The Evolution of Geographic Variation in Birdsong

ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 37

The Evolution of Geographic Variation in Birdsong Jeffrey Podos* and Paige S. Warren{ *department of biology, graduate program in organismic and evolutionary biology, university of massachusetts amherst, massachusetts 01003 { department of natural resources conservation, graduate program in organismic and evolutionary biology, university of massachusetts amherst, massachusetts 01003

I. INTRODUCTION Evolutionary biologists have a long‐standing interest in how organisms vary geographically. This interest is motivated in part by recognition of a relationship between geographic variation and the process of speciation. Traits that vary over a given species’ range may serve as neutral, noncontributing indicators of the early stages of divergence, for example as different populations adapt to distinct environments and undergo corresponding genotypic and phenotypic divergence (Schluter, 2000). In other cases, traits that vary geographically might contribute to the speciation process. Primary examples of such traits are mating ornaments and displays which, in many animals, are centrally involved in mate recognition and mate selection (Andersson, 1994; Boughman, 2001; Foster, 1999; Panhuis et al., 2001; Wells and Henry, 1998; West‐Eberhard, 1983). Geographic divergence of mating signals can, under particular circumstances, facilitate assortative mating, reproductive isolation, and thus continued divergence among populations (Irwin et al., 2001; Lachlan and Servedio, 2004; Liou and Price, 1994; Payne et al., 2000; Ptacek, 2000; Slabbekoorn and Smith, 2002a). Divergence of mating signals and mate recognition systems is increasingly recognized as an important factor in speciation (Ryan, 1986). Studies of vocal signals in birds offer potentially useful opportunities for empirical tests of the relationships among geographic signal divergence, reproductive isolation, and speciation (reviewed by Edwards et al., 2005). Many bird vocalizations express significant geographic variation, a phenomenon that can be attributed largely to the tendency for many birds to 403 0065-3454/07 $35.00 DOI: 10.1016/S0065-3454(07)37009-5

Copyright 2007, Elsevier Inc. All rights reserved.

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learn to vocalize through imitation. Imitative vocal learning enables the ready generation and rapid transmission of novel patterns of vocal structure (Slabbekoorn and Smith, 2002a; Slater, 1989). Indeed, it has been argued that consequent plasticity of the vocal phenotype has been instrumental in generating the high species diversity that characterizes some avian taxa (Fitzpatrick, 1988; Vermeij, 1988; cf. Baptista and Trail, 1992; see also Irwin and Price, 1999; ten Cate, 2000). A Science Citation Index query (Fig. 1A) attests to rapidly expanding activity, over the past 15 years, in the study of the causes and consequences of geographic variation in birdsong. Prospects for this field of inquiry, however, did not look very promising just a few decades ago. Mounting unease centered especially on questions about the evolution of ‘‘dialects,’’ a particular form of vocal geographic variation. This unease was well illustrated in Baker and Cunningham’s influential review of the phenomenon of dialects, a main goal of which was to articulate a ‘‘synthetic theory’’ on dialect origins and maintenance (Baker and Cunningham, 1985). Responses to this review, provided by a panel of

Number of literature citations

90

60

30

0 1965– 1970– 1975– 1980– 1985– 1990– 1995– 2000– 1969 1974 1979 1984 1989 1994 1999 2004 5-year span FIG. 1. Number of primary literature citations, binned over 5‐year spans, for two searches on Thompson’s ISI Web of Science. (A) Filled bars, search query as follows: topic ! [bird* AND song AND (geographic* OR dialect*)]. At the time of the search, Web of Science queries did not include abstract text for literature pre‐1990; thus only data post‐1990 are presented. A marked increase in citations is evident, pointing to increased activity in the field. (B) Open bars, number of publications that Marler and Tamura (1964), a classic paper on birdsong dialects. The post‐1990 trend mirrors that of the broader query (closed bars). Additionally, this search reveals a temporary reduction in activity in the field in the early 1990s, corresponding to the timing of the Baker and Cunningham (1985) exchange.

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peer experts, were forceful and included laments about difficulties in quantifying patterns of vocal geographic variation, in comparing data across taxa, in extrapolating laboratory data to field situations, and, most critically for present purposes, in identifying evolutionary factors that facilitate dialect formation and maintenance (Baptista, 1985; Brenowitz, 1985; Kroodsma, 1985a; Lemon, 1985; McGregor, 1985). With regard to this latter issue, Baker and Cunningham argued that dialects are necessarily maintained through adaptive processes, and thus, in order to understand their evolution we must examine their present function. A broad alternative hypothesis, that dialects emerge as epiphenomena of other evolutionary processes (Andrew, 1962), was summarily dismissed as being ‘‘pointless’’ (Baker and Cunningham, 1985, p. 86). The peer expert panel generally found Baker and Cunningham to be overly supportive of local adaptation hypotheses of dialect evolution and overly dismissive of alternative hypotheses (Baptista, 1985; McGregor, 1985; Waser, 1985). Given the wide range of stated unresolved issues, along with limited evidence at the time that could support any particular hypothesis of dialect evolution, it is perhaps not surprising that publication of the Baker and Cunningham exchange was followed by a period of dampened enthusiasm for the field (Fig. 1B). Our goal in this chapter is to assess the present state of the field, from both empirical and conceptual perspectives. Prior reviews on the topic of geographic variation in bird vocalizations have been numerous (Baker and Cunningham, 1985; Krebs and Kroodsma, 1980; Mundinger, 1982; Slabbekoorn and Smith, 2002a), and readers may question the value of yet another contribution. Yet new information continues to accrue. Moreover, recent general advances in the study of birdsong—a field that has remained active in realms ranging from mechanisms to ecology and evolution—have renewed the way that we can study geographic variation, in at least two ways. First, we have gained numerous insights into the range of possible functions of song learning, particularly in the arenas of social and sexual selection (Beecher and Brenowitz, 2005; Kroodsma and Byers, 1991; Nowicki et al., 2002). Our increasingly detailed understanding of the myriad functions of song learning suggests that patterns of vocal geographic variation may emerge as secondary by‐products of selection on other functions, rather than through direct selection for geographic patterns themselves (Slater, 1989). Second, advances in our understanding of mechanisms of vocal production suggest additional ‘‘non‐functional’’ scenarios by which geographic vocal variation may emerge. Whereas earlier models of song evolution focused on vocal imitation and cultural evolution, geographic variation in song may also emerge through evolution of the morphological and physiological underpinnings of song production.

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We begin with a brief overview of the study of geographic variation in birdsong, focusing in particular on song dialects and hypothesis that have traditionally been put forward to explain their evolution (Section II). To evaluate the status of these hypotheses, we next conduct a comparative survey based on data gleaned from published literature (Section III). Our focus on dialects in these two sections is not motivated by an opinion that dialects are inherently more interesting than other patterns of geographic variation, or by an opinion that dialects are a unique phenomenon requiring special explanation. Rather it is simply because dialects have been the focus of the majority of published studies in this area. We then discuss how recent advances in the study of birdsong have enriched our ability to address questions about vocal geographic variation, arguing in particular that modern studies on mechanisms of song learning and production support ‘‘by‐ product’’ models of vocal geographic evolution (Section IV). As we argue below, the by‐product hypothesis of vocal geographic evolution can be regarded as a broader version of the ‘‘epiphenomenon’’ hypothesis of vocal dialect evolution. In Section V, we summarize the factors that may contribute together to the evolution of geographic variation in bird vocalizations.

II. EVOLUTION OF GEOGRAPHIC VARIATION IN SONG: LITERATURE OVERVIEW A. THE IMPORTANCE OF LEARNING MECHANISMS AND DISPERSAL PATTERNS Perhaps the most appropriate place to begin an overview of the literature on geographic variation in birdsong is with Marler and Tamura’s classic work on white‐crowned sparrows, Zonotrichia leucophrys nuttalli (Marler and Tamura, 1962, 1964). The phenomenon of geographic variation in birdsong had been observed and reported on previously, but only a handful of published studies (Borror, 1961) had made use of sound spectrograms, which provide an invaluable visual aid for assessing patterns of vocal geographic variation. Marler and Tamura described, for Z. leucophrys nuttalli of Northern California, the now‐classic ‘‘dialect’’ pattern of geographic variation, in which songs within particular populations ‘‘all share certain salient characteristics . . . which differ in certain consistent respects from the patterns found in neighboring populations’’ (Marler and Tamura, 1964, pp. 1483–1484). A Berkeley population of birds, for instance, was found to sing trills with a specific structure (fewer notes, of descending frequencies) distinct from trills sung by other nearby populations (Marler and Tamura, 1962). As suggested by Marler and Tamura and as generally corroborated by later studies (reviewed by Kroodsma et al., 1985), two main

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factors appear to facilitate dialect evolution in Z. leucophrys nuttalli. The first is that songs are culturally transmitted across generations, via vocal learning. A central role for vocal learning in song development in these birds was first demonstrated in a series of laboratory studies in which young males were exposed to various training regimes (Marler and Tamura, 1964). Training models presented over loudspeakers, during the sensitive phase of song acquisition, were shown to be copied precisely by these birds, even when training models had been recorded from nonnatal localities (Marler and Tamura, 1964). By contrast, in the absence of training models, birds were found to develop songs with atypical, degraded acoustic structure. It was thus argued that song learning enables the ready transmission of song patterns across generations, not just from fathers to sons but also potentially to other young males in a population. Song learning facilitates dialect formation by providing a mechanism for generating vocal novelties, which may emerge through copying ‘‘errors’’ (Marler and Tamura, 1964; see also Baptista, 1977; Lemon, 1975; Marler and Peters, 1987, 1988; Slater, 1986, 1989). The second main factor that appears to contribute to song dialect evolution in Z. leucophrys nuttalli is that of limited or biased dispersal. Toward this end, Marler and Tamura (1962) suggested two possibilities: that dialects may emerge if male birds remain on the grounds where they learned to sing, or that dialects may emerge if males do disperse but then settle preferentially in locations where they hear songs similar to their own. Either explanation would be consistent with the observation of vocal ‘‘neighborhoods.’’ Banding/recapture studies (Baker and Mewaldt, 1978; Petrinovich et al., 1981) confirmed that dispersal distances in this subspecies are indeed moderate, most commonly within a few hundred meters and rarely exceeding 1 km. By contrast, data in support of biased dispersal in this subspecies have been equivocal, and their interpretation controversial (Baker and Mewaldt, 1981; Baker et al., 1985; Hafner and Peterson, 1985; Petrinovich et al., 1981). The Z. leucophrys nuttalli system turns out to be comparatively, although by no means absolutely, straightforward; other systems examined to date feature their own complications and deviations from the nuttalli scenario. This point is well illustrated by variation found even within this one species. Two additional white‐crowned sparrow subspecies, Z. leucophrys pugetensis and Z. leucophrys oriantha, also exhibit song dialects. In the former subspecies, dialects occur over larger neighborhood areas (Baptista, 1977; Chilton and Lein, 1996a; DeWolfe and Baptista, 1995; Heinemann, 1981; Nelson and Soha, 2004), and in the latter subspecies, songs vary systematically among subalpine meadow populations (Chilton et al., 1995; Harbison et al., 1999; Orejuela and Morton, 1975). Birds of both subspecies turn out

