the evolution of geographic variation in birdsong...evolution in z. leucophrys nuttalli is that of...

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 andevolutionary biology, university of massachusetts

amherst, massachusetts 01003{department of natural resources conservation, graduate program inorganismic and evolutionary biology, university of massachusetts

amherst, massachusetts 01003

I. INTRODUCTION

Evolutionary biologists have a long‐standing interest in how organismsvary geographically. This interest is motivated in part by recognition of arelationship between geographic variation and the process of speciation.Traits that vary over a given species’ range may serve as neutral, noncon-tributing indicators of the early stages of divergence, for example as differ-ent populations adapt to distinct environments and undergo correspondinggenotypic and phenotypic divergence (Schluter, 2000). In other cases, traitsthat vary geographically might contribute to the speciation process. Primaryexamples of such traits are mating ornaments and displays which, in manyanimals, 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 ofmating signals can, under particular circumstances, facilitate assortativemating, reproductive isolation, and thus continued divergence among popu-lations (Irwin et al., 2001; Lachlan and Servedio, 2004; Liou and Price, 1994;Pa yne et al. , 2000; Ptacek, 2000 ; Slabbekoo rn and Smith, 2002a). Diver-gence of mating signals and mate recognition systems is increasingly recog-nized as an important factor in speciation (Ryan, 1986).

Studies of vocal signals in birds offer potentially useful opportunities forempirical 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 phe-nomenon that can be attributed largely to the tendency for many birds to

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404 JEFFREY PODOS AND PAIGE S. WARREN

learn to vocalize through imitation. Imitative vocal learning enables theready generation and rapid transmission of novel patterns of vocal structure(Slabbekoorn and Smith, 2002a; Slater, 1989). Indeed, it has been arguedthat consequent plasticity of the vocal phenotype has been instrumental ingenerating the high species diversity that characterizes some avian taxa(Fitzpatrick, 1988; Vermeij, 1988; cf. Baptista and Trail, 1992; see alsoIrwin and Price, 1999; ten Cate, 2000). A Science Citation Index query(Fig. 1A) attests to rapidly expanding activity, over the past 15 years, in thestudy of the causes and consequences of geographic variation in birdsong.

Prospects for this field of inquiry, however, did not look very promisingjust a few decades ago. Mounting unease centered especially on questionsabout the evolution of ‘‘dialects,’’ a particular form of vocal geographicvariation. This unease was well illustrated in Baker and Cunningham’sinfluential review of the phenomenon of dialects, a main goal of which wasto articulate a ‘‘synthetic theory’’ on dialect origins andmaintenance (Bakerand Cunningham, 1985). Responses to this review, provided by a panel of

90

60

30

0

5-year span

Num

ber

of li

tera

ture

cita

tions

1965–1969

1970–1974

1975–1979

1980– 1984

1985– 1989

1990– 1994

1995– 1999

2000– 2004

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.

GEOGRAPHIC VARIATION IN BIRDSONG 405

peer experts, were forceful and included laments about difficulties in quan-tifying patterns of vocal geographic variation, in comparing data across taxa,in extrapolating laboratory data to field situations, and, most critically forpresent purposes, in identifying evolutionary factors that facilitate dialectformation 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 maintainedthrough adaptive processes, and thus, in order to understand their evolutionwe 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 andCunningham, 1985, p. 86). The peer expert panel generally found Baker andCunningham to be overly supportive of local adaptation hypotheses ofdialect evolution and overly dismissive of alternative hypotheses (Baptista,1985; McGregor, 1985; Waser, 1985). Given the wide range of stated unre-solved issues, along with limited evidence at the time that could support anyparticular hypothesis of dialect evolution, it is perhaps not surprising thatpublication of the Baker and Cunningham exchange was followed by aperiod of dampened enthusiasm for the field (Fig. 1B).

Our goal in this chapter is to assess the present state of the field, from bothempirical and conceptual perspectives. Prior reviews on the topic of geo-graphic variation in bird vocalizations have been numerous (Baker andCunningham, 1985; Krebs and Kroodsma, 1980; Mundinger, 1982;Slabbekoorn and Smith, 2002a), and readers may question the value of yetanother contribution. Yet new information continues to accrue. Moreover,recent general advances in the study of birdsong—a field that has remainedactive in realms ranging from mechanisms to ecology and evolution—haverenewed theway that we can study geographic variation, in at least twoways.First, we have gained numerous insights into the range of possible functionsof 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 ofsong learning suggests that patterns of vocal geographic variation mayemerge as secondary by‐products of selection on other functions, ratherthan through direct selection for geographic patterns themselves (Slater,1989). Second, advances in our understanding of mechanisms of vocalproduction suggest additional ‘‘non‐functional’’ scenarios by which geo-graphic vocal variation may emerge. Whereas earlier models of song evolu-tion focused on vocal imitation and cultural evolution, geographic variationin song may also emerge through evolution of the morphological and physi-ological underpinnings of song production.


We begin with a brief overview of the study of geographic variationin birdsong, focusing in particular on song dialects and hypothesis thathave traditionally been put forward to explain their evolution (Section II).To evaluate the status of these hypotheses, we next conduct a comparativesurvey based on data gleaned from published literature (Section III). Ourfocus on dialects in these two sections is not motivated by an opinion thatdialects are inherently more interesting than other patterns of geographicvariation, or by an opinion that dialects are a unique phenomenon requiringspecial explanation. Rather it is simply because dialects have been the focusof the majority of published studies in this area. We then discuss how recentadvances in the study of birdsong have enriched our ability to addressquestions about vocal geographic variation, arguing in particular that mod-ern studies on mechanisms of song learning and production support ‘‘by‐product’’ models of vocal geographic evolution (Section IV). As we arguebelow, the by‐product hypothesis of vocal geographic evolution can beregarded as a broader version of the ‘‘epiphenomenon’’ hypothesis ofvocal dialect evolution. In Section V, we summarize the factors that maycontribute together to the evolution of geographic variation in birdvocalizations.

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 literatureon geographic variation in birdsong is with Marler and Tamura’s classicwork on white‐crowned sparrows, Zonotrichia leucophrys nuttalli (Marlerand Tamura, 1962, 1964). The phenomenon of geographic variation inbirdsong had been observed and reported on previously, but only a handfulof published studies (Borror, 1961) had made use of sound spectrograms,which provide an invaluable visual aid for assessing patterns of vocalgeographic variation. Marler and Tamura described, for Z. leucophrysnuttalli of Northern California, the now‐classic ‘‘dialect’’ pattern of geo-graphic variation, in which songs within particular populations ‘‘all sharecertain salient characteristics . . . which differ in certain consistent respectsfrom the patterns found in neighboring populations’’ (Marler and Tamura,1964, pp. 1483–1484). A Berkeley population of birds, for instance, wasfound to sing trills with a specific structure (fewer notes, of descendingfrequencies) distinct from trills sung by other nearby populations (Marlerand Tamura, 1962). As suggested by Marler and Tamura and as generallycorroborated by later studies (reviewed by Kroodsma et al., 1985), twomain


factors appear to facilitate dialect evolution in Z. leucophrys nuttalli. Thefirst is that songs are culturally transmitted across generations, via vocallearning. A central role for vocal learning in song development in thesebirds was first demonstrated in a series of laboratory studies in which youngmales were exposed to various training regimes (Marler and Tamura, 1964).Training models presented over loudspeakers, during the sensitive phase ofsong acquisition, were shown to be copied precisely by these birds, evenwhen training models had been recorded from nonnatal localities (Marlerand Tamura, 1964). By contrast, in the absence of training models, birdswere found to develop songs with atypical, degraded acoustic structure. Itwas thus argued that song learning enables the ready transmission of songpatterns across generations, not just from fathers to sons but also potential-ly to other young males in a population. Song learning facilitates dialectformation by providing a mechanism for generating vocal novelties, whichmay emerge through copying ‘‘errors’’ (Marler and Tamura, 1964; see alsoBaptista, 1977; Lemon, 1975; Marler and Peters, 1987, 1988; Slater, 1986,1989).

The second main factor that appears to contribute to song dialectevolution 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 theylearned to sing, or that dialects may emerge if males do disperse but thensettle preferentially in locations where they hear songs similar to theirown. 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 subspeciesare indeed moderate, most commonly within a few hundred meters andrarely exceeding 1 km. By contrast, data in support of biased dispersal inthis subspecies have been equivocal, and their interpretation controversial( Baker and Mewaldt, 1981 ; Baker et al. , 1985; Hafn er and Peters on, 1985;Petrinovich et al., 1981).

The Z. leucophrys nuttalli system turns out to be comparatively, althoughby no means absolutely, straightforward; other systems examined to datefeature 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 pugetensisand Z. leucophrys oriantha, also exhibit song dialects. In the former sub-species, 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 systemati-cally among subalpine meadow populations (Chilton et al., 1995; Harbisonet al., 1999; Orejuela and Morton, 1975). Birds of both subspecies turn out


to be less philopatric than their nuttalli counterparts, especially Z. leu-cophrys oriantha, which is fully migratory. Recent field and laboratorystudies by Nelson and colleagues indicate that birds of both subspeciesmemorize multiple models at their natal grounds, and then, postdispersal,‘‘select’’ among memorized models to best match song types present attheir breeding grounds (‘‘overproduction’’ and ‘‘selective attrition’’:Nelson, 2000; Nelson et al., 1995, 1996a). An alternative model of songlearning, postdispersal acquisition and memorization of song models, asposited by Heinemann (1981), appears not to apply in these birds. A fourthsubspecies, Z. leucophrys gambelli, a long‐distance migrant that breeds inthe sub‐Arctic, shows no evidence of dialects (Austen and Handford, 1991;DeWolfe et al., 1974; Nelson, 1998). Nelson (1999) argues that the post-dispersal, prebreeding time frame in this subspecies is too brief to allow thekinds of extended interactions that are required for song matching viaselective attrition.

