song analysis ...... 7

2.4 Morphometric analysis...... 8

2.5 Patterns across the elevational gradient ...... 8

2.6 Evolutionary rates and gradient effect ...... 9

2.7 Age distribution and diversification rates ...... 10

Results ...... 11

3.1 Songs ...... 11

3.2 Morphometrics ...... 11

3.3 Patterns across the elevational gradient ...... 12

3.4 Age distribution and diversification rates ...... 12

Discussion ...... 13

4.1 Speciation ...... 16

4.2 Conclusions ...... 18

References ...... 20

Appendices ...... 43

List of Tables

Table 1: Loadings of the PCA analysis for song measurements...... 31

Table 2: Loadings of the PCA analysis for morphometric measurements...... 31

Table 3: Comparison of support for Brownian Motion (BM) and Ornstein–Ulhenbeck models of trait evolution. ΔAICc scores (AICc for each model – smallest AICc score) and Akaike weights (AICc weight) were used as metrics of model support. N is the number of model parameters. The best-fit model (bolded) has the smallest ΔACc (a value of 0) and largest AICc weight. Values for the slope of β (change in evolutionary rate with elevational difference per 1000 meters) and 95% confidence intervals are shown for models that test for the elevational difference. Change in β shows the proportional increase in β gained with 2000 meters increase in elevational difference...... 32

List of Figures

Figure 1: Map of the study region, outlined in orange, includes the Guiana shield, Amazonian biome and adjacent eastern slopes of the Andes (from Bogotá-Colombia to Cochabamba-Bolivia) that drain into the Amazon basin...... 36

Figure 2: Measurements taken from an example song spectrogram...... 36

Figure 3: Example of morphometric measurements taken from museum specimens...... 37

Figure 4: Consensus phylogenetic tree of species pairs with cyt b within my dataset. Three clades are differentiated: Furnarioidea in blue, Tyrannoides in green, and Oscines in orange...... 39

Figure 5: Change in evolutionary rates of traits as a function of midpoint elevational difference between species pairs of birds from the Amazon basin and adjacent Andean slopes. Maximum likelihood estimates for the best fitting model are shown for songs (frequency (PC1) in green and length (PC2), in black) and morphometrics (body size (PC1) in orange, bill width (PC2) in purple, tail length (PC3) in yellow, bill length (PC4) in red, wing length (PC5) in blue and bill depth (PC6) in pink). All characters, except song length and body size, best fit models in which rates of evolution increased with increasing difference in midpoint elevation. Song frequency bill width, tail length, bill length, and wing length were best fitted by Ornsetin-Ulhenbeck (OU) models, with other models best fit by Brownian Motion (BM). ML estimates for morphometrics are shown with a solid line for oscines and with a dashed line for suboscines wing length and bill depth, because those traits best supported a model with separate rates between these groups. At the bottom-right, I show an example of a morphologically-differentiated species pair, Campylorhamphus pusillus – C. procurvoides, with a Euclidean distance for bill length is 0.43 and difference in midpoint elevational ranges of 1320m. In contrast, the bottom-left corner illustrates a sister species pair, Dendroplex kienerii – D. picus, with weak morphometric differentiation (Euclidean distance for bill length = 0.18) that occur at similar elevations (Δ midpoint elevational range = 400m). Bird illustrations were taken with permission from Schulenberg et al. (2010)...... 39

Figure 6: Mean PC values for behavioral and morphometric traits for individual species as a response of increasing elevation. Song traits are shown in (a-b) and morphometric traits in (c-h). Lines represent phylogenetically-corrected regressions...... 40

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Figure 7: (a) Age distribution of sister species pairs belonging to the study region (Fig.1), taken from the phylogeny of birds of the world with genetic data of Pulido-Santacruz & Weir (2016), within three elevational categories: lowland Amazonian species (midpoint elevational range <1000m), highland Andean species (midpoint elevational range ≥1000m), and lowland Amazonian versus highland Andean species (midpoint elevational range of one member in a sister pair is <1000m and ≥1000m for the other member). (b) Phylogenetically-corrected regression of diversification rates as a function of the difference in midpoint elevational range...... 41

Figure 8: Histogram of midpoint elevational difference across sister species pairs from the phylogeny of Pulido-Santacruz & Weir (2016). Approximately 20% of the sister pairs in this dataset differ by at least 1000 meters in their midpoint elevational range...... 42

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List of Appendices

Appendix 1: Dataset for song analysis ...... 43

Appendix 2: Dataset for morphometric analysis...... 47

Appendix 3: Data sources for song files, museum specimens and genetic sequences...... 52

Appendix 4: Neotropical sister species pairs belonging to the study site (Fig. 1), extracted from the phylogeny of Pulido-Santacruz & Weir (2016)...... 77

Chapter 1 Elevational Differentiation Increseses Rates of Trait Evolution but not Diversification in Neotropical Passerine Birds Introduction

The evolution of reproductive isolation is influenced by many factors, including rates of gene flow, strength of geographic barriers, and strength of divergent natural selection (Harvey et al.

2017). These mechanisms may eventually lead to the evolution of reproductive barriers through the accumulation of genetic differences. While genetic differentiation is expected to result in the formation of reproductive isolation for any set of populations not exchanging genes, this process can be accelerated under strong divergent selection mediated by environmental differences – a process termed “ecological speciation” (Schluter 2000). As the strength of ecologically-driven divergent selection increases, the amount of time required to evolve reproductive isolation declines

(Schluter 2000; Price 2008). This potential to speed up the diversification process could be an important driver of species richness gradients, but most studies on the role of ecological divergence in accelerating evolution have been focused on species-poor faunas of island archipelagoes (e.g.

Galapagos finches) and in temperate regions at high latitudes. However, our current understanding of the role of ecological divergence in accelerating evolution in species-rich habitats of the continental tropics is limited.

Several studies have demonstrated the importance of ecologically-mediated divergent selection for speciation. In animals, adaptation to distinct foraging niches has resulted in divergence of morphological traits such as body size, body shape and nuptial colouration, which has led to the evolution of reproductive isolation (Nagel & Schluter 1998; Boughman 2001; Nosil

& Crespi 2006). For instance, the extraordinary phenotypic diversification of ovenbirds

1 2

(Furnariidae), Hawaiian honeycreepers (Drepanidini), Galapagos finches (Geospiza), and vangas

(Vangidae) may have occurred by the action of divergent selection pressures for habitat use, leading to extensive diversification of bill morphology, which facilitated the rapid specialization of foraging strategies among lineages (e.g. Claramunt 2010; Lovette et al. 2002; Burns et al. 2002;

Jønsson et al. 2012). A recent study provides evidence that climatic niche divergence is a primary driver of diversification and species richness in the Furnariidae radiation (Seeholzer et al. 2017).

Other forces – such as sexual selection – are likely to interact with environmental conditions in driving phenotypic diversification on traits important for species recognition

(Schluter 2001). For example, several aspects of the song structure and performance in passerine birds are known to be directly influenced by both habitat divergence (e.g Seddon 2005; Weir et al.

2012; Derryberry et al. 2018) and sexual selection (e.g Price 1998; Slabbekoorn & Smith 2002;

Mason et al. 2017). Female preference for aspects of male song may evolve in such a way as to maximize male song transmission through the environment. Populations exposed to different environments may therefore have accelerated rates of both female preferences and male song traits, facilitating premating isolation. Hence, when acing together, natural and sexual selection, may speed up the speciation process.

The importance of speciation through ecological differentiation has been mostly stressed in high latitudes or island ecosystems, with relatively few studies undertaken in the most species- rich biome of the planet – wet forests of the Amazon basin (e.g. Tobias et al. 2014). The tropics are often believed to harbor a greater number of ecological niches than high latitude biomes. If this is true, then tropical regions might generate greater ecological opportunity, leading to faster trait divergence and speciation than high latitudes (e.g. Ecological Opportunity Hypothesis sensu

Schluter 2016). However, comparative analyses across closely-related avian species demonstrate less divergence in climatic niche towards the equator, which is also associated with reduced

3 evolutionary rates in body size and song divergence (Lawson & Weir 2014), as well as differentiation of plumage colouration (Martin et al. 2010). Recent speciation rates are also estimated to be slower in the tropics (Weir & Schluter 2007). Together, these results question the validity of the Ecological Opportunity Hypothesis as an explanation for high species richness in the continental tropics.

The Amazon basin represents the most species-rich tropical regions of the planet (Hurlbert

& Jetz 2007). Lowland rainforests of the Amazon basin are bisected by broad rivers, that are known to restrict gene flow and drive speciation in taxa with limited dispersion abilities, such as understory forest birds (e.g. Haffer 2008; Leite & Rogers 2013; Weir et al. 2015). However, habitat, climate and other ecological and environmental variables are unlikely to be greatly differentiated on opposing river banks. Thus, divergent selection is unlikely to be strong in the lowlands of Amazonia. In contrast, environmental (temperature, precipitation, atmospheric pressure, wind, radiation and humidity) and ecological variables (e.g habitat, phenology, predation, competition, parasitism, foraging strategies, etc.) change rapidly as one leaves lowland forests of the Amazon basin and ascends up the slopes of the adjacent Andes. The extensive variety of habitats among elevational zones of the Andes (Lynch & Duellman 1997) could promote strong divergent selection between populations living at different elevations, leading to increased rates of trait evolution (Kricher 1997). Thus, the Amazon basin and adjacent Andean slopes represent an ideal geographic region to test the effect of ecologically-mediated divergent selection in increasing rates of trait evolution.

Here, I analyzed whether or not difference in elevation plays an important role in increasing rates of trait divergence across closely-related passerine birds from the Amazon basin and adjacent east-facing slope of the Andes. I calculated evolutionary rates of song and morphometrics between

135 pairs of species distributed along an elevational gradient from sea level to 5000m and used

Brownian motion and Ornsetin-Ulhenbeck models of trait evolution to test whether evolutionary rates vary as a function of elevational difference separating members of each species pair. I chose to analyze song and morphometrics due to the importance of these traits in the formation of reproductive isolation. Song divergence is associated with premating reproductive isolation in birds (see Wilkins et al. 2013), and morphometric traits are believed to be fine-tuned to the ecological niche in birds (e.g. Miles & Ricklefs 1984; Miles et al. 1987; Smith et al. 1997).

Divergence in morphometrics, thus, is likely to lead to extrinsic postmating reproductive isolation mediated by local adaptation to environmental conditions at different elevations. If ecological differences associated with elevation influence trait divergence, I expect to find faster evolution of song and morphometrics in species pairs with greater differences in their elevational ranges.

Similarly, I tested if the high species richness of the Andes resulted as a consequence of increased ecological opportunity along different elevations, by comparing diversification rates between sister pairs living in similar versus different elevations.

Methods 2.1 Data collection

Pairs of closely-related species or genetically differentiated subspecies (e.g. phylogroups) of passerine birds from the humid Amazonian basin (including the eastern slope of the tropical

Andes whose drainages flow into the Amazon river, but not the southern Andes (below 18° S latitude or western Andean slopes) and adjacent Guaianan shield region (Fig. 1) were identified from published, nearly complete species-level molecular phylogenetic trees. This region is collectively referred to as the Amazon basin throughout this paper. Many pairs in the dataset involve sister species or pairs of closely-related phylogroups from the same species. In addition, to increase the sample size of pairs which differ in elevation, I included several pairs of closely- related congeneric species or clades of such species, which are not sister species. For example, a species in a sister pair may be more closely-related to another species outside of the study area, which is not included, or I may compare a clade of species that occur at the same elevation to its sister clade, which occurs at a different elevation. The resulting dataset included a total of 135 species pairs, 125 for song analyses and 119 pairs for morphometric analyses (see Appendices 1 and 2). Differences in the numbers of species pairs across these datasets resulted from some sister pairs lacking data for either song or morphometrics (i.e. appropriate specimens for the latter were not available from the museum collection visited).

I used the field guide of the Birds of Peru (Schulenberg et al. 2010) as primary source for gathering information of altitudinal ranges per each species, because it uses data not only from well documented sigtht observations but also from museum specimens. This source represents one of the most comprehensive and robust assesments of altitutinal ranges currently available. When a species in the dataset did not occurred in Peru or its elevational range was not described, I used

5 6 other field guides or available datasets (e.g Ridgely & Greenfield 2001; McMullan & Donegan

2010; Del Hoyo et al. 2018; Stotz et al. 1996). Then, I calculated the absolute difference of the midpoint of elevational range between the two species or subspecies within each phylogroup.

Species pairs in which one or both species’ elevational range spanned from less than 500m to more than 1600m or whose elevational range (maximum minus minimum elevation) exceeded 2000m were excluded. This eliminated species with broad elevational ranges spanning lowland and highland regions, whose midpoint elevations provide a less meaningful comparison. The degree of elevational overlap has also been used as a metric in macroevolutionary studies, but was not investigated here.

2.2 Time estimation

Mitochondrial sequences of cytochome b (cyt b) and NADH dehydrogenase subunit 2

(ND2) genes, with at least 500bp, were obtained for 1 to 5 individuals per species or subspecies from GenBank (Appendix 3). Sequences were aligned in MEGA7 (Kumar et al. 2016) and GTR- gamma genetic distances (with model permeameters estimated using maximum likelihood along a neighbor joining tree, with Acanthisitta chloris as outgroup) between members of each species pair were obtained for each gene, using the software PAUP 4.0b10 (Swofford 2001).

I performed separate least squares linear regression fits, through the origin, for GTR- gamma distances of ND2 versus cyt b for the following three subclades of passerines: Oscines,

Tyrannoides, and Furnaroidea. The slope of these regressions indicated that such subclades evolve

10 to 40% faster in ND2 compared to cyt b (Oscines: 33%, Tyrannoides: 9.4%, Furnaroidea: 42%) and thus, I corrected the ND2 genetic distance in each of these subclades by the appropriate factor, so that ND2 genetic distances were comparable in scale to cyt b distances. Then, I took the averages of the genetic distances of both genes and calibrated them with the cyt b 2% avian

7 molecular clock – a mean mutation rate of 0.01 per million years – (Weir & Schluter 2008), to estimate approximate times of divergence. Following Weir and Wheatcroft (2011), species pairs with genetic distances less than 0.75% were excluded. This distance equates to only 4 to 9 base pair differences depending on sequencing length used. Given the stochastic nature of sequence evolution, considerable imprecision in the estimates of genetic distances obtained from such few base pair differences generates inaccuracy in species divergence dates – which may adversely affect the fit of models of trait evolution (see Weir et al. 2012).

2.3 Song analysis

1096 song files were obtained from field recordings belonging to Jason Weir, and the online databases xeno-canto (http://www.xeno-canto.org) and Macaulay Library

(http://macaulaylibrary.org). Avian vocalizations were analyzed using the software Raven Pro 1.4

(http://www.birds.cornell.edu/raven) for 1 to 5 individuals (mean = 4.14) per species, with individuals sampled from the same general geographic region when possible. I followed previously published methods (Weir et al. 2012) to obtain 6 measures of song frequency (low frequency, high frequency, first and third quartile frequency, center frequency, delta frequency), as well as song length and the number of notes per song (Fig. 2). Frequency measurements were taken from the fundamental song (in hertz) and song duration was measured in seconds. All measurements were log-transformed to perform a principal component analysis (PCA) with the prcomp function in R

3.4.1 (R Core Team 2017), using the correlation matrix. Then, I took Euclidean distances of the mean PC values for each of the first two PCs, as measures of song divergence. Measurement errors were calculated from the variances across individuals. I applied the variance of the other species, within a phylogroup, to species with a single song file.

2.4 Morphometric analysis

I took morphometric measurements of 616 museum specimens for 1 to 3 individual adult males (mean=2.58) per species belonging to the same general geographic region. I took length measurements of the bill: total bill length (from the start point of the culmen to the tip of the bill), depth (distance between the upper and lower closed mandible at the anterior end of the nostrils), and width (distance of the widest lateral mandible at the anterior end of the nostrils); wing arc; central feather of the tail; tarsus (measured from the junction to the last undivided scale); and hallux’ claw. Bill and tarsus were measured with a Marathon digital caliper (8 in/200 mm), and a flexible stainless-steel ruler (12 in/30 cm) was used to measure wing and tail (Fig. 3). Because I did not include a metric measurement of body size, the morphometric analysis for this later variable was corrected using a PCA approach. The first principal component (PC1) – of the log-transformed morphometric measurements (in mm) – represents overall body size variation, thus, the remaining

PCs are considered to be largely size-independent (Berner 2011). I analyzed each remaining PC, that explained more than 2% of the variation, separately. When only one specimen of a species was available, measurement errors were calculated using the variance of the other member of a phylogroup.

2.5 Patterns across the elevational gradient

I generated a relaxed-clock tree (with rate variation following a log-normal distribution and

Yule speciation prior, using Acanthositta as an outgroup) in BEAST v2.4.8 (Bouckaert et al. 2014).

The tree topology was fixed following phylogenetic studies (relationships between oscine families from Barker et al. 2015 and between suboscines families from Prum et al. 2015). Within family relationships were determined from species-level molecular phylogenetic studies. Branch lengths were established using the GTR-gamma model of sequence evolution for the cyt b sequences. The

9 analysis was run for 100 million generations, with trees sampled every 1000 generations. The consensus phylogenetic tree, with mean branch lengths, was generated from a sample of 10,000 post burning trees (Fig. 4). Using this latter, I performed phylogenetically-corrected regressions with the R package Caper v0.5 (Orme et al. 2013), to test whether the association between mean

PC trait values and the midpoint elevational range (in meters) per each individual species (i.e. not species pairs) showed significant trends. Following Freckleton et al. (2002), I used generalized least-square models and transformed branch lengths with maximum likelihood estimates of Pagel’s lambda (λ).

