Every morning, people all over the world are greeted by the sound of songbirds singing to attract mates and defend their homes. Each type of songbird has its own unique song that it learns from other birds in the same species. Maintaining these signature songs is important, because songbirds that sing the wrong tune are unlikely to succeed in breeding. This type of vocal learning is rare in the animal kingdom and is similar to how humans learn to speak.

The time it takes for songbirds to learn their unique song varies between species: some species pick up their song within a few months after hatching, while others may continue learning for years or even the rest of their lives. It remains unclear, however, why certain species spend a longer time learning, and how changing this length of time affects the song they sing. One possibility is that differences in the amount of time spent learning evolved from female songbirds preferring mates with more impressive songs.

To address this possibility, Robinson et al. used previously published data to compare the song characteristics, learning periods, and mating strategies of 67 songbird species. This confirmed that longer learning windows allowed songbirds to develop a larger repertoire of songs with more unique sounds. Robinson et al. also found songbirds that learn elaborate songs over a short period of time quickly evolve either simpler songs or longer phases of learning. Meanwhile, songbirds that learn simple songs over a longer period tended to evolve more elaborate songs or shorten the amount of time they spent learning. Furthermore, species with longer learning were more likely to switch between having one mating partner and multiple partners over several generations.

These findings suggest that by choosing mates with elaborate songs and larger repertoires, female songbirds can end up favoring the evolution of longer learning. Studying the conditions that led to longer or shorter learning in songbirds could help scientists understand why some things, like human language, are easier to learn early in life.

Song is a learned behavior with a complex evolutionary history in the oscine songbirds. Birds’ songs have multiple functions, including species recognition, territory defense, and mate attraction (Catchpole and Slater, 2003). All species studied in this expansive clade have certain life stages during which they are more likely to learn and acquire songs, termed sensitive periods (Brenowitz and Beecher, 2005; Marler, 1990; Marler and Peters, 1987; Rauschecker and Marler, 1987). In some species, learning is restricted to a short sensitive period early in life, also called a ‘critical period’ (e.g. ~day 25–90 in zebra finches), after which no new song elements are acquired (Böhner, 1990; Immelman, 1969; Nottebohm, 1984). Other species appear to delay song crystallization until some time in adulthood (Dowsett-Lemaire, 1979; Kipper and Kiefer, 2010; Martens and Kessler, 2000); for example, chipping sparrows appear to have a second sensitive period immediately after their first migration, following which their song crystallizes (Liu and Kroodsma, 2006; Liu and Nottebohm, 2007). Still other species can continue to acquire new syllables or songs throughout their lives (Adret-Hausberger et al., 2010; Espmark and Lampe, 1993; Gil et al., 2001; Hausberger et al., 1991; Mountjoy and Lemon, 1995; Price and Yuan, 2011). Typically, this spectrum of variation in the timing of the sensitive period is simplified into a dichotomy of ‘open-ended learning’ and ‘closed-ended learning.’ While these temporal differences in the song-learning window have been studied for decades, it is unknown how they interact with the evolution of song itself. Previous hypotheses have suggested that seasonal factors, such as environmental variation and breeding season length, play a role in shaping adult song learning (Nottebohm et al., 1986; Smith et al., 1997; Tramontin et al., 2001). However, evidence from a small-scale comparative analysis suggests that a longer learning window in a species may be associated with larger average syllable repertoire sizes (Creanza et al., 2016).

Birdsong is composed of both culturally and genetically inherited features, any of which may be subject to evolutionary pressures. Two key modes of selection on song might act in conjunction: female choice can favor certain song characteristics, such as superior repertoire size, learning quality, or song performance (Beecher and Brenowitz, 2005; Gil and Gahr, 2002; Searcy and Marler, 1984; Searcy and Andersson, 1986), while the inherent metabolic cost of neuroplasticity should theoretically favor a shorter song-learning window and thus reduce the a for a bird to alter its song in adulthood (Garamszegi and Eens, 2004; Nottebohm et al., 1986; Tramontin and Brenowitz, 2000). Therefore, while learning in adulthood or elongated sensitive periods have not been shown to be directly under positive selection (sexual or natural) or to play an explicit role in female preferences, sexual selection acting on certain song features could indirectly favor longer or shorter song-learning windows. However, this theorized connection between sexual selection and adult learning hinges on establishing the evolutionary relationship between song as the target of sexual selection and the neurobehavioral phenotype of song learning, which has not yet been done.