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to be less philopatric than their nuttalli counterparts, especially Z. leucophrys oriantha, which is fully migratory. Recent field and laboratory studies by Nelson and colleagues indicate that birds of both subspecies memorize multiple models at their natal grounds, and then, postdispersal, ‘‘select’’ among memorized models to best match song types present at their breeding grounds (‘‘overproduction’’ and ‘‘selective attrition’’: Nelson, 2000; Nelson et al., 1995, 1996a). An alternative model of song learning, postdispersal acquisition and memorization of song models, as posited by Heinemann (1981), appears not to apply in these birds. A fourth subspecies, Z. leucophrys gambelli, a long‐distance migrant that breeds in the sub‐Arctic, shows no evidence of dialects (Austen and Handford, 1991; DeWolfe et al., 1974; Nelson, 1998). Nelson (1999) argues that the postdispersal, prebreeding time frame in this subspecies is too brief to allow the kinds of extended interactions that are required for song matching via selective attrition. Studies of white‐crowned sparrows thus demonstrate that learning strategies, dispersal patterns, and resulting geographic song patterns can be highly variable even within a single species. This variation of course represents just a tiny sample of the diversity present in the songbirds as a group (Krebs and Kroodsma, 1980; Kroodsma, 1996; Slater, 1989). Indeed, a primary message of prior reviews of geographic song variation has been of caution in extrapolating results across species (Kroodsma, 1996). One widespread phenomenon that does not apply in white‐crowned sparrows, for instance, is postdispersal acquisition and learning of song models, as has been shown, for example, in the brood‐parasitic brown‐headed and bronzed cowbirds, Molothrus ater and M. aeneus (Rothstein and Fleischer, 1987; Warren, 2002). The white‐crowned sparrow system also does not address the potential influence of improvisation on geographic song patterns (Kroodsma and Verner, 1978; Kroodsma et al., 1997; Marler et al., 1972). Moreover, unlike many other species, adult white‐crowned sparrows tend to produce only a single song type. Patterns of geographic variation, and the ecological and learning‐based causes of these patterns, tend to be more challenging although sometimes still possible to document in species with song repertoires (Searcy et al., 2002; Slater et al., 1984; Williams and Slater, 1990). Explaining the formation of dialects might be relatively straightforward if patterns of geographic variation in song evolved solely as an incidental by‐product of particular learning mechanisms and patterns of dispersal (Fig. 2A). However, since the earliest studies of avian vocal geographic variation, authors have also spent considerable energy contemplating the potential fitness benefits of particular geographic song patterns. Implicit in this exercise is the hypothesis that selection for particular geographic patterns

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FIG. 2. Traditional framework to explain the evolution of vocal geographic variation in songbirds. (A) Solid lines: Geographic variation in song emerges, in a proximate sense, as a by‐ product of specific learning mechanisms and dispersal patterns. The evolution of learning mechanisms that foster early, accurate imitation, combined with the evolution of limited dispersal distances, for example, will result in the evolution of sharp dialects. (B) Dashed lines: Selection for particular patterns of geographic variation may alter the evolution of learning mechanisms and patterns of dispersal, in a feedback loop. For instance, selection favoring strong assortative mating (local adaptation hypothesis) may conceivably favor the evolution of early song imitation and limited dispersal, and thus the evolution of sharp dialects.

alters, in a feedback loop, the evolution of the learning mechanisms and dispersal patterns that shape vocal geographic divergence (Fig. 2B; Jenkins, 1985). To explore this scenario further we turn again to the phenomenon of dialects and hypotheses that have been put forward to explain their evolution. We provide brief commentary on definitions of song dialects, and then examine three broad categories of hypotheses that have traditionally been forwarded to explain their evolution.

B. DEFINITION OF SONG DIALECTS As noted by Slater (1989, p. 33), ‘‘geographic variation [in song] is seldom the simple matter that the word ‘dialect’ might imply.’’ Indeed, ‘‘dialects’’ have been described across a range of geographic scales (e.g., compare Leader et al., 2000 with Warren, 2002; see also Mundinger, 1982) and for a variety of vocal parameters. Schematically, we can represent geographic structure in vocal parameters as taking a range of spatial patterns, including random variation (Fig. 3A), gradual and shallow clines (Fig. 3B), or steep clines with stepped variation (Fig. 3C). This latter form is consistent with classic definitions of dialects (Marler and Tamura, 1962; Mundinger, 1982). Numerous researchers have noted that strict dialects (Fig. 3C) may be a comparatively rare phenomenon (Slater, 1986, 1989). The functional hypotheses that we review in Section II.C have drawn on the presence of sharp boundaries between dialect neighborhoods as justification for regarding dialects as being actively maintained by selection.

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Vocal parameter (s)

Random

Clinal

Dialect

Geographic locality FIG. 3. Schematic representation of three patterns of geographic variation in song. Geographic variation can be manifested at varying scales. Dialect variation features sharp transitions in vocal parameters between localities, and consistency in vocal parameters within localities.

C. HYPOTHESES TO EXPLAIN THE EVOLUTION OF SONG DIALECTS 1. Local Adaptation Hypothesis The local adaptation hypothesis, alluded to by Marler and Tamura (1962) and then formalized by Nottebohm (1969), posits that females gain fitness advantages when they are able to mate with males from their natal regions, in preference to males from other regions. According to this hypothesis, birds that select mates from their natal regions will gain fitness advantages because their offspring will more likely express adaptations to local ecological conditions. Because song is a key mating signal in many species of birds, song structure is thus posited to diverge by locality, under selection for accurately marking birds’ natal localities. Baker and colleagues argued more specifically that dialects serve as markers for ‘‘coadapted gene complexes,’’ and that dialect boundaries represent secondary contact zones between partially

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isolated populations (Baker, 1982; Baker and Thompson, 1985; Baker et al., 1982). The local adaptation hypothesis makes four predictions, each of which has been subject to considerable scrutiny. The first prediction is that birds should learn their vocalizations early, before dispersing from their natal regions (MacDougall‐Shackleton and MacDougall‐Shackleton, 2001; Payne, 1981; Rothstein and Fleischer, 1987). Decades of study have now shown wide diversity in the timing of song acquisition, and so this prediction may hold for some species but clearly does not hold for others. Brown‐headed cowbirds, for instance, learn songs postdispersal, and thus dialects in this species can best be attributed to other hypotheses (Rothstein and Fleischer, 1987). The timing of learning has traditionally been studied in laboratory conditions, and a general point of discussion concerns the applicability of laboratory results to field contexts. In particular, it has been noted that species that only learn early in the laboratory, from taped tutor songs, may still retain the ability to learn songs later in life, if trained by live tutors (Baptista and Petrinovich, 1984). The relative impact of social influences on song learning continues as an area of active study (Beecher and Brenowitz, 2005; Johannessen et al., 2006). Second, the local adaptation hypothesis predicts that dispersing birds will tend to settle, more than would be expected by chance, in localities where birds sing their natal dialects, as opposed to localities in which birds sing ‘‘foreign’’ dialects. Tests of this prediction require mark/recapture studies, and results so far have been inconclusive (Baker and Mewaldt, 1978; Baker and Mewaldt, 1981; Baker et al., 1985; Hafner and Peterson, 1985; Petrinovich et al., 1981). A call by Kroodsma (1985a) for additional empirical work in this area still rings true. Third, the local adaptation hypothesis predicts that dialect groups should come to be genetically differentiated, as a result of recent histories of assortative mating. The main challenge in tests of this prediction has been to identify genetic parameters in which dialect neighborhoods differ. Again the evidence has been inconclusive and open to interpretation (Baker et al., 1982; Lougheed and Handford, 1992; Payne and Westneat, 1988; Zink and Barrowclough, 1984). In the most comprehensive study on this topic to date, MacDougall‐Shackleton and MacDougall‐Shackleton (2001) analyzed variation in microsatellite allele frequencies among eight dialect regions of Z. leucophyrs oriantha, and concluded that ‘‘dialect borders are associated with some reduction in gene flow, but they appear to be very low walls rather than barriers in any strict sense’’ (MacDougall‐Shackleton and MacDougall‐Shackleton, 2001, p. 2574). The fourth prediction of the local adaptation hypothesis is that females should evolve mating preferences for males from their natal dialects. A number of assays have been developed to measure the impact of vocal

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variation on female preferences (Searcy, 1992). In tests of the influence of dialect variation on female preferences, researchers have generally turned to the copulation solicitation display assay. In this assay, females are captured, acclimated to laboratory conditions, and then treated with exogenous estradiol, normally via silastic implants, to increase sexual receptivity. Songs of different dialects are then presented over loudspeakers, and the strength and vigor of resulting solicitation displays documented. Females of a number of species have been found to respond more often and more vigorously to local songs than to foreign songs (Baker et al., 1981, 1987a; Searcy et al., 1997, 2002). Differential preferences for local songs presumably emerge because of greater familiarity with local dialects (Baker et al., 1981), or through learned associations between local songs and social feedback experienced early in life (Riebel, 2003). Learned preferences for males that sing local songs could foster, through coevolution of dialect and preference, the divergence of dialect forms (Riebel, 2003). It is useful to note that of these four predictions, only the second is unique to the local adaptation hypothesis. The others predictions are also consistent with other hypotheses of dialect evolution (see below). Early studies of vocal geographic variation in birds focused on the local adaptation hypothesis (Marler and Tamura, 1962; Nottebohm, 1969), which is perhaps not surprising given that birdsong studies of that era were generally geared toward questions about species recognition. Many birds produce species‐specific vocalizations, and ornithologists have long hypothesized that vocalizations thus aid conspecific recognition (Marler, 1957, 1960). With the advent of portable tape recorders and loudspeakers, this hypothesis was tested and supported in a host of playback studies, which showed time and again that birds respond more strongly to playback of conspecific than heterospecific song (Falls, 1963; Gill and Murray, 1972; Martin and Martin, 2001; Milligan, 1966; reviewed by Becker, 1982). Similar patterns of elevated responses to playback of conspecific versus heterospecific song have also been demonstrated in females from numerous species (Searcy, 1992). From an evolutionary perspective, interspecific divergence in vocal structure is thought to be driven by two related factors, namely selection for avoiding interspecific acoustic competition (Nelson and Marler, 1990) and selection against cross‐species mating and hybrid production (Butlin, 1995; de Kort and ten Cate, 2001; Haavie et al., 2004; Seddon, 2005; see Sætre et al., 1997, for a parallel argument for the evolution of plumage divergence). According to these hypotheses, individuals within a population that best transmit the correct species identity, through production of the most species‐typical songs, experience lower probabilities of fitness‐reducing interspecific hybridization (Noor, 1999). It was a small logical step from the species recognition hypothesis to the local adaptation