Studies of white‐crowned sparrows thus demonstrate that learning stra-tegies, dispersal patterns, and resulting geographic song patterns can behighly variable even within a single species. This variation of course repre-sents just a tiny sample of the diversity present in the songbirds as a group(Krebs and Kroodsma, 1980; Kroodsma, 1996; Slater, 1989). Indeed, aprimary message of prior reviews of geographic song variation has beenof caution in extrapolating results across species (Kroodsma, 1996). Onewidespread phenomenon that does not apply in white‐crowned sparrows,for instance, is postdispersal acquisition and learning of song models, as hasbeen shown, for example, in the brood‐parasitic brown‐headed and bronzedcowbirds, Molothrus ater and M. aeneus (Rothstein and Fleischer, 1987;Warren, 2002). The white‐crowned sparrow system also does not addressthe 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 tendto produce only a single song type. Patterns of geographic variation, and theecological and learning‐based causes of these patterns, tend to be morechallenging although sometimes still possible to document in species withsong repertoires (Searcy et al., 2002; Slater et al., 1984; Williams andSlater, 1990).

Explaining the formation of dialects might be relatively straightforward ifpatterns of geographic variation in song evolved solely as an incidentalby‐product of particular learning mechanisms and patterns of dispersal(Fig. 2A). However, since the earliest studies of avian vocal geographicvariation, authors have also spent considerable energy contemplating thepotential fitness benefits of particular geographic song patterns. Implicit inthis exercise is the hypothesis that selection for particular geographic patterns

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 anddispersal patterns that shape vocal geographic divergence (Fig. 2B; Jenkins,1985). To explore this scenario further we turn again to the phenomenon ofdialects and hypotheses that have been put forward to explain their evolution.We provide brief commentary on definitions of song dialects, and then exam-ine three broad categories of hypotheses that have traditionally been for-warded to explain their evolution.

B. DEFINITION OF SONG DIALECTS

As noted by Slat er (1989 , p. 33), ‘‘geograph ic vari ation [in song] is seldomthe simple matter that the word ‘dialect’ might imply.’’ Indeed, ‘‘dialects’’have been described across a range of geographic scales (e.g., compareLea der et al ., 2000 with Warre n, 2002; see also Mund inger, 1982 ) and fora variety of vocal parameters. Schematically, we can represent geographicstructure in vocal parameters as taking a range of spatial patterns, includingrandom variation (Fig. 3A), gradual and shallow clines (Fig. 3B), or steepclines with stepped variation (Fig. 3C). This latter form is consistent withclassic definitions of dialects (Marler and Tamura, 1962; Mundinger, 1982).Numerous researchers have noted that strict dialects (Fig. 3C) may be acomparatively rare phenomenon (Slater, 1986, 1989). The functional hypoth-eses that we review in Section II.C have drawn on the presence of sharpboundaries between dialect neighborhoods as justification for regardingdialects as being actively maintained by selection.

Random

Clinal

Dialect

Geographic locality

Voc

al p

aram

eter

(s)

FIG. 3. Schematic representation of three patterns of geographic variation in song. Geograph-

ic 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 fitnessadvantages when they are able tomate withmales from their natal regions, inpreference to males from other regions. According to this hypothesis, birdsthat select mates from their natal regions will gain fitness advantages becausetheir offspring will more likely express adaptations to local ecological condi-tions. Because song is a key mating signal in many species of birds, songstructure is thus posited to diverge by locality, under selection for accuratelymarking birds’ natal localities. Baker and colleagues arguedmore specificallythat dialects serve as markers for ‘‘coadapted gene complexes,’’ and thatdialect boundaries represent secondary contact zones between partially


isolated populations (Baker, 1982; Baker and Thompson, 1985; Baker et al.,1982). The local adaptation hypothesis makes four predictions, each of whichhas been subject to considerable scrutiny.

The first prediction is that birds should learn their vocalizations early,before dispersing from their natal regions (MacDougall‐Shackleton andMacDougall‐Shackleton, 2001; Payne, 1981; Rothstein and Fleischer,1987). Decades of study have now shown wide diversity in the timing ofsong acquisition, and so this predictionmay hold for some species but clearlydoes not hold for others. Brown‐headed cowbirds, for instance, learn songspostdispersal, and thus dialects in this species can best be attributed to otherhypotheses (Rothstein and Fleischer, 1987). The timing of learning hastraditionally been studied in laboratory conditions, and a general point ofdiscussion concerns the applicability of laboratory results to field contexts.In particular, it has been noted that species that only learn early in thelaboratory, from taped tutor songs, may still retain the ability to learnsongs later in life, if trained by live tutors (Baptista and Petrinovich, 1984).The relative impact of social influences on song learning continues as an areaof active study (Beecher and Brenowitz, 2005; Johannessen et al., 2006).

Second, the local adaptation hypothesis predicts that dispersing birds willtend to settle, more than would be expected by chance, in localities wherebirds 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; BakerandMewaldt, 1981; Baker et al., 1985; Hafner and Peterson, 1985; Petrinovichet al., 1981). A call by Kroodsma (1985a) for additional empirical work in thisarea still rings true.

Third, the local adaptation hypothesis predicts that dialect groups shouldcome to be genetically differentiated, as a result of recent histories ofassortative mating. The main challenge in tests of this prediction has beento identify genetic parameters in which dialect neighborhoods differ. Againthe evidence has been inconclusive and open to interpretation (Baker et al.,1982; Lougheed and Handford, 1992; Payne and Westneat, 1988; Zink andBarrowclough, 1984). In the most comprehensive study on this topic todate, MacDougall‐Shackleton and MacDougall‐Shackleton (2001) ana-lyzed variation in microsatellite allele frequencies among eight dialectregions of Z. leucophyrs oriantha, and concluded that ‘‘dialect borders areassociated with some reduction in gene flow, but they appear to be very lowwalls rather than barriers in any strict sense’’ (MacDougall‐Shackleton andMacDougall‐Shackleton, 2001, p. 2574).

The fourth prediction of the local adaptation hypothesis is that femalesshould evolve mating preferences for males from their natal dialects.A number of assays have been developed to measure the impact of vocal


variation on female preferences (Searcy, 1992). In tests of the influence ofdialect variation on female preferences, researchers have generally turnedto the copulation solicitation display assay. In this assay, females are cap-tured, acclimated to laboratory conditions, and then treated with exoge-nous estradiol, normally via silastic implants, to increase sexual receptivity.Songs of different dialects are then presented over loudspeakers, and thestrength and vigor of resulting solicitation displays documented. Females ofa number of species have been found to respond more often and morevigorously to local songs than to foreign songs (Baker et al., 1981, 1987a;Searcy et al., 1997, 2002). Differential preferences for local songs presum-ably emerge because of greater familiarity with local dialects (Baker et al.,1981), or through learned associations between local songs and socialfeedback experienced early in life (Riebel, 2003). Learned preferences formales that sing local songs could foster, through coevolution of dialect andpreference, the divergence of dialect forms (Riebel, 2003).

It is useful to note that of these four predictions, only the second is uniqueto the local adaptation hypothesis. The others predictions are also consistentwith other hypotheses of dialect evolution (see below).

Early studies of vocal geographic variation in birds focused on the localadaptation hypothesis (Marler and Tamura, 1962; Nottebohm, 1969), whichis perhaps not surprising given that birdsong studies of that era weregenerally geared toward questions about species recognition. Many birdsproduce species‐specific vocalizations, and ornithologists have longhypothesized 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, whichshowed time and again that birds respond more strongly to playback ofconspecific than heterospecific song (Falls, 1963; Gill and Murray, 1972;Martin andMartin, 2001; Milligan, 1966; reviewed by Becker, 1982). Similarpatterns of elevated responses to playback of conspecific versus heterospe-cific song have also been demonstrated in females from numerous species(Searcy, 1992). From an evolutionary perspective, interspecific divergencein vocal structure is thought to be driven by two related factors, namelyselection for avoiding interspecific acoustic competition (Nelson andMarler, 1990) and selection against cross‐species mating and hybrid produc-tion (Butlin, 1995; de Kort and ten Cate, 2001; Haavie et al., 2004; Seddon,2005 ; see Sætre et al., 1997, for a paral lel argumen t for the evo lution ofplumage divergence). According to these hypotheses, individuals within apopulation that best transmit the correct species identity, through produc-tion of the most species‐typical songs, experience lower probabilities offitness‐reducing interspecific hybridization (Noor, 1999). It was a smalllogical step from the species recognition hypothesis to the local adaptation


hypothesis. Both hypotheses focus on the same evolutionary factors, notablyselection against mating ‘‘errors.’’ The only substantive difference betweenthe hypotheses is thatmating errors occur at inter‐ versus intraspecific scales.As with tests of conspecific recognition, tests of local recognition abilitieshave relied heavily on playback designs (Baker et al., 1987a; Harris andLemon, 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 therole of song in recognition, but with regard to social groups rather thanlocality. These ‘‘social adaptation hypotheses’’ suggest that males gainfitness advantages by singing songs similar to those of other males in theirregion, whereas males that sing nonlocal songs are subject to social penal-ties. One version of this hypothesis, the deceptive mimicry hypothesis(Payne, 1981), posits that subordinate males that successfully mimic thevocalizations of dominant males are able to improve their access to mates,and also to reduce probabilities of aggressive interactions with dominantmales. Under this scenario, dialects should be temporally unstable anddepend on which individuals are dominant at a given time (Payne, 1981;Rothstein and Fleischer, 1987). A related hypothesis is that of ‘‘honestconvergence,’’ which posits that dialects serve as honest signals of long‐term residence (Rothstein and Fleischer, 1987). Similarly, the colony pass-word hypothesis (Feekes, 1977) proposes that dialects can serve as markersof group membership in colonial species, and thus facilitate the identifica-tion of intruders into a colony.

