Visual and acoustic communication signals in birds: taxonomy, evolution and

transfer between signals mediated by sensory drive

Oscar Laverde-R.

Advisor: Carlos Daniel Cadena

Universidad de los Andes Departamento de Ciencias Biologicaś November 2015

Table of Contents

Introduction 2

Taxonomy and conservation: a tale of two tinamou species groups (Tinamidae,

Crypturellus). 17

Bird songs in the shelf: assessing activity and output using data hidden in sound archives. 27

Evolution of bird communication signals: transference between signals mediated by sensory drive. 60

Songs in the understory and colors in the canopy: sensory drive leads to different avian communication strategies in a tropical montane forest. 84

Introduction

The evolution of communication signals in animals likely involves a compromise between sexual selection for conspicuousness and natural selection for crypsis; the outcome of such compromise is expected to vary in space along with variation in ecological factors (Endler 1980, 1992, 1993). Previous research on communication has often examined single signals in isolation (Seddon 2005; Tobias et al. 2010;

Wilkins et al. 2013), but communication is mostly multimodal: it typically involves complex signaling and the combination of more than one type of signal (Hebets and

Papaj 2004; Partan 2004; Partan and Marler 2005). The sensory drive hypothesis, for instance, postulates that signals, sensory systems, and signaling behavior coevolve as a function of habitat structure (Endler 1992). Nevertheless, most research on this topic has evaluated the effect of habitat on single communication signals (Morton 1975;

Slabbekoorn and Smith 2002; Seddon 2005; Kirschel et al. 2009; Tobias et al. 2010), and few studies have addressed the possible outcomes of the interaction between multiple signals in different communication channels with regard to the properties of the signalling environment (Uy et al. 2009; Uy and Safran 2013).

Multimodal communication has been noticed since the times of early naturalists. In

1871, Darwin referred to a negative relationship between bright colors and the “power of songs” in birds. Observations of negative relationships of this sort later resulted in the postulation of the trade-off or transfer hypothesis, which states that the complexity of acoustic signals and the degree of elaboration of visual signals are negatively related (Gilliard 1956, 1963; Shutler and Weatherhead 1990; Repentigny et al. 2000;

Ornelas et al. 2009; Shutler 2011; Gonzalez-Voyer et al. 2013; Mason et al. 2014).

2 Owing to internal limitations in energy expenditure, birds are expected to invest either on song or plumage elaboration (Shutler 2011). However, the transfer between acoustic and visual signal elaboration might also be affected by external factors such as predation, parasitism, or physical conditions of the habitat (Endler 1992; Zuk and

Kolluru 1998). If differences in color and songs between species are due primarily to spatial differences in light environment, vegetation structure, noise and predation, then interspecific variation in colors and songs —and the relationships between those signals— should be associated with variation in habitat use (Morton 1975; Wiley

1991; Endler 1992; Seehausen et al. 2008).

Every sensory modality has special properties that may make it more or less appropriate in different types of habitats; signals have different ranges and limits

(Candolin 2003; “efficacy trade-off” in Hebets and Papaj 2004). Visual signals, for instance, are easily obstructed by vegetation, whereas acoustic signals are more effective in dense vegetation because they can travel around obstacles and transmit through air; thus, forest animals often rely more on acoustic than in visual signals (Uy and Safran 2013). In addition, every modality has properties affecting its suitability for communication over different spatial scales; visual signals work well in middle to close-range communication, whereas acoustic signals are especially effective for long-range communication (Hebets and Papaj 2004; Partan and Marler 2005).

Similarly, in some groups visual signals are used for close-range communication, whereas chemical signals are used for long-range communication (Uy and Safran

2013). Multimodal signals can be assessed sequentially, as communication often requires individuals first identifying conspecifics from a distance (e.g. by assesing songs) before approaching to refine assessments (e.g. by assesing colors); in open

3 areas where visual signals are more easily perceived, songs may be assessed simultaneously with plumage color (Uy and Safran 2013).

The signaling environment includes the physical properties of the habitat, the sensory physiology of receivers and the set of predators and parasites living in the area

(Endler 1992; Zuk and Kolluru 1998). It is well known that natural selection by predations shapes visual (Endler 1980, 1982; Gotmark and Post 1996; Cummings et al. 2003; Godin and McDonough 2003; Ruiz-Rodríguez et al. 2013) and acoustic signals (Marler 1955; Krams 2001; Trillo et al. 2013). If predation pressure is strong, then individuals may be more cryptic, reduce the number of signals, alter their signaling schedule, or switch between communication channels (Zuk and Kolluru

1998; Pinheiro 2007). For example, ground-nesting passerine birds are under greater risk of predation and then they tend to be considerably more cryptic than off-ground nesters; this is especially clear in females, which spend more time in the nest

(Gotmark and Post 1996; Martin and Badyaev 1996). In some passerines, alarm calls are high-pitched to avoid the hearing threshold of their predators (Marler 1955). In the absence of predators, sexual selection increases the conspicuousness and color pattern diversity in guppies and ciclhids, but when predation pressure is strong conspicuousness is reduced (Endler 1980, 1982; Godin and McDonough 2003; Trillo et al. 2013; Vane-Wright and Boppre 1993; Pinheiro 2004, 2011; Santos et al. 2015).

A variety of studies have tested the transfer hypothesis in birds, with mixed results: finches and avenue-building bowerbirds exhibit negative relationships between acoustic and visual signals (Kusmierski et al. 1997; Badyaev et al. 2002), but no evidence of such apparent transference has been found in trogons (Ornelas et al.

4 2009) or tanagers (Mason et al. 2014), and positive relations were found in Asian barbets (Gonzalez-Voyer et al. 2013). In other groups like butterflies (Vane-Wright and Boppre 1993), fishes (Rafferty and Boughman 2006; Heuschele et al. 2009) and plants (Raguso 2008), there is evidence of negative relationships between signals driven either by the properties of habitats or by local predation pressure. For example, in turbid waters sticklebacks use more olfactory cues to attract mates that in clear waters (Heuschele and Candolin 2007; Heuschele et al. 2009), suggesting a transference mediated by habitat from visual signals to chemical signals. Likewise, in noisy habitats, visual signals act as a complementary mode of communication to acoustic signals: some male frogs calling near noisy rivers have evolved visual signals to compensate for the loud background noise of flowing water (Grafe et al. 2012): these frogs transfer the information from the acoustic to the visual domain.

The evolution of communication signals is a central, integrative topic in evolutionary biology. In this dissertation, I propose that achieving a comprehensive understanding of the evolution of such signals requires not only considering different theories in isolation (i.e. the transfer and sensory drive hypotheses), but also possible interactions between theories and disciplines. For example, the negative association between communication signals observed by Darwin might be strongly determined by adaptation for communication to different habitats (Endler 1992): each habitat has its own features affecting the efficacy of communication, and animals should evolve strategies to optimize signal salience in the face of resolving potential tradeoffs

(Hebets and Papaj 2004). Based on this overall framework, my collaborators an I tested hypotheses about the evolution of and relationships between acoustic and visual signals in relation to habitat features. I used birds as a model system, but the approach

5 and conclusions likely apply to other animals using both acoustic and visual signals for communication. Specifically, my work focused on three different study systems which allowed me to approach the relationships between types of signals from different perspectives: (1) two species complexes in the tinamou family (Tinamidae) differing in patterns of variation in plumage and vocalizations, (2) a widely distributed family of passerine birds with species exhibiting different communication strategies and occurring in contrasting habitats (i.e. the New World warblers,

Parulidae), and (3) an assemblage of multiple species coexisting locally in tropical montane forest site. Additionally, I explored the use and relevance of information contained in sound collections for studies on the ecology and evolution of animal communication.

In the first chapter, we studied vocal variation as a tool to examine species limits in tinamous. Tinamou plumages are typically cryptic, but they are active singers with disctinctive vocalizations (Cabot 1992). Classical taxonomy has relied mainly on plumage features, but vocal variation could likely inform studies on species limits aimed at better understanding the diversity of this group of birds (Laverde-R and

Cadena 2014; Negret and Laverde-R. 2015). We studied two groups of tinamous; in one of them songs were very similar among populations but plumage colors were slightly different, due to the effect of the variation in humidity on plumages (i.e.

Gloger’s rule, Burtt and Ichida 2004), while in the other songs were very diverse in and color variation was limited. Classical taxonomists focused mainly on one axis of variation (plumages) and ignored the other (songs), leading to an incomplete and likely incorrect understanding of species limits. Our analyses revealed the existence of at least one overlooked species-level taxon in one group and indicated that

6 populations are likely oversplit in the other, a result having potential conservation implications. Because tinamous are terrestrial birds of the dark understory of tropical forests, visual signals are likely not an optimal strategy for communication in this family; instead, because acoustic signals can travel throughout the vegetation, one would expect selection for conspicuousness to act more strongly on acoustic traits.

Tinamous are terrestrial birds with limited flight abilities; they are cryptic, shy and elusive, and show limited variation in their dull plumage patterns. These features may be the outcome of strong predation pressure plus low light availability in the understory of tropical lowland forests favoring communcation primarily in the acoustic channel. Thus, we suggest that songs are likely more appropriate to establish species limits and to resolve taxonomic problems in these birds.

In the second chapter we evaluated whether information in sound archives may allow one to understand the frequency with which animals emit communication signals over various time frames, which is arguably a critical component of investment in acoustic communication that is seldom examined. Specifically, we asked whether the number of recordings of vocalizations of mutliple species of birds in three different sound archives and the times when such recordings were obtained reflect estimates of vocal output and temporal patterns of vocal activity obtained through systematic monitoring of wild populations of these species in three tropical forest sites. We found that recordings in sound collections contain valuable information about the vocal output and temporal patterns in vocal activity of birds, which opens the possibility of considering vocal output as a variable of interest in multiple studies on the ecology and evolution of acoustic communication. In chapter 4 (see below) we made use of

7 information in sound archives to address questions about signal evolution, demonstrating an empirical application of the approach.

In the third chapter, we used phylogenetic comparative methods tested for family- wide negative relationships between elaboration in song and plumage coloration in

New World warblers in the context of the signaling habitat, linking the transfer hypothesis with the sensory drive hypothesis. We found strong support for the transfer hypothesis, but only when considered in the context of sensory drive. Our results suggest that the negative relationships between signals not occur because of internal physiological or developmental restrictions as had been previously suggested, but can be generated by the saliency of signals in their local habitat.

Finally, in chapter 4, we focused on a tropical montane forest bird assemblage in which we studied the songs and colors of species to test the transfer and sensory drive hypotheses posed to explain the evolution of communication signals. In addition, we tested the hypothesis that apparent transfer effects between signals are the result of the occurrence of different optimal signaling strategies in each particular habitat. We found that understory birds sing more than canopy birds and that canopy birds are more colorful than understory birds. In addition, we found that acoustic and visual signals are negatively related especially when one considers species from different habitats, which employ differing signaling strategies. We thus found partial support for both the transfer and sensory drive hypotheses, but also for the hypothesis that negative relationships expected under the transfer hypothesis are the result of sensory drive and need not involve tradeoffs in energy allocation.

8 Overall, these results are a novel and integrative contribution to our understanding of the evolution of animal communication signals. I suggest that studying behavioral traits related to investment in signaling over longer time scales than that of single songs, which has been the focus of the vast majority of studies so far, is critical for adequate evaluations of the transfer and sensory drive hypotheses in order to achieve a more complete understanding of investment in signaling and its consequences for the evolution of signals. Considering that communication is multimodal and that optimal signaling strategies often differ between habitats, I argue that examining multiple communication channels and jointly considering the efficiency of various signals in different environments is essential to elucidate the mechanisms underlying the evolution of animal communication strategies.

Literature cited

Badyaev, A. V, G. E. Hill, and B. V Weckwort. 2002. Species divergence in sexually selected traits: increase in song elaboration is related to decrease in plumage ornamentation in finches. Evolution 56:412–419.

Burtt, J. E. H., and J. M. Ichida. 2004. Gloger’s rule, feather-degrading bacteria, and color variation among song sparrows. Condor 106:681–686.

Cabot, J. 1992. Familia Tinamidae (tinamous). Pp. 112–139 in J. Del Hoyo et al., ed.

Handbook of the Birds of the World - Ostrich to Ducks.

Candolin, U. 2003. The use of multiple cues in mate choice. Biol. Rev. Camb. Philos.

Soc. 78:575–595.

9 Cummings, M. E., G. G. Rosenthal, and M. J. Ryan. 2003. A private ultraviolet channel in visual communication. Proc. Biol. Sci. 270:897–904.

Endler, J. A. 1982. Convergent and Divergent Effects of Natural Selection on Color

Patterns in Two Fish Faunas. Evoution 36:178–188.

Endler, J. A. 1980. Natural Selection on Color Patterns in Poecilia reticulata.

Evolution. 34:76–91.

Endler, J. A. 1992. Signals, signal conditions, and the direction of evolution. Am. Nat.

139:S125–S153.

Endler, J. A. 1993. The color of light in forests and its implications. Ecol. Monogr.

63:1–27.

Gilliard, T. 1956. Bower ornamentation versus plumage characters in Bowerbirds.

Auk 73:450–451.

Gilliard, T. E. 1963. The evolution of bowerbirds evolutionary processes. Sci. Am.

209:38–46.

Godin, J. G. J., and H. E. McDonough. 2003. Predator preference for brightly colored males in the guppy: A viability cost for a sexually selected trait. Behav. Ecol. 14:194–

200.

Gonzalez-Voyer, A., R.-J. Den Tex, A. Castelló, and J. A. Leonard. 2013. Evolution of acoustic and visual signals in Asian barbets. J. Evol. Biol. 26:647–659.

10 Gotmark, F., and P. Post. 1996. Prey selection by sparrowhawks , Accipiter nisus: relative predation risk for breeding passerine birds in relation to their size, ecology and behaviour. Philos. Trans. R. Soc. B Biol. Sci. 351:1559–1577.

Grafe, T. U., D. Preininger, M. Sztatecsny, R. Kasah, J. M. Dehling, S. Proksch, and

W. Hödl. 2012. Multimodal communication in a noisy environment: a case study of the Bornean rock frog Staurois parvus. PLoS One 7:e37965.

Hebets, E. a., and D. R. Papaj. 2004. Complex signal function: developing a framework of testable hypotheses. Behav. Ecol. Sociobiol. 57:197–214.

Heuschele, J., and U. Candolin. 2007. An increase in pH boosts olfactory communication in sticklebacks. Biol. Lett. 3:411–413.

Heuschele, J., M. Mannerla, P. Gienapp, and U. Candolin. 2009. Environment- dependent use of mate choice cues in sticklebacks. Behav. Ecol. 20:1223–1227.

Isler, M. L., P. R. Isler, and B. M. Whitney. 1998. Use of vocalizations to establish species limits in antbirds (Passeriformes: Thamnophilidae). Auk 115:577–590.

Kirschel, A. N. G., D. T. Blumstein, R. E. Cohen, W. Buermann, T. B. Smith, and H.

Slabbekoorn. 2009. Birdsong tuned to the environment: green Hylia song varies with elevation, tree cover, and noise. Behav. Ecol. 20:1089–1095.

Krabbe, N., and T. Schulenber. 1997. Species limits and natural history of Scytalopus tapaculos (Rhinocryptidae), with descriptions of the Ecuadorian taxa, including three new species. Pp. 47–88 in J. Remsen, J. V., ed. Studies in Neotropical ornithology honoring Ted Parker. Ornithological Monographs No. 48.

11 Krams, I. 2001. Communication in crested tits and the risk of predation. Anim.

Behav. 61:1065–1068.

Kusmierski, R., G. Borgia, A. C. Uy, and R. H. Crozier. 1997. Labile evolution of display traits in bowerbirds indicates reduced effects of phylogenetic constraint. Proc.

Biol. Sci. 264:307–313.

Laverde-R, O., and C. D. Cadena. 2014. Taxonomy and conservation: a tale of two tinamou species groups (Tinamidae, Crypturellus). J. Avian Biol. 484–492.

Marler, P. 1955. Characteristics of some animal calls. Nature 176:6–7.

Martin, T. E., and A. V Badyaev. 1996. Sexual Dichromatism in Birds: Importance of

Nest Predation and Nest Location for Females Versus Males. Evolution. 50:2454–

2460.

Mason, N. A., A. J. Shultz, and K. J. Burns. 2014. Elaborate visual and acoustic signals evolve independently in a large, phenotypically diverse radiation of songbirds.

Proc. R. Soc. B Biol. Sci. 281:1–10.

Morton, E. S. 1975. Ecological sources of selection on avian sounds. Am. Nat.

109:17–43.

Narins, P. M., A. S. Feng, W. Lin, H.-U. Schnitzler, A. Denzinger, R. A. Suthers, and

C. Xu. 2004. Old World frog and bird vocalizations contain prominent ultrasonic harmonics. J. Acoust. Soc. Am. 115:910–913.

12 Negret, P. J., and O. Laverde-R. 2015. The enigmatic Black Tinamou: Do distribution, climate, and vocalizations reveal more than one species? Auk 132:132–

139.

Ornelas, J. F., C. González, and A. Espinosa de los Monteros. 2009. Uncorrelated evolution between vocal and plumage coloration traits in the trogons: a comparative study. J. Evol. Biol. 22:471–484.

Partan, S. R. 2004. Multisensory Animal Communication. Pp. 225–240 in G. A.

Calvert, C. Spence, and B. E. Stein, eds. The Handbook of Multisensory Processes.

MIT Press, Cambridge.

Partan, S. R., and P. Marler. 2005. Issues in the classification of multimodal signals.

Am. Nat. 166:231–245.

Pinheiro, C. E. G. 2007. Asynchrony in daily activity patterns of butterfly models and mimics. J. Trop. Ecol. 23:119.

Pinheiro, C. E. G. 2004. Jacamars (Aves , Galbulidae) as selective agents of mimicry in neotropical butterflies. Ibis (Lond. 1859). 12:137–139.

Pinheiro, C. E. G. 2011. On the evolution of warning coloration, Batesian and

Müllerian mimicry in Neotropical butterflies: the role of jacamars (Galbulidae) and tyrant-flycatchers (Tyrannidae). J. Avian Biol. 42:277–281.

Rafferty, N. E., and J. W. Boughman. 2006. Olfactory mate recognition in a sympatric species pair of three-spined sticklebacks. Behav. Ecol. 17:965–970.

13 Raguso, R. A. 2008. Wake up and smell the roses: the ecology and evolution of floral scent. Annu. Rev. Ecol. Evol. Syst. 39:549–569.

Repentigny, Y. D. E., H. Ouellet, and R. McNeil. 2000. Song versus plumage in some

North American oscines: testing Darwin’s hypothesis. Ecoscience 7:137–148.

Ruiz-Rodríguez, M., J. M. Avilés, J. J. Cuervo, D. Parejo, F. Ruano, C. Zamora-

Muñoz, F. Sergio, L. López-Jiménez, a Tanferna, and M. Martín-Vivaldi. 2013. Does avian conspicuous colouration increase or reduce predation risk? Oecologia 173:83–

93.

Santos, J. C., M. Baquero, C. Barrio-Amorós, L. A. Coloma, L. K. Erdtmann, A. P.

Lima, and D. C. Cannatella. 2015. Aposematism increases acoustic diversification and speciation in poison frogs. Proc. R. Soc. B Biol. Sci. 281:1–9.

Seddon, N. 2005. Ecological adaptation and species recognition drives vocal evolution in neotropical suboscine birds. Evolution 59:200–15.

Seehausen, O., Y. Terai, I. S. Magalhaes, K. L. Carleton, H. D. J. Mrosso, R. Miyagi,

I. van der Sluijs, M. V Schneider, M. E. Maan, H. Tachida, H. Imai, and N. Okada.

2008. Speciation through sensory drive in cichlid fish. Nature 455:620–6.

Shutler, D. 2011. Sexual selection: when to expect trade-offs. Biol. Lett. 7:101–104.

Shutler, D., and P. J. Weatherhead. 1990. Targets of sexual selection: song and plumage of wood warblers. Evolution. 44:1967–1977.

14 Slabbekoorn, H., and T. B. Smith. 2002. Habitat-dependent song divergence in the little greenbul: an analysis of environmental selection pressures on acoustic signals.

Evolution. 56:1849–58.

Tobias, J. A., J. Aben, R. T. Brumfield, E. P. Derryberry, W. Halfwerk, H.

Slabbekoorn, and N. Seddon. 2010. Song divergence by sensory drive in Amazonian birds. Evolution. 64:2820–39.

Touchton, J. M., N. Seddon, and J. A. Tobias. 2014. Captive rearing experiments confirm song development without learning in a tracheophone suboscine bird. PLoS

One 9.