2.6 Evolutionary rates and gradient effect

I used the R package EvoRAG (Weir & Lawson 2015), to compare Brownian motion (BM) and Ornstein Uhlenbeck (OU) null models – in which a single rate of evolution, β, is estimated for all sister pairs regardless of their difference in elevation – to models in which β varies as a linear function of the absolute difference in the midpoint elevational range separating each of the species in a pair. In addition to β estimated by the BM and OU models, the OU model estimates a constraint parameter, α, which represents the “pull” towards an optimal trait value, that in the EvoRAG modelling framework is the intermediate trait value between a sister pair. For linear OU models testing the effect of elevation difference, only β changes with elevation difference while α was held constant. I also fitted more complex models that tested the effect of elevational difference separately for oscines and suboscines which represent two major groups within passerines, the former which culturally inherit certain aspects of their song and the latter which rarely do. Models were compared with Akaike Information Criterion and Akaike Weights. Simulations with avian datasets of similar size have previously shown that an AIC difference in models of ca. 2 is comparable to an alpha cutoff of 0.05 in a frequentist approach (Weir & Lawson 2015), so I use

10 this value here as indicating substantial support between competing models. These models were fitted separately to PC1 and PC2 for song data, and to PC1 to PC6 for morphometric data.

2.7 Age distribution and diversification rates

I cannot use the species pairs in my dataset to infer diversification rates, because it includes intraspecific splits as well as some pairs which are not true sister species. Instead, for purposes of comparing diversification rates between sister species pairs that occur at similar versus different elevations, I obtained the genetic estimates of divergence times for all sister species from the

Amazon basin in the Pulido-Santacruz & Weir (2016) phylogeny. This phylogeny adds genetic data of the Furnariidae family to the complete phylogeny of birds of the World (Jetz et al. 2012).

Sister species represented on this phylogeny without genetic data were excluded. Sister species were categorized into three datasets. The highland and lowland datasets included sister species in which the midpoint elevation of each species was greater than or less than 1000m, respectively.

The mixed lowland and highland dataset included sisters in which one species had a midpoint elevation less than 1000m and the other greater than 1000m, with an elevational difference of at least 500m between their midpoints.

I calculated diversification rates of the tropical sister pairs from the updated Jetz et al.

(2012) phylogeny (Appendix 4). Following Harvey & Rabosky (2018), I used a tip-specific metric,

TB, to estimate time since the last speciation event for the diversification analysis. The TB statistic measures the inverse of the terminal branch lengths and has been recently used in other studies of trait-dependent diversification (e.g. Bromham et al. 2016; Gomes et al. 2016). To assess the impact of ecological divergence in assembling lineage diversification in the Amazon basin and adjacent

Andean slopes, I performed a phylogenetically-corrected regression of the log-transformed diversification rates as a function of difference in midpoint elevational range per sister species. If

11 ecological opportunity promoted lineage diversification along the Andes, I expect to find faster diversification rates with increasing elevational difference between sister pairs.

Results 3.1 Songs

PC1 explained 65% of the variation and was heavily weighted by the six measurements of frequency (Table 1). I interpret PC1 as representing song frequency. PC2 explained 22% of the variation and was weighted heavily by song duration and number of notes. PC2 represents song length. The OU model with a linear effect of elevation difference had substantially better support for PC1 than models without an elevational effect. The evolutionary rate of song frequency increased substantially with increasing difference in elevation. In contrast, the dynamic of song length evolution was best fitted by models with elevational difference not included, with a single rate of evolution across elevations (Table 3, Fig. 5).

3.2 Morphometrics

PC1 was positively loaded towards all morphometric measurements, explaining 73% of the variation and represents body size. Each remaining PC was positively loaded towards a particular trait – PC2: bill width, PC3: tail length, PC4: bill length, PC5: wing length and PC6: bill depth – and explained 9%, 7%, 3%, 2.8% and 2.5% of the variation, respectively (Table 2). I found no evidence of body size evolving as a consequence of the ecological differentiation between species pairs. The best fitting model of evolution for PC1 was Brownian motion without a gradient effect. In contrast, the evolutionary rates of all remaining PCs were best fitted by models including elevational difference. I found significant differences in the evolution of wing length and bill depth between oscines and suboscines. Wing lengths differ faster between suboscine pairs when increasing the differences of elevation, whereas the speed of bill depth evolution increases faster

12 in oscine versus suboscine pairs when the difference in elevation is more than 700 m (Table 3, Fig.

5).

3.3 Patterns across the elevational gradient

Elevational trends of characters are shown in figure 6. For the song analysis, PC1 (song frequency) had a borderline significant decrease with elevation (slope=-0.0002±0.0001 SE,

P=0.0534), while PC2 (song length) did not show any significant trend along the elevational gradient (slope=7.2x10-5±6.7x10-5 SE, P=0.3098). For morphometric analyses, PC1 (body size) had a borderline significant increase with elevation (slope=2.1x10-4±1.1x10-4 SE, P=0.04863) as predicted by Bergman’s rule of increase body size in colder environments (Bergmann 1847). Three of the remaining morphometric PCs had significant elevational trends (bill width: slope=-1.32x10-

4±4.31x10-5 SE, P=0.002504; tail length: slope=1.2x10-4±3.82x10-5 SE, P=0.002051; bill length: slope=-0.0001±0.00003 SE, P=0.0003384) and two PC’s lacked elevational trends (wing length: slope=2.4x10-5±2.5x10-5 SE, P=0.3459; bill depth: slope=-3.45x10-6±2.32x10-5 SE, P=0.882).

3.4 Age distribution and diversification rates

Sister species age distributions and their associated TB values are shown in Figure 7. For

Andean sister species, 42% dated to the past 2 million years while only 29% dated to this time- period in the Amazon and 17% for the mixed lowland and highland sister pairs (Fig. 7a). A chi- squared test comparing the proportions of sister species less than 2 million years between the three categories was significant (X2=7.88, P= 0.01941), and pairwise chi-squared tests between the categories indicate that the Andean and Mixed (X2=16.54, P=0.00004), Amazonian and Mixed

(X2=6.56, P=0.010426), and the Andean and Amazonian (X2=11.94, P= 0.00055) datasets were each significantly different. The phylogenetically-corrected regressions showed a non-significant decline in log(TB) with increasing elevational difference (Fig. 7b).

Discussion I found that evolutionary rates for key morphometric and behavioural traits are positively correlated with elevational difference between diverging pairs of species from the Amazon basin and adjacent Andean slopes. While a large number of studies have found an important role for ecological divergence in increasing rates of trait evolution in specific species (e.g. Schluter 1996;

Jiggins et al. 2001; Grant & Grant 2008; Matsubayashi et al. 2010; Tobias et al. 2014), this is one of the first broad-scale comparative projects to determine whether ecological divergence is more generally associated with increased rates of evolution in traits important to speciation (see also

Lawson & Weir 2014; Funk et al. 2006). Importantly, I demonstrate the role of ecology in increasing rates of trait evolution in the most species-rich regions, the continental Neotropics.

However, I did not find that increased rates of trait evolution were associated with increased diversification rates. A number of factors could result in increased rates of trait evolution with increasing elevational difference.

Traits may show elevational trends, in which case sister pairs which differentiate in elevation should diverge more rapidly than sister pairs at the same elevation. I saw clear evidence of this in several traits. Bill width (PC2, Fig. 6d) and bill length (PC4, Fig. 6f) decreased, while tail length (PC3, Fig. 6e) increased significantly with increasing elevation. These trends are likely to be directly or indirectly related to ecological factors that also vary with elevation. For example, differentiation of bill size and shape is strongly associated with prey size (e.g. Kleindorfer et al.

2006). Though not tested for the Andes, insect prey size (almost all passerine birds feed on insects) has been shown to decline with elevation in other montane systems (e.g. Janes 1994), likely due to slower rates of insect growth and development in colder temperatures (Hodkinson 2005). Tail length is an important trait in birds, as they use it as a rudder for steering and for braking during flight (Tubaro et al. 2002; Bears et al. 2008; Claramunt 2010). These locomotor features can be

14 affected by windy conditions and lower barometric pressures, usually found at high elevations, facilitating selection towards longer tails to aid for balance and steering (Maybury et al. 2001).

Song frequency (PC1) showed a borderline significant decline with elevation (Fig. 6a).

Different aspects of bird song are believed to be finetuned across environments in order to communicate effectively. Frequency is a structural component of the song that is highly influenced by the habitat but not by cultural learning (e.g. Nemeth et al. 2001; Boncoraglio & Saino 2007).

Birds sing at lower frequencies in tropical forests than either temperate forests or open tropical habitats, likely because tropical forests with dense vegetation have louder ambient noise and result in greater signal attenuation (Seddon 2005; Kirschel et al. 2009; Weir et al. 2012). Most of the species pairs within the dataset occupy forest habitats. Canopy height declines and density of vegetation typically increases with elevation as one leaves lowland terra firme forests and enters montane cloud forests. Dense cloud-forest vegetation is expected to differentially filter out high- pitched sounds, thus selecting for lower-pitched sounds, which transmit further and with less distortion (Tobias et al. 2010). As a result, species pairs diverging in elevation are likely to experience different forest densities and thus different selective pressures optimizing sound transmission, leading to song frequency divergence.

I also found increased evolutionary rates for wing length (PC5) and bill depth (PC6) with differences in elevation, but these traits showed no general increase or decrease in length with elevation (Figs. 6g, 6h). For traits that do not show elevational trends, it is possible that other mechanisms rather than environmental differentiation have promoted ecological divergence between populations. For instance, species interactions for resource availability could result in adaptations minimizing competition, such as specialization for distinctive foraging strategies, character displacement or interspecific aggregations. Bird species living at different elevational zones could show distictive foraging behavior, as a response of different community interactions.

The lack of evolutionary trends of trait values could also have occurred due to different responses to elevational gradients among clades, and thus no overall elevation trend across all passerine bird groups. For example, wing length and shape is correlated with flight speed (long pointed wings) and maneuverability (short rounded wings) (Savile 1957; Pennycuick 1975; Rayner 1988). Wing shape optimization across elevational gradients may evolve differently depending on the family of birds and their foraging niche, position in the canopy, need to escape predators, etc. (e.g.

Fitzpatrick 1985; Marchetti et al. 1995; Whelan 2001). Despite this different response to elevation by different taxonomic groups, the end result is that changes in elevation result in accelerated wing length evolution.

In contrast to the other characteristics, body size (PC1) and song length (PC2) (Fig. 5) did not show increased rates of evolution with increasing elevational difference between diverging species. Bergman’s rule predicts that body size in endotherms should increase in colder environments (Bergmann 1847), possibly, as a way to reduce heat loss (though this explanation is not universally accepted, e.g. Watt et al. 2010; Meiri 2011; Olalla-Tárraga 2011) and appears to hold true across elevational and latitudinal gradients in many parts of the world (e.g. Bulgarella et al. 2007). One study found that avian species pairs differing more in their temperature niches also experienced increased rates of body size evolution (Lawson & Weir 2014) consistent with these predictions, though the effect was present only at high latitudes and not in the tropics. I found a borderline significant increase in body size with elevation in passerine birds (compare with

Remsen 1993; Gutiérrez-Pinto et al. 2014; Freeman 2017 who found no effect), though the effect is minimal (Fig. 6c) and apparently not sufficient to drive a significant increase in rates of body size with increasing elevational difference. Other factors, such as sexual selection and competition, may constrain the evolution of body size across elevational gradients in the tropics (Freeman

2017). I also found no effect of elevational difference on evolutionary rates of song length. While

16 song length is typically heritable (in both oscines and suboscines, Slabbekoorn & Smith 2002), it is nevertheless a highly conserved component of avian vocalization, whose variation might be phylogenetically-dependent rather than habitat-dependent (e.g. Read & Weary 1992; Price &

Lanyon 2002; Derryberry et al. 2018). Here, I found that song length showed no trend with elevation across the dataset, which could explain why elevational divergence is not associated with elevated rates of song length.

4.1 Speciation Speciation is a two-step process that is generally initiated when populations become isolated by a geographic barrier to gene flow and is completed when populations evolve reproductive isolation. Many geographic barriers that initiate speciation and that operate within the Amazon basin are unlikely to drive substantial ecological differentiation that might accelerate trait evolution and reproductive isolation. For example, in the Amazonian lowlands, wide rivers are known to be important barriers to gene flow, with many closely-related species differentiating on opposing river banks (Mayr 1969; Haffer 2008; Leite & Rogers 2013; Weir et al. 2015). Arid river valleys play a similar role in the Andes, with cloud-forest populations of birds fragmented by these barriers and differentiating on opposite sides of them. However, these geographic barriers are unlikely to be associated with strong divergent ecological selection pressures that would cause populations on opposing river banks or valley sides to experience elevated rates of trait divergence

(Lawson & Weir 2014). The resulting species produced across these barriers usually represent only regional replacements rather than ecologically-distinct lineages that can interact in broad sympatry

(e.g. Weir 2009; Caro et al. 2013; Weir et al. 2015; Winger & Bates 2015). Recent studies show that lowland pairs differentiating across lowland Amazonian rivers often require millions of years to evolve reproductive isolation (Weir & Price 2011; Weir et al. 2015; Pulido-Santacruz et al.

2018), suggesting that the speciation process is protracted.

In contrast, the orogeny of the Andes created a series of ecologically-distinct elevational zones (Cracraft 1985), which provided the potential for ecological opportunity to rapidly accelerate the speciation process for species pairs that differentiated in elevation. Elevational differentiation could accelerate both premating and postmating reproductive isolation. Premating isolation would be promoted through divergent selection for different song features (or plumage colours, not tested here) that may optimize a males’ attractiveness to females in different habitats at different elevational zones. Here, I found evidence for elevated rates of song frequency evolution with increasing elevational difference. Frequency is a key aspect of song that is likely to be important for premating isolation (e.g. Slabbekoorn & Smith 2002; Seddon 2005; Tobias et al. 2010). Also, direct adaptation to differences in the abiotic and biotic environments at different elevational zones could drive extrinsic postmating isolation, whereby intermediate hybrids are unfit. Here, I found that a number of morphological features including those of the bill, which likely play a key role in ecological foraging niches, are evolving faster in pairs separated in elevation. Divergence in these traits likely contribute to extrinsic postmating isolation. Together, the results for song and morphometrics demonstrate that the evolution of reproductive isolation, marking the completion of speciation, is likely accelerated in sister pairs undergoing elevational differentiation and suggests that ecological speciation is occurring within a subset of species within the Amazon basin.

Approximately 20% of sister species pairs from the Amazon basin (including the eastern slope of the Andes) differ in their midpoint elevations by at least 1000 meters (Fig. 8) suggesting while ecological speciation mediated by elevational differentiation may play a role, it is unlikely to be a key driving factor behind speciation.

I next addressed whether rates of diversification also increased along a gradient of elevational midpoint difference within each sister species by performing an age distribution analysis and phylogenetically-corrected regressions of the TB statistic. Contrary to the expectation

18 that species diverging in elevation will experience increased speciation rates and thus will be younger on average, I found that sister species differing in elevation experienced slightly lower

(though not significantly so) diversification rates and rarely dated to the past two million years.

Rather, the bulk of these sister species diverged between 2 and 5 million years ago. The lack of an increased diversification rate may indicate that faster evolution in song and morphometrics experienced by sister pairs which differ in elevation may not translate into faster diversification rates and may thus have limited influence on patterns of species richness (see also Rabosky &

Matute 2013). However, the patterns uncovered may have resulted from greater opportunity for lowland to highland transitions when the eastern Andes were rapidly uplifted between 2 to 5 Ma

(Gregory-Wodzicki 2000). Uplift of the eastern cordilleras of the Andes may have rapidly transported lowland populations to high elevations where they would have experienced distinct ecological pressures compared to their lowland counterparts and would have diverged in song and morphometrics rapidly. Redcued Andean orogeny over the past 2 million years may have resulted in less opportunity for lowland to highland transitions and producing the apparent lower diversification rate of sister species pairs that differ in elevation. It, thus, seems plausible that elevational differences could lead to bursts of rapid diversification during periods of rapid Andean uplift, but that such bursts have not occurred recently and are thus not reflected in the TB statistic used to infer diversification.

4.2 Conclusions Speciation in birds from the Amazon basin may generally involve limited input from ecologically-mediated divergent selection, especially for those species groups which closely track their preferred climatic environment or elevation through time. Many speciation events simply involve gradual differentiation on opposite sides of rivers or similar barriers, in which limited ecological difference occurs on either side of the barrier (e.g. Weir et al. 2015). However, these

19 results demonstrate that for those species pairs which differentiate in elevation (a substantial component of birds from the Amazon basin together with its eastern Andean slopes – Fig. 8), the rapid evolution of traits important for reproductive isolation is driven by ecologically-mediated divergent selection associated with elevational differentiation. This elevated divergence is likely to have contributed importantly to the evolution of reproductive isolation for lineages differentiating in elevation during the uplift of the eastern cordilleras, but these results fail to demonstrate that elevational differentiation resulted in increased overall rates of diversification.

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Table 1: Loadings of the PCA analysis for song measurements.

Loadings Measurements PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Number of notes 0.00477641 0.69368 0.36967 0.61792 0.01538 0.00552 -0.0049 -0.003 Low Freq -0.3712 -0.0685 0.54502 -0.2605 0.63015 0.09393 0.29445 -0.0038 High Freq -0.4287816 0.02477 -0.1892 0.07675 0.33479 -0.3194 -0.7332 -0.1491 Q1 Freq -0.4278241 0.00011 0.1755 -0.0993 -0.389 0.66594 -0.3284 0.27133 Q3 Freq -0.4355725 -0.0005 -0.0064 0.02446 -0.273 -0.5546 0.25327 0.60277 Center Freq -0.4345018 -0.0035 0.07646 -0.0281 -0.4318 -0.1363 0.24308 -0.7351 Delta Freq -0.3410419 0.1035 -0.6725 0.28181 0.27965 0.34369 0.38034 0.01812 Delta Time 0.02181284 0.70906 -0.2038 -0.6736 -0.0089 -0.0365 0.00475 0.00188 Proportion of variance 0.6479 0.1847 0.09174 0.06178 0.0082 0.00342 0.00156 0.00077 Standard deviation 2.2766 1.2154 0.8567 0.70303 0.2561 0.16541 0.11173 0.0784

Table 2: Loadings of the PCA analysis for morphometric measurements.