Social adaptation hypotheses predict that individuals will learn newvocalizations on dispersal to a new dialect area, in order to match (mimic)vocalizations at the new locality (Payne, 1981). If individuals are con-strained to acquire and crystallize new vocalizations early, prior to juveniledispersal, then the social dynamics of adults cannot play a role in themaintenance of dialects. Postdispersal learning has been implicated in anumber of species, including the cowbird systems mentioned earlier(Rothstein and Fleischer, 1987; Warren, 2002).

Not surprisingly, male–male interactions have been a primary focus intests of social adaptation hypotheses. The deceptive mimicry hypothesispredicts that accurate mimics should incite less aggressive responses fromdominant males, and moreover that males should be more aggressivetoward other males from neighboring dialects as opposed to from theirhome dialects (Rothstein and Fleischer, 1987). The colony passwordhypothesis similarly predicts that members of a colony should be more


aggressive toward males from foreign rather than local dialects, becauseforeign dialects would indicate potential intruders from other colonies(Feekes, 1977).

Dialect size is also an important component of social adaptation hypoth-eses. The colony password and deceptive mimicry hypotheses predict thatdialect areas should be small, corresponding to ‘‘socially cohesive units’’(Payne, 1981). The honest convergence hypothesis does not require thatdialects be small, but does, however, require them to be not substantiallylarger than average adult dispersal distance (Warren, 2002). If a dialect areais too large, dialect identity can no longer serve as an honest signal of localresidence because newly arrived males would produce vocal signals indis-tinguishable from those of local residents (Warren, 2002). In general, it isdifficult to conceive of a scenario in which social adaptation maintainsboundaries between larger regional dialects; if dialects were too large,most individuals within a dialect would be unlikely to ever encounterother dialects.

3. Epiphenomenon Hypothesis

The epiphenomenon hypothesis, first articulated (although only briefly)by Andrew (1962), posits that dialects emerge as incidental by‐products ofparticular patterns of learning and dispersal, both of which evolve underselection pressures unrelated to dialect formation. In other words, selectionon dialect patterns per se need not produce dialects or maintain the discreteboundaries that characterize dialects. This has been considered by someto be the null hypothesis for dialect evolution, and it indeed makesfewer assum ptions than do the other classes of hypothese s ( Fig. 2A vs B;Lemon, 1975; Wiens, 1982). As we understand it, the epiphenomenonhypothesis differs from functional hypotheses of dialect evolution in thatit does not require a history of continuous contact between birds fromdifferent dialect groups or a history of negative selection against birdsthat sing foreign songs. Continuous contact is necessary in the functionalhypotheses, for example as intruders that sing foreign songs are rejected, inorder to generate the negative selection pressures implied therein.

Most simply, nonfunctional divergence among dialect groups can resultfrom differential trajectories of selection among isolated populations.To illustrate, numerous lines of evidence now suggest that songs undergoselection for optimal transmission through the acoustic environment(Nottebohm, 1969, 1985;Wiley andRichards, 1978; reviewedbySlabbekoorn,2004). Such selection pressures may drive intraspecific vocal divergence, andthus the emergence of dialect patterns, when different populations come tooccupy distinct habitats (Doutrelant et al., 1999; Handford and Lougheed,1991; Hunter and Krebs, 1979; Patten et al., 2004; Slabbekoorn and


Smith, 2002b). Limited dispersal, and thus limited contact among localities,may facilitate the nonfunctional evolution of dialects, as vocal noveltiespersevere only in limited regions (Lemon, 1975; Slater, 1989). (A note onterminology: vocal divergence through acoustic adaptation to differenthabitats itself clearly has functional bases. However, any resulting geo-graphic patterns would be considered nonfunctional, given that the locusof acoustic adaptation was site specific and thus did not favor geographicdiversification per se).

Slater (1985, 1986, 1989) offered a compelling defense of the epiphenom-en on hypo thesis for dialect evolution. As noted by Slat er (1986 , p. 96), ‘‘It ispossible that dialects have no functional significance, but that the differ-ences they represent are simply spurious byproducts of vocal learning.If song is learnt and dispersal after learning is restricted, some sort ofvariation in both time and space seems inevitable. . .’’ Slater defened theepiphenomenon hypothesis by raising three points. First, song learning mayevolve for a range of functions besides dialect recognition. Such functionsmay include helping birds to better match the local acoustic environment orto 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 indispersal patterns and the accuracy of song learning, as studied in computersimulations, illustrate that the epiphenomenon mechanism can indeed gen-erate discrete dialect patterns, particularly in species with small repertoiresizes (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 interpopulationdivergence refer generally to situations in which selection on one trait orsuite of traits drives incidental changes in, or limits on the expression of,other traits or suites of traits. Correlated evolution of multiple traits mayarise through shared genetic or phenotypic mechanisms, such as whenmultiple functions make use of the same anatomical or physiological com-ponents (Patek et al., 2006; Podos and Hendry, 2006). Correlated evolutionof multiple traits may also arise as result of life history constraints, asmetabolic or energetic resources allocated to certain traits limit the develop-ment or expression of other traits (Roff, 1992). By‐product mechanismshave been of particular interest in studies of ‘‘ecological speciation,’’ whichposits that incidentally modified trait(s) can impact patterns of mating andreproductive isolation (Boughman, 2001; Dobzhansky, 1951; Mayr, 1942;Orr and Smith, 1998; Podos, 2001; Podos and Hendry, 2006; Rice andHostert, 1993; Ruegg et al., 2006; Schluter, 2000, 2001). Rundle et al.(2000), for instance, provided evidence that adaptive divergence of bodysize in benthic versus limnetic stickleback fishes has facilitated assortative


mating by morph, presumably because body size is used as a cue in mateassessment and selection. According to by‐product models of trait diver-gence, the emergence of reproductive isolation among populations is notnecessarily a product of selection for that isolation, but potentially a sec-ondary consequence of ecological adaptation in other traits (Podos andHendry, 2006).

III. ASSESSING HYPOTHESES OF DIALECT EVOLUTION

The potential relevance of the three hypothesis classes outlined abovehas now been addressed in a moderate number of species. Our goal in thissection is to survey the primary literature, in order to identify potentiallycommon conditions that underlie dialect formation and maintenance. Ofparticular interest is the timing of song acquisition, given that a key predic-tion distinguishing the two functional hypotheses is whether acquisitionoccurs pre‐ or postdispersal. Other potential correlates of dialect patternsexamined here include social systems, degree of seasonal mobility, andvocal repertoire size. Examination of these parameters cannot providedefinitive support for or against the three classes of dialect hypotheses,especially given difficulties noted by many other researchers in comparingdialect data across species. Rather, our intent is to gain a sense of trends orconditions 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 evi-dence of sharp geographic boundaries between signal forms, of discretedifferences among signal forms, and of uniformity of signal forms withingiven localities (Fig. 3C). Thus, a species exhibiting geographic variation ina continuous character such as pulse rate or dominant frequency was notincluded unless this variation was shown to be partitioned discretely amongsites (e.g., as shown in Leader et al., 2000). We excluded studies for whichdata were ambiguous about one or more of the above criteria (Naguib et al.,2001; Podos, 2007). Moreover, a species exhibiting many discretely differ-ent signal forms was not included unless these signal types were geographi-cally partitioned such that narrow boundaries between types could beclearly 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 Common name Family Signal References

1 Amazona auropalliata Yellow‐naped Amazon Psittacidae group calls Wright, 1996; Wright and Wilkinson, 2001;

Wright et al., 2005

2 Phaethornis

longuemareus

Little Hermit Trochilidae song Wiley, 1971

3 Miliaria calandra Corn bunting Fringillidae song Holland et al., 1996; McGregor, 1980, 1983;

McGregor and Krebs, 1984; McGregor

and Thompson, 1988; McGregor et al.,

1988

4 Emberiza citrinella Yellowhammer Fringillidae song Baker et al., 1987a; Møller, 1982; Hansen,

1985, 1999; Glaubrecht, 1989, 1991;

Rutkowska‐Guz and Osiejuk, 2004

5 Emberiza hortulana Ortolan Fringillidae song Conrads, 1976; Conrads and Conrads, 1971;

Thielcke, 1969

6 Zonotrichia leucrophrys

nuttalli

Nuttall’s white‐crownedSparrow

Fringillidae song 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

7 Zonotrichia leucophrys

oriantha

Montane White‐crownedSparrow

Fringillidae song Baptista, 1977; Chilton and Lein, 1996a,b;

Nelson, 2000; Nelson et al., 1996b, 2004;

Soha et al., 2004

(Continued)

417

TABLE I (Continued)


8 Zonotrichia leucophrys

pugetensis

Puget Sound White‐crownedSparrow

Fringillidae song 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

9 Zonotrichia capensis Rufous‐crownedSparrow

Fringillidae song King, 1972; Lougheed and Handford, 1992;

Nottebohm, 1969, 1975; Tubaro and

Segura, 1994; Zink et al., 1991

10 Pooecetes gramineus Vesper Sparrow Fringillidae song Kroodsma, 1972

11 Amphispiza belli Sage Sparrow Fringillidae song Rich, 1981

12 Pipilo maculatus Spotted Towhees Fringillidae song Borror, 1975

13 Passerina cyanea Indigo Bunting Fringillidae song Payne, 1981, 1982, 1983; Payne and

Westneat, 1988

14 Cacicus cela Yellow‐rumped Cacique

(Surinam)

