Song evolution in American Screech-owls: the distinctive ecology of nocturnal top predators can lead to unexpected acoustic patterns

Song evolution in American Screech-owls: the distinctive ecology of nocturnal top predators can lead to unexpected acoustic patterns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Song evolution in American Screech-owls: the distinctive ecology of nocturnal top predators can lead to unexpected acoustic patterns Luis Felipe Peixoto¹, Luiz P. Gonzaga¹, Paulo C. Paiva¹, Fábio Hepp¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8834994/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract The evolution of birdsong has been predominantly studied in diurnal passerines, leaving nocturnal species underexplored. Owls provide an exceptional model: their cryptic plumage contrasts with conspicuous vocalizations that function in mate attraction and territorial defense, in low-visibility environments. We investigated the evolutionary drivers of acoustic diversity in Megascops , the largest New World owl genus, using phylogenetic comparative methods on the most comprehensive vocal dataset assembled for the group. We compiled 5,652 recordings from all species and Gymnasio nudipes , measured 40 acoustic variables from primary songs, and analyzed their evolutionary patterns using an updated molecular phylogeny. Three traits showed significant phylogenetic signal (phrase duration, number of notes, and relative time at 5% of phrase energy), indicating evolutionary conservatism with clade-specific innovations overlaying a shared ancestral motif. Contrary to predictions from the Acoustic Adaptation Hypothesis, forest-dwelling species produced higher-pitched songs than those in open habitats. We propose that this deviation reflects eavesdropping avoidance: as small-bodied owls vulnerable to larger sympatric species, Megascops may favor higher frequencies that attenuate rapidly, reducing detectability. Additionally, forest species showed intra-phrase acceleration, which combined with higher frequencies could enhance distance assessment (ranging), allowing receivers to gauge intruder proximity, a critical adaptation for territorial, armed birds. Elevation showed complex associations, with higher altitudes correlating with increased frequencies, likely mediated by habitat structure. Sexual vocal dimorphism was associated with subtle spectral patterns rather than overall frequency differences, suggesting that sexual selection acts on fine-scale modulation capacity. Unexpectedly, body mass showed no correlation with any acoustic trait after phylogenetic correction. Our findings reveal that owl vocal evolution reflects a complex interplay between phylogenetic constraint and ecological pressures distinct from diurnal songbirds, highlighting the need to expand bioacoustics research beyond traditional model systems to understand communication under nocturnal, predation-driven conditions. Megascops Strigidae Phylogeny Acoustic Adaptation Hypothesis Sexual selection Eavesdropping avoidance Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Birdsong represents a cornerstone of avian biology (Catchpole and Slater 2008 ), fascinating humans since antiquity and inspiring ancient observations that resonate with modern scientific concepts. Pliny the Elder, in The Natural History (Book X, Chap. 43; Bostock et al. 1855), described the remarkable vocal artistry of male nightingales vigorously competing with one another. He highlighted male-male competition, one of the foundational elements of sexual selection, nearly two millennia before Darwin formalized the concept in 1871. On the other hand, early observations also contributed to historical biases. For instance, first documented birdsong observations focused on oscine passerines from north-temperate regions, whose males produce loud and elaborate vocalizations during the reproductive season (Rose et al. 2022 ). These first impressions led to the view of sexual selection as the primary driver of trait elaboration in birdsong (Podos et al. 2004 ). Nowadays, the interpretation of drivers of the evolution of birdsong traits has broadened, recognizing that multiple forces, not only sexual selection, might shape vocal phenotypes, and that disentangling their effects requires explicit consideration of phylogenetic history (Felsenstein 1985 ; Price and Lanyon 2002 ). Among these interacting forces, sexual and natural selection appear as common drivers of changes in vocalizations (e.g., Slater 1983 ; West-Eberhard 1983 ; Yasukawa 1989 ; Price 2009 ; Tobias et al. 2011 ; Lyon and Montgomerie 2012 ), while phylogenetic history, morphology, and energetic or social costs impose constraints (e.g., Gould and Lewontin 1979 ; Bradbury and Vehrencamp 1998 ; Blomberg and Garland 2002 ; Blomberg et al. 2003 ). One effect of natural selection on vocalizations has received special attention: the Acoustic Adaptation Hypothesis (AAH), which poses that signal structure is shaped by habitat-dependent propagation and noise overlap challenges (Morton 1975 ; Wiley and Richards 1982 ; Brumm and Naguib 2009 , Bradbury and Vehrencamp 2011 ). According to this hypothesis, vocalizations in closed habitats are expected to have lower frequencies, narrower bandwidths, and slower pace than those in open habitats, because these acoustic features are less affected by, or compensate for, degradation caused by dense vegetation, thus optimizing propagation (Morton 1975 ; Wiley and Richards 1982 ). Despite being a longstanding hypothesis, empirical support for these predictions has been mixed (e.g., Boncoraglio and Saino 2007 ; Ey and Fischer 2009 ; Bezerra et al. 2021 ; Mikula et al. 2021 ). Habitat features are often correlated with elevation, which represents a composite proxy encompassing vegetation structure, temperature, humidity, and air density. Elevation can affect sound propagation indirectly, as it influences vegetation structure (e.g., Kirschel et al. 2009 ), but also directly through differences in atmospheric conditions (Larom et al. 1997 ; Boyle et al. 2016 ). However, effects of elevation on vocal variation have been studied mostly at the intraspecific level, comparing populations across altitudinal gradients (Gillam et al. 2009 ; Funk et al. 2016 ; Villegas et al. 2018 ), and remain less explored than habitat effects. Moreover, the direction and magnitude of altitudinal effects on vocalizations are harder to predict and vary considerably across taxa and geographic regions (e.g., Branch and Pravosudov 2015 ; Villegas et al. 2018 ). Beyond the physical environment, acoustic signals are also shaped by the reactions of receivers. In birds, songs mostly serve the primary dual purpose of attracting mates and defending territories (Collins 2004 ; Catchpole and Slater 2008 ), and sexual selection is historically considered one of the most important drivers of signal elaboration, typically favoring more vigorous and costly displays (Bradbury and Vehrencamp 2011 ). Some species favor vocal complexity (e.g., Cardoso et al. 2010), while others favor consistency and repetition (e.g., Sierro et al. 2023 ). Some favor lower-pitched vocalizations (e.g. Appleby and Redpath 1997 ; Hardouin et al. 2007 ), while others favor higher amplitude (Podos and Cohn-Haft 2019 ). As males are typically subject to female choice and male-male competition, the degree of sexual dimorphism often reflects intensity of sexual selection (Trivers 1972 ; Fairbairn 1997 ; Badyaev and Martin 2000 ). Additionally, in territorial contexts, signals may serve a secondary function: ranging — the use of acoustic cues such as signal degradation to estimate signaler distance (Naguib and Wiley 2001 ), adding another social dimension to signal design. However, signals cannot change freely; features that are beneficial in one way may be costly in another. Not all receivers are the intended audience of a given signal. Eavesdroppers such as predators and competitors can locate or exploit signalers using acoustic cues (Tuttle and Ryan 1981 ; Mougeot and Bretagnolle 2000 ; Naguib et al. 2004 ), selecting for signals that favor privacy over broadcast range and conspicuousness (Dabelsteen et al. 1998 ; McGregor 2005 ; Peake 2005 ). Beyond this social constraint, morphology and phylogenetic history also limit signal evolution. For instance, larger-bodied animals generally have larger phonating membranes, enabling the production of lower-frequency sounds, and larger lungs, allowing longer vocalizations (Bradbury and Vehrencamp 1998 , 2011 ; Fitch and Hauser 2003 ), and thus certain acoustic traits can generally provide information about the size of the animal, although there are exceptions (e.g., Fitch 1999 ; Peters et al. 2009 ). Finally, phylogenetic inertia may constrain divergence, as closely related species tend to share similar vocal traits regardless of current selective pressures (Blomberg and Garland 2002 ; Blomberg et al. 2003 ). Most of the theoretical framework presented above derives from studies focused on diurnal birds, especially passerines. However, these are not the only birds that engage vocally in social interactions. Owls stand out as a group with a markedly different biology in several aspects that are potentially key for the evolution of vocal signals. Firstly, most owls are nocturnal predators that communicate in low-visibility environments where visual signals are limited (Marks et al. 1999 ; Penteriani et al. 2007 ; Penteriani and Delgado 2017 ). Indeed, their plumage is generally cryptic with minimal sexual dichromatism (König et al. 2008 ), making acoustic signals the primary channel for mate attraction and territorial defense (König et al. 2008 ; Mikkola 2014 ). Further, unlike oscine passerines, owl songs are predominantly innate (König et al. 2008 ), minimizing confounding effects of cultural transmission on the evolution of their vocalizations. Furthermore, owls occur within acoustic networks that include dangerous potential eavesdroppers: larger sympatric species that frequently engage in intraguild predation (Sergio and Hiraldo 2008 ), an interaction that appears to be particularly common among owls (Sergio and Hiraldo 2008 ), as well as conspecific rivals armed with sharp talons that heighten the risks of physical confrontations (Enquist and Leimar 1990 ). These pressures may favor signals that prioritize privacy over long-range propagation and/or facilitate the ability to estimate intruder distance through signal degradation (ranging) — especially relevant at night, as owls are known to be active in darkness when even they are unable to see (Martin 1977 , 1986 ), placing further significance on acoustic assessment of adversaries. Finally, owls often exhibit reverse sexual size dimorphism — females being larger than males (Earhart and Johnson 1970 ; Segall et al. 2017 ) — yet males consistently produce lower-frequency vocalizations (Appleby and Redpath 1997 ; Galeotti 1998 ; Segall et al. 2022 ), a pattern attributed to disproportionately larger male syringes rather than overall body size (Miller 1934 ; Segall 2013 ). This decoupling between body size and vocal frequency challenges straightforward morphological predictions. Together, these peculiarities make owls a compelling system for testing whether nocturnal ecology generates vocal evolutionary patterns distinct from those documented in diurnal songbirds. These peculiarities motivated us to begin investigation on vocal evolution in owls using phylogenetic comparative methods. Among owls, the genus Megascops Kaup, 1848 is particularly suitable for this purpose. It is the largest New World owl genus, spanning from southern Canada to southern South America across broad altitudinal and habitat gradients. Morphology and plumage are often insufficient for species identification, and taxa are identified primarily by vocalizations (Dantas et al. 2016 ; Krabbe 2017 ). The genus exhibits high vocal diversity and, in some species, multiple song types (König et al. 2008 ; Krabbe 2017 ), making it an ideal system for investigating vocal trait evolution. We inferred an updated phylogeny of Megascops by integrating published sequence datasets (Dantas et al. 2016 , 2021 ) with recent taxonomic updates. Using the largest vocal dataset for the genus to date, we reviewed the vocal repertoire of all species, identifying primary songs and obtaining acoustic measurements from them. We used phylogenetic comparative methods to investigate whether variation in primary-song traits correlates with phylogenetic history, habitat structure, elevation, vocal dimorphism, and body size. Our hypotheses follow classical predictions derived from other taxa. We expect (1) to find significant phylogenetic signals in vocal traits across Megascops . Following the Acoustic Adaptation Hypothesis, we expect (2) that species inhabiting forested habitats produce songs with lower frequency and slower pace than those in open habitats. We also predict (3) that sexual selection and morphological constraints leave detectable signatures in song traits, such that species with stronger vocal dimorphism or larger body size will produce lower-frequency or longer vocalizations. Our results shed light on the selective and historical processes shaping the evolution of owl song, providing a framework for understanding the diversification of a lineage of birds with the unique ecology of being highly vocal and nocturnal top predators. Material and methods Taxonomy and datasets The genus Megascops comprises 27 currently recognized species (Clements Checklist v2024, supplemented with taxa from Dantas et al. 2021 ). Our study involved different subsets depending on data availability. The molecular phylogeny included 26 Megascops species ( M. seductus lacks sequence data) and seven outgroup taxa ( Otus megalotis , Bubo bubo , Lophostrix cristata , Strix aluco , Asio otus , Psiloscops flammeolus and Gymnasio nudipes ); the concatenated matrix totaled 3,917 bp, with sequence coverage ranging from one to six genes per species (Table S1 ). Sequences labeled as M. vermiculatus in Dantas et al. ( 2016 ) correspond to localities consistent with M. centralis (Hekstra 1982 ; Greeney and Freile 2020 ) and were treated accordingly (see N. Krabbe in Lane 2018 ). Phylogenetic comparative analyses used the 26 Megascops species with Gymnasio nudipes (Daudin, 1800) as the sole outgroup, due to homology constraints on vocal comparisons (see Acoustic data below). Molecular data We obtained sequences from three mitochondrial genes (Cytochrome-b, ND2, COI) and three nuclear genes (β-fibrinogen intron 5, CHD1 intron 18, MUSK intron 4). GenBank accession numbers are provided in Table S1 . Phylogenetic analysis Sequences were aligned using MAFFT v7 (Katoh and Standley 2013 ; Katoh et al, 2019 ) with default parameters, translated to amino acids to check for unexpected indels or stop codons, and assessed for substitution saturation using DAMBE v7.3.32 following Xia and Lemey ( 2009 ). The concatenated matrix comprised 2,411 bp of mitochondrial and 1,506 bp of nuclear genes (including gaps). Mitochondrial genes were partitioned by codon position; each nuclear gene comprised a separate partition. Best-fit substitution models were selected using ModelFinder (Kalyaanamoorthy et al. 2017 ; see ModelFinder output in Supplementary Material 1; partition and models in Table S2). Bayesian inference was performed in MrBayes v3.2.7 (Ronquist et al. 2012 ) with two independent runs of four Markov chains each (10⁶ generations, 25% burn-in). Gaps were treated as missing data. Convergence was assessed in Tracer v1.5 (Rambaut et al. 2013 ). To assess topological robustness, we also performed Maximum Likelihood analysis in IQ-TREE 2 (Minh et al. 2020 ) with SH-aLRT (Guindon et al. 2010 ) and 1,000 ultrafast bootstrap replicates (Hoang et al. 2018 ). Both approaches yielded highly congruent topologies (see Supplementary Material 1). Acoustic data Data gathering and screening Our dataset comprises 5,652 audio recordings of all 27 Megascops species and Gymnasio nudipes (see Outgroup selection below), obtained from institutional sound archives and commercial sources (Table S3). Recordings with artifacts, excessive background noise, or inadequate signal-to-noise ratios were excluded. All recordings underwent aural and spectrographic screening to classify vocalizations into putative types within each species, independent of published nomenclature; classifications were then cross-checked against repertoire descriptions in the literature. This approach aimed to establish an unbiased characterization of each species' vocal repertoire, free from historical nomenclature inconsistencies and a priori homology assumptions. Primary song selection Some owl species exhibit multiple song types (König et al. 2008 ; Krabbe 2017 ), and vocal homology assignments are often inconsistent across studies. We qualitatively characterized vocal diversity across the genus, identifying song types, motif structure, and clade-associated deviations from the basic pattern (see Results: Bioacoustical diversity overview; a detailed description of Megascops vocal repertoires will be published elsewhere). For quantitative analyses, we analyzed only the primary song, operationally defined as the most frequently recorded vocalization for each species in our sample. We treated primary songs as homologous structures across Megascops following the hierarchical homology framework (Striedter and Northcutt 1991 ; Hall 2013 ), under which vocalizations may undergo evolutionary shifts, including functional ones (Hepp and Pombal 2019). Acoustic terminology and measurements We define 'song' as loud, multi-note vocalizations that serve to attract mates and defend territories (the functional equivalent of passerine song, regardless of learning, sex of the emitter and seasonality); 'call' as shorter, simpler vocalizations associated with contact, alarm, or non-territorial contexts; 'note' as a continuous sound emission; 'syllable' as notes repeatedly rendered together in the same pattern; and 'phrase' as a group of syllables regularly repeated during vocal bouts. When feasible (some species are poorly sampled) we randomly selected up to five recordings per species, avoiding sampling from the same locality and date to minimize pseudoreplication (Hurlbert 1984 ). From each recording, we measured one randomly selected phrase. Audio files were band-pass filtered (approximately 50 Hz below and above each species' vocal frequency range) to isolate target signals. We measured 22 acoustic variables encompassing spectral, temporal, energy-distributional, and sectional parameters; variables were measured at two hierarchical levels: (1) entire phrases and (2) individual notes and inter-note intervals (Table 1 ). For note-level measurements, each phrase was divided into three temporal sections (initial, middle, final), with some variables measured separately for each section, totaling 40 acoustic variables. Sampling strategy varied with phrase length (Fig. 1 ). In phrases with ≤ 9 notes, we measured one note and one interval per section. In longer phrases, we sampled three notes and three intervals per section and calculated section means — providing more robust estimates, since initial and terminal notes are often weaker or partially lost. All measurements were taken using Raven Pro 1.6 (Cornell Lab of Ornithology 2023 ): temporal and amplitude variables from oscillograms, spectral variables from spectrograms (Hann window, 25.7 ms; 99% overlap; DFT 8,192 samples). For a justification of measuring spectral variables from spectrograms, see Peixoto et al. ( 2025 ). Table 1 Acoustic variables measured from owl songs and used in comparative analyses. Variables are grouped into categories according to the type of information they represent: frequency, energy distribution, temporal, and sectional metrics describing intra-phrase variation. Measurements were taken either at the level of the entire phrase, individual notes, or inter-note intervals, as detailed in the table. Variable Category Variable Name Measurement Level Description Frequency Minimum frequency Phrase, Note Minimum frequency of vocalization Maximum frequency Phrase, Note Maximum frequency of vocalization Bandwidth Phrase, Note Difference between high and low frequency Dominant frequency Phrase, Note Frequency with maximum energy Energy distribution Frequency 5% Phrase Frequency below which 5% of energy occurs Frequency 25% Phrase Frequency below which 25% of energy occurs Frequency 75% Phrase Frequency below which 75% of energy occurs Frequency 95% Phrase Frequency below which 95% of energy occurs Time 5% relative Phrase Time at which 5% of cumulative energy is reached Time 25% relative Phrase Time at which 25% of cumulative energy is reached Time 75% relative Phrase Time at which 75% of cumulative energy is reached Time at 95% relative Phrase Time at which 95% of cumulative