Trillo, P. A., K. A. Athanas, D. H. Goldhill, K. L. Hoke, and W. C. Funk. 2013. The influence of geographic heterogeneity in predation pressure on sexual signal divergence in an Amazonian frog species complex. J. Evol. Biol. 26:216–222.

Uy, J. A. C., R. G. Moyle, and C. E. Filardi. 2009. Plumage and song differences mediate species recognition between incipient flycatcher species of the Solomon

Islands. Evolution. 63:153–64.

Uy, J. A. C., and R. J. Safran. 2013. Variation in the temporal and spatial use of signals and its implications for multimodal communication. Behav. Ecol. Sociobiol.

67:1499–1511.

Vane-Wright, R. I., and M. Boppre. 1993. Visual and chemical signalling in butterflies: functional and phylogenetic perspectives. Philos. Trans. R. Soc. B Biol.

Sci. 340:197–205.

15 Wiley, R. H. 1991. Associations of song properties with habitats for territorial Oscine birds of eastern North-America. Am. Nat. 138:973–993.

Wilkins, M. R., N. Seddon, and R. J. Safran. 2013. Evolutionary divergence in acoustic signals: causes and consequences. Trends Ecol. Evol. 28:156–166.

Zuk, M., and G. Kolluru. 1998. Exploitation of sexual signals by predators and parasitoids. Q. Rev. Biol. 73:415–438.

16 Chapter 1

Taxonomy and conservation: a tale of two tinamou species groups (Tinamidae,

Crypturellus)

Short title: Taxonomy and Conservation

Oscar Laverde-R. & Carlos Daniel Cadena

Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias

Biológicas, Universidad de los Andes.

Journal of Avian Biology 45: 484–492, 2014 doi: 10.1111/jav.00298 © 2014 Th e Authors. Journal of Avian Biology © 2014 Nordic Society Oikos Subject Editor: Martin Paeckert. Accepted 7 March 2014

Taxonomy and conservation: a tale of two tinamou species groups (Tinamidae, Crypturellus )

Oscar Laverde-R. and Carlos Daniel Cadena

O. Laverde-R. ([email protected]) and C. D. Cadena, Laboratorio de Biologí a Evolutiva de Vertebrados, Depto de Ciencias Biol ó gicas, Univ. de los Andes, Apartado 4976, Bogot á , Colombia.

Species delimitation has important consequences for the management of endangered species. Species-level taxonomy in the genus Crypturellus (Tinamidae) has been based largely on plumage characters and species limits in several groups have been diffi cult to establish. Because some of the forms of uncertain taxonomic status are currently threatened with extinction, a basic understanding of species limits is crucial not only for taxonomists but also for conservation biologists and managers. We analysed vocal variation to assess species limits in two Crypturellus species-groups, the red-legged complex (Crypturellus erythropus and allied forms) and the brown tinamou Crypturellus obsoletus. In the red-legged complex, where several species-level taxa have been recognized by some authors, there is no obvious geographic variation in vocalizations and populations appear mostly continuously distributed, with plumage variation largely explicable in terms of environmental conditions. In the brown group, a single species is recognized, but we found marked geographic variation in vocalizations and populations have disjunct distributions; we propose that at least one of the populations in this group likely merits recognition as a separate species. We conclude that incomplete knowledge of patterns of variation in relevant traits in addition to the momentum carried by traditional taxonomy may potentially mislead conservation actions.

Taxonomic, systematic, and evolutionary knowledge exerts a reaches its greatest diversity in tropical South America, with major infl uence on policy and decision-making in conserva- two major groups recognized: forest and steppe tinamous tion biology (Samper 2004, Hendry et al. 2010). For exam- (Cabot 1992, Bertelli et al. 2002, Bertelli and Porzecanski ple, species concepts and the criteria used to establish limits 2004). Tinamous are cryptic, shy and elusive but their between species have important consequences regarding vocalizations are distinctive, being among the most charac- strategies for the management of endangered species and teristic sounds of Neotropical forests (Cabot 1992). Th e biodiversity (Agapow et al. 2004, Samper 2004, Frankham songs of tinamous are believed to be innate (i.e. genetically et al. 2012, Heller et al. 2013). Because species arguably rep- determined) and thereby unaff ected by cultural evolution resent the currency of biodiversity, much of conservation (Kroodsma and Miller 1996). Th us, assuming that vocaliza- biology has relied on species as the main units for protection tions play an important role in species recognition and and management (but see Moritz 2002, Santamarí a and mating in tinamous (Cabot 1992), vocal variation could Mé ndez 2012), and this requires that species be properly be used to establish species limits in tinamous under the delimited. Furthermore, alternative criteria to recognize biological species concept, much as has been done in species can infl uence perceived priorities for conservation other avian groups lacking song learning (e.g. suboscine action (Cadena 2003, Agapow et al. 2004, Isaac et al. 2004, passerines; Isler et al. 1998, Remsen 2005). However, to our Dillon and Fjelds å 2005, Frankham et al. 2012) and could knowledge, vocalizations have only been used in one study divert limited resources away from the conservation of of species limits in tinamous (Maijer 1996). threatened species of unquestionable taxonomic status Th e forest-tinamou genus Crypturellus is the most (Garnett and Christidis 2007). Despite its critical impor- diverse in the family, with 21 species currently recognized tance, basing conservation action on sound species-level (Cabot 1992, Remsen et al. 2014). However, high inter- taxonomy is not always feasible: with regard to taxonomic specifi c similarity and ample intraspecifi c diversity have knowledge, conservation biology is often a crisis discipline in caused disagreement with regard to the number of species which one must act before knowing all the facts (Soulé accepted (Cabot 1992). Tinamou species-level taxonomy is 1985). Th is is true even in well studied groups, such as birds. largely based on plumage characters and species limits among Tinamous (Tinamidae) are terrestrial birds with limited some populations of Crypturellus are not well established. fl ight abilities. Th e family is endemic to the Neotropics and Because some of the forms of uncertain taxonomic status are

484 currently threatened with extinction, an adequate under- Methods standing of species limits is crucial not only for taxonomists but also for conservation biologists and managers. Study taxa We conducted analyses of vocal variation to assess species limits in two Crypturellus species groups, the red-legged Crypturellus erythropus (red-legged tinamou) is widespread in complex ( Crypturellus erythropus and allied forms) and the the lowlands of northern South America (Fig. 1); it is brown tinamou Crypturellus obsoletus. Th e taxonomy of found in wet to dry forest and also in disturbed habitats the red-legged, medium-sized Crypturellus inhabiting (Olivares 1958, Schwartz and Lentino 1984, Cabot 1992, lowland areas in northern South America has been contro- Remsen et al. 2014). Seven subspecies of C. erythropus versial for decades; this group has received much attention have been described (Clements et al. 2010) and are currently in part because two forms (columbianus and saltuarius ) have considered as such by the South American Classifi cation been suggested to be at risk of extinction and some authors Committee of the American Ornithologists’ Union (Remsen consider them to be good species (Cabot 1992, Renjifo et al. 2014). However, some of these forms have often et al. 2002, Birdlife International 2008, Remsen et al. been regarded as separate species (Stotz et al. 1996, Renjifo 2014). On the other hand, the taxonomy of the brown et al. 2002, Donegan et al. 2003) and two of them, tinamou has not been evaluated recently and it is considered C. e. columbianus (Colombian tinamou) and C. e. saltuarius of least concern from a conservation standpoint (Birdlife (Magdalena tinamou), are currently considered threatened International 2008). Our interest in the brown tinamou was (Collar 1992, Renjifo et al. 2002). Crypturellus e. saltuarius spurred by the rediscovery of one of its members in Colom- was described from a single male specimen on the basis bia (Alvarez-Rebolledo et al. 2007), where it was formerly of its stronger pattern of barring in the plumage, but known only from ‘ Bogot á ’ trade skins. Because preliminary because this type of pattern is characteristic of immature analyses indicated this population was vocally distinct, males in this group, the taxonomic distinctness of this we suspected that the brown tinamou could include form has been questioned (F. G. Stiles pers. comm.). Th e species-level taxa at risk of extinction that had been over- taxonomic status of Crypturellus kerriae (choco tinamou) looked owing to the lack of modern taxonomic studies. has also been matter of dispute, but it has not been treated

C. boucardi C. e. idoneus C. e. erythropus

erythropus idoneus saltuarius kerriae C. kerriae

C. e. saltuarius C. e. erythropus

Figure 1. Geographic distribution and vocal variation of the red-legged tinamou complex in northern South America. Circles represent the location of recordings used in acoustic analyses. A potential distribution map is shown in grayscale, with darker tones indicating greater probability of presence. Th e map was constructed using the maximum entropy method implemented in package DISMO for R (Hijmans et al. 2011) based on the localities from which recordings were available and using 19 climate surfaces obtained from WORLDCLIM, ver. 1.2 (Hijmans et al. 2005). Representative sonograms of two-second sections of songs of each form are shown: the horizontal axis indicates time and the vertical axis indicates frequency (range 0– 2 kHz). A sonogram for the extralimital Central American species C. boucardi is shown but its geographical distribution is not depicted in the map.

485 as conspecifi c with C. erythropus. Some authors have consid- Cordillera Oriental west of Bogot á obtained the fi rst record- ered C. kerriae and C. erythropus as sister taxa, forming a ings of C. o. castaneus (Alvarez-Rebolledo et al. 2007). Since superspecies with the Middle American C. boucardi this discovery, this form has been repeatedly recorded at this (slaty-breasted tinamou; Sibley and Monroe 1990, Bertelli locality and a few others in the same region (O. Cort é s, et al. 2002). It has been suggested that C. e. saltuarius might A. Hern á ndez, O. Laverde. and C. D. Cadena unpubl.). be a subspecies of C. kerriae (Blake 1977), but also that kerriae could instead be a subspecies of C. boucardi Vocal analysis (Remsen et al. 2014). Crypturellus obsoletus (brown tinamou) is a polytypic We examined good-quality tinamou recordings deposited species with nine recognized subspecies inhabiting in the Macaulay Library of Natural Sounds (MLS), disjunct areas in both the highlands and lowlands of South Xeno-Canto (XC), and the Banco de Sonidos Animales of the America (Venezuela, Colombia, Ecuador, Peru, Bolivia, Inst. Alexander von Humboldt (BSA) (Supplementary mate- Brasil, Paraguay and Argentina; Fig. 2; Olivares 1958, rial Appendix 1). Th ese recordings represented 82 individuals Cabot 1992, Clements et al. 2010). Th e nominate form in the red-legged tinamou complex and related forms: C. o. obsoletus was described based on skins from localities C. e. erythropus (71), C. e. idoneus (3), C. e. saltuarius (2), in southeastern Brazil (Ipanema, Itarar é , Mato-Dentro; C. kerriae (2) and C. boucardi (4). We included recordings of Hellmayr and Conover 1942). Four decades later, C. o. cas- C. kerriae and C. boucardi in analyses because both species have taneus was described based on ‘ Bogot á ’ skins of uncertain been considered to be allied to the red-legged group. For geographic origin (Sclater 1858 in Hellmayr and Conover C. obsoletus, we analyzed recordings from 42 individuals of the 1942), and specimens from Peru and Ecuador were following taxa: griseiventris (5), obsoletus (13), ochraceiventris later referred to this taxon (Hellmayr and Conover 1942). In (10, we here include subspecies traylori due to its uncertain September 2006 an expedition of the Inst. Alexander von elevational distribution and to marked vocal similarity with Humboldt to a cloud forest site on the western slope of the ochraceiventris ), punensis (8) and castaneus (6). Crypturellus

C. o. castaneus

castaneus punensis obsoletus griseiventris ochraceiventris

C. o. griseiventris C. o. ochraceiventris

C. o. obsoletus C. o. punensis

Figure 2. Geographic distribution and vocal variation of the brown tinamou complex in South America. Circles represent the location of recordings used in acoustic analyses. A potential distribution map is shown in grayscale, with darker tones indicating greater probability of presence. Th e map was constructed using the maximum entropy method implemented in package DISMO for R (Hijmans et al. 2011) based on the localities from which recordings were available and using 19 climate surfaces obtained from WORLDCLIM, ver. 1.2 (Hijmans et al. 2005). Representative sonograms of two-second songs of each form are depicted; the horizontal axis indicates time and the vertical axis indicates frequency (range 0 – 2 kHz).

486 erythropus has one type of vocalization emitted constantly, its between this entity, C. obsoletus (including C. o. castaneus, song. In C. obsoletus , long vocalizations are rarely produced and C. o. griseiventris, C. o. ochraceiventris, C. o. obsoletus and short vocalizations are commonly used; we chose short vocal- C. o. punensis), and C. boucardi; in this scheme, we grouped izations for analyses. We asume homology between the vocal- C. erythropus and C. kerriae due to their remarkably izations selected for analyses and refer to them as songs, defi ned similar songs (see below). Finally, we examined discrimina- as loud vocalizations functioning to establish and defend terri- tion between C. boucardi, broad-sense C. erythropus tories and to attract mates (Kroodsma 2007). as defi ned above, C. obsoletus (including C. o. griseiventris , We selected a single song from each recording for C. o. ochraceiventris , C. o. obsoletus and C. o. punensis analysis. Songs were analyzed with the RAVEN bioacoustics and excluding C. o. castaneus ) and C. o. castaneus ; we left program ver. 1.4 (Cornell Lab of Ornithology, Ithaca, New C. o. castaneus apart from the rest of C. obsoletus due to its York) using default settings: FFT window 512, window strikingly diff erent songs (see below). overlap hann method. Song structure was quantifi ed on sonograms using on-screen cursors to measure time and fre- quency characteristics. Sonograms for publication were built Results using the Seewave library (Sueur et al. 2008) for R (R Core Team). A visual inspection of sonograms reveals very similar For each recording, we selected a single song for analysis, songs among members of the red-legged tinamou complex in which we measured the following variables: 1) total length (Fig. 1) but considerable variation in songs among members of song; 2) length of the fi rst, middle and fi nal note; of the brown tinamou complex (Fig. 2). Songs of members 3) highest frequency of the fi rst, middle and fi nal of the red-legged tinamou complex are stereotyped, low- note; 4) lowest frequency of the fi rst, middle and fi nal note; paced tremulous whistles comprising two to three pure tones, 5)maximum frequency of the fi rst, middle and fi nal note; with unmodulated frequency. Brown tinamou songs are 6) bandwidth of the fi rst, middle and fi nal note, and highly variable among populations, consisting of one to 7) average entropy of the fi rst, middle and fi nal note. nine notes, with greater frequency modulation and variable Peak frequency was defi ned as the frequency at which most note shape between forms. Th e songs of C. o. castaneus energy in the note selected is found (Charif et al. 2010). stand out as especially divergent from those of other Th e average entropy measurement describes the amount of populations because they only show three unmodulated pure disorder for a selected spectrum; high entropy values tones whereas other taxa show longer and tremulous songs correspond to sounds with greater disorder whereas zero with more modulated frequencies. entropy would characterize a pure tone with energy in only Th e fi rst principal component (PC1) accounted for one frequency bin (Charif et al. 2010). When the song 40% of the variation in song measurements in C. erythropus selected on a given recording had an even numbers of notes and allies (Fig. 3) and is associated largely with duration of (e.g. 4, 6), we measured the fi rst of the two middle notes. the middle note (loading coeffi cient 0.68) and total length of song (0.32). PC2 accounted for 30% of the variation Statistical analysis and correlated most with length of fi rst note (0.62). In the acoustic space described by the fi rst two PCA axes, songs We fi rst examined overall patterns of variation in songs of C. e. idoneus , C. e. saltuarius and even of C. kerriae within each species complex (red-legged and brown tinamou) exhibit characteristics within the range of variation existing using principal component analyses (PCA) based on the in songs of C. e. erythropus (see also sonograms in Fig. 1). correlation matrices derived from acoustic variables. Prior PC1 accounted for 46% of the variation in song to PCA, variables not normally distributed were log- measurements in C. obsoletus (Fig. 4), with frequency transformed. Coeffi cients of variation were used to compare bandwidth of the fi rst, middle and fi nal note loading the extent of intra-taxon vocal variation between C. obsoletus heavily on this axis (loadings 0.45, 0.53, and 0.38, respec- and C. erythropus . tively). PC2 accounted for 31% of the variation, with total We tested whether taxa could be distinguished in length of songs and length of middle and fi nal notes loading multivariate vocal space using discriminant analysis (DA), heavily on it (loadings 0.58, 0.28, and 0.38, respectively). combining all measurements of songs of: fi ve subspecies of Songs of C. o. castaneus stand out as distinct from those C. obsoletus (castaneus , griseiventris , ochraceiventris , obsoletus of other taxa; they tend to have notes with narrower band- and punensis), the three forms of C. erythropus (erythropus , width, and shorter songs and notes relative to others idoneus and saltuarius ), C. kerriae and C. boucardi . We con- members of C. obsoletus group (Fig. 2, 4). Coeffi cients of ducted three separate discriminant analyses to examine the variation of most song characteristics were considerably sensitivity of results to establishing diff erent sets of taxa as larger in C. obsoletus than in C. erythropus (Table 2). groupings. Th ese sets were defi ned based on 1) existing Discriminant function analysis of song measurements hypotheses about species limits (Hellmayr and Conover for all 10 taxa combined revealed signifi cant diff erences 1942, Cabot 1992, Clements et al. 2010) and 2) our among centroids (Wilks’ L 0.001, DF 117, p 0.0001, preliminary inspections of vocal variation based on PCA, Fig. 5, Table 1). Th is analysis classifi ed correctly 83.8% of which revealed the existence of taxa with apparently distinct individuals to their respective population designation, vocalizations. First, we treated all ten taxa as independent with most of the misclassifi ed songs corresponding to entities. Second, we lumped C. e. erythropus , C. e. idoneus , members of the red-legged group. When we treated C. e. saltuarius and C. kerriae into one entity (i.e. broad- C. e. erythropus, C. e. idoneus , C. e. saltuarius and C. kerriae as sense C. erythropus), and then tested for discrimination one entity (i.e. broad-sense C. erythropus ) separate from

487 erythropus 0.3 idoneus saltuarius kerriae 0.2

0.1

0.0

–0.1 Principal component 2 –0.2

–0.3

–0.3 –0.2 –0.1 0.0 0.1 0.2 0.3 Principal component 1

Figure 3. Principal component analysis of measurements taken on recordings of songs of red-legged tinamou and allies. Note the marked variation existing in songs of C. erythropus , which overlap with those of saltuarius , considered by some to represent a diff erent species, and even with those of C. kerriae.

C. obsoletus and C. boucardi , 96.9% of individuals were races of species diff erent from C. erythropus (Blake 1977, classifi ed correctly in their respective taxon designation Cabot 1992). For instance, Meyer de Schauensee (1970) (Wilks’ L 0.0006, DF 117, p 0.0001, Fig. 5, Table 1). considered saltuarius to be a distinct species rather than a Finally, when we tested for discrimination between subspecies of C. erythropus , but Blake (1977) proposed that C. boucardi , broad-sense erythropus , C. obsoletus, and this taxon might instead be a subspecies of C. kerriae . C. o. castaneus , 100% of individuals were classifi ed correctly Hilty (2003) indicated that forms of erythropus are all rather (Wilks ’ L 0.0005, DF 117, p 0.0001, Fig. 5, similar, with variation within each subspecies almost as great Table 1). as variation between them. In keeping with this suggestion, our vocal analyses show that C e. idoneus , C. e. erythropus , C. e. saltuarius and even C. kerriae (sometimes considered Discussion conspecifi c with C. boucardi, Cabot 1992) have very similar songs. Unfortunately, we were unable to obtain con- Much uncertainty exists regarding species limits in the fi rmed recordings of C. e. columbianus, but assuming this red-legged tinamou group and about the taxonomic status of population is in contact with idoneus and saltuarius , related forms. Crypturellus e. columbianus , C. e. saltuarius we expect its song to fall within the variation existing in and C. e. idoneus have all been recognized as full species or as C. erythropus .

1.5 castaneus obsoletus punensis 1.0 griseiventris ochraceiventris

0.5

0.0 Principal component 2

–1.0 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 Principal component 1

Figure 4. Principal component analysis of measurements taken on recordings of brown tinamou songs. Note that songs of each taxon tend to cluster together and the marked separation of songs of castaneus.

488 4

2

0

–2 erythropus idoneus –4 saltuarius Canonical axis 2 kerriae boucardi obsoletus –6 punensis griseiventris ochraceiventris –8 castaneus –5 0 5 10 Canonical axis 1

Figure 5. Call variation in 10 tinamou taxa summarized by the fi rst two discriminant canonical functions derived from 13 acoustic variables. Th e fi rst two canonical functions explained 89.4% of the variation in vocal variables (81.8 and 7.6%, respectively; Table 1). Note the marked discrimination in members of the brown tinamou group relative to discrimination among forms of the red-legged group.