Fringillidae song Feekes, 1977

15 Cacicus cela Yellow‐rumped Cacique

(Panama)

Fringillidae song Trainer, 1988, 1989; Trainer and Parsons,

2002

16 Molothrus ater Brown‐headed Cowbird Fringillidae song 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

17 Molothrus aeneus Bronzed Cowbird (Winter) Fringillidae song Dolbeer, 1982; Rothstein, 1980; Warren,

2000, 2003

418

18 Molothrus aeneus Bronzed Cowbird

(Breeding)

Fringillidae song Carter, 1984; Clotfelter, 1995; Dolbeer,

1982; Rothstein, 1980; Warren, 2000,

2002, 2003

19 Dolichonyx oryzivorus Bobolink Fringillidae song Avery and Oring, 1977; Trainer and

Peltz, 1995

20 Fringilla coelebs Chaffinch Fringillidae call (‘‘rain

call’’)

Baptista, 1990; Sick, 1939; Sorjonen, 2001;

Thielcke, 1969, 1988a,b, 1989

21 Chloris chloris Greenfinch Fringillidae song Guttinger, 1974, 1976

22 Carpodacus mexicanus House Finch Fringillidae song Mundinger, 1975, 1982

23 Calcarius pictus Smith’s longspur Fringillidae song Briskie, 1999

24 Himatione sanguinea Apapane Fringillidae song Ward, 1964

25 Thryomanes bewickii Bewicks Wren Certhiidae song Kroodsma, 1974, 1985b

26 Troglodytes troglodytes European Wren (UK) Certhiidae song Catchpole and Rowell, 1993

27 Troglodytes troglodytes European Wren (France) Certhiidae song Kreutzer, 1974

28 Certhia brachydactyla Short‐toed Treecreeper Certhiidae song Seitz et al., 1994; Thielcke, 1969, 1984, 1986,

1987; Thielcke and Wuestenberg, 1985

29 Turdus iliacus Redwing Passeridae song Bjerke, 1974, 1980, 1982, 1984; Bjerke and

Bjerke, 1981; Espmark, 1981, 1982;

Fonstad et al., 1984; Mork, 1974

30 Vidua chalybeata Village Indigobird Passeridae song Payne, 1973, 1981, 1987; Payne and Payne,

1977

31 Vidua purpurascens Dusky Indigobird Passeridae song Payne, 1973, 1981, 1987

32 Poecile atricapillus Black‐capped Chickadee Paridae call

(‘‘gargle’’)

Ficken et al., 1978, 1985

33 Poecile atricapillus Black‐capped Chickadee Paridae song (‘‘fee

bee’’)

Kroodsma et al., 1999

34 Poecile carolinensis Carolina Chickadee Paridae song (‘‘fee

bee’’)

Ward, 1966

(Continued)

419

TABLE I (Continued)


35 Pachycephala olivacea Olive Whistler Corvidae song White, 1985, 1986, 1987

36 Cyanocitta cristata Blue Jay Corvidae group calls

(‘‘bell’’

call)

Kramer and Thompson, 1979

37 Creadion carunculatus Saddleback Callaeatidae song Jenkins, 1978

38 Menura

novaehollandiae

Superb Lyrebird Menuridae song Powys, 1995; Robinson and Curtis, 1996

39 Nectarinia osea Orange‐tufted Sunbirds Nectariniidae song Leader et al., 2000, 2002, 2005

40 Orcinus orca Orca, Killer Whale Cetacea group calls Ford, 1991

41 Pyseter macrocephalus Sperm Whale Certacea group calls Weilgart and Whitehead, 1997

42 Saguinus labiatus

labiatus

Red‐chested Moustached

Tamarin

Callithricidae long calls Masataka, 1988

420


from each other. This is not to say that vocal vari ation in speci es distri butedacross islands cannot be consid ered dial ectal; our criteria here were set forpur poses of analys is only.

B. LITE RATURE S URVEY

Our searc h for published studies of dialect s was con ducted using bothdigi tal da tabases (ISI Web of Scienc e) and search es of citati on sect ions ofpubl ished works on dial ects. We ident ified abou t 200 studi es of 52 species inwhi ch the authors suggest ed that the vocal system could be descri bed asdial ectal. Of these, 42 cases met our present definition of diale cts ( Fig. 3C).We include data from 141 studies of these 42 cases in our analysis ( Table I ).We compile d data for them ( Table II ), as follow s.

1. Dialect Chara cteristics

We coded three descripti ve charac terist ics of dialects: spatial extent(dial ect scale), temporal stabi lity of dialect bounda ries , and presenc e orab sence of bilingual ism at bounda ries be tween dialect s. Di alect scale wascod ed according to four categ ories: microgeo graphic, small , medi um, andlarg e, coded, respec tively, as 0, 1, 2, or 3. Microgeogr aph ic dialect s feature10 indi viduals or less in each dialect area, an d span less than 2 km in anygiven direction . Small dial ects con sisted of dial ects areas that spa n 2–10 km.Med ium dialects were consi dered to span 10–100 km, and larg e dial ectsgreat er than 100 km. In some cases , dialect s were only described in terms ofnum bers of individual s. Smal l, medi um, and large dialects were then classi-fie d as containing less than 100 indi viduals, less than 1000 indi viduals, andmore than 1000 indi viduals, respec tively. Most dial ect areas do not greatlyexceed 1000 km.

The temp oral stabi lity of dialect bounda ries was described explicitl y bymany authors . Some taxa retain similar bounda ries and acousticcharac terist ics of son g types over long pe riods 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 dialectsmaintained 2–6 years of stability. In the case of most passerine species,this probably corresponds to the life span of individuals of the species(Weat herhead and Forbes, 1994). Mod erate ‐ term dialect s were defined asbeing stable for 6–20 years, and long‐term dialects as being stable for greaterthan 20 years. It is probable that some taxa coded with moderate‐termdialects actually are stable over the long‐term, and that long‐term stabilityhas not yet been demonstrated.

TABLE II

VOCAL AND ECOLOGICAL PARAMETERS OF DIALECT S PECIES a

# Species

Spatial

scale

Temporal

stability Bilingual Territoriality Mobility

Repertoire

size max

Timing of

learning

1 Amazona auropalliata 2 1 0 0 1 post

2 Phaethornis longuemareus 0 2 1 0 1

3 Miliaria calandra 1 2 1 1 1 3 post

4 Emberiza citrinella 3 3 1 1 1 5

5 Emberiza hortulana 2 1 1 1 1 2

6 Zonotrichia leucrophrys nuttalli 1 3 0 1 0 1 pre

7 Zonotrichia leucophrys oriantha 3 3 1 1 1 2 pre

8 Zonotrichia leucophrys pugetensis 3 2 1 1 1 2 pre

9 Zonotrichia capensis 2 3 1 1 1 1 pre

10 Pooecetes gramineus 1 1 1 43

11 Amphispiza belli 1 0 1 1 1

12 Pipilo maculatus 1 1 1 2

13 Passerina cyanea 0 1 1 1 1 post

14 Cacicus cela 2 0 0 1 4 post

15 Cacicus cela 2 0 0 0 1 8 post

16 Molothrus ater 2 2 1 1 1 3 post

17 Molothrus aeneus 2 1 1 0 1 1

18 Molothrus aeneus 3 1 1 1 1 1

19 Dolichonyx oryzivorus 1 1 0 1 43 post

20 Fringilla coelebs 2 3 1 1 1 1

21 Chloris chloris 2 0 0 1 0 35 post

422

22 Carpodacus mexicanus 1 1 1 0 1 4 pre

23 Calcarius pictus 0 2 0 1 1 post

24 Himatione sanguinea 1 1 0 1

25 Thryomanes bewickii 1 1 1 0 16 post

26 Troglodytes troglodytes 0 0 1 0 6

27 Troglodytes troglodytes 1 1 1 1 0 1 pre

28 Certhia brachydactyla 3 3 1 1 5 pre

29 Turdus iliacus 1 2 1 1 1 2 post

30 Vidua chalybeata 1 0 1 1 0 12 post

31 Vidua purpurascens 1 0 1 1 0 12 post

32 Poecile atricapillus 1 1 1 1

33 Poecile atricapillus 1 0 1 0 9 post

34 Poecile carolinensis 2 1 0

35 Pachycephala olivacea 2 0 1 0 10

36 Cyanocitta cristata 2 1 0 2

37 Creadion carunculatus 0 1 1 1 0 1 post

38 Menura novaehollandiae 1 2 1 0 3

39 Nectarinia osea 0 1 1 1 0 1

40 Orcinus orca 2 3 0 0 0 17 pre

41 Pyseter macrocephalus 1 1 0 0 1 30 pre

42 Saguinus labiatus labiatus 2 1 0 1 post

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

423


Bilingualism refers to the propensity of individuals to acquire more thanone dialect, particularly in areas near a dialect boundary. Following ourdefinition of dialects, the proportion of a population that is bilingual shouldbe low in most cases. But bilingualism was reported regularly among thespecies we reviewed. We classified bilingualism as either absent or present(coded as 0 or 1, Table II) based on the authors’ reporting. We classifiedbilingualism as absent only when an author either stated it to be so or gavesufficiently exhaustive detail on vocal behavior of the focal populations toallow us to make this inference.

2. Ecological Correlates

We compiled data for two ecological and life history characteristics com-monly cited in the literature: social system and degree of seasonal mobility.First, we assessed, based on the literature, whether taxa with dialects werepredominantly territorial or, alternatively, social (e.g., colonial) on theirbreeding grounds. With regard to seasonal mobility, taxa were classified aseither sedentary or migratory, again on the basis of published descriptions.