energy is reached Temporal Phrase duration Phrase Total duration of entire phrase Note duration Note Duration of individual notes (by section) Interval duration Note (Interval) Duration of silent gaps between notes Number of notes Phrase Total count of notes per phrase Overall pace Phrase Notes per second across entire phrase Sectional metrics Section pace Phrase sections Notes per second within each phrase section Pace change (initial-middle) Derived from Phrase sections Change in pace (initial to middle) Pace change (middle-final) Derived from Phrase sections Change in pace (middle to final) Frequency change (initial-middle) Derived from Phrase sections Change in dominant frequency (initial to middle) Frequency change (middle-final) Derived from Phrase sections Change in dominant frequency (middle to final) Outgroup selection We used Gymnasio nudipes as the sole outgroup for vocal analyses. The molecular phylogeny included additional outgroup taxa (see above; Table S1 ), but these were excluded from acoustic comparisons due to homology constraints. Psiloscops flammeolus , the closest relative to Megascops + G. nudipes (tribe Megascopini; Wink et al. 2009 ; Salter et al. 2020 ), produces songs with fewer than three notes per phrase (König et al. 2008 ), precluding sectional measurements. The remaining outgroups belong to distantly related tribes (Bubonini, Pulsatrigini, Strigini, Otini) with divergent vocal structures, making homology assessments unreliable. In contrast, G. nudipes shares the multi-note phrase structure typical of Megascops (> 4 notes), allowing direct application of all acoustic variables. Using a single outgroup admittedly increases uncertainty in ancestral state reconstructions; however, we prioritized robust homology over taxon sampling, since incorrect homology assumptions would yield spurious analytical precision rather than genuine phylogenetic insight. Habitat and elevational data Habitat associations were classified from species accounts in Birds of the World (Billerman et al. 2022 ) as: (1) forest-dependent (species restricted to or predominantly associated with forested habitats), (2) open habitats (species restricted to or predominantly associated with open habitats such as savannas, scrublands, deserts), or (3) forest + open (species regularly occurring in both habitat types). For brevity, 'forest + open' is sometimes discussed as ‘habitat generalists’ or, when grouped with forest-dependent species as 'species that occur in forests', given the shared use of forests. This broad classification was adopted deliberately: given the continental scale of Megascops distributions and the scarcity of comparable fine-scale habitat data across all species, finer categorizations would likely introduce noise rather than meaningful biological resolution. Text excerpts supporting each assignment are provided in Table S4. Elevational data were also extracted from Birds of the World and summarized as minimum elevation, maximum elevation, and elevational range for each species (Table S5). Data were available for 23 of the 27 species. Sexual selection and body size proxies We quantified vocal dimorphism as an index of sexual selection intensity (Trivers 1972 ; Fairbairn 1997 ) using sex-specific dominant frequencies from Krabbe ( 2017 ). For each species, we calculated the percentage difference between male and female dominant frequency: [(female − male) / female] × 100. This approach relies on published averages rather than individually sexed recordings, and although this limits precision, it remains standard for comparative analyses where sexed samples are scarce. More rigorous approaches, such as those advocated by Segall et al. ( 2022 ), require controlled sampling of known pairs and are beyond the scope of the present study. Data were available for 14 of the 27 species (Table S5). Body mass data were compiled from König et al. ( 2008 ) as a proxy for vocal apparatus size, a standard practice in comparative bioacoustics. Although body mass is an imperfect proxy for syringeal dimensions, it remains the most widely available size metric for owls; linear measurements (such as total length) are highly subjective and sensitive to specimen condition, while wing length, (though more precise) is less commonly reported across species (Segall et al. 2017 ). When ranges were reported, arithmetic means were used to reduce the effects of sexual size dimorphism. Data were available for 22 of the 27 species (Table S5). Phylogenetic Comparative Analyses All analyses were conducted in R v4.4.1 (R Core Team 2024 ; script in Supplementary Material 1). The Bayesian phylogeny was prepared for the phylogenetic comparative analyses by removing outgroup taxa used only for rooting (retaining G. nudipes as root), resolving polytomies with multi2di in ape (Paradis and Schliep 2019 ), and transforming the topology into an ultrametric tree. Prior to analysis, acoustic variables were standardized using scale(), shifted to positive values, and log-transformed. Ultrametricization was performed using the penalized-likelihood method in chronos (Paradis 2013 ). In the absence of fossil calibration points for Megascops , we selected the smoothing parameter (λ = 0.8) that maximized correlation between patristic distances of original and ultrametric trees (r = 0.98) while minimizing RMSE (0.84; Figure S1 ), thus preserving the covariance structure among species (Felsenstein 1985 ). Phylogenetic Signal Phylogenetic signal was assessed for each acoustic trait using Blomberg's K (Blomberg et al. 2003 ) and Pagel's λ (Pagel 1999 ) with phylosig in phytools (Revell 2012 ). Significance was evaluated via tip-label randomization for K (1,000 simulations) and likelihood ratio tests (λ = 0) for λ. We interpreted the signal as robust only when both metrics were significant (p < 0.05), given their complementary properties: K can exceed 1.0 indicating stronger-than-Brownian structure, while λ is bounded between 0 and 1. For interpretation and discussion of the results, we focused exclusively on traits that exhibited significant phylogenetic signals for both K and λ, ensuring robustness across complementary estimators of phylogenetic dependence. Evolutionary models We compared nine continuous-trait evolutionary models using fitContinuous in geiger (Harmon et al. 2008 ): Brownian Motion (BM), Ornstein–Uhlenbeck (OU), Early Burst (EB), Lambda (LB), Pagel's λ, δ, KP, Time-Dependent (TD), Drift (DF), and White-Noise (WN) via AICc and Akaike weights for all 40 acoustic variables and the first five principal components from pPCA (see below). Because most variables were best fit by WN or BM, indicating low to moderate phylogenetic structure, we adopted Pagel's λ-transformed Brownian model (with λ optimized by maximum likelihood) as the general correlation structure for pPCA and PGLS analyses. For the few traits better described by KP or OU models, we ran additional PGLS analyses using model-specific parameters estimated by fitContinuous. Character mapping and ancestral state reconstruction Ancestral states were reconstructed using maximum likelihood under Brownian motion with contMap in phytools (Revell 2012 ). Visualizations were generated using plotSimmap and fancyTree. We reconstructed ancestral states for acoustic variables showing significant phylogenetic signals for both K and λ. For habitat (discrete), we used stochastic character mapping under an equal-rates model with make.simmap, also from phytools. Because treating 'forest + open' as a discrete state rather than a polymorphism could affect evolutionary inferences, we compared four models of discrete character evolution using AICc: Equal Rates (ER), Symmetric (SYM), All Rates Different (ARD), and an Ordered model constraining 'forest + open' as an obligatory intermediate between forest and open (i.e., direct forest ↔ open transitions prohibited). The ER and Ordered models showed identical fit (AICc = 47.16, ΔAICc = 0), followed by SYM (ΔAICc = 1.08) and ARD (ΔAICc = 9.59). Given their identical fit, we retained the ER model, which does not impose a priori constraints on transitions that may occur over long evolutionary timescales. Phylogenetic regression models Associations between acoustic traits and predictors (body mass, vocal dimorphism, habitat and elevation metrics) were tested using Phylogenetic Generalized Least Squares (PGLS) with gls() in nlme (Pinheiro et al. 2023 ), specifying Pagel's λ correlation structure via corPagel() in ape (Paradis and Schliep 2019 ). This allows phylogenetic covariance to be scaled according to λ rather than assuming strict Brownian evolution (λ = 1). For traits better fit by KP or OU models (see Evolutionary models), we used pgls() in caper (Orme et al. 2013 ) with model-specific parameters from fitContinuous. Significance was set at p < 0.05. Phylomorphospace and clade visualization To visualize evolutionary patterns, we plotted a phylomorphospace in phytools (Revell 2012 ) using two acoustic variables with significant phylogenetic signals for both K and λ. Six major clades were color-coded to highlight lineage-level differentiation. Phylogenetic principal component analysis Although we primarily analyzed variables in univariate models — an approach that provides clearer relationships and is preferable when variables may evolve under distinct selective or developmental constraints (Odom et al. 2021 ) — we also conducted a phylogenetic Principal Component Analysis (pPCA) to allow comparison with multivariate approaches common in comparative bioacoustics. We used phyl.pca() in phytools (Revell 2012 ) with λ optimization. Prior to analysis, collinear variables were identified via corrplot (Wei and Simko 2021 ) and removed to ensure the number of predictors remained well below the number of taxa. Results Phylogenetic reconstruction analysis Within Megascops , we recovered two major clades (Fig. 2 ). Clade 1 (strongly supported, PP = 1.00 throughout) comprises five species: (( M. trichopsis , M. clarkii )( M. albogularis ( M. koepckeae , M. choliba ))). The second major clade encompasses all remaining Megascops species and is divided into four subclades, treated hereafter as Clades 2 to 5. Clade 2 (PP = 1) contains five species: ( M. sanctaecatarinae ( M. barbarus ( M. cooperi ( M. kennicottii , M. asio )))). Clades 3–5 form a soft polytomy. Clade 3 includes four species: ( M. gilesi ( M. guatemalae ( M. roraimae , M. centralis ))), though the placement of M. gilesi as sister to the other three species receives weak support (PP = 0.52). Clade 4 comprises five species: (( M. colombianus , M. ingens )( M. petersoni ( M. hoyi , M. marshalli ))). Clade 5 includes seven species: ( M. roboratus ( M. watsonii ( M. usta ( M. stangiae , M. ater , ( M. atricapilla , M. alagoensis ))))). Bioacoustical diversity overview We found two distinct song types in 11 of the 27 Megascops species. All other species have only one type; Clade 3 is the only clade where all species have a single song type. Several species also present a ‘harsh’ variant (energy more evenly distributed across lower harmonics; "voice type II" sensu Peixoto et al. 2021 ). The primary songs of Megascops spp. and G. nudipes share a general motif: phrases with a simple syntax consisting of a single softly ascending-descending frequency-modulated syllable composed of repetitive, narrow-bandwidth hoots (notes) delivered at roughly regular intervals (Fig. 2 ). Interval duration, note duration, number of notes and resulting pace vary considerably among species. The descriptive labels used below (e.g., ‘double-trill’, ‘bouncing ball’, ‘plateau notes’) are not intended as homology assertions. We observed clade-associated deviations from this basic motif. In Clade 3, a subtle frequency dip (‘step’) near the end of the first third of the phrase occurs in all species except M. gilesi ; this feature was not captured by our quantitative metrics. ‘Double-trill’ songs (two ascending-descending syllables) were recorded only in a Clade 2 subclade ( M. cooperi , M. kennicottii , M. asio ), and in M. seductus — thought to be also a Clade 2 species (Sibley and Monroe 1990 ) — as either the primary or secondary song depending on the species. Regular directional changes in pace occur across multiple clades: intervals becoming progressively shorter (‘bouncing ball’) in M. kennicottii , M. seductus , and M. atricapilla ; or progressively longer (‘reversed bouncing ball’) in M. koepckeae and M. seductus . Clade 1 species are especially distinct. Megascops clarkii and some individuals of M. trichopsis produce phrases with few, long, plateau-shaped notes (sharp attack/decay; minimal frequency modulation). Megascops choliba has a detached, accented final syllable (highly variable among subspecies; pers. obs.), while its sister M. koepckeae occasionally shows phrase-final interval expansions yielding somewhat similar detached notes. Additionally, M. koepckeae is aurally very distinctive because the second harmonic, rather than the fundamental, is dominant. Evolution of the primary song Phylogenetic signal and evolutionary models Three of the 40 acoustic variables showed significant phylogenetic signal for both λ and K (Table 2 ; see full results in Table S6): number of notes (λ = 0.84, K = 0.81), phrase duration (λ = 0.97, K = 1.09), and Time 5% relative (λ = 0.66, K = 0.87). None of these followed Brownian motion: KP models best explained phrase duration and Time 5% relative, while number of notes fit an OU model (Table S7). Table 2 Phylogenetic signal estimates for 40 acoustic variables measured from primary songs of Megascops species. Pagel's λ and Blomberg's K were calculated using the phylosig function in the R package phytools . Values of λ ≈ 0 indicate no phylogenetic signal; λ ≈ 1 indicates trait variation consistent with Brownian motion. K 1 suggests greater similarity. Bold rows indicate variables with significant phylogenetic signal (p < 0.05) for both metrics. Variable λ p (λ) K p (K) Minimum frequency of initial notes 1.028 0.662 0.557 0.058 Maximum frequency of initial notes < 0.001 1.000 0.393 0.194 Bandwidth of initial notes 0.623 0.230 0.458 0.127 Note duration of initial notes 0.915 0.057 0.538 0.063 Interval duration of initial intervals < 0.001 1.000 0.407 0.197 Dominant frequency of initial notes < 0.001 1.000 0.386 0.238 Minimum frequency of central notes < 0.001 1.000 0.548 0.059 Maximum frequency of central notes < 0.001 1.000 0.411 0.167 Bandwidth of central notes < 0.001 1.000 0.262 0.691 Note duration of central notes 0.944 0.051 0.529 0.067 Interval duration of central intervals < 0.001 1.000 0.410 0.146 Dominant frequency of central notes < 0.001 1.000 0.395 0.258 Minimum frequency of final notes < 0.001 1.000 0.522 0.065 Maximum frequency of final notes < 0.001 1.000 0.396 0.207 Bandwidth of final notes < 0.001 1.000 0.288 0.531 Note duration of final notes 0.872 0.055 0.506 0.073 Interval duration of final intervals < 0.001 1.000 0.487 0.079 Dominant frequency of final notes 0.703 0.149 0.444 0.118 Phrase minimum frequency 1.032 0.462 0.552 0.049 Phrase maximum frequency < 0.001 1.000 0.451 0.080 Phrase bandwidth 0.647 0.516 0.391 0.209 Phrase duration 0.966 < 0.001 1.091 0.001 Phrase dominant frequency < 0.001 1.000 0.506 0.071 Frequency 25% < 0.001 1.000 0.485 0.089 Frequency 5% < 0.001 1.000 0.532 0.064 Frequency 75% < 0.001 1.000 0.476 0.093 Frequency 95% < 0.001 1.000 0.468 0.088 Time 25% relative 0.490 0.141 0.595 0.033 Time 5% relative 0.659 0.028 0.867 0.001 Time 75% relative 0.412 0.286 0.500 0.065 Time 95% relative < 0.001 1.000 0.449 0.100 Initial pace < 0.001 1.000 0.398 0.187 Central pace < 0.001 1.000 0.399 0.172 Final pace 0.911 0.333 0.480 0.066 Pace change (initial–middle) < 0.001 1.000 0.407 0.260 Pace change (middle–final) 0.934 0.080 0.510 0.082 Frequency change (initial–middle) < 0.001 1.000 0.311 0.474 Frequency change (middle–final) < 0.001 1.000 0.363 0.416 Number of notes 0.843 0.003 0.811 0.002 Overall pace < 0.001 1.000 0.425 0.141 The phylomorphospace (Fig. 3 ) reveals clade-level structuring in acoustic space (phrase duration was not plotted because it is a composite variable determined by number of notes, note duration, and interval duration). Clade 1 occupies the region of fewest notes and fastest energy deposition (low Time 5% relative), while Clade 5 shows the opposite pattern. Clades 2, 3, and 4 are intermediate, with Clade 2 showing the greatest within-clade variation. Two Clade 2 species ( M. cooperi and M. kennicottii ) cluster with Clade 1, reflecting convergent short-phrase structure. Character mapping and ancestral reconstructions Ancestral states were reconstructed for the three acoustic variables with significant phylogenetic signal (Table S8; Figure S2) and for habitat (Fig. 4 ). All values reported below are standardized scores. Phrase duration: The Megascops ancestor had moderate-to-short phrases (0.64 ± 0.08), a condition retained in Clade 1 (0.61–0.63) but increasing in Clades 2–5, and reaching extreme values in Clades 4–5 (up to 0.86 in M. colombianus , 0.85 in M. ater , 0.84 in M. usta ). Secondary reductions occurred independently in Clade 2 ( M. cooperi and M. kennicottii : 0.61) and in Clade 5 ( M. roboratus : 0.62). Number of notes: The ancestral state (0.65 ± 0.09) resembles the outgroup G. nudipes (0.65). Clade 1 shows reduction (down to 0.59 in M. clarkii ), while Clades 2–5 show increases (ancestor: 0.69 ± 0.05), peaking in the M. atricapilla + M. alagoensis lineage (0.81 ± 0.03). Time 5% relative: Values increased from 0.55 in G. nudipes to 0.64 ± 0.08 in the Megascops ancestor. Clade 1 retained similar values (0.63 ± 0.10), with M. koepckeae showing marked increase (0.80). Clades 2–5 show an overall tendency toward higher values (ancestor: 0.69 ± 0.06), except for M. cooperi (0.44); M. atricapilla and M. alagoensis show the highest values (0.75). Habitat: The Megascops ancestor was forest-dependent, the predominant condition across the genus. Independent transitions to open habitats occurred in M. choliba , M. sanctaecatarinae , M. cooperi , and M. roboratus . Transitions to forest + open occurred in the ancestor of M. koepckeae + M. choliba , in M. kennicottii + M. asio , and in M. guatemalae . Evolutionary model fit Most acoustic traits were best described by White Noise (n = 35) or Brownian Motion (n = 6) models. Among structured models, Pagel's λ-transformed Brownian was the most frequent best fit, supporting its use as the correlation structure for subsequent analyses (see Methods). Four traits departed from this pattern: KP models best fit phrase duration and Time 5% relative, while number of notes and pace change (middle–final) fit Ornstein–Uhlenbeck models. Full model comparisons are provided in Table S7. PGLS models Univariate PGLS revealed significant associations between acoustic traits and habitat, elevation, and vocal dimorphism (Tables 3 – 5 ; see Supplementary Material 1 for full results from PGLS using evolutionary models “OU” and “Kappa); body mass showed no significant effects (Table S9). Because many acoustic variables are correlated (e.g., note- and phrase-level frequency measures), effects on related variables should be interpreted as shared patterns rather than independent responses. A complementary pPCA using 12 non-collinear variables produced similar outcomes, with PGLS on the first five PCs mirroring the univariate results (Figures S3–S6). Table 3 Summary of significant associations between acoustic traits and habitat type in Megascops species based on Phylogenetic Generalized Least Squares (PGLS) models. Positive estimates indicate higher trait values in species occurring in forested or generalist habitats relative to open-habitat species (reference category). Frequency-related variables consistently show increased pitch in forest-dependent and forest + open species, contrary to Acoustic Adaptation Hypothesis (AAH) predictions. Temporal variables indicate intra-phrase acceleration (pace change) and slower initial pacing in forest species, suggesting modifications in signal temporal structure linked to habitat acoustics. Asterisk (*) marks variable for which PGLS models were fitted under the Ornstein–Uhlenbeck (OU) evolutionary model, identified as the best-fitting model for that trait. Category Variable Forest-dependent Forest + open Interpretation Estimate p value Estimate p value Frequency Frequency 5% 0.166 0.0002 0.120 0.0003 Tendency to increase in frequency in species that occur in forests Frequency 75% 0.141 0.0018 0.107 0.0105 Frequency 95% 0.136 0.0030 0.103 0.0169 Mean maximum frequency of the central notes 0.188 0.0005 0.166 0.0001 Mean maximum frequency of the final notes 0.012 0.0068 0.011 0.0174 Mean maximum frequency of the initial notes 0.147 0.0002 0.118 0.0006 Phrase maximum frequency 0.118 0.0040 0.090 0.0227 Mean minimum frequency of the central notes 0.135 0.0002 0.076 0.0136 Mean minimum frequency of the final notes 