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hypothesis. Both hypotheses focus on the same evolutionary factors, notably selection against mating ‘‘errors.’’ The only substantive difference between the hypotheses is that mating errors occur at inter‐ versus intraspecific scales. As with tests of conspecific recognition, tests of local recognition abilities have relied heavily on playback designs (Baker et al., 1987a; Harris and Lemon, 1974; Lemon, 1967; McGregor, 1983; Milligan and Verner, 1971; Petrinovich and Patterson, 1981; Ratcliffe and Grant, 1985; Searcy et al., 1997, 2002). 2. Social Adaptation Hypotheses A second class of hypotheses for dialect evolution also focuses on the role of song in recognition, but with regard to social groups rather than locality. These ‘‘social adaptation hypotheses’’ suggest that males gain fitness advantages by singing songs similar to those of other males in their region, whereas males that sing nonlocal songs are subject to social penalties. One version of this hypothesis, the deceptive mimicry hypothesis (Payne, 1981), posits that subordinate males that successfully mimic the vocalizations of dominant males are able to improve their access to mates, and also to reduce probabilities of aggressive interactions with dominant males. Under this scenario, dialects should be temporally unstable and depend on which individuals are dominant at a given time (Payne, 1981; Rothstein and Fleischer, 1987). A related hypothesis is that of ‘‘honest convergence,’’ which posits that dialects serve as honest signals of long‐ term residence (Rothstein and Fleischer, 1987). Similarly, the colony password hypothesis (Feekes, 1977) proposes that dialects can serve as markers of group membership in colonial species, and thus facilitate the identification of intruders into a colony. Social adaptation hypotheses predict that individuals will learn new vocalizations on dispersal to a new dialect area, in order to match (mimic) vocalizations at the new locality (Payne, 1981). If individuals are constrained to acquire and crystallize new vocalizations early, prior to juvenile dispersal, then the social dynamics of adults cannot play a role in the maintenance of dialects. Postdispersal learning has been implicated in a number of species, including the cowbird systems mentioned earlier (Rothstein and Fleischer, 1987; Warren, 2002). Not surprisingly, male–male interactions have been a primary focus in tests of social adaptation hypotheses. The deceptive mimicry hypothesis predicts that accurate mimics should incite less aggressive responses from dominant males, and moreover that males should be more aggressive toward other males from neighboring dialects as opposed to from their home dialects (Rothstein and Fleischer, 1987). The colony password hypothesis similarly predicts that members of a colony should be more

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aggressive toward males from foreign rather than local dialects, because foreign dialects would indicate potential intruders from other colonies (Feekes, 1977). Dialect size is also an important component of social adaptation hypotheses. The colony password and deceptive mimicry hypotheses predict that dialect areas should be small, corresponding to ‘‘socially cohesive units’’ (Payne, 1981). The honest convergence hypothesis does not require that dialects be small, but does, however, require them to be not substantially larger than average adult dispersal distance (Warren, 2002). If a dialect area is too large, dialect identity can no longer serve as an honest signal of local residence because newly arrived males would produce vocal signals indistinguishable from those of local residents (Warren, 2002). In general, it is difficult to conceive of a scenario in which social adaptation maintains boundaries between larger regional dialects; if dialects were too large, most individuals within a dialect would be unlikely to ever encounter other dialects. 3. Epiphenomenon Hypothesis The epiphenomenon hypothesis, first articulated (although only briefly) by Andrew (1962), posits that dialects emerge as incidental by‐products of particular patterns of learning and dispersal, both of which evolve under selection pressures unrelated to dialect formation. In other words, selection on dialect patterns per se need not produce dialects or maintain the discrete boundaries that characterize dialects. This has been considered by some to be the null hypothesis for dialect evolution, and it indeed makes fewer assumptions than do the other classes of hypotheses (Fig. 2A vs B; Lemon, 1975; Wiens, 1982). As we understand it, the epiphenomenon hypothesis differs from functional hypotheses of dialect evolution in that it does not require a history of continuous contact between birds from different dialect groups or a history of negative selection against birds that sing foreign songs. Continuous contact is necessary in the functional hypotheses, for example as intruders that sing foreign songs are rejected, in order to generate the negative selection pressures implied therein. Most simply, nonfunctional divergence among dialect groups can result from differential trajectories of selection among isolated populations. To illustrate, numerous lines of evidence now suggest that songs undergo selection for optimal transmission through the acoustic environment (Nottebohm, 1969, 1985; Wiley and Richards, 1978; reviewed by Slabbekoorn, 2004). Such selection pressures may drive intraspecific vocal divergence, and thus the emergence of dialect patterns, when different populations come to occupy distinct habitats (Doutrelant et al., 1999; Handford and Lougheed, 1991; Hunter and Krebs, 1979; Patten et al., 2004; Slabbekoorn and

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Smith, 2002b). Limited dispersal, and thus limited contact among localities, may facilitate the nonfunctional evolution of dialects, as vocal novelties persevere only in limited regions (Lemon, 1975; Slater, 1989). (A note on terminology: vocal divergence through acoustic adaptation to different habitats itself clearly has functional bases. However, any resulting geographic patterns would be considered nonfunctional, given that the locus of acoustic adaptation was site specific and thus did not favor geographic diversification per se). Slater (1985, 1986, 1989) offered a compelling defense of the epiphenomenon hypothesis for dialect evolution. As noted by Slater (1986, p. 96), ‘‘It is possible that dialects have no functional significance, but that the differences they represent are simply spurious byproducts of vocal learning. If song is learnt and dispersal after learning is restricted, some sort of variation in both time and space seems inevitable. . .’’ Slater defened the epiphenomenon hypothesis by raising three points. First, song learning may evolve for a range of functions besides dialect recognition. Such functions may include helping birds to better match the local acoustic environment or to enable accurate transmission of complex songs (Slater, 1986). Second, selection on bird vocalizations generally acts at the level of individuals, whereas dialects are population‐level phenomena. Third, variations in dispersal patterns and the accuracy of song learning, as studied in computer simulations, illustrate that the epiphenomenon mechanism can indeed generate discrete dialect patterns, particularly in species with small repertoire sizes (Goodfellow and Slater, 1986; Lachlan et al., 2004; Slater, 1989). We regard the epiphenomenon hypothesis as a type of ‘‘by‐product’’ hypothesis of vocal evolution. By‐product mechanisms of interpopulation divergence refer generally to situations in which selection on one trait or suite of traits drives incidental changes in, or limits on the expression of, other traits or suites of traits. Correlated evolution of multiple traits may arise through shared genetic or phenotypic mechanisms, such as when multiple functions make use of the same anatomical or physiological components (Patek et al., 2006; Podos and Hendry, 2006). Correlated evolution of multiple traits may also arise as result of life history constraints, as metabolic or energetic resources allocated to certain traits limit the development or expression of other traits (Roff, 1992). By‐product mechanisms have been of particular interest in studies of ‘‘ecological speciation,’’ which posits that incidentally modified trait(s) can impact patterns of mating and reproductive isolation (Boughman, 2001; Dobzhansky, 1951; Mayr, 1942; Orr and Smith, 1998; Podos, 2001; Podos and Hendry, 2006; Rice and Hostert, 1993; Ruegg et al., 2006; Schluter, 2000, 2001). Rundle et al. (2000), for instance, provided evidence that adaptive divergence of body size in benthic versus limnetic stickleback fishes has facilitated assortative

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mating by morph, presumably because body size is used as a cue in mate assessment and selection. According to by‐product models of trait divergence, the emergence of reproductive isolation among populations is not necessarily a product of selection for that isolation, but potentially a secondary consequence of ecological adaptation in other traits (Podos and Hendry, 2006).

III. ASSESSING HYPOTHESES OF DIALECT EVOLUTION The potential relevance of the three hypothesis classes outlined above has now been addressed in a moderate number of species. Our goal in this section is to survey the primary literature, in order to identify potentially common conditions that underlie dialect formation and maintenance. Of particular interest is the timing of song acquisition, given that a key prediction distinguishing the two functional hypotheses is whether acquisition occurs pre‐ or postdispersal. Other potential correlates of dialect patterns examined here include social systems, degree of seasonal mobility, and vocal repertoire size. Examination of these parameters cannot provide definitive support for or against the three classes of dialect hypotheses, especially given difficulties noted by many other researchers in comparing dialect data across species. Rather, our intent is to gain a sense of trends or conditions that may favor dialect evolution as a general phenomenon.

A. OUR APPROACH We surveyed the literature for species reported to exhibit dialects (Table I). Our survey included not just birds but also primates, cetaceans, anurans, and insects. To meet our definition of dialects, we required evidence of sharp geographic boundaries between signal forms, of discrete differences among signal forms, and of uniformity of signal forms within given localities (Fig. 3C). Thus, a species exhibiting geographic variation in a continuous character such as pulse rate or dominant frequency was not included unless this variation was shown to be partitioned discretely among sites (e.g., as shown in Leader et al., 2000). We excluded studies for which data were ambiguous about one or more of the above criteria (Naguib et al., 2001; Podos, 2007). Moreover, a species exhibiting many discretely different signal forms was not included unless these signal types were geographically partitioned such that narrow boundaries between types could be clearly mapped. We thus excluded from our analysis ‘‘island’’ dialects (Ratcliffe, 1981), in which dialect localities are geographically isolated

TABLE I SPECIES REPORTED TO EXHIBIT VOCAL DIALECTS, INCLUDED IN OUR ANALYSES #

Species

417

Common name

Family

Signal

1 Amazona auropalliata

Yellow‐naped Amazon

Psittacidae

group calls

2 Phaethornis longuemareus 3 Miliaria calandra

Little Hermit

Trochilidae

song

Corn bunting

Fringillidae

song

4 Emberiza citrinella

Yellowhammer

Fringillidae

song

5 Emberiza hortulana

Ortolan

Fringillidae

song

6 Zonotrichia leucrophrys Nuttall’s white‐crowned nuttalli Sparrow

Fringillidae

song

7 Zonotrichia leucophrys Montane White‐crowned oriantha Sparrow

Fringillidae

song

References Wright, 1996; Wright and Wilkinson, 2001; Wright et al., 2005 Wiley, 1971 Holland et al., 1996; McGregor, 1980, 1983; McGregor and Krebs, 1984; McGregor and Thompson, 1988; McGregor et al., 1988 Baker et al., 1987a; Møller, 1982; Hansen, 1985, 1999; Glaubrecht, 1989, 1991; Rutkowska‐Guz and Osiejuk, 2004 Conrads, 1976; Conrads and Conrads, 1971; Thielcke, 1969 Baker, 1974, 1975, 1983; Baker and Mewaldt, 1978; Baker and Thompson, 1985; Baker et al., 1981, 1982, 1984a,b, 1987b; Baptista, 1975; Baptista et al., 1997; Cunningham et al., 1987; Marler and Tamura, 1962, 1964; Milligan and Verner, 1971; Petrinovich and Patterson, 1981; Trainer, 1983; Zink and Barrowclough, 1984 Baptista, 1977; Chilton and Lein, 1996a,b; Nelson, 2000; Nelson et al., 1996b, 2004; Soha et al., 2004 (Continued)