3. Vocal Correlates

Data on one vocal parameter, repertoire size, was compiled for eachspecies or subspecies in our analysis. Rather than analyzing the ranges ofrepertoire sizes within each species, we included in our analyses onlymaximum repertoire size. We focus on maximum repertoire size becausethis provides a good indication of the potential for individuals to acquiresongs of more than one dialect. Coding each cell with a single number aidedour multivariate analyses below.

4. Timing of Song Acquisition

While the song acquisition phase of song learning has not been charac-terized in detail for many species, it can sometimes be inferred fromobservational studies of song acquisition in the field. This is particularlyso in demonstrations of postdispersal learning, in which individuals areshown to match their songs to the local dialect after dispersing from theirnatal grounds. Thus, we classified any taxa in which individuals regularlyalter their songs after dispersal as a ‘‘postdispersal learner’’ for the purposesof this analysis. This includes species that delete song types learned earlierthat do not match neighbors. We classified taxa as predispersal learnerswhen the predominant pattern appears to be early song acquisition, with noknown examples of modification of song after natal dispersal. These cate-gories parallel the open‐ended and close‐ended song learning categories as


defined by Beecher and Brenowitz (2005) and others. Notably, songlearning in some species likely bridges pre‐ and postdispersal periods.Z. leucophrys oriantha and Z. leucophrys pugetensis, for instance, are be-lieved to memorize song models predispersal but then crystallize only asubset of memorized models postdispersal, based on vocal interactions withneighbors (Nelson, 2000). For such species, we coded song learning as pri-marily predispersal, based on the supposed period of song model acquisition.

One notable shortcoming in our analysis is that we did not includedispersal distances, in spite of the fact that these are considered a key factorin shaping vocal geographic variation. Unfortunately, dispersal is difficult tomeasure and therefore not available in the literature for many species.Moreover, our system of classifying taxa as pre‐ versus postdispersal lear-ners incorporates dispersal distance, at least to some extent. Taxa identifiedas having postdispersal learning have most likely been so identified becauseindividuals have been observed dispersing across dialect boundaries. Thus,these taxa will have longer dispersal distances than will predispersal lear-ners, almost by definition.

5. Analytical Approaches

While our survey included a wide range of taxa, we decided to limit ourquantitative analysis to birds, which constituted the large majority of vocaldialect cases identified (Table I, taxa #1–39). We first tested whether dialectcharacteristics in birds differed systematically between pre‐ and postdispersallearners. The lack of information on vocal ontogeny for many species greatlylimited our sample size, and therefore the number of parameters we couldinclude in a multivariate analysis. Because dialect characteristics such asspatial scale are potentially linked to ecological variables such as territoriality,we conducted two separate discriminant function analyses with song ontoge-ny as the grouping variable in both cases. In the first, we asked whether ourthree ecological and vocal parameters classified pre‐ and postdispersal lear-ners as distinct groups. In the second, we asked whether the three dialectparameters classified pre‐ and postdispersal learners as distinct groups.

We explored potential correlates of dialect variation using nonparametricmethods, comparing, using univariate analyses, each of the dialect para-meters to the ecological and vocal parameters shown in Table II. It wouldhave been preferable to apply the comparative method, that is, takingphylogenetic relationships into account in our analysis. This was not practi-cal, however, given the limited information currently available about therelationships of the taxa examined. It seems unlikely that phylogeneticfactors would have strongly influenced our findings, given that dialectcharacteristics such as spatial scale can vary widely even among closelyrelated taxa (see, e.g., Zonotrichia species and subspecies, #6–9, Table II).


C. RESULTS

The majority of research on vocal geographic variation has been con-ducted on songbirds from temperate regions. Considerably, less is knownabout vocal geographic variation in other bird groups in other regions,notably tropical suboscine passerines. Thus, the sample analyzed heredoes not represent an unbiased snapshot of natural vocal variation. Ourfirst impression from our survey is that the absolute number of speciesexhibiting sharp dialects is lower than the abundant literature on dialectsmight lead one to suspect. Most entries are for dialects in song (n¼ 36), but5 are for group calls. In five cases for which intraspecific variation could noteasily be summarized, we use multiple entries for a single species. One ofthese is the black‐capped chickadee, for which dialects are reported in twodifferent vocal signals, the ‘‘gargle’’ and the ‘‘fee bee’’ (Table II). In theother cases, populations or subspecies are so distinct in their reporteddialect or other characteristics as to warrant separate entries. For example,three white‐crowned sparrow subspecies (Z. leucophrys nuttalli, Z. leu-cophrys oriantha, and Z. leucophrys pugetensis, #6–7) differ both in thecharacteristics of the dialects they exhibit as well as in reported patterns ofmigratory behavior (Table II). In the bronzed cowbird (M. aeneus), dialectsoccur in distinct geographic patterns in breeding versus wintering popula-tions (Warren, 2002).

The majority of the cases of vocal dialects we identified occur in passerinebirds (n ¼ 37). These were distributed among just eight families (sensuSibley and Monroe, 1990), with most examples from four subfamilies of theFringillidae (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 passer-ines, occurring in less than a quarter of passerine families, at least as basedon available data. Yet, dialects may actually be a common form of geo-graphic variation in the Fringillids, a group with a high propensity for songsharing (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 bilin-gualism, 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 compari-son). Spatial scale and temporal stability were each normally distributedacross our sample. It was more difficult to assess bilingualism than the other

2018

18

16

14

12

10

8

6

4

2

0

3 32 2

1 1 1 1 1

Fringil

lidae

Certh

iidae

Passe

ridae

Corvid

aePar

idae

Callae

atida

eM

enur

idae

Necta

riniid

aePsit

tacid

aeTro

chilid

ae

Num

ber

of s

peci

es

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 inwhich this could be assessed with confidence, the majority, 74%, exhibitedsome level of bilingualism.

2. Ecological Correlates of Dialect Characteristics

The 42 dialect systems we found exhibit a wide variety of ecologicalcharacteristics, with representatives of both territorial and social speciesand of both sedentary and migratory species (Fig. 5, Table II). Given thedominance of passerine birds in the sample, we were not surprised to findthat a majority of the birds with dialects are territorial (79%, Fig. 5). It wasmore surprising to find that sedentary species were the minority in thesample (Fig. 5), since it is thought that dialects should evolve more readilyin sedentary species. Across our dialect sample, territorial species tended tobe 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 thedominance in our sample of temperate and arctic species, many of whichare seasonally migratory.

100%

80%

60%

40%

20%

0%Territoriality Seasonal mobility

Mig

Sed

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 variablesand dialect variables, but the trends uncovered suggest potential mechan-isms underlying variation among dialect systems. Migratory species tend tohave larger dialect regions (Kruskal‐Wallis, Z ¼ �1.85, p ¼ 0.06), and weresomewhat more likely than sedentary species to show bilingualism atdialect boundaries (w2 ¼ 2.82, p ¼ 0.09). But we found no relationshipbetween seasonal mobility and the stability of dialect regions over time.By contrast, territorial species tend to have more temporally stable dialectsthan do social species (Kruskal‐Wallis, Z ¼ �1.9, p ¼ 0.05). But we find noeffect of territoriality on spatial scale of dialects or the propensity forbilingualism.

3. Vocal Correlates of Dialect Characteristics

We find that 32% (12 of 37) of songbird species with dialects in ourreview have repertoires of only a single song type, and that 68% (25 species)have repertoires of fewer than five song types. According to Beecher andBrenowitz (2005), the corresponding percentages for all passerines are 30%of single song type and 50% repertoires of less than five song types. The twodata sets thus correspond closely in this regard.

As with the ecological variables, there are no statistically significantcorrelations 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 withdialect scale.


4. Song Ontogeny

Dialect species were somewhat more likely to show postdispersal ratherthan predis persal learn ing (Fig. 6). While this lends greate r suppo rt for thesocial adaptation than local adaptation hypothesis, neither is strongly sup-ported. The social adaptation hypothesis can be rejected in the nine dialectsystems in which individuals rarely disperse across dialect boundaries andapparently do not acquire the local song type when they do cross bound-aries. 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 songacquisition and memorization relative to natal dispersal is not known formore than a third of the cases in our table.

Multivariate approaches identified few good predictors of the timing ofsong acquisition. First, we conducted a discriminant function analysis usingthe ecological and vocal parameters, territoriality, seasonal mobility andrepertoire size, and song ontogeny as the grouping variable. This discrimi-nant 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 con-ducted a second discriminant function analysis using three factors specifi-cally describing the dialect systems, the spatial scale of dialects, their

20

18

16

14

12

1010

16 16

8

6

4

2

0Predispersal Postdispersal Unknown

Num

ber

of ta

xa

FIG. 6. Numbers of all taxa with dialects that exhibit either predispersal learning or post-

dispersal 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.


temporal stability, and presence of bilingualism. This was also not signifi-cant (Exact F ¼ 1.89, df ¼ 3, N ¼ 10, p ¼ 0.20) with a canonical correlationof 0.60 for the first canonical axis.

Eliminating bilingualism from the analysis, a variable with many missingdata 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 thevariation among the cases of dialects. Temporal stability shows the highestcorrelation with Canon1 (r ¼ 0.88, p < 0.0001), but dialect size is alsostrongly correlated with Canon1 (r ¼ 0.59, p ¼ 0.002). Thus, the temporaland spatial features of dialects appear to be moderately successful predic-tors of learning ‘‘strategy’’ (sensu Beecher and Brenowitz, 2005).

Temporal stability of dialects remained a significant predictor of learningstrategy in univariate tests. This and repertoire size were the only para-meters to show a tendency to differ between pre‐ and postdispersal lear-ners. Postdispersal learners were slightly more likely to have largermaximum repertoire sizes than predispersal learners (Kruskal‐Wallis,

3.5

3.0

2.5

2.0

1.5

1.0

0.5

−0.51.0 1.5 2.0 2.5 3.0

Canonical 1

Can

onic

al 2

3.5 4.0 4.5 5.0 5.5

0.0

14, 15, 21

5

18

8

4, 7, 28

9

16

3, 29, 38

6

2, 23

37, 39

19, 22,24, 27

30, 31

Pos

t

Pre

Spatial scale

+ +

Temporal stability

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.