Loadings Measurements PC1 PC2 PC3 PC4 PC5 PC6 PC7 Bill length 0.385967 -0.16456 -0.20786 -0.84921 0.025163 0.071307 -0.23209 Bill depth 0.396129 0.274453 -0.18936 0.064293 -0.10478 -0.83657 0.130124 Bill width 0.33669 0.710587 -0.23056 0.121853 0.388899 0.402685 -0.0174 Wing length 0.393586 0.179709 0.310343 0.072963 -0.79613 0.277464 -0.01886 Tarsus length 0.371323 -0.44715 -0.38217 0.496053 0.016298 0.0717 -0.51443 Hallux claw 0.402486 -0.39018 -0.06718 0.079809 0.139229 0.188936 0.787276 Tail length 0.354916 -0.09045 0.787973 0.045752 0.428578 -0.1228 -0.21 Proportion of variance 0.7307 0.09374 0.07067 0.0382 0.02785 0.02559 0.01323 Standard deviation 2.2616 0.81006 0.70336 0.5171 0.4415 0.42326 0.30428

31 32

Elevational difference Elevational difference in model not in model Models of trait evolution AICc AICc N Δ AICc N Δ AICc β slope (± 95% CI) Change in β weight weight

PC1 (frequency)

All (BM) 1 24.10 0.00 2 5.36 0.06 0.2359 (0.0446–0.481) 7.084

All (OU) 2 22.67 0.00 3 0.00 0.90 0.519 (0.0755–*) 8.872

Oscine/Suboscine Oscine: 0.218 (2.93x10-10–0.514) / 2 26.14 0.00 4 9.53 0.01 6.484 / 7.595

(BM) Suboscine: 0.249 (6.9x10-10 – 0.689) Songs Oscine/Suboscine Oscine: 0.725 (1.16x10-09–*) / 4 26.36 0.00 6 6.70 0.03 7.158 / 10.178 (OU) Suboscine: 0.495 (2.99x10-07–*)

PC2 (length)

All (BM) 1 4.85 0.06 2 6.92 0.02 3.69x10-09 (-0.0519–0.0641) 0.998

All (OU) 2 5.60 0.04 3 7.70 0.02 0.00154 (1.98x10-74–*) 1.073

Oscine: 2.28x10-09 (-0.0713–0.112) / Oscine/Suboscine 2 6.64 0.03 4 10.88 0.00 Suboscine: 1.23x10-17 (-0.0553– 1.004 / 1.001 (BM) 0.0796)

Oscine/Suboscine Oscine: -0.691 (-10–*) / Suboscine: 4 0.00 0.73 6 4.19 0.09 -0.642 / 1.001 (OU) 1.76x10-14 (-0.0495–*)

PC1 (body size)

All (BM) 1 0.00 0.29 2 0.96 0.18 0.0173 (7.91x10-23–0.0546) 1.65

All (OU) 2 2.07 0.10 3 3.06 0.06 0.0172 (1.85x10-27–*) 1.66

Oscine: 4.57x10-09 (-0.0263–0.0649) / Oscine/Suboscine 2 0.66 0.21 4 2.08 0.10 Suboscine: 0.0263 (8.23x10-12– 0.998 / 2.331 (BM)

0.0713)

Oscine/Suboscine Oscine: 4.2x10-10 (-1.04–*) / 4 4.90 0.03 6 6.27 0.01 -09 1.002 / 2.466 metrics (OU) Suboscine: 0.0313 (1.63x10 –*)

PC2 (bill width)

Morpho All (BM) 1 7.66 0.01 2 8.54 0.01 4.57x10-09 (7.17x10-23–0.00841) 0.998

All (OU) 2 2.54 0.13 3 0.00 0.45 0.0162 (2.05x10-09–*) 2.454

Oscine: 0.0125 (2.54x10-11–0.0232) / Oscine/Suboscine 2 6.74 0.02 4 7.01 0.01 Suboscine: 0.00225 (3.1x10-14– 8.284 / 1.366 (BM) 0.00813)

Oscine/Suboscine Oscine: 0.0266 (-1.01–*) / Suboscine: 4 3.59 0.07 6 0.74 0.31 10.386 / 2.284 (OU) 0.0231 (5.34x10-14–*)

PC3 (tail length)

All (BM) 1 16.38 0.00 2 16.02 0.00 0.0574 (3.55x10-22–0.0144) 2.176

All (OU) 2 5.71 0.04 3 0.00 0.74 0.0439 (0.00603–*) 3.964

Oscine: 0.00723 (7.94x10-14–0.027) / Oscine/Suboscine 2 16.32 0.00 4 17.95 0.00 Suboscine: 0.00572 (3.89x10-13– 3.078 / 1.975 (BM) 0.017)

Oscine/Suboscine Oscine: 0.0218 (2.79x10-13–*) / 4 5.63 0.04 6 2.94 0.17 2.555 / 5.261 (OU) Suboscine: 0.0882 (0.00436–*)

PC4 (bill length)

All (BM) 1 53.18 0.00 2 40.74 0.00 0.013 (0.000346–0.0304) 7.163

All (OU) 2 11.47 0.00 3 0.00 0.85 33.1 (0.00833–*) 5.742

Oscine: 0.0263 (1.04x10-12–0.0539) / Oscine/Suboscine 2 50.01 0.00 4 34.94 0.00 Suboscine: 0.00343 (6.27x10-13– 41.7 / 2.267 (BM) 0.00754)

Oscine/Suboscine Oscine: 0.104 (5.77x10-22–*) / 4 13.87 0.00 6 3.53 0.15 11.913 / 3.822 (OU) Suboscine: 0.136 (0.00353–*)

PC5 (wing length)

All (BM) 1 53.12 0.00 2 53.14 0.00 0.00148 (2.5x10-22–0.00358) 2.16

All (OU) 2 55.19 0.00 3 55.07 0.00 0.00167 (5.68x10-22–*) 1.99

Oscine: 0.00187 (3.15x10-13–0.00614) Oscine/Suboscine 2 54.92 0.00 4 57.06 0.00 / Suboscine: 0.00131 (6.53x10-13– 2.531 / 1.967 (BM) 0.00348)

Oscine/Suboscine Oscine: 0.00214 (2.45x10-18–*) / 4 59.14 0.00 6 0.00 1.00 2.750 / 1.766 (OU) Suboscine: 0.00156 (1.35x10-12–*)

PC6 (bill depth)

All (BM) 1 135.56 0.00 2 132.39 0.00 0.00474 (2.68x10-21–0.0131) 2.992

All (OU) 2 128.83 0.00 3 119.46 0.00 0.02298 (0.00184–*) 4.501

Oscine/Suboscine Oscine: 0.00867 (2.3x10-11–0.0202 / 2 137.45 0.00 4 0.00 1.00 8.342 / 2000.98 (BM) Suboscine: 6.58 (8.33x10-14–0.0116)

Oscine/Suboscine Oscine: 0.017 (1.03x10-09–*) / 4 132.22 0.00 6 123.65 0.00 6.897 / 5.004 (OU) Suboscine: 0.147 (0.000797–*)

* The upper end of the confidence interval can't be determined in OU models with elevation included because the maximum likelihood estimate occurs along a ridge in the likelihood surface.

Song duration

Number of notes High Freq.

Quartile 3 Freq.

Center Freq. Quartile 1 Freq.

Low Freq.

1 2 2 1 4 3 Δ Frequency

Frequency(kHz)

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Time (s)

Figure 2: Measurements taken from an example song spectrogram.

36 37

Figure 3: Example of morphometric measurements taken from museum specimens.

oscines

subosc

ines

Figure 4: Consensus phylogenetic tree of species pairs with cyt b within my dataset. Three clades are differentiated: Furnarioidea in blue, Tyrannoides in green, and Oscines in orange.

0.9

)

0.6

0.3

Evolutionaryrate (

0 0 500 1000 1500 2000

Δ Midpoint elevation (m)

I show an example of a morphologically-differentiated species pair, Campylorhamphus pusillus –

C. procurvoides, with a Euclidean distance for bill length is 0.43 and difference in midpoint elevational ranges of 1320m. In contrast, the bottom-left corner illustrates a sister species pair,

Dendroplex kienerii – D. picus, with weak morphometric differentiation (Euclidean distance for bill length = 0.18) that occur at similar elevations (Δ midpoint elevational range = 400m). Bird illustrations were taken with permission from Schulenberg et al. (2010).

(a) Song frequency (b) Song length

P = 0.05 P = 0.31

P < 0.05 P < 0.01 P < 0.01

(f) Bill length (g) Wing length (h) Bill depth

P < 0.001 P = 0.35 P = 0.88

(a) Amazonian lowlands Andean highlands Lowlands vs. Highlands (b)

P = 0.1666

Log (TB) (TB) (TB) Log Log Log Number of sister species sister of Number

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Age (Myr) Δ Midpoint elevational range

Figure 7: (a) Age distribution of sister species pairs belonging to the study region (Fig.1), taken

from the phylogeny of birds of the world with genetic data of Pulido-Santacruz & Weir (2016),

within three elevational categories: lowland Amazonian species (midpoint elevational range

<1000m), highland Andean species (midpoint elevational range ≥1000m), and lowland

Amazonian versus highland Andean species (midpoint elevational range of one member in a sister

pair is <1000m and ≥1000m for the other member). (b) Phylogenetically-corrected regression of

diversification rates as a function of the difference in midpoint elevational range.

Number of sister species pairs species sister of Number

Δ Midpoint elevational range (m)

Appendices

Appendix 1: Dataset for song analysis Genetic Δ Midpoint Euclidean Euclidean Time (Mya) Species1 Species2 distance elevation range distance PC1 distance PC2 Thamnophilus nigrocinereus Thamnophilus cryptoleucus 0.56 1.11273 0 0.011632 0.364436 Thamnophilus 1.8 3.595643 150 0.124312 0.256924 Thamnophilus punctatus stictocephalus Thamnomanes ardesiacus Thamnomanes saturninus 3.22 6.431345 375 0.765734 0.259409 Isleria hauxwelli Isleria guttata 4.23 8.454064 200 0.080945 0.174763 Myrmotherula ambigua Myrmotherula sclateri 3.69 7.382516 175 0.340016 0.474001 Myrmotherula multostriata Myrmotherula surinamensis 1.66 3.313974 50 0.882431 0.479488 Myrmotherula cherriei Myrmochanes hemileucus 5.26 10.52642 125 1.374831 2.509046 Myrmotherula menetriesii Myrmotherula assimilis 7.45 14.89491 350 1.183624 0.499951 Hypocnemoides 3.34 6.682496 100 0.089756 0.841513 Hypocnemoides melanopogon maculicauda Akletos melanoceps Akletos goeldii 1.61 3.214375 0 0.25068 0.200383 Rhegmatorhina gymnops Rhegmatorhina hoffmannsi 0.69 1.373577 50 0.592113 0.038669 Gymnopithys leucaspis Gymnopithys rufigula 0.46 0.918563 150 0.338805 0.0272 Gymnopithys lunulatus Gymnopithys salvini 2.54 5.079289 25 0.110866 0.502314 Thamnophilus aethiops Thamnophilus aroyae 2.49 4.989493 750 0.975273 0.04692 Thamnophilus torquatus Thamnophilus ruficapillus 1.92 3.838313 1850 0.098103 0.168313 Myrmotherula klagesi Myrmotherula longicauda 1.98 3.963712 700 0.922329 0.332539 Drymophila devillei Drymophila caudata 4.8 9.593494 1300 0.296579 0.096013 Thamnomanes caesius Thamnomanes schistogynus 2.23 4.45659 300 0.314161 0.744151 Thamnophilus tenuepunctatus Thamnophilus palliatus 0.5 0.992673 200 0.292116 0.111567 Grallaria dignissima Grallaria eludens 5.58 11.16366 100 0.066425 0.020036 Hylopezus fulviventris Hylopezus berlepschi 2.73 5.468582 250 0.332371 0.330096 Grallaria squamigera Grallaria varia 7.54 15.0747 1850 1.980804 2.319456 Grallaria rufula Grallaria blakei 5.7 11.39145 700 0.954923 0.672089 Grallaricula lineifrons Grallaricula flavirostris 7.85 15.70292 1650 1.427235 2.397441

43 44

Scytalopus parvirostris Scytalopus spillmanni 5.68 11.36039 50 1.25307 1.635583 Formicarius colma Formicarius analis 6.6 13.19757 250 1.481361 1.535731 Dendrexetastes rufigula Nasica longirostris 5.8 11.60431 225 1.635363 0.945225 Hylexetastes stresemanni Hylexetastes perrotii 1.91 3.817267 0 0.223686 0.162431 Xiphorhynchus elegans Xiphorhynchus spixii 1.99 3.989438 50 1.468178 0.217929 Dendroplex picus Dendroplex kienerii 4.21 8.416854 400 1.080734 0.445222 Thripophaga cherriei Thripophaga fusciceps 2.12 4.24921 165 0.765673 1.259909 Cranioleuca vulpina Cranioleuca muelleri 0.84 1.683981 100 0.425293 2.008547 Certhiaxis mustelinus Certhiaxis cinnamomeus 4.16 8.316397 175 0.65863 0.537646 Cinclodes excelsior Cinclodes aricomae 1.03 2.059533 150 0.075947 0.61434 Thripadectes flammulatus Thripadectes scrutator 1.09 2.173623 25 0.588376 0.174423 Asthenes virgata Asthenes maculicauda 1.78 3.568407 225 0.240784 0.183557 Cranioleuca marcapatae Cranioleuca albiceps 0.39 0.779683 150 0.263613 0.170846 Synallaxis azarae Synallaxis courseni 0.76 1.525623 475 0.416658 0.142626 Xiphocolaptes promeropirhynchus 0.68 1.363074 1950 0.284585 0.129714 promeropirhynchus X. p. orenocensis Dendrocincla tyrannina Dendrocincla merula 7.97 15.94968 1550 1.792347 4.551958 Xenops rutilans Xenops tenuirostris 4.02 8.046067 950 0.312825 0.288726 Synallaxis cabanisi Synallaxis macconnelli 1.63 3.267579 1175 0.36539 0.370202 Xiphorhynchus 2.61 5.215263 650 0.328754 0.039241 Xiphorhynchus ocellatus chunchotambo Xiphorhynchus guttatus 1.13 2.254988 300 0.149765 0.076979 guttatoides X. g. eytoni Clibanornis rubiginosus 2.2 4.408395 375 0.114453 0.01149 brunnescens C. r. obscurus Syndactyla ucayalae Syndactyla striata 1.39 2.780933 75 0.382089 0.178482 Anabacerthia variegaticeps Anabacerthia ruficaudata 2.52 5.03808 1125 1.659062 0.60471 Campylorhamphus 3.47 6.941212 1325 1.051115 0.522916 procurvoides Campylorhamphus pusillus Myiopagis flavivertex Myiopagis viridicata 7.03 14.06311 400 0.806185 2.062706 Elaenia ruficeps Elaenia cristata 15.11 30.22535 550 0.435071 1.017044

Elaenia albiceps Elaenia pallatangae 5 9.992964 600 0.738079 1.668401 Uromyias agilis Uromyias agraphia 3.71 7.423472 550 0.266954 0.960266 Leptopogon rufipectus Leptopogon taczanowskii 2.96 5.910503 50 0.325291 3.229776 Myiopagis caniceps Myiopagis olallai 5.07 10.14435 545 0.812773 0.036621 Elaenia parvirostris Elaenia flavogaster 5.91 11.81154 250 0.154043 0.483502 Zimmerius cinereicapilla Zimmerius villarejoi 10.19 20.38447 370 1.512556 0.593691 Zimmerius bolivianus Zimmerius gracilipes 2.82 5.630783 1615 0.903235 0.197678 Mionectes striaticollis Mionectes olivaceus 3.21 6.428737 850 5.325999 1.043345 Colonia colonus fuscicapillus C. c. poecilonota 3.51 7.011496 850 0.9582 0.062531 Phoenicircus nigricollis Phoenicircus carnifex 1.38 2.752523 100 1.610631 1.774824 Gymnoderus foetidus Conioptilon mcilhennyi 10.48 20.96893 100 1.274257 0.957185 Pipreola riefferii Pipreola intermedia 4.68 9.358575 750 0.242528 0.453921 Pipreola jucunda Pipreola pulchra 3.5 7.00131 425 0.062696 0.020105 Pipreola frontalis Pipreola chlorolepidota 9.1 18.20852 600 2.585193 1.355496 Doliornis remseni Doliornis sclateri 3.91 7.815909 225 0.452981 0.353588 Ampelion rufaxilla Ampelion rubrocristata 5.39 10.77494 955 0.195159 0.422762 Lipaugus fuscocinereus Lipaugus uropygialis 6.2 12.4059 225 1.231874 0.15968 Rupicola peruviana Rupicola rupicola 3.93 7.85177 800 1.53104 0.582265 Tyranneutes stolzmanni Tyranneutes virescens 4.08 8.165711 0 0.036586 0.449622 Neopelma chrysocephalum Neopelma sulphureiventer 4.17 8.344289 150 0.20356 0.568529 Lepidothrix nattereri Lepidothrix iris 0.93 1.854245 150 0.338112 0.059222 Pipra aureola Pipra fasciicauda 0.98 1.963447 150 0.569245 0.305349 Chloropipo flavicapilla Chloropipo unicolor 3.95 7.909985 225 1.023453 0.284781 Lepidothrix isidorei Lepidothrix coeruleocapilla 2.05 4.095002 200 1.08454 0.589856 Dixiphia pipra coracina Dixiphia pipra pipra 2.46 4.9129 1200 3.729944 0.261199 Tityra cayana Tityra semifasciata 3.84 7.683221 350 1.469015 0.237302 Iodopleura isabellae Iodopleura fusca 4.63 9.254109 0 0.01757 0.864594 Schiffornis turdina steinbachi S. t. amazona 2.29 4.589906 600 0.339009 0.137054 Pachyramphus polychopterus Pachyramphus albogriseus 3.72 7.441554 1250 0.149385 0.102494 Hylophilus brunneiceps Hylophilus thoracicus 3.95 7.907619 0 0.260499 1.012714 Cyclarhis gujanensis Cyclarhis nigrirostris 4.89 9.7845 900 0.162195 0.253037 Hylophilus olivaceus Hylophilus pectoralis 3.04 6.076156 950 0.74844 0.472947