TABLE I (Continued) #

Species

Common name

Family

Signal

418

8 Zonotrichia leucophrys Puget Sound White‐crowned pugetensis Sparrow

Fringillidae

song

9 Zonotrichia capensis

Rufous‐crowned Sparrow

Fringillidae

song

Vesper Sparrow Sage Sparrow Spotted Towhees Indigo Bunting

Fringillidae Fringillidae Fringillidae Fringillidae

song song song song

Fringillidae

song

Fringillidae

song

16 Molothrus ater

Yellow‐rumped Cacique (Surinam) Yellow‐rumped Cacique (Panama) Brown‐headed Cowbird

Fringillidae

song

17 Molothrus aeneus

Bronzed Cowbird (Winter)

Fringillidae

song

10 11 12 13

Pooecetes gramineus Amphispiza belli Pipilo maculatus Passerina cyanea

14 Cacicus cela 15 Cacicus cela

References Baptista and King, 1980; Baptista and Morton, 1982, 1988; Harbison et al., 1999; MacDougall‐Shackleton and MacDougall‐Shackleton, 2001; MacDougall‐Shackleton et al., 2002; Nelson et al., 1996a King, 1972; Lougheed and Handford, 1992; Nottebohm, 1969, 1975; Tubaro and Segura, 1994; Zink et al., 1991 Kroodsma, 1972 Rich, 1981 Borror, 1975 Payne, 1981, 1982, 1983; Payne and Westneat, 1988 Feekes, 1977 Trainer, 1988, 1989; Trainer and Parsons, 2002 Alderson et al., 1999; Dolbeer, 1982; Fleischer and Rothstein, 1988; O’Loghlen, 1995; O’Loghlen and Rothstein, 1993, 1995; Rothstein and Fleischer, 1987; Rothstein et al., 1999; Teather and Robertson, 1986; Yokel, 1986 Dolbeer, 1982; Rothstein, 1980; Warren, 2000, 2003

419

18 Molothrus aeneus

Bronzed Cowbird (Breeding)

Fringillidae

song

19 Dolichonyx oryzivorus

Bobolink

Fringillidae

song

20 Fringilla coelebs

Chaffinch

Fringillidae

21 22 23 24 25 26 27 28

Greenfinch House Finch Smith’s longspur Apapane Bewicks Wren European Wren (UK) European Wren (France) Short‐toed Treecreeper

Fringillidae Fringillidae Fringillidae Fringillidae Certhiidae Certhiidae Certhiidae Certhiidae

call (‘‘rain call’’) song song song song song song song song

29 Turdus iliacus

Redwing

Passeridae

song

30 Vidua chalybeata

Village Indigobird

Passeridae

song

31 Vidua purpurascens 32 Poecile atricapillus

Dusky Indigobird Black‐capped Chickadee

Passeridae Paridae

33 Poecile atricapillus

Black‐capped Chickadee

Paridae

34 Poecile carolinensis

Carolina Chickadee

Paridae

song call (‘‘gargle’’) song (‘‘fee bee’’) song (‘‘fee bee’’)

Chloris chloris Carpodacus mexicanus Calcarius pictus Himatione sanguinea Thryomanes bewickii Troglodytes troglodytes Troglodytes troglodytes Certhia brachydactyla

Carter, 1984; Clotfelter, 1995; Dolbeer, 1982; Rothstein, 1980; Warren, 2000, 2002, 2003 Avery and Oring, 1977; Trainer and Peltz, 1995 Baptista, 1990; Sick, 1939; Sorjonen, 2001; Thielcke, 1969, 1988a,b, 1989 Gu¨ttinger, 1974, 1976 Mundinger, 1975, 1982 Briskie, 1999 Ward, 1964 Kroodsma, 1974, 1985b Catchpole and Rowell, 1993 Kreutzer, 1974 Seitz et al., 1994; Thielcke, 1969, 1984, 1986, 1987; Thielcke and Wuestenberg, 1985 Bjerke, 1974, 1980, 1982, 1984; Bjerke and Bjerke, 1981; Espmark, 1981, 1982; Fonstad et al., 1984; Mork, 1974 Payne, 1973, 1981, 1987; Payne and Payne, 1977 Payne, 1973, 1981, 1987 Ficken et al., 1978, 1985 Kroodsma et al., 1999 Ward, 1966

(Continued)

TABLE I (Continued) #

Species

Common name

Family

420

35 Pachycephala olivacea 36 Cyanocitta cristata

Olive Whistler Blue Jay

Corvidae Corvidae

37 Creadion carunculatus 38 Menura novaehollandiae 39 Nectarinia osea 40 Orcinus orca 41 Pyseter macrocephalus 42 Saguinus labiatus labiatus

Saddleback Superb Lyrebird Orange‐tufted Sunbirds Orca, Killer Whale Sperm Whale Red‐chested Moustached Tamarin

Signal

References White, 1985, 1986, 1987 Kramer and Thompson, 1979

Callaeatidae Menuridae

song group calls (‘‘bell’’ call) song song

Nectariniidae Cetacea Certacea Callithricidae

song group calls group calls long calls

Leader et al., 2000, 2002, 2005 Ford, 1991 Weilgart and Whitehead, 1997 Masataka, 1988

Jenkins, 1978 Powys, 1995; Robinson and Curtis, 1996

GEOGRAPHIC VARIATION IN BIRDSONG

421

from each other. This is not to say that vocal variation in species distributed across islands cannot be considered dialectal; our criteria here were set for purposes of analysis only.

B. LITERATURE SURVEY Our search for published studies of dialects was conducted using both digital databases (ISI Web of Science) and searches of citation sections of published works on dialects. We identified about 200 studies of 52 species in which the authors suggested that the vocal system could be described as dialectal. Of these, 42 cases met our present definition of dialects (Fig. 3C). We include data from 141 studies of these 42 cases in our analysis (Table I). We compiled data for them (Table II), as follows. 1. Dialect Characteristics We coded three descriptive characteristics of dialects: spatial extent (dialect scale), temporal stability of dialect boundaries, and presence or absence of bilingualism at boundaries between dialects. Dialect scale was coded according to four categories: microgeographic, small, medium, and large, coded, respectively, as 0, 1, 2, or 3. Microgeographic dialects feature 10 individuals or less in each dialect area, and span less than 2 km in any given direction. Small dialects consisted of dialects areas that span 2–10 km. Medium dialects were considered to span 10–100 km, and large dialects greater than 100 km. In some cases, dialects were only described in terms of numbers of individuals. Small, medium, and large dialects were then classified as containing less than 100 individuals, less than 1000 individuals, and more than 1000 individuals, respectively. Most dialect areas do not greatly exceed 1000 km. The temporal stability of dialect boundaries was described explicitly by many authors. Some taxa retain similar boundaries and acoustic characteristics of song types over long periods of time (Hansen, 1999; Thielcke, 1987) while others change rapidly, even within a season (Trainer, 1989). We classified dialect stability as short‐term, moderate‐ term, and long‐term, coded as 1, 2, or 3, respectively. Short‐term dialects maintained 2–6 years of stability. In the case of most passerine species, this probably corresponds to the life span of individuals of the species (Weatherhead and Forbes, 1994). Moderate‐term dialects were defined as being stable for 6–20 years, and long‐term dialects as being stable for greater than 20 years. It is probable that some taxa coded with moderate‐term dialects actually are stable over the long‐term, and that long‐term stability has not yet been demonstrated.

TABLE II VOCAL AND ECOLOGICAL PARAMETERS OF DIALECT SPECIESa

422

#

Species

Spatial scale

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Amazona auropalliata Phaethornis longuemareus Miliaria calandra Emberiza citrinella Emberiza hortulana Zonotrichia leucrophrys nuttalli Zonotrichia leucophrys oriantha Zonotrichia leucophrys pugetensis Zonotrichia capensis Pooecetes gramineus Amphispiza belli Pipilo maculatus Passerina cyanea Cacicus cela Cacicus cela Molothrus ater Molothrus aeneus Molothrus aeneus Dolichonyx oryzivorus Fringilla coelebs Chloris chloris

2 0 1 3 2 1 3 3 2 1 1 1 0 2 2 2 2 3 1 2 2

Temporal stability

2 2 3 1 3 3 2 3

Bilingual

Territoriality

Mobility

Repertoire size max

1

0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1

0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 3 5 2 1 2 2 1 43 1 2 1 4 8 3 1 1 43 1 35

1 1 1 0 1 1 1 0 1

0 0 2 1 1 1 3 0

0 1 1 1 1 0

Timing of learning post post

pre pre pre pre

post post post post

post post

423

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 a

Carpodacus mexicanus Calcarius pictus Himatione sanguinea Thryomanes bewickii Troglodytes troglodytes Troglodytes troglodytes Certhia brachydactyla Turdus iliacus Vidua chalybeata Vidua purpurascens Poecile atricapillus Poecile atricapillus Poecile carolinensis Pachycephala olivacea Cyanocitta cristata Creadion carunculatus Menura novaehollandiae Nectarinia osea Orcinus orca Pyseter macrocephalus Saguinus labiatus labiatus

1 0 1 1 0 1 3 1 1 1 1 1 2 2 2 0 1 0 2 1 2

1 2 1

1 3 2 0 0

1

1 0 1 1 1 1 0 0

1 2 1 3 1

1 1 0 0

0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1

See text for an explanation of coding; blank cells indicate a lack of sufficient available data.