Z ¼ �1.54, p ¼ 0.12). This finding is supported by the observation that allseven 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 considerablevariation in both groups. Some dialect systems in postdispersal learners arequite stable over time, for example in brown‐headed cowbirds (M. ater)(Fleischer and Rothstein, 1988). Dialects in predispersal learners thus occurin species with smaller repertoires, and when they occur, they tend to berelatively stable in time. By contrast, dialects in postdispersal learners tendto cover smaller regions and be less stable over time, though there isconsiderable variation among dialects exhibited by postdispersal learners.

D. DISCUSSION

Our survey illustrates both the rarity and diversity of dialect systems innature. No single functional hypothesis can account for all or even amajority of published examples of dialects, and a substantial portion ofthe cases reject at least one of these hypotheses. The local adaptationhypothesis is rejected for 16 cases of dialects in which individuals arecapable of modifying songs after dispersal to new dialect regions (Fig. 6).Likewise, the social adaptation hypothesis is rejected for 10 cases in whichindividuals appear not to disperse across dialect boundaries or to modifytheir signals after natal dispersal (Fig. 6). Although timing of song acquisi-tion remains unknown for more than a third of all dialect systems (Table II),it seems unlikely that further work will support one of these functionalhypotheses absolutely over the other. We suggest that the diversity ofdialect systems, occurring as it does across a range of social systems, scales,and ecological conditions, argues against any given functional hypothesis ofdialect evolution.

The assembled data provide additional insights into the selective forcesunderlying 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‐studiedacoustic performers as insects and anurans, we only found dialects inspecies with evidence of vocal learning. In birds, these were the passer-ines, psitticines, and trochilids. Among mammals, the cetaceans and pri-mates were the only groups represented. The majority of the cases (37 of42) were in passerine birds. The predominance of passerines in the sampleno doubt reflects, to some extent, historical and taxonomic biases inresearch on geographic variation in acoustic signaling. However, itremains noteworthy that no examples of dialects to date have been


found in species lacking imitative learning. We conclude that imitativelearning, as predicted by Kroodsma (1996) among others, is a necessarycondition 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 withHandley and Nelson, 2005). At the surface, this relationship suggests poten-tial support for social adaptation hypotheses such as the deceptive mimicryhypothesis. However, as we argue in Section IV, and as was argued by Slater(1989), evidence of relationships between song sharing and vocal dialectevolution provides more direct support for by‐product hypotheses of dialectevolution.

1. Ecological and Vocal Correlates of Dialect Parameters

Our comparisons of vocal and ecological parameters in dialect speciessuggest several issues worth pursuing in future work. First, the spatialcharacteristics of dialects appeared to be associated with seasonal mobility,with migratory species tending to evolve larger dialect regions. Theseinterspecific comparisons appear to corroborate patterns described for thethree subspecies of the white‐crowned sparrow with dialects, which rangefrom the sedentaryZ. leucophrys nuttalliwith its small dialect regions to themigratory Z. leucophrys oriantha and Z. leucophrys pugetensis with theirlarger dialect region (Nelson, 1999). Further refinement of comparisons toinclude distances traveled by migratory populations may reveal an evenstronger relationship with dialect size.

Second, territorial species tend to maintain the same dialects for longerperiods of time than do social species such as the colonial yellow‐rumpedcacique (Trainer, 1989). Perhaps more revealing is the lack of a relationshipbetween migratory behavior and stability of dialects. This suggests thatseasonal mobility alone does not disrupt dialect patterns. Other life historycharacteristics that could be addressed include the degree of site fidelity andthe rate of population turnover (Handley and Nelson, 2005; Kroodsma,1996; Wiens, 1982).

Third, larger song repertoires are associated with more stable dialectregions, but not with other dialect characteristics. This may reflect under-lying selection on either repertoire size or accuracy of vocal imitation(Beecher and Brenowitz, 2005). Species in which accurate imitation isadvantageous typically have lower repertoire sizes and are expected tohave more stable song neighborhoods (Beecher and Brenowitz, 2005). Wenote, however, that predispersal learners also tend to have smaller reper-toires and more stable dialects. The significant correlation between reper-toire size and dialect stability disappears when we treat pre‐ andpostdispersal learners separately. Thus, an unresolved question is whether


the stability of geographic variation in song is a consequence of timing ofsong acquisition, or of selection on traits such as repertoire size or imitativelearning. The timing of song acquisition itself may be a consequence ofselection on other traits such as dispersal distances or length of breedingseasons (Nelson, 1999).

2. Role of Song Ontogeny

The evolutionary consequences of geographic variation in song arethought to be determined in large part by song ontogeny, in particular bythe timing of song acquisition and memorization (Slabbekoorn and Smith,2002a). Our survey of birds with dialects found examples of species withboth pre‐ and postdispersal learning. These groups differ somewhat in thecharacteristics of their dialects, particularly in the stability of dialects overtime (Fig. 7). But these differences are not overwhelming, and lack ofinformation on song ontogeny in our sample significantly hampers ourability to draw conclusions about its role in dialect evolution. Nevertheless,it seems clear that song ontogeny is only one of many characteristics ofspecies 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 empiricalsupport for functional hypotheses of dialect evolution (Kroodsma, 1985a).Our survey in the preceding section suggests that little has changed on thisfront. There is and probably will never be a simple, universal explanationfor the phenomenon of dialects. Perhaps more importantly, we suggest thatthe traditional focus on dialects has eclipsed investigation into the broaderphenomenon of vocal geographic evolution, of which dialects are but oneform expressed. In this section, we argue that by‐product hypotheses ofvocal geographic evolution—the only one of the three sets of dialecthypotheses that seems to have broader applicability—have been receivingnew sources of support, through recent general advances in the study ofbirdsong.

A. PHYLOGENETIC SIGNAL IN VOCAL EVOLUTION

Over decades, the study of behavioral evolution has been transformedby increased attention to phylogenetic factors (Brooks and McLennan,1991; Martins, 1996). This has been the case for the study of birdsongs, inspite of the prior presumption that these signals are too plastic to permit


historical analyses (Irwin, 1996; Payne, 1986; Price and Lanyon, 2002; tenCate, 2004). The principal approach used so far to account for historicalfactors in bird vocal evolution has involved comparative analyses(Catchpole, 1980; Irwin, 1990; Kroodsma, 1977; Podos, 1997; Read andWeary, 1992; Slabbekoorn et al., 1999; Wiley, 1991). Toward this end,reference to hypotheses of phylogenetic relationships has proven particu-larly helpful. Phylogenetic hypotheses enable researchers to estimate vocalancestral 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, Priceand Lanyon (2002, 2004) used ancestral state reconstruction to infer thatsome vocal parameters (e.g., note structure, peak frequencies) are highlyvariable, whereas other vocal parameters (e.g., the presence or absence of atrill or a click within songs, and note and song duration) have remainedstable over evolutionary time. Phylogenetic hypotheses have also allowedfor independent contrast and similar statistical analyses, to assess correlatedevolution in other taxonomic groups between song traits and neural, mor-phological, or ecological parameters (Podos, 2001; Seddon, 2005; Szekelyet al., 1996; Van Buskirk, 1997). Returning to the example of oropendolasand caciques, phylogenetic reconstruction enabled analysis of correlationsbetween rates of vocal evolution and the intensity of sexual selection indifferent lineages (Price and Lanyon, 2002, 2004).

B. MULTIPLE FUNCTIONS AND TRADE‐OFFS IN VOCAL EVOLUTION

Evidence of historical signal in vocal evolution suggests alternativesto functional hypotheses of evolution, which assume that vocal traitsare sufficiently plastic to be easily molded to whatever selective pressuresare presently in play. Instead, vocal features may be evolutionarily con-served when they are subject to multiple evolutionary pressures (Beecherand Brenowitz, 2005; Gil and Gahr, 2002; Nowicki and Podos, 1993).A rigorous empirical example of how song may evolve under multipleselection pressures was provided by Seddon (2005), who documented con-current impacts of morphological adaptation, interspecific competition, andacoustic adaptation on the divergence of vocal structure in Neotropical ant-birds (Passeriformes: Thamnophilidae). When traits are subject to multipleselection pressures, they may face trade‐offs in their evolution, that is, byresponding only partially to some pressures (Roff, 1992). Evolutionaryresponses to specific functions, through their impacts on morphology, phys-iology, and behavior, may also alter how other traits are expressed, or evenprovide opportunities for the evolution of new functions (Gould and Vrba,1982; Patek et al., 2006). The relevance of these issues for questions about


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 ofvocal geographic divergence.

1. Mechanisms of Vocal Learning

Researchers have long wondered why some animals have evolved imita-tive learning as a mechanism in vocal signal ontogeny (Nottebohm, 1972).After all, many other animals develop effective vocal signals withoutrecourse to imitation. Two traditional explanations for the evolution ofvocal imitation are that it enables transmission of particularly complexpatterns of vocal structure across generations, and that it helps animals toadapt 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 territorialneighbors One of the main documented functions of birdsong is to mediateinteractions among neighboring territorial males (Catchpole and Slater, 1995;Hyman, 2002; Searcy andAndersson, 1986; Todt andNaguib, 2000).Researchhas focused on the use of ‘‘shared’’ songs by interacting neighbors and thepotential 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 toexplain song sharing are that it provides a reliable means by which males candiscriminate neighbors from strangers, and that it enables increased precisionin communicating aggressive intent among territorial neighbors. Birds dowellto distinguish neighbors from strangers because, unlike neighbors, strangersare ‘‘inherently expansionist’’ (Beecher andBrenowitz, 2005, p. 147), and thusrequire closer monitoring. Research by Beecher and colleagues on songsparrows (Melospiza melodia) illustrates how neighboring males may employshared songs to escalate or de‐escalate aggressive interactions. To escalate aninteraction, males may first respond to a singing neighbor with a nonsharedsong, then by singing a ‘‘repertoire match’’ (a shared song, although not theone being sung by the neighbor at the moment), and then by singing a precisetype match (Beecher and Brenowitz, 2005).