Cyanolyca viridicyanus Cyanolyca turcosa 4.45 8.893709 200 0.441763 0.265473 Orochelidon murina Orochelidon andecola 2.57 5.148565 550 0.465578 0.161611 Pygochelidon cyanoleuca Pygochelidon melanoleuca 5.26 10.51527 2050 4.385287 1.145856 Pheugopedius genibarbis Pheugopedius coraya 5.54 11.07203 250 1.052829 0.428471 Cantorchilus leucotis Cantorchilus guarayanus 3.63 7.254591 50 1.403676 0.306274 Pheugopedius mystacalis Pheugopedius euophrys 5.37 10.74755 875 0.761146 0.814872 Henicorhina leucosticta Henicorhina leucoptera 5.77 11.54654 1250 0.976123 0.102389 Turdus haplochrous Turdus nudigenis 0.77 1.546649 250 0.824129 0.150673 Catharus dryas Catharus fuscater 5.99 11.97123 250 1.412934 0.665392 Entomodestes leucotis Entomodestes coracinus 1.3 2.60323 875 2.689835 0.025401 Turdus chiguanco Turdus serranus 1.44 2.878071 850 0.382425 0.365766 Tachyphonus rufiventer Tachyphonus luctuosus 5.56 11.11903 125 0.099277 0.086303 Tangara velia Tangara callophrys 1.47 2.944495 50 1.22097 0.632021 Dacnis flaviventer Dacnis cayana 4.19 8.37904 300 0.26135 0.249336 Conirostrum bicolor Conirostrum margaritae 3.38 6.758246 25 0.094209 0.586442 Creurgops verticalis Creurgops dentatus 4.74 9.480791 300 0.328812 0.043226 Hemispingus calophrys Hemispingus parodii 0.6 1.209709 295 0.103165 1.231916 Hemispingus frontalis Hemispingus melanotis 3.72 7.442343 250 0.223644 1.258318 Hemispingus 4.13 8.257695 125 0.745552 1.168978 Hemispingus goeringi rufosuperciliaris Hemispingus 3.03 6.068249 375 0.95788 0.978471 Hemispingus verticalis xanthophthalmus Cnemathraupis aureodorsalis Cnemathraupis eximia 3.86 7.726615 400 1.614659 1.853242 Anisognathus lacrymosus Anisognathus igniventris 2.94 5.87904 350 0.539478 0.855253 Dubusia taeniata Dubusia castaneoventris 4.11 8.225586 0 0.190427 0.464853 Pipraeidea melanonota Pipraeidea bonariensis 6.92 13.84316 725 0.436477 0.390088 Conirostrum cinereum Conirostrum rufum 1.13 2.25763 50 0.821125 1.358147 Diglossa caerulescens Diglossa cyanea 3.74 7.476346 275 0.974847 0.116855 Urothraupis stolzmanni Nephelornis oneilli 4.07 8.135498 100 0.730995 1.034947 Phrygilus atriceps Phrygilus punensis 1.25 2.496711 450 0.107649 0.696407 Phrygilus plebejus Phrygilus unicolor 3.2 6.396549 700 0.771401 1.14192 Catamenia inornata Catamenia homochroa 2.69 5.376142 300 0.100931 0.750822

Thlypopsis sordida Thlypopsis inornata 0.88 1.765024 775 0.823218 0.343085 Ramphocelus melanogaster Ramphocelus carbo 0.55 1.100715 800 0.526502 0.089029 Chlorospingus parvirostris Chlorospingus flavigularis 3.74 7.481781 700 0.656974 0.146582 Pheucticus chrysogaster Pheucticus aureoventris 0.69 1.378868 775 0.124036 0.661635 Piranga rubriceps Piranga leucoptera 5.1 10.19105 1050 0.220921 0.790003 Myiothlypis fulvicauda Myiothlypis rivularis 2.97 5.935801 50 0.969401 0.341791 Myiothlypis nigrocristata Myiothlypis signata 3.79 7.583324 575 0.221127 1.202636 Basileuterus tristriatus Basileuterus trifasciatus 1.93 3.86698 250 0.60593 0.190418 Cacicus sclateri Cacicus koepckeae 1.15 2.308151 250 0.116242 0.111723 Cacicus cela Cacicus uropygialis 4.06 8.119421 1175 1.909731 0.575624 Psarocolius angustifrons Psarocolius atrovirens 2.17 4.349814 750 0.026386 0.417762 Euphonia rufiventris Euphonia cayennensis 2.92 5.84733 250 0.61504 0.041476 Spinus spinescens Spinus olivaceus 0.68 1.365733 850 0.736968 0.891696

Appendix 2: Dataset for morphometric analysis Δ Euclidean Euclidean Euclidean Euclidean Euclidean Euclidean Euclidean Time Genetic Midpoint distance distance distance distance distance distance distance (Mya) distance elevation PC1 PC2 PC3 PC4 PC5 PC6 PC7 Species1 Species2 range Thamnophilus 0.56 1.11273 0 0.60427 0.29359 0.23759 0.09969 0.12164 0.23487 0.57922 Thamnophilus nigrocinereus cryptoleucus Thamnophilus 1.8 3.595643 150 0.11209 0.27237 0.0426 0.07478 0.06916 0.26478 0.32086 Thamnophilus punctatus stictocephalus Thamnophilus murinus Thamnophilus schistaceus 5.223 10.44695 375 0.32245 0.23202 0.19523 0.2122 0.1771 0.33481 0.77701 Thamnomanes ardesiacus Thamnomanes saturninus 3.22 6.431345 200 0.30158 0.02723 0.33755 0.26067 0.07811 0.01838 0.03428 Isleria hauxwelli Isleria guttata 4.23 8.454064 350 0.23515 0.05742 0.12655 0.03442 0.09016 0.01785 0.01501 Myrmotherula 1.66 3.313974 100 0.06413 0.13317 0.0473 0.142 0.02171 0.03063 0.01993 Myrmotherula multostriata surinamensis Myrmotherula menetriesii Myrmotherula assimilis 7.45 14.89491 0 0.01481 0.07711 0.08894 0.10118 0.01901 0.09088 0.15466

Hypocnemoides Hypocnemoides 3.34 6.682496 150 0.33968 0.31086 0.2735 0.1481 0.15438 0.18337 0.11178 melanopogon maculicauda Akletos melanoceps Akletos goeldii 1.61 3.214375 25 0.35803 0.46554 0.17425 0.17192 0.07066 0.08142 0.37658 Gymnopithys leucaspis Gymnopithys rufigula 0.46 0.918563 750 0.08042 0.00234 0.23804 0.10252 0.06907 0.26102 0.01907 Gymnopithys lunulatus Gymnopithys salvini 2.54 5.079289 1850 0.18011 0.11629 0.06507 0.13481 0.13709 0.085 0.16272 Thamnophilus aethiops Thamnophilus aroyae 2.49 4.989493 700 0.89771 0.1353 0.08289 0.20338 0.082 0.05718 0.26654 Thamnophilus torquatus Thamnophilus ruficapillus 1.92 3.838313 1300 0.18855 0.18038 0.55418 0.43644 0.03918 0.0836 0.04584 Myrmotherula klagesi Myrmotherula longicauda 1.98 3.963712 300 0.17738 0.02117 0.45503 0.20287 0.18796 0.07269 0.35673 Drymophila devillei Drymophila caudata 4.8 9.593494 100 0.63089 0.49465 0.47982 0.01923 0.32065 0.25177 0.04386 Thamnomanes 2.23 4.45659 250 0.00118 0.06448 0.31105 0.21362 0.08459 0.12653 0.14759 Thamnomanes caesius schistogynus Thamnophilus 0.5 0.992673 1850 0.26731 0.35601 0.14678 0.09595 0.01958 0.06898 0.26897 tenuepunctatus Thamnophilus palliatus Grallaria dignissima Grallaria eludens 5.58 11.16366 700 0.42529 0.14197 0.24633 0.0562 0.29655 0.20214 0.2638 Hylopezus fulviventris Hylopezus berlepschi 2.73 5.468582 50 0.40047 0.13275 0.14649 0.11763 0.01287 0.03208 0.07119 Grallaria squamigera Grallaria varia 7.54 15.0747 250 2.16559 0.96555 0.25406 0.29215 0.2375 0.28151 0.80041 Grallaria rufula Grallaria blakei 5.7 11.39145 225 0.33087 0.30414 0.52751 0.25036 0.15549 0.05734 0.33572 Scytalopus parvirostris Scytalopus spillmanni 5.68 11.36039 50 0.94965 0.37813 0.0856 0.02271 0.02339 0.29887 0.03317 Formicarius colma Formicarius analis 6.6 13.19757 175 0.19099 0.22252 0.28907 0.10133 0.03245 0.12119 0.21567 Dendrexetastes rufigula Nasica longirostris 5.8 11.60431 25 0.08757 3.80597 1.26931 1.93447 2.10841 1.17814 0.29973 Hylexetastes stresemanni Hylexetastes perrotii 1.91 3.817267 0 0.01792 0.20796 0.32605 0.0755 0.1598 0.23307 0.0105 Xiphorhynchus elegans Xiphorhynchus spixii 1.99 3.989438 225 0.03761 0.08607 0.31953 0.00028 0.1508 0.02331 0.11155 Dendroplex picus Dendroplex kienerii 4.21 8.416854 150 0.0363 0.26428 0.19754 0.18141 0.16336 0.06149 0.06748 Certhiaxis mustelinus Certhiaxis cinnamomeus 4.16 8.316397 1950 0.38015 0.34506 0.24006 0.1416 0.03768 0.01348 0.33514 Thripadectes flammulatus Thripadectes scrutator 1.09 2.173623 1550 0.37957 0.13458 0.062 0.01206 0.03541 0.07197 0.00378 Asthenes virgata Asthenes maculicauda 1.78 3.568407 950 0.82765 0.32841 0.05932 0.08928 0.25362 0.04451 0.30608 Cranioleuca marcapatae Cranioleuca albiceps 0.39 0.779683 1175 0.36374 0.01066 0.04609 0.2306 0.16352 0.06808 0.06396 Xiphocolaptes promeropirhynchus 0.68 1.363074 650 0.15356 0.30393 0.01715 0.2123 0.08985 0.04343 0.17402 promeropirhynchus X. p. orenocensis Dendrocincla tyrannina Dendrocincla merula 7.97 15.94968 300 1.70382 0.06988 0.24274 0.28964 0.18932 0.43401 0.44058 Xenops rutilans Xenops tenuirostris 4.02 8.046067 1325 0.25129 0.60332 0.22191 0.03718 0.25398 0.45945 0.23834 Synallaxis cabanisi Synallaxis macconnelli 1.63 3.267579 400 0.33222 0.0301 0.07556 0.19408 0.02359 0.18512 0.00593

Xiphorhynchus 2.61 5.215263 650 0.09063 0.18408 0.1862 0.07346 0.08134 0.00222 0.12236 Xiphorhynchus ocellatus chunchotambo Xiphorhynchus guttatus 1.13 2.254988 600 0.05893 0.06128 0.21112 0.22614 0.00484 0.02439 0.0164 guttatoides X. g. eytoni Campylorhamphus Campylorhamphus 3.47 6.941212 1750 0.66788 0.33088 0.18514 0.43335 0.34535 0.25733 0.72334 procurvoides pusillus Myiopagis flavivertex Myiopagis viridicata 7.03 14.06311 550 0.0857 0.11643 0.372 0.10541 0.01525 0.02344 0.53527 Mionectes oleagineus Mionectes macconnelli 1.443 2.885914 50 1.00769 0.05722 0.27816 0.06207 0.00141 0.01701 0.13003 Elaenia albiceps Elaenia pallatangae 5 9.992964 250 0.21516 0.25154 0.28284 0.02003 0.29383 0.17603 0.11721 Uromyias agilis Uromyias agraphia 3.71 7.423472 1615 0.00561 0.10823 0.04597 0.01668 0.1079 0.08571 0.03763 Leptopogon rufipectus Leptopogon taczanowskii 2.96 5.910503 850 0.00632 0.06982 0.00632 0.12385 0.02281 0.11684 0.12952 Elaenia parvirostris Elaenia flavogaster 5.91 11.81154 1650 1.03936 0.07836 0.32563 0.17951 0.37099 0.28803 0.67817 Zimmerius bolivianus Zimmerius gracilipes 2.82 5.630783 100 1.23718 0.46544 0.52254 0.42973 0.1615 0.48334 0.4293 Mionectes striaticollis Mionectes olivaceus 3.21 6.428737 150 0.34762 0.08774 0.04865 0.1225 0.13421 0.08987 0.28122 Knipolegus poecilurus Knipolegus poecilocercus 5.855 11.7107 100 0.25967 0.2952 0.76043 0.11856 0.38034 0.35098 0.24023 Colonia colonus fuscicapillus C. c. poecilonota 3.51 7.011496 750 0.92402 0.30624 0.33073 0.25612 0.32918 0.85313 0.23298 Phoenicircus nigricollis Phoenicircus carnifex 1.38 2.752523 600 0.39611 0.04593 0.12936 0.11502 0.10598 0.00398 0.15129 Gymnoderus foetidus Conioptilon mcilhennyi 10.48 20.96893 225 1.95386 0.12854 0.19094 0.03896 0.35143 0.07941 0.32218 Pipreola riefferii Pipreola intermedia 4.68 9.358575 955 0.54049 0.06462 0.27154 0.03154 0.05512 0.05427 0.17318 Pipreola jucunda Pipreola pulchra 3.5 7.00131 800 0.03103 0.23748 0.50067 0.05322 0.01706 0.12294 0.35037 Pipreola frontalis Pipreola chlorolepidota 9.1 18.20852 0 1.13811 0.15399 0.28108 0.06303 0.36159 0.16449 0.32324 Doliornis remseni Doliornis sclateri 3.91 7.815909 150 0.16535 0.33315 0.32796 0.07909 0.09956 0.28266 0.21645 Ampelion rufaxilla Ampelion rubrocristata 5.39 10.77494 150 0.13102 0.15378 0.19923 0.04709 0.13076 0.28831 0.33263 Rupicola peruviana Rupicola rupicola 3.93 7.85177 150 0.23781 0.11034 0.64192 0.06599 0.11402 0.10569 0.45619 Tyranneutes stolzmanni Tyranneutes virescens 4.08 8.165711 225 0.12281 0.19382 0.20205 0.212 0.04656 0.03035 0.28111 Neopelma chrysocephalum Neopelma sulphureiventer 4.17 8.344289 200 0.3888 0.71095 0.29947 0.18119 0.03693 0.27191 0.12193 Lepidothrix nattereri Lepidothrix iris 0.93 1.854245 850 0.37016 0.30154 0.42998 0.08389 0.06213 0.19883 0.31457 Pipra aureola Pipra fasciicauda 0.98 1.963447 350 0.1241 0.13805 0.17234 0.13421 0.06729 0.02963 0.11059 Chloropipo flavicapilla Chloropipo unicolor 3.95 7.909985 600 0.34265 0.35311 0.27962 0.00573 0.07599 0.13345 0.07004 Lepidothrix 2.05 4.095002 1250 0.66325 0.15994 0.05978 0.32962 0.17205 0.06492 0.01603 Lepidothrix isidorei coeruleocapilla Dixiphia pipra coracina D. p. pipra 2.46 4.9129 900 0.07563 0.14061 0.21145 0.03463 0.04949 0.04305 0.1534 Ceratopipra rubrocapilla Ceratopipra chloromeros 2.22 4.439217 950 0.3085 0.25341 0.35866 0.21596 0.12969 0.20342 0.00476

Tityra cayana Tityra semifasciata 3.84 7.683221 200 0.51648 0.23432 0.07848 0.04575 0.09097 0.0444 0.14139 Iodopleura isabellae Iodopleura fusca 4.63 9.254109 1950 0.26859 0.33104 0.1129 0.21051 0.01902 0.01864 0.36054 Schiffornis turdina 2.29 4.589906 2050 0.26345 0.05109 0.30068 0.16466 0.03398 0.15881 0.07089 steinbachi S. t. amazona Pachyramphus Pachyramphus 3.72 7.441554 250 0.95366 0.36404 0.02234 0.0982 0.03234 0.06582 0.25281 polychopterus albogriseus Hylophilus brunneiceps Hylophilus thoracicus 3.95 7.907619 50 0.56485 0.06862 0.05451 0.07355 0.08517 0.16491 0.22619 Cyclarhis gujanensis Cyclarhis nigrirostris 4.89 9.7845 875 0.00926 0.05598 0.25074 0.1445 0.02379 0.34316 0.10957 Hylophilus olivaceus Hylophilus pectoralis 3.04 6.076156 1250 0.41299 0.37068 0.33636 0.00269 0.24118 0.20351 0.06995 Cyanolyca viridicyanus Cyanolyca turcosa 4.45 8.893709 250 0.12759 0.16377 0.42197 0.03527 0.04493 0.0733 0.19582 Orochelidon murina Orochelidon andecola 2.57 5.148565 150 0.18231 0.48351 0.14075 0.34355 0.13504 0.19568 0.7149 Pygochelidon 5.26 10.51527 250 0.02062 0.00203 0.21507 0.0949 0.12121 0.101 0.23855 Pygochelidon cyanoleuca melanoleuca Pheugopedius genibarbis Pheugopedius coraya 5.54 11.07203 850 0.1745 0.01221 0.24092 0.06635 0.04513 0.14194 0.20372 Cantorchilus leucotis Cantorchilus guarayanus 3.63 7.254591 450 1.34129 0.08695 0.15208 0.05743 0.01629 0.2111 0.35603 Pheugopedius mystacalis Pheugopedius euophrys 5.37 10.74755 125 0.1935 0.58876 0.32828 0.01304 0.06654 0.60144 0.03694 Henicorhina leucosticta Henicorhina leucoptera 5.77 11.54654 50 0.49087 0.15875 0.13825 0.04355 0.36786 0.1188 0.27497 Turdus haplochrous Turdus nudigenis 0.77 1.546649 300 0.55801 0.11285 0.14454 0.02277 0.07558 0.01954 0.21146 Turdus hauxwelli Turdus fumigatus 4.32 4.320497 150 0.4848 0.00419 0.16152 0.01725 0.01608 0.02062 0.37333 Catharus dryas Catharus fuscater 5.99 11.97123 25 0.20525 0.1139 0.30354 0.20571 0.30773 0.09195 0.29779 Entomodestes leucotis Entomodestes coracinus 1.3 2.60323 300 0.17957 0.16969 0.09249 0.93405 0.02426 0.6422 0.36774 Turdus chiguanco Turdus serranus 1.44 2.878071 295 0.77693 0.27641 0.111 0.00689 0.04377 0.06389 0.21202 Tachyphonus rufiventer Tachyphonus luctuosus 5.56 11.11903 250 1.48179 0.26234 0.03806 0.23416 0.07249 0.25806 0.1843 Tangara velia Tangara callophrys 1.47 2.944495 375 0.17631 0.11533 0.03239 0.05011 0.07658 0.16179 0.08563 Dacnis flaviventer Dacnis cayana 4.19 8.37904 250 0.86376 0.08152 0.15665 0.11375 0.00299 0.07337 0.16342 Cyanerpes caeruleus Cyanerpes cyaneus 2.084 4.168989 400 0.5975 0.35455 0.42038 0.58291 0.08551 0.10375 0.12801 Conirostrum bicolor Conirostrum margaritae 3.38 6.758246 350 0.65711 0.20557 0.17959 0.06555 0.17459 0.18766 0.48824 Creurgops verticalis Creurgops dentatus 4.74 9.480791 0 0.82424 0.51431 0.00347 0.0153 0.03954 0.07282 0.00773 Hemispingus calophrys Hemispingus parodii 0.6 1.209709 325 0.25859 0.06977 0.05783 0.04347 0.13196 0.04808 0.02523 Hemispingus frontalis Hemispingus melanotis 3.72 7.442343 0 0.69227 0.07383 0.11836 0.10192 0.07234 0.20099 0.2836 Hemispingus 3.03 6.068249 725 0.6332 0.17356 0.00608 0.19916 0.13541 0.05267 0.09966 Hemispingus verticalis xanthophthalmus Thlypopsis ornata Thlypopsis pectoralis 0.856 1.711468 300 0.47602 0.16709 0.12575 0.05365 0.10676 0.05252 0.17897