0.125 0.0001 0.085 0.0026 Mean minimum frequency of the initial notes 0.140 0.0001 0.075 0.0148 Table 5 Summary of significant associations between acoustic traits and vocal dimorphism in Megascops species based on Phylogenetic Generalized Least Squares (PGLS) models. Positive or negative estimates indicate the direction of trait change with increasing dimorphism. More dimorphic species tend to exhibit broader bandwidths in the initial notes, stronger concentration of energy at lower frequencies, and steeper downward modulation in frequency across the phrase, suggesting that sexual selection may act on fine-scale spectral modulation rather than overall pitch. These species also show greater pace acceleration and sharper initial attack, indicating more dynamic temporal structuring. The asterisk (*) marks the variable analyzed under the kappa evolutionary model, identified as the best-fitting model for that trait Category Variable Minimum altitude Maximum altitude Altitudinal ranges Interpretation Estimate p value Estimate p value Estimate p value Interval duration Mean interval duration of the central intervals 0.00001 0.0272 — — — — Tendency of longer intervals in the first half of the phrase, in species occurring at higher minimum altitudes Mean interval duration of the initial intervals 0.000013 < 0.001 — — — — Note duration Mean note duration of the central notes 0.00003 < 0.001 — — — — Tendency of longer note durations in species occurring at higher minimum altitudes. Mean note duration of the final notes 0.000049 < 0.001 — — — — Mean note duration of the initial notes 0.000025 < 0.001 — — — — Frequency Frequency 25% — — 0.000039 0.0333 — — Tendency of higher overall frequencies (increased pitch) in species occurring at higher maximum altitudes. Frequency 75% — — 0.000038 0.0334 — — Frequency 95% — — 0.000037 0.0426 — — Mean maximum frequency of the final notes — — 0.000004 0.0271 — — Mean maximum frequency of the initial notes — — 0.00003 0.0415 — — Category Variable Minimum altitude Maximum altitude Altitudinal ranges Interpretation Estimate p value Estimate p value Estimate p value Frequency Mean minimum frequency of the central notes 0.000043 0.0004 0.000044 < 0.001 — — Tendency of higher overall frequencies (increased pitch) in species occurring at higher maximum altitudes, and restricted to the last half of the phrases of species that occur in higher minimum altitudes Mean minimum frequency of the final notes 0.000039 0.0465 0.000057 < 0.001 — — Frequency Phrase minimum frequency — — 0.000035 < 0.001 — — Tendency of higher overall frequencies (increased pitch) in species occurring at higher maximum altitudes. Mean dominant frequency of the central notes — — 0.000038 0.031 — — Mean dominant frequency of the final notes — — 0.000046 0.0152 — — Mean dominant frequency of the initial notes — — 0.000033 0.0451 — — Phrase dominant frequency — — 0.000039 0.0394 — — Number of notes Number of notes* — — — — 0.000056 0.005 Tendency of fewer notes per phrase in species occupying broader altitudinal ranges Pace Central pace — — — — − 0.000036 0.0297 Tendency of a slower pace in the first half of the phrase in species occupying broader altitudinal ranges. Initial pace — — — — − 0.000036 0.0417 Variable Estimate p-value Interpretation Mean bandwidth of the initial notes 0.1021 0.0119 Tendency of broader bandwidth in the initial notes in more dimorphic species Frequency 5% − 0.1855 0.0285 Tendency of energy being focused on the lower frequencies in more dimorphic species Frequency change (middle–final) − 0.1458 0.0015 Tendency of frequency decreasing throughout the phrase in more dimorphic species Frequency change (initial–middle) − 0.1668 0.0347 Tendency of frequency decreasing throughout the phrase in more dimorphic species Pace change (middle–final) 0.491 0.0317 Tendency of pace acceleration in more dimorphic species Time 5% relative * − 0.288 0.0113 Tendency to a steeper initial attack in more dimorphic species Effects of habitat on vocal traits The songs of species occurring in forests showed consistently and significantly higher frequencies than those of open-habitat species across nearly all spectral variables (Table 3 ). This occurred in both forest-dependent and forest + open species at note and phrase level, with coefficients ranging from β = 0.01–0.19 (p < 0.01 for most). Forest-dependent species also showed significant reduction in bandwidth from the initial to middle portion of the phrase (β = −0.09, p = 0.02), a pattern absent in forest + open species. Temporal traits varied systematically with habitat. Forest-associated species exhibited slower initial pace (forest: β = -0.1, p = 0.02; forest + open: β = -0.1, p = 0.03) and progressive acceleration of the phrase (beginning–middle: forest, β = 0.13, p = 0.025; forest + open, β = 0.18, p = 0.013; middle–end: forest, β = 0.12, p = 0.019; forest + open, β = 0.14, p = 0.009). Effects of elevation on vocal traits Elevation metrics showed distinct associations with acoustic parameters (Table 4 ). Minimum altitude was positively associated with temporal variables: species at higher minimum elevations produced longer notes (all phrase sections, β = 0.000025–0.000050, p < 0.001) and longer intervals (initial and central sections, β = 0.000010–0.000013, p < 0.03). Altitudinal range was negatively associated with pace in the first half of the phrase (β = −0.000036, p < 0.05) and with number of notes (β = −0.000056, p = 0.005). Table 4 Summary of significant associations between acoustic traits and elevation metrics in Megascops species based on Phylogenetic Generalized Least Squares (PGLS) models. Positive estimates indicate higher trait values with increasing (minimum or maximum) elevation or broader altitudinal ranges. Species occurring at higher minimum altitudes tend to exhibit longer notes and intervals, suggesting slower phrase pacing, while those at higher maximum altitudes show an overall increase in frequency (higher pitch). In contrast, species occupying broader altitudinal ranges tend to produce slower and shorter phrases with fewer notes. The asterisk (*) marks the variables for which PGLS models were fitted under the Ornstein–Uhlenbeck (OU) evolutionary model, identified as the best-fitting model for that trait. Category Variable Forest-dependent Forest + open Interpretation Estimate p value Estimate p value Frequency Phrase minimum frequency 0.135 0.0001 0.077 0.0085 Tendency to increase in frequency in species that occur in forests Mean dominant frequency of the central notes 0.181 0.0002 0.157 < 0.0001 Mean dominant frequency of the final notes 0.151 0.0006 0.134 0.0017 Mean dominant frequency of the initial notes 0.164 0.0001 0.126 0.0003 Phrase dominant frequency 0.132 0.0029 0.099 0.0201 Change in frequency Frequency change (initial-middle) − 0.086 0.0202 — — Tendency to a downward modulation in frequency from the beginning to the middle of the phrases, only in forest-exclusive species Change in pace Pace change (middle–final)* 0.122 0.0188 0.140 0.009 Intra-phrase acceleration in pace in species that occur in forests Pace change (initial–middle) 0.130 0.0250 0.182 0.0136 Sectional pace Initial pace − 0.104 0.0191 − 0.102 0.0307 Slower initial pace in species that occur in forests Spectral variables were more strongly associated with maximum altitude: species reaching higher elevations showed higher frequencies across multiple measures (β = 0.000035–0.000057, p < 0.05). Minimum frequencies of central and final notes were also positively associated with minimum altitude, indicating that both elevational limits correlate with upward spectral shifts. Effects of proxies for sexual selection and body size on vocal traits Vocal dimorphism was significantly associated with both spectral and temporal parameters (Table 5 ). More dimorphic species showed a broader bandwidth in the initial notes (β = 0.10, p = 0.01), energy concentrated at lower frequencies (Frequency 5%: β = −0.19, p = 0.03), and progressive frequency decrease through the phrase (beginning–middle: β = −0.17, p = 0.04; middle–end: β = −0.15, p = 0.002). Temporal variables also varied with dimorphism: Time 5% relative decreased (β = −0.29, p = 0.01), indicating faster initial energy deposition, while pace acceleration toward phrase end increased (β = 0.49, p = 0.03). No acoustic variable was significantly associated with body mass (see Table S9). Discussion Our phylogenetic reconstruction of Megascops spp., integrating recent taxonomic developments, largely supports previous hypotheses (Dantas et al. 2016 , 2021 ). However, we recovered M. gilesi (Krabbe 2017 ) in a different position than that in Dantas et al. ( 2016 ), where it was treated as an ‘unnamed taxon’. Here, this species is placed as sister of a clade containing M. guatemalae , M. roraimae , and M. centralis , instead of M. roboratus, M. watsonii , and M. atricapilla in that study. In any case, its position remains uncertain due to limited molecular data (single CytB sequence; PP ≈ 0.52). Contrary to König et al. ( 2008 ), we found that two song types were not universal across the genus, which corroborates Krabbe’s ( 2017 ) assertion of having found two song types in only about half of the species. We also corroborate van der Weyden’s ( 1975 ) characterization of a genus-wide basic song motif, and we found that several deviations of this motif are clade-associated, which is reflected in traits with significant phylogenetic signal. These patterns indicate that acoustic characters can be useful in the taxonomy of Megascops , especially in poorly resolved lineages (e.g., M. seductus, M. gilesi ). However, this potential is currently hampered by the lack of explicit and testable homology criteria for song types (e.g., Marshall 1967 ; Klatt and Ritchison 1993 ; Schulenberg et al. 2007 ; Krabbe 2017 ), which needs to be addressed. Prior terminologies (“A/B song”: König et al. 1999 ; “long/short song”: Krabbe 2017 ) have mixed functional labels with putative homologies in inconsistent ways, complicating cross-taxon alignment (see Hepp and Pombal 2019). Notably, most variables (37 of 40) lacked significant phylogenetic signal. The three exceptions (phrase duration, number of notes and Time 5% relative) describe phrase length and initial energy deposition. Spectral variables (frequencies, bandwidth and energy distribution), note-level temporal parameters (durations, intervals and pace), and intra-phrase modulation (pace change, frequency change) showed no significant signal. This suggests that what is phylogenetically conserved in Megascops songs is the overall phrase duration and how abruptly energy is delivered at the onset, while spectral and fine-scale temporal characteristics are labile. This interpretation is consistent with our PGLS results (see below). Clade 1 stands out as vocally distinct, with shorter phrases and fewer notes, while Clades 4–5 show the longest phrases. Although Clades 2–5 share cohesive temporal architectures, localized modifications occur (e.g., ‘reverse bouncing-ball’ in M. cooperi and M. kennicottii recalls M. koepckeae of Clade 1). Overall, most variation in Megascops appears to arise from spectral and syntactic novelties within a phylogenetically conserved temporal framework. Our ancestral state reconstruction indicates that the Megascops ancestor was forest-dependent, a condition retained across most of the genus, with restriction to open habitats representing a derived state in a few lineages. This result provides a framework for evaluating the selective pressures predicted by the Acoustic Adaptation Hypothesis (AAH). Contrary to these predictions, forest-dwelling Megascops spp. produced higher-frequency songs, with a broad bandwidth and a progressive acceleration of the phrase. Deviations from the AAH are not unprecedented in birds (e.g., Boncoraglio and Saino 2007 ; Hardt and Benedict 2020 ; Mikula et al. 2021 ) and suggest that competing selective pressures may override propagation efficiency in shaping vocal evolution (Ryan and Brenowitz 1985 ; Briseño-Jaramillo et al. 2015 ). We consider three hypotheses that might explain this unexpected association between higher-pitched broadcast songs and forest habitats, given that higher frequencies are more affected by reverberation and attenuate more quickly in dense vegetation (Bradbury and Vehrencamp 2011 ): signal reinforcement driven by reverberation, eavesdropping avoidance, and ranging. In some conditions, reflected sound can reinforce signals, resulting in longer and louder songs (Slabbekoorn et al. 2002 ), and forest-dwelling Megascops spp. could exploit this reinforcement. However, this mechanism favors narrow-bandwidth signals, whereas most Megascops songs are broadband, and we found no relationship between bandwidth and habitat. Therefore, our data do not support the first of these hypotheses. Eavesdropping avoidance offers a more compelling explanation. Since higher-frequency vocalizations attenuate more quickly, and are more directional, detectability by unintended receivers may be reduced (Dabelsteen 2005 : 52). This is particularly relevant for Megascops , as owls frequently engage in intraguild predation, nocturnal predators are acoustically oriented (Polis et al. 1989 ; Sergio and Hiraldo 2008 ), and Megascops spp. are small-bodied compared with sympatric owls ( Lophostrix , Strix , Pulsatrix ). Indeed, M. choliba has been reported as prey of Pulsatrix perspicillata (Schubart et al. 1965 ). Reducing amplitude would also reduce detectability (Dabelsteen et al. 1998 ), but low- and high-amplitude (broadcast) songs serve distinct functions in owls (Peixoto et al. 2025 ), which may constrain this strategy. Increasing frequency may thus be the most viable route to reduce eavesdropping risk while preserving signal identity. Unlike diurnal birds — which can rely on vision to detect approaching predators — owls remain active even on nights too dark for them to see (Martin 1977 , 1986 ), making acoustic cues the primary, and sometimes only, channel for detecting tracking targets (Konishi 1973 ). This sensory asymmetry likely intensifies selection for acoustic discretion. Vocal suppression near potential predators, frequently reported in owls (Zuberogoitia et al. 2008 ; Vrezec and Bertoncelj 2018 ), further supports the likely importance of eavesdropping avoidance for these birds. Nonexclusive to the eavesdropping-avoidance hypothesis, the higher-frequency songs in forest-dwelling species may also be related to ranging (Holland et al. 2001 ; Naguib and Wiley 2001 ; Ringler et al. 2017 ). For territorial species such as owls, optimal response to an intruder depends on perceived distance (Naguib and Wiley 2001 ). Higher-pitched multi-note songs are more susceptible to "interval filling" by reverberation (Slabbekoorn 2004 : 180), causing distant faster-paced phrases to sound more continuous or "whistled", which would potentially encode reliable distance information in the note intervals. In line with this prediction, we found a significant positive relationship between phrase acceleration and forest habitat. Thus, nearby individuals could perceive all notes distinctly, whereas reverberation would progressively blur final intervals as distances increased, yielding a gradual series of temporal cues that would improve ranging accuracy. These distance-sensitive cues may reduce uncertainty and prevent unnecessary aggressive escalation, being beneficial for both signaler and receiver (Enquist and Leimar 1990 ; Briffa and Hardy 2013 ). The presence of low-amplitude songs in Megascops (Peixoto et al. 2025 ) and their possible use in ranging (Peixoto 2025 ) further support this interpretation. For owls active at night — including nights too dark for visual orientation (Martin 1977 , 1986 ), acoustic assessment of adversary distance becomes particularly relevant, consistent with their suite of adaptations for sound localization (König et al. 2008 ). Given the inconclusive existing evidence for elevation effects on song traits across taxa, we interpret our findings as exploratory. In our study, Maximum elevation (and to a lesser extent, minimum elevation) was positively correlated with higher frequencies, paralleling the pattern observed for denser habitats. A plausible interpretation is that high-elevation Megascops spp. tend to inhabit structurally complex environments, as most species restricted to high elevations occupy humid, mossy cloud or montane forests, particularly in the Andes (König et al. 2008 ; Billerman et al. 2022 ). Additionally, species with greater altitudinal ranges produce slower and shorter songs, potentially allowing effective propagation with fewer repetitive elements (Slabbekoorn 2004 ). Contrary to our prediction, vocal dimorphism was not significantly associated with dominant frequency, phrase duration, or vocal stability (i.e., low intra-phrase variation in pace and frequency). Instead, species with greater dimorphism concentrated more energy in the lower portion of the spectrum (Freq 5%) and exhibited a steeper descending frequency modulation within phrases, suggesting that sexual selection in Megascops may operate through finer spectral adjustments rather than overall frequency. Notably, in Otus scops , lower-frequency vocalizations were initially interpreted as indicators of male quality (Hardouin et al. 2007 ), but subsequent work showed that males can voluntarily modulate hoot frequency (Grieco 2022 ), suggesting that previous correlations may have reflected modulation capacity rather than fixed differences. A similar mechanism may be present in Megascops . Since this modulation likely depends on the level of arousal and the context, interspecific datasets based on heterogeneous recordings (such as ours) may obscure selection-related trends; standardized playback experiments could help clarify this. Furthermore, vocal dimorphism showed a positive correlation with larger bandwidths at the beginning of phrases, reinforcing the interpretation that spectral modulation may be a primary target of sexual selection in Megascops . Notably, during our screening, we observed abrupt frequency shifts — consistent with performance constraints (Sierro et al. 2023 ) — in several species of Clades 4–5, which produce the longest, most repetitive songs in the genus. Gonzaga and Castiglioni ( 2015 ) described a similar “Tarzan yell” effect in M. atricapilla . Whether such performance breaks carry communicative value (e.g., as indicators of the signaler quality or individuality) remains untested and warrants experimental investigation. Given prior evidence of mass-acoustics relationships in owls (e.g., Appleby and Redpath 1997 ), and also contrary to our expectation, we found no associations between body mass and any song trait after controlling for phylogeny. In owls, the relationship between body size and song frequency is complicated by reverse sexual size dimorphism, in which females are larger than males (Krüger 2005 ; Segall et al. 2017 ), yet males produce lower-frequency vocalizations due to disproportionately larger syringes (Miller 1934 ; Segall 2013 ). Crucially, while syrinx width correlates with body mass, syrinx length (which also affects frequency) varies independently (Segall 2020), weakening the predictive power of mass in interspecific comparisons. Additionally, ecological pressures may override morphological constraints, as suggested by our finding that forest-dwelling species produce higher frequency songs. Practical limitations also warrant consideration. Body mass, despite seasonal and nutritional variation, remains the best available proxy for syrinx size (Segall 2020) and the most practical metric for interspecific comparisons, though published values for the same species can differ substantially across studies (Segall et al. 2017 ; pers. obs.). Furthermore, very few song recordings of sexed owls exist (Segall et al. 2022 ), and our sample may have inadvertently included more than one sex. Comparative studies pairing syringeal morphology of known-sex individuals and controlled audio recordings would help to disentangle these effects. Concluding remarks This study is based on the largest acoustic dataset yet assembled for Megascops and represents, to our knowledge, the first application of phylogenetic comparative methods to acoustic communication of nocturnal birds. Beyond our core findings, we emphasize the severe disparities in basic natural history and bioacoustic information among Megascops species, underscoring the need for comprehensive vocal repertoire descriptions and homology reassessment across the genus. Most of the expected correlations were either nonsignificant or opposite to classical predictions. Forest-dwelling species produced higher-pitched songs, contrary to the Acoustic Adaptation Hypothesis, and body mass showed no relationship with frequency. Because forest use represents the ancestral condition in the genus, lower frequencies in open-habitat species appear derived rather than primitive. We propose that this pattern reflects the distinctive ecology of nocturnal predators balancing long-range communication against eavesdropping risk from both prey and larger owls. Sexual selection also appears to act on finer-scale spectral modulation rather than on dominant frequency, with more dimorphic species concentrating energy in lower spectral regions and exhibiting steeper frequency modulation. Taken together, our results reveal that the vocal evolution in owls reflects a complex interplay between shared ancestry and ecological pressures, distinct from that observed in diurnal passerines. Megascops thus provides a promising model for investigating how communication systems evolve under nocturnal and predation-driven conditions, highlighting the importance of extending bioacoustics research beyond traditional model systems. Declarations Acknowledgements This paper is part of the Ph.D. requirements of Luis Felipe Peixoto at the Biodiversity and Evolutionary Biology Graduate Program of the Federal University of Rio de Janeiro. We thank the curators and staff of the following sound archives for granting access to their collections: Arquivo Sonoro Prof. Elias Coelho (UFRJ), Fonoteca Neotropical Jacques Vielliard (Unicamp), Macaulay Library (Cornell University), Borror Laboratory of Bioacoustics (Ohio State University), Bird Sound Collection (Florida Museum), Animal Sound Archive (Museum für Naturkunde, Berlin), Fonoteca Zoológica (Museo Nacional de Ciencias Naturales, Madrid), Soundlibrary Archives (Muséum National d’Histoire Naturelle, Paris), and Colección de Sonidos Ambientales (Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Colombia). We also used publicly available recordings from Xeno-canto.org and commercial sound compilations, including Chants d’Oiseaux de Guyane , Aves do Brasil , and Birds of Costa Rica . We thank José Leonardo Mattos for his help with phylogenetic analyses, and Judit Szabo, José Pombal Junior, Ana Galvão, Andressa Bezerra, Gloria Denise Castiglioni and Wilson Costa for their valuable comments and suggestions on this work. Funding This study was supported by fellowships and grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) awarded to LFP, FH, and PCP. Conflicts of interest/Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material The datasets generated and/or analyzed during the current study are available in the Supplementary Material. Code availability The code used for the analyses is available in the Supplementary Material. Authors' contributions LFP conceived the idea and formulated questions. LFP and FH created the study design and collected data. FH and LFP analyzed data. LFP , FH and LPG wrote the manuscript. PCP, FH and LPG critically reviewed and substantially edited the manuscript. All authors approved the final version of the manuscript. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 References Appleby BM, Redpath SM (1997) Variation in the male territorial hoot of the Tawny Owl Strix aluco in three English populations. Ibis 139:152–158 Badyaev AV, Martin TE (2000) Sexual dimorphism in relation to current selection in the house finch. Evolution 54:987–997 Bezerra AM, de Carvalho-e-Silva SP, Gonzaga LP (2021) Evolution of acoustic signals in Neotropical leaf frogs. 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Supplementary Files Supplementarymaterial1Peixotoetal.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 13 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 13 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8834994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598354697,"identity":"8b23dea2-3cd5-49c5-a3c6-55ecf1ed93c1","order_by":0,"name":"Luis Felipe Peixoto¹","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Felipe","lastName":"Peixoto¹","suffix":""},{"id":598354699,"identity":"1f1830c9-f4dd-47ab-af48-833ee5f936b9","order_by":1,"name":"Luiz P. Gonzaga¹","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Luiz","middleName":"P.","lastName":"Gonzaga¹","suffix":""},{"id":598354701,"identity":"cc5363b8-d1ce-43ed-b846-c665260b83e6","order_by":2,"name":"Paulo C. Paiva¹","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"C.","lastName":"Paiva¹","suffix":""},{"id":598354703,"identity":"dc37b00f-d69f-400a-a462-13dadfcc71d1","order_by":3,"name":"Fábio Hepp¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBAC9gYehgOMDWAWkDCwIKyF5wBMC88BkBYJ4rQwgLVIJID4xGhhP3vwwMcdNnnmks+vbvhRIMHA396dgF8LT17CwZln0ootZ+eU3ewBOkzizNkNeLXYM+QYHOZtO5y44XZO2g0eoBYDiVz8Wnj434C0/E/ccPNM2s0/RGmRANtyIHHDDfZjt4mzReId0C9tycWWPTlst2UMJHgI+oWHP/fwh49tdnnm7Mef3Xzzx0aOv70XvxYYSDBg4DEAm0GUcqgW9gdEqx4Fo2AUjIKRBQAf600SXgF5SwAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":true,"prefix":"","firstName":"Fábio","middleName":"","lastName":"Hepp¹","suffix":""}],"badges":[],"createdAt":"2026-02-10 00:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8834994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8834994/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103885295,"identity":"ec755912-51d6-428f-9c3c-fbc8db768267","added_by":"auto","created_at":"2026-03-04 06:49:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110907,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of sampling within phrases. Each row represents a different phrase type varying in total number of notes (odd or even) and phrase length (short ≤ 9 notes; long \u0026gt; 9 notes). Red dashed boxes indicate measured notes and green brackets indicate the intervals measured within each section (blue dotted boxes), which correspond to the initial, central, and final portions of the phrase. In short phrases (top two rows), one note and one interval were measured per section; in long phrases (two bottom rows), three notes and three intervals per section were sampled and averaged. In even-numbered phrases, the central section was centered on the median interval, whereas in odd-numbered phrases it was centered on the middle note.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8834994/v1/fed24659e9df67c12c81a2ac.jpg"},{"id":104402037,"identity":"f9a8cacd-e436-4013-b788-5fcce8f5f8b7","added_by":"auto","created_at":"2026-03-11 12:14:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":284676,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian phylogeny of the genus \u003cem\u003eMegascops\u003c/em\u003e showing representative primary songs displayed as oscillograms (black) and spectrograms (color), both oriented with their beginning closest to the species names. Node support values (posterior probabilities, PP) are shown when \u0026lt; 0.99. Branch colors indicate the five major clades recovered. Clade 1 (red) represents the most vocally distinct group, characterized by overall shorter songs. Clade 2 (green) shows marked intra-clade variation in song duration, with two species whose songs closely resemble those of Clade 1. Clade 3 (turquoise) is vocally similar to Clade 2, though the song of \u003cem\u003eM. centralis\u003c/em\u003e is notably short. Clade 4 (blue) and Clade 5 (magenta) comprise species with simple, monotone songs, although species in Clade 5 and \u003cem\u003eM. colombianus\u003c/em\u003e produce particularly long vocalizations. \u003cem\u003eGymnasio nudipes \u003c/em\u003esong is similar to the \u003cem\u003eMegascops \u003c/em\u003espp. basic motif. Species in the clade marked with an asterisk (*) have their songs shown at 50% scale for visualization purposes.\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8834994/v1/cb136d0afe1eab942b44295a.jpg"},{"id":103885296,"identity":"94b1126b-c050-4300-8b6a-b4c30094692f","added_by":"auto","created_at":"2026-03-04 06:49:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126755,"visible":true,"origin":"","legend":"\u003cp\u003ePhylomorphospace with number of notes and the relative time at 5% of phrase duration (Time 5%) of songs of 26 \u003cem\u003eMegascops\u003c/em\u003e species and \u003cem\u003eGymnasio nudipes \u003c/em\u003e(black dot). Branch colors correspond to the five major clades recovered in the phylogeny. The plot shows a clear phylogenetically structured pattern of acoustic differentiation, with species from the same clades tending to cluster in the morphospace. Clade 1 occupies a distinct region characterized by song with fewer note and fast energy deposition, while two species from Clade 2 closely resemble this pattern. Clades 2 and 3 show partial overlap, reflecting intermediate acoustic profiles, whereas Clades 4 and 5 are broadly grouped and comprise species with large number of notes. Together, these trends reinforce the qualitative patterns observed in Figure 2 and highlight the strong association between phylogenetic relatedness and acoustic structure in \u003cem\u003eMegascops\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8834994/v1/6f9fc7be2760ae7c1de2d24e.png"},{"id":104401269,"identity":"804fd0b0-c675-43af-922b-e022e88d4ce1","added_by":"auto","created_at":"2026-03-11 12:12:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":242611,"visible":true,"origin":"","legend":"\u003cp\u003eAncestral character state reconstruction of acoustic traits with significant phylogenetic signal and habitat type across the \u003cem\u003eMegascops\u003c/em\u003e phylogeny. Branch colors in the continuous trait maps (top and bottom right panels) represent reconstructed ancestral values for each trait, with warmer colors (yellow to red) indicating higher values and cooler colors (green to blue) indicating lower values. Traits shown include the number of notes (top left) and phrase duration (bottom right), which display overall similar patterns, reflecting their strong correlation. These traits highlight consistent clade-level differentiation, with species from Clade 1 generally producing shorter songs, Clade 5 longer ones, and the remaining clades showing intermediate values with some localized deviations. The Relative Time 5% map (top right) indicates that species from Clade 1 tend to concentrate more energy at the beginning of the phrase, resembling \u003cem\u003eGymnasio nudipes\u003c/em\u003e, whereas most \u003cem\u003eMegascops\u003c/em\u003e species exhibit intermediate tendencies, and species from Clade 5 show particularly gradual, soft initial energy distributions. The categorical mapping of habitat type (bottom left) distinguishes species classified as open-habitat (dark blue), forest-dependent (green), and forest + open (teal), emphasizing the forest-dependent condition as both ancestral and predominant, with multiple independent transitions toward broader (forest + open) or more open-habitat specialization. Colored vertical bars indicate the five major \u003cem\u003eMegascops\u003c/em\u003eclades (see Figure 2).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8834994/v1/6b963b6d69998bc5a845d966.png"},{"id":104779421,"identity":"abb099dc-36eb-4d56-832e-4a40f39bd1f3","added_by":"auto","created_at":"2026-03-17 07:40:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2698148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8834994/v1/ad9e5d7f-7f6f-4d10-a3dc-d7bbe120ffc3.pdf"},{"id":103885299,"identity":"39eeff9c-6990-48e2-898b-9e711ce05d62","added_by":"auto","created_at":"2026-03-04 06:49:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2480675,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1Peixotoetal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8834994/v1/af85695a34ae11cff8fafbd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Song evolution in American Screech-owls: the distinctive ecology of nocturnal top predators can lead to unexpected acoustic patterns","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBirdsong represents a cornerstone of avian biology (Catchpole and Slater \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), fascinating humans since antiquity and inspiring ancient observations that resonate with modern scientific concepts. Pliny the Elder, in \u003cem\u003eThe Natural History\u003c/em\u003e (Book X, Chap.\u0026nbsp;43; Bostock et al. 1855), described the remarkable vocal artistry of male nightingales vigorously competing with one another. He highlighted male-male competition, one of the foundational elements of sexual selection, nearly two millennia before Darwin formalized the concept in 1871. On the other hand, early observations also contributed to historical biases. For instance, first documented birdsong observations focused on oscine passerines from north-temperate regions, whose males produce loud and elaborate vocalizations during the reproductive season (Rose et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These first impressions led to the view of sexual selection as the primary driver of trait elaboration in birdsong (Podos et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Nowadays, the interpretation of drivers of the evolution of birdsong traits has broadened, recognizing that multiple forces, not only sexual selection, might shape vocal phenotypes, and that disentangling their effects requires explicit consideration of phylogenetic history (Felsenstein \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Price and Lanyon \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong these interacting forces, sexual and natural selection appear as common drivers of changes in vocalizations (e.g., Slater \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; West-Eberhard \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Yasukawa \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Price \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tobias et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lyon and Montgomerie \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while phylogenetic history, morphology, and energetic or social costs impose constraints (e.g., Gould and Lewontin \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Bradbury and Vehrencamp \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Blomberg and Garland \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Blomberg et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne effect of natural selection on vocalizations has received special attention: the Acoustic Adaptation Hypothesis (AAH), which poses that signal structure is shaped by habitat-dependent propagation and noise overlap challenges (Morton \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Wiley and Richards \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Brumm and Naguib \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Bradbury and Vehrencamp \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). According to this hypothesis, vocalizations in closed habitats are expected to have lower frequencies, narrower bandwidths, and slower pace than those in open habitats, because these acoustic features are less affected by, or compensate for, degradation caused by dense vegetation, thus optimizing propagation (Morton \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Wiley and Richards \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Despite being a longstanding hypothesis, empirical support for these predictions has been mixed (e.g., Boncoraglio and Saino \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ey and Fischer \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bezerra et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mikula et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHabitat features are often correlated with elevation, which represents a composite proxy encompassing vegetation structure, temperature, humidity, and air density. Elevation can affect sound propagation indirectly, as it influences vegetation structure (e.g., Kirschel et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), but also directly through differences in atmospheric conditions (Larom et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Boyle et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, effects of elevation on vocal variation have been studied mostly at the intraspecific level, comparing populations across altitudinal gradients (Gillam et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Funk et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Villegas et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and remain less explored than habitat effects. Moreover, the direction and magnitude of altitudinal effects on vocalizations are harder to predict and vary considerably across taxa and geographic regions (e.g., Branch and Pravosudov \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Villegas et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond the physical environment, acoustic signals are also shaped by the reactions of receivers. In birds, songs mostly serve the primary dual purpose of attracting mates and defending territories (Collins \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Catchpole and Slater \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and sexual selection is historically considered one of the most important drivers of signal elaboration, typically favoring more vigorous and costly displays (Bradbury and Vehrencamp \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Some species favor vocal complexity (e.g., Cardoso et al. 2010), while others favor consistency and repetition (e.g., Sierro et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some favor lower-pitched vocalizations (e.g. Appleby and Redpath \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hardouin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while others favor higher amplitude (Podos and Cohn-Haft \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As males are typically subject to female choice and male-male competition, the degree of sexual dimorphism often reflects intensity of sexual selection (Trivers \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Fairbairn \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Badyaev and Martin \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Additionally, in territorial contexts, signals may serve a secondary function: ranging \u0026mdash; the use of acoustic cues such as signal degradation to estimate signaler distance (Naguib and Wiley \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), adding another social dimension to signal design.\u003c/p\u003e \u003cp\u003eHowever, signals cannot change freely; features that are beneficial in one way may be costly in another. Not all receivers are the intended audience of a given signal. Eavesdroppers such as predators and competitors can locate or exploit signalers using acoustic cues (Tuttle and Ryan \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Mougeot and Bretagnolle \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Naguib et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), selecting for signals that favor privacy over broadcast range and conspicuousness (Dabelsteen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; McGregor \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Peake \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Beyond this social constraint, morphology and phylogenetic history also limit signal evolution. For instance, larger-bodied animals generally have larger phonating membranes, enabling the production of lower-frequency sounds, and larger lungs, allowing longer vocalizations (Bradbury and Vehrencamp \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fitch and Hauser \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and thus certain acoustic traits can generally provide information about the size of the animal, although there are exceptions (e.g., Fitch \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Peters et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Finally, phylogenetic inertia may constrain divergence, as closely related species tend to share similar vocal traits regardless of current selective pressures (Blomberg and Garland \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Blomberg et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost of the theoretical framework presented above derives from studies focused on diurnal birds, especially passerines. However, these are not the only birds that engage vocally in social interactions. Owls stand out as a group with a markedly different biology in several aspects that are potentially key for the evolution of vocal signals. Firstly, most owls are nocturnal predators that communicate in low-visibility environments where visual signals are limited (Marks et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Penteriani et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Penteriani and Delgado \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Indeed, their plumage is generally cryptic with minimal sexual dichromatism (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), making acoustic signals the primary channel for mate attraction and territorial defense (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mikkola \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Further, unlike oscine passerines, owl songs are predominantly innate (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), minimizing confounding effects of cultural transmission on the evolution of their vocalizations. Furthermore, owls occur within acoustic networks that include dangerous potential eavesdroppers: larger sympatric species that frequently engage in intraguild predation (Sergio and Hiraldo \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), an interaction that appears to be particularly common among owls (Sergio and Hiraldo \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), as well as conspecific rivals armed with sharp talons that heighten the risks of physical confrontations (Enquist and Leimar \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). These pressures may favor signals that prioritize privacy over long-range propagation and/or facilitate the ability to estimate intruder distance through signal degradation (ranging) \u0026mdash; especially relevant at night, as owls are known to be active in darkness when even they are unable to see (Martin \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1977\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), placing further significance on acoustic assessment of adversaries. Finally, owls often exhibit reverse sexual size dimorphism \u0026mdash; females being larger than males (Earhart and Johnson \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Segall et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) \u0026mdash; yet males consistently produce lower-frequency vocalizations (Appleby and Redpath \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Galeotti \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Segall et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a pattern attributed to disproportionately larger male syringes rather than overall body size (Miller \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1934\u003c/span\u003e; Segall \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This decoupling between body size and vocal frequency challenges straightforward morphological predictions. Together, these peculiarities make owls a compelling system for testing whether nocturnal ecology generates vocal evolutionary patterns distinct from those documented in diurnal songbirds.