In contrast to the situation of the red-legged tinamou are not contiguous. Lowland subspecies are well isolated group, there has been little debate with regard to species by distance and major rivers (Fig. 2): C. o. obsoletus of the limits in the brown tinamou (Cabot 1992, Clements et al. southern Atlantic forest is isolated by more than 1000 km 2010). Th e extensive variation in songs revealed by our from C. o. griseiventris of north-central Brazil and analyses suggests that C. obsoletus likely comprises more than C. o. hypochraceus is separated from C. o. griseiventris by the one species. Because only one species is recognized in the Madeira River. In turn, montane forms ( C. o. cerviniventris, brown tinamou group, whereas as many as three species have C. o. knoxi, C. o. castaneus , C. o. ochraceiventris, C. o. traylori been suggested to exist in the red-legged tinamou group, and C. o. punensis) have seemingly disjunct distributions vocalizations suggest that current taxonomy may not ade- from Venezuela to Bolivia (Fig. 2, Cabot 1992). Finally, it quately represent species-level diversity in these birds. appears that C. o. castaneus diff ers ecologically from other Populations of C. erythropus from east and west of the populations: its elevational range (n 6 localities, 2650 5 5 Andes are likely connected through the lowlands north of m, mean standard deviation) does not overlap with that of the Andes (Fig. 1). Such connection would facilitate gene C. o. griseiventris (n 34, 270 39 m) or C. o. obsoletus fl ow (Brumfi eld and Capparella 1996) and could account for (n 23, 647 370 m), and only narrowly overlaps with the lack of vocal diff erentiation we observed. In contrast, the highland forms C. o. punensis (n 13, 1750 470 m) geographic distributions of populations of C. obsoletus and C. o. ochraceiventris (n 13, 1650 680 m). Populations with contrasting elevational distributions and diff erent voices have been shown to represent diff erent Table 1. Standarized canonical discriminant function coeffi cients of species in recent studies in other Neotropical birds (Cadena the discriminant function analysis of vocal variation in C. erythopus , and Cuervo 2010, Caro et al. 2013) including tinamous C. boucardi , C. castaneus and C. obsoletus based on 13 measure- ments taken on songs. Bold numbers highlight the most important (Maijer 1996). variables in the discriminant functions. Th e patterns uncovered by our analyses reveal greater vocal diff erentiation among populations of the brown Function tinamou group relative to the red-legged tinamou group, Variable 1 2 which contrasts with the patterns observed in plumage Length of song 0.061 0.148 variation. Because traditional taxonomy of tinamous has Lower frequency fi rst note 0.384 0.380 Higher frequency fi rst note0.819 0.411 Table 2. Coeffi cients of variation of song features in the red-legged Max frequency fi rst note0.691 0.381 and the brown tinamou groups. Note the greater variation in most Bandwidth fi rst note0.444 0.096 measurements in the brown tinamou group. Entropy fi rst note 0.473 0.294 Length of the fi rst note 0.239 0.225 Red-legged group Brown group Lower frequency second note 0.447 ؊ 0.542 Number of notes 16.54 58.10 Higher frequency second note0.812 0.321 Song length (s) 16.80 63.20 Max frequency second note0.647 0.292 Pace (notes/s) 23.29 21.99 Bandwidth second note0.485 0.335 Bandwidth (Hz) 16.89 46.89 Entropy second note0.484 0.412 Peak frequency (Hz) 12.41 16.57 Length of the second note 0.196 0.380 Entropy 2.79 20.76

489 relied largely on plumage coloration (Hellmayr and Conover their marked diff erences in plumage coloration and the lack 1942, Blake 1977), the recognition of several species in the of evidence about the importance of such diff erences as red-legged tinamou group is based on marked geographic indicators of species limits in tinamous. Moreover, because variation in plumage coloration, especially of the crown and a cladistic analysis based on 80 integumentary characters belly (Cabot 1992). Although plumage traits carry some indicates that C. erythropus and C. kerriae are distant rela- phylogenetic signal in tinamous (Bertelli et al. 2002, tives within the Tinamidae (Bertelli et al. 2002), any pro- Bertelli and Porzecanski 2004), we suggest they may not be posal to lump these taxa in a single species should be based reliable taxonomic markers for species delimitation. In par- on additional data. In addition to analyses directed at estab- ticular, color intensity may be highly sensitive to the local lishing the function of plumage and vocal traits, molecular- environment and thus result in within-species geographic based assessments of phylogenetic relationships and variation in association with humidity (i.e. Gloger’s population-genetic diff erentiation are sorely needed. rule, Burtt and Ichida 2004) and light availability in the Another important priority for future analyses would be to environment (Gomez and Th é ry 2007). Consistent acquire and analyze recordings of the songs of taxa with such scenario, an examination of specimens and not included in our analyses owing to lack of material, espe- photographs of members of the red-legged tinamou group cially in the brown tinamou group (i.e. C. o. cerviniventris , by F. G. Stiles revealed that all of the forms share a similar C. o. knoxi , C. o. hypochaceus ). overall plumage pattern and diff er mainly in intensity of Th e uncertain taxonomic status of threatened bird coloration, with variation seemingly following Gloger’ s rule populations is particularly evident among the Neotropical (i.e. darker plumages in more humid areas; Remsen et al. avifauna. On one hand, some Neotropical bird species of 2014). Th us, the phenotypic variation that led to recogni- particular conservation concern (e.g. the critically tion of several taxa in this group may refl ect geographic endangered tumaco seedeater Sporophila insulata and variation over environmental gradients within a single lin- Entre R í os seedeater S. zelichi ) have been shown to be eage. In contrast, geographic variation in plumage in the invalid taxa (Stiles 2004, Areta 2008), whereas other pop- brown tinamou group is much more subtle and this may ulations that might merit species recognition are currently explain why this group has been subject to far less debate treated only as subspecies of more widespread species (e.g. concerning species delimitation. Pyrrhura picta subandina, Cistothorus apolinari apolinari ) If one follows the biological species concept (BSC), are rapidly vanishing owing to a lack of formal protection which focuses on reproductive isolation as a criterion for (Joseph 2000, Cadena 2003). In addition, the existence species delimitation (Mayr 1963), the taxonomic implica- of threatened species that only recently have been discov- tions of the patterns of variation revealed by our study ered or recognized (Krabbe and Schulenberg 1997, Krabbe must necessarily be tentative in the absence of studies et al. 1999, 2005, Cuervo et al. 2005, Carantó n-Ayala assessing the role of songs and plumage in species recogni- and Certuche-Cubillos 2010, Lara et al. 2012), or whose tion (Uy et al. 2009), especially considering the allopatric validity as species has only been confi rmed through recent distributions of most taxa. Pending such additional analyses work (Krabbe et al. 2005, Krabbe and Cadena 2010), and also examinations of genetic variation and phylogenetic highlights the importance of studies on species-level tax- relationships, however, it appears that, minimally, the onomy to enable sound conservation of Neotropical montane Colombian form castaneus merits recognition birds. as a distinct biological species based on its remarkably dif- Our study of two groups of tinamous highlights that ferent song and distinct elevational distribution (Fig. 2, 4). incomplete knowledge of patterns of variation in relevant Th e degree of vocal and ecological diff erentiation between traits, in addition to adhering to the momentum carried by castaneus and other populations is comparable to, or even traditional taxonomy, may lead to potential pitfalls in con- greater than, variation existing among good (i.e. reproduc- servation planning. On one hand, the red-legged tinamou tively isolated) species of tinamous (Cabot 1992, Maijer group, in which much attention by conservationists 1996), a criterion often used to treat allopatric populations has been devoted to threatened populations (BirdLife as diff erent species under the BSC (reviewed by Remsen International 2008), may actually consist of a single lineage 2005). Alternatively, if one were to follow species concepts with a suffi ciently large range and population sizes so as to based on diagnosability (e.g. the phylogenetic species con- be relatively safe from a conservation standpoint. In cept, Cracraft 1983), which are often used in ornithology contrast, the brown tinamou, long considered of least (Sangster 2014), then it is likely that several species would concern by conservationists owing to its large geographical need to be recognized in the brown tinamou group. In turn, distribution, may actually comprise several species of uncer- vocal variation does not support the split of C. erythropus tain, and likely worrisome conservation status. Our data into more species than currently recognized (Remsen et al. indicate that taxon castaneus should be considered a species 2014) as has been suggested by some authors (Collar 1992, separate from the rest of C. obsoletus; this formerly unrecog- Renjifo et al. 2002, BirdLife International 2008). Intrigu- nized species is likely under risk. Its distribution range is ingly, our analyses indicate that C. kerriae, which has quite small and largely deforested, and is known to never been considered part of this group, exhibits songs occur only within a single small reserve (Parque Natural within the range of vocal variation existing in C. erythropus , Chicaque; ca 250 ha). Other recent records west of Bogot á suggesting these two taxa may well be conspecifi c and that (La Aguadita and Subachoque) are from a region lacking their phenotypic distinctiveness should be recognized at protected areas; thus, this tinamou occurs in areas entirely the subspecies level. We refrain, however, from formally rec- lacking offi cial protection (Franco et al. 2007, BirdLife ommending lumping C. kerriae and C. erythropus given International 2013).

490 More generally, our work serves to illustrate that Cadena , C. D. and Cuervo , A. M . 2010 . Molecules, ecology, conservation biologists and managers should bear in mind morphology, and songs in concert: how many species is that taxonomy has the potential to infl uence, and even mis- “Arremon torquatus ” (Aves, Emberizidae)? – Biol . J. Linn. lead, perceived priorities for conservation action, especially Soc. 99 : 152 – 176 . Carant ó n-Ayala , D. and Certuche-Cubillos , K . 2010 . A new in groups and areas where taxonomic revisions of many species of antpitta (Grallaridae: Grallaria ) from the northern groups are still required (e.g. many birds in the Neotropics). sector of the western Andes of Colombia. – Ornitol . Colomb. Conservation would clearly benefi t from consultation 9 : 56 – 70 . with taxonomists and from consideration of alternative met- Caro , L. M. , Caycedo , P. C. , Bowie , R. C. K. , Slabbekoorn , H. rics of biodiversity in addition to a species-centered approach and Cadena , C. D . 2013 . Ecological speciation along relying on prevailing (and possibly incorrect) taxonomy an elevational gradient in a tropical passerine bird? – J. Evol. (Faith 1992, Moritz 2002, Cadena 2003). Biol. 26 : 357 – 374 . Charif , R. A. , Strickman , L. M. and Waack , A. M . 2010 . Raven Pro 1.4 user’ s manual. – Cornell Lab of Ornithology, Acknowledgements – We are grateful to Greg Budney, Matthew Ithaca, NY . Medler and Tammy Bishop of the Macaulay Library of Natural Clements , J. F. , Schulenberg , T. S. , Iliff , M. J. , Sullivan , B. L. Sounds, Paula Caycedo of the Colecció n de Sonidos Ambientales of and Wood , C. L . 2010 . Th e eBird/Clements checklist of birds the Inst. Alexander von Humboldt, and all the recordists who of the world: version 6.5. – www.birds.cornell.edu/ uploaded recordings in Xeno Canto for making them available for clementschecklist/ . the present study. Oswaldo Corté s and Richard Webster provided Collar , N. J . 1992 . Th reatened birds of the Americas: the ICBP/ useful recordings. We thank Mauricio Á lvarez, Gustavo Bravo, IUCN Red Data Book (3rd ed., part 2). – Smithsonian Fernando Forero and Socorro Sierra for being part of the team Inst. Press . who rediscovered C. o. castaneus in the fi eld. F. G. Stiles, Cracraft , J . 1983 . Species concepts and speciation analysis. J. V. Remsen off ered valuable comments on the manuscript. – Curr. Ornithol. 3 : 159 – 187 . Cuervo , A. M. , Cadena , C. D. , Krabbe , N. and Renjifo , L. M . 2005 . Scytalopus stilesi, a new species of tapaculo (Rhinocryptidae) from the Cordillera Central of Colombia. – Auk 122 : References 445 – 463 . Dillon , S. and Fjelds å , J . 2005 . Th e implications of diff erent Agapow , P.-M. , Bininda-Edmonds , O. R. P., Crandall , K. A., species concepts for describing biodiversity patterns and Gittleman , J. L. , Mace , G. M. , Marshall , J. C. and Purvis , A . assessing conservation needs for African birds. – Ecography 2004 . Th e impact of species concept on biodiversity studies . 28 : 682 – 692 . – Q. Rev. Biol. 79 : 161 – 179 . Donegan T. M. , Huertas , B. C. and Brice ñ o , E. R . 2003 . Status Alvarez-Rebolledo , M. , Caro , V. , Laverde , O. and Cuervo , A. M. of the Magdalena tinamou Crypturellus saltuarius in the 2007 . Gu í a sonora de las aves de los Andes colombianos . type locality and surrounding lower Magdalena Valley. – Inst. Alexander von Humboldt, Macaulay Library of – Cotinga 19 : 34 – 39 . Natural Sounds . Faith , D. P . 1992 . Conservation evaluation and phylogenetic Areta , J. I . 2008 . Entre R í os seedeater (Sporophila zelichi ): diversity. – Biol. Conserv. 61 : 1 – 10 . a species that never was. – J. Field Ornithol. 79 : Franco , P. , Saavedra-Rodr í guez , C. A. and Kattan , G. H . 352 – 363 . 2007 . Bird species diversity captured by protected areas in Bertelli , S. and Porzecanski , A. L. 2004 . Tinamou (Tinamidae) the Andes of Colombia: a gap analysis . – Oryx 41 : 57 – 61 . systematics: a preliminary combined analysis of morphology Frankham , R. , Ballou , J. D. , Dudash , M. R. , Eldridge , M. D. B. , and molecules . – Ornitol. Neotrop. 15 : 1 – 8 . Fenster , C. B. , Lacy , R. C. , Mendelson III , J. R., Porton , I. J. , Bertelli , S. , Giannini , N. P. and Goloboff , P. A . 2002 . A phylogeny Ralls , K. and Ryder , O. A . 2012 . Implications of diff erent of the tinamous (Aves: Palaeognathiformes) based species concepts for conserving biodiversity. – Biol. Conserv. on integumentary characters . – Syst. Biol. 51 : 959 – 579 . 153 : 25 – 31 . BirdLife International 2008 . Critically endangered birds: Garnett , S. T. and Christidis , L . 2007 . Implications of a global audit . – BirdLife International, Cambridge, UK . changing species defi nitions for conservation purposes. BirdLife International 2013 . Important bird areas, factsheet: – Bird Conserv. Int. 17 : 187 – 195 . Bosques de la Falla del Tequendama. – www.birdlife. Gomez , D. and Th é ry , M . 2007 . Simultaneous crypsis and org . conspicuousness in color patterns : comparative analysis Blake , E. R. 1977 . Manual of Neotropical Birds, Volume 1. of a Neotropical rainforest bird community. – Am. Nat. 169 : – Univ. of Chicago Press . S42 – S41 . B r u m fi eld, R. T. and Capparella , A. P. 1996 . Historical Heller , R. , Frandsen , P. , Lorenzen , E. and Siegismund , H. R . diversifi cation of birds in northwestern South America: 2013 . Are there really twice as many bovid species as we a molecular perspective on the role of vicariant events . thought? – Syst . Biol. 62 : 490 – 493 . – Evolution 50 : 1607 – 1624 . Hellmayr , C. E. and Conover , B . 1942 . Catalogue of birds Burtt , J. E. H. and Ichida , J. M . 2004 . Gloger ’ s rule, feather- of the Americas. – Field Museum Natural History, degrading bacteria, and color variation among song Zoological Series 13 : 1 . sparrows . – Condor 106 : 681 – 686 . Hendry , A. P. , Lohmann , L. G. , Conti , E. , Cracraft , J. , Cabot , J . 1992 . Family Tinamidae (tinamous). – In: del Hoyo, J., Crandall , K. A. , Faith , D. P. , Hauser , C. , Joly , C. A. , Elliott, J. A. and Sargatal, J. (eds) , Handbook of the birds Kogure , K. , Larigauderie , A. , Magallon , S. , Moritz , C. , of the World, Vol. 1 – ostrich to ducks. Lynx Edicions , Tillier , S. , Zardoya , R. , Prieur-Richard , A. H. , Walther , B. A. , pp. 112 – 139 . Yahara , T. and Donoghue , M. J . 2010 . Evolutionary biology Cadena , C. D . 2003 . Taxonom í a de Cistothorus apolinari in biodiversity science, conservation, and policy: a call to (Troglodytidae), conceptos de especie y conservació n de las action . – Evolution 64 : 1517 – 1528 . aves amenazadas de Colombia: un comentario. – Ornitol . Hijmans , R. J. , Cameron , S. E. , Parra , J. L. , Jones , P. G. and Colomb. 1 : 71 – 75 . Jarvis , A . 2005 . Very high resolution interpolated climate

491 surfaces for global land areas. – Int. J. Climatol. 25 : Moritz , C . 2002 . Strategies to protect biological diversity and 1965 – 1978 . the evolutionary processes that sustain it. – Syst. Biol. 51 : Hijmans , R. J., Phillips , S. , Leathwick , J. and Elith , J . 238 – 254 . 2011 . Package ‘ dismo ’ . – http://cran.rproject.org/web/ Olivares , A . 1958 . Monograf í a de los tinam ú es colombianos. packages/dismo/index.html . – Rev. Univ. Nac. Colomb. 23 : 245 – 301 . Hilty , S. L . 2003 . Birds of Venezuela. – Princeton Univ. Press . Remsen , J. V . 2005 . Pattern, process, and rigor to meet Isaac , N. J. B. , Mallet , J. and Mace , G. M . 2004 . Taxonomic clasifi cation . – Auk 122 : 403 – 413 . infl ation: its infl uence on macroecology and conservation. Remsen , J. V. Jr , Cadena , C. D. , Jaramillo , A. , Nores , M. , – Trends Ecol. Evol. 19 : 464 – 469 . Pacheco , J. F. , P é rez-Em á n , J. , Robbins , M. B. , Stiles , F. G. , Isler , M. L. , Isler , P. R. and Whitney , B. M . 1998 . Use of Stotz , D. F. and Zimmer , K. J . 2014 . A classifi cation of the vocalizations to establish species limits in antbirds bird species of South America . – www.museum.lsu. (Passeriformes: Th amnophilidae) . – Auk 115 : 577 – 590 . edu/ ∼ Remsen/Baseline.html . Joseph , L . 2000 . Beginning an end to 63 years of uncertainty: Renjifo , L. M. , Franco-Maya , A. M. , Amaya-Espinel , J. D. , the Neotropical parakeets known as Pyrrhura picta and Kattan , G. H. and L ó pez-Lans , B . 2002 . Libro rojo de aves P. leucotis comprise more than two species. – Proc. Acad. de Colombia . – Serie Libro Rojos de Especies Amenazadas Nat. Sci. Phil. 150 : 279 – 292 . de Colombia, Inst. de Investigació n de Recursos Krabbe , N. and Schulenber , T. S. 1997 . Species limits and Biol ó gicos Alexander von Humboldt Ministerio del Medio natural history of Scytalopus tapaculos (Rhinocryptidae), Ambiente . with descriptions of the Ecuadorian taxa, including three Samper , C . 2004 . Taxonomy and environmental policy. new species. – Ornithol. Monogr. 48 : 47 – 88 . – Phil. Trans. Biol. Sci. 359 : 721 – 728 . Krabbe , N. and Cadena, C. D. 2010 . A taxonomic revision of Sangster , G . 2014 . Th e application of species criteria in the Paramo tapaculo Scytalopus canus (Aves: Rhinocryptidae), avian taxonomy and its implications for the debate over with description of a new subspecies from Ecuador and species concepts . – Biol. Rev. 89 : 199 – 214 . Peru. – Zootaxa 2354 : 56 – 66 . Santamar í a , L. and M é ndez , P. F. 2012 . Evolution in biodiversity Krabbe , N. , Agro , D. J., Rice , N. H., Jacome , M. , Navarrete , policy – current gaps and future needs . – Evol. Appl. 5 : L. and Soronza , M. F. 1999 . A new species of antpitta 202 – 218 . (Formicariidae: Grallaria ) from the southern Ecuadorian Schwartz , P. and Lentino , M . 1984 . Relaciones de los tin á midos Andes. – Auk 116 : 882 – 890 . venezolanos del grupo Crypturellus noctivagus indicados Krabbe , N. , Salaman , P. , Cort é s , A. , Quevedo , A. , Ortega , L. por su voz (Aves: Tinamidae) . – Serie Informes Cient í fi cos A. and Cadena , C. D. 2005 . A new species of Scytalopus DGSIIA/IC/23, Ministerio del Ambiente y de Recursos tapaculo from the upper Magdalena Valley, Colombia . – Naturales Renovables, Caracas, Venezuela. Bull. Br. Ornithol. Club 125 : 3 – 18 . Sibley , C. G. and Monroe , B. L . 1990 . Distribution and taxonomy Kroodsma , D . 2007 . Th e singing life of birds: the art and science of birds of the world . – Yale Univ. Press . of listening to birdsong . – Houghton Miffl in Harcourt. Soul é , M . 1985 . What is conservation biology ? – Bioscience Kroodsma , D. E. and Miller , E. H. 1996 . Ecology and 35 : 727 – 734 . evolution of acoustic communication in birds. – Cornell Stiles , F. G . 2004 . Th e tumaco seedeater (Sporophila insulata , Univ. Press . Emberizidae): a species that never was? – Ornitol . Neotrop. Lara , C. E., Cuervo , A. M., Valderrama , S. V., Calderó n-F., D. 15 : 17 – 30 . and Cadena , C. D . 2012 . A new species of wren Stotz , D. F. , Fitzpatrick , J. W. , Parker , T. A. and Moskovits , D. (Troglodytidae: Th ryophilus) from the dry Cauca River K . 1996 . Neotropical birds: ecology and conservation . – Canyon, northwestern Colombia. – Auk 129 : 537 – 550 . Univ. of Chicago Press . Maijer , S . 1996 . Distinctive song of highland form maculicollis Sueur , J. , Aubin , T. and Simonis , C . 2008 . Seewave: a free of the red-winged tinamou (Rhynchotus rufescens): evidence modular tool for sound analysis and synthesis. – Bioacoustics for species rank. – Auk 113 : 695 – 697 . 18 : 213 – 226 . Mayr , E . 1963 . Animal species and evolution. – Belknap Uy , J. A. C. , Moyle , R. G. and Filardi , C . 2009 . Plumage and Press. song diff erences mediate species recognition between Meyer de Schauensee, R . 1970 . A guide to the birds of incipient fl ycatcher species of the Solomon Islands . – South America. – Livingston Publishing. Evolution 63 : 153 – 164 .