Selection for song sharingmay influence the evolution of vocal geographicpatterns—beyond obvious effects on the finest‐scale geographic patterns,that is, among neighbors—because of how it shapes the evolution of songlearning strategies in populations (Beecher and Brenowitz, 2005; Handleyand Nelson, 2005; Slater, 1989). First of all, selection for song sharing maypresumably influence the number of song types that birds will evolve tolear n, that is song rep ertoire size. As stated by Beec her and Brenowitz (2005 ,


p. 147), ‘‘selection for song sharing and selection for large song repertoiresare at least partially contrary . . . as a logical consequence of the fact that asong learning strategy cannot optimize both goals.’’ This is because selectionfor song sharing may favor, Beecher and Brenowitz (2005) argue, the evolu-tion of smaller repertoires with correspondingly greater percentages ofsongs shared among neighbors. Expanding to a broader geographic scale,species that evolve smaller repertoires may be more likely to express theclassic dialect pattern (Williams and Slater, 1990). A second potential im-pact of selection for song sharing is on the timing of song learning. Songsharing may be promoted in species that retain sufficient flexibility to matchsongs produced by neighbors on their breeding grounds, after dispersal fromnatal 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 suchlearning programs increase the likelihood that birds would be able tomatch 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 expressboth song sharing and dialects (Handley and Nelson, 2005; Section III)supports this hypothesis.

b. Vocal imitation and male quality Another proposed function of vocalimitation is that it enables song structure to be used as an accurate indicatorof male quality (Buchanan et al., 1999, 2003; Nowicki et al., 1998, 2002). Thishypothesis suggests that learned songs serve as honest indicators of malequality because their accurate reproduction requires successful brain devel-opment in the face of potentially severe nutritional and developmentalstress. Males who accurately reproduce vocal features of song tutors, orwho are able to develop complex vocal features, in effect advertise high‐quality genes, developmental histories, and learning abilities. Such maleswould presumably offer higher quality genetic input and rearing environ-ments for females that choose them as mates.

Studies suggest that nutritional or developmental stress may indeedimpair the normal de velopmen t of vocal brain nuclei (Buc hananet al., 2004; MacD onald et al. , 2006; Now icki et al ., 2002 ; but see Gil et al .,2006). Empirical research on the potential consequences of developmentalstress for song development has focused especially on three vocal structuralparameters: syllable or song repertoire size, the accuracy of vocal tutormatching, and rates or durations of vocal output (Buchanan et al., 2003;Nowicki et al., 2002; Spencer et al., 2003, 2004, 2005). Presumably, males ofhigher quality will be able to develop larger vocal repertoires, given thecorrelations between early stress, song nuclei volume, and vocal repertoire


size (Nowicki et al., 2002). An honest indicator mechanism such as thismight help to explain female preferences for large vocal repertoires, asshown in some species (Searcy and Yasukawa, 1996). Accuracy in imitationmay similarly provide an honest indicator of a male’s brain developmentalstatus, given the complexity of the auditory and neural systems that aredevoted to the process, and given the numerous environmental challengesthat may impede song nucleus development (Nowicki et al., 2002). Rates ordurations of vocal output may provide an indicator of the quality of a male’soverall health and developmental history (Spencer et al., 2005). Nowickiet al. (1998) noted a possible trade‐off in the evolution of copying accuracyand 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 develop-ment of larger repertoires if the brain space and developmental resourcesrequired for accurate imitation secondarily limit the quantity of vocalmaterial that can be imitated in the first place.

Our point with this example is that selective pressures on repertoiresize and the accuracy of vocal tutor matching may impose, either indepen-dently or jointly, secondary effects on the evolution of vocal geographicpatterns. Species with larger repertoires are generally thought not to evolvestringent dialects (reviewed by Searcy et al., 2002), a supposition that is partlysupported by our analysis in Section III. Beyond the question of whetherdialects occur or not, species that have evolved a premium on copying accu-racy may evolve greater stability and within‐neighborhood similarity in thestructure of their songs (Slater, 1986; see also our analyses in Section III).

Vocal geographic structure may thus arise indirectly through selectionfavoring males with learning programs that render them more adeptat ‘‘managing’’ interactions for selfish gain (Kroodsma, 1996), or throughselection on song structure as an honest indicator of male genetic and devel-opmental quality (Nowicki et al., 2002). These arguments echo Slater’s sug-gestion that selection at the level of individuals drives geographic patternsthat appear at the level of populations (Slater, 1989).

2. Mechanisms of Vocal Production

The ontogeny and evolution of vocalizations are impacted not just bylearning but also by the mechanisms that underlie vocal production(Elemans et al., 2004; Fee et al., 1998; Nowicki et al., 1992; Podos andNowicki, 2004a; Suthers and Goller, 1997). Some recent studies have empha-sized the fact that vocal production in birds requires the input and activity ofnot just the sound source (the syrinx) but also other motor components,including the respiratory system and vocal tract (Beckers et al., 2003; Hoese


et al., 2000; Nowicki, 1987; Nowicki and Marler, 1988; Riede et al., 2006;Suthers, 2004; Suthers et al., 1999). These additional motor components ofvocal production can impose performance limits on the expression and evo-lution of song features (reviewed by Podos and Nowicki, 2004a). Movementsof respiratory muscles, to illustrate, are coordinated precisely with syrinxactivity, and appear to be essential in controlling the timing of vocal output(Suthers et al., 1999). Maximal rates of breathing cycles may thus limit theevolution 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 theproduction of pure‐tonal songs (Beckers et al., 2003; Hoese et al., 2000;Nowicki and Marler, 1988; Riede et al., 2006; Westneat et al., 1993). Maximalrates of vocal tract reconfiguration, such as those achieved through changesin beak gape, can limit trill rates and frequency bandwidth within trilledvocalizations (Nowicki et al., 1992; Podos, 1997).

Recent empirical studies suggest two related effects of production con-straints on vocal evolution. First, production constraints can bias the evolu-tion of individual vocal features. The evolution of trill rate in swampsparrows (Melospiza georgiana), to illustrate, appears to be limited byindividual birds’ vocal performance abilities. This was revealed in a studyin which young male swamp sparrows were trained with tutor songs inwhich trill rates had been artificially elevated (Podos, 1996; see also Podoset al., 1999). Birds proved able to memorize the rapid tutor songs, butunable to produce accurate copies of these songs, in manners consistentwith a hypothesis of motor constraints on song production (Podos, 1996).Second, production constraints on vocal evolution may be manifest not onlyin individual features but also as trade‐offs among multiple vocal features.Songs of birds of the sparrow family Emberizidae, to illustrate, exhibit atrade‐off between trill rates and frequency bandwidth (Podos, 1997). Simi-lar patterns have now been described in additional taxa (Ballentine et al.,2004; Draganoiu et al., 2002; Illes et al., 2006), and evidence also suggeststhat songs produced at higher performance levels—that is, with greater trillrates and/or frequency bandwidths—are more effective with regard to bothinter‐ and intraspecific function (Ballentine et al., 2004; Illes et al., 2006).An acoustic trade‐off between trill rate and frequency bandwidth is consis-tent with a hypothesis of physical constraint: in order to achieve particularlyrapid trill rates, the requirement for pure‐tonal quality (and thus rapid vocaltract reconfigurations) sets performance limits on the ranges of frequenciesthat can be produced over a given time interval (Podos and Nowicki,2004a). Physical limits or trade‐offs in vocal evolution are worth attentionbecause they may counter selection for particular functions. Thus, to


illustrate, selection for songs with particularly rapid trills, for exampleunder a local adaptation scenario, would presumably be impeded by physi-cal constraints on trill production.

Selection on components of the vocal apparatus for nonvocal functionsmay also invoke secondary effects on vocal evolution (Podos and Hendry,2006 ; Podos and Now icki, 2004b). Body size, to illustrat e, evolve s in manyanimals 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 onthe fundamental frequencies of bird vocalizations, given tight correlationsbetween body size and syrinx size, and the functional relationship of syrinxsize and vocal frequency production (Bertelli and Tubaro, 2002; Cutler,1970; Ryan and Brenowitz, 1985). A second scenario, involving beak andsong evolution, has been illustrated recently for Darwin’s finches of theGalapagos Islands, Ecuador. In these birds, beak form and function hasbeen shown to evolve in precise correspondence with varying ecologicalparameters, namely food availability and interspecific competition (Grantand Grant, 1995, 2002, 2006). Analyses of songs of birds with knownmorphologies have now revealed that the same two vocal parametersmentioned above, trill rate and frequency bandwidth, correlate with varia-tion in beak morphology (Huber and Podos, 2006; Podos, 2001). Thiscorrelation seems likely to be the result of proximate constraints on beakgape changes and thus vocal tract configurations during vocal production.Consistent with this hypothesis, birds with larger beaks, predicted to suffergreater constraints on vocal performance (Nowicki et al., 1992), haveevolved songs with slower trill rates and narrower frequency bandwidths(Huber and Podos, 2006; Podos, 2001; Podos and Nowicki, 2004b; Podoset al., 2004a). Thus, within given lineages of Darwin’s finches, morphologicaladaptation under selection for feeding opportunities is predicted to impactvocal performance, and thus the evolution of some vocal features, that isthose vocal features that require precise tract reconfigurations for theirproduction.