1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0

4 1

pre post

16 6 1 5 2 12 12 1 9

post

10 2 1 3 1 17 30 1

pre pre post post post post

post

pre pre post

424

JEFFREY PODOS AND PAIGE S. WARREN

Bilingualism refers to the propensity of individuals to acquire more than one dialect, particularly in areas near a dialect boundary. Following our definition of dialects, the proportion of a population that is bilingual should be low in most cases. But bilingualism was reported regularly among the species we reviewed. We classified bilingualism as either absent or present (coded as 0 or 1, Table II) based on the authors’ reporting. We classified bilingualism as absent only when an author either stated it to be so or gave sufficiently exhaustive detail on vocal behavior of the focal populations to allow us to make this inference. 2. Ecological Correlates We compiled data for two ecological and life history characteristics commonly cited in the literature: social system and degree of seasonal mobility. First, we assessed, based on the literature, whether taxa with dialects were predominantly territorial or, alternatively, social (e.g., colonial) on their breeding grounds. With regard to seasonal mobility, taxa were classified as either sedentary or migratory, again on the basis of published descriptions. 3. Vocal Correlates Data on one vocal parameter, repertoire size, was compiled for each species or subspecies in our analysis. Rather than analyzing the ranges of repertoire sizes within each species, we included in our analyses only maximum repertoire size. We focus on maximum repertoire size because this provides a good indication of the potential for individuals to acquire songs of more than one dialect. Coding each cell with a single number aided our multivariate analyses below. 4. Timing of Song Acquisition While the song acquisition phase of song learning has not been characterized in detail for many species, it can sometimes be inferred from observational studies of song acquisition in the field. This is particularly so in demonstrations of postdispersal learning, in which individuals are shown to match their songs to the local dialect after dispersing from their natal grounds. Thus, we classified any taxa in which individuals regularly alter their songs after dispersal as a ‘‘postdispersal learner’’ for the purposes of this analysis. This includes species that delete song types learned earlier that do not match neighbors. We classified taxa as predispersal learners when the predominant pattern appears to be early song acquisition, with no known examples of modification of song after natal dispersal. These categories parallel the open‐ended and close‐ended song learning categories as

GEOGRAPHIC VARIATION IN BIRDSONG

425

defined by Beecher and Brenowitz (2005) and others. Notably, song learning in some species likely bridges pre‐ and postdispersal periods. Z. leucophrys oriantha and Z. leucophrys pugetensis, for instance, are believed to memorize song models predispersal but then crystallize only a subset of memorized models postdispersal, based on vocal interactions with neighbors (Nelson, 2000). For such species, we coded song learning as primarily predispersal, based on the supposed period of song model acquisition. One notable shortcoming in our analysis is that we did not include dispersal distances, in spite of the fact that these are considered a key factor in shaping vocal geographic variation. Unfortunately, dispersal is difficult to measure and therefore not available in the literature for many species. Moreover, our system of classifying taxa as pre‐ versus postdispersal learners incorporates dispersal distance, at least to some extent. Taxa identified as having postdispersal learning have most likely been so identified because individuals have been observed dispersing across dialect boundaries. Thus, these taxa will have longer dispersal distances than will predispersal learners, almost by definition. 5. Analytical Approaches While our survey included a wide range of taxa, we decided to limit our quantitative analysis to birds, which constituted the large majority of vocal dialect cases identified (Table I, taxa #1–39). We first tested whether dialect characteristics in birds differed systematically between pre‐ and postdispersal learners. The lack of information on vocal ontogeny for many species greatly limited our sample size, and therefore the number of parameters we could include in a multivariate analysis. Because dialect characteristics such as spatial scale are potentially linked to ecological variables such as territoriality, we conducted two separate discriminant function analyses with song ontogeny as the grouping variable in both cases. In the first, we asked whether our three ecological and vocal parameters classified pre‐ and postdispersal learners as distinct groups. In the second, we asked whether the three dialect parameters classified pre‐ and postdispersal learners as distinct groups. We explored potential correlates of dialect variation using nonparametric methods, comparing, using univariate analyses, each of the dialect parameters to the ecological and vocal parameters shown in Table II. It would have been preferable to apply the comparative method, that is, taking phylogenetic relationships into account in our analysis. This was not practical, however, given the limited information currently available about the relationships of the taxa examined. It seems unlikely that phylogenetic factors would have strongly influenced our findings, given that dialect characteristics such as spatial scale can vary widely even among closely related taxa (see, e.g., Zonotrichia species and subspecies, #6–9, Table II).

426

JEFFREY PODOS AND PAIGE S. WARREN

C. RESULTS The majority of research on vocal geographic variation has been conducted on songbirds from temperate regions. Considerably, less is known about vocal geographic variation in other bird groups in other regions, notably tropical suboscine passerines. Thus, the sample analyzed here does not represent an unbiased snapshot of natural vocal variation. Our first impression from our survey is that the absolute number of species exhibiting sharp dialects is lower than the abundant literature on dialects might lead one to suspect. Most entries are for dialects in song (n ¼ 36), but 5 are for group calls. In five cases for which intraspecific variation could not easily be summarized, we use multiple entries for a single species. One of these is the black‐capped chickadee, for which dialects are reported in two different vocal signals, the ‘‘gargle’’ and the ‘‘fee bee’’ (Table II). In the other cases, populations or subspecies are so distinct in their reported dialect or other characteristics as to warrant separate entries. For example, three white‐crowned sparrow subspecies (Z. leucophrys nuttalli, Z. leucophrys oriantha, and Z. leucophrys pugetensis, #6–7) differ both in the characteristics of the dialects they exhibit as well as in reported patterns of migratory behavior (Table II). In the bronzed cowbird (M. aeneus), dialects occur in distinct geographic patterns in breeding versus wintering populations (Warren, 2002). The majority of the cases of vocal dialects we identified occur in passerine birds (n ¼ 37). These were distributed among just eight families (sensu Sibley and Monroe, 1990), with most examples from four subfamilies of the Fringillidae (n ¼ 18 species, Fig. 4). The Fringillids represent roughly 40% of the species that were found to exhibit some degree of song sharing, according to a survey of the Fringillids by Handley and Nelson (2005). Slater (1989) and others have argued that dialects are a rare phenomenon. According to our chapter, they certainly seem to be uncommon in passerines, occurring in less than a quarter of passerine families, at least as based on available data. Yet, dialects may actually be a common form of geographic variation in the Fringillids, a group with a high propensity for song sharing (45 of the 65 taxa reviewed by Handley and Nelson, 2005).

1. Variation in Dialect Characteristics The three dialect characteristics, spatial scale, temporal stability, and bilingualism, were not correlated with one another (all Spearman’s r < 0.25, p > 0.2, N ¼ 27, except N ¼ 20 for temporal stability‐bilingualism comparison). Spatial scale and temporal stability were each normally distributed across our sample. It was more difficult to assess bilingualism than the other

427

GEOGRAPHIC VARIATION IN BIRDSONG

20 18

18

Number of species

16 14 12 10 8 6 4

3

3 2

2

2 1

1

1

1

1

ae lid

Tr

oc

hi

id ac

iid

Ps

itt

in ar ct

ae

ae

ae

ae

id

ur

tid ea

en

M

lla Ca

Ne

ae

ae

rid

id rv

Co

Pa

ae id

ae

er

iid

ss Pa

llid

Ce

gi in Fr

rth

ae

0

FIG. 4. Number of species in which vocal dialects were identified, according to family, in birds. All but two families, the Psittacidae and Trochilidae, are passerines.

two variables, with many cases lacking sufficient detail. Of the 27 cases in which this could be assessed with confidence, the majority, 74%, exhibited some level of bilingualism.

2. Ecological Correlates of Dialect Characteristics The 42 dialect systems we found exhibit a wide variety of ecological characteristics, with representatives of both territorial and social species and of both sedentary and migratory species (Fig. 5, Table II). Given the dominance of passerine birds in the sample, we were not surprised to find that a majority of the birds with dialects are territorial (79%, Fig. 5). It was more surprising to find that sedentary species were the minority in the sample (Fig. 5), since it is thought that dialects should evolve more readily in sedentary species. Across our dialect sample, territorial species tended to be sedentary more frequently than did social species (w2 ¼ 3.83, p ¼ 0.05). This relative dearth of sedentary species with dialects might be due to the dominance in our sample of temperate and arctic species, many of which are seasonally migratory.

428

JEFFREY PODOS AND PAIGE S. WARREN

100% 80% Mig 60% 40% Sed 20% 0%

Territoriality

Seasonal mobility

FIG. 5. Distribution of ecological correlates among species with vocal dialects. Bars indicate the percentage of species that are territorial (black) versus nonterritorial (white) and sedentary (‘‘sed’’) versus migratory (‘‘mig’’).

We found few significant relationships between ecological variables and dialect variables, but the trends uncovered suggest potential mechanisms underlying variation among dialect systems. Migratory species tend to have larger dialect regions (Kruskal‐Wallis, Z ¼
1.85, p ¼ 0.06), and were somewhat more likely than sedentary species to show bilingualism at dialect boundaries (w2 ¼ 2.82, p ¼ 0.09). But we found no relationship between seasonal mobility and the stability of dialect regions over time. By contrast, territorial species tend to have more temporally stable dialects than do social species (Kruskal‐Wallis, Z ¼
1.9, p ¼ 0.05). But we find no effect of territoriality on spatial scale of dialects or the propensity for bilingualism. 3. Vocal Correlates of Dialect Characteristics We find that 32% (12 of 37) of songbird species with dialects in our review have repertoires of only a single song type, and that 68% (25 species) have repertoires of fewer than five song types. According to Beecher and Brenowitz (2005), the corresponding percentages for all passerines are 30% of single song type and 50% repertoires of less than five song types. The two data sets thus correspond closely in this regard. As with the ecological variables, there are no statistically significant correlations between repertoire size and dialect characteristics. However, species with smaller repertoires tend to have more stable dialects over time (Spearman’s r ¼
0.38, p ¼ 0.05) and a lower propensity for bilingualism (Spearman’s r ¼
0.36, p ¼ 0.12). Repertoire size is not associated with dialect scale.

429

GEOGRAPHIC VARIATION IN BIRDSONG

4. Song Ontogeny

Number of taxa

Dialect species were somewhat more likely to show postdispersal rather than predispersal learning (Fig. 6). While this lends greater support for the social adaptation than local adaptation hypothesis, neither is strongly supported. The social adaptation hypothesis can be rejected in the nine dialect systems in which individuals rarely disperse across dialect boundaries and apparently do not acquire the local song type when they do cross boundaries. While some of the cases of predispersal learning have been disputed (Baptista and Petrinovich, 1984) or are based on single studies (Kreutzer, 1974), many others are well established (Lachlan and Slater, 2003). Thus, each of the functional hypotheses can be rejected in at least some cases. Furthermore, there are many missing cells in the table. The timing of song acquisition and memorization relative to natal dispersal is not known for more than a third of the cases in our table. Multivariate approaches identified few good predictors of the timing of song acquisition. First, we conducted a discriminant function analysis using the ecological and vocal parameters, territoriality, seasonal mobility and repertoire size, and song ontogeny as the grouping variable. This discriminant function was not significant (Exact F ¼ 1.20, df ¼ 3, N ¼ 18, p ¼ 0.33), with a canonical correlation of 0.45 for the first canonical axis. We conducted a second discriminant function analysis using three factors specifically describing the dialect systems, the spatial scale of dialects, their

20 18 16 14 12 10 8 6 4 2 0

16

16

Postdispersal

Unknown

10

Predispersal

FIG. 6. Numbers of all taxa with dialects that exhibit either predispersal learning or postdispersal learning, or for whom timing of learning has not been described. The two functional hypotheses, local adaptation and social adaptation, make mutually exclusive predictions regarding timing of song memorization and acquisition.