\u003c/p\u003e \u003cp\u003eThese peculiarities motivated us to begin investigation on vocal evolution in owls using phylogenetic comparative methods. Among owls, the genus \u003cem\u003eMegascops\u003c/em\u003e Kaup, 1848 is particularly suitable for this purpose. It is the largest New World owl genus, spanning from southern Canada to southern South America across broad altitudinal and habitat gradients. Morphology and plumage are often insufficient for species identification, and taxa are identified primarily by vocalizations (Dantas et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Krabbe \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The genus exhibits high vocal diversity and, in some species, multiple song types (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Krabbe \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), making it an ideal system for investigating vocal trait evolution.\u003c/p\u003e \u003cp\u003eWe inferred an updated phylogeny of \u003cem\u003eMegascops\u003c/em\u003e by integrating published sequence datasets (Dantas et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with recent taxonomic updates. Using the largest vocal dataset for the genus to date, we reviewed the vocal repertoire of all species, identifying primary songs and obtaining acoustic measurements from them. We used phylogenetic comparative methods to investigate whether variation in primary-song traits correlates with phylogenetic history, habitat structure, elevation, vocal dimorphism, and body size.\u003c/p\u003e \u003cp\u003eOur hypotheses follow classical predictions derived from other taxa. We expect (1) to find significant phylogenetic signals in vocal traits across \u003cem\u003eMegascops\u003c/em\u003e. Following the Acoustic Adaptation Hypothesis, we expect (2) that species inhabiting forested habitats produce songs with lower frequency and slower pace than those in open habitats. We also predict (3) that sexual selection and morphological constraints leave detectable signatures in song traits, such that species with stronger vocal dimorphism or larger body size will produce lower-frequency or longer vocalizations. Our results shed light on the selective and historical processes shaping the evolution of owl song, providing a framework for understanding the diversification of a lineage of birds with the unique ecology of being highly vocal and nocturnal top predators.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomy and datasets\u003c/h2\u003e \u003cp\u003eThe genus \u003cem\u003eMegascops\u003c/em\u003e comprises 27 currently recognized species (Clements Checklist v2024, supplemented with taxa from Dantas et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our study involved different subsets depending on data availability. The molecular phylogeny included 26 \u003cem\u003eMegascops\u003c/em\u003e species (\u003cem\u003eM. seductus\u003c/em\u003e lacks sequence data) and seven outgroup taxa (\u003cem\u003eOtus megalotis\u003c/em\u003e, \u003cem\u003eBubo bubo\u003c/em\u003e, \u003cem\u003eLophostrix cristata\u003c/em\u003e, \u003cem\u003eStrix aluco\u003c/em\u003e, \u003cem\u003eAsio otus\u003c/em\u003e, \u003cem\u003ePsiloscops flammeolus\u003c/em\u003e and \u003cem\u003eGymnasio nudipes\u003c/em\u003e); the concatenated matrix totaled 3,917 bp, with sequence coverage ranging from one to six genes per species (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sequences labeled as \u003cem\u003eM. vermiculatus\u003c/em\u003e in Dantas et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) correspond to localities consistent with \u003cem\u003eM. centralis\u003c/em\u003e (Hekstra \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Greeney and Freile \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and were treated accordingly (see N. Krabbe in Lane \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Phylogenetic comparative analyses used the 26 \u003cem\u003eMegascops\u003c/em\u003e species with \u003cem\u003eGymnasio nudipes\u003c/em\u003e (Daudin, 1800) as the sole outgroup, due to homology constraints on vocal comparisons (see Acoustic data below).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular data\u003c/h3\u003e\n\u003cp\u003eWe obtained sequences from three mitochondrial genes (Cytochrome-b, ND2, COI) and three nuclear genes (β-fibrinogen intron 5, CHD1 intron 18, MUSK intron 4). GenBank accession numbers are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003ePhylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eSequences were aligned using MAFFT v7 (Katoh and Standley \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Katoh et al, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with default parameters, translated to amino acids to check for unexpected indels or stop codons, and assessed for substitution saturation using DAMBE v7.3.32 following Xia and Lemey (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The concatenated matrix comprised 2,411 bp of mitochondrial and 1,506 bp of nuclear genes (including gaps). Mitochondrial genes were partitioned by codon position; each nuclear gene comprised a separate partition. Best-fit substitution models were selected using ModelFinder (Kalyaanamoorthy et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; see ModelFinder output in Supplementary Material 1; partition and models in Table S2).\u003c/p\u003e \u003cp\u003eBayesian inference was performed in MrBayes v3.2.7 (Ronquist et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with two independent runs of four Markov chains each (10⁶ generations, 25% burn-in). Gaps were treated as missing data. Convergence was assessed in Tracer v1.5 (Rambaut et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To assess topological robustness, we also performed Maximum Likelihood analysis in IQ-TREE 2 (Minh et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with SH-aLRT (Guindon et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and 1,000 ultrafast bootstrap replicates (Hoang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Both approaches yielded highly congruent topologies (see Supplementary Material 1).\u003c/p\u003e\n\u003ch3\u003eAcoustic data\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData gathering and screening\u003c/h2\u003e \u003cp\u003eOur dataset comprises 5,652 audio recordings of all 27 \u003cem\u003eMegascops\u003c/em\u003e species and \u003cem\u003eGymnasio nudipes\u003c/em\u003e (see Outgroup selection below), obtained from institutional sound archives and commercial sources (Table S3). Recordings with artifacts, excessive background noise, or inadequate signal-to-noise ratios were excluded. All recordings underwent aural and spectrographic screening to classify vocalizations into putative types within each species, independent of published nomenclature; classifications were then cross-checked against repertoire descriptions in the literature. This approach aimed to establish an unbiased characterization of each species' vocal repertoire, free from historical nomenclature inconsistencies and \u003cem\u003ea priori\u003c/em\u003e homology assumptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrimary song selection\u003c/h2\u003e \u003cp\u003eSome owl species exhibit multiple song types (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Krabbe \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and vocal homology assignments are often inconsistent across studies. We qualitatively characterized vocal diversity across the genus, identifying song types, motif structure, and clade-associated deviations from the basic pattern (see Results: Bioacoustical diversity overview; a detailed description of \u003cem\u003eMegascops\u003c/em\u003e vocal repertoires will be published elsewhere). For quantitative analyses, we analyzed only the primary song, operationally defined as the most frequently recorded vocalization for each species in our sample. We treated primary songs as homologous structures across \u003cem\u003eMegascops\u003c/em\u003e following the hierarchical homology framework (Striedter and Northcutt \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Hall \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), under which vocalizations may undergo evolutionary shifts, including functional ones (Hepp and Pombal 2019).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcoustic terminology and measurements\u003c/h3\u003e\n\u003cp\u003eWe define 'song' as loud, multi-note vocalizations that serve to attract mates and defend territories (the functional equivalent of passerine song, regardless of learning, sex of the emitter and seasonality); 'call' as shorter, simpler vocalizations associated with contact, alarm, or non-territorial contexts; 'note' as a continuous sound emission; 'syllable' as notes repeatedly rendered together in the same pattern; and 'phrase' as a group of syllables regularly repeated during vocal bouts.\u003c/p\u003e \u003cp\u003eWhen feasible (some species are poorly sampled) we randomly selected up to five recordings per species, avoiding sampling from the same locality and date to minimize pseudoreplication (Hurlbert \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). From each recording, we measured one randomly selected phrase. Audio files were band-pass filtered (approximately 50 Hz below and above each species' vocal frequency range) to isolate target signals.\u003c/p\u003e \u003cp\u003eWe measured 22 acoustic variables encompassing spectral, temporal, energy-distributional, and sectional parameters; variables were measured at two hierarchical levels: (1) entire phrases and (2) individual notes and inter-note intervals (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For note-level measurements, each phrase was divided into three temporal sections (initial, middle, final), with some variables measured separately for each section, totaling 40 acoustic variables. Sampling strategy varied with phrase length (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In phrases with \u0026le;\u0026thinsp;9 notes, we measured one note and one interval per section. In longer phrases, we sampled three notes and three intervals per section and calculated section means \u0026mdash; providing more robust estimates, since initial and terminal notes are often weaker or partially lost. All measurements were taken using Raven Pro 1.6 (Cornell Lab of Ornithology \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e): temporal and amplitude variables from oscillograms, spectral variables from spectrograms (Hann window, 25.7 ms; 99% overlap; DFT 8,192 samples). For a justification of measuring spectral variables from spectrograms, see Peixoto et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcoustic variables measured from owl songs and used in comparative analyses. Variables are grouped into categories according to the type of information they represent: frequency, energy distribution, temporal, and sectional metrics describing intra-phrase variation. Measurements were taken either at the level of the entire phrase, individual notes, or inter-note intervals, as detailed in the table.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase, Note\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum frequency of vocalization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase, Note\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum frequency of vocalization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBandwidth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase, Note\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifference between high and low frequency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominant frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase, Note\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency with maximum energy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency below which 5% of energy occurs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency below which 25% of energy occurs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency below which 75% of energy occurs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency below which 95% of energy occurs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime 5% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime at which 5% of cumulative energy is reached\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime 25% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime at which 25% of cumulative energy is reached\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime 75% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime at which 75% of cumulative energy is reached\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime at 95% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime at which 95% of cumulative energy is reached\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhrase duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal duration of entire phrase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNote duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration of individual notes (by section)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterval duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNote (Interval)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration of silent gaps between notes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal count of notes per phrase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNotes per second across entire phrase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSectional metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSection pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrase sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNotes per second within each phrase section\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePace change (initial-middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDerived from Phrase sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in pace (initial to middle)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePace change (middle-final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDerived from Phrase sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in pace (middle to final)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency change (initial-middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDerived from Phrase sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in dominant frequency (initial to middle)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency change (middle-final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDerived from Phrase sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in dominant frequency (middle to final)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOutgroup selection\u003c/h3\u003e\n\u003cp\u003eWe used \u003cem\u003eGymnasio nudipes\u003c/em\u003e as the sole outgroup for vocal analyses. The molecular phylogeny included additional outgroup taxa (see above; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), but these were excluded from acoustic comparisons due to homology constraints. \u003cem\u003ePsiloscops flammeolus\u003c/em\u003e, the closest relative to \u003cem\u003eMegascops\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eG. nudipes\u003c/em\u003e (tribe Megascopini; Wink et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Salter et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), produces songs with fewer than three notes per phrase (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), precluding sectional measurements. The remaining outgroups belong to distantly related tribes (Bubonini, Pulsatrigini, Strigini, Otini) with divergent vocal structures, making homology assessments unreliable. In contrast, \u003cem\u003eG. nudipes\u003c/em\u003e shares the multi-note phrase structure typical of \u003cem\u003eMegascops\u003c/em\u003e (\u0026gt;\u0026thinsp;4 notes), allowing direct application of all acoustic variables. Using a single outgroup admittedly increases uncertainty in ancestral state reconstructions; however, we prioritized robust homology over taxon sampling, since incorrect homology assumptions would yield spurious analytical precision rather than genuine phylogenetic insight.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHabitat and elevational data\u003c/h2\u003e \u003cp\u003eHabitat associations were classified from species accounts in Birds of the World (Billerman et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as: (1) forest-dependent (species restricted to or predominantly associated with forested habitats), (2) open habitats (species restricted to or predominantly associated with open habitats such as savannas, scrublands, deserts), or (3) forest\u0026thinsp;+\u0026thinsp;open (species regularly occurring in both habitat types). For brevity, 'forest\u0026thinsp;+\u0026thinsp;open' is sometimes discussed as \u0026lsquo;habitat generalists\u0026rsquo; or, when grouped with forest-dependent species as 'species that occur in forests', given the shared use of forests. This broad classification was adopted deliberately: given the continental scale of \u003cem\u003eMegascops\u003c/em\u003e distributions and the scarcity of comparable fine-scale habitat data across all species, finer categorizations would likely introduce noise rather than meaningful biological resolution. Text excerpts supporting each assignment are provided in Table S4. Elevational data were also extracted from Birds of the World and summarized as minimum elevation, maximum elevation, and elevational range for each species (Table S5). Data were available for 23 of the 27 species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSexual selection and body size proxies\u003c/h2\u003e \u003cp\u003eWe quantified vocal dimorphism as an index of sexual selection intensity (Trivers \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Fairbairn \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) using sex-specific dominant frequencies from Krabbe (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For each species, we calculated the percentage difference between male and female dominant frequency: [(female\u0026thinsp;\u0026minus;\u0026thinsp;male) / female] \u0026times; 100. This approach relies on published averages rather than individually sexed recordings, and although this limits precision, it remains standard for comparative analyses where sexed samples are scarce. More rigorous approaches, such as those advocated by Segall et al. (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), require controlled sampling of known pairs and are beyond the scope of the present study. Data were available for 14 of the 27 species (Table S5).