Cnemathraupis 3.86 7.726615 275 0.57085 0.23154 0.01107 0.15381 0.11387 0.1092 0.12539 aureodorsalis Cnemathraupis eximia Anisognathus lacrymosus Anisognathus igniventris 2.94 5.87904 450 0.59496 0.08994 0.11591 0.11093 0.04499 0.11062 0.18357 Dubusia taeniata Dubusia castaneoventris 4.11 8.225586 700 1.01905 0.07951 0.16089 0.14256 0.18261 0.10392 0.05291 Iridosornis rufivertex Iridosornis reinhardti 1.766 3.532898 450 0.3864 0.01524 0.05229 0.09015 0.12494 0.10863 0.05324 Pipraeidea melanonota Pipraeidea bonariensis 6.92 13.84316 150 2.21163 0.47776 0.35805 0.19657 0.62326 0.16674 0.05829 Conirostrum cinereum Conirostrum rufum 1.13 2.25763 650 0.14005 0.13763 0.23347 0.08226 0.149 0.08641 0.31646 Diglossa caerulescens Diglossa cyanea 3.74 7.476346 300 1.21662 0.27333 0.14705 0.00794 0.07484 0.03721 0.13571 Urothraupis stolzmanni Nephelornis oneilli 4.07 8.135498 950 1.14004 0.13156 0.27014 0.10021 0.08171 0.27628 0.07733 Phrygilus atriceps Phrygilus punensis 1.25 2.496711 775 0.52501 0.08511 0.20366 0.12799 0.00851 0.06228 0.02431 Phrygilus plebejus Phrygilus unicolor 3.2 6.396549 800 1.12811 0.05564 0.18813 0.20321 0.07068 0.09541 0.03145 Catamenia inornata Catamenia homochroa 2.69 5.376142 625 0.09294 0.15973 0.26054 0.18129 0.06432 0.07571 0.18273 Thlypopsis sordida Thlypopsis inornata 0.88 1.765024 800 0.13761 0.18069 0.49925 0.10606 0.21333 0.18771 0.10823 Ramphocelus melanogaster Ramphocelus carbo 0.55 1.100715 650 0.36868 0.1721 0.04691 0.13358 0.06973 0.0812 0.02224 Tangara cyanicollis Tangara nigrocincta 2.586 5.172284 700 0.0446 0.14558 0.28794 0.04685 0.07169 0.025 0.08219 Trichothraupis melanops Eucometis penicillata 4.727 9.454985 1050 0.31818 0.3555 0.01269 0.07944 0.02003 0.2304 0.24811 Chlorospingus parvirostris Chlorospingus flavigularis 3.74 7.481781 775 0.47898 0.14061 0.01901 0.24483 0.05923 0.09811 0.05209 Pheucticus chrysogaster Pheucticus aureoventris 0.69 1.378868 1050 0.12103 0.01426 0.04452 0.02395 0.0391 0.00173 0.1677 Piranga rubriceps Piranga leucoptera 5.1 10.19105 50 1.8536 0.25513 0.18519 0.3865 0.14219 0.29161 0.36846 Myiothlypis fulvicauda Myiothlypis rivularis 2.97 5.935801 575 0.16425 0.02679 0.17839 0.04181 0.14352 0.00503 0.16441 Myiothlypis nigrocristata Myiothlypis signata 3.79 7.583324 300 0.49943 0.11157 0.02155 0.07902 0.0668 0.01581 0.17232 Basileuterus tristriatus Basileuterus trifasciatus 1.93 3.86698 250 0.58356 0.20972 0.26377 0.05198 0.01724 0.20594 0.30604 Cacicus sclateri Cacicus koepckeae 1.15 2.308151 800 0.002 0.12498 0.21625 0.07503 0.06359 0.08716 0.16275 Cacicus cela Cacicus uropygialis 4.06 8.119421 1175 0.19118 0.13978 0.47431 0.14682 0.08726 0.11539 0.21003 Psarocolius angustifrons Psarocolius atrovirens 2.17 4.349814 750 0.50743 0.31232 0.17524 0.15202 0.03608 0.12166 0.22181 Euphonia rufiventris Euphonia cayennensis 2.92 5.84733 250 0.66527 0.19902 0.09159 0.07323 0.01116 0.05735 0.21667 Spinus spinescens Spinus olivaceus 0.68 1.365733 850 0.03589 0.19875 0.177478 0.481427 0.001231 0.000529 0.125004

Appendix 3: Data sources for song files, museum specimens and genetic sequences.

Voucher number of GenBank Accession Number Species Pair Catalog number of song files number museum specimens CytB ND2 XC_119458, XC_87717, Thamnophilus nigrocinereus 1 LSU_165767, XC_5391, XC_5050, EF030331.1 EF030300.1 UMMZ_65126 XC_283088 WL_4141, WL_4142, AY676941.1, T. cryptoleucus 2 LSU_116224, LSU_172899, XC_276563, XC_230712, EF030295.1 EF030326.1 LSU_116222 XC_15085 XC_8242, XC_230782, Thamnophilus punctatus 1 XC_230783, XC_87729, EF030334.1 EF030303.1 LSU_178340, LSU_178341 XC_44175 XC_241711, XC_241616, T. stictocephalus 2 LSU_150730, LSU_150732, XC_241613, XC_108090, EF030335.1 EF030304.1 LSU_150726 XC_87744 XC_202225, XC_230626, Thamnomanes ardesiacus 1 LSU_70546, LSU_70545, ML_61960, ML_60961, HM637152.1 HM637242.1 LSU_83067 ML_222376 XC_108525, XC_237563, T. saturninus 2 LSU_115214, LSU_115217, XC_66679, ML_52247, EF639990.1 EF640057.1 LSU_115222 ML_52246 JQ445867.1, JQ445869.1, Thamnophilus murinus 1 JQ445870.1, UMMZ_215384 JQ445864.1 UMMZ_106835, T. schistaceus 2 EF030306.1 UMMZ_222844 WL_4383, XC_14558, KM236413.1, Isleria hauxwelli 1 LSU_62290, LSU_161754, XC_258407, XC_77027, JQ445636.1 AF118160.1 LSU_64193 XC_4386

KM236309.1, KM236415.1, KM236308.1, I. guttata 2 XC_65487, XC_81221, XC_226514, XC_226516, KM236414.1 JQ445635.1, LSU_68545, LSU_165714 XC_272556 JQ445634.1 XC_226454, XC_88432, HM637161.1, HM637250.1, Myrmotherula ambigua 1 XC_66632, XC_81413, KM236404.1 KM236300.1 XC_6023 XC_343519, XC_338571, HM637162.1, HM637251.1, M. sclateri 2 XC_333051, XC_47958, KM236405.1 KM236301.1 XC_236977 WL_4361, WL_4362, HM637163.1, HM637252.1, Myrmotherula multostriata 1 XC_70284, XC_134973, KM236407.1, LSU_137134, LSU_137135, KM236303.1 LSU_137136 XC_276273 GU215267.1 KM236406.1, XC_303199, XC_226609, KM236302.1, M. surinamensis 2 JX213550.1, UMMZ_135161, XC_226612, ML_61936, JX213510.1 UMMZ_22212 ML_64561 GU215271.1 XC_124254, XC_121821, KM236410.1, HM637254.1, Myrmotherula cherriei 1 XC_6021, XC_81759, KM236409.1, KM236305.1 XC_81758 GU215261.1 XC_296597, XC_270824, EF639969.1, EF640036.1, M. hemileucus 2 XC_47698, XC_29542, KM236442.1 KM236335.1 ML_188634 WL_4439, WL_4440, Myrmotherula menetriesii 1 LSU_172921, LSU_109971, WL_4441, XC_276272, KM236429.1 KM236323.1 LSU_109970 XC_249569 XC_340141, XC_316958, HM637172.1, KM236323.1, M. assimilis 2 LSU_119761, LSU_119762, XC_5046, XC_139989, KM236430.1 KM236324.1 LSU_119765 ML_115268

ML_64569, XC_224545, Weir Lab (CN003, Hypocnemoides 1 XC_224547, ML_62046, CN013, CN060, CN092, melanopogon ML_62047 CN094) LSU_115399, LSU_115398, LSU_110047

EF639955.1, Weir Lab H. maculicauda 2 (AMA463, AMZ426, XC_5681, XC_80223, OM029, OM143) LSU_137197, LSU_137194, XC_89689, XC_270743, LSU_153387 XC_144513 XC_120542, XC_226356, Akletos melanoceps 1 LSU_110083, LSU_172972, XC_61544, XC_258536, EF640004.1 LSU_110081 XC_17532 XC_10807, XC_102725, A. goeldii 2 LSU_157134, LSU_92422, XC_115159, XC_226321, EF640002.1 LSU_188963 XC_226322 XC_37949, XC_229056, EF639998.1, EF640065.1, Rhegmatorhina gymnops 1 XC_66666, XC_37951, KM260398.1 KM260378.1 XC_229057 XC_167289, XC_169990, EF640050.1, R. hoffmannsi 2 XC_24623, ML_43377, EF639983.1 KM260379.1 XC_38720 JQ445450.1, XC_276055, XC_258549, JQ445451.1, EF639995.1, Gymnopithys leucaspis 1 XC_223803, XC_249014, EF640062.1, GU215232.1 LSU_83253, LSU_83260, XC_8071 JQ445454.1, LSU_83242 JQ445453.1

JQ445442.1, JQ445448.1, G. rufigula 2 XC_316488, XC_272285, EF639997.1 JQ445447.1, XC_138904, XC_59203, JQ445444.1, LSU_67344, LSU_178460 XC_49637 JQ445443.1 XC_66231, XC_93707, EF640063.1, Gymnopithys lunulatus 1 LSU_161783, LSU_172981, XC_96577, XC_258554, EF639996.1 KM260377.1 LSU_110124 XC_97548 XC_223815, XC_203177, EF640016.1, G. salvini 2 EF639949.1 LSU_188989, LSU_188990, XC_123091, XC_10809, KM260376.1 LSU_188991 XC_223817 KF664032.1, XC_258353, XC_258352, KF685944.1, KF664030.1, Thamnophilus aethiops 1 XC_8070, WL_4149, KF685941.1, KF664031.1, LSU_170862, LSU_115175, WL_4150 F685940.1, KF685943.1 KF664051.1, LSU_64180 KF664047.1 WL_AV1, WL_AV32, KF686017.1, T. aroyae 2 LSU_90707, LSU_90706, XC_3542, ML_33742, EF030291.1 KF686016.1 LSU_96048 ML_33737 XC_116395, XC_230842, Thamnophilus torquatus 1 XC_13335, XC_84273, EF030339.1 EF030308.1 LSU_150738, LSU_150739 XC_116394 XC_296327, XC_64262, EF030336.1, T. ruficapillus 2 LSU_124160, LSU_124156, XC_4576, XC_1643, EF030305.1 GU215289.1 LSU_124157 WL_VAB40 ML_180962, XC_98027, Thamnophilus 1 XC_72713, XC_260744, EF030338.1 EF030307.1 tenuepunctatus LSU_1739667 XC_249460 XC_87617, XC_39945, T. palliatus 2 XC_38489, XC_230763, EF030332.1 EF030301.1 LSU_63531, UMMZ_23094 XC_119500

ML_88291, ML_88290, Thamnomanes caesius 1 LSU_109860, LSU_109862, ML_39434, ML_88292, EF030320.1 EF030288.1 LSU_109859 ML_39414 XC_76073, XC_230655, T. schistogynus 2 LSU_102091, LSU_102088, ML_101788, ML_121760, HM637153.1 HM637243.1 LSU_102095 ML_110462 XC_317423, XC_284364, HM637165.1, HM637255.1, Myrmotherula klagesi 1 XC_88549, XC_316961, KM236411.1 KM236306.1 LSU_165771, LSU_184616 XC_88547 XC_3598, WL_AV2, EF639974.1, M. longicauda 2 LSU_171311, LSU_171312, ML_120976, ML_17112, EF640041.1 KM236412.1 LSU_36095 ML_30052 XC_222492, WL_SA29, AF118176.1, Drymophila devillei 1 LSU_132784, LSU_132781, XC_2533, ML_101617, AF118175.1 LSU_132778 ML_94797 XC_304520, XC_301219, JQ913136.1, D. caudata 2 LSU_173991, LSU_173990, ML_89399, XC_303390, JQ913134.1, LSU_81978 ML_89300 JQ913133.1 XC_296361, WL_AV5, Scytalopus parvirostris 1 LSU_102256, LSU_102262, XC_3404, ML_33650, KM065770.1, U06161.1 LSU_102257 ML_168073 XC_258645, XC_248869, S. spillmanni 2 XC_229620, XC_62343, AY065716.1 LSU_83371, LSU_83372 XC_249603 XC_264640, XC_258612, AY370547.1, Grallaria dignissima 1 XC_258611, XC_248840, AF127199.1 LSU_49044 XC_90430 XC_20617, XC_23230, G. eludens 2 LSU_62316, LSU_62317, XC_20616, WL_ANT347, AY370546.1 LSU_52135 ML_18386 XC_258616, XC_264641, AY370589.1, Hylopezus fulviventris 1 XC_249450, XC_135215, AY065721.1 LSU_173005, LSU_173007, AF127216.1 LSU_173006 XC_102321

XC_2291, ML_90182, JQ775825.1, H. berlepschi 2 ML_90181, ML_90174, EF640018.1 JQ775824.1 LSU_102247 ML_127024 XC_258589, XC_250406, KP277585.1, AY139638.1, Grallaria squamigera 1 XC_250035, XC_248605, KP277584.1, LSU_92438, LSU_92437, AY065720.1 LSU_92439 XC_102304 KP277583.1 ML_182511, ML_134564, AF127189.1, G. varia 2 ML_115734, XC_59325, AF127204.1 AY370541.1 LSU_67359 ML_74349 XC_258603, XC_248385, AY370542.1, Grallaria rufula 1 XC_101056, XC_250040, LSU_102244, LSU_102241, AF127190.1 LSU_96073 XC_248630 XC_296749, XC_223589, AY370543.1, G. blakei 2 XC_102694, XC_223593, AF127191.1 LSU_74098 XC_20139 XC_250226, XC_250112, AY370538.1, Grallaricula lineifrons 1 XC_246084, XC_59414, AF127215.1 AF127200.1 XC_17214 WL_AV32, XC_82788, G. flavirostris 2 ML_92150, ML_92671, AY370539.1 AY370576.1 ML_92643 AY612460.1, XC_223084, XC_202916, AY612461.1, Formicarius colma 1 XC_223085, XC_24225, AY612462.1, LSU_83325, LSU_83322, XC_272084 AY612458.1, LSU_83328 AY612459.1 FJ175932.1, FJ175931.1,

F. analis 2 XC_114137, XC_65193, FJ175930.1, LSU_110238, LSU_110242, XC_59314, ML_145113, FJ175929.1, LSU_110241 XC_74995 FJ175928.1

XC_272281, XC_66206, KY510784.1, KY510695.1, Dendrexetastes rufigula 1 XC_55538, XC_316506, KY510783.1, LSU_170814, LSU_161690, KY510694.1 LSU_34138 XC_6103 KY510782.1 XC_39458, XC_270039, KY510781.1, N. longirostris 2 LSU_109674, LSU_115014, XC_91127, XC_226632, AY089797.1 GQ906714.1 LSU_119513 XC_91128 XC_117998, ML_219442, Hylexetastes stresemanni 1 XC_73640, ML_80095, JF975341.1 LSU_132436 ML_38887 XC_9162, XC_21167, GQ906716.1, H. perrotii 2 XC_293855, XC_119141, KF705461.1 LSU_184604, ROM_147668 XC_44062 AY504918.1, XC_258298, XC_264573, AY504923.1, Xiphorhynchus elegans 1 XC_267520, XC_258300, AY504922.1, KF001185.1 LSU_70528, LSU_82940, XC_89462 AY504921.1, LSU_82945 AY504920.1 AY504941.1, AY504940.1, JF975355.1, X. spixii 2 AY504939.1, ML_115123, XC_232475, KF001187.1 LSU_67202, LSU_67201, XC_232474, XC_108532, AY504938.1, LSU_67189 XC_39448 AY504937.1 UMMZ_23133, XC_91273, XC_91271, AY089813.1, Dendroplex picus 1 UMMZ_23090, XC_80513, XC_345321, GU215385.1 AY089802.1 UMMZ_215370 XC_287916 XC_23906, XC_270680, D. kienerii 2 AY089818.1 JF975351.1 LSU_165765 XC_144817, XC_144816 ML_190931, ML_190930, Thripophaga cherriei 1 JF975236.1 ML_139298, ML_190929 ML_44457, ML_139300, T. fusciceps 2 ML_44438, ML_44436, JF975237.1 XC_74270