Furthermore, sexual selection is hypothesized to be amplified in species with polygynous social mating systems or high rates of extra-pair paternity (EPP). A recent large-scale study found that polygyny drives faster, but non-directional, evolution of syllable repertoire size, and that syllable repertoire size is negatively correlated with the rate of EPP (Snyder and Creanza, 2019). Because of the evolutionary links between these mating strategies and song features, higher rates of EPP and polygyny could potentially have an effect on learning windows. We therefore investigate the evolutionary relationship between the critical learning period and mating strategies.

Here, we take a comparative, computational approach to the evolutionary history of open- and closed-ended song learning. We mined the literature for longitudinal field and laboratory observations to classify species as exhibiting ‘adult song stability’ or ‘adult song plasticity’. This classification is a quantifiable proxy for closed- and open-ended learning, as the true length of the song-learning window is difficult to assess directly in nature; ultimately, we obtained data for the classification of 67 species. For these species, we also compiled a database of seven species-level song characteristics that can represent either song complexity (syllable repertoire, syllables per song, and song repertoire) or song performance (song duration, inter-song interval, song rate, and song continuity). We then performed phylogenetically controlled analyses to evaluate the evolution of song and mating strategies alongside the relative plasticity of song over time. We find that adult song plasticity has evolved numerous times in bird species. Further, we find evidence of correlated evolution between adult song stability and plasticity and social mating system, with shifts in social mating system occurring more rapidly in lineages with adult song plasticity. In addition, we find a significant evolutionary pattern: species with plastic songs generally have larger repertoires than species with stable songs. Specifically, the evolution of larger syllable and song repertoires appears to drive an evolutionary transition toward open-ended learning.

In this study, we analyzed the length of the song-learning window using three classification schemes. First, we classified 67 species as having either adult song plasticity (i.e. those that change their syllable repertoire after their first breeding season ends) or adult song stability. Second, when possible, we reclassified the species into three categories: 1) those that stop changing their song before their first breeding season, 2) those that modify their songs during their first breeding season but not after, and 3) those that modify their songs after their first breeding season has ended. This three-state classification was possible for 59 out of the 67 species. Third, for the same 59 species, we used reported estimates of the ages at which song stabilizes to create a continuous measure of the song-learning window. There were exceedingly few studies that examined song changes after the second breeding season, so our continuous metric ranges from 0 to 2 years. We then examined the evolutionary patterns of adult song stability and plasticity, as well as their interactions with species-level song characteristics and mating behaviors.

How has adult song plasticity evolved across clades?

We were first interested in examining the rate of evolution of adult song stability versus adult song plasticity, as well as when and where evolutionary transitions in these traits occurred on a phylogenetic tree (Jetz et al., 2012) using ancestral state reconstruction. As with any reconstruction of evolutionary history, these simulations cannot exactly predict the ancestral states but aim to approximate them. Furthermore, we note that only a subset of oscine families were represented in our analysis, and most of the early branching lineages that would be required to assess the ancestral state for all oscine species were not included in our dataset. Ultimately, we could not make a conclusion about whether the last common ancestor for the species included in this study had adult song plasticity, but our results hint that there might have been several early transitions in this trait, leading to clades that predominantly have adult song stability or plasticity, coupled with a number of more recent transitions (see pie charts in Figure 1A for the predicted likelihood of each state at each node). We found that a model allowing the transition rate from song stability to plasticity to be different from the transition rate from plasticity to stability (all-rates-different model [ARD]) did not fit the data significantly better than a simpler model allowing for only one rate of transition back and forth between song stability and plasticity (equal rates model [ER]) (LogLikelihood_ER = −38.22, LogLikelihood_ARD = −38.21, p=0.87). At least 14 transitions were required to explain the current binary song-stability states of our subset of bird species. Explaining the distribution of song plasticity in our subset of species most parsimoniously requires at least nine transitions to adult song plasticity if the last common ancestor was song-stable and seven transitions to song stability if the common ancestor was song-plastic (Figure 1—figure supplement 1).