\u003c/p\u003e \u003cp\u003eBody mass data were compiled from K\u0026ouml;nig et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) as a proxy for vocal apparatus size, a standard practice in comparative bioacoustics. Although body mass is an imperfect proxy for syringeal dimensions, it remains the most widely available size metric for owls; linear measurements (such as total length) are highly subjective and sensitive to specimen condition, while wing length, (though more precise) is less commonly reported across species (Segall et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). When ranges were reported, arithmetic means were used to reduce the effects of sexual size dimorphism. Data were available for 22 of the 27 species (Table S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic Comparative Analyses\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R v4.4.1 (R Core Team \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; script in Supplementary Material 1). The Bayesian phylogeny was prepared for the phylogenetic comparative analyses by removing outgroup taxa used only for rooting (retaining \u003cem\u003eG. nudipes\u003c/em\u003e as root), resolving polytomies with multi2di in ape (Paradis and Schliep \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and transforming the topology into an ultrametric tree. Prior to analysis, acoustic variables were standardized using scale(), shifted to positive values, and log-transformed.\u003c/p\u003e \u003cp\u003eUltrametricization was performed using the penalized-likelihood method in chronos (Paradis \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the absence of fossil calibration points for \u003cem\u003eMegascops\u003c/em\u003e, we selected the smoothing parameter (λ\u0026thinsp;=\u0026thinsp;0.8) that maximized correlation between patristic distances of original and ultrametric trees (r\u0026thinsp;=\u0026thinsp;0.98) while minimizing RMSE (0.84; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), thus preserving the covariance structure among species (Felsenstein \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic Signal\u003c/h2\u003e \u003cp\u003ePhylogenetic signal was assessed for each acoustic trait using Blomberg's K (Blomberg et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Pagel's λ (Pagel \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) with phylosig in phytools (Revell \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Significance was evaluated via tip-label randomization for K (1,000 simulations) and likelihood ratio tests (λ\u0026thinsp;=\u0026thinsp;0) for λ. We interpreted the signal as robust only when both metrics were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), given their complementary properties: K can exceed 1.0 indicating stronger-than-Brownian structure, while λ is bounded between 0 and 1.\u003c/p\u003e \u003cp\u003eFor interpretation and discussion of the results, we focused exclusively on traits that exhibited significant phylogenetic signals for both K and λ, ensuring robustness across complementary estimators of phylogenetic dependence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEvolutionary models\u003c/h2\u003e \u003cp\u003eWe compared nine continuous-trait evolutionary models using fitContinuous in geiger (Harmon et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e): Brownian Motion (BM), Ornstein\u0026ndash;Uhlenbeck (OU), Early Burst (EB), Lambda (LB), Pagel's λ, δ, KP, Time-Dependent (TD), Drift (DF), and White-Noise (WN) via AICc and Akaike weights for all 40 acoustic variables and the first five principal components from pPCA (see below).\u003c/p\u003e \u003cp\u003eBecause most variables were best fit by WN or BM, indicating low to moderate phylogenetic structure, we adopted Pagel's λ-transformed Brownian model (with λ optimized by maximum likelihood) as the general correlation structure for pPCA and PGLS analyses. For the few traits better described by KP or OU models, we ran additional PGLS analyses using model-specific parameters estimated by fitContinuous.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCharacter mapping and ancestral state reconstruction\u003c/h2\u003e \u003cp\u003eAncestral states were reconstructed using maximum likelihood under Brownian motion with contMap in phytools (Revell \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Visualizations were generated using plotSimmap and fancyTree. We reconstructed ancestral states for acoustic variables showing significant phylogenetic signals for both K and λ.\u003c/p\u003e \u003cp\u003eFor habitat (discrete), we used stochastic character mapping under an equal-rates model with make.simmap, also from phytools. Because treating 'forest\u0026thinsp;+\u0026thinsp;open' as a discrete state rather than a polymorphism could affect evolutionary inferences, we compared four models of discrete character evolution using AICc: Equal Rates (ER), Symmetric (SYM), All Rates Different (ARD), and an Ordered model constraining 'forest\u0026thinsp;+\u0026thinsp;open' as an obligatory intermediate between forest and open (i.e., direct forest \u0026harr; open transitions prohibited). The ER and Ordered models showed identical fit (AICc\u0026thinsp;=\u0026thinsp;47.16, ΔAICc\u0026thinsp;=\u0026thinsp;0), followed by SYM (ΔAICc\u0026thinsp;=\u0026thinsp;1.08) and ARD (ΔAICc\u0026thinsp;=\u0026thinsp;9.59). Given their identical fit, we retained the ER model, which does not impose \u003cem\u003ea priori\u003c/em\u003e constraints on transitions that may occur over long evolutionary timescales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic regression models\u003c/h2\u003e \u003cp\u003eAssociations between acoustic traits and predictors (body mass, vocal dimorphism, habitat and elevation metrics) were tested using Phylogenetic Generalized Least Squares (PGLS) with gls() in nlme (Pinheiro et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), specifying Pagel's λ correlation structure via corPagel() in ape (Paradis and Schliep \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This allows phylogenetic covariance to be scaled according to λ rather than assuming strict Brownian evolution (λ\u0026thinsp;=\u0026thinsp;1). For traits better fit by KP or OU models (see Evolutionary models), we used pgls() in caper (Orme et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with model-specific parameters from fitContinuous. Significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePhylomorphospace and clade visualization\u003c/h2\u003e \u003cp\u003eTo visualize evolutionary patterns, we plotted a phylomorphospace in phytools (Revell \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) using two acoustic variables with significant phylogenetic signals for both K and λ. Six major clades were color-coded to highlight lineage-level differentiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic principal component analysis\u003c/h2\u003e \u003cp\u003eAlthough we primarily analyzed variables in univariate models \u0026mdash; an approach that provides clearer relationships and is preferable when variables may evolve under distinct selective or developmental constraints (Odom et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) \u0026mdash; we also conducted a phylogenetic Principal Component Analysis (pPCA) to allow comparison with multivariate approaches common in comparative bioacoustics. We used phyl.pca() in phytools (Revell \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with λ optimization. Prior to analysis, collinear variables were identified via corrplot (Wei and Simko \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and removed to ensure the number of predictors remained well below the number of taxa.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic reconstruction analysis\u003c/h2\u003e \u003cp\u003eWithin \u003cem\u003eMegascops\u003c/em\u003e, we recovered two major clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Clade 1 (strongly supported, PP\u0026thinsp;=\u0026thinsp;1.00 throughout) comprises five species: ((\u003cem\u003eM. trichopsis\u003c/em\u003e, \u003cem\u003eM. clarkii\u003c/em\u003e)(\u003cem\u003eM. albogularis\u003c/em\u003e (\u003cem\u003eM. koepckeae\u003c/em\u003e, \u003cem\u003eM. choliba\u003c/em\u003e))). The second major clade encompasses all remaining \u003cem\u003eMegascops\u003c/em\u003e species and is divided into four subclades, treated hereafter as Clades 2 to 5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClade 2 (PP\u0026thinsp;=\u0026thinsp;1) contains five species: (\u003cem\u003eM. sanctaecatarinae\u003c/em\u003e (\u003cem\u003eM. barbarus\u003c/em\u003e (\u003cem\u003eM. cooperi\u003c/em\u003e (\u003cem\u003eM. kennicottii\u003c/em\u003e, \u003cem\u003eM. asio\u003c/em\u003e)))). Clades 3\u0026ndash;5 form a soft polytomy. Clade 3 includes four species: (\u003cem\u003eM. gilesi\u003c/em\u003e (\u003cem\u003eM. guatemalae\u003c/em\u003e (\u003cem\u003eM. roraimae\u003c/em\u003e, \u003cem\u003eM. centralis\u003c/em\u003e))), though the placement of \u003cem\u003eM. gilesi\u003c/em\u003e as sister to the other three species receives weak support (PP\u0026thinsp;=\u0026thinsp;0.52). Clade 4 comprises five species: ((\u003cem\u003eM. colombianus\u003c/em\u003e, \u003cem\u003eM. ingens\u003c/em\u003e)(\u003cem\u003eM. petersoni\u003c/em\u003e (\u003cem\u003eM. hoyi\u003c/em\u003e, \u003cem\u003eM. marshalli\u003c/em\u003e))). Clade 5 includes seven species: (\u003cem\u003eM. roboratus\u003c/em\u003e (\u003cem\u003eM. watsonii\u003c/em\u003e (\u003cem\u003eM. usta\u003c/em\u003e (\u003cem\u003eM. stangiae\u003c/em\u003e, \u003cem\u003eM. ater\u003c/em\u003e, (\u003cem\u003eM. atricapilla\u003c/em\u003e, \u003cem\u003eM. alagoensis\u003c/em\u003e))))).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eBioacoustical diversity overview\u003c/h2\u003e \u003cp\u003eWe found two distinct song types in 11 of the 27 \u003cem\u003eMegascops\u003c/em\u003e species. All other species have only one type; Clade 3 is the only clade where all species have a single song type. Several species also present a \u0026lsquo;harsh\u0026rsquo; variant (energy more evenly distributed across lower harmonics; \"voice type II\" sensu Peixoto et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary songs of \u003cem\u003eMegascops\u003c/em\u003e spp. and \u003cem\u003eG. nudipes\u003c/em\u003e share a general motif: phrases with a simple syntax consisting of a single softly ascending-descending frequency-modulated syllable composed of repetitive, narrow-bandwidth hoots (notes) delivered at roughly regular intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interval duration, note duration, number of notes and resulting pace vary considerably among species. The descriptive labels used below (e.g., \u0026lsquo;double-trill\u0026rsquo;, \u0026lsquo;bouncing ball\u0026rsquo;, \u0026lsquo;plateau notes\u0026rsquo;) are not intended as homology assertions.\u003c/p\u003e \u003cp\u003eWe observed clade-associated deviations from this basic motif. In Clade 3, a subtle frequency dip (\u0026lsquo;step\u0026rsquo;) near the end of the first third of the phrase occurs in all species except \u003cem\u003eM. gilesi\u003c/em\u003e; this feature was not captured by our quantitative metrics. \u0026lsquo;Double-trill\u0026rsquo; songs (two ascending-descending syllables) were recorded only in a Clade 2 subclade (\u003cem\u003eM. cooperi\u003c/em\u003e, \u003cem\u003eM. kennicottii\u003c/em\u003e, \u003cem\u003eM. asio\u003c/em\u003e), and in \u003cem\u003eM. seductus\u003c/em\u003e \u0026mdash; thought to be also a Clade 2 species (Sibley and Monroe \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) \u0026mdash; as either the primary or secondary song depending on the species. Regular directional changes in pace occur across multiple clades: intervals becoming progressively shorter (\u0026lsquo;bouncing ball\u0026rsquo;) in \u003cem\u003eM. kennicottii\u003c/em\u003e, \u003cem\u003eM. seductus\u003c/em\u003e, and \u003cem\u003eM. atricapilla\u003c/em\u003e; or progressively longer (\u0026lsquo;reversed bouncing ball\u0026rsquo;) in \u003cem\u003eM. koepckeae\u003c/em\u003e and \u003cem\u003eM. seductus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eClade 1 species are especially distinct. \u003cem\u003eMegascops clarkii\u003c/em\u003e and some individuals of \u003cem\u003eM. trichopsis\u003c/em\u003e produce phrases with few, long, plateau-shaped notes (sharp attack/decay; minimal frequency modulation). \u003cem\u003eMegascops choliba\u003c/em\u003e has a detached, accented final syllable (highly variable among subspecies; pers. obs.), while its sister \u003cem\u003eM. koepckeae\u003c/em\u003e occasionally shows phrase-final interval expansions yielding somewhat similar detached notes. Additionally, \u003cem\u003eM. koepckeae\u003c/em\u003e is aurally very distinctive because the second harmonic, rather than the fundamental, is dominant.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEvolution of the primary song\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003ePhylogenetic signal and evolutionary models\u003c/h2\u003e \u003cp\u003eThree of the 40 acoustic variables showed significant phylogenetic signal for both λ and K (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; see full results in Table S6): number of notes (λ\u0026thinsp;=\u0026thinsp;0.84, K\u0026thinsp;=\u0026thinsp;0.81), phrase duration (λ\u0026thinsp;=\u0026thinsp;0.97, K\u0026thinsp;=\u0026thinsp;1.09), and Time 5% relative (λ\u0026thinsp;=\u0026thinsp;0.66, K\u0026thinsp;=\u0026thinsp;0.87). None of these followed Brownian motion: KP models best explained phrase duration and Time 5% relative, while number of notes fit an OU model (Table S7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhylogenetic signal estimates for 40 acoustic variables measured from primary songs of \u003cem\u003eMegascops\u003c/em\u003e species. Pagel's λ and Blomberg's K were calculated using the \u003cem\u003ephylosig\u003c/em\u003e function in the R package \u003cem\u003ephytools\u003c/em\u003e. Values of λ\u0026thinsp;\u0026asymp;\u0026thinsp;0 indicate no phylogenetic signal; λ\u0026thinsp;\u0026asymp;\u0026thinsp;1 indicates trait variation consistent with Brownian motion. K\u0026thinsp;\u0026lt;\u0026thinsp;1 suggests less similarity among relatives than expected under Brownian motion; K\u0026thinsp;\u0026gt;\u0026thinsp;1 suggests greater similarity. Bold rows indicate variables with significant phylogenetic signal (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for both metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep (λ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep (K)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum frequency of initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum frequency of initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBandwidth of initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote duration of initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval duration of initial intervals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant frequency of initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum frequency of central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum frequency of central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBandwidth of central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote duration of central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval duration of central intervals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant frequency of central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum frequency of final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum frequency of final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBandwidth of final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote duration of final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval duration of final intervals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant frequency of final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhrase minimum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhrase maximum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhrase bandwidth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhrase duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.966\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.091\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhrase dominant frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency 25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency 5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency 75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency 95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime 25% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime 5% relative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.659\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.867\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime 75% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime 95% relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePace change (initial\u0026ndash;middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePace change (middle\u0026ndash;final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency change (initial\u0026ndash;middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency change (middle\u0026ndash;final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of notes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.843\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.811\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe phylomorphospace (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveals clade-level structuring in acoustic space (phrase duration was not plotted because it is a composite variable determined by number of notes, note duration, and interval duration). Clade 1 occupies the region of fewest notes and fastest energy deposition (low Time 5% relative), while Clade 5 shows the opposite pattern. Clades 2, 3, and 4 are intermediate, with Clade 2 showing the greatest within-clade variation. Two Clade 2 species (\u003cem\u003eM. cooperi\u003c/em\u003e and \u003cem\u003eM. kennicottii\u003c/em\u003e) cluster with Clade 1, reflecting convergent short-phrase structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCharacter mapping and ancestral reconstructions\u003c/h2\u003e \u003cp\u003eAncestral states were reconstructed for the three acoustic variables with significant phylogenetic signal (Table S8; Figure S2) and for habitat (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All values reported below are standardized scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePhrase duration: The \u003cem\u003eMegascops\u003c/em\u003e ancestor had moderate-to-short phrases (0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08), a condition retained in Clade 1 (0.61\u0026ndash;0.63) but increasing in Clades 2\u0026ndash;5, and reaching extreme values in Clades 4\u0026ndash;5 (up to 0.86 in \u003cem\u003eM. colombianus\u003c/em\u003e, 0.85 in \u003cem\u003eM. ater\u003c/em\u003e, 0.84 in \u003cem\u003eM. usta\u003c/em\u003e). Secondary reductions occurred independently in Clade 2 (\u003cem\u003eM. cooperi\u003c/em\u003e and \u003cem\u003eM. kennicottii\u003c/em\u003e: 0.61) and in Clade 5 (\u003cem\u003eM. roboratus\u003c/em\u003e: 0.62).\u003c/p\u003e \u003cp\u003eNumber of notes: The ancestral state (0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09) resembles the outgroup \u003cem\u003eG. nudipes\u003c/em\u003e (0.65). Clade 1 shows reduction (down to 0.59 in \u003cem\u003eM. clarkii\u003c/em\u003e), while Clades 2\u0026ndash;5 show increases (ancestor: 0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05), peaking in the \u003cem\u003eM. atricapilla\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eM. alagoensis\u003c/em\u003e lineage (0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eTime 5% relative: Values increased from 0.55 in \u003cem\u003eG. nudipes\u003c/em\u003e to 0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 in the \u003cem\u003eMegascops\u003c/em\u003e ancestor. Clade 1 retained similar values (0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10), with \u003cem\u003eM. koepckeae\u003c/em\u003e showing marked increase (0.80). Clades 2\u0026ndash;5 show an overall tendency toward higher values (ancestor: 0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06), except for \u003cem\u003eM. cooperi\u003c/em\u003e (0.44); \u003cem\u003eM. atricapilla\u003c/em\u003e and \u003cem\u003eM. alagoensis\u003c/em\u003e show the highest values (0.75).