XC_12461, XC_38599, Cranioleuca vulpina 1 ML_64720, XC_91265, JF975218.1 XC_5448 ML_115267, ML_115264, C. muelleri 2 ML_115266, XC_333319, JF975233.1 XC_181829 XC_270648, XC_270647, Certhiaxis mustelinus 1 UMMZ_22369, XC_41695, ML_190496, JF975235.1 UMMZ_22387 ML_91493 XC_153570, XC_30510, C. cinnamomeus 2 LSU_183820, LSU_124015, ML_518503, XC_220805, JF975234.1 LSU_124014 XC_41489 XC_275868, XC_249479, Cinclodes excelsior 1 XC_248666, XC_20846, JF975144.1 XC_20847 XC_73370, XC_73371, C. aricomae 2 XC_73369, XC_41015, JF975145.1 ML_143793 KP277975.1, XC_275000, ML_90402, KP277974.1, Thripadectes flammulatus 1 XC_249513, XC_58594, KP277973.1, XC_58589 KP277972.1, LSU_82975 KP277969.1 XC_189610, ML_26917, T. scrutator 2 LSU_107721, LSU_74058, ML_17351, ML_186925, JF975316.1 LSU_74056 ML_173913 XC_82630, XC_72984, Asthenes virgata 1 LSU_181849, LSU_181847, XC_47596, XC_38997, JF975250.1 LSU_181848 XC_20671 XC_350973, XC_296379, A. maculicauda 2 XC_296378, XC_150429, JF975251.1 LSU_102007, LSU_98260 XC_2010

XC_221715, XC_74363, Cranioleuca marcapatae 1 LSU_78319, LSU_78321, XC_221716, XC_74480, JF975216.1 LSU_78318 XC_221714 LSU_101976, LSU_101979, C. albiceps 2 JF975217.1 LSU_101973 XC_73463, ML_148121 ML_186936, XC_23058, Synallaxis azarae 1 XC_92330, ML_173954, JF975191.1 ML_147870 XC_74420, XC_47796, S. courseni 2 XC_47795, ML_132347, JF975192.1 ML_82841 KF001211.1, KF001210.1, Xiphorhynchus ML_55841, XC_232357, 1 KF001209.1, chunchotambo XC_232358 LSU_188742, LSU_188743, KF001208.1, LSU_188744 KF001207.1 KF001179.1, KF001192.1, KF001178.1, KF001191.1, X. ocellatus 2 KF001177.1, KF001214.1, LSU_172870, LSU_172871, KF001156.1, KF001213.1, LSU_109691 ML_127733, WL_2 KF001155.1 KF001215.1 ML_180924, XC_258218, Clibanornis rubiginosus KC835414.1, 1 XC_258217, XC_249444, (Andean) KC835413.1 XC_249423 Clibanornis rubiginosus 2 KC835412.1 (Amazonian) ML_131116, XC_7520 ML_52296, ML_64765, Syndactyla ucayalae 1 F975297.1 ML_52273, ML_52234 ML_110621, ML_101694, S. striata 2 JF975298.1 ML_110614, XC_3126 ML_50688, XC_217480, Anabacerthia variegaticeps 1 JF975288.1 XC_260679

ML_221320, ML_219487, A. ruficaudata 2 JF975300.1 XC_66650 XC_262915, XC_260739, Campylorhamphus pusillus 1 LSU_87717, LSU_169791, XC_258323, XC_258324, GQ906725.1 LSU_169790 XC_260741, KC242883.1, KC242870.1, C. procurvoides 2 KC242869.1, ML_127712, ML_127699, KC242867.1, LSU_165703 ML_127697, ML_127698 KC242866.1 Xiphocolaptes XC_260714, XC_275359, promeropirhynchus 1 LSU_128404, LSU_128402, WL_3864, WL_3865, JF975342.1 (Andean) LSU_128405 WL_3868 Xiphocolaptes XC_258283, XC_275004, promeropirhynchus 2 XC_251776, XC_249938, KF705463.1 (Amazonian) LSU_75186, ROM_45954 XC_258284 KP775743.1, KP775797.1, KP775741.1, KP775796.1, Xiphorhynchus guttatus ML_121764, ML_121789, 1 KP775746.1, KP775795.1, (Andean) ML_101811, ML_52332 ROM_103997, KP775745.1, KP775794.1, ROM_99008, ROM_103995 KP775744.1 KP775793.1 AY089845.1, KP775766.1, Xiphorhynchus guttatus 2 AY089794.1 KP775764.1, (Amazonian) ML_126709, ML_115088, LSU_67211, LSU_67210, ML_115080, ML_115167, KP775763.1, LSU_67209 ML_115168 KP775762.1 XC_260711, XC_258243, JN622106.1, JF975334.1, Dendrocincla tyrannina 1 XC_248976, XC_258244, LSU_97630, LSU_97631, GU215186.1 GU215380.1 LSU_87660 ML_78333

JF975336.1, JQ445358.1, D. merula 2 ML_184976, XC_63389, JN622102.1 JQ445361.1, LSU_109580, LSU_109581, WL_VAB22, XC_40360, JQ445360.1, LSU_114866 ML_39522 JQ445346.1 XC_264559, XC_264558, Xenops rutilans 1 XC_260705, XC_86392, KM081607.1 JF975331.1 LSU_81946, LSU_64165 XC_17698 XC_39933, XC_39438, X. tenuirostris 2 KM081611.1 JF975327.1 LSU_150701, LSU_150700 XC_108530, XC_147053 ML_147481, ML_147450, HM449848.1, Synallaxis cabanisi 1 ML_147387, ML_147324, KC437445.1 LSU_105895, LSU_105896, KC437514.1 LSU_105893 ML_30060 JF975204.1, KC437444.1, KC437517.1, S. macconnelli 2 ML_177676, XC_5152, KC437441.1, KC437516.1, XC_230322, XC_230326, KC437440.1 LSU_178427, LSU_178426 XC_230325 KC437515.1 EU310994.1, ML_106383, ML_89859, EU310984.1, Myiopagis flavivertex 1 ML_106324, ML_89849, EU310978.1, ML_77986 EU310977.1, LSU_173013, LSU_173014 KM370001.1 EU310985.1, M. viridicata 2 LSU_150875, LSU_137579, ML_69912, XC_226117, EU310995.1, LSU_137581 ML_69911 EU310993.1 EF110827.1, EF110830.1, Mionectes oleagineus 1 EF110848.1 EF110828.1, LSU_110776, LSU_110780, EF110829.1, LSU_110781 EF110831.1 EF110704.1, M. macconnelli 2 EF110845.1 LSU_178362, LSU_178489 EF110705.1

LSU_169920, LSU_169919, EU311190.1, Elaenia albiceps 1 XC_250809, XC_250320 LSU_82097 EU311188.1 LSU_128865, LSU_128859, ML_100943, XC_23056, EU311048.1, E. pallatangae 2 LSU_128863 XC_252397, XC_264672 EU311101.1 LSU_112631, LSU_112629, Uromyias agilis 1 XC_21962, WL_5492 JX273103.1 LSU_112630 LSU_128819, LSU_128822, ML_168642, ML_195154, U. agraphia 2 JX273104.1 LSU_128821 ML_28700, XC_10532 XC_17748, XC_203560, Leptopogon rufipectus 1 LSU_169930, LSU_169929, XC_95042, WL_5546, KP297424.1 LSU_88509 WL_5547 LSU_128888, LSU_128892, WL_AV55, ML_92276, KP297434.1, L. taczanowskii 2 LSU_106359 ML_92670, ML_92180 KP297421.1 ML_112809, ML_46978, EU310974.1, Myiopagis caniceps 1 ML_39450, ML_88338, KM370007.1 XC_333042 XC_264668, XC_262955, EU310976.1, M. olallai 2 XC_250304, ML_60232, EU310975.1 XC_251172 KJ810369.1, EU311102.1, Elaenia parvirostris 1 ML_120843, ML_120803 KJ810462.1 LSU_124583, LSU_124582, EU311081.1, LSU_124581 EU311076.1 ML_145035, ML_145029, EU311103.1, E. flavogaster 2 AF453807.1 LSU_184630, LSU_67519, ML_145028, ML_145007, EU311070.1 UMMZ_23129 ML_72431 LSU_90816, LSU_90820, XC_74345, XC_74344, Zimmerius bolivianus 1 EU311008.1 LSU_90818 ML_132715

JQ445329.1, JQ445638.1,

Z. gracilipes 2 ML_187970, ML_187952, JQ445901.1, LSU_71789, LSU_71787, XC_258668, XC_298890, JQ445909.1, LSU_71785 XC_271017 JQ446033.1 LSU_128914, LSU_128913, ML_148296, XC_4741, Mionectes striaticollis 1 EF110693.1 LSU_128911 XC_150533, ML_121831 LSU_189077, LSU_189076, WL_5517, WL_5518, M. olivaceus 2 EF501926.1 LSU_189075 WL_AV53 JQ288128.1, Knipolegus poecilurus 1 LSU_128749, LSU_106213, JQ288134.1, LSU_128751 JQ288132.1

K. poecilocercus 2 UMMZ_119509 JQ288125.1 ROM_111352, FJ899342.1, Colonia colonus (Andean) 1 ROM_103606, XC_258922, XC_250703 KM079953.1 ROM_103607 Colonia colonus ML_69687, ML_69689, 2 KM079947.1 (Amazonian) UMMZ_90536 ML_52900 ML_30661, ML_30641, Phoenicircus nigricollis 1 LSU_92536, LSU_92535, ML_30639, XC_12076, KJ810483.1 KJ810396.1 LSU_92533 XC_187528 ML_115234, ML_115156, P. carnifex 2 ML_115135, XC_287715, KJ810481.1 KJ810392.1 LSU_67427, LSU_67437 XC_84116 LSU_35227, LSU_171043, Gymnoderus foetidus 1 ML_55850 KJ810464.1 KJ810371.1 LSU_171044 ML_55847, ML_187945, C. mcilhennyi 2 LSU_52148, LSU_52147, ML_132292, ML_129523, KJ810458.1 KJ810363.1 LSU_64245 XC_12024 LSU_174155, LSU_174154, XC_228269, XC_102732, Pipreola riefferii 1 KJ810497.1 KJ810412.1 LSU_94220 XC_102731, ML_515147

LSU_98412, LSU_98414, ML_132683, XC_74110, P. intermedia 2 KJ810492.1 KJ810407.1 LSU_98416 ML_107135 UMMZ_98168, Pipreola jucunda 1 ML_168754, XC_22339 KJ810493.1 KJ810408.1 UMMZ_87646 LSU_74158, LSU_75248, XC_97700, XC_121290, DQ363985.1, P. pulchra 2 KJ810495.1 LSU_80609 XC_17897, XC_90482 KJ810410.1 XC_262972, XC_98738, KJ810406.1, Pipreola frontalis 1 XC_98122, XC_98740, KJ810491.1 DQ363984.1 LSU_88184 XC_276398 P. chlorolepidota 2 LSU_117002, LSU_130276 XC_157955, XC_250309 KJ810488.1 KJ810403.1 Doliornis remseni 1 LSU_159471 XC_165648, XC_165650 KJ810460.1 KJ810367.1 LSU_128650, LSU_128651, XC_41676, XC_296819, D. sclateri 2 KJ810461.1 KJ810368.1 LSU_128653 XC_20615, XC_222458 LSU_78594, LSU_78596, XC_259063, XC_248606, Ampelion rubrocristata 1 KJ810441.1 KJ810340.1 LSU_80604 XC_260981 XC_47859, XC_275719, A. rufaxilla 2 WL_AV72, ML_35801, KJ810442.1 KJ810341.1 LSU_90752, LSU_98409 XC_165782 DQ363980.1, Lipaugus fuscocinereus 1 XC_29815, XC_25527 KJ810378.1

L. uropygialis 2 XC_40334, XC_40335 KJ810382.1 XC_259086, XC_248871, KJ810504.1, Rupicola peruviana 1 KJ810429.1 LSU_61655, LSU_40812 XC_260997, ML_58870 DQ435459.1 XC_202351, XC_247154, R. rupicola 2 WL_BoV137, WL_BoV27, KJ810505.1 KJ810430.1 LSU_68568 WL_BoV03 GU985516.1, ML_117016, ML_115257, GU985515.1, Tyranneutes stolzmanni 1 ML_127665, ML_88299, KF228515.1, LSU_110629, LSU_115849, ML_88295 JQ445919.1, LSU_115848 JQ445918.1

DQ363977.1, XC_3439, WL_BoV18, KF228516.1, T. virescens 2 XC_203242, ML_221696, JQ445922.1, ML_65755 JQ445921.1, LSU_165774 JQ445920.1 ML_88345, ML_88343, EF501893.1, Neopelma chrysocephalum 1 LSU_165744, WL_BoV79, ML_48566, DQ363978.1, UMMZ_150318 WL_BoV44 KF228517.1 ML_129520, ML_128948, GU985507.1, N. sulphureiventer 2 LSU_102423, LSU_102427, ML_129519, XC_39786, GU985506.1, LSU_102430 ML_132278 KF228519.1 KR781209.1, ML_88508, ML_106158, KR781208.1, Lepidothrix nattereri 1 ML_106107, XC_47965, KR781206.1, KF228535.1 LSU_137424, LSU_137430, ML_117149 KR781205.1, LSU_137421 KR781218.1 KR781212.1, KR781216.1, L. iris 2 ML_115232, ML_115231, KR781215.1, KF228533.1 LSU_67474, ML_114976, ML_143909, KR781214.1, UMMZ_134368 WL_VAB24 KR781212.1 LSU_184699, LSU_64972, Pipra aureola 1 XC_202219, WL_4325 EF633401.1 KF228543.1 LSU_67438 ML_147902, ML_28835, EF633400.1, P. fasciicauda 2 LSU_189243, LSU_189244, ML_29762, ML_76000, KF228544.1 EF633399.1 LSU_189245 XC_123093 Chloropipo flavicapilla 1 LSU_113503 WL_50 KF228525.1 GU985518.1, GU985517.1, C. unicolor 2 XC_262979, XC_249846, LSU_128676, LSU_128677, XC_86139, XC_276653, KJ810353.1, LSU_128674 ML_92919 KF228526.1

LSU_117087, LSU_117076, ML_191413, XC_157570, Lepidothrix isidorei 1 KF228534.1 LSU_117081 XC_150872 LSU_130293, LSU_130300, ML_140585, ML_140588, L. coeruleocapilla 2 KF228531.1 LSU_130303 ML_138828 UMMZ_23116, Ceratopipra rubrocapilla 1 KF228555.1 UMMZ_2313 UMMZ_111782, C. chloromeros 2 KF228551.1 UMMZ_219488 ML_180919, XC_249340, Dixiphia pipra (Andean) 1 LSU_117064, LSU_117063, XC_86334, XC_16629, KF228547.1 LSU_117058 XC_250787 ML_218647, XC_188535, Dixiphia pipra (Amazonian) 2 LSU_67471, LSU_67470, XC_287393, XC_200179, KF228546.1 UMMZ_22359 ML_48620 ML_48609, ML_31504, Tityra cayana 1 LSU_120092, LSU_52159, ML_32427, ML_32431, KM081392.1 DQ363963.1 LSU_52167 XC_76385 FJ899399.1, FJ899393.1, T. semifasciata 2 ML_88858, ML_126760, FJ899397.1, DQ363967.1 LSU_52173, LSU_171001, ML_126762, XC_39144, KM081384.1, LSU_52172 WL_BVAB33 KM081385.1 LSU_110271, LSU_173127, JQ445531.1, Iodopleura isabellae 1 XC_61335, XC_70279 DQ435455.1 LSU_115612 JQ445535.1 DQ363973.1, KJ810374.1, I. fusca 2 KJ810467.1 UMMZ_90571, JQ445533.1, UMMZ_90572 XC_42087 JQ445532.1 KM081079.1, EF458515.1, XC_2886, XC_3049, KM081078.1, Schiffornis turdina (Andean) 1 EF458516.1, ML_94724, ML_120903 KM081080.1, LSU_162814, LSU_162815, EF458518.1 LSU_162818 KM081113.1

FJ899403.1, EF458514.1, Schiffornis turdina XC_248992, XC_248988, 2 KM081105.1, EF458512.1, (Amazonian) LSU_115866, LSU_115867, XC_248993, XC_281923, LSU_115868 XC_27841 KM081108.1 EF458513.1 ML_127586, ML_117224, Pachyramphus 1 XC_328497, ML_89016, DQ363947.1 polychopterus LSU_124241, LSU_183835, LSU_133371 ML_110028 XC_264751, XC_262970, P. albogriseus 2 DQ363935.1 LSU_172282, LSU_42029 WL_6500, XC_249348 WL_A25B, WL_B38B, KM115224.1, Hylophilus brunneiceps 1 LSU_184701, WL_A38A, ML_117028, KM115223.1 UMMZ_201932 XC_316977 ML_127630, ML_127619, KM115277.1, H. thoracicus 2 ML_127566, ML_48663, KM115276.1, LSU_184703 ML_127331 KM115275.1 XC_211108, XC_188530, AY030129.1, Cyclarhis gujanensis 1 ML_127509, ML_32419, LSU_71828, LSU_71829, KM115191.1 LSU_71830 ML_185548 WL_6916, WL_6917, C. nigrirostris 2 LSU_47288, LSU_38848, WL_6918, WL_6919, KM115218.1 LSU_162123 WL_6920 XC_30299, ML_36021, KM115263.1, Hylophilus olivaceus 1 XC_13091, XC_251008, KM115262.1 LSU_85326, LSU_174170 XC_86391 KM115267.1, KM115266.1, H. pectoralis 2 WL_A22B, WL_B32A, ML_516420, XC_2285, KM115265.1, LSU_71840, LSU_71830 XC_63531 KM115264.1 FJ598146.1, ML_110754, XC_222122, Cyanolyca viridicyanus 1 FJ598147.1, LSU_96732, LSU_96733, XC_222121 LSU_102861 FJ598148.1