Supplementary material (Appendix JAV-00298 at www. avianbiology.org/readers/appendix ). Appendix 1.

492 Chapter 2

Bird songs on the shelf: assessing vocal activity and output using data hidden in

sound archives

Short title: Bird songs on the shelf.

Oscar Laverde-R.a, Paula Caycedo-Rosales b, Paulo-C. Pulgarín-R.a & Carlos Daniel

Cadena a

a Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias

Biológicas, Universidad de los Andes.

b Laboratorio de Biogeografía Aplicada y Bioacústica. Instituto de Investigación de

Recursos Biológicos, Alexander von Humboldt.

Address correspondence to Oscar Laverde-R.

Phone: 57-314 8889598

Address:

E-mail: [email protected]

Abstract

Understanding the frequency with which animals sing is of critical importance to address a variety of research questions in behavioral ecology and sexual selection.

However, information on vocal output, a central component of the investment in signaling, is lacking for most species. We asked whether the number of recordings of avian vocalizations in three different sound archives and the times when such recordings were obtained reflect estimates of vocal output and temporal patterns of vocal activity obtained through systematic monitoring of wild bird populations in three tropical forest sites. Based on a sample of 43 montane forest species, we found significant relationships between the number of recordings of species detected through continuous monitoring over several months and the number of recordings archived in sound collections, especially when accounting for the area of distribution of each species. In addition, daily activity patterns based on data collected through continuous monitoring over several days did not differ from those based on recordings archived in sound collections in 12 of 15 species of lowland forest birds.

Annual patterns in vocal activity of two species estimated based on recordings in collections closely resembled previously published patterns. We conclude that recordings in sound collections contain valuable information about the vocal output and temporal patterns in vocal activity of birds. This opens the possibility of using sound collections to assess vocal output and to consider it as a variable of interest in studies on the ecology and evolution of birds and other animals that use acoustic signals for communication.

Keywords: avian recordings, bioacoustics, Colombia, Neotropics, vocal output, vocal activity.

29 INTRODUCTION

Understanding the frequency with which animals emit communication signals over various time frames is of critical importance to address a variety of research questions in behavioral and evolutionary biology. In particular, birdsong is a model system for the study of animal communication and sexual selection (Read & Weary 1992; Slater

2003). Mate choice by females in many bird species is influenced by male singing behavior and song structure, with different attributes of songs being targets of sexual selection (Cardoso & Hu 2011). For example, because traits such as song length, consistency, rate, repertoire size, syllable variety, and trill syntax are attributes commonly selected by females, they are often the focus of behavioral and evolutionary studies on acoustic signals (Arvidsson & Neergaard 1991; Podos 1997;

Gil & Slater 2000; Gil & Gahr 2002; Ballentine 2004; Botero et al. 2009; Cardoso &

Hu 2011; Woodgate et al. 2012; Derryberry et al. 2012). Because singing is time- consuming, energetically costly, and may entail other costs (e.g., increased predation), studies on acoustic communication and sexual selection would benefit from understanding the costs that birds accrue when singing over longer time frames (e.g., complete breeding seasons, Krams 2001; Gil and Gahr 2002; Shutler 2011). Although some properties of single vocalizations are related to song elaboration or complexity and may thus represent partially adequate proxies of some of the costs involved in singing, they cannot fully capture the variation existing among individuals and species in singing strategies and their associated costs. What is more costly: to emit a highly elaborate song sporadically, or to emit a simple song constantly? One cannot begin to address questions like this one without basic information about when and how frequently do birds sing.

30 Patterns of circadian variation in the vocal activity of birds are not well-known, except for the fact that, generally, birds sing more at dawn (and sometimes at dusk) than at other times of the day (Aide et al. 2013). Daily patterns in the vocal activity of tropical birds have not been studied in detail, except for a few studies finding that canopy birds begin singing earlier than understory birds during dawn choruses (Berg et al. 2006), or that vocal activity in understory birds declines markedly one hour after sunrise while vocal activity in canopy birds tends to increase 1-2 hours after dawn and then declines (Blake 1992). Nonetheless, some tropical birds are more active at different times of the day (e.g., at around noon in some woodcreepers, Antunes 2008).

Beyond these general observations, however, quantitative data on daily patterns in avian vocal activity are notoriously scarce, especially for tropical species.

It is generally assumed that most birds from the temperate zone exhibit marked seasonal patterns of vocal activity over the year, but this is often thought not to be the case for species from tropical, more stable environments (Stutchbury & Morton

2001). However, many tropical species show breeding seasonality in response to pulses in resource abundance and climate (Wikelski et al. 2000), and because reproduction correlates with increased vocal activity, one should expect seasonal variation in singing behavior. Some species sing throughout the year (e.g., Koloff &

Mennill, 2012; Topp & Mennill, 2008), whereas others show markedly seasonal patterns of vocal activity correlated with reproduction (e.g., Negret et al. 2015;

Stutchbury & Morton 2001). Even in species that sing constantly during the year, vocal activity may vary seasonally, increasing during the breeding period (e.g., Koloff

& Mennill 2012; Chiver et al. 2015).

31 In sum, it appears clear that the singing behavior of birds needs to be described in more detail, especially in the tropics. Because this would require direct study of the behavior of individual species over long periods of time (minimally one year) and in several places, which would be highly time-consuming and rather expensive, developing alternative means to study temporal patterns in vocal activity and vocal output (i.e., the frequency with which birds sing) would be highly desirable. Based on the idea that information associated with museum or herbarium specimens has been used to characterize the annual cycles of organisms (e.g., flowering or fruiting phenology in plants; Borchert 1996; Boulter et al. 2006; Zalamea et al. 2011), here we explore the possibility of using information archived in sound collections to describe patterns of variation in avian vocal activity and vocal output.

Sound archives are collections of field recordings of animal vocalizations focusing mainly on birds, frogs, fishes, mammals and insects. For instance, the Colección de

Sonidos Animales (CSA) of the Instituto Alexander von Humboldt, Colombia, created 20 years ago, holds around 20,000 bird recordings from this country. The

Macaulay Library of Natural Sounds (MLS) at Cornell University holds around

175,000 audio recordings of more than 7,500 bird species from around the world.

Xeno-canto (XC; http://www.xeno-canto.org/), a website where field researchers and bird watchers share recordings of sounds of wild birds from all over the world, has accumulated ca. 225,000 recordings of more than 9,000 bird species since it was launched in May 2005. Recordings deposited in these and other sound collections have primarily been used for species identification, general bioacoustic analyses, and as records for distributional and biogeographical studies; we propose that these

32 archives might also offer ample yet unexplored information about vocal output and temporal patterns of vocal activity.

Because recordists rely on the vocal activity of birds to record songs and calls, one may expect the number of recordings archived in sound collections to reflect the frequency with which birds sing. For instance, the Gray-breasted Wood-Wren

(Henicorhina leucophrys) is a common Neotropical bird that sings throughout the year; accordingly, sound collections contain a large number of recordings of this species (380 in CSA, 182 in MLS and 288 in XC as of May 2015; Table S1). The

Spangled Cotinga (Cotinga cayana), in contrast, is a widespread species that apparently does not use acoustic signals as its main communication channel; not a single recording of it is to be found in CSA, MLS or XC. If this pattern is consistent

(i.e. that species investing more heavily in vocal communication are better represented in sound archives), then one may potentially use the number of recordings in sound collections as an index of vocal output. In addition, because recordists rely on temporal patterns of vocal activity to record birds, then one may further expect that information on the date and time when recordings in sound collections were obtained would give insights about daily and annual patterns of vocal activity. However, to make valid inferences about vocal output and temporal patterns in vocal activity based on archived recordings, one would first need to validate this approach with data on vocal activity measured directly in the field.

We asked whether the number of recordings in sound archives and when such recordings were obtained reflect estimates of vocal output and temporal patterns of vocal activity obtained through systematic monitoring of wild bird populations.

33 Specifically, we evaluated the hypothesis that one can obtain adequate estimates of vocal output and temporal patterns in vocal activity from sound archives by testing the predictions that (1) there is a positive relation between the number of recordings of each species deposited in archives and the number of vocalizations detected via direct field monitoring in three contrasting tropical forest sites, and (2) that there are no differences in the temporal distributions of recordings available in sound archives and of recordings obtained through continuous monitoring. Our results suggest that sound recordings in collections may in fact be used to assess vocal output and to describe temporal patterns of variation in the singing activity of birds for various types of studies.

MATERIAL AND METHODS

We evaluated whether recordings in sound archives reflect vocal output and temporal patterns in vocal activity described on the basis of systematic monitoring of bird populations using sound recordings obtained with autonomous recording units

(ARUs, Rempel et al. 2013). To accomplish this, we first used ARUs (Wildlife

Acoustics SongMeter II) to (1) measure vocal output over a period of seven months

(including dry and wet seasons) in a tropical montane forest and (2) to quantify daily patterns in vocal activity in two tropical lowland humid forests. We then compared our data from these field sites with information extracted from sound archives.

Measuring vocal output

We studied vocal output of avian species over several months in a tropical montane forest in Chingaza National Park, eastern Andes of Colombia. Chingaza is located ca.

40 km east of the city of Bogotá in the departments of Cundinamarca and Meta.

34 Approximately 190 bird species in 40 families occur in the park (Vargas & Pedraza

2004). The region has an average annual precipitation of 1800 mm, with two distinct peaks. The dry season extends from November to March with minimum rainfall in

January and February, and the wet season extends from April to October with maximum rainfall in June and July (Vargas & Pedraza 2004).

We sampled avian vocal activity in the Palacio sector of Chingaza National Park

(4°41’ N, 73°50’ W) in six different locations (Table 1). ARUs were located in forests between 2950 and 3170 m elevation and placed more than 500 m away from each other to avoid recording the same individuals in more than one unit. Each ARU was programmed to record for three minutes every 30 minutes. We sampled vocal activity over several months in 2013 (Table 1): February (four ARUs), April (three ARUs),

May (four ARUs), July (two ARUs), and August (two ARUs). We listened to recordings obtained from 5:30 am to 12:00 m (vocal activity decreasead markedly later in the day) and identified vocalizing species (90% of avian vocalizations detected were identified to species). For each identified sound we registered the species, type of vocalization (song or call), date, and time of day. We included in analyses 43 species with more than two recordings (i.e, we excluded nine species recorded only once or twice).

We set to test whether the number of recordings of a given species deposited in sound archives represents a valid proxy of its vocal output (i.e., the frequency with which it sings). Thus, for the 43 species detected in field recordings in Chingaza and included in the analyses, we tallied the number of recordings archived in three different sound archives: CSA, MLS and XC. CSA holds about 20,000 recordings from Colombia,

35 with special focus in the Andes, the Amazon Basin and the Caribbean Region. MLS is one of the largest collections of bird recordings in the world, with material from the

Neotropics mainly from Peru (ca. 13,000), Brazil (ca. 10,000), Venezuela (ca. 8,500), and Ecuador (ca. 6,000); Colombia is not as well represented in this collection, with only ca. 1000 recordings. XC is a web-based, rapidly growing archive with ca.

120,000 recordings from South America; in the region, the best represented countries in terms of number of recordings are Brazil (ca. 25,000), Colombia (ca. 13,000), Peru

(ca. 12,000), and Ecuador (ca. 9,000).

Distributional range size likely affects the number of recordings of each species in archives (i.e., one expects more available recordings of more widespread species).

Therefore, we controlled for the effect of area of the distributional range (see below) on number of recordings for each species using Nature Serve range maps (Ridgely et al. 2003). Because the abundance of birds may also affect the number of recordings in archives, we also sought to correct for abundance using bird-count data collected over ten months in Finca Cárpatos, Chingaza (2800-3100 m; 4°42′ N, 73°51′ W), a location very close to our study site in Palacio (Stiles and Roselli 1998; see below).

Quantifying temporal activity patterns

We studied vocal activity along the day for avian species assemblages in two lowland forest sites. First, Barbacoas (6°41′ N, 74°21′ W) is a humid lowland forest located in

Antioquia Department, in the middle Magdalena Valley of Colombia at 300 m elevation. Pastures for cattle dominate the area, but some fragments of primary forest remain. Approximately 250 bird species in 53 families have been recorded in the area

(O. Laverde-R. et al., unpubl. data). The region has an average annual precipitation

36 of 2200 mm, with two distinct peaks. The dry season extends from December to

March, with minimum rainfall in January, and the wet season extends from April to

October with maximum rainfall in September and October (IDEAM, http://bart.ideam.gov.co/cliciu/barran/precipitacion.htm). Second, Bahía Málaga

(3°58′ N, 77°19′ W) is located on the Colombian Pacific coast in the Chocó biogeographic region, in Chocó Department. There are no published avian inventories of the area, but more than 300 species are expected to occur in the region (Hilty &

Brown 1986). The region has an average annual precipitation of 7300 mm, with two distinct peaks. The region is wet year-round, but rainfall is lower from January to

February, and the wet season extends from March to November with maximum rainfall in September and October (IDEAM, http://bart.ideam.gov.co/cliciu/buena/precipitacion.htm).

To sample avian vocal activity during the day, we set two ARUs in Barbacoas for 19 days (18 December 2012 to 5 January 2013) and two ARUs in Bahía Málaga for five days (2 July 2014 to 6 July 2014). ARUs were placed in tall primary forest and were programmed to record for 3 minutes every 30 minutes, from 05:30 h to 18:00 h. We listened to recordings and identified vocalizing species; for each identified sound we registered the species, type of vocalization (song or call), and time of day. We selected the 15 most frequently recorded species (i.e., those with at least 30 recordings in ARUs) for analyses of variation in vocal activity along the day (Table

2). For the above 15 lowland species, we extracted information on the time of day associated with all the recordings deposited in XC. We analyzed all archived recordings of these species (i.e., not exclusively those from our study region or from

Colombia) to ensure sufficient sample sizes were available for analyses.

37

In addition to our interest in examining daily patterns of vocal activity, we explored whether annual patterns of vocal activity may also be studied using recordings deposited in sound collections. We selected two species known to exhibit seasonal patterns of vocal activity along the year and which are well represented in sound collections: the Clay-colored Thrush (Turdus grayi) and the Great Tinamou (Tinamus major). The Clay-colored Thrush exhibits a marked peak in vocal activity in the first part of the year (March-April; Figure 6.1 in Stutchbury & Morton 2001), whereas the vocal activity of tinamous is known to vary seasonally, with peaks especially in the dry season (Lancaster 1964; Negret et al. 2015). Because Great Tinamou has a broad latitudinal distribution and precipitation regimes (hence likely breeding and vocal activity) differ between hemispheres, we analyzed data only from the northern hemisphere. We obtained the dates of recordings for these two species in MLS and

XC, grouped them by month, and examined whether annual patterns of activity estimated using the recordings in archives matched expected patterns given published data.

Statistical analyses

To evaluate the hypothesis that the information archived in sound collections can be used to estimate vocal output and temporal patterns in avian vocal activity we tested the predictions that (1) vocal output measured via continuous monitoring using ARUs correlates positively with the number of recordings in collections and (2) that the hourly distribution of recordings obtained via continuous monitoring does not differ from the hourly distribution of recordings available in collections. To evaluate our first prediction, we regressed the number of recordings in sound collections against

38 the area of the geographic range of each species, and then regressed the residuals of this analysis on the total number of recordings counted in our field recordings. To evaluate the effect of abundance in the number of recordings in sound collections,we regressed the number of recordings in sound collections against the abundance of each species (Stiles & Rosselli 1992), and then regressed the residuals of this analysis on the total number of recordings counted in our field recordings. Variables were log- transformed to meet the normality assumptions of linear models. We also ran phylogenetic generalized least-square models (PGLS) to account for phylogenetic effects using the caper package for R (Orme et al. 2012) based on a comprehensive avian phylogeny (Jetz et al. 2012).

To examine our second prediction, we used circular statistics. First, we evaluated whether the data obtained from ARUs and from XS were uniformly distributed along the day using a non-parametric Rayleigh test of uniformity; second, we used the

Watson-Williams test to evaluate the null hypothesis that the two daily activity patterns (i.e., those obtained from ARUs and XC) are not statistically different

(Kovach 2011). We also used Rayleigh tests of uniformity to assess whether vocalizations of Clay-colored Robin and Great Tinamou are uniformly distributed along the year, and qualtiatively compared the annual patterns for these species revealed by recordings in MLS and XC with published data.

RESULTS

Vocal output

We examined 607 three-minute recordings from our tropical montane forest site in

Chingaza; in 237 of these, no avian vocalizations were detected. We detected a total

39 of 2192 vocalizations in 370 recordings obtained using ARUs (Table S1). Of this total, we identified 1522 vocalizations as songs (70%) and 433 as calls (20%); we were unable to identify 237 vocalizations (10%). Among the 1522 identified songs we detected 52 species from 19 families; for subsequent analyses we focused on 43 species having more than three recordings of songs during our sampling period (total

1512 individual songs; Table S1). For the species detected in ARUs, we found a total of 1363 recordings of songs in CSA, 1892 in MLS, and 1651 in XC.

The number of recordings of each species recorded in ARUs and in each of the sound collections were significantly and positively related (Figs. 1a-c): CSA (p<0.0001,

β=0.63, R2=0.32), MLS (p=0.040, β=0.27, R2= 0.076), XC (p<0.0001, β=0.42,

R2=0.36). Analyses correcting for size of the distributional range of species based on residuals (Figure 1) also revealed significant relationships and the explanatory power of models was considerably larger than that of models based on raw data (Figs. 1d-f):

CSA (p<0.0001, R2=0.41), MLS (p<0.0001, R2=0.42) and XC (p<0.0001, R2=0.40).

Similar results were obtained in PGLS analyses: CSA (p<0.0001, β=0.69, R2=0.36),

MLS (p=0.002, β=0.36, R2= 0.14), XC (p<0.0001, β=0.48, R2=0.43), with the explanatory power of models increasing when we included area of distributional range as a covariate: CSA (p < 0.0001, R2=0.43), MLS (p<0.0001, R2=0.48) and XC

(p<0.0001, R2=0.45). Analyses correcting for abundance based on residuals did not improve the explanatory power of models CSA (p = 0.0003, R2=0.25), MLS (p =

0.13, R2=0.03) and XC (p=0.0001, R2=0.29) even when accounting for phylogeny;

CSA (p=0.07, R2=0.04), MLS (p=0.81, R2= -0.02), XC (p=0.0001, R2=0.23).

Vocal activity

40 We examined 708 and 697 three-minute recordings from Barbacoas and Bahía

Málaga, respectively. Of these, 431 and 430 recordings, respectively, contained avian vocalizations, resulting in a total of 1144 individual detections of species.