\u003c/p\u003e \u003cp\u003eHabitat: The \u003cem\u003eMegascops\u003c/em\u003e ancestor was forest-dependent, the predominant condition across the genus. Independent transitions to open habitats occurred in \u003cem\u003eM. choliba\u003c/em\u003e, \u003cem\u003eM. sanctaecatarinae\u003c/em\u003e, \u003cem\u003eM. cooperi\u003c/em\u003e, and \u003cem\u003eM. roboratus\u003c/em\u003e. Transitions to forest\u0026thinsp;+\u0026thinsp;open occurred in the ancestor of \u003cem\u003eM. koepckeae\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eM. choliba\u003c/em\u003e, in \u003cem\u003eM. kennicottii\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eM. asio\u003c/em\u003e, and in \u003cem\u003eM. guatemalae\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eEvolutionary model fit\u003c/h2\u003e \u003cp\u003eMost acoustic traits were best described by White Noise (n\u0026thinsp;=\u0026thinsp;35) or Brownian Motion (n\u0026thinsp;=\u0026thinsp;6) models. Among structured models, Pagel's λ-transformed Brownian was the most frequent best fit, supporting its use as the correlation structure for subsequent analyses (see Methods). Four traits departed from this pattern: KP models best fit phrase duration and Time 5% relative, while number of notes and pace change (middle\u0026ndash;final) fit Ornstein\u0026ndash;Uhlenbeck models. Full model comparisons are provided in Table S7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003ePGLS models\u003c/h2\u003e \u003cp\u003eUnivariate PGLS revealed significant associations between acoustic traits and habitat, elevation, and vocal dimorphism (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e; see Supplementary Material 1 for full results from PGLS using evolutionary models \u0026ldquo;OU\u0026rdquo; and \u0026ldquo;Kappa); body mass showed no significant effects (Table S9). Because many acoustic variables are correlated (e.g., note- and phrase-level frequency measures), effects on related variables should be interpreted as shared patterns rather than independent responses. A complementary pPCA using 12 non-collinear variables produced similar outcomes, with PGLS on the first five PCs mirroring the univariate results (Figures S3\u0026ndash;S6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of significant associations between acoustic traits and habitat type in \u003cem\u003eMegascops\u003c/em\u003e species based on Phylogenetic Generalized Least Squares (PGLS) models. Positive estimates indicate higher trait values in species occurring in forested or generalist habitats relative to open-habitat species (reference category). Frequency-related variables consistently show increased pitch in forest-dependent and forest\u0026thinsp;+\u0026thinsp;open species, contrary to Acoustic Adaptation Hypothesis (AAH) predictions. Temporal variables indicate intra-phrase acceleration (pace change) and slower initial pacing in forest species, suggesting modifications in signal temporal structure linked to habitat acoustics. Asterisk (*) marks variable for which PGLS models were fitted under the Ornstein\u0026ndash;Uhlenbeck (OU) evolutionary model, identified as the best-fitting model for that trait.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eForest-dependent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eForest\u0026thinsp;+\u0026thinsp;open\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eTendency to increase in frequency in species that occur in forests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean maximum frequency of the central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean maximum frequency of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean maximum frequency of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhrase maximum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean minimum frequency of the central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean minimum frequency of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean minimum frequency of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of significant associations between acoustic traits and vocal dimorphism in \u003cem\u003eMegascops\u003c/em\u003e species based on Phylogenetic Generalized Least Squares (PGLS) models. Positive or negative estimates indicate the direction of trait change with increasing dimorphism. More dimorphic species tend to exhibit broader bandwidths in the initial notes, stronger concentration of energy at lower frequencies, and steeper downward modulation in frequency across the phrase, suggesting that sexual selection may act on fine-scale spectral modulation rather than overall pitch. These species also show greater pace acceleration and sharper initial attack, indicating more dynamic temporal structuring. The asterisk (*) marks the variable analyzed under the kappa evolutionary model, identified as the best-fitting model for that trait\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMinimum altitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMaximum altitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eAltitudinal ranges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eInterval duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean interval duration of the central intervals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTendency of longer intervals in the first half of the phrase, in species occurring at higher minimum altitudes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean interval duration of the initial intervals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNote duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean note duration of the central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTendency of longer note durations in species occurring at higher minimum altitudes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean note duration of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean note duration of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTendency of higher overall frequencies (increased pitch) in species occurring at higher maximum altitudes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency 95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean maximum frequency of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean maximum frequency of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMinimum altitude\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eMaximum altitude\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eAltitudinal ranges\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean minimum frequency of the central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTendency of higher overall frequencies (increased pitch) in species occurring at higher maximum altitudes, and restricted to the last half of the phrases of species that occur in higher minimum altitudes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean minimum frequency of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e0.0465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhrase minimum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTendency of higher overall frequencies (increased pitch) in species occurring at higher maximum altitudes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean dominant frequency of the central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean dominant frequency of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean dominant frequency of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhrase dominant frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of notes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of notes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.000056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTendency of fewer notes per phrase in species occupying broader altitudinal ranges\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.000036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.0297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTendency of a slower pace in the first half of the phrase in species occupying broader altitudinal ranges.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.000036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.0417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean bandwidth of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTendency of broader bandwidth in the initial notes in more dimorphic species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency 5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.1855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTendency of energy being focused on the lower frequencies in more dimorphic species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency change (middle\u0026ndash;final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.1458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTendency of frequency decreasing throughout the phrase in more dimorphic species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency change (initial\u0026ndash;middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.1668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTendency of frequency decreasing throughout the phrase in more dimorphic species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePace change (middle\u0026ndash;final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTendency of pace acceleration in more dimorphic species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime 5% relative *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTendency to a steeper initial attack in more dimorphic species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eEffects of habitat on vocal traits\u003c/h2\u003e \u003cp\u003eThe songs of species occurring in forests showed consistently and significantly higher frequencies than those of open-habitat species across nearly all spectral variables (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This occurred in both forest-dependent and forest\u0026thinsp;+\u0026thinsp;open species at note and phrase level, with coefficients ranging from β\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.19 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for most). Forest-dependent species also showed significant reduction in bandwidth from the initial to middle portion of the phrase (β = \u0026minus;0.09, p\u0026thinsp;=\u0026thinsp;0.02), a pattern absent in forest\u0026thinsp;+\u0026thinsp;open species.\u003c/p\u003e \u003cp\u003eTemporal traits varied systematically with habitat. Forest-associated species exhibited slower initial pace (forest: β = -0.1, p\u0026thinsp;=\u0026thinsp;0.02; forest\u0026thinsp;+\u0026thinsp;open: β = -0.1, p\u0026thinsp;=\u0026thinsp;0.03) and progressive acceleration of the phrase (beginning\u0026ndash;middle: forest, β\u0026thinsp;=\u0026thinsp;0.13, p\u0026thinsp;=\u0026thinsp;0.025; forest\u0026thinsp;+\u0026thinsp;open, β\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.013; middle\u0026ndash;end: forest, β\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.019; forest\u0026thinsp;+\u0026thinsp;open, β\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eEffects of elevation on vocal traits\u003c/h2\u003e \u003cp\u003eElevation metrics showed distinct associations with acoustic parameters (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Minimum altitude was positively associated with temporal variables: species at higher minimum elevations produced longer notes (all phrase sections, β\u0026thinsp;=\u0026thinsp;0.000025\u0026ndash;0.000050, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and longer intervals (initial and central sections, β\u0026thinsp;=\u0026thinsp;0.000010\u0026ndash;0.000013, p\u0026thinsp;\u0026lt;\u0026thinsp;0.03). Altitudinal range was negatively associated with pace in the first half of the phrase (β = \u0026minus;0.000036, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and with number of notes (β = \u0026minus;0.000056, p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of significant associations between acoustic traits and elevation metrics in \u003cem\u003eMegascops\u003c/em\u003e species based on Phylogenetic Generalized Least Squares (PGLS) models. Positive estimates indicate higher trait values with increasing (minimum or maximum) elevation or broader altitudinal ranges. Species occurring at higher minimum altitudes tend to exhibit longer notes and intervals, suggesting slower phrase pacing, while those at higher maximum altitudes show an overall increase in frequency (higher pitch). In contrast, species occupying broader altitudinal ranges tend to produce slower and shorter phrases with fewer notes. The asterisk (*) marks the variables for which PGLS models were fitted under the Ornstein\u0026ndash;Uhlenbeck (OU) evolutionary model, identified as the best-fitting model for that trait.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eForest-dependent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eForest\u0026thinsp;+\u0026thinsp;open\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhrase minimum frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTendency to increase in frequency in species that occur in forests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean dominant frequency of the central notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean dominant frequency of the final notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean dominant frequency of the initial notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhrase dominant frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChange in frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency change (initial-middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTendency to a downward modulation in frequency from the beginning to the middle of the phrases, only in forest-exclusive species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eChange in pace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePace change (middle\u0026ndash;final)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntra-phrase acceleration in pace in species that occur in forests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePace change (initial\u0026ndash;middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSectional pace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlower initial pace in species that occur in forests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpectral variables were more strongly associated with maximum altitude: species reaching higher elevations showed higher frequencies across multiple measures (β\u0026thinsp;=\u0026thinsp;0.000035\u0026ndash;0.000057, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Minimum frequencies of central and final notes were also positively associated with minimum altitude, indicating that both elevational limits correlate with upward spectral shifts.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffects of proxies for sexual selection and body size on vocal traits\u003c/h3\u003e\n\u003cp\u003eVocal dimorphism was significantly associated with both spectral and temporal parameters (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). More dimorphic species showed a broader bandwidth in the initial notes (β\u0026thinsp;=\u0026thinsp;0.10, p\u0026thinsp;=\u0026thinsp;0.01), energy concentrated at lower frequencies (Frequency 5%: β = \u0026minus;0.19, p\u0026thinsp;=\u0026thinsp;0.03), and progressive frequency decrease through the phrase (beginning\u0026ndash;middle: β = \u0026minus;0.17, p\u0026thinsp;=\u0026thinsp;0.04; middle\u0026ndash;end: β = \u0026minus;0.15, p\u0026thinsp;=\u0026thinsp;0.002). Temporal variables also varied with dimorphism: Time 5% relative decreased (β = \u0026minus;0.29, p\u0026thinsp;=\u0026thinsp;0.01), indicating faster initial energy deposition, while pace acceleration toward phrase end increased (β\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;0.03). No acoustic variable was significantly associated with body mass (see Table S9).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur phylogenetic reconstruction of \u003cem\u003eMegascops\u003c/em\u003e spp., integrating recent taxonomic developments, largely supports previous hypotheses (Dantas et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, we recovered \u003cem\u003eM. gilesi\u003c/em\u003e (Krabbe \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in a different position than that in Dantas et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), where it was treated as an \u0026lsquo;unnamed taxon\u0026rsquo;. Here, this species is placed as sister of a clade containing \u003cem\u003eM. guatemalae\u003c/em\u003e, \u003cem\u003eM. roraimae\u003c/em\u003e, and \u003cem\u003eM. centralis\u003c/em\u003e, instead of \u003cem\u003eM. roboratus, M. watsonii\u003c/em\u003e, and \u003cem\u003eM. atricapilla\u003c/em\u003e in that study. In any case, its position remains uncertain due to limited molecular data (single CytB sequence; PP\u0026thinsp;\u0026asymp;\u0026thinsp;0.52).\u003c/p\u003e \u003cp\u003eContrary to K\u0026ouml;nig et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), we found that two song types were not universal across the genus, which corroborates Krabbe\u0026rsquo;s (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) assertion of having found two song types in only about half of the species. We also corroborate van der Weyden\u0026rsquo;s (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) characterization of a genus-wide basic song motif, and we found that several deviations of this motif are clade-associated, which is reflected in traits with significant phylogenetic signal. These patterns indicate that acoustic characters can be useful in the taxonomy of \u003cem\u003eMegascops\u003c/em\u003e, especially in poorly resolved lineages (e.g., \u003cem\u003eM. seductus, M. gilesi\u003c/em\u003e). However, this potential is currently hampered by the lack of explicit and testable homology criteria for song types (e.g., Marshall \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Klatt and Ritchison \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Schulenberg et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Krabbe \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which needs to be addressed. Prior terminologies (\u0026ldquo;A/B song\u0026rdquo;: K\u0026ouml;nig et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; \u0026ldquo;long/short song\u0026rdquo;: Krabbe \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have mixed functional labels with putative homologies in inconsistent ways, complicating cross-taxon alignment (see Hepp and Pombal 2019).\u003c/p\u003e \u003cp\u003eNotably, most variables (37 of 40) lacked significant phylogenetic signal. The three exceptions (phrase duration, number of notes and Time 5% relative) describe phrase length and initial energy deposition. Spectral variables (frequencies, bandwidth and energy distribution), note-level temporal parameters (durations, intervals and pace), and intra-phrase modulation (pace change, frequency change) showed no significant signal. This suggests that what is phylogenetically conserved in \u003cem\u003eMegascops\u003c/em\u003e songs is the overall phrase duration and how abruptly energy is delivered at the onset, while spectral and fine-scale temporal characteristics are labile. This interpretation is consistent with our PGLS results (see below). Clade 1 stands out as vocally distinct, with shorter phrases and fewer notes, while Clades 4\u0026ndash;5 show the longest phrases. Although Clades 2\u0026ndash;5 share cohesive temporal architectures, localized modifications occur (e.g., \u0026lsquo;reverse bouncing-ball\u0026rsquo; in \u003cem\u003eM. cooperi\u003c/em\u003e and \u003cem\u003eM. kennicottii\u003c/em\u003e recalls \u003cem\u003eM. koepckeae\u003c/em\u003e of Clade 1). Overall, most variation in \u003cem\u003eMegascops\u003c/em\u003e appears to arise from spectral and syntactic novelties within a phylogenetically conserved temporal framework.