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Figure 1—source data 1

Predicted values for internal nodes for each trait.

There are three plots with associated tables for each song characteristic: The first plot and table set gives the values when the binary categorization of song stability (song-stable and song-plastic) is used. The second plot and table set gives the values when the ternary categorization of early song-stable, delayed song-stable, and song-plastic is used. The final plot and table set gives values when the continuous categorization is used. In the table, ‘Node’ corresponds to the numbered boxes on the tree nodes. ‘State Likelihood’ gives the probability that the common ancestor at a node was in a given leaning state when discrete categorizations are used. S = song stable, p=song plastic, E = early song-stable, D = delayed song-stable. ‘Length of Plasticity’ predicts the value along a continuous spectrum. ‘Trait’ is the predicted value of the song trait being examined in the associated tree for each internal node.

https://doi.org/10.7554/eLife.44454.012

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Which song traits differ between species with stable versus plastic songs?

We next tested whether song characteristics were affected by the length of the song-learning window on an evolutionary scale. Intuitively, it makes sense that a species that has a longer time-window to learn might be able to accumulate a larger repertoire. Indeed, this relationship is consistent with the pattern of song stability and repertoire size in several clades, such as the Phylloscopus species (Figure 2). However, many individual species do not follow this prediction: for example, Acrocephalus palustris appears to learn a large repertoire in a single year (Dowsett-Lemaire, 1979), and Philesturnus rufusater modifies its song for multiple years but maintains a small repertoire (Jenkins, 1978). Further, numerous species with adult song plasticity do not significantly increase their repertoire sizes over time (Eriksen et al., 2011; Galeotti et al., 2001; Garamszegi et al., 2005; Nicholson et al., 2007). Thus, an evolutionary link between adult song plasticity and larger repertoire sizes cannot be assumed. Using a phylogenetically controlled ANOVA (Garland et al., 1993; Revell, 2012), we found that species with adult song plasticity did possess significantly larger syllable repertoires than species with adult song stability (Figures 1A and 3A, Table 1). This concurs with a previous analysis using a smaller dataset (Creanza et al., 2016). Similarly, we found that song-plastic species had significantly larger song repertoires than song-stable species (Figure 3B, Figure 1—figure supplement 2, and Table 1).

Table 1

Song trait	Song-stable	Song-plastic	F-Value	Corrected α	p-value
Syllable repertoire	1.8807	3.946	41.5064	0.0071	<0.001*
Song repertoire	1.1055	3.8688	33.8334	0.0083	<0.001*
Syllables/song	1.2556	2.2962	9.2658	0.01	0.094
Duration	0.7736	1.2927	2.0783	0.0125	0.42
Continuity	-1.3453	-1.0286	2.1537	0.0167	0.474
Interval	1.6075	1.218	1.3879	0.025	0.567
Song rate	1.8969	2.0971	0.6079	0.05	0.713

1. *Denotes traits with significantly different groups.

Figure 2

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Figure 3

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There were no significant differences between song-plastic and song-stable species for the other song characteristics that we tested: syllables per song, inter-song interval, song duration, song rate (calculated as 60/(interval + duration)), or song continuity (calculated as duration/(duration + interval)) (Table 1, Figure 1—figure supplements 3–7). When we used the classification scheme with three states, we could only test for differences in syllable repertoire, song repertoire, and syllables per song between groups, as there were very few early song-stable species for which we had data on the other song traits. We found no significant differences between early song-stable and delayed song-stable species for any tested traits, but both of these groups had significantly smaller syllable and song repertoires compared to song-plastic species (Figures 1B and 3C, Tables 2 and 3). When performing a phylogenetic generalized least squares (PGLS) analysis using continuous estimates of the duration of song plasticity, we found similar results; both syllable repertoire and song repertoire were correlated with duration of song plasticity, such that repertoire size increased with the song-plasticity duration (Figure 3D, Table 4).