FJ598169.1, FJ598168.1,

C. turcosa 2 ML_90401, XC_186581, FJ598167.1, LSU_179125, LSU_179126, XC_186582, XC_34602, FJ598166.1, LSU_170129 XC_62348 FJ598164.1 LSU_127746, LSU_64418, XC_261055, XC_62333, Orochelidon murina 1 AY825951.1 AY826002.1 LSU_64419 XC_261056 UMMZ_154177, O.andecola 2 AY825980.1 AY826010.1 UMMZ_57354 XC_16106 XC_245165, XC_245166, Pygochelidon cyanoleuca 1 LSU_128925, LSU_128924, XC_259260, XC_251252, AF074586.1 AY826003.1 LSU_128922 XC_259261 P. melanoleuca 2 LSU_67554, LSU_67555 ML_31050 AY825954.1 AY826007.1 ML_37296, ML_35559, Pheugopedius genibarbis 1 LSU_102820, LSU_102817, ML_135136, ML_135167, DQ415683.1 LSU_102819 XC_122580 ML_127481, ML_66980, P.coraya 2 LSU_110814, LSU_110818, XC_76174, XC_144168, DQ415688.1 LSU_110819 ML_41032 ML_35422, XC_56715, Cantorchilus leucotis 1 LSU_83501, LSU_83502, XC_270627, ML_30890, AY352544.2 LSU_83503 ML_31772 LSU_124699, LSU_124697, XC_148841, XC_296321, C. guarayanus 2 AY352543.1 LSU_124698 ML_81108 ML_80822, ML_80885, Pheugopedius mystacalis 1 XC_54382, XC_93888, DQ415687.1 LSU_77618 XC_80692 ML_83163, ML_211533, P. euophrys 2 LSU_172336, LSU_172333, ML_191951, ML_83151, DQ415684.1 LSU_179107 ML_57272 ML_53421, XC_8080, EU983541.1, Henicorhina leucosticta 1 LSU_117382, LSU_117383, XC_4229, XC_86623, EU983540.1 LSU_117384 ML_28559

EU983454.1, EU983453.1, H. leucoptera 2 ML_78073, ML_79696, LSU_117392, LSU_117394, ML_79694, ML_79683, EU983452.1, LSU_117395 XC_86353 EU983451.1 LSU_38084, LSU_183841, Turdus haplochrous 1 ML_166345, ML_166340 DQ911077.1 LSU_183843 KU569395.1, T. nudigenis 2 LSU_175519, LSU_175520 ML_66276, XC_213238 N049521.1 DQ911078.1, KU569389.1, DQ910949.1, Turdus hauxwelli 1 KU569388.1, EU154620.1 LSU_54345, LSU_31422, KU569387.1, LSU_52347 KU569386.1 LSU_67621, LSU_67622, T. fumigatus 2 DQ910945.1 DQ911076.1 LSU_67623 ML_33761, ML_120901, AY049491.1, Catharus dryas 1 ML_13899, ML_110634, AY049516.1 LSU_96674, LSU_96675, EU154579.1 LSU_96677 ML_120872 XC_259183, XC_32386, AY049494.1, C. fuscater 2 XC_6675, XC_4214, AY049518.1 HM633265.1 LSU_112683, LSU_112685 ML_63456 ML_148187, ML_148193, AY049483.1, AY049509.1, Entomodestes leucotis 1 ML_148169, XC_45844, LSU_98643, LSU_98646, HM633295.1 AY752341.1 LSU_98645 ML_193144 XC_262987, XC_275973, E. coracinus 2 XC_275972, XC_264774, AY752382.1 AY752340.1 ROM_118727 XC_264773 XC_90101, XC_47186, Turdus chiguanco 1 ML_26996, XC_20667, EU154609.1 AY752352.1 LSU_102845, LSU_162916 XC_20812

XC_276625, XC_276624, T. serranus 2 LSU_90913, LSU_90912, ML_85026, XC_261049, EU154673.1 DQ911118.1 LSU_90914 XC_259196 LSU_52468, LSU_64593, Tachyphonus rufiventer 1 ML_163824, ML_29571 FJ799895.1, FJ799894.1 JN810550.1 LSU_52467 LSU_52461, LSU_52466, XC_108693, ML_88293, JN810548.1, T. luctuosus 2 FJ799892.1 LSU_64595 ML_48039 GU215423.1 LSU_133936, LSU_133938, XC_188656, ML_127716, Tangara velia 1 AY383158.1 EU648094.1 LSU_133939 ML_88322 LSU_110986, LSU_171136, T. callophrys 2 AY383107.1 EU648056.1 LSU_31435 XC_259637, XC_86611 Dacnis flaviventer 1 LSU_52953, LSU_110991 XC_73511, XC_73510 JN810066.1 JN810457.1 LSU_72793, LSU_62469, D. cayana 2 AF006227.1 JN810456.1 LSU_28574 XC_257135, XC_257134 LSU_116381, LSU_173187, Cyanerpes caeruleus 1 FJ899500.1, FJ899498.1 JN810449.1 LSU_171141 FJ176139.1, FJ176138.1, C. cyaneus 2 GU215300.1 FJ176137.1, LSU_151596, LSU_38162, FJ176136.1, LSU_38163 FJ176135.1 LSU_120376, LSU_120379, Conirostrum bicolor 1 ML_127486, XC_215602 AF489883.1 AF383141.1 LSU_120380 ML_34270, ML_31797, C. margaritae 2 LSU_116327, LSU_116328, ML_29341, ML_34247, EU647892.1 EU647925.1 LSU_120383 ML_33820 LSU_170240, LSU_172408, AY190166.1, Creurgops verticalis 1 XC_261215, XC_261216 JN810448.1 LSU_172409 FJ799872.1 LSU_98831, LSU_79308, FJ799871.1, C. dentatus 2 JN810447.1 LSU_79309 XC_45833, XC_45832 AF006224.1 LSU_103004, LSU_103003, Hemispingus calophrys 1 XC_150476, XC_124015 JN810078.1 LSU_103006

ML_35433, ML_32010, H. parodii 2 ML_32011, ML_30403, JN810080.1 LSU_75589 ML_32013 XC_97809, XC_97810, KP297416.1, Hemispingus frontalis 1 LSU_172407, LSU_170218, ML_35767, ML_195122, EU372679.1 AF383136.1 LSU_170219 ML_31271 ML_148304, XC_4297, H. melanotis 2 ML_13844, ML_13823, EU647914.1 EU647947.1 LSU_96870, LSU_90988 ML_121713 ML_67284, ML_67283, Hemispingus goeringi 1 JN810079.1 ML_67282 ML_28779, ML_26987, H. rufosuperciliaris 2 ML_26988, XC_189622, JN810082.1 XC_54552 ML_21788, ML_21652, KP297417.1, Hemispingus verticalis 1 LSU_88929, LSU_97894, XC_98770, XC_98774, JN810085.1 JN810469.1 LSU_170225 XC_98771 LSU_98804, LSU_98808, ML_17382, ML_17364, H. xanthophthalmus 2 JN810086.1 JN810470.1 LSU_98805 ML_17253 LSU_129139, LSU_129141, XC_243168, XC_243175, Thlypopsis ornata 1 JN810157.1 JN810555.1 LSU_129142 XC_243176, XC_243166 XC_41675, XC_190594, T. pectoralis 2 JN810158.1 JN810556.1 LSU_74678, LSU_74673 XC_41674 Cnemathraupis LSU_93353, LSU_93355, ML_17273, XC_36692, 1 EU647974.1 aureodorsalis LSU_93357 XC_54556 LSU_97907, LSU_97908, C. eximia 2 EU647975.1 LSU_97909 XC_149161, XC_294408 ML_26919, ML_26911, Anisognathus lacrymosus 1 LSU_174243, LSU_174242, ML_17291, XC_218597, EU647964.1 EU648008.1 LSU_174249 XC_218596 ML_135310, ML_135303, A. igniventris 2 LSU_79234, LSU_79235, ML_147867, ML_35463, EU647961.1 EU648006.1 LSU_79239 ML_31282

XC_259668, ML_78960, AF006230.1, Dubusia taeniata 1 XC_341438, XC_250801, LSU_89050, LSU_89051, AY383098.1 LSU_89052 XC_242585 LSU_98838, LSU_98840, AF006228.1, D. castaneoventris 2 LSU_98842 ML_197224, ML_100965 AY383097.1 LSU_179169, LSU_179170, Iridosornis rufivertex 1 JN810094.1 JN810476.1 LSU_179171 LSU_174252, LSU_174251, I. reinhardti 2 EU647985.1 EU648041.1 LSU_174255 LSU_129223, LSU_129228, XC_259563, XC_259564, AF006246.1, Pipraeidea melanonota 1 LSU_129226 XC_73150 AY383101.1 XC_230888, XC_230887, AY383103.1, P. bonariensis 2 LSU_79273, LSU_113627, XC_230886, ML_21842, AF489898.1, LSU_74666 ML_21612 EU647997.1 ML_35460, ML_24095, Conirostrum cinereum 1 JN810057.1 UMMZ_98190 XC_91307, XC_20920 LSU_113515, C. rufum 2 UMMZ_215708, JN810060.1 ROM_94449 XC_11249, XC_57454 XC_261125, XC_293902, Diglossa caerulescens 1 LSU_82310, LSU_170338, XC_222420, XC_242539, EU647941.1 LSU_82313 XC_242544 ML_21672, ML_21798, D. cyanea 2 LSU_129307, LSU_129315, ML_203074, XC_259541, EU647942.1 LSU_129319 XC_250776 Urothraupis stolzmanni 1 UMMZ_223143 XC_262665 JN810160.1 JN810560.1 LSU_119143, LSU_119151, N. oneilli 2 AF006243.1 JN810481.1 LSU_119152 ML_195167, XC_41680 LSU_124863, LSU_38459, XC_16131, XC_60859, EF529982.1, Phrygilus atriceps 1 LSU_183876 XC_60858 KP965526.1

JN417901.1, P. punensis 2 LSU_181887, LSU_34761, JN417900.1, LSU_62629 ML_171858, ML_171857 JN417899.1 LSU_124887, LSU_124889, Phrygilus plebejus 1 XC_28910, XC_28945 EF529979.1 EF529865.1 LSU_183877 LSU_96753, LSU_124876, XC_261278, XC_259881, P. unicolor 2 EF529980.1 EF529866.1 LSU_102877 XC_259880, XC_259879 XC_23530, XC_23586, Catamenia inornata 1 EF529989.1 EF529875.1 LSU_74773, LSU_93142 ML_35445 ML_35445, ML_36018, C. homochroa 2 JN810052.1 JN810432.1 LSU_96802, LSU_96803 ML_36016, ML_35467 ML_34287, ML_30829, Thlypopsis sordida 1 LSU_116310, LSU_116309, ML_30788, ML_45576, AF006256.1 JN810558.1 LSU_116313 ML_45947 LSU_82232, LSU_80852, XC_296841, XC_42867, T. inornata 2 JN810156.1 JN810554.1 LSU_64599 XC_230877, XC_230878 LSU_72857, LSU_28603, Ramphocelus melanogaster 1 XC_14124, ML_191409 FJ799883.1 JN810505.1 LSU_62592 ML_76184, ML_26566, KP995896.1, R. carbo 2 LSU_116180, LSU_110943, ML_30031, ML_135115, JN810502.1 KP995895.1 LSU_110944 ML_28924,

FJ899482.1, FJ899481.1, Tangara cyanicollis 1 EU648061.1 FJ899479.1, LSU_163080, LSU_163081, XC_230490, XC_118717, FJ899476.1, FJ899475.1 LSU_163079 XC_230489 LSU_162004, LSU_162005, XC_86345, ML_190306, T. nigrocincta 2 AY383143.1 EU648081.1 LSU_162003 ML_190192 FJ799900.1, Trichothraupis melanops 1 LSU_96901, LSU_96898, FJ799899.1, JN810559.1 LSU_96903 KP965513.1

FJ799876.1, FJ799875.1, E. penicillata 2 JN810464.1 LSU_42841, LSU_173154, AF006231.1, LSU_110917 GU215311.1 Chlorospingus parvirostris 1 LSU_88868, LSU_88869 XC_261252, XC_183319 FJ222650.1 XC_259740, XC_263010, EF529935.1, C. flavigularis 2 LSU_88874, LSU_88870, XC_242371, XC_242364, FJ222655.1, FJ547250.1 LSU_88872 XC_156316 XC_259810, XC_242928, Pheucticus chrysogaster 1 LSU_85702, LSU_80946, ML_28447, ML_74684, KM224974.1 KM225015.1 LSU_172386 XC_242927 LSU_125198, LSU_125197, ML_68180, XC_259815, P. aureoventris 2 EF530011.1 EF529904.1 LSU_125195 XC_242922, XC_235463 XC_251401, XC_98763, Piranga rubriceps 1 LSU_97901, LSU_97902, XC_5522, XC_98762, AF011781.1 KM225014.1 LSU_170237 XC_98761 LSU_85512, LSU_85513, AF011770.1, P. leucoptera 2 KM225012.1 LSU_117500 XC_150845, XC_150608 AF011769.1 LSU_52372, LSU_64507, XC_88587, XC_61021, Myiothlypis fulvicauda 1 FJ899537.1 AY327400.1 LSU_52374 ML_24260, ML_168814 ML_127461, ML_48642, AY340217.1, M. rivularis 2 ML_48644, ML_31477, GU932069.1 GU932387.1 LSU_67679 XC_72870 ML_21650, ML_21772, Myiothlypis nigrocristata 1 LSU_97979, LSU_179238, XC_219673, XC_83043, GU932386.1 JQ727428.1 LSU_179235 ML_515365 ML_29420, ML_197219, M. signata 2 LSU_103244, LSU_103228, ML_186976, XC_92329, GU932388.1 GU932070.1 LSU_103229 XC_65601

JQ727425.1, ML_148148, ML_148146, JQ727424.1, AF383013.1, Basileuterus tristriatus 1 ML_148199, XC_63185, JQ727423.1, GU932374.1 LSU_82209, LSU_97996, XC_219751 JQ727422.1, LSU_179248 JQ727421.1 JQ727225.1, JQ727224.1, B. trifasciatus 2 XC_259488, XC_206016, GU932373.1 ML_68185, XC_118557, JQ727209.1, LSU_98005 XC_206829 JQ727226.1 XC_94879, XC_242291, Cacicus sclateri 1 XC_259973, XC_21086, AY117718.1 LSU_85728, LSU_83618 ML_37554 C. koepckeae 2 LSU_31429 ML_96000, XC_63203 KF810927.1 LSU_173193, LSU_111029, ML_39024, ML_29705, AF089015.1, Cacicus cela 1 AY117728.1 LSU_171155 ML_39038, ML_45954 AY117702.1 AF089019.1, AY117739.1, AY117711.1, AY117738.1, C. uropygialis 2 AY117710.1, AY117737.1, LSU_179263, LSU_179264, AY117709.1, AY117736.1, LSU_179265 XC_259969, XC_220037 AY117708.1 AY117735.1 LSU_120397, LSU_116394, ML_136639, ML_23959, AF472364.1, Psarocolius angustifrons 1 AF472389.1 LSU_120398 ML_135052 FJ899539.1 AF472367.1, ML_17148, ML_120932, AF472392.1, P. atrovirens 2 AF472366.1, LSU_91133, LSU_163128, ML_110383, ML_17168, AF472391.1 LSU_97298 ML_13803 AF089049.1 JN715452.1, ML_115140, ML_126732, JQ445376.1, Euphonia rufiventris 1 LSU_50410, LSU_70602, ML_127533 JQ445375.1, LSU_83588 JQ445374.1 ML_39395, ML_46971, JQ445373.1, E. cayennensis 2 ML_46919, XC_326689, JQ445372.1 LSU_67772, UMMZ_83361 XC_217463

UMMZ_216556, ML_63546, ML_63545, KT221230.1, Spinus spinescens 1 KT221360.1 ROM_78564 XC_6868 KT221229.1 LSU_97306, LSU_91142, ML_78516, XC_260072, KT221354.1, KT221224.1, S. olivaceus 2 LSU_91141 XC_98247 KT221353.1 KT221223.1

-Museums: LSU: Louisiana State University Museum of Natural Science, UMMZ: University of Michigan Museum of Zoology, and ROM: Royal Ontario Museum. -Song files: ML: Macaulay Library, XC: xeno-canto, and WL: either recordings belonging to the Weir Lab or song files from commercial CDs. - GenBank accession numbers: Weir Lab: sequences available at the Weir Lab that have not been submitted to GenBank yet.

Appendix 4: Neotropical sister species pairs belonging to the study site (Fig. 1), extracted from the phylogeny of Pulido-Santacruz & Weir (2016).