430

JEFFREY PODOS AND PAIGE S. WARREN

temporal stability, and presence of bilingualism. This was also not significant (Exact F ¼ 1.89, df ¼ 3, N ¼ 10, p ¼ 0.20) with a canonical correlation of 0.60 for the first canonical axis. Eliminating bilingualism from the analysis, a variable with many missing data points, improves the classification considerably, as shown in Fig. 7. This classification is statistically significant (Exact F ¼ 6.01, df ¼ 3, N ¼ 15, p ¼ 0.01), with the first canonical axis (Canon1) accounting for 67% of the variation among the cases of dialects. Temporal stability shows the highest correlation with Canon1 (r ¼ 0.88, p < 0.0001), but dialect size is also strongly correlated with Canon1 (r ¼ 0.59, p ¼ 0.002). Thus, the temporal and spatial features of dialects appear to be moderately successful predictors of learning ‘‘strategy’’ (sensu Beecher and Brenowitz, 2005). Temporal stability of dialects remained a significant predictor of learning strategy in univariate tests. This and repertoire size were the only parameters to show a tendency to differ between pre‐ and postdispersal learners. Postdispersal learners were slightly more likely to have larger maximum repertoire sizes than predispersal learners (Kruskal‐Wallis, 3.5 18

14, 15, 21

3.0

8

2.5

Spatial scale

5

16

+

1.5

Pre

Post

Canonical 2

4, 7, 28 30, 31

2.0

+

19, 22, 24, 27

9 Temporal stability

1.0 3, 29, 38 37, 39

0.5

6 0.0

2, 23

−0.5 1.0

1.5

2.0

2.5

3.5 3.0 Canonical 1

4.0

4.5

5.0

5.5

FIG. 7. Results of discriminant function analysis, using the spatial scale and temporal stability of dialect regions as independent variables and the timing of song acquisition as a grouping variable. Circles indicate the 50% confidence interval for predispersal (dark circle) and postdispersal learners (light circle). All bird species are plotted and numbered according to their entries in Table II.

GEOGRAPHIC VARIATION IN BIRDSONG

431

Z ¼
1.54, p ¼ 0.12). This finding is supported by the observation that all seven predispersal cases have repertoires with five or fewer song types. Dialects in predispersal learners are also significantly more stable over time (Kruskal‐Wallis, Z ¼ 2.43, p ¼ 0.015), though there is clearly considerable variation in both groups. Some dialect systems in postdispersal learners are quite stable over time, for example in brown‐headed cowbirds (M. ater) (Fleischer and Rothstein, 1988). Dialects in predispersal learners thus occur in species with smaller repertoires, and when they occur, they tend to be relatively stable in time. By contrast, dialects in postdispersal learners tend to cover smaller regions and be less stable over time, though there is considerable variation among dialects exhibited by postdispersal learners.

D. DISCUSSION Our survey illustrates both the rarity and diversity of dialect systems in nature. No single functional hypothesis can account for all or even a majority of published examples of dialects, and a substantial portion of the cases reject at least one of these hypotheses. The local adaptation hypothesis is rejected for 16 cases of dialects in which individuals are capable of modifying songs after dispersal to new dialect regions (Fig. 6). Likewise, the social adaptation hypothesis is rejected for 10 cases in which individuals appear not to disperse across dialect boundaries or to modify their signals after natal dispersal (Fig. 6). Although timing of song acquisition remains unknown for more than a third of all dialect systems (Table II), it seems unlikely that further work will support one of these functional hypotheses absolutely over the other. We suggest that the diversity of dialect systems, occurring as it does across a range of social systems, scales, and ecological conditions, argues against any given functional hypothesis of dialect evolution. The assembled data provide additional insights into the selective forces underlying the evolution of these diverse patterns of dialect variation. First, we note some common features across the species in our chapter. Although we searched intensively for cases of dialects in such well‐studied acoustic performers as insects and anurans, we only found dialects in species with evidence of vocal learning. In birds, these were the passerines, psitticines, and trochilids. Among mammals, the cetaceans and primates were the only groups represented. The majority of the cases (37 of 42) were in passerine birds. The predominance of passerines in the sample no doubt reflects, to some extent, historical and taxonomic biases in research on geographic variation in acoustic signaling. However, it remains noteworthy that no examples of dialects to date have been

432

JEFFREY PODOS AND PAIGE S. WARREN

found in species lacking imitative learning. We conclude that imitative learning, as predicted by Kroodsma (1996) among others, is a necessary condition for dialect formation. Our survey also reveals a strong incidence of dialects within the Fringillids, including species that share songs with neighbors (compare our data with Handley and Nelson, 2005). At the surface, this relationship suggests potential support for social adaptation hypotheses such as the deceptive mimicry hypothesis. However, as we argue in Section IV, and as was argued by Slater (1989), evidence of relationships between song sharing and vocal dialect evolution provides more direct support for by‐product hypotheses of dialect evolution. 1. Ecological and Vocal Correlates of Dialect Parameters Our comparisons of vocal and ecological parameters in dialect species suggest several issues worth pursuing in future work. First, the spatial characteristics of dialects appeared to be associated with seasonal mobility, with migratory species tending to evolve larger dialect regions. These interspecific comparisons appear to corroborate patterns described for the three subspecies of the white‐crowned sparrow with dialects, which range from the sedentary Z. leucophrys nuttalli with its small dialect regions to the migratory Z. leucophrys oriantha and Z. leucophrys pugetensis with their larger dialect region (Nelson, 1999). Further refinement of comparisons to include distances traveled by migratory populations may reveal an even stronger relationship with dialect size. Second, territorial species tend to maintain the same dialects for longer periods of time than do social species such as the colonial yellow‐rumped cacique (Trainer, 1989). Perhaps more revealing is the lack of a relationship between migratory behavior and stability of dialects. This suggests that seasonal mobility alone does not disrupt dialect patterns. Other life history characteristics that could be addressed include the degree of site fidelity and the rate of population turnover (Handley and Nelson, 2005; Kroodsma, 1996; Wiens, 1982). Third, larger song repertoires are associated with more stable dialect regions, but not with other dialect characteristics. This may reflect underlying selection on either repertoire size or accuracy of vocal imitation (Beecher and Brenowitz, 2005). Species in which accurate imitation is advantageous typically have lower repertoire sizes and are expected to have more stable song neighborhoods (Beecher and Brenowitz, 2005). We note, however, that predispersal learners also tend to have smaller repertoires and more stable dialects. The significant correlation between repertoire size and dialect stability disappears when we treat pre‐ and postdispersal learners separately. Thus, an unresolved question is whether

GEOGRAPHIC VARIATION IN BIRDSONG

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the stability of geographic variation in song is a consequence of timing of song acquisition, or of selection on traits such as repertoire size or imitative learning. The timing of song acquisition itself may be a consequence of selection on other traits such as dispersal distances or length of breeding seasons (Nelson, 1999). 2. Role of Song Ontogeny The evolutionary consequences of geographic variation in song are thought to be determined in large part by song ontogeny, in particular by the timing of song acquisition and memorization (Slabbekoorn and Smith, 2002a). Our survey of birds with dialects found examples of species with both pre‐ and postdispersal learning. These groups differ somewhat in the characteristics of their dialects, particularly in the stability of dialects over time (Fig. 7). But these differences are not overwhelming, and lack of information on song ontogeny in our sample significantly hampers our ability to draw conclusions about its role in dialect evolution. Nevertheless, it seems clear that song ontogeny is only one of many characteristics of species that influence how geographic variation in song evolves.

IV. RECENT STUDIES OF AVIAN VOCAL EVOLUTION, AND HOW THEY SUPPORT BY‐PRODUCT MODELS OF VOCAL GEOGRAPHIC DIVERGENCE As of two decades ago, researchers had mustered little direct empirical support for functional hypotheses of dialect evolution (Kroodsma, 1985a). Our survey in the preceding section suggests that little has changed on this front. There is and probably will never be a simple, universal explanation for the phenomenon of dialects. Perhaps more importantly, we suggest that the traditional focus on dialects has eclipsed investigation into the broader phenomenon of vocal geographic evolution, of which dialects are but one form expressed. In this section, we argue that by‐product hypotheses of vocal geographic evolution—the only one of the three sets of dialect hypotheses that seems to have broader applicability—have been receiving new sources of support, through recent general advances in the study of birdsong. A. PHYLOGENETIC SIGNAL IN VOCAL EVOLUTION Over decades, the study of behavioral evolution has been transformed by increased attention to phylogenetic factors (Brooks and McLennan, 1991; Martins, 1996). This has been the case for the study of birdsongs, in spite of the prior presumption that these signals are too plastic to permit

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historical analyses (Irwin, 1996; Payne, 1986; Price and Lanyon, 2002; ten Cate, 2004). The principal approach used so far to account for historical factors in bird vocal evolution has involved comparative analyses (Catchpole, 1980; Irwin, 1990; Kroodsma, 1977; Podos, 1997; Read and Weary, 1992; Slabbekoorn et al., 1999; Wiley, 1991). Toward this end, reference to hypotheses of phylogenetic relationships has proven particularly helpful. Phylogenetic hypotheses enable researchers to estimate vocal ancestral character states, as has now been done in a number of bird groups (de Kort and ten Cate, 2004; Irwin, 1988; Payne, 1986; Price and Lanyon, 2002, 2004). In their studies of oropendolas and caciques, to illustrate, Price and Lanyon (2002, 2004) used ancestral state reconstruction to infer that some vocal parameters (e.g., note structure, peak frequencies) are highly variable, whereas other vocal parameters (e.g., the presence or absence of a trill or a click within songs, and note and song duration) have remained stable over evolutionary time. Phylogenetic hypotheses have also allowed for independent contrast and similar statistical analyses, to assess correlated evolution in other taxonomic groups between song traits and neural, morphological, or ecological parameters (Podos, 2001; Seddon, 2005; Sze´kely et al., 1996; Van Buskirk, 1997). Returning to the example of oropendolas and caciques, phylogenetic reconstruction enabled analysis of correlations between rates of vocal evolution and the intensity of sexual selection in different lineages (Price and Lanyon, 2002, 2004). B. MULTIPLE FUNCTIONS AND TRADE‐OFFS IN VOCAL EVOLUTION Evidence of historical signal in vocal evolution suggests alternatives to functional hypotheses of evolution, which assume that vocal traits are sufficiently plastic to be easily molded to whatever selective pressures are presently in play. Instead, vocal features may be evolutionarily conserved when they are subject to multiple evolutionary pressures (Beecher and Brenowitz, 2005; Gil and Gahr, 2002; Nowicki and Podos, 1993). A rigorous empirical example of how song may evolve under multiple selection pressures was provided by Seddon (2005), who documented concurrent impacts of morphological adaptation, interspecific competition, and acoustic adaptation on the divergence of vocal structure in Neotropical antbirds (Passeriformes: Thamnophilidae). When traits are subject to multiple selection pressures, they may face trade‐offs in their evolution, that is, by responding only partially to some pressures (Roff, 1992). Evolutionary responses to specific functions, through their impacts on morphology, physiology, and behavior, may also alter how other traits are expressed, or even provide opportunities for the evolution of new functions (Gould and Vrba, 1982; Patek et al., 2006). The relevance of these issues for questions about