Table 2

Song trait	Early	Delayed	Plastic	F-Value	Corrected α	p-value
Syllable repertoire	1.6436	2.0062	3.946	17.1099	0.0071	0.003*
Song repertoire	0.6788	1.4819	3.8688	12.88	0.0083	0.011*
Syllables/song	1.2852	1.2467	2.2962	3.6877	0.01	0.252

1. *Denotes traits with significantly different groups.

Table 3

Song trait	State 1	State 2	T-Value	p-value
Syllable repertoire	Plastic	Delayed	4.8995	0.012*
Syllable repertoire	Early	Plastic	4.6091	0.003*
Syllable repertoire	Early	Delayed	0.6872	0.659
Song repertoire	Plastic	Delayed	4.0268	0.044*
Song repertoire	Early	Plastic	4.3074	0.015*
Song repertoire	Early	Delayed	1.0444	0.55

1. *Denotes traits with significantly different groups.

Table 4

Song trait	Slope	Std error	λ	T-Value	Corrected α	p-value
Syllable repertoire	0.9067	0.2449	0.8913	3.7021	0.0071	<0.001*
Song repertoire	1.1013	0.3123	0.8316	3.5263	0.0083	<0.001*
Syllables/song	0.3701	0.2224	0.4699	1.6642	0.01	0.1029
Interval	0.4221	0.2646	0.8823	1.5953	0.0125	0.1215
Continuity	-0.2135	0.1439	0.8832	-1.4838	0.0167	0.1486
Duration	0.3702	0.2569	1.0163	1.441	0.025	0.1578
Song rate	-0.2113	0.25	0.7307	-0.8453	0.05	0.4048

1. *Denotes significant slopes.

Do song characteristics evolve at different rates in song-stable or song-plastic species?

Our result that species with adult song plasticity had significantly larger syllable and song repertoires raised the question of whether song stability versus plasticity also affected the rate of evolution for any of the song characteristics. To examine this possibility, we used the Brownie algorithm (O'Meara et al., 2006), which tests whether a model with two rates of evolution for each song characteristic—one rate for ancestral periods of song stability and another rate for song plasticity—fits the data significantly better than a model that allows for only a single rate of evolution of each song characteristic regardless of the ancestral states of song stability. Each calculation of the two-rate model is based on one stochastic projection of the ancestral traits across the phylogenetic tree, so we generated 1300 different stochastic simulation maps to use with Brownie. We plotted the distribution of potential rates (Figures 4–5) and compared the average log likelihood of the two-rate models to the log likelihood of the one-rate model (Table 5).

Table 5

Song trait	One rate	Two rates	p-value
Syllables/song	-110.6482	-100.7673	<0.001*
Song rate	-43.4397	-38.4938	0.002*
Interval	-45.2842	-40.5004	0.002*
Duration	-71.2042	-66.3122	0.002*
Continuity	-25.6471	-24.7285	0.175
Syllable repertoire	-120.2983	-120.0695	0.499
Song repertoire	-113.5829	-113.3706	0.515

1. *Denotes traits where the more complex model fit the data significantly better than the simpler model.

Figure 4

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Figure 5

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We found that allowing for two different rates of song trait evolution depending on song stability or plasticity did not lead to a significantly better fit model than using one Brownian-motion rate for either syllable repertoire size or song repertoire size, even though syllable repertoires and song repertoires were both significantly larger in species with adult song plasticity (Figure 4A and B and Table 5). In contrast, the two-rate model led to a significantly better fit for syllables per song, song rate, inter-song interval, and song duration (Figures 4C and 5 and Table 5), indicating that evolution of these song characteristics was faster in song-plastic lineages (Figures 4C and 5, red traces).