Vocalizations recorded in the field using ARUs and those available in XC were not uniformly distributed over the day in any of our study species (Table S2; Figure 2). In

12 out of 15 species, the hourly distribution of vocalizations did not differ between data collected using ARUs and recordings available in XC, suggesting that both sources reveal similar circadian patterns of activity (Table 3). In two species of pigeon (Pale-vented Pigeon, Patagioenas cayennensis and Scaled Pigeon, P. speciosa) and one toucan (Black-billed Toucan, Ramphastos ambiguus) daily activity patterns were significantly different between data sets. In the pigeons, our recordings obtained using ARUs showed two clear peaks in vocal activity: one in the early morning and a second one in the late afternoon (Figure S1). Information in the XC collection did not reveal the same pattern: the peak in the morning was also clear, but the peak in the afternoon was not as evident. ARUs detected a bimodal pattern with clear peaks in the early morning and late afternoon in the singing activity of the toucan, but XC recordings showed a single peak during the mid-morning.

Annual patterns of vocal activity assessed using recordings available in XC and MLS were significantly seasonal (i.e., not uniform over time) for the Great Tinamou (MLS, z= 18.816, p<0.0001; XC, z= 3.056, p=0.04) and the Clay-colored Thrush (MLS, z=

36.186, p<0.0001, XC, z= 34.877, p<0.0001). The recordings in sound archives indicate that Great Tinamou sings mostly from January to May with a peak in singing in March; the Clay-colored Thrush sings mostly from January to June with a peak in singing in April. These annual patterns based on archived recordings were similar to

41 those described previously in the literature for species in the tinamou family

(Lancaster, 1964; Negret et al. 2015) and for the Clay-colored Thrush (Stutchbury &

Morton 2001).

DISCUSSION

The energetic costs of singing and how these costs are related to song complexity or elaboration is matter of debate (Eberhardt 1994; Oberweger and Goller 2001; Ward

2004; Garamszegi et al. 2006; Hasselquist and Bensch 2008). Some studies indicate that complex songs are not necessarily more costly than simple songs (Oberweger and

Goller 2001), but others suggest that complex songs can indeed be especially costly

(Garamszegi et al. 2006). Regardless of any potential costs associated to song complexity, it is clear that some birds vocalize more frequently than others, implying that vocal output is a crucial variable one must consider when characterizing the variation in energy investment in vocal communication existing among species.

However, vocal output is rarely evaluated in studies examining the costs of communication signals because information on how frequently do birds sing over various temporal scales (e.g. times of the day, months in a year) is lacking for most species. We suggest that a possible way to remedy the lack of consideration of vocal output in many studies is to use information deposited in sound collections. Our study supported the hypothesis that one can obtain meaningful estimates of vocal output and temporal patterns in vocal activity of tropical birds from information available in sound archives. This overlooked source of information may represent a crucial resource for researchers in acoustic communication, sexual selection, and other aspects of avian biology.

42 First, we found that recordings in sound collections can be used as a relatively accurate proxy of vocal output because the number of recordings of 43 species obtained through continuous monitoring over several months in a tropical montane forest were significantly related to the number of recordings collected non- systematically by various field workers over multiple years and deposited in sound collections; this relation was stronger when correcting the number of recordings in collections by the size of the distributional range of species but not when correcting by species abundance. Second, in 12 out of 15 lowland species similar daily patterns of vocal activity were detected using data from continuous monitoring and from sound collections. Third, for two species, circannual patterns in vocal activity determined using information in sound collections matched patterns documented in the literature based on systematic studies. Although these results encourage the use of information in sound archives to characterize vocal output and temporal patterns in vocal activity, our analyses also revealed some possible sources of bias that researchers must consider; we discuss caveats related to such biases below.

Our analyses suggest that the suitability of archived recordings to characterize vocal output may vary among collections depending on their geographic focus. For example, the relationship between recordings archived at the MLS (the collection with the lowest numbers of recordings from Colombia; ca. 1,000) and recordings we obtained through continuous monitoring was weak relative to relationships obtained using recordings archived in the XC and CSA collections, which have much larger numbers of recordings from Colombia (ca. 13,000 and 20,000, respectively). This effect may be due to regional variation in vocal activity or abundance of species in different sectors of their distribution ranges, or may reflect bias resulting from

43 differences in recording intensity effort (i.e., collections with fewer recordings may not adequately capture patterns in vocal output due to insufficient sampling).

Accordingly, the data we collected in the field for this project revealed the importance of sampling over long periods of time to adequately characterize the vocal output of species. We explored whether vocal output estimated using recordings made using

ARUs over a few days reflected information in sound collections for the lowland species that we used to study daily patterns of vocal activity, finding this was not the case (F=0.04, p=0.83) even when controlling for the size of the distributional range of species (F=0.72, p=0.5). We attribute this to the fact that sound archives contain information obtained over long periods of time (i.e., multiple years including dry and wet seasons); this long-term sampling captures effects of seasonality in vocal activity that cannot possibly be revealed by data collected over short periods of time. Thus, we suggest that information in collections is not only useful to study vocal output, but also that it may provide more accurate characterizations of long-term vocal output than data collected by short-term studies.

Our work revealed that the number of recordings of species in collections is significantly related to the number of recordings of these species obtained at a single site via continuous monitoring; however, a considerable fraction of the variation in vocal output measured locally was unexplained by the frequency with which species were represented in collections. This is not unexpected given the nature of the data because collections contain information from many different sites obtained by dozens of field workers across time and space lacking the specific purpose of documenting vocal output. We explored whether aspects related to the singing behavior of species influenced the extent to which recordings in sound collections reflected vocal output

44 estimated through continuous monitoring by examining the residuals of regression analyses relating the number of recordings in collections to vocal output assessed using ARUs, after accounting for area of distribution. First, we hypothesized that information in collections should be a worse predictor of vocal output in species with more seasonal singing activity; however, residuals did not differ between vocally seasonal and non-seasonal species (CSA, t= -0.67, p = 0.51; MLS, t = -1.10, p = 0.28;

XC, t = -1.03, p = 0.30). We considered a species as non-seasonal if it was present constantly in recordings throughout the five months sampled in Chingaza, and seasonal if it was only present in recordings from 1-3 months (no species was present only in recordings from four months). Second, we also considered whether residual variation could be accounted for phylogenetic affinities; however residuals did not differ between non-passerines, suboscines and oscines (CSA, F=0.17, p = 0.83; MLS, t = -1.10, p = 0.28; XC, t = -1.03, p = 0.30).

It seemed reasonable to think that abundance influences the number of recordings in sound collections; however, we found that correcting for species abundance in our analyses did not improve the explanatory power of models relating data in sound collections to data obtained through continuous monitoring. This likely implies that birds are not recorded in proportion to their abundance by field workers contributing to sound collections. Specifically, field workers are likely to only record few of the individuals of common species they detect, while they often try to record rare birds.

That common birds may be under represented in collections while rare birds may be over represented relative to their abundance reflects not only that rarity is attractive to ornithologists, but also that most of the recordings archived in sound collections were obtained for the purpose of documenting diversity (i.e., establishing the occurrence of

45 rare species at sites, sampling geographical variation in vocalizations of widespread species) and not for the purpose of documenting the frequency with which species were encountered.

Although daily activity patterns based on data collected through continuous monitoring were similar to those based on recordings archived in sound collections in most of our study species, this was not the case in all of them. In particular, information in collections failed to reveal a peak in singing activity in the afternoon in two species of pigeons and a toucan. Recordists, like most birds, are more active during the morning, and this certainly has an impact on the daily patterns of activity one may infer based on recordings in sound collections. Our data and recordings in sound collections reveal that tropical birds exhibit varying strategies in vocal activity through the day (Figure 2, Figure S1); some species sing in the early morning and late afternoon, others during the whole morning, and a few probably during the whole day. Therefore, to the valuable recommendations for recording bird sounds offered by previous workers (Parker III 1991; Budney and Grotke 1997), we would add the importance of recording constantly along the day and of recording as many singing birds as possible. By doing so, recordists may considerably increase the usefulness of their material for studies on various aspects of avian biology.

The idea that one may use information in sound collections to study vocal seasonality along the year as shown by our analyses focused on Great Tinamou and Clay-colored

Thrush offers exciting opportunities for future study. For example, information in sound collections may allow one to assess the effect of climate on singing behavior within and across sites; because singing correlates with reproduction, information in

46 sound collections may further be used as a proxy to examine geographic variation in breeding activity and its consequences for population differentiation (Quintero et al.

2014). In the long term, sound recordings in collections may allow one to monitor changes in vocal activity patterns and annual phenological cycles in wild bird populations in relation to local and global change.

We conclude that recordings in sound collections contain valuable information about the vocal output of birds, which opens the possibility of considering vocal output as a variable of interest in studies on the ecology and evolution of birds and other animals that use acoustic signals for communication. Sound collections also contain much information about temporal patterns in vocal activity over various time frames; this information is relevant to studies in many areas of animal biology. Just as traditional museum specimens capture valuable information about the time in which they were collected (Pyke and Ehrlich 2010), properly archived sound recordings may be considered acoustic specimens with great potential to be used in various ways in the future. Traditional specimens have been used for purposes that original collectors could not have imagined, including the documentation of the effects of environmental contaminants (Berg et al. 1966; Ratcliffe 1967) or the prediction of shifts in species distributions as a consequence of climatic change (Peterson et al. 2002). The same could be true of future studies on vocal activity and vocal output based on properly curated and publicly available sound collections. Now that sound recordings are relatively easy to collect and archive, we encourage field researchers to record animal sounds along the day and the year, and to make recordings and associated information available to the public. To finalize, we emphasize that much of data one may extract

47 from recordings in sound collections is waiting to be used; part of the information needed for many different studies is already sitting on the shelf.

ACKNOWLEDGMENTS

We thank Chingaza National Park for allowing us to record bird songs. Greg Budney and Matt Medler of the Macaulay Library of Natural Sounds at Cornell University, and the Colección de Sonidos Ambientales of the Instituto Alexander von Humboldt gave us unrestricted access to the information archived in their collections. We thank all the recordists who have deposited their recordings in these archives and who have made them available through xeno-canto. We are grateful to Laura Céspedes, Simón

Quintero, Ignacio Areta, Mecky Hollzmann, Trevor Price and Alex Kirschel for asistance in the field. This work was supported by funds from the Facultad de

Ciencias at Universidad de los Andes, Fundación Biodiversa-Colombia, and the

BIOREDD+ project (USAID & Instituto de Investigación en Recursos Biológicos

Alexander von Humboldt).

REFERENCES

Aide TM, Corrada-Bravo C, Campos-Cerqueira M, Milan C, Vega G, Alvarez R.

2013. Real-time bioacoustics monitoring and automated species identification. PeerJ

1:e103.

Antunes AZ. 2008. Diurnal and seasonal variability in bird counts in a forest fragment in southeastern Brazil. Rev. Bras. Zool. 25:228–237.

Arvidsson BL, Neergaard R. 1991. Mate choice in the willow warbler - a field experiment. Behav. Ecol. Sociobiol. 29:225–229.

48 Ballentine B. 2004. Vocal performance influences female response to male bird song: an experimental test. Behav. Ecol. 15:163–168.

Berg KS, Brumfield RT, Apanius V. 2006. Phylogenetic and ecological determinants of the neotropical dawn chorus. Proc. R. Soc. B Biol. Sci. 273:999–1005.

Berg W, Johnels A, Sjostrand B, Westermark T. 1966. Mercury content in feathers of

Swedish birds from the past 100 years. Oikos 17:71–83.

Blake JG. 1992. Temporal variation in point counts of birds in a lowland wet forest in

Costa Rica. Condor 94:265–275.

Borchert R. 1996. Phenology and flowering periodicity of Neotropical dry forest species: evidence from herbarium collections. J. Trop. Ecol. 12:65-80.

Botero CA, Rossman RJ, Caro LM, Stenzler LM, Lovette IJ, De Kort SR,

Vehrencamp SL. 2009. Syllable type consistency is related to age, social status, and reproductive Success in the Tropical Mockingbird. Anim. Behav. 77:701–706.

Boulter SL, Kitching RL, Howlett BG. 2006. Family, visitors and the weather:

Patterns of flowering in tropical rain forests of northern Australia. J. Ecol. 94:369–

382.

Budney GF, Grotke RW. 1997. Techniques for audio recording vocalizations of tropical birds. Ornithol. Monogr. 48:147–163.

Cardoso GC, Hu Y. 2011. Birdsong performance and the evolution of simple (rather than elaborate) sexual signals. Am. Nat. 178:679–686.

49 Chiver, I., Stutchbury, B. J. M., & Morton, E. S. 2015. The function of seasonal song in a tropical resident species, the Red-throated Ant-tanager (Habia fuscicauda). J.

Ornithol. 156: 55-63.

Derryberry EP, Seddon N, Claramunt S, Tobias JA, Baker A, Aleixo A, Brumfield

RT. 2012. Correlated evolution of beak morphology and song in the Neotropical woodcreeper radiation. Evolution 66:2784–2797.

Eberhardt LS. 1994. Oxygen Consumption During Singing by Male Carolina Wrens

(Thryothorus ludovicianus). Auk 111:124–130.

Garamszegi LZ, Moreno J, Møller AP. 2006. Avian song complexity is associated with high field metabolic rate. Evol. Ecol. Res. 8:75–90.

Gaunt AS, Bucher TL, Gaunt SLL, Baptista LA. 1994. Is singing costly? Auk

113:718–721.

Gil D, Gahr M. 2002. The honesty of bird song: multiple constraints for multiple traits. Trends Ecol. Evol. 17:133–141.

Gil D, Slater PJB. 2000. Multiple song repertoire characteristics in the willow warbler

(Phylloscopus trochilus): correlations with female choice and offspring viability.

Behav. Ecol. Sociobiol. 47:319–326.

Hasselquist D, Bensch S. 2008. Daily energy expenditure of singing great reed warblers Acrocephalus arundinaceus. J. Avian Biol. 39:384–388.

Jetz W, Thomas GH, Joy JB, Hartmann K, Mooers AO. 2012. The global diversity of birds in space and time. Nature 491:444–448.

50 Koloff J, Mennill DJ. 2012. Vocal behaviour of Barred Antshrikes, a Neotropical duetting suboscine bird. J. Ornithol. 154:51–61.

Kovach Computing Services. 2011. Oriana – Circular Statistics for Windows.

Krams I. 2001. Communication in crested tits and the risk of predation. Anim. Behav.

61:1065–1068.

Lancaster DA. 1964. Life History of the Boucard Tinamou in British Honduras Part

II: Breeding Biology. Condor 66:253–276.

Negret PJ, Garzon O, Stevenson PR, Laverde-R O. 2015. New ecological information for the Black Tinamou (Tinamus osgoodi hershkovitzi). Auk 132:533–539.

Oberweger K, Goller F. 2001. The metabolic cost of birdsong production. J. Exp.

Biol. 204:3379–88.

Orme D, Freckleton R, Thomas G, Petzoldt T, Fritz S, Isaac N, Pearse W. 2012.

Caper: comparative analyses of phylogenetics and evolution in R.

Parker III TA. 1991. On the use of tape recorders in avifaunal surveys. Auk 108:443–

444.

Peterson AT, Ortega-Huerta MA, Bartley J, Sanchez-Cordero V, Soberon J,

Buddenmeier RH, Stockwell DRB. 2002. Future projections for Mexican faunas under global climate change scenarios. Nature 416:626–629.

Podos J. 1997. A performance constraint on the evolution of trilled vocalizations in a songbird family (Passeriformes: Emberizidae). Evolution 51:537–551.

51 Pyke GH, Ehrlich PR. 2010. Biological collections and ecological/environmental research: A review, some observations and a look to the future. Biol. Rev. 85:247–

266.

Quintero I, González-Caro S, Zalamea P-C, Cadena CD. 2014. Asynchrony of seasons: genetic differentiation associated with geographic variation in climatic seasonality and reproductive phenology. Am. Nat. 184:352–363.

Ratcliffe DA. 1967. Decrease in eggshell weight in certain birds of prey. Science

215:208–210.

Read AF, Weary DM. 1992. The evolution of bird song : comparative analyses.

Philos. Trans. R. Soc. B Biol. Sci. 338:165–187.

Rempel RS, Francis CM, Robinson JN, Campbell M. 2013. Comparison of audio recording system performance for detecting and monitoring songbirds. J. F. Ornithol.

84:86–97.

Ridgely RS, Allnutt TF, Brooks T, McNicoll DK, Mehlman DW, Young BE, Zook

JR. 2003. Digital Distribution Maps of the Birds of the Western Hemisphere, version

1.0. NatureServe, Arlington, Virginia, USA.

Shutler D. 2011. Sexual selection: when to expect trade-offs. Biol. Lett. 7:101–104.

Slater PJB. 2003. Fifty years of bird song research: a case study in animal behaviour.

Anim. Behav. 65:633–639.

Stiles FG, Rosselli L. 1998. Inventario de las aves de un bosque altoandino: comparación de dos métodos. Caldasia 20:29–43.

52 Stutchbury BJM, Morton ES. 2001. Behavioral ecology of tropical birds. Academic

Press.

Topp SM, Mennill DJ. 2008. Seasonal variation in the duetting behaviour of rufous- and-white wrens (Thryothorus rufalbus). Behav. Ecol. Sociobiol. 62:1107–1117.

Vargas O, Pedraza P. 2004. Parque Nacional Natural Chingaza. Bogotá, D. C.: Gente

Nueva Editorial.

Ward S. 2004. Singing is not energetically demanding for pied flycatchers, Ficedula hypoleuca. Behav. Ecol. 15:477–484.

Wikelski M, Hau M, Wingfield J. 2000. Seasonality of reproduction in a Neotropical rain forest bird. Ecology 81:2458–2472.

Woodgate JL, Mariette MM, Bennett ATD, Griffith SC, Buchanan KL. 2012. Male song structure predicts reproductive success in a wild zebra finch population. Anim.

Behav. 83:773–781.

Zalamea P-C, Munoz F, Stevenson PR, Paine CET, Sarmiento C, Sabatier D, Heuret

P. 2011. Continental-scale patterns of Cecropia reproductive phenology: evidence from herbarium specimens. Proc. Biol. Sci. 278:2437–2445.

53 a) R2= 0.323, β = 0.63 d) R2= 0.378, β = 0.65 6 2 5 1 4 0 3 -1 2 -2 Ln CSA Recordings Ln Residuals CSA-Area Residuals 1 -3

1 2 3 4 5 6 1 2 3 4 5 6

b) R2= 0.076, β = 0.27 e) R2= 0.169, β = 0.31 6 2 5 1 4 0 3 -1 2 -2 Ln MLS Recordings Ln Residuals MLS-Area Residuals 1 -3

1 2 3 4 5 6 1 2 3 4 5 6

c) R2= 0.358, β = 0.42 f) R2= 0.394, β = 0.44 6 2 5 1 4 0 3 -1 2 -2 Ln XC Recordings Ln Residuals XC-Area Residuals 1 -3

1 2 3 4 5 6 1 2 3 4 5 6 Ln Chingaza Recordings Ln Chingaza Recordings

Figure 1. Relationships between vocal output estimated through continuous monitoring based on recordings obtained in Chingaza and the number of recordings archived in sound collections (1a – 1c), and the residual number of recordings in sound collections unaccounted for by area of distributional range (1d – 1f). Results of linear regression analyses are significant in all cases, but note the increased fit of models when accounting for area of distributional range. These patterns suggest that information in sound archives is an appropriate proxy of vocal output.

54

Great Tinamou White-bearded Manakin 12 Tinamus major 12 12 Manacus manacus 12

6 18 6 18 6 18 6 18

0 0 0 0

12 Pale-vented Pigeon 12 12 Bright-rumped Attila 12 Patagioenas cayennensis Attila spadiceus

6 18 6 18 6 18 6 18

0 0 0 0 Slaty Antshrike Black-headed Tody-flycatcher 12 Thamnophilus atrinucha 12 12 Todirostrum nigriceps 12

6 18 6 18 6 18 6 18

0 0 0 0

Figure 2. Circular plots showing daily vocal activity for six lowland species estimated via continuous monitoring (black) and based on recordings deposited in the xeno- canto archive (blue). We selected six of the 15 species studied to illustrate general patterns observed in the data. Note the strong similarity between sources of data in patterns in all species except for the Pale-vented Pigeon (Patagioenas cayennensis), in which the information in xeno-canto failed to detect a peak in singing activity revealed through continuous monitoring.