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bird vocal evolution has been highlighted by recent advances in two areas: on mechanisms of vocal learning and on mechanisms of vocal production. Advances in both areas, we argue below, support by‐product models of vocal geographic divergence. 1. Mechanisms of Vocal Learning Researchers have long wondered why some animals have evolved imitative learning as a mechanism in vocal signal ontogeny (Nottebohm, 1972). After all, many other animals develop effective vocal signals without recourse to imitation. Two traditional explanations for the evolution of vocal imitation are that it enables transmission of particularly complex patterns of vocal structure across generations, and that it helps animals to adapt their vocal signals to local acoustic environments (Slater, 1986). Recent work in songbirds has expanded this list in two significant directions. a. Vocal imitation and the development of song sharing among territorial neighbors One of the main documented functions of birdsong is to mediate interactions among neighboring territorial males (Catchpole and Slater, 1995; Hyman, 2002; Searcy and Andersson, 1986; Todt and Naguib, 2000). Research has focused on the use of ‘‘shared’’ songs by interacting neighbors and the potential fitness benefits of song sharing (Brown and Farabaugh, 1997; Handley and Nelson, 2005; Lachlan et al., 2004; Molles and Vehrencamp, 2001; Payne and Payne, 1997; Todt and Naguib, 2000). Two hypotheses to explain song sharing are that it provides a reliable means by which males can discriminate neighbors from strangers, and that it enables increased precision in communicating aggressive intent among territorial neighbors. Birds do well to distinguish neighbors from strangers because, unlike neighbors, strangers are ‘‘inherently expansionist’’ (Beecher and Brenowitz, 2005, p. 147), and thus require closer monitoring. Research by Beecher and colleagues on song sparrows (Melospiza melodia) illustrates how neighboring males may employ shared songs to escalate or de‐escalate aggressive interactions. To escalate an interaction, males may first respond to a singing neighbor with a nonshared song, then by singing a ‘‘repertoire match’’ (a shared song, although not the one being sung by the neighbor at the moment), and then by singing a precise type match (Beecher and Brenowitz, 2005). Selection for song sharing may influence the evolution of vocal geographic patterns—beyond obvious effects on the finest‐scale geographic patterns, that is, among neighbors—because of how it shapes the evolution of song learning strategies in populations (Beecher and Brenowitz, 2005; Handley and Nelson, 2005; Slater, 1989). First of all, selection for song sharing may presumably influence the number of song types that birds will evolve to learn, that is song repertoire size. As stated by Beecher and Brenowitz (2005,

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p. 147), ‘‘selection for song sharing and selection for large song repertoires are at least partially contrary . . . as a logical consequence of the fact that a song learning strategy cannot optimize both goals.’’ This is because selection for song sharing may favor, Beecher and Brenowitz (2005) argue, the evolution of smaller repertoires with correspondingly greater percentages of songs shared among neighbors. Expanding to a broader geographic scale, species that evolve smaller repertoires may be more likely to express the classic dialect pattern (Williams and Slater, 1990). A second potential impact of selection for song sharing is on the timing of song learning. Song sharing may be promoted in species that retain sufficient flexibility to match songs produced by neighbors on their breeding grounds, after dispersal from natal grounds (Beecher and Brenowitz, 2005; Martens and Kessler, 2000; Trainer, 1989). For species or subspecies with significant postnatal dispersal, ‘‘open‐ended’’ song learning programs should be favored because such learning programs increase the likelihood that birds would be able to match the songs of neighboring males (Rothstein and Fleischer, 1987). Evolution of the classic dialect pattern may thus be facilitated indirectly, via selection for song sharing. The observation that many Fringillids express both song sharing and dialects (Handley and Nelson, 2005; Section III) supports this hypothesis. b. Vocal imitation and male quality Another proposed function of vocal imitation is that it enables song structure to be used as an accurate indicator of male quality (Buchanan et al., 1999, 2003; Nowicki et al., 1998, 2002). This hypothesis suggests that learned songs serve as honest indicators of male quality because their accurate reproduction requires successful brain development in the face of potentially severe nutritional and developmental stress. Males who accurately reproduce vocal features of song tutors, or who are able to develop complex vocal features, in effect advertise high‐ quality genes, developmental histories, and learning abilities. Such males would presumably offer higher quality genetic input and rearing environments for females that choose them as mates. Studies suggest that nutritional or developmental stress may indeed impair the normal development of vocal brain nuclei (Buchanan et al., 2004; MacDonald et al., 2006; Nowicki et al., 2002; but see Gil et al., 2006). Empirical research on the potential consequences of developmental stress for song development has focused especially on three vocal structural parameters: syllable or song repertoire size, the accuracy of vocal tutor matching, and rates or durations of vocal output (Buchanan et al., 2003; Nowicki et al., 2002; Spencer et al., 2003, 2004, 2005). Presumably, males of higher quality will be able to develop larger vocal repertoires, given the correlations between early stress, song nuclei volume, and vocal repertoire

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size (Nowicki et al., 2002). An honest indicator mechanism such as this might help to explain female preferences for large vocal repertoires, as shown in some species (Searcy and Yasukawa, 1996). Accuracy in imitation may similarly provide an honest indicator of a male’s brain developmental status, given the complexity of the auditory and neural systems that are devoted to the process, and given the numerous environmental challenges that may impede song nucleus development (Nowicki et al., 2002). Rates or durations of vocal output may provide an indicator of the quality of a male’s overall health and developmental history (Spencer et al., 2005). Nowicki et al. (1998) noted a possible trade‐off in the evolution of copying accuracy and repertoire size development, in which a premium on imitation accuracy (quality of copying) might impede the development of large repertoires (quantity of copying) and vice versa. In terms of neural mechanisms, selection for high imitation accuracy may perhaps constrain the development of larger repertoires if the brain space and developmental resources required for accurate imitation secondarily limit the quantity of vocal material that can be imitated in the first place. Our point with this example is that selective pressures on repertoire size and the accuracy of vocal tutor matching may impose, either independently or jointly, secondary effects on the evolution of vocal geographic patterns. Species with larger repertoires are generally thought not to evolve stringent dialects (reviewed by Searcy et al., 2002), a supposition that is partly supported by our analysis in Section III. Beyond the question of whether dialects occur or not, species that have evolved a premium on copying accuracy may evolve greater stability and within‐neighborhood similarity in the structure of their songs (Slater, 1986; see also our analyses in Section III). Vocal geographic structure may thus arise indirectly through selection favoring males with learning programs that render them more adept at ‘‘managing’’ interactions for selfish gain (Kroodsma, 1996), or through selection on song structure as an honest indicator of male genetic and developmental quality (Nowicki et al., 2002). These arguments echo Slater’s suggestion that selection at the level of individuals drives geographic patterns that appear at the level of populations (Slater, 1989). 2. Mechanisms of Vocal Production The ontogeny and evolution of vocalizations are impacted not just by learning but also by the mechanisms that underlie vocal production (Elemans et al., 2004; Fee et al., 1998; Nowicki et al., 1992; Podos and Nowicki, 2004a; Suthers and Goller, 1997). Some recent studies have emphasized the fact that vocal production in birds requires the input and activity of not just the sound source (the syrinx) but also other motor components, including the respiratory system and vocal tract (Beckers et al., 2003; Hoese

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et al., 2000; Nowicki, 1987; Nowicki and Marler, 1988; Riede et al., 2006; Suthers, 2004; Suthers et al., 1999). These additional motor components of vocal production can impose performance limits on the expression and evolution of song features (reviewed by Podos and Nowicki, 2004a). Movements of respiratory muscles, to illustrate, are coordinated precisely with syrinx activity, and appear to be essential in controlling the timing of vocal output (Suthers et al., 1999). Maximal rates of breathing cycles may thus limit the evolution of temporal modulations in song. Components of the vocal tract, including the trachea, larynx, and beak, modify the spectral structure of song, and in particular serve to dampen harmonic overtones and thus enable the production of pure‐tonal songs (Beckers et al., 2003; Hoese et al., 2000; Nowicki and Marler, 1988; Riede et al., 2006; Westneat et al., 1993). Maximal rates of vocal tract reconfiguration, such as those achieved through changes in beak gape, can limit trill rates and frequency bandwidth within trilled vocalizations (Nowicki et al., 1992; Podos, 1997). Recent empirical studies suggest two related effects of production constraints on vocal evolution. First, production constraints can bias the evolution of individual vocal features. The evolution of trill rate in swamp sparrows (Melospiza georgiana), to illustrate, appears to be limited by individual birds’ vocal performance abilities. This was revealed in a study in which young male swamp sparrows were trained with tutor songs in which trill rates had been artificially elevated (Podos, 1996; see also Podos et al., 1999). Birds proved able to memorize the rapid tutor songs, but unable to produce accurate copies of these songs, in manners consistent with a hypothesis of motor constraints on song production (Podos, 1996). Second, production constraints on vocal evolution may be manifest not only in individual features but also as trade‐offs among multiple vocal features. Songs of birds of the sparrow family Emberizidae, to illustrate, exhibit a trade‐off between trill rates and frequency bandwidth (Podos, 1997). Similar patterns have now been described in additional taxa (Ballentine et al., 2004; Draganoiu et al., 2002; Illes et al., 2006), and evidence also suggests that songs produced at higher performance levels—that is, with greater trill rates and/or frequency bandwidths—are more effective with regard to both inter‐ and intraspecific function (Ballentine et al., 2004; Illes et al., 2006). An acoustic trade‐off between trill rate and frequency bandwidth is consistent with a hypothesis of physical constraint: in order to achieve particularly rapid trill rates, the requirement for pure‐tonal quality (and thus rapid vocal tract reconfigurations) sets performance limits on the ranges of frequencies that can be produced over a given time interval (Podos and Nowicki, 2004a). Physical limits or trade‐offs in vocal evolution are worth attention because they may counter selection for particular functions. Thus, to

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illustrate, selection for songs with particularly rapid trills, for example under a local adaptation scenario, would presumably be impeded by physical constraints on trill production. Selection on components of the vocal apparatus for nonvocal functions may also invoke secondary effects on vocal evolution (Podos and Hendry, 2006; Podos and Nowicki, 2004b). Body size, to illustrate, evolves in many animals in response to a range of selective factors such as thermoregulation, fecundity, reproductive rate, and dispersal (Blanckenhorn, 2000; Roff, 1992). Resulting changes in body size may impose secondary impacts on the fundamental frequencies of bird vocalizations, given tight correlations between body size and syrinx size, and the functional relationship of syrinx size and vocal frequency production (Bertelli and Tubaro, 2002; Cutler, 1970; Ryan and Brenowitz, 1985). A second scenario, involving beak and song evolution, has been illustrated recently for Darwin’s finches of the Gala´pagos Islands, Ecuador. In these birds, beak form and function has been shown to evolve in precise correspondence with varying ecological parameters, namely food availability and interspecific competition (Grant and Grant, 1995, 2002, 2006). Analyses of songs of birds with known morphologies have now revealed that the same two vocal parameters mentioned above, trill rate and frequency bandwidth, correlate with variation in beak morphology (Huber and Podos, 2006; Podos, 2001). This correlation seems likely to be the result of proximate constraints on beak gape changes and thus vocal tract configurations during vocal production. Consistent with this hypothesis, birds with larger beaks, predicted to suffer greater constraints on vocal performance (Nowicki et al., 1992), have evolved songs with slower trill rates and narrower frequency bandwidths (Huber and Podos, 2006; Podos, 2001; Podos and Nowicki, 2004b; Podos et al., 2004a). Thus, within given lineages of Darwin’s finches, morphological adaptation under selection for feeding opportunities is predicted to impact vocal performance, and thus the evolution of some vocal features, that is those vocal features that require precise tract reconfigurations for their production. These scenarios of vocal evolution suggest that adaptation to divergent environments may produce, on its own account, structured patterns of vocal geographic variation. To continue with the example of Darwin’s finches, populations of some species have diverged in genetics and morphology on different islands, presumably as a result of the different selective environments on those islands, and a result of limited gene flow between islands (Lack, 1947; Petren et al., 2005). Given the role of the beak in vocal production, adaptive divergence in beak morphology may thus have driven intraspecific, between‐island vocal divergence, with, for example, the largest‐beaked

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populations of a given species experiencing the most severe constraints on trill evolution (Podos and Nowicki, 2004b). The main point of this example, for present purposes, is that geographic patterns of song variation may emerge as incidental by‐products of selection for nonvocal functions, without need for selection for the patterns themselves, as is implied by functional adaptation hypotheses of vocal geographic evolution.