We repeated this analysis with the three-state categorization for syllable repertoire, song repertoire, and syllables per song; for other song characteristics, we did not have enough species in the early song stability group. We found that the three-rate model was significantly better than the one-rate model for syllables per song and song repertoire, but not for syllable repertoire (Figure 4D–F and Table 6). However, the three-rate model was only significantly better than the two-rate model for song repertoire (Table 7). Thus, the two-rate model sufficiently approximated the evolution of syllables per song. We noticed that for both song repertoire and syllable repertoire, the rate of evolution in delayed song-stable lineages (purple traces in Figure 4D,E) was very similar to the rate in song-plastic lineages (corresponding red traces). We tested one more set of models where we combined delayed song-stable species with song-plastic species to create a ‘longer learning’ group, while early song-stable species were assigned to a ‘shorter learning’ group. For this comparison of shorter versus longer learning, the two-rate model was significantly better than the one-rate model for song repertoire and trending in that direction for syllable repertoire (Figure 4G,H and Table 8). The three-rate model was not significantly better than the longer/shorter-learning two-rate model for either syllable or song repertoire (Table 9). Taken together with our phylANOVA results, this pattern suggests that species with early song stability evolve their song repertoires and potentially their syllable repertoires at a slower rate than delayed song-stable and song-plastic species; however, only song-plastic species directionally evolve towards larger song and syllable repertoires.

Table 6

Song trait	One rate	Three rates	p-value
Syllables/song	-97.8349	-86.3206	<0.001*
Song repertoire	-100.812	-97.7647	0.014*
Syllable repertoire	-107.3206	-105.5895	0.063

1. *Denotes traits where the more complex model fit the data significantly better than the simpler model.

Table 7

Song trait	Two rates	Three rates	p-value
Song repertoire	-100.691	-97.7148	0.015*
Syllable repertoire	-107.1332	-105.5532	0.075
Syllables/song	-86.3125	-86.3447	1

1. *Denotes traits where the more complex model fit the data significantly better than the simpler model.

Table 8

Song trait	One rate	Two rates	p-value
Song repertoire	-100.812	-97.9918	0.018*
Syllable repertoire	-107.3206	-105.8488	0.086

1. *Denotes traits where the more complex model fit the data significantly better than the simpler model.

Table 9

Song trait	Two rates	Three rates	p-value
Syllable repertoire	-105.8156	-105.5532	0.469
Song repertoire	-97.9372	-97.7148	0.505

1. *Denotes traits where the more complex model fit the data significantly better than the simpler model.

Is song stability or plasticity influenced by the evolution of song traits and mating strategies, or vice versa?

While the Brownie algorithm tested whether adult song plasticity affected the rate of evolution for song characteristics, it did not address whether the opposite might be true. We used BayesTraits (Pagel, 1994; Pagel and Meade, 2006) to test whether the rate and order of evolutionary transitions in one trait is dependent on the state of another trait. Because the song features were continuous variables, we binarized them by setting a series of threshold values to delineate ‘low’ and ‘high’ categories, using each observed song feature value as a threshold in turn. We then tested whether there was correlated evolution between the binary classifications of adult song plasticity versus stability and each of the seven song characteristics.

In the lowest third of syllable repertoire thresholds, adult song plasticity with small syllable repertoires was an evolutionarily unstable state, with rapid transitions primarily toward a song-stable state and secondarily toward larger repertoires (82% of runs significant in this range, Figure 6). In the middle third of syllable repertoire thresholds, song stability with smaller syllable repertoires is an evolutionarily stable attractor state, with high rates of transition observed from large to small syllable repertoires in song-stable species and from plasticity to stability with a small syllable repertoire. These rate differences are highly significant (100% of runs significant in this range). In the highest third of syllable repertoire thresholds, adult song stability with a large syllable repertoire is an evolutionarily unstable state, transitioning primarily toward adult song plasticity (86% of runs significant in this range, Figure 6). We found similar trends when using two, four, and five bins for the song characteristic threshold values, with subtle differences. When using four or five bins, we still observe that song stability with larger syllable repertoires is an unstable combination. However, for the highest bin of threshold values, the transition rates are faster when changing to song plasticity, whereas for the second-highest bin, we observe faster transition rates toward repertoire size increases (Figure 6—source data 1). To rule out the possibility that syllable repertoire size evolution is faster in species with larger repertoire sizes regardless of learning program, we tested the rates of evolution of syllable repertoire size across monophyletic species pairs in our dataset. We found that lineages with larger syllable repertoire sizes do not systematically undergo faster or slower syllable repertoire size evolution (Figure 1—figure supplement 8).