Midpoint Min elevation Max elevation Elevation_diff Age TB Species Elevation Tyranneutes virescens 100 500 300 0 2.563671 0.390066 Tyranneutes stolzmanni 100 500 300 0 2.563671 0.390066 Rupicola rupicola 100 1200 650 800 4.177002 0.239406 Rupicola peruvianus 500 2400 1450 800 4.177002 0.239406 Snowornis subalaris 1350 2300 1825 750 8.701239 0.114926 Snowornis cryptolophus 800 1350 1075 750 8.701239 0.114926 Gymnoderus foetidus 100 300 200 100 5.636952 0.177401 Conioptilon mcilhennyi 100 500 300 100 5.636952 0.177401 Ampelion rubrocristatus 1750 2740 2245 955 5.236546 0.190966 Ampelion rufaxilla 2500 3900 3200 955 5.236546 0.190966 Tityra semifasciata 100 1200 650 350 3.348066 0.29868 Tityra cayana 100 500 300 350 3.348066 0.29868 Pachyramphus albogriseus 800 2300 1550 1250 2.738489 0.365165 Pachyramphus polychopterus 100 500 300 1250 2.738489 0.365165

Pachyramphus castaneus 100 800 450 100 2.650384 0.377304 Pachyramphus cinnamomeus 100 1000 550 100 2.650384 0.377304 Nephelomyias lintoni 2200 3700 2950 375 1.289791 0.77532 Nephelomyias ochraceiventris 2400 2750 2575 375 1.289791 0.77532 Myiopagis caniceps 890 1500 1195 545 2.248506 0.44474 Myiopagis olallai 100 1200 650 545 2.248506 0.44474 Myiopagis flavivertex 100 1100 600 400 6.16677 0.162159 Myiopagis viridicata 100 300 200 400 6.16677 0.162159 Elaenia parvirostris 100 1500 800 250 5.053562 0.19788 Elaenia flavogaster 100 2000 1050 250 5.053562 0.19788 Elaenia obscura 750 2800 1775 275 1.464368 0.682889 Elaenia dayi 1400 2700 2050 275 1.464368 0.682889 Elaenia ruficeps 100 300 200 550 9.896798 0.101043 Elaenia cristata 100 1400 750 550 9.896798 0.101043 Uromyias agraphia 2700 3600 3150 550 0.354789 2.818577 Uromyias agilis 1800 3400 2600 550 0.354789 2.818577 Anairetes alpinus 1900 4100 3000 1150 3.348271 0.298662 Anairetes flavirostris 3700 4600 4150 1150 3.348271 0.298662 Stigmatura napensis 1800 4600 3200 3000 0.574473 1.740725 Mecocerculus leucophrys 100 300 200 3000 0.574473 1.740725 Mecocerculus minor 1100 2400 1750 300 1.789334 0.558867 Mecocerculus calopterus 1600 2500 2050 300 1.789334 0.558867 Pitangus lictor 100 1200 650 225 6.843522 0.146124 Pitangus sulphuratus 100 750 425 225 6.843522 0.146124 Empidonomus aurantioatrocristatus 100 1200 650 300 0.966209 1.034973 Empidonomus varius 100 1800 950 300 0.966209 1.034973 Ramphotrigon ruficauda 100 1050 575 175 3.335017 0.299849 Ramphotrigon fuscicauda 100 700 400 175 3.335017 0.299849 Fluvicola pica 100 450 275 25 0.075171 13.30297 Fluvicola albiventer 100 400 250 25 0.075171 13.30297 Myiophobus flavicans 900 1800 1350 550 0.422942 2.364388 Myiophobus roraimae 1500 2300 1900 550 0.422942 2.364388

Myiophobus cryptoxanthus 100 1800 950 375 3.713673 0.269275 Myiophobus fasciatus 900 1750 1325 375 3.713673 0.269275 Muscisaxicola cinereus 2700 4200 3450 900 0.413996 2.415482 Muscisaxicola rufivertex 4000 4700 4350 900 0.413996 2.415482 Hemitriccus zosterops 100 900 500 225 0.373787 2.675321 Hemitriccus griseipectus 100 1350 725 225 0.373787 2.675321 Mionectes striaticollis 500 1600 1050 850 3.04358 0.32856 Mionectes olivaceus 1300 2500 1900 850 3.04358 0.32856 Mionectes macconnelli 100 500 300 650 1.411764 0.708333 Mionectes oleagineus 100 1800 950 650 1.411764 0.708333 Myiobius barbatus 100 1000 550 125 0.946522 1.0565 Myiobius atricaudus 100 750 425 125 0.946522 1.0565 Myrmotherula menetriesii 100 200 150 350 10.86061 0.092076 Myrmotherula assimilis 100 900 500 350 10.86061 0.092076 Myrmotherula longipennis 100 1050 575 75 8.995054 0.111172 Myrmotherula axillaris 100 900 500 75 8.995054 0.111172 Myrmochanes hemileucus 100 300 200 125 6.915516 0.144602 Myrmotherula cherriei 100 550 325 125 6.915516 0.144602 Myrmotherula ambigua 100 700 400 175 5.179988 0.193051 Myrmotherula sclateri 100 350 225 175 5.179988 0.193051 Myrmotherula brachyura 100 1000 550 0 2.726398 0.366784 Myrmotherula ignota 100 1000 550 0 2.726398 0.366784 Myrmotherula longicauda 600 1200 900 700 2.598317 0.384864 Myrmotherula klagesi 100 300 200 700 2.598317 0.384864 Cymbilaimus lineatus 100 1450 775 225 7.775892 0.128603 Cymbilaimus sanctaemariae 100 1000 550 225 7.775892 0.128603 Frederickena viridis 100 700 400 100 6.951752 0.143849 Frederickena unduliger 100 500 300 100 6.951752 0.143849 Thamnomanes saturninus 100 300 200 375 4.220703 0.236927 Thamnomanes ardesiacus 100 1050 575 375 4.220703 0.236927 Thamnomanes caesius 100 1200 650 300 2.89242 0.345731 Thamnomanes schistogynus 100 600 350 300 2.89242 0.345731

Sakesphorus canadensis 100 250 175 325 3.612937 0.276783 Sakesphorus luctuosus 100 900 500 325 3.612937 0.276783 Thamnophilus aroyae 600 1700 1150 750 2.032064 0.49211 Thamnophilus aethiops 100 700 400 750 2.032064 0.49211 Thamnophilus stictocephalus 100 700 400 150 1.789742 0.55874 Thamnophilus punctatus 100 1000 550 150 1.789742 0.55874 Thamnophilus cryptoleucus 100 300 200 0 0.610549 1.637871 Thamnophilus nigrocinereus 100 300 200 0 0.610549 1.637871 Thamnophilus melanothorax 100 400 250 25 4.458199 0.224306 Thamnophilus amazonicus 0 550 275 25 4.458199 0.224306 Thamnophilus schistaceus 100 1100 600 50 6.182578 0.161745 Thamnophilus murinus 100 1000 550 50 6.182578 0.161745 Thamnophilus torquatus 1750 3050 2400 1850 2.006015 0.498501 Thamnophilus ruficapillus 100 1000 550 1850 2.006015 0.498501 Sciaphylax hemimelaena 100 1350 725 75 2.502342 0.399626 Sciaphylax castanea 100 1500 800 75 2.502342 0.399626 Drymophila devillei 1200 2500 1850 1300 4.516307 0.22142 Drymophila caudata 100 1000 550 1300 4.516307 0.22142 Cercomacra carbonaria 100 1150 625 525 0.146286 6.835909 Cercomacra cinerascens 100 100 100 525 0.146286 6.835909 Cercomacra tyrannina 100 1100 600 350 4.223056 0.236795 Cercomacra serva 100 1800 950 350 4.223056 0.236795 Pithys castaneus 100 1350 725 500 4.216328 0.237173 Pithys albifrons 200 250 225 500 4.216328 0.237173 Phlegopsis nigromaculata 100 150 125 325 2.810921 0.355755 Phlegopsis borbae 100 800 450 325 2.810921 0.355755 Gymnopithys leucaspis 100 600 350 150 0.368578 2.713132 Gymnopithys rufigula 100 900 500 150 0.368578 2.713132 Gymnopithys lunulatus 100 450 275 25 2.064316 0.484422 Gymnopithys salvini 100 400 250 25 2.064316 0.484422 Akletos melanoceps 100 500 300 0 2.307916 0.433291 Akletos goeldii 100 500 300 0 2.307916 0.433291

Epinecrophylla leucophthalma 100 1000 550 100 5.728396 0.174569 Epinecrophylla erythrura 100 800 450 100 5.728396 0.174569 Epinecrophylla haematonota 700 1500 1100 900 2.718905 0.367795 Epinecrophylla spodionota 100 300 200 900 2.718905 0.367795 Xenops rutilus 100 1500 800 950 3.695629 0.27059 Xenops tenuirostris 700 2800 1750 950 3.695629 0.27059 Cinclodes aricomae 3500 4600 4050 150 1.620039 0.617269 Cinclodes excelsior 3200 5200 4200 150 1.620039 0.617269 Philydor erythropterum 1000 1700 1350 850 4.394222 0.227572 Philydor rufum 100 900 500 850 4.394222 0.227572 Automolus melanopezus 100 700 400 125 2.968988 0.336815 Automolus rufipileatus 100 450 275 125 2.968988 0.336815 Automolus subulatus 100 1400 750 0 2.718783 0.367812 Automolus ochrolaemus 100 1400 750 0 2.718783 0.367812 Thripadectes flammulatus 2450 3200 2825 25 1.671606 0.598227 Thripadectes scrutator 2200 3500 2850 25 1.671606 0.598227 Anabacerthia ruficaudata 100 850 475 1125 2.892461 0.345726 Anabacerthia variegaticeps 700 2500 1600 1125 2.892461 0.345726 Syndactyla ucayalae 650 900 775 75 1.829327 0.546649 Syndactyla striata 100 1300 700 75 1.829327 0.546649 Cranioleuca muelleri 100 200 150 100 1.500403 0.666488 Cranioleuca vulpina 100 400 250 100 1.500403 0.666488 Cranioleuca marcapatae 2200 3400 2800 150 0.858144 1.165305 Cranioleuca albiceps 2400 3500 2950 150 0.858144 1.165305 Thripophaga cherriei 500 50 275 155 2.641879 0.378519 Thripophaga fusciceps 600 2900 120 155 2.641879 0.378519 Synallaxis azarae 2450 3500 2975 475 1.08439 0.922177 Synallaxis courseni 1500 3500 2500 475 1.08439 0.922177 Synallaxis cabanisi 250 1350 800 50 1.839798 0.543538 Synallaxis moesta 100 1400 750 50 1.839798 0.543538 Synallaxis rutilans 500 1100 800 625 2.428146 0.411837 Synallaxis cherriei 100 250 175 625 2.428146 0.411837

Certhiaxis cinnamomeus 0 500 250 175 4.44487 0.224978 Certhiaxis mustelinus 0 150 75 175 4.44487 0.224978 Asthenes ottonis 3000 3700 3350 100 1.062544 0.941138 Asthenes palpebralis 2900 4000 3450 100 1.062544 0.941138 Asthenes griseomurina 2800 3300 3050 50 0.188334 5.309728 Asthenes fuliginosa 2900 3300 3100 50 0.188334 5.309728 Asthenes coryi 3000 3400 3200 250 1.664922 0.600629 Asthenes perijana 2800 4100 3450 250 1.664922 0.600629 Asthenes maculicauda 3000 4300 3650 225 1.884124 0.530751 Asthenes virgata 3250 4500 3875 225 1.884124 0.530751 Asthenes sclateri 2000 4000 3000 750 0.652204 1.533262 Asthenes wyatti 3000 4500 3750 750 0.652204 1.533262 Leptasthenura striata 2000 4000 3000 0 2.676507 0.373621 Leptasthenura pileata 2500 3500 3000 0 2.676507 0.373621 Sittasomus griseicapillus 685 1570 1127.5 227.5 8.892729 0.112451 Deconychura longicauda 100 1700 900 227.5 8.892729 0.112451 Dendrocincla tyrannina 100 600 350 1550 8.152721 0.122658 Dendrocincla merula 1300 2500 1900 1550 8.152721 0.122658 Dendroplex kienerii 100 200 150 400 5.009951 0.199603 Dendroplex picus 100 1000 550 400 5.009951 0.199603 Campylorhamphus procurvoides 100 800 450 200 1.809575 0.552616 Campylorhamphus trochilirostris 100 400 250 200 1.809575 0.552616 Xiphorhynchus elegans 100 600 350 50 2.685893 0.372316 Xiphorhynchus spixii 100 500 300 50 2.685893 0.372316 Hylexetastes stresemanni 100 300 200 0 2.081899 0.480331 Hylexetastes perrotii 100 300 200 0 2.081899 0.480331 Nasica longirostris 100 950 525 225 7.673149 0.130325 Dendrexetastes rufigula 100 500 300 225 7.673149 0.130325 Sclerurus rufigularis 100 1100 600 150 6.538162 0.152948 Sclerurus mexicanus 100 800 450 150 6.538162 0.152948 Cyanolyca turcosa 1500 3500 2500 200 4.160223 0.240372 Cyanolyca viridicyanus 1600 3000 2300 200 4.160223 0.240372

Pygochelidon melanoleuca 100 300 200 2050 5.674867 0.176216 Pygochelidon cyanoleuca 1000 3500 2250 2050 5.674867 0.176216 Orochelidon andecola 2500 4400 3450 550 2.358167 0.424058 Orochelidon murina 1800 4000 2900 550 2.358167 0.424058 Turdus hauxwelli 100 1100 600 150 1.261194 0.792899 Turdus fumigatus 100 800 450 150 1.261194 0.792899 Turdus maranonicus 100 1200 650 550 0.308034 3.246391 Turdus ignobilis 400 2000 1200 550 0.308034 3.246391 Turdus serranus 2400 4200 3300 1400 2.612984 0.382704 Turdus fuscater 900 2900 1900 1400 2.612984 0.382704 Catharus dryas 800 2600 1700 250 4.003405 0.249787 Catharus fuscater 600 2300 1450 250 4.003405 0.249787 Entomodestes leucotis 600 1900 1250 875 1.122329 0.891005 Entomodestes coracinus 900 3350 2125 875 1.122329 0.891005 Pheugopedius coraya 2200 3200 2700 1650 5.054838 0.19783 Pheugopedius euophrys 100 2000 1050 1650 5.054838 0.19783 Cantorchilus leucotis 0 400 200 50 3.691673 0.27088 Cantorchilus guarayanus 0 300 150 50 3.691673 0.27088 Piranga leucoptera 600 2000 1300 1050 8.518069 0.117397 Piranga rubriceps 1700 3000 2350 1050 8.518069 0.117397 Creurgops dentatus 1200 2500 1850 300 5.861262 0.170612 Creurgops verticalis 1500 2800 2150 300 5.861262 0.170612 Chlorophanes spiza 1100 2000 1550 700 4.568441 0.218893 Iridophanes pulcherrimus 100 1600 850 700 4.568441 0.218893 Ramphocelus carbo 100 1200 650 800 0.630484 1.586084 Ramphocelus melanogaster 800 2100 1450 800 0.630484 1.586084 Tachyphonus cristatus 100 1000 550 50 5.213894 0.191795 Tachyphonus luctuosus 100 900 500 50 5.213894 0.191795 Coryphospingus cucullatus 100 450 275 275 3.93283 0.25427 Coryphospingus pileatus 400 700 550 275 3.93283 0.25427 Eucometis penicillata 100 1000 550 800 4.37177 0.22874 Trichothraupis melanops 1000 1700 1350 800 4.37177 0.22874

Phrygilus unicolor 800 5300 3050 700 2.392311 0.418006 Phrygilus plebejus 2500 5000 3750 700 2.392311 0.418006 Diglossa lafresnayii 2000 3750 2875 525 0.672056 1.487971 Diglossa gloriosissima 3000 3800 3400 525 0.672056 1.487971 Diglossa caerulescens 1450 3600 2525 275 3.635895 0.275035 Diglossa cyanea 1300 3200 2250 275 3.635895 0.275035 Poospiza hypochondria 2900 3500 3200 150 2.184928 0.457681 Poospiza caesar 2500 4200 3350 150 2.184928 0.457681 Compsospiza garleppi 2500 3100 2800 650 0.641605 1.55859 Compsospiza baeri 2700 4200 3450 650 0.641605 1.55859 Cyanerpes caeruleus 100 1000 550 200 4.973843 0.201052 Cyanerpes nitidus 100 1400 750 200 4.973843 0.201052 Tangara parzudakii 100 1600 850 950 4.141515 0.241458 Tangara schrankii 1100 2500 1800 950 4.141515 0.241458 Tangara velia 100 600 350 50 1.457415 0.686146 Tangara callophrys 100 500 300 50 1.457415 0.686146 Tangara xanthogastra 600 2000 1300 875 2.56059 0.390535 Tangara punctata 100 750 425 875 2.56059 0.390535 Tangara heinei 1100 2200 1650 150 0.392513 2.547683 Tangara argyrofenges 1100 1900 1500 150 0.392513 2.547683 Thraupis sayaca 100 1500 800 250 0.726732 1.376024 Thraupis episcopus 100 2000 1050 250 0.726732 1.376024 Anisognathus somptuosus 800 2200 1500 400 5.519785 0.181166 Anisognathus notabilis 1200 2600 1900 400 5.519785 0.181166 Cnemathraupis aureodorsalis 2000 3800 2900 400 4.385306 0.228034 Cnemathraupis eximia 3100 3500 3300 400 4.385306 0.228034 Anisognathus lacrymosus 2600 3500 3050 350 1.950166 0.512777 Anisognathus igniventris 2100 3300 2700 350 1.950166 0.512777 Buthraupis wetmorei 1500 3100 2300 600 7.096101 0.140922 Thraupis cyanocephala 2900 2900 2900 600 7.096101 0.140922 Dubusia castaneoventris 2000 3500 2750 0 4.09738 0.244058 Dubusia taeniata 2000 3500 2750 0 4.09738 0.244058

Chlorochrysa nitidissima 1300 2200 1750 300 1.661345 0.601922 Chlorochrysa calliparaea 900 2000 1450 300 1.661345 0.601922 Bangsia aureocincta 1600 2200 1900 650 0.558309 1.791124 Bangsia edwardsi 400 2100 1250 650 0.558309 1.791124 Iridosornis reinhardti 2050 3500 2775 325 2.244627 0.445508 Iridosornis rufivertex 2500 3700 3100 325 2.244627 0.445508 Myiothlypis rivularis 0 1000 500 50 2.857693 0.349933 Myiothlypis fulvicauda 100 1000 550 50 2.857693 0.349933 Basileuterus tristriatus 500 2000 1250 250 3.182564 0.314212 Basileuterus trifasciatus 1000 2000 1500 250 3.182564 0.314212 Myioborus melanocephalus 2000 4000 3000 300 0.365386 2.736835 Myioborus ornatus 2000 3400 2700 300 0.365386 2.736835 Oreopsar bolivianus 100 3200 1650 800 4.415158 0.226492 Agelaioides badius 1400 3500 2450 800 4.415158 0.226492 Lampropsar tanagrinus 1200 2700 1950 1700 5.290485 0.189019 Hypopyrrhus pyrohypogaster 100 400 250 1700 5.290485 0.189019 Chlorospingus flavigularis 300 1800 1050 700 4.003285 0.249795 Chlorospingus parvirostris 1400 2100 1750 700 4.003285 0.249795 Atlapetes fulviceps 700 3100 1900 650 0.36147 2.766482 Atlapetes citrinellus 1500 3600 2550 650 0.36147 2.766482