These scenarios of vocal evolution suggest that adaptation to divergentenvironments may produce, on its own account, structured patterns of vocalgeographic variation. To continue with the example of Darwin’s finches,populations of some species have diverged in genetics and morphology ondifferent islands, presumably as a result of the different selective environ-ments 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 produc-tion, adaptive divergence in beakmorphologymay thus have driven intraspe-cific, between‐island vocal divergence, with, for example, the largest‐beaked


populations of a given species experiencing themost severe constraints on trillevolution (Podos and Nowicki, 2004b). The main point of this example, forpresent purposes, is that geographic patterns of song variationmay emerge asincidental by‐products of selection for nonvocal functions, without need forselection for the patterns themselves, as is implied by functional adaptationhypotheses of vocal geographic evolution.

V. EVOLUTION OF GEOGRAPHIC VARIATION IN AVIAN VOCAL

SIGNALS: PROSPECTUS

As we argued above, advances on both empirical and conceptual frontsprovide increasing support for a role of by‐product models of vocal geo-graphic variation. We do not, however, intend to suggest that all facets ofsong evolution are explained through by‐product mechanisms. Rather,there are myriad factors that may impact geographic divergence of thevocal phenotype. This final section, which follows closely from Podoset al. (2004b), is devoted to surveying the range of scenarios by whichsong 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 ituseful to distinguish two distinct ‘‘substrates’’ of vocal evolution: memes andmechanisms. Memes refer to song parameters that are transmitted acrossgenerations via learning, whereas mechanisms refer to phenotypic bases ofvocal expression (development and production) that are transmitted acrossgenerations via genetic inheritance (Podos et al., 2004b). Traditional explana-tions for patterns of vocal geographic evolution have, in our viewpoint, beenhampered by a nearly exclusive focus on meme evolution. Part of the reasonfor the relative neglect of mechanisms, we believe, is that their effect isnormally manifest over comparatively broad timescales (Podos, 1997; Ryanand Brenowitz, 1985) and are thus more difficult to identify and study.

In the evolution of learned vocalizations, vocal memes and vocalmechanisms may evolve on nonintersecting trajectories. Thus, for instance,selection for increased trill rates may augment trill rates in a population, inthe event that the mechanisms responsible for trill production in thatlineage are able to accommodate such increases. Similarly, evolutionarychanges in body size may have no impact on the vocal frequenciesexpressed in a population, in the event that vocal frequencies were initiallynot produced near their limits of possibility. But memes and mechanismsmay also interact in vocal evolution. Evolutionary changes in mechanisms


of vocal production and learning may adjust potential routes of memeevolution, and evolutionary stability in vocal mechanisms may limit theresponse of memes to directional selection (Podos et al., 2004b). Recogni-tion of the interplay of memes and mechanisms in vocal evolution allows usto identify five categories of potential causes for the evolution of vocalgeographic variation (Podos et al., 2004b).

B. POTENTIAL CAUSES OF VOCAL GEOGRAPHIC EVOLUTION

Songs, like any other phenotype, evolve through the combined effects ofdrift and selection. We identify two scenarios that involve drift and threethat involve selection. We do not claim the scenarios to be mutually exclu-sive or collectively exhaustive. Rather, song divergence likely involves all ofthese processes, emphasized to varying degrees and at different times in anylineage’s evolutionary history. We do not attempt to integrate details aboutdispersal patterns or the timing of learning, which must play a central role invocal geographic evolution (Ellers and Slabbekoorn, 2003; Krebs andKroodsma, 1980). Nor do we attempt to evaluate how long‐term ecologicalprocesses, such as changes in land use or impacts of fire on habitat, mayinfluence bird distributions and thus dialect formation (Laiolo and Tella,2005).

1. Cultural Drift

Song features may evolve as a result of inaccurate transmission of songmemes across generations because of ‘‘errors’’ in learning (Grant and Grant,1996; Payne, 1996). Distinct trajectories of cultural evolution via copy errorsmay explain vocal differences among diverging populations, especially duringthe initial stages of divergence (Lemon, 1975; Slabbekoorn and Smith, 2002a).To illustrate, evolutionary divergence in the phonology (fine structure) ofnotes, resulting from inaccurate imitation,may explain interisland differencesin note structure in some species ofDarwin’s finches (Grant andGrant, 1996).Lineages that readily express cultural errors, along with some isolation ofdescendent 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 pro-duction—and, more specifically, in the genetic loci that underpin thesemechanisms. Genetic drift may presumably impact vocal geographic evolu-tion when song memes in a lineage are produced at or near some anatomical,developmental, or performance limit. Consider, for instance, drift in thegenetic loci that underlie syrinx mass. Syrinx mass appears to set lower limits


on vocal frequencies, such that only larger syringes can produce lower fre-quency sounds. If genetic drift leads to reduced syrinx mass within a givenpopulations,wewould expect the potential for frequencyproduction to followsuit. Thus, vocal frequencies, if initially produced near maximal performancecapacities (comparatively low frequencies), may accordingly be ‘‘bumped’’ tohigher levels in the offshoot population (Podos et al., 2004b). Genetic driftmay also alter the structure and function of the brain nuclei involved in songlearning, for instance, through randomalterations in the timing of interactionsbe twe en s on g n uc le i ( Li vi ng st on et al., 2000). Such random changes may haveconsequences for the timing and content of song acquisition.

3. Cultural Selection

Cultural selection occurs when certain vocal memes are favored overothers, as a result of the differential effectiveness of those memes in theprocess of communication. A primary example concerns selection for optimalsound transmission. Songs are known to vary in how well they transmit indifferent environments, and cultural selection is thought to thus shape certainvocal parameters (Slabbekoorn, 2004; Wiley and Richards, 1978). Songs withslow repetition rates and low frequencies, to illustrate, have been shown toevolve more often in forested habitats than in other habitats, presumablybecause slow, low‐frequency songs suffer relatively less degradation in forest-ed habitats than elsewhere. Songs withmore effective transmission propertiesmay be favored by selection not only in the context of interactions amongadults but also in song model imitation by juveniles (Hansen, 1979). Culturalselection for optimal sound transmission has been implicated in the diver-gence 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 influ-ence on either memes or mechanisms. With respect to memes, natural selec-tion may facilitate vocal divergence via ‘‘reinforcement,’’ in which selectionagainst hybrid production favors those birds that produce the most species,p o pu l a ti on , o r l oc al it y d i st i n c ti ve s o n g s ( Butlin and Ritchie, 1994; Marler,1957, 1960; Nelson and Marler, 1990; Ptacek, 2000). This is the broadercontext in which we would place the local adaptation hypothesis of dialectevolution. The example of the multifunctional role of the beak in singing andfeeding, discussed in the previous section, illustrates how natural selection onmechanisms can cause incidental vocal evolution (Nowicki et al., 1992; Podosand Nowicki, 2004a,b). To reiterate, natural selection in the context of selec-tion for food availability, food type, and interspecific competition is known to


drive precise changes in beak formand function (Grant andGrant, 1995, 2002,2006).Given the role of the beak in vocal production, such evolution can drivevocal changes as a secondary consequence (Podos and Nowicki, 2004b).

5. Sexual Selection

Sexual selection is traditionally regarded as favoring elaborate or complexforms of vocal signals, especially as a result of female choice (Andersson,1994; Catchpole and McGregor, 1985; Searcy and Andersson, 1986; Searcyand Yasukawa, 1996). Sexual selection may also favor vocal features thatchallenge males’ developmental and performance capacities (Nowicki et al.,2002) or that enable increased precision in communication in male–maleinteractions (Beecher and Brenowitz, 2005; Todt and Naguib, 2000). As ageneral observation, the course of sexual selection is often haphazard, withdifferent signal parameters favored or exaggerated in different lineages(Boughman, 2001; Panhuis et al., 2001). Divergent pathways of sexual selec-tion on songmay 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. Insome lineages, female preferences for complex signals may favor the evolu-tion of large repertoires, whereas in other lineages female preferences foraccurate imitation may favor small repertoires. Moreover, selection for songsharing amongmales in other lineages may favor moderate‐sized repertoires.Divergence of sexual selection pressures among populations may thuspresumably lead to geographic divergence in repertoire size.

VI. SUMMARY

Our goal in this chapter has been to evaluate, from both empirical andconceptual perspectives, the factors that facilitate the evolution of geo-graphic variation in bird vocalizations. Studies on this topic have tradition-ally focused on the evolution of song ‘‘dialects,’’ and have emphasizedfunctional hypotheses to explain their evolution. Two such hypotheses,‘‘local adaptation’’ and ‘‘social adaptation’’ hypotheses, focus on the poten-tial role of song in aiding recognition of males, either by locality or by socialgroup. A quantitative survey of results from papers published on dialects,between 1962 and 2006, however, suggests limited direct support for func-tional hypotheses. An alternative set of hypotheses suggests that songfeatures may diverge through ‘‘by‐product’’ scenarios, in which selectionfor nonrecognition functions drives incidental changes in song structure,and geographic variation therein. Examples of such functions involve theevolution of song learning in neighbor–neighbor song sharing and the


evolution of song learning in the context of sexual selection for malequality. We also describe scenarios by which songs may diverge indirectlythrough selection on components of the vocal apparatus such as body sizeand beak form and function. To conclude, we outline scenarios by whichsongs may diverge geographically; via cultural drift, genetic drift, culturalselection, natural selection, and sexual selection. Empirical study of thesescenarios, together with countinued descriptions of vocal learning strategiesand patterns of dispersal, may provide insights into vocal geographic evolu-tion 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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