55 Great Tinamou Clay-colored Robin Tinamus major Turdus grayi

June July June July May August May August

April September April September

March October March OctoberOctober

February November February November January December January DecemberDecember

Figure 3. Circular plots showing annual vocal activity for the Great Tinamou and the

Clay-colored Thrush in the Northern Hemisphere. Data from the MLS are shown in black and data from XC in white. Note that estimated vocal activity is very similar based on the information obtained from the two collections for both species: Great

Tinamou vocal activity peaks between March and April and Clay-colored Robin vocal activity peaks in April; these patterns coincide with patterns reported in the literature based on systematic monitoring (see text).

56 Table 1. Number of recordings of avian vocalizations obtained through continuous monitoring by month and elevation in Chingaza National Park from February to

August 2013.

ARU Geographical February April May July August Total Elevation (m) No Coordinates 1 248 248 4°41’47’’; 73°50’53’’ 2950

2 121 138 92 351 4°41’54’’; 73°50’50’’ 2960

3 158 158 4°42’08’’; 73°50’50’’ 3015

4 233 121 246 246 846 4°41’44’’; 73°50’39’’ 3030

5 88 38 127 4°41’27’’; 73°50’24’’ 3015

6 131 132 150 50 465 4°42’42’’; 73°50’21’’ 3170

Total 760 427 383 396 296 2192

57 Table 2. Number of recordings obtained via continuous monitoring using ARUs in

Barbacoas and Bahía Málaga and number of recordings found in the xeno-canto (XC) archive of lowland species used to study daily patterns of vocal activity.

Family Species ARU XC Total Tinamidae Tinamus major 31 55 86 Cracidae Ortalis columbiana 60 29 89 Columbidae Patagioenas cayennensis 116 41 157 Columbidae Patagioenas speciosa 102 83 185 Ramphastidae Ramphastos ambiguus 95 107 202 Thamnophilidae Thamnophilus atrinucha 69 94 163 Thamnophilidae Myrmeciza exsul 74 238 312 Thamnophilidae Myrmeciza berlepschi 48 21 69 Tyrannidae Mionectes oleagineus 48 68 116 Tyrannidae Todirostrum nigriceps 63 16 79 Tyrannidae Tolmomyias flaviventris 165 177 342 Tyrannidae Attila spadiceus 33 325 358 Pipridae Lepidothrix coronata 83 63 146 Pipridae Manacus manacus 95 118 213 Troglodytidae Pheugopedius fasciatoventris 62 39 101 Total 1144 1474 2618

58 Table 3. Results of Watson-Williams circular tests for differences between the distributions of recordings obtained via continuous monitoring and based on recordings deposited in the xeno-canto archive (see Figure 2). Except for three species

(two pigeons and one toucan shown in bold), daily activity patterns obtained via continuous monitoring and based on information in xeno-canto did not differ, validating the use of information in sound archives.

Watson- Family Species Williams F-test Tinamidae Tinamus major 1.999 Cracidae Ortalis colombiana 2.978 Columbidae Patagioenas cayennensis 5.662 * Columbidae Patagioenas speciosa 16.768 *** Ramphastidae Ramphastos ambiguus 16.95 *** Thamnophilidae Thamnophilus atrinucha 4.233 Thamnophilidae Myrmeciza exsul 0.752 Thamnophilidae Myrmeciza berlepshchi 1.029 Tyrannidae Mionectes oleagineus 0.649 Tyrannidae Todirostrum nigriceps 0.288 Tyrannidae Tolmomyias flaviventris 0.064 Tyrannidae Attila spadiceus 3.639 Pipridae Lepidothrix coronata 0.72 Pipridae Manacus manacus 0.48 Pheugopedius Troglodytidae 1.523 fasciatoventris

59 Chapter 3

Evolution of bird communication signals: transference between signals mediated

by sensory drive

Oscar Laverde-Ra, Michael J. Ryanb & Carlos Daniel Cadenaa

aLaboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias

Biológicas, Universidad de los Andes.

bSmithsonian Tropical Research Institute, Balboa, PanamacDepartment of Integrative

Biology, University of Texas at Austin, Austin.

Address correspondence to Oscar Laverde-R.

E-mail: [email protected]

Animals communicate using signals perceived via multiple sensory modalities but usually invest more heavily in one of type of signal. This pattern, observed by

Darwin1 and many researchers since, led to development of the transfer hypothesis (see also transferal effect2 and tradeoff hypothesis3,4), which predicts a negative relationship between investment in different signaling modalities dictated by the relative costs and benefits variety of each. One factor that influences costs and benefits —and is central to the sensory drive hypothesis5 posed to account for signal evolution— is the suitability of the environment for different types of signals. Movement into a dark habitat, for example, should favor investment in acoustic over visual signals. We use phylogenetic comparative methods to analyze the joint effect of transfer and sensory drive on plumage and song variation in 52 species of New World warblers (Parulidae), and to estimate temporal patterns in the accumulation of differences in visual and vocal signals and habitat along the evolutionary history of this clade. We found evidence for the predicted negative correlations between a variety of song and plumage traits that vary with habitat type. Plumage contrast to background and chromatic diversity both were negatively related to syllable variety when vegetation structure was a covariate: birds with a greater variety of song syllables and less colorful plumages live in closed or darker habitats. Also as predicted, achromatic or brightness diversity was related to vegetation structure.

In addition, disparity-through-time analyses showed that when one set of traits diversified at a relatively high rate the other did not, as predicted by the transfer hypothesis. Our results show that sensory drive influences the transfer of investment between traits in different sensory modalities. This interaction

61 between mechanisms shaping signals may be a major determinant in the evolution of animal communication.

Ever since Darwin1, naturalists have noted a negative relationship between bright colors and the elaboration of songs involved in animal communication. For example,

“... in territorial passerine birds, there tends to be an inverse relation between the development of auditory distinctiveness (song of species with cryptic behavior and coloration, e.g., grasshopper warbler, chiffchaff) and visual distinctiveness (pattern of species with conspicuous behavior, e.g., stonechat, pied flycatcher)...” 6. Such observations led to the suggestion of the transferal effect2,7 or tradeoff hypothesis3

(later named transfer hypothesis), which states that the complexity of signals in different modalities should be negatively related. Animals are expected to invest primarily in one type of signal (i.e. vocal, chemical, or visual8) owing to various factors, including internal limitations in energy expenditure (classical life-history tradeoffs9,10), predators11, parasites12, physical conditions of the habitat2, and the conflicting forces of sexual and natural selection12. Despite the generality and popularity of this hypothesis, there have been few attempts to test it with a robust, phylogenetically informed data set that includes variation in multiple signal modalities and covariation with the signaling habitat that influences the salience of these signals13–15.

Here, we test for a negative relationship between elaboration in song and plumage coloration in the context of the signaling habitat, thus linking the transfer hypothesis with another leading hypothesis posed to account for signal evolution, namely sensory drive5. The sensory drive hypothesis posits that variation in habitat features leads to

62 variation in selection pressures by affecting the ease with which different traits are perceived. For example, because the use of visual signals for communication in dark habitats (e.g. forest understory, turbid waters) is expected to be ineffective5,16, acoustic or olfactory signals should be more prevalent as targets of sexual selection in such environments. In turn, in open areas or clear waters one expects visual signals to be more elaborate than vocal or chemical signals. Although several studies have supported the sensory drive hypothesis, they have largely focused on a single communication channel (i.e. visual15,17–20, olfactory16, or acoustic signals21–23) and have not considered interactions among channels as those implied by the transfer hypothesis. Similarly, the few existing examinations of the transfer hypothesis13–15,24 have ignored the influence of habitat on signal salience.

Using data on plumage, song, and habitat, we examined correlations between visual and acoustic signals mediated by the environment across a large radiation of passerine birds. The New World warblers (Parulidae) are a group of small, often colorful, primarily insectivorous oscines with diverse songs and a broad diversity of habitat affinities and life histories25. The tuning of the visual system of warblers varies with habitat and this should generate differential, habitat-specific selection on visual signals. Expression of opsins varies with the light environment occupied by different species26; specifically, short-wavelength sensitive type 2 opsins (SWS2) are shifted to longer wavelengths in species living in closed forest environments27 where most of the short wavelengths are filtered by vegetation28. In addition, female expression of these same opsins varies with plumage dichromatism, suggesting a role for sensory drive26.

63 In analyses that did not account for the habitat preferences of species, we found no support for the prediction of the transfer hypothesis that elaboration in plumage coloration (chromatic diversity of plumage and chromatic contrast to background) and song traits (vocal deviation —a measure of vocal performance—, syllable variety and song length) should be negatively related (Table 1, 2). However, when we included vegetation structure as a covariate (i.e. open vs. closed habitats), significant negative relationships between variables emerged, suggesting that apparent transfers in investment between signals in different communication channels are mediated by habitat. Specifically, chromatic contrast to the background (β= -6.79, t= -2.69, p=0.01) and chromatic diversity of plumage were negatively related to song syllable variety (β= -1.51, t= -3.31, p=0.001, Table 1), such that species with less colorful plumage exhibit songs with a greater variety of syllables and live in closed habitats (β

= 1.96, t= 2.76, p=0.008). Additionally, achromatic diversity of plumage was significantly related to vegetation structure (β= 3.22, t= -2.16, p=0.03, Table 2) and to vocal deviation (β= 1.6290, t=2.0145, p=0.05, Fig. 1); this indicates that birds with darker backs live in darker habitats and communicate using songs demanding greater performance. Thus, it appears that in forest species natural selection to reduce conspicuousness due to predation has resulted in a match between plumage coloration and background color; this has presumably triggered an investment transfer between visual and vocal signals resulting in more elaborate songs favored by sexual selection.

These patterns we found are uniquely predicted by a combination of the transfer and sensory drive hypotheses.

A more specific prediction of the transfer hypothesis considers not only the evolutionary end points of visual versus vocal signal elaboration, but the dynamic

64 processes that gave rise to their negative relationship. Analyses of the accumulation of disparity in color and vocal traits along the New World warbler phylogeny suggest a negative evolutionary correlation between these two different types of signals across time. Relative to a null process of accumulation of phenotypic disparity (i.e., under a

Brownian-motion process of evolution), chromatic (chromatic diversity of plumage and chromatic contrast to background) and achromatic (achromatic diversity of plumage) disparity in coloration accumulated at a faster rate near the tips of the phylogeny (Fig. 1), indicating divergence among closely related taxa. Accumulation in disparity in song length and syllable variety also differed from the null but, by contrast, peaked around the middle of the clade’s history and not near the present.

Taken together, patterns of evolutionary change in plumage and song traits appear to be decoupled: when there were bursts in accumulation in disparity in song length and syllable variety, disparity in plumage coloration did not deviate from the null and vice versa (Fig. 1). In other words, it appears that when one set of traits diversified at a relatively high rate the other did not, as predicted by the transfer hypothesis. The pattern of disparity through time estimated for vegetation structure closely mirrored patterns observed for plumage coloration variables (Fig. 1). This suggests that diversification in coloration in warblers may have been linked to shifts between habitats differing in light availability and color properties as predicted by the sensory drive hypothesis. In contrast to traits involved in visual and acoustic communication, morphological traits (i.e., body mass and bill size) accumulated disparity as expected under a Brownian-motion model of evolution.

Considering vegetation structure allowed us to reveal a negative association between vocal and visual signals that was otherwise absent when vegetation structure was not

65 considered a covariate. We thus conclude that the signaling environment mediates the evolution of the relationship between vocal and visual signals, resulting in negative relationships between the degree of elaboration of different types of signals (i.e., apparent transferal effects). A variety of studies have tested the transfer hypothesis in birds, with mixed results: finches and avenue-building bowerbirds exhibit negative relationships between acoustic and visual signals24,29, but no evidence of such apparent transference has been found in trogons15, Asian barbets14 or tanagers13. In other groups like butterflies30, fishes16,31 and plants32, there is evidence of negative relationships between signals driven by properties of habitats. For example, in turbid waters sticklebacks rely more on olfactory cues to attract mates than in clear waters31,33, suggesting a transference from visual signals to chemical signals mediated by habitat.

Our study offers strong support for the transfer hypothesis, but only when it is considered in the context of sensory drive. Transfer in signal investment occurs when there are differential costs and benefits of signals in different modalities. Our results suggest that these differential costs and benefits need not occur because of internal physiological or developmental constraints as previously suggested, but can be generated by the saliency of signals in their local habitat. We conclude that to understand the diversity of multimodal communication strategies it is necessary to address not only the magnitude of investment in signaling modalities but also the selective forces that influence their salience.

METHODS SUMMARY

We measured coloration in New World warblers using museum specimens with an

Ocean Optics (Dunedin, FL) USB2000 spectrophotometer and a PX-2 pulsed xenon

66 light source to record reflectance across the avian visual spectrum. For each specimen, we measured all plumage patches we observed that appeared to be differently colored to the human eye along 40 measurement points in ventral and dorsal sides. With these data, we constructed three variables describing colors: (1) chromatic and (2) achromatic diversity, and (3) contrast to the background assuming that the color in of the backs matched the color of the background. Chromatic and achromatic diversity were calculated by comparing the contrast between every color measured on each specimen. We used data on song length, syllable variety and vocal deviation from a previous study46 as measurements of song elaboration13 in New

World warblers. We explored the evolutionary history of communication signals

(visual and acoustic) in New World warblers (Parulidae) using comparative methods designed to understand the patterns of evolution of traits along a phylogeny34 (DTT plots) and phylogenetic generalized least squares (PGLS)35 models to test for relationships between variables predicted by the transfer hypothesis and sensory drive.

Literature cited

1. Darwin, C. The Descent of Man and Selection in Relation to Sex. (1871).

2. Gilliard, T. Bower ornamentation versus plumage characters in Bowerbirds.

Auk 73, 450–451 (1956).

3. Repentigny, Y. D. E., Ouellet, H. & McNeil, R. Song versus plumage in some

North American oscines: testing Darwin’s hypothesis. Ecoscience 7, 137–148

(2000).

67 4. Shutler, D. Sexual selection: when to expect trade-offs. Biol. Lett. 7, 101–104

(2011).

5. Endler, J. A. Signals, signal conditions, and the direction of evolution. Am. Nat.

139, S125–S153 (1992).

6. Huxley, J. S. Darwin’s theory of sexual selection and the data subsumed by it,

in the light of recent research. Am. Nat. 72, 416–433 (1938).

7. Gilliard, T. E. The evolution of bowerbirds evolutionary processes. Sci. Am.

209, 38–46 (1963).

8. Gil, D. & Gahr, M. The honesty of bird song: multiple constraints for multiple

traits. Trends Ecol. Evol. 17, 133–141 (2002).

9. Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in

animals. Annu. Rev. Ecol. Syst. 32, 95–126 (2001).

10. Roff, D. a & Fairbairn, D. J. The evolution of trade-offs: where are we? J.

Evol. Biol. 20, 433–447 (2007).

11. Tuttle, M. D. & Ryan, M. J. Bat predation and the evolution of frog

vocalizations in the Neotropics. Science 214, 677–678 (1981).

12. Partan, S. R. & Marler, P. Issues in the classification of multimodal signals.

Am. Nat. 166, 231–245 (2005).

13. Mason, N. A., Shultz, A. J. & Burns, K. J. Elaborate visual and acoustic signals

evolve independently in a large, phenotypically diverse radiation of songbirds.

Proc. R. Soc. B Biol. Sci. 281, 1–10 (2014).

68 14. Gonzalez-Voyer, A., Den Tex, R.-J., Castelló, A. & Leonard, J. A. Evolution

of acoustic and visual signals in Asian barbets. J. Evol. Biol. 26, 647–59

(2013).

15. Ornelas, J. F., González, C. & Espinosa de los Monteros, A. Uncorrelated

evolution between vocal and plumage coloration traits in the trogons: a

comparative study. J. Evol. Biol. 22, 471–484 (2009).

16. Rafferty, N. E. & Boughman, J. W. Olfactory mate recognition in a sympatric

species pair of three-spined sticklebacks. Behav. Ecol. 17, 965–970 (2006).

17. Doucet, S. M., Mennill, D. J. & Hill, G. E. The Evolution of Signal Design in

Manakin Plumage Ornaments. Am. Nat. 169, S62–S80 (2007).

18. Cardoso, G. C. & Mota, P. G. Evolution of female carotenoid coloration by

sexual constraint in Carduelis finches. BMC Evol. Biol. 10, 1471– (2010).

19. Price, J. J. & Whalen, L. M. Plumage evolution in the oropendolas and

caciques: different divergence rates in polygynous and monogamous taxa.

Evolution 63, 2985–2998 (2009).

20. Mcnaught, M. K. & Owens, I. Interspecific variation in plumage colour among

birds: species recognition or light environment? J. Evol. Biol. 15, 505–514

(2002).

21. Seddon, N. Ecological adaptation and species recognition drives vocal

evolution in neotropical suboscine birds. Evolution 59, 200–15 (2005).

69 22. Kirschel, A. N. G. et al. Birdsong tuned to the environment: green hylia song

varies with elevation, tree cover, and noise. Behav. Ecol. 20, 1089–1095

(2009).

23. Slabbekoorn, H. & Smith, T. B. Habitat-dependent song divergence in the little

greenbul: an analysis of environmental selection pressures on acoustic signals.

Evolution (N. Y). 56, 1849–58 (2002).

24. Badyaev, A. V, Hill, G. E. & Weckwort, B. V. Species divergence in sexually

selected traits: increase in song elaboration is related to decrease in plumage

ornamentation in finches. Evolution 56, 412–9 (2002).

25. Lovette, I. J. et al. A comprehensive multilocus phylogeny for the wood-

warblers and a revised classification of the Parulidae (Aves). Mol. Phylogenet.

Evol. 57, 753–70 (2010).

26. Bloch, N. I. Evolution of opsin expression in birds driven by sexual selection

and habitat. Proc. R. Soc. B Biol. Sci. 282, (2014).

27. Bloch, N. I., Morrow, J. M., Chang, B. S. W. & Price, T. D. SWS2 visual

pigment evolution as a test of historically contingent patterns of plumage color

evolution in warblers. Evolution (N. Y). 69, 341–356 (2015).

28. Gomez, D. & Théry, M. Simultaneous crypsis and conspicuousness in color

patterns: comparative cnalysis of a Neotropical rainforest bird community. Am.

Nat. 169, S42–S41 (2007).

70 29. Kusmierski, R., Borgia, G., Uy, A. C. & Crozier, R. H. Labile evolution of

display traits in bowerbirds indicates reduced effects of phylogenetic

constraint. Proc. Biol. Sci. 264, 307–13 (1997).

30. Vane-Wright, R. I. & Boppre, M. Visual and chemical signalling in butterflies:

functional and phylogenetic perspectives. Philos. Trans. R. Soc. B Biol. Sci.

340, 197–205 (1993).

31. Heuschele, J., Mannerla, M., Gienapp, P. & Candolin, U. Environment-

dependent use of mate choice cues in sticklebacks. Behav. Ecol. 20, 1223–1227

(2009).

32. Raguso, R. A. Wake up and smell the roses: the ecology and evolution of floral

scent. Annu. Rev. Ecol. Evol. Syst. 39, 549–569 (2008).

33. Heuschele, J. & Candolin, U. An increase in pH boosts olfactory

communication in sticklebacks. Biol. Lett. 3, 411–413 (2007).

34. Harmon, L. J., Schulte, J. a, Larson, A. & Losos, J. B. Tempo and mode of

evolutionary radiation in iguanian lizards. Science 301, 961–964 (2003).

35. Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and

comparative data: a test and review of evidence. Am. Nat. 160, 712–726

(2002).

36. Doucet, S. M. & Hill, G. E. Do museum specimens accurately represent wild

birds? A case study of carotenoid, melanin, and structural colours in long-tailed

manakins Chiroxiphia linearis. J. Avian Biol. 40, 146–156 (2009).

71 37. Clarke, J. M. & Schluter, D. Colour plasticity and background matching in a

threespine stickleback species pair. Biol. J. Linn. Soc. 102, 902–914 (2011).

38. Goldsmith, T. H. Optimization, constraint and history in the evolution of eyes.

Quaterly Rev. Biol. 65, 281–322 (1990).

39. Endler, J. A. & Mielke, P. W. Comparing entire colour patterns as birds see

them. Biol. J. Linn. Soc. 86, 405–431 (2005).

40. Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour

thresholds. Proc. R. Soc. B Biol. Sci. 265, 351–358 (1998).

41. Cardoso, G. C. & Hu, Y. Birdsong performance and the evolution of simple

(rather than elaborate) sexual signals. Am. Nat. 178, 679–686 (2011).

42. Dunning, J. B. J. CRC Handbook of Avian Body Masses. (CRC Press, 2008).

43. Ballentine, B. Vocal performance influences female response to male bird

song: an experimental test. Behav. Ecol. 15, 163–168 (2004).

44. Podos, J. A Performance constraint on the evolution of trilled vocalizations in a

songbird family (Passeriformes: Emberizidae). Evolution (N. Y). 51, 537–551

(1997).