\u003c/p\u003e \u003cp\u003eOur ancestral state reconstruction indicates that the \u003cem\u003eMegascops\u003c/em\u003e ancestor was forest-dependent, a condition retained across most of the genus, with restriction to open habitats representing a derived state in a few lineages. This result provides a framework for evaluating the selective pressures predicted by the Acoustic Adaptation Hypothesis (AAH). Contrary to these predictions, forest-dwelling \u003cem\u003eMegascops\u003c/em\u003e spp. produced higher-frequency songs, with a broad bandwidth and a progressive acceleration of the phrase. Deviations from the AAH are not unprecedented in birds (e.g., Boncoraglio and Saino \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hardt and Benedict \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mikula et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and suggest that competing selective pressures may override propagation efficiency in shaping vocal evolution (Ryan and Brenowitz \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Brise\u0026ntilde;o-Jaramillo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe consider three hypotheses that might explain this unexpected association between higher-pitched broadcast songs and forest habitats, given that higher frequencies are more affected by reverberation and attenuate more quickly in dense vegetation (Bradbury and Vehrencamp \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e): signal reinforcement driven by reverberation, eavesdropping avoidance, and ranging. In some conditions, reflected sound can reinforce signals, resulting in longer and louder songs (Slabbekoorn et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and forest-dwelling \u003cem\u003eMegascops\u003c/em\u003e spp. could exploit this reinforcement. However, this mechanism favors narrow-bandwidth signals, whereas most \u003cem\u003eMegascops\u003c/em\u003e songs are broadband, and we found no relationship between bandwidth and habitat. Therefore, our data do not support the first of these hypotheses.\u003c/p\u003e \u003cp\u003eEavesdropping avoidance offers a more compelling explanation. Since higher-frequency vocalizations attenuate more quickly, and are more directional, detectability by unintended receivers may be reduced (Dabelsteen \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e: 52). This is particularly relevant for \u003cem\u003eMegascops\u003c/em\u003e, as owls frequently engage in intraguild predation, nocturnal predators are acoustically oriented (Polis et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Sergio and Hiraldo \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and \u003cem\u003eMegascops\u003c/em\u003e spp. are small-bodied compared with sympatric owls (\u003cem\u003eLophostrix\u003c/em\u003e, \u003cem\u003eStrix\u003c/em\u003e, \u003cem\u003ePulsatrix\u003c/em\u003e). Indeed, \u003cem\u003eM. choliba\u003c/em\u003e has been reported as prey of \u003cem\u003ePulsatrix perspicillata\u003c/em\u003e (Schubart et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). Reducing amplitude would also reduce detectability (Dabelsteen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), but low- and high-amplitude (broadcast) songs serve distinct functions in owls (Peixoto et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which may constrain this strategy. Increasing frequency may thus be the most viable route to reduce eavesdropping risk while preserving signal identity. Unlike diurnal birds \u0026mdash; which can rely on vision to detect approaching predators \u0026mdash; owls remain active even on nights too dark for them to see (Martin \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1977\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), making acoustic cues the primary, and sometimes only, channel for detecting tracking targets (Konishi \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). This sensory asymmetry likely intensifies selection for acoustic discretion. Vocal suppression near potential predators, frequently reported in owls (Zuberogoitia et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vrezec and Bertoncelj \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), further supports the likely importance of eavesdropping avoidance for these birds.\u003c/p\u003e \u003cp\u003eNonexclusive to the eavesdropping-avoidance hypothesis, the higher-frequency songs in forest-dwelling species may also be related to ranging (Holland et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Naguib and Wiley \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ringler et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For territorial species such as owls, optimal response to an intruder depends on perceived distance (Naguib and Wiley \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Higher-pitched multi-note songs are more susceptible to \"interval filling\" by reverberation (Slabbekoorn \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2004\u003c/span\u003e: 180), causing distant faster-paced phrases to sound more continuous or \"whistled\", which would potentially encode reliable distance information in the note intervals. In line with this prediction, we found a significant positive relationship between phrase acceleration and forest habitat. Thus, nearby individuals could perceive all notes distinctly, whereas reverberation would progressively blur final intervals as distances increased, yielding a gradual series of temporal cues that would improve ranging accuracy. These distance-sensitive cues may reduce uncertainty and prevent unnecessary aggressive escalation, being beneficial for both signaler and receiver (Enquist and Leimar \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Briffa and Hardy \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The presence of low-amplitude songs in \u003cem\u003eMegascops\u003c/em\u003e (Peixoto et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and their possible use in ranging (Peixoto \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) further support this interpretation. For owls active at night \u0026mdash; including nights too dark for visual orientation (Martin \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1977\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), acoustic assessment of adversary distance becomes particularly relevant, consistent with their suite of adaptations for sound localization (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the inconclusive existing evidence for elevation effects on song traits across taxa, we interpret our findings as exploratory. In our study, Maximum elevation (and to a lesser extent, minimum elevation) was positively correlated with higher frequencies, paralleling the pattern observed for denser habitats. A plausible interpretation is that high-elevation \u003cem\u003eMegascops\u003c/em\u003e spp. tend to inhabit structurally complex environments, as most species restricted to high elevations occupy humid, mossy cloud or montane forests, particularly in the Andes (K\u0026ouml;nig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Billerman et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, species with greater altitudinal ranges produce slower and shorter songs, potentially allowing effective propagation with fewer repetitive elements (Slabbekoorn \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContrary to our prediction, vocal dimorphism was not significantly associated with dominant frequency, phrase duration, or vocal stability (i.e., low intra-phrase variation in pace and frequency). Instead, species with greater dimorphism concentrated more energy in the lower portion of the spectrum (Freq 5%) and exhibited a steeper descending frequency modulation within phrases, suggesting that sexual selection in \u003cem\u003eMegascops\u003c/em\u003e may operate through finer spectral adjustments rather than overall frequency. Notably, in \u003cem\u003eOtus scops\u003c/em\u003e, lower-frequency vocalizations were initially interpreted as indicators of male quality (Hardouin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), but subsequent work showed that males can voluntarily modulate hoot frequency (Grieco \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that previous correlations may have reflected modulation capacity rather than fixed differences. A similar mechanism may be present in \u003cem\u003eMegascops\u003c/em\u003e. Since this modulation likely depends on the level of arousal and the context, interspecific datasets based on heterogeneous recordings (such as ours) may obscure selection-related trends; standardized playback experiments could help clarify this. Furthermore, vocal dimorphism showed a positive correlation with larger bandwidths at the beginning of phrases, reinforcing the interpretation that spectral modulation may be a primary target of sexual selection in \u003cem\u003eMegascops\u003c/em\u003e. Notably, during our screening, we observed abrupt frequency shifts \u0026mdash; consistent with performance constraints (Sierro et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u0026mdash; in several species of Clades 4\u0026ndash;5, which produce the longest, most repetitive songs in the genus. Gonzaga and Castiglioni (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) described a similar \u0026ldquo;Tarzan yell\u0026rdquo; effect in \u003cem\u003eM. atricapilla\u003c/em\u003e. Whether such performance breaks carry communicative value (e.g., as indicators of the signaler quality or individuality) remains untested and warrants experimental investigation.\u003c/p\u003e \u003cp\u003eGiven prior evidence of mass-acoustics relationships in owls (e.g., Appleby and Redpath \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and also contrary to our expectation, we found no associations between body mass and any song trait after controlling for phylogeny. In owls, the relationship between body size and song frequency is complicated by reverse sexual size dimorphism, in which females are larger than males (Kr\u0026uuml;ger \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Segall et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), yet males produce lower-frequency vocalizations due to disproportionately larger syringes (Miller \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1934\u003c/span\u003e; Segall \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Crucially, while syrinx width correlates with body mass, syrinx length (which also affects frequency) varies independently (Segall 2020), weakening the predictive power of mass in interspecific comparisons. Additionally, ecological pressures may override morphological constraints, as suggested by our finding that forest-dwelling species produce higher frequency songs. Practical limitations also warrant consideration. Body mass, despite seasonal and nutritional variation, remains the best available proxy for syrinx size (Segall 2020) and the most practical metric for interspecific comparisons, though published values for the same species can differ substantially across studies (Segall et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; pers. obs.). Furthermore, very few song recordings of sexed owls exist (Segall et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and our sample may have inadvertently included more than one sex. Comparative studies pairing syringeal morphology of known-sex individuals and controlled audio recordings would help to disentangle these effects.\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eConcluding remarks\u003c/h2\u003e \u003cp\u003eThis study is based on the largest acoustic dataset yet assembled for \u003cem\u003eMegascops\u003c/em\u003e and represents, to our knowledge, the first application of phylogenetic comparative methods to acoustic communication of nocturnal birds. Beyond our core findings, we emphasize the severe disparities in basic natural history and bioacoustic information among \u003cem\u003eMegascops\u003c/em\u003e species, underscoring the need for comprehensive vocal repertoire descriptions and homology reassessment across the genus.\u003c/p\u003e \u003cp\u003eMost of the expected correlations were either nonsignificant or opposite to classical predictions. Forest-dwelling species produced higher-pitched songs, contrary to the Acoustic Adaptation Hypothesis, and body mass showed no relationship with frequency. Because forest use represents the ancestral condition in the genus, lower frequencies in open-habitat species appear derived rather than primitive. We propose that this pattern reflects the distinctive ecology of nocturnal predators balancing long-range communication against eavesdropping risk from both prey and larger owls. Sexual selection also appears to act on finer-scale spectral modulation rather than on dominant frequency, with more dimorphic species concentrating energy in lower spectral regions and exhibiting steeper frequency modulation.\u003c/p\u003e \u003cp\u003eTaken together, our results reveal that the vocal evolution in owls reflects a complex interplay between shared ancestry and ecological pressures, distinct from that observed in diurnal passerines. \u003cem\u003eMegascops\u003c/em\u003e thus provides a promising model for investigating how communication systems evolve under nocturnal and predation-driven conditions, highlighting the importance of extending bioacoustics research beyond traditional model systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is part of the Ph.D. requirements of Luis Felipe Peixoto at the Biodiversity and Evolutionary Biology Graduate Program of the Federal University of Rio de Janeiro. We thank the curators and staff of the following sound archives for granting access to their collections: Arquivo Sonoro Prof. Elias Coelho (UFRJ), Fonoteca Neotropical Jacques Vielliard (Unicamp), Macaulay Library (Cornell University), Borror Laboratory of Bioacoustics (Ohio State University), Bird Sound Collection (Florida Museum), Animal Sound Archive (Museum f\u0026uuml;r Naturkunde, Berlin), Fonoteca Zool\u0026oacute;gica (Museo Nacional de Ciencias Naturales, Madrid), Soundlibrary Archives (Mus\u0026eacute;um National d\u0026rsquo;Histoire Naturelle, Paris), and Colecci\u0026oacute;n de Sonidos Ambientales (Instituto de Investigaci\u0026oacute;n de Recursos Biol\u0026oacute;gicos Alexander von Humboldt, Colombia). We also used publicly available recordings from Xeno-canto.org and commercial sound compilations, including \u003cem\u003eChants d\u0026rsquo;Oiseaux de Guyane\u003c/em\u003e, \u003cem\u003eAves do Brasil\u003c/em\u003e, and \u003cem\u003eBirds of Costa Rica\u003c/em\u003e. We thank Jos\u0026eacute; Leonardo Mattos for his help with phylogenetic analyses, and Judit Szabo, Jos\u0026eacute; Pombal Junior, Ana Galv\u0026atilde;o, Andressa Bezerra, Gloria Denise Castiglioni and Wilson Costa for their valuable comments and suggestions on this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This study was supported by fellowships and grants from the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES), Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq), and Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado do Rio de Janeiro (FAPERJ) awarded to LFP, FH, and PCP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e The datasets generated and/or analyzed during the current study are available in the Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e The code used for the analyses is available in the Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e LFP conceived the idea and formulated questions. LFP and FH created the study design and collected data. FH and LFP analyzed data. LFP , FH and LPG wrote the manuscript. PCP, FH and LPG critically reviewed and substantially edited the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the link to the electronic supplementary material.\u003c/p\u003e\n\u003cp\u003eSupplementary Material 1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAppleby BM, Redpath SM (1997) Variation in the male territorial hoot of the Tawny Owl Strix aluco in three English populations. Ibis 139:152\u0026ndash;158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadyaev AV, Martin TE (2000) Sexual dimorphism in relation to current selection in the house finch. 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Ardea 96:109\u0026ndash;113\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"evolutionary-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evec","sideBox":"Learn more about [Evolutionary Ecology](https://www.springer.com/journal/10682)","snPcode":"10682","submissionUrl":"https://submission.nature.com/new-submission/10682/3","title":"Evolutionary Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Megascops, Strigidae, Phylogeny, Acoustic Adaptation Hypothesis, Sexual selection, Eavesdropping avoidance","lastPublishedDoi":"10.21203/rs.3.rs-8834994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8834994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe evolution of birdsong has been predominantly studied in diurnal passerines, leaving nocturnal species underexplored. Owls provide an exceptional model: their cryptic plumage contrasts with conspicuous vocalizations that function in mate attraction and territorial defense, in low-visibility environments. We investigated the evolutionary drivers of acoustic diversity in \u003cem\u003eMegascops\u003c/em\u003e, the largest New World owl genus, using phylogenetic comparative methods on the most comprehensive vocal dataset assembled for the group. We compiled 5,652 recordings from all species and \u003cem\u003eGymnasio nudipes\u003c/em\u003e, measured 40 acoustic variables from primary songs, and analyzed their evolutionary patterns using an updated molecular phylogeny. Three traits showed significant phylogenetic signal (phrase duration, number of notes, and relative time at 5% of phrase energy), indicating evolutionary conservatism with clade-specific innovations overlaying a shared ancestral motif. Contrary to predictions from the Acoustic Adaptation Hypothesis, forest-dwelling species produced higher-pitched songs than those in open habitats. We propose that this deviation reflects eavesdropping avoidance: as small-bodied owls vulnerable to larger sympatric species, \u003cem\u003eMegascops\u003c/em\u003e may favor higher frequencies that attenuate rapidly, reducing detectability. Additionally, forest species showed intra-phrase acceleration, which combined with higher frequencies could enhance distance assessment (ranging), allowing receivers to gauge intruder proximity, a critical adaptation for territorial, armed birds. Elevation showed complex associations, with higher altitudes correlating with increased frequencies, likely mediated by habitat structure. Sexual vocal dimorphism was associated with subtle spectral patterns rather than overall frequency differences, suggesting that sexual selection acts on fine-scale modulation capacity. Unexpectedly, body mass showed no correlation with any acoustic trait after phylogenetic correction. Our findings reveal that owl vocal evolution reflects a complex interplay between phylogenetic constraint and ecological pressures distinct from diurnal songbirds, highlighting the need to expand bioacoustics research beyond traditional model systems to understand communication under nocturnal, predation-driven conditions.\u003c/p\u003e","manuscriptTitle":"Song evolution in American Screech-owls: the distinctive ecology of nocturnal top predators can lead to unexpected acoustic patterns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 06:49:42","doi":"10.21203/rs.3.rs-8834994/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T23:48:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:53:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T23:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207592838557454855193190280388553917320","date":"2026-05-11T19:12:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7901329242089934974881322461128261213","date":"2026-04-22T18:54:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T01:27:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T05:11:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T05:11:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Evolutionary Ecology","date":"2026-02-10T00:23:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"evolutionary-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evec","sideBox":"Learn more about [Evolutionary Ecology](https://www.springer.com/journal/10682)","snPcode":"10682","submissionUrl":"https://submission.nature.com/new-submission/10682/3","title":"Evolutionary Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e11f1c0d-47c8-4f85-b60d-56f878012c5b","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T23:48:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:53:40+00:00","index":32,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T23:13:00+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"207592838557454855193190280388553917320","date":"2026-05-11T19:12:00+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T23:53:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 06:49:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8834994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8834994","identity":"rs-8834994","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}