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Figure 6—source data 1

Mean transition rates from BayesTraits analysis between song stability and song features averaged over 2, 4, and 5 bins.

On page 1, the top two panels depict the mean rates from all runs across the values of syllable repertoire in the lower and upper half of thresholds, respectively. The bottom panel shows the p-values from the 100 runs per threshold, plotted against threshold. Colored bars denote low and high threshold segments. The blue line denotes p=0.05. On page 2, the top four panels depict the mean rates from all runs across the values of syllable repertoire in the first (top left), second (top right), third (middle left), and fourth (middle right) quartiles of the thresholds. The bottom panel follows the pattern of the bottom panel from page 1. On page 3, the five panels (top left and right, middle left and right, and bottom left) depict the mean rates from all runs across the values of syllable repertoire in the five quintiles of the of the thresholds, respectively. The bottom panel follows the pattern of the bottom panels from pages 1 and 2. The three-page pattern repeats for each song feature.

https://doi.org/10.7554/eLife.44454.027

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Figure 6—source data 2

BayesTraits analysis of syllable repertoire and learning window, jackknifed across families.

Labeling the same as in Figure 6 and Figure 6—figure supplement 1.

https://doi.org/10.7554/eLife.44454.034

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Figure 6—source data 3

BayesTraits analysis of syllables per song and learning window, jackknifed across families.

Labeling the same as in Figure 6 and Figure 6—figure supplement 1.

https://doi.org/10.7554/eLife.44454.035

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At low song repertoire threshold values, song plasticity with a small repertoire is an evolutionarily unstable state; there is rapid transition away from this combination, predominantly trending toward song stability but also transitioning secondarily to a larger song repertoire, with very high significance (100% of runs significant in this range). At moderate song repertoire thresholds, the highest rate is observed for species with small repertoires transitioning from song plasticity to song stability, also with very high significance (100% of runs significant in this range). At high song repertoire thresholds, the primary shift is from song stability to song plasticity in species with large song repertoires (89% of runs significant in this range) (Figure 7). When analyzing the results using five bins, transitions in the upper range of song repertoire values becomes more nuanced; in the highest bin, song stability with a larger song repertoire is very unstable, but is relatively stable in the second-highest bin. In the lower four bins, the dominant transition is from plastic to stable song with smaller song repertoires (Figure 6—source data 1). The results for syllables per song show some general trends that are complicated by the strong effect of the mimid species (see results of jackknife analysis below). We did not find evidence for correlated evolution between adult song stability or plasticity and any of the other song characteristics (Figure 6—figure supplements 1–5).

Figure 7

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Figure 7—source data 1

BayesTraits analysis of song repertoire and learning window, jackknifed across families.

Labeling the same as in Figure 6 and Figure 6—figure supplement 2.

https://doi.org/10.7554/eLife.44454.037

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In addition, it has been proposed that polygyny and extra-pair paternity (EPP) may increase sexual selection pressures on sexually selected traits, including song (Emlen and Oring, 1977; Payne, 1984), and increased selection pressure due to polygyny was theorized to accelerate the evolution of song learning in a mathematical model (Aoki, 1989). We tested for correlated evolution between adult song plasticity versus stability and both social mating system (polygyny vs. monogamy) and extra-pair paternity (low vs. high EPP), with the caveat that many species in our dataset lacked mating behavior classifications (57 species with social mating system data, 41 with EPP data). We did not find evidence for correlated evolution between song stability and EPP. There was, however, evidence for correlated evolution between polygynous/monogamous mating systems and song plasticity (100% of runs significant), with elevated rates of transition between polygyny and monogamy in the song-plastic state (Figure 6—figure supplement 6).