45. Curson, J., Quinn, D. & Beadle, D. The Warblers of the Americas. An

Identification Guide. (Houghton Mifflin, 1994).

46. Orme, D. et al. Caper: comparative analyses of phylogenetics and evolution in

R. (2012).

72 47. Maia, R., Eliason, C. M., Bitton, P.-P., Doucet, S. M. & Shawkey, M. D. Pavo :

an R package for the analysis, visualization and organization of spectral data.

Methods Ecol. Evol. 906–913 (2013). doi:10.1111/2041-210X.12069

73

2.0 2.0 Chromatic%JNDs 2.0 Song%Length Bill%Size 1.5 1.5 1.5 Bill 1.0 1.0 1.0 Song Length Song Chromatic JNDs Chromatic 0.5 0.5 0.5 0.0 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 2.0 2.0 2.0 RelativeAchromatic%JNDs time RelativeSyllable%Variety time Relative time Body%Size 1.5 1.5 1.5 1.0 1.0 1.0 Body Size Body Sylable variety Sylable 0.5 0.5 0.5 Achromatic JNDs Achromatic 0.0 0.0 0.0 Disparity 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 2.0 2.0 2.0 RelativeContrast%to%BackG time RelativeVocal%Deviation time Relative timeVegetation 1.5 1.5 1.5 1.0 1.0 1.0 Vegetation Vocal Deviation Vocal Dist. Back-Body 0.5 0.5 0.5 0.0 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Relative time Relative%TimeRelative time Relative time

Figure 1. Disparity through time (DTT) plots for plumage, songs, morphology and vegetation suggest a negative evolutionary correlation between plumage traits and attributes of songs possibly mediated by habitat. Disparity in color traits (in red) and habitat (in green) accumulated at a faster rate near the tips of the phylogeny.

However, disparity in variables related to song elaboration (in blue) peaked around the middle of the clade’s history. Morphological traits like body size and bill size (in black) followed a null evolutionary model.

74 Table 1. Phylogenetic generalized least squares multiple regressions of plumage contrast to background on different sets of predictors. Only when we included vegetation structure as a covariate did significant negative relationships between variables emerge (the model that best fit the data is shown in bold), suggesting that transfers in investment between signals in different communication channels are mediated by habitat.

Independent variables AIC p-value Adjusted R-squared

Syllable variety 84.18 0.89 -0.02

Vocal Deviation 84.28 0.98 -0.02

Song Length 83.90 0.68 -0.01

Vegetation 83.95 0.71 -0.02

Syllable Variety * Vegetation 76.77 0.01 0.17

Song Length * Vegetation 87.36 0.88 -0.05

Vocal Deviation * Vegetation 86.95 0.80 -0.04

75 Table 2. Phylogenetic generalized least squares multiple regressions of achromatic diversity (dL) on different sets of predictors. Achromatic diversity of plumage was significantly related to vocal deviation and vegetation structure. Vocal deviation was a good predictor of achromatic diversity, but the explanatory power of the model increased when vegetation was included in the model. This indicates that birds with darker backs live in darker habitats and communicate using songs demanding greater performance.

Independent variables AIC p-value Adjusted R-squared

Syllable variety 283.99 0.32 0.00

Vocal Deviation 281.67 0.02 0.07

Song Length 284.30 0.44 0.00

Vegetation 280.76 0.02 0.10

Syllable Variety * Vegetation 283.52 0.14 0.12

Song Length * Vegetation 282.39 0.08 0.08

Vocal Deviation * Vegetation 281.69 0.03 0.12

76 METHODS

We explored the evolutionary history of communication signals using comparative methods designed to understand the patterns of evolution of traits along a phylogeny34

(disparity through time [DTT] plots) and phylogenetic generalized least squares

(PGLS)35 to evaluate different models. We focused on New World warblers

(Parulidae), which have evolved in contrasting habitat types, and differ in communication strategies to study the evolution of communication signals.

Plumage conspicuousness

We measured coloration in New World warblers using museum specimens. In total we measured 231 study skins of 51 species (see Supplement) in the collections at the

Museum of Natural Science at Louisiana State University (LSUMZ). To avoid any effects of specimen age on reflectance measures17,36 the most recent specimens with the freshest looking plumage for each species were chosen. We used an Ocean Optics

(Dunedin, FL) USB2000 spectrophotometer with a PX-2 pulsed xenon light source to record reflectance across the avian visual spectrum. All measurements were taken at a

45-degree angle to the feather surface. For each species and each sex, we measured all colors we observed in all patches that appeared to be differently colored to the human eye along on 40 measurement points in the ventral and dorsal sides of each specimen.

These measurements encompassed all major plumage patches (dorsal: crown, mantle and rump; ventral: throat, breast and belly).

To quantify the conspicuousness of plumage coloration we used two approaches.

First, we calculated euclidean distances between the colors in the belly, rump and crown against the color in the mantle. Birds increase crypsis by displaying

77 countershaded patterns (lighter ventral than dorsal parts) and by matching background contrast (Fig. S1), particularly on their dorsal parts, which are often more exposed to predators 28,37. For this reason, being unable to measure light environments for all species in the field, we assumed that dorsal plumage approximately emulates the color of the background where birds live. Therefore, the contrast between the back and the belly, crown and rump colors was used as a proxy for conspicuousness. For this approach, we used an avian visual model38,39 to analyze colors in a tetrahedral color space. We called this variable contrast to background. Our second approach used a discrimination model which calculates a contrast, for the chromatic and achromatic domain, in avian color space defined by the quantum catches of each receptor type

(i.e., cone cell) in the avian retina40. The model assumes that color discrimination in this perceptual space is limited by noise originating in the receptors, and that no visual signal results when stimulus and background differ only in intensity40. We calculated the chromatic (dS) and achromatic (dL) diversity of plumages as follows. First, we measured the contrasts between every pair of points (40) measured on each specimen; second, we calculated the centroid for the chromatic and the achromatic dimension; third, we measured the distance from each point to the centroid for the chromatic and achromatic measures of contrast and we averaged these measurements to have a mean value of the distribution of the contrasts in the chromatic and achromatic dimension of colors (Fig S2.). We called these measurements achromatic (dL) and chromatic (dS) diversity of plumage.

Song measurements

We used data on song length, syllable variety and vocal performance from a previous study41 as measurements of song elaboration in New World warblers 13. We also used

78 existing data on bill size41 and body size42 to study the evolution of morphological traits compared to the evolution of communication traits.

As songs increase in either frequency bandwidth or trill rate, the demands of vocal performance increase. Vocal deviation (i.e. the orthogonal distance from the upper- bound regression line between frequency bandwidth and pace of each trill) is strongly related to the energetic demands of vocal performance43,44. Songs far below the regression line (i.e. those with higher values of vocal deviation) are assumed to be less vocally demanding than those closer to the regression line44; thus, we interpret larger values of vocal deviation to reflect greater investment in song production.

Vegetation structure

Vegetation structure was scored using habitat descriptions45 and in accordance with previous work41, as follows: 1 – open, 2 – semiclosed with low vegetation, 3 – semiclosed with high vegetation and 4 – closed. Intermediate scores were used when species used more than one habitat type.

Analyses

To evaluate the transfer hypothesis and the effect of vegetation structure on the relationship between communication signals, we used a generalized least square model for comparative phylogenetics (PGLS) implemented in caper46. We run PGLS setting contrast to background, chromatic (dS) and achromatic (dL) diversity of plumage as response variables including different set of predictors. First, to test for transfer effects we run models independently with syllable variety, vocal deviation and song length as predictors. Then, to test the effect of habitat on the relationship

79 between visual and acoustical signals, we added vegetation structure as another predictor in the models.

To examine patterns of evolutionary differentiation in vocal performance and color conspicuousness among lineages of New World warblers, we constructed disparity- through-time (DTT) plots34. DTT plots allow one to examine the accumulation in trait disparity over evolutionary time in a clade by estimating the dispersion of points in multivariate space across time intervals in a phylogeny. This method calculates dissimilarity as the mean square (pair-wise) Manhattan distance among points in trait- space for subclades relative to the variance of the entire clade. Disparity is calculated for individual nodes by moving up the phylogeny from the root of the tree. Relative disparity values close to 0.0 indicate that subclades contain only a small proportion of the total variation and therefore overlap in occupation of phenotypic space is minimal between the different subclades; conversely, relative disparity values close to 1.0 indicate extensive phenotypic overlap34. For all the comparative analyses mentioned above, we used a molecular phylogeny of New World warblers based on several mitochondrial and nuclear gene sequences25.

36. Doucet, S. M. & Hill, G. E. Do museum specimens accurately represent wild

birds? A case study of carotenoid, melanin, and structural colours in long-tailed

manakins Chiroxiphia linearis. J. Avian Biol. 40, 146–156 (2009).

37. Clarke, J. M. & Schluter, D. Colour plasticity and background matching in a

threespine stickleback species pair. Biol. J. Linn. Soc. 102, 902–914 (2011).

80 38. Goldsmith, T. H. Optimization, constraint and history in the evolution of eyes.

Quaterly Rev. Biol. 65, 281–322 (1990).

39. Endler, J. A., Westcott, D. A., Madden, J. R. & Robson, T. Animal visual

systems and the evolution of color patterns: sensory processing illuminates

signal evolution. Evolution 59, 1795–1818 (2005).

40. Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour

thresholds. Proc. R. Soc. B Biol. Sci. 265, 351–358 (1998).

41. Cardoso, G. C. & Hu, Y. Birdsong performance and the evolution of simple

(rather than elaborate) sexual signals. Am. Nat. 178, 679–686 (2011).

42. Dunning, J. B. J. CRC Handbook of Avian Body Masses. (CRC Press, 2008).

43. Ballentine, B. Vocal performance influences female response to male bird

song: an experimental test. Behav. Ecol. 15, 163–168 (2004).

44. Podos, J. A Performance constraint on the evolution of trilled vocalizations in a

songbird family (Passeriformes: Emberizidae). Evolution 51, 537–551 (1997).

45. Curson, J., Quinn, D. & Beadle, D. The Warblers of the Americas. An

Identification Guide. (Houghton Mifflin, 1994).

46. Orme, D. et al. Caper: Comparative analyses of phylogenetics and evolution in

R. (2012).

47. Foote, M. The evolution of morphological diversity. Annu. Rev. Ecol. Syst. 28,

129–152 (1997).

81

Figure S1. Two photographs of the Tropical Kingbird (Tyrannus melancholicus) as an example of how dorsal colors may match the background color while the ventral color is conspicuous relative to the background. We assume background color matching by the dorsal parts as shown in this bird also occurs in the New World warblers.

82 CardellinaCardellina rubrifrons MyiothlypisBasileuterus bellibelli MyiothlypisBasileuterus coronatuscoronata ErgaticusErgaticus ruber ruber 80 80 80 80 60 60 60 60 40 40 40 40 20 20 20 20 Achormatic JNDs Achormatic JNDs Achormatic JNDs Achormatic JNDs Achormatic 0 0 0 0

0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70

Chromatic JNDs Chromatic JNDs Chromatic JNDs Chromatic JNDs

SetophagaDendroica nigrescensnigrescens HelmitherosHelmitheros vermivorusvermivorus MniotiltaMniotilta varia varia ParkesiaParkesia noveboracensis noveboracensis 80 80 80 80 60 60 60 60 40 40 40 40 20 20 20 20 Achormatic JNDs Achormatic JNDs Achormatic JNDs Achormatic JNDs Achormatic 0 0 0 0

0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70

Chromatic JNDs Chromatic JNDs Chromatic JNDs Chromatic JNDs

Figure S2. Variation in achromatic and chromatic diversity of colors among selected species of warblers. Each point is a comparison between the discrimination thresholds or ‘just noticeable differences’ (JND) between two reflectance measures. Values in the 1–3 range indicate that two objects are likely to be indistinguishable to an avian observer, whereas values >3 are increasingly likely to lead to detection and discrimination. Notice the distribution of points in Mniotilta varia and Parkesia noveboracensis, which exhibit wide variation in the achromatic axis relative to low variation in the chromatic axis. In contrast, Cardellina rubrifrons and Myiothlypis belli exhibit ample variation along both achromatic and chromatic axes.

83 Chapter 4

Songs in the understory and colors in the canopy: habitat structure leads to

different avian communication strategies in a tropical montane forest

Short title: Songs in the understory and colors in the canopy

Oscar Laverde-R. & Carlos Daniel Cadena

Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias

Biológicas, Universidad de los Andes.

Abstract

Birds inhabit a variety of habitats and they communicate using primarily visual and acoustic signals; two central hypotheses have been postulated to study the evolution of such a signals. The sensory drive hypothesis posits that variation in the physical properties of habitats leads to variation in natural selection pressures by affecting the ease with which different types of signals are perceived. Assuming that resources are limited for animals, the transfer hypothesis predicts a negative relationship between the investments in different types of signals. We evaluated these two hypotheses in a tropical montane forest bird assemblage. We also postulate a possible interaction between these two hypotheses: we predicted that the negative relationships between signals should be observed only when jointly considering birds from different environments (e.g. understory and canopy) due to the expected differences in communication strategies between habitats. The sensory drive hypothesis was supported by the differences we found between strata in vocal output, patch contrast to background and color conspicuousness, but not for the variables associated to song elaboration and hue disparity. We found support for the transfer hypothesis: birds with colors contrasting less against the background sing more frequently and birds with lower diversity of colors produce longer songs, understory birds showed also a negative relationship between signals, but only when accounting for phylogeny. We found partial support for the interaction between the sensory drive and the transfer hypotheses: hue disparity and vocal output were negatively related only when analyzing together birds from the canopy and the understory, but not when analyzing them separately. We conclude that the study of the evolution of communication signals needs to consider more than one channel and the functional interactions

85 between them. The results of the interaction of optimal signaling strategies in two communication channels in the local habitats where animals signaling, are the patterns of colors and songs we revealed in a tropical montane forest bird assemblage.

Keywords: bioacoustics, bird communication, montane forest, multimodal communication, Neotropical, sensory drive, transfer hypothesis.

Introduction

Birds communicate using primarily visual and acoustic signals, and the evolution of these signals likely involves a compromise between sexual selection for conspicuousness and natural selection for crypsis (Endler 1992). Assuming that producing communication signals (i.e. plumage colors, songs) is costly and that resources devoted to producing such signals are limited, one expects that animals should invest predominantly in one type of signal (Shutler, 2011). Accordingly, the trade-off or transfer hypothesis predicts a negative relationship between the investment in different types of signals: birds with complex songs are expected to exhibit dull plumages, whereas birds with colorful plumages are expected to have simple songs (Darwin 1871; Huxley 1938; Gilliard 1956; Shutler and Weatherhead

1990; Kusmierski et al. 1997; Ornelas et al. 2009; Shutler 2011). In addition, the direction in which potential trade-offs between plumage and song elaboration are resolved is likely to be affected by spatially variable biotic and abiotic factors. For example, if high predation in a given environment selects for reduced conspicuousness in plumage, then song elaboration may replace plumage characteristics as a main target of sexual selection, resulting in increased elaboration of vocal signals (Badyaev et al. 2002).

86

The sensory drive hypothesis posits that variation in the physical properties of the habitat in which animals communicate leads to variation in natural selection pressures by affecting the ease with which different types of signals are perceived (Endler

1992). The canopy and understory of tropical forests, for example, are highly contrasting habitats in terms of light availability (Gomez and Théry 2004, 2007) and acoustic properties affecting sound transmission (Ryan and Brenowitz 1985; Nemeth et al. 2001; Slabbekoorn and Smith 2002; Seddon 2005). Therefore, one may hypothesize that birds occupying different forest strata are likely to be differentially selected with regard to colors and songs (Nemeth et al. 2001; Gomez and Théry 2004,

2007; Seddon 2005).

The forest understory is a dark environment in which ultraviolet (UV) light is especially scarce, and in which the visual background is mostly brown due to tree trunks and leaf litter (Endler 1993). In contrast, light is abundant and rich in UV wavelengths in the forest canopy, where the background is largely green due to leaves. Work on an avian assemblage inhabiting a Neotropical lowland forest suggests that the plumage patterns of birds vary between understory and canopy species in ways consistent with adaptation to contrasting light environments (Gomez and Théry

2007). For example, UV patches and colors rich in short wavelengths (i.e. blue and violet) are relatively frequent in the plumage of canopy birds but are nearly absent in the understory. Understory birds are usually monochromatic and often exhibit brown dorsal plumage showing little contrast to their background, whereas conspicuous yellow and orange are more common colors in ventral plumages. In contrast, the most common colors in the canopy are green, black and brown in males, and brown and

87 green in females. The contrast in brightness between dorsal plumage patches and the background is greater in canopy species than in understory species. In sum, under the avian visual system, canopy birds are more conspicuous relative to their background than understory birds in a lowland tropical forest (Gomez and Théry 2007). Greater conspicuousness in the canopy may originate from less intense selection by predators for crypsis or from the greater efficiency of UV signals in this environment, which allows for such signals being used for communication with conspecifics while being undetected by some predators (i.e., a private communication channel; Gomez and

Théry 2007). Whether this applies to other forest sites remains to be seen.

As with colors, songs and calls are thought to be shaped by habitat structure. The acoustic adaptation hypothesis postulates that structural properties of habitats influence signal evolution through their effects on signal transmission (Jønsson et al.,

2012; Kirschel et al., 2009; Morton, 1975; Nemeth et al., 2001; Ryan & Brenowitz,

1985; Seddon, Merrill, & Tobias, 2008; Slabbekoorn & Smith, 2002; Tobias et al.,

2010). Dense habitats are expected to select for lower-pitched vocalizations with narrower frequency bands and for songs with longer notes and inter-notes relative to open habitats (Morton 1975; Ryan & Brenowitz 1985). In forest environments the understory is more densely vegetated than the canopy, while stems and branches are thinner and leaves smaller in the canopy; this structural variation in the environment results in height-specific patterns of sound degradation affecting the design of vocal signals (Nemeth et al. 2001). Specifically, birds living in the understory are expected to use low-frequency songs to minimize the effects of frequency attenuation and to emit long, well-spaced notes to avoid distortion by reverberation (Nemeth et al.

2001). But some narrow frequency bandwidth vocalizations reverberations may be

88 beneficial leading to longer and louder notes after signal transmission (Slabbekoorn et al. 2002). In the canopy, the effects of frequency attenuation and reverberation are less strong; thus, songs of canopy species are expected to be higher pitched, with shorter notes and less time between notes relative to songs of understory species

(Nemeth et al. 2001).

Many comparative studies have tested and supported the sensory drive hypothesis, but these studies have largely focused only on single communication channels, emphasizing the study of either colors (Mcnaught and Owens 2002; Doucet et al.

2007; Ornelas et al. 2009; Price and Whalen 2009; Cardoso and Mota 2010) or songs

(Kirschel, et al., 2009; Smith & Bernatchez, 2008; Kirschel, et al., 2009; Seddon &

Tobias, 2010; Seddon, 2005; Podos, 2001; Nowicki et al., 1998; Price & Lanyon,

2002; Price, 2004). However, if there are tradeoffs in the investment between visual and acoustic signals, then the direction of evolution will depend on the influence of the environment (i.e. sensory drive) on how such tradeoffs are resolved. For example, a recent study on the evolution of communication signals in New World Warblers

(Parulidae) showed that sensory drive influenced the transfer of investment between traits in different sensory modalities (Laverde et al., Chapter 3). In that study, negative relationships between measures of song and plumage elaboration were only apparent when considering the influence of habitat: plumage contrast to the background and chromatic diversity were negatively related to song syllable variety only when vegetation structure was included as a covariate in analyses (e.g., birds with a greater variety of song syllables and less colorful plumage live in closed or darker habitats). Thus, negative relationships between investment in different types of signals do not necessarily exist because of internal physiological or energetic

89 restrictions, but may arise as a consequence of selection for optimal signaling strategies in the local habitat of species (Laverde et al., Chapter 3).

A tropical montane forest is an appropriate scenario for studying the variation in visual and acoustic signals of birds in relation to habitat structure. At a single locality, one may find a large number of bird species exhibiting a wide variety of colors and songs, and differing in microhabitat use within the forest (Stiles and Rosselli 1998). In addition, the gradient of physical properties from the canopy to the understory within a montane forest offers a good framework for testing the sensory drive and the transfer hypotheses while exploring possible interactions between them. Here, we studied an assemblage of tropical montane birds to examine whether mechanisms underlying negative relationships between signals (i.e. transfer hypothesis) may be understood as the result of interactions between suitability of different types of signals for communication given habitat features (i.e. sensory drive hypothesis; Seehausen et al. 2008; Tobias and Seddon 2009; Laverde et al., Chapter 3).