V. EVOLUTION OF GEOGRAPHIC VARIATION IN AVIAN VOCAL SIGNALS: PROSPECTUS As we argued above, advances on both empirical and conceptual fronts provide increasing support for a role of by‐product models of vocal geographic variation. We do not, however, intend to suggest that all facets of song evolution are explained through by‐product mechanisms. Rather, there are myriad factors that may impact geographic divergence of the vocal phenotype. This final section, which follows closely from Podos et al. (2004b), is devoted to surveying the range of scenarios by which song features may diverge among different populations of a species. A. INTERPLAY OF MEMES AND MECHANISMS IN VOCAL EVOLUTION To better address the range of factors involved in vocal evolution we find it useful to distinguish two distinct ‘‘substrates’’ of vocal evolution: memes and mechanisms. Memes refer to song parameters that are transmitted across generations via learning, whereas mechanisms refer to phenotypic bases of vocal expression (development and production) that are transmitted across generations via genetic inheritance (Podos et al., 2004b). Traditional explanations for patterns of vocal geographic evolution have, in our viewpoint, been hampered by a nearly exclusive focus on meme evolution. Part of the reason for the relative neglect of mechanisms, we believe, is that their effect is normally manifest over comparatively broad timescales (Podos, 1997; Ryan and Brenowitz, 1985) and are thus more difficult to identify and study. In the evolution of learned vocalizations, vocal memes and vocal mechanisms may evolve on nonintersecting trajectories. Thus, for instance, selection for increased trill rates may augment trill rates in a population, in the event that the mechanisms responsible for trill production in that lineage are able to accommodate such increases. Similarly, evolutionary changes in body size may have no impact on the vocal frequencies expressed in a population, in the event that vocal frequencies were initially not produced near their limits of possibility. But memes and mechanisms may also interact in vocal evolution. Evolutionary changes in mechanisms

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of vocal production and learning may adjust potential routes of meme evolution, and evolutionary stability in vocal mechanisms may limit the response of memes to directional selection (Podos et al., 2004b). Recognition of the interplay of memes and mechanisms in vocal evolution allows us to identify five categories of potential causes for the evolution of vocal geographic variation (Podos et al., 2004b). B. POTENTIAL CAUSES OF VOCAL GEOGRAPHIC EVOLUTION Songs, like any other phenotype, evolve through the combined effects of drift and selection. We identify two scenarios that involve drift and three that involve selection. We do not claim the scenarios to be mutually exclusive or collectively exhaustive. Rather, song divergence likely involves all of these processes, emphasized to varying degrees and at different times in any lineage’s evolutionary history. We do not attempt to integrate details about dispersal patterns or the timing of learning, which must play a central role in vocal geographic evolution (Ellers and Slabbekoorn, 2003; Krebs and Kroodsma, 1980). Nor do we attempt to evaluate how long‐term ecological processes, such as changes in land use or impacts of fire on habitat, may influence bird distributions and thus dialect formation (Laiolo and Tella, 2005). 1. Cultural Drift Song features may evolve as a result of inaccurate transmission of song memes across generations because of ‘‘errors’’ in learning (Grant and Grant, 1996; Payne, 1996). Distinct trajectories of cultural evolution via copy errors may explain vocal differences among diverging populations, especially during the initial stages of divergence (Lemon, 1975; Slabbekoorn and Smith, 2002a). To illustrate, evolutionary divergence in the phonology (fine structure) of notes, resulting from inaccurate imitation, may explain interisland differences in note structure in some species of Darwin’s finches (Grant and Grant, 1996). Lineages that readily express cultural errors, along with some isolation of descendent populations, seem likely to generate vocal geographic variation. 2. Genetic Drift Song features may also evolve via random changes in the anatomical, physiological, and neural mechanisms that underlie vocal ontogeny and production—and, more specifically, in the genetic loci that underpin these mechanisms. Genetic drift may presumably impact vocal geographic evolution when song memes in a lineage are produced at or near some anatomical, developmental, or performance limit. Consider, for instance, drift in the genetic loci that underlie syrinx mass. Syrinx mass appears to set lower limits

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on vocal frequencies, such that only larger syringes can produce lower frequency sounds. If genetic drift leads to reduced syrinx mass within a given populations, we would expect the potential for frequency production to follow suit. Thus, vocal frequencies, if initially produced near maximal performance capacities (comparatively low frequencies), may accordingly be ‘‘bumped’’ to higher levels in the offshoot population (Podos et al., 2004b). Genetic drift may also alter the structure and function of the brain nuclei involved in song learning, for instance, through random alterations in the timing of interactions between song nuclei (Livingston et al., 2000). Such random changes may have consequences for the timing and content of song acquisition. 3. Cultural Selection Cultural selection occurs when certain vocal memes are favored over others, as a result of the differential effectiveness of those memes in the process of communication. A primary example concerns selection for optimal sound transmission. Songs are known to vary in how well they transmit in different environments, and cultural selection is thought to thus shape certain vocal parameters (Slabbekoorn, 2004; Wiley and Richards, 1978). Songs with slow repetition rates and low frequencies, to illustrate, have been shown to evolve more often in forested habitats than in other habitats, presumably because slow, low‐frequency songs suffer relatively less degradation in forested habitats than elsewhere. Songs with more effective transmission properties may be favored by selection not only in the context of interactions among adults but also in song model imitation by juveniles (Hansen, 1979). Cultural selection for optimal sound transmission has been implicated in the divergence of song among populations of a number of species (Doutrelant et al., 1999; Handford and Lougheed, 1991; Hunter and Krebs, 1979; Ruegg et al., 2006; Slabbekoorn and Smith, 2002b; Wiley, 1991). 4. Natural Selection Natural selection may drive vocal geographic divergence through its influence on either memes or mechanisms. With respect to memes, natural selection may facilitate vocal divergence via ‘‘reinforcement,’’ in which selection against hybrid production favors those birds that produce the most species, population, or locality distinctive songs (Butlin and Ritchie, 1994; Marler, 1957, 1960; Nelson and Marler, 1990; Ptacek, 2000). This is the broader context in which we would place the local adaptation hypothesis of dialect evolution. The example of the multifunctional role of the beak in singing and feeding, discussed in the previous section, illustrates how natural selection on mechanisms can cause incidental vocal evolution (Nowicki et al., 1992; Podos and Nowicki, 2004a,b). To reiterate, natural selection in the context of selection for food availability, food type, and interspecific competition is known to

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drive precise changes in beak form and function (Grant and Grant, 1995, 2002, 2006). Given the role of the beak in vocal production, such evolution can drive vocal changes as a secondary consequence (Podos and Nowicki, 2004b). 5. Sexual Selection Sexual selection is traditionally regarded as favoring elaborate or complex forms of vocal signals, especially as a result of female choice (Andersson, 1994; Catchpole and McGregor, 1985; Searcy and Andersson, 1986; Searcy and Yasukawa, 1996). Sexual selection may also favor vocal features that challenge males’ developmental and performance capacities (Nowicki et al., 2002) or that enable increased precision in communication in male–male interactions (Beecher and Brenowitz, 2005; Todt and Naguib, 2000). As a general observation, the course of sexual selection is often haphazard, with different signal parameters favored or exaggerated in different lineages (Boughman, 2001; Panhuis et al., 2001). Divergent pathways of sexual selection on song may similarly result in signal divergence in offshoot populations, at least to the extent that populations remain in genetic and cultural isolation. To illustrate we turn again to potential trade‐offs involving repertoire size. In some lineages, female preferences for complex signals may favor the evolution of large repertoires, whereas in other lineages female preferences for accurate imitation may favor small repertoires. Moreover, selection for song sharing among males in other lineages may favor moderate‐sized repertoires. Divergence of sexual selection pressures among populations may thus presumably lead to geographic divergence in repertoire size.

VI. SUMMARY Our goal in this chapter has been to evaluate, from both empirical and conceptual perspectives, the factors that facilitate the evolution of geographic variation in bird vocalizations. Studies on this topic have traditionally focused on the evolution of song ‘‘dialects,’’ and have emphasized functional hypotheses to explain their evolution. Two such hypotheses, ‘‘local adaptation’’ and ‘‘social adaptation’’ hypotheses, focus on the potential role of song in aiding recognition of males, either by locality or by social group. A quantitative survey of results from papers published on dialects, between 1962 and 2006, however, suggests limited direct support for functional hypotheses. An alternative set of hypotheses suggests that song features may diverge through ‘‘by‐product’’ scenarios, in which selection for nonrecognition functions drives incidental changes in song structure, and geographic variation therein. Examples of such functions involve the evolution of song learning in neighbor–neighbor song sharing and the

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evolution of song learning in the context of sexual selection for male quality. We also describe scenarios by which songs may diverge indirectly through selection on components of the vocal apparatus such as body size and beak form and function. To conclude, we outline scenarios by which songs may diverge geographically; via cultural drift, genetic drift, cultural selection, natural selection, and sexual selection. Empirical study of these scenarios, together with countinued descriptions of vocal learning strategies and patterns of dispersal, may provide insights into vocal geographic evolution and thus propensities for speciation by reproductive isolation.

Acknowledgments J.P. gratefully acknowledges financial support from the National Science Foundation (NSF IOB‐0347291). P.W. gratefully acknowledges financial support from the National Science Foundation (NSF IBN‐98‐01490) and the Zoology Scholarship Fund for Excellence (Dorothea Stengl) at University of Texas. Helpful comments on previous versions of this chapter were provided by M. Naguib, P. Slater, L. Higgins, D. Hillis, M. Kirkpatrick, C. Sexton, M. Ryan, and W. Wilczynski.

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