Previously, it was unknown whether the song-learning window evolved in concert with song features associated with sexual selection, as predicted by a computational model of song learning (Creanza et al., 2016). This is a critical missing piece of the puzzle of song learning evolution, as previous evidence has suggested that sexual selection only acts upon the features of song and not the length of the song-learning window or maintenance of the song-learning pathways in the brain. Here, we performed phylogenetically controlled analyses to assess the interactions between the length of the song-learning window—using adult song stability versus plasticity as a proxy—and the evolution of song characteristics. Interestingly, we noted that several evolutionary events relatively early in passerine evolution accounted for much of the diversity in the song-plasticity period in our sample of species. We show that a bird’s ability to modify its song as an adult affects the characteristics of its species' song: adult song plasticity corresponds to larger syllable and song repertoires. Further, our results suggest that sexual selection for large repertoires could indirectly favor individuals with longer learning windows, driving the evolution of increased song plasticity.

We found two key trends in the trait correlation (phylANOVA) and evolutionary rate (Brownie) analyses. First, song plasticity affected the direction of evolution in traits that can be considered metrics of song complexity (syllable and song repertoire size, Figures 1 and 3, Table 1), leading to larger repertoires in species with adult song plasticity. Further, species with early song stability evolved their repertoires at a slower rate than species with longer learning (delayed song stability and song plasticity, Table 5), but song-plastic species did not evolve their repertoires at a faster rate than species with early or delayed song stability combined (Tables 6–9). Thus, while repertoires only evolve directionally in song-plastic species (Tables 2–3), our results suggest that extended learning through the first breeding season allows for faster, but nondirectional, evolution of repertoire size. A possible explanation is that delayed song learning allows individuals to modify their songs after migration and thus adapt their song to their new surroundings once they establish a territory, without necessarily corresponding with sexual selection for larger repertoires. This ability for an individual to adapt to a new local song might be beneficial, particularly when species have local dialect structure; however, this would not lead to directional evolution for any particular song feature, consistent with our findings. Second, song plasticity increased the rate of evolution primarily in traits that can be considered metrics of song performance (song duration, intersong interval, and song rate Figure 5, Table 2). While these performance-related song traits evolved faster in song-plastic lineages, this rapid evolution did not lead to significant differences in those song characteristics compared to song-stable lineages. A possible explanation may be that increases in repertoire size necessitate changes to song structure, but multiple structural aspects of song can be altered to accommodate these changes. Thus, there is no overall pattern of directional evolution in these other song characteristics. Alternatively, bird species may be required to adapt to increasing repertoire sizes while maintaining species-specific constraints imposed by innate aspects of song structure or female preferences for different performance characteristics. In the latter case, if information about innate characteristics and female preferences are known, it may be possible to predict how song traits will change in response to increasing repertoire sizes and greater adult song plasticity.

With our analyses of correlated evolution, we aimed to detect whether the state of the repertoire size or adult song stability versus plasticity consistently changes first in evolutionary history, facilitating a change in the other trait. Overall, our results suggest that there is not a consistent order of evolutionary transitions (Figures 6–7). For example, song stability with very large syllable or song repertoires, and song plasticity with very small syllable or song repertoires both formed evolutionarily unstable states, with high evolutionary rates of transition in both repertoire size and song-learning window. However, we do note that the fastest rates of transition in our analyses were those switching between song stability and song plasticity to leave those unstable states. This trend suggests that the magnitude of a species’ repertoire may be more likely to drive the evolution of learning window than vice versa (Figures 6–7). This is consistent with the idea that selection acting upon song features could indirectly place selective pressures on the learning window. We propose several hypotheses that could explain these evolutionary dynamics: 1) it may be disproportionately costly to maintain song plasticity when syllable or song repertoire sizes are very small, perhaps because the benefit of extra time to learn does not outweigh the metabolic cost of maintaining plasticity, or 2) species with small syllable or song repertoires may have highly stereotyped songs which are selected for based on accuracy of learning and consistency of song production, favoring males that only learn from their fathers or early-life neighbors. Alternatively, in species where females prefer larger repertoires, 3) it may simply require more time to learn a large song or syllable repertoire than is available with a short learning window, or 4) learning large repertoires may require too much energy devoted to song practice during the crucial period of development before and during fledging, favoring birds that can learn for longer periods. Further research into the physiological or reproductive costs of song plasticity is needed.

Beginning with Darwin (1872), numerous researchers have proposed that polygynous mating systems could lead to amplified sexual selection (Emlen and Oring, 1977; Payne, 1984). The elevated rates of transition that we observed between social mating systems in song-plastic lineages suggest an interesting hypothesis for further investigation: perhaps having a plastic song-learning program facilitates evolutionary transitions in mating systems.

Our results provide key evidence that sexual selection upon song characteristics might indirectly act upon the song-learning window. We do not fully understand the mechanisms underlying the maintenance or reopening of the song learning window in adulthood, but genetic, environmental, hormonal, and social factors are likely contributors (Eales, 1987; Eales, 1985; Kroodsma and Pickert, 1980; Morrison and Nottebohm, 1993; Nottebohm, 1969). For example, when zebra finches were reared in isolation, their sensitive periods were lengthened. These isolated birds maintained both gene expression profiles associated with song learning in the song system and high levels of neuronal addition to the HVC (a key region in the song system of the songbird brain) for longer than birds reared with an adult male tutor (Kelly et al., 2018; Wilbrecht et al., 2006), linking the neural underpinnings of song learning to the length of the song-learning window (Brenowitz and Beecher, 2005; Nordeen and Nordeen, 1990; Nordeen, 1997; Nottebohm, 1992). Furthermore, a positive association between HVC volume and song repertoire size has been demonstrated both intraspecifically (in song sparrows; Pfaff et al., 2007) and interspecifically (Devoogd et al., 1993). In light of our findings that adult song plasticity correlates with an increase in song repertoire size, there is a logical prediction that extended song learning may be associated with increased HVC volume across species. This is an important avenue for future research.

Although our dataset includes species from 24 different songbird families, many families are not represented due to a lack of data about song stability. It will be important to expand this dataset in future studies. It would also be interesting to explore the evolutionary interactions between adult song plasticity and mimicry of heterospecific sounds, which has been observed in Mimidae and numerous other clades across the songbird lineage (Goller and Shizuka, 2018). With our current dataset, we could not adequately explore the effects of mimicry on the evolution of song learning outside of the mimids, but the repeated evolution of mimicry makes it a particularly interesting topic for follow-up studies on the length of the song-learning window. In addition, different song metrics that are tailored to mimicry would be important in studying the evolution of vocal mimics and the dynamics of their unique vocal patterns. Furthermore, there is increasing interest in the importance of female song in species, which is more common than previously thought (Langmore, 1998; Odom et al., 2014). Our dataset includes numerous species wherein females are known to sing at least occasionally (Langmore, 1998), but the length of the song-learning window in females has not been assessed in any of these species. There is, however, some evidence that female birds can modify their song preferences in adulthood (Nagle and Kreutzer, 1997). Thus, it remains an open question whether song plasticity affects the evolution of female song in the same way it affects male song, and whether species with adult song plasticity in males also have adult song plasticity in females.

Our findings shed new light on the broader subject of song evolution, specifically the evolution of the process of song learning. We hypothesized that sexual selection on certain aspects of song could in turn alter the selection pressures on the length of the song-learning window. Here, we performed phylogenetically controlled analyses across 67 songbird species to assess the evolutionary interactions between song characteristics and song plasticity in adulthood. With these analyses, we show the first evidence for this evolutionary relationship. Adult song stability versus plasticity may be evolutionarily dependent upon the properties of the song itself: large syllable and song repertoires appear to drive the evolution of adult song plasticity and thus open-ended song learning. This provides context for the remarkable interspecific variation in song-learning windows across the songbird lineage and suggests an evolutionary mechanism by which sexual selection might have influenced the evolution of songbird brains.

All data are made available as supplementary information provided with this manuscript, and are also provided at https://github.com/CreanzaLab/SongLearningEvolution (copy archived at https://github.com/elifesciences-publications/SongLearningEvolution).

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Author details

1. Cristina M Robinson

   Department of Biological Sciences, Vanderbilt University, Nashville, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Kate T Snyder

   Competing interests

   No competing interests declared

2. Kate T Snyder

   Department of Biological Sciences, Vanderbilt University, Nashville, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Cristina M Robinson

   Competing interests

   No competing interests declared

3. Nicole Creanza

   Department of Biological Sciences, Vanderbilt University, Nashville, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   nicole.creanza@vanderbilt.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8821-7383

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