First, we tested the sensory drive hypothesis (i.e. the effect of contrasting habitats: understory vs. canopy) using variables related to song elaboration and vocal output and variables related to color diversity and conspicuousness. The hypothesis that signal evolution is driven by sensory drive predicts that in the understory, where light availability is low and visual signals are easily obstructed by vegetation, birds would rely more in acoustic than in visual signals; this should be manifested in greater vocal output (i.e. in singing more frequently) or in greater song elaboration relative to canopy species (Fig. 1a). By contrast, visual signals (i.e. colorful or contrasting plumages) are predicted to be favored in canopy birds due to greater light availability

90 and less obstacles. However, because singing in colorful species in a more open habitat like the canopy would further increase detectability by predators (Marler 1955;

Gotmark and Post 1996), one expects vocal output to be lower or songs to be less elaborate in canopy species than in understory species (Fig. 1b). Second, we tested the transfer hypothesis between songs and colors, which predicts negative relationships between song elaboration or vocal output and variables indicating plumage conspicuousness regardless of habitat (i.e. negative relationships should exist among species from the same forest stratum; Fig. 1c and 1d). Finally, we tested our hypothesis (Laverde et al., Chapter 3) that negative relationships expected under the transfer hypothesis are the result of sensory drive and need not involve tradeoffs in energy allocation; this hypothesis predicts that negative relationships between signals should be observed only when jointly considering birds from the understory and canopy due to the expected differences in communication strategies between habitats

(Fig. 1e).

Methods

We studied the bird assemblage in a tropical montane forest in Chingaza National

Park, eastern Andes of Colombia. Chingaza is located ca. 40 km east of the city of

Bogotá in the departments of Cundinamarca and Meta. The region has an average annual precipitation of 1800 mm, with two distinct peaks. The dry season extends from November to March with minimum rainfall in January and February, and the wet season extends from April to October with maximum rainfall in June and July

(Vargas and Pedraza 2004). Approximately 190 bird species in 40 families occur in the park (Stiles and Rosselli 1998; Vargas and Pedraza 2004), but we focused on 52 species of 20 families that were common in forest in our study area (Table S1). We

91 categorized each species as occurring in canopy or understory habitat based on our field observations and on information from the literature (Hilty and Brown 1986;

Stiles and Rosselli 1998).

QUANTIFYING INVESTMENT IN SONGS

We sought to characterize the investment of avian species in vocal signals by measuring (1) the frequency with which songs are emitted over relatively long time frames (i.e., vocal output), (2) the rate at which songs are delivered during singing

(i.e., song rate), and (3) properties of individual songs, namely song length and syllable variety. Recordings for these measures were obtained by continuous sampling of avian vocalizations in six different locations in the Palacio sector of Chingaza

National Park (4°41’ N, 73°50’ W; Table 1). Autonomous recording units (ARUs;

Songmeter II, WildLife Acoustics) were located in forests between 2950 and 3170 m elevation and placed more than 500 m away from each other to avoid recording the same individuals in more than one unit. Each ARU was programmed to record for three minutes every 30 minutes. We sampled vocal output over several months in

2013: February (4 ARUs), April (3 ARUs), May (4 ARUs), July (2 ARUs), and

August (2 ARUs; Table 1 and Table S1). We listened to recordings obtained from

5:30 am to 12:00 m (vocal activity decreased markedly later in the day) and identified vocalizing species (90% of avian vocalizations detected were identified to species).

We measured vocal output as the total number of recordings in which each species was present in the recordings obtained by the ARUs. Although data gathered by

ARUs gave us information about the frequency with birds sang during our study period in Chingaza, they can not reveal patterns in vocal output over longer time

92 frames. For example, monitoring during few months may be inadequate to characterize vocal output in species with markedly seasonal patterns of annual vocal activity (e.g. tinamous and thrushes). To obtain complementary information about vocal output over longer time frames, we also used data on recordings archived in sound collections; in a previous study we validated this approach by demonstrating that the number of recordings in archives reflects vocal output and temporal patterns in vocal activity measured through continuous monitoring (Laverde et al. Chapter 2).

We thus tallied the total number of recordings of each of our study species archived in three major sound collections (Colección de Sonidos Animales at Instituto Alexander von Humboldt [CSA], Macaulay Library of Natural Sounds [MLS] and xeno-canto

[XC]) corrected by the size of its distributional range (Laverde et al. Chapter 2) as an additional proxy of vocal output.

In addition to vocal output, we measured three variables related to song elaboration: song rate, song length, and syllable diversity (Cardoso and Hu 2011). We measured song rate as the mean number of songs of each species in the recordings in which we detected each species singing. We defined a song as a cluster of single vocal units separated from other clusters by a longer time span than that between any inter-unit time interval; songs may also be identified by the pauses between them, which may be on the order of several seconds (Thompson et al. 1994). We measured song length and counted the number of different types of syllables in Raven version 1.5 (Cornell Lab of Ornithology, Ithaca, New York, USA) using the following combination of settings:

FFT window = 512, window type = Hann, and window overlap = 50%. We defined a syllable as the minimum discrete unit forming a song (Cardoso and Hu 2011); we recognized types of syllables by their shape on spectrograms and then counted them

93 as our measure of syllable diversity. We measured syllable variety and song length using between 2 and 175 songs per species obtained from the xeno-canto archive

(total 1352 songs; Table S2).

QUANTIFYING PLUMAGE COLORATION AND CONSPICUOUSNESS

Colors of birds

To quantify plumage color heterogeneity and conspicuousness we measured 297 specimens of 52 species from 29 families (range 1-8 individuals per species; see suplementary material) in the ornithological collection of the Instituto de Ciencias

Naturales, Universidad Nacional de Colombia. On each specimen we measured five color patches on different body parts (crown, rump, back, throat and belly) and also any other differently colored patch we observed in other body parts. Reflectance measurements were taken using an Ocean Optics USB-4000 Spectrometer and a DH-

2000 deuterium halogen light source coupled with an optic fiber QP400-2-UV-VIS with a 400 um diameter. Light was reflected on the surfaces at a 45° angle. The spectrometer was calibrated using a white and a black standard before measuring each specimen. Each color patch was measured twice and measures averaged using the pavo package for R (Maia et al. 2013). We measured males and females for all species, but because none of the species was sexually dichromatic, we combined all the individuals for analyses.

We used the tetrahedral color space model (Endler and Mielke 2005; Stoddard and

Prum 2008) to describe plumage coloration for each species. We used models of cone sensitivity spectra corresponding to the UV-sensitive (UVS) and V-sensitive (VS) types to account for the two main types of color vision in diurnal birds (Ödeen and

94 Håstad 2009); because results were similar using both models, we only show results for UV-type vision. We considered two measurements indicating investment in visual signals as a basis for our tests of the transfer and sensory drive hypotheses: hue disparity and color conspicuousness. Hue disparity is an estimate of color heterogeneity, measured as the magnitude of the angle between two color vectors in the tetrahedral color space (Stoddard and Prum 2008). Color conspicuousness measures the mean contrast of plumage patches to the background (see below).

Determining how conspicuous avian plumages are requires characterizing the visual background in which species signal. Thus, we collected 35 leaves from different canopy trees along a 50 m transect, and randomly took 50 samples of litter and tree bark in the same transect in the understory to quantify background colors in the canopy and understory, respectively. We measured the reflectance spectra of these materials using the same equipment and protocol used to measure bird specimens.

Each sample of each element (i.e. one leave, a piece of litter or bark) was measured twice and measurements were averaged. We then averaged measurements of all canopy leaves to obtain a mean canopy background reflectance spectrum, and averaged all the litter and bark measurements to obtain the mean background spectrum for the understory (Gomez & Thery, 2007). To quantify the chromatic contrast (ΔS) between plumage patches (crown, back, rump, throat and breast) and the background color we used the receptor noise model (Vorobyeb et al. 1998). This model considers the sensitivity of cones in the avian visual system and estimates of cone abundance and noise in photoreceptors to calculate discrimination thresholds or

‘just noticeable differences’ (JND). Values in the 1–3 range indicate that two objects are likely to be indistinguishable to an avian observer, whereas values >3 are

95 increasingly likely to lead to detection and discrimination (Vorobyev et al. 1998). We contrasted each color patch in bird plumages against the color of leaves in the canopy or litter and tree bark in the understory depending on the habitat occupied by each species using the coldist function in pavo. Finally, we estimated overall color conspicuousness as the mean chromatic contrast of plumage patches against background color expressed in JND units.

STATISTICAL ANALYSES

To evaluate the effect of habitat on the elaboration of visual and acoustic signals (i.e. sensory drive hypothesis) we tested the predictions that (1) colors are more diverse and more conspicuous in the canopy than in the understory, and that (2) vocal output is greater and songs are more elaborate in the understory than in the canopy. To evaluate these predictions we ran phylogenetic analyses of variance (ANOVAs) with habitat as predictor variable and measures of color and song elaboration as response variables. Owing to multiple comparisons we used the Bonferroni correction to adjust the p-values: this is a method in which the p-values are multiplied by the number of comparisons. To evaluate the transfer hypothesis we tested the prediction that measurements of elaboration in vocal and visual signals are negatively related using linear models and phylogenetic generalized least-square multiple regressions (PGLS), with hue disparity and color conspicuousness as response variables and song length, song rate, syllable variety and vocal output as explanatory variables. Finally, to evaluate the hypothesis that the negative relationships between songs and colors are the result of sensory drive operating separately on two types of communication signals we tested the prediction that negative relationships between signals should be observed only when considering birds from the understory and canopy together. We

96 ran linear models and PGLS of understory and canopy birds separately, and including all species occurring in the understory and canopy. If negative relationships are due to the effect of sensory drive, then they should be observed only when both canopy and understory birds are included in analyses; negative relationships between signals in species from the same forest stratum would be consistent with tradeoffs. All PGLS analyses were ran using the caper package for R (Orme et al. 2012) based on a comprehensive avian phylogeny (Jetz et al. 2012). Because we obtained color measurements for our 52 study species but were able to obtain good quality recordings only for 38 species for which we quantified song length and syllable variety, our final analyses of the transfer hypothesis focused on 38 species having complete data for colors and songs (Table S2).

Some of our results depended on whether or not analyses accounted for phylogenetic effects (see below). Therefore, we explored variation in vocal output and measurements of elaboration of visual signals (i.e. hue disparity and color conspicuousness) between understory and canopy birds separately for species in different taxonomic groups (non-passerines, and suboscines and oscine passerines).

For these analyses, we ran linear models setting forest stratum and taxonomic group as predictors.

Results

As predicted by the sensory drive hypothesis, vocal output (measured by our continuous field monitoring using ARUs) differed between forest strata: understory birds sing more frequently than canopy birds (Table 1, Fig. 2), but the number of recordings in sound collections were marginally significant (Table 1, Fig. 2). By

97 contrast, variables related to song elaboration (i.e. song rate, song length and syllable variety) did not differ significantly between understory and canopy species (Table 1,

Fig. 2).

Variation in visual signals partially supported predictions of the sensory drive hypothesis. Although plumage hue disparity did not differ between understory and canopy birds, the contrast of two color patches (i.e. back and breast) to the background and color conspicuousness were greater in canopy than in understory species as we had predicted (Table 2, Fig. 3). The contrast of crown, throat and rump color with the background did not differ between understory and canopy species.

Phylogenetic multiple regressions including our two measures of vocal output

(number of recordings obtained by continuous monitoring using ARUs and the total number of recordings found in three sound collections), song length, syllable variety, and song rate as predictors of plumage hue diversity and conspicuousness revealed significantly negative relationships irrespective of habitat consistent with the transfer hypothesis. However, the most relevant variables explaining variation in our measures of investment in color signals differed between models (Table 3 and 4). Color conspicuousness was negatively related to the number of recordings in ARUs (Table

3): birds with colors contrasting less against the background sing more frequently. In turn, hue disparity was negatively related to song length (birds with lower diversity of colors produce longer songs) but not to any other measure of vocal output or song elaboration (Table 4).

98 As predicted by our hypothesis that negative relationships as those expected under the transfer hypothesis are the result of sensory drive (Fig. 4), linear regressions between hue disparity and our two measures of vocal output were significantly negative only when we considered understory and canopy birds together (Table 5); when we ran separate analyses for understory and canopy birds we did not find any relationship.

As predicted by our hypothesis that negative relationships as those expected under the transfer hypothesis are the result of sensory drive (Fig. 4), linear regressions between hue disparity and our two measures of vocal output were significantly negative only when we considered understory and canopy birds together (Table 5); when we ran separate analyses for understory and canopy birds we did not find any relationship.

However, PGLS gave slightly different results: we found negative significant relationships only between hue disparity and the recordings obtained by the ARUs

(Table 5), but not between hue disparity and the number of recordings archived in sound collections. As expected given our hypothesis that negative relationships in the investment in different types of signals are mediated by habitat, hue disparity and our measures of vocal output were unrelated in canopy birds; however, in understory birds these variables were negatively related, a pattern consistent with tradeoffs

(Table 5). We found no significant relationships between measures of vocal output and color conspicuousness when considering canopy and understory birds together, neither when we ran separate analyses for canopy and understory birds (Table 5).

Finally, when examining relationships between vocal output and visual signals separately for major taxonomic groups, we found different patterns in oscines and suboscines (Fig. 5). In oscines, vocal output (number of recordings in ARUs, t=-2.54, p=0.02; number of recordings in collections, t=-2.69, p=0.01) and color

99 conspicuousness (t=3.29, p=0.004) were significantly different between strata in the direction we had predicted (i.e. greater vocal output in the understory and more conspicuous plumages in the canopy), but this was not the case for suboscines

(number of recordings in ARUs, t=-0.90, p=0.38; number of recordings in collections, t=0.09, p=0.92; Hue disparity, t=-0.89, p=0.38, Conspicuousness, t=-0.69, p=0.49).

Data for non-passerines were too limited to draw any definitive conclusions.

Discussion

Our work demonstrates that in a tropical montane forest assemblage, birds from the understory sing more frequently, whereas birds from the canopy exhibit more diverse and more conspicuous colors in their plumage. These two different signaling strategies are consistent with predictions of the sensory drive hypothesis and likely represent adaptations to maximize the perception of signals in two contrasting habitats

(Endler 1992; Seehausen et al. 2008; Tobias et al. 2010). Our work goes a step further relative to other tests of the sensory drive hypothesis by linking it to another leading explanation for signal evolution, namely the transfer hypothesis (Gilliard 1956;

Shutler and Weatherhead 1990; Shutler 2011). Our study (see also Laverde et al.

Chapter 3) suggests that negative relationships between songs and colors observed by early naturalists that led to the postulation of the transfer hypothesis (Darwin 1871;

Huxley 1938; Gilliard 1956) do not necessarily arise as a result of tradeoffs. Rather, such negative relationships may be a consequence of selection for optimal signaling strategies in the local habitat of each species.

The sensory drive hypothesis postulates that natural selection favors signals, receptors, and signaling behavior that maximize signal reception relative to

100 background noise and minimize signal degradation (Endler 1992). Remarkably, most of the studies on this topic have evaluated changes in signal design and in sensory systems in relation to properties of habitats (e.g., Seddon 2005; Seehausen et al. 2008;

Tobias et al. 2010), but we are aware of a single study examining the potential consequences of sensory drive on behavioral traits associated to signaling (i.e. courtship behavior in sticklebacks, Heuschele et al. 2009). Our work shows that not considering aspects of signaling behavior may lead to incorrect inferences about the validity of the sensory drive hypothesis. Specifically, had we not considered behavior we would have likely rejected the sensory drive hypothesis because, contrary to its predictions, variables associated to the elaboration of songs (song length and syllable variety) were not affected by habitat in our study system. However, we found differences in vocal output between understory and canopy birds in the direction predicted by sensory drive: understory birds sing significantly more often than canopy birds, likely because producing vocalizations more frequently may increase the chance of the reception of acoustic signals in the dense understory of montane tropical forests. Because vocal output can be measured relatively easily using ARUs and can be reasonably approximated using data in sound collections (Laverde et al. Chapter

2), we suggest that future studies testing for sensory drive should consider such measures as an important dimension along which animal signaling may respond adaptively to environmental variation. For example, in other avian groups like trogons

(Ornelas et al. 2009) and tanagers (Mason et al. 2014) there is seemingly no negative relation between investment in song and in colors, but these studies only focused on variables associated to elaboration of songs and did not consider variables related to vocal output. Examining vocal output as an indication of investment in vocal signals may reveal negative relationships between types of signals in these groups.

101

The features related to song elaboration (i.e. song length, syllable diversity and song rate) were not significantly different between canopy and understory birds. Songs of birds contain multiple traits that can be sexually selected, which are not equally important for all species: females of some species prefer males that produce very diverse repertoires, but females of other species may prefer longer songs or higher song rates (Gil and Gahr 2002). Even within the same group of birds (i.e. wood warblers), females can select different traits of songs, sometimes resulting in simple signals (Cardoso and Hu 2011). Therefore, an assemblage-base approach may place together a diversity of preferences from different species. The classical hypotheses proposed to explain the evolution of complex songs assumed to be the outcome of sexual selection acting on males (Price 2015), but in the tropics female bird song is much more common than previously thought, suggesting that the classical view of sexual selection acting on the complexity or elaboration of male songs, must be re- evaluated (Odom et al. 2014; Price 2015).

We also found evidence consistent with sensory drive in plumage coloration; overall, canopy birds contrasted more relative to the background color than understory birds, a result consistent with previous work on a lowland assemblage (Gomez & Thery

2007). In addition, although the diversity of colors (i.e. mean hue disparity) was not different between forest strata, variation in color among canopy birds (coefficient of variation [cv]=69.5) was wider than variation among understory birds (cv=47.8). In the understory, the majority of the birds are mostly cryptic and not very colorful, but in the canopy one can find species with dull and uniform plumages (e.g. Smoky Bush- tyrant Myiotheretes fumigatus, Great Thrush Turdus fuscater) or conspicuous/colorful

102 (many tanagers) plumages. We suggest that exhibiting colorful plumages in the canopy might not be as risky as in the understory due to varying predation pressure between strata (Gotmark and Post 1996). However, more data on abundance of predators, their distribution and their effects on the evolution of communication signals is required to effectively link variation in signals with habitat-specific predation pressures, especially in the tropics.

In principle, our results support the idea of transference between communication signals: birds less contrasting against the background sing more often, and birds with more diversity of colors produce shorter songs. Furthermore, the negative relationship we observed between vocal and visual signals of understory birds seems to support the idea of classical tradeoffs: hue disparity and our two measures of vocal output were negatively related when accounting for phylogeny. Nevertheless, we argue that the effect of habitat is without doubt a crucial factor shaping communication signals and some of the observed negative relationships between them.

Suboscine and oscine passserines showed different responses to the effect of the habitat with regard to visual and acoustic signals. In oscines, songs and colors varied between habitats as predicted by sensory drive (songs more important in the understory, colors in the canopy), but this was not the case in suboscines. Suboscines and oscines exhibit striking differences in habitat, diet, behavior, syrinx complexity and song development (Kroodsma 1984; Ricklefs 2002) which may account for the different patterns in signal evolution documented by our study. Foremost, vocal learning is crucial to the production of acoustic signals in oscines, but it is thought to be absent in the majority of suboscines, in which songs are developed without an

103 influence of experience (Kroodsma 1984, 2007; Touchton et al. 2014). In addition, the greater complexity of the oscine syrinx allows for greater versatility (hence presumably greater evolutionary lability; see Weir and Wheatcroft 2011) in sound production relative to suboscines (Kroodsma 2007; Amador et al. 2008). Ecology may also help to explain the different patterns observed in oscines and suboscines. In the

Neotropics, oscines dominate the luminous forest canopy and habitats outside the forest, but most of the birds of the dark forest understory belong to the suboscines, although a few suboscines (i.e. particularly flycatchers) have colonized the canopy and open areas (Ricklefs 2002). Most suboscines feed largely on insects whereas oscines make greater use of fruit resources (consistent with their dominance in the canopies of forests; Ricklefs 2002) and this may reflect on the ease with which plumages may evolve adaptively in response to habitat variation because various elements of visual displays are based on carotenoid pigments obtained form the diet and deposited on feathers (Hill and McGraw 2006). In sum, we hypothesize that vocal learning and greater complexity in anatomical structures involved in producing vocalizations coupled with a more diverse color palette associated with diets rich in carotenoids jointly allow for greater opportunities for adaptive evolution in oscine signals. This may partly account for our results showing signal evolution is consistent with sensory drive in oscines but not suboscines. However, other studies on suboscines have provided evidence that sensory drive shapes spectral and temporal properties of songs in this clade (Seddon 2005, Tobias et al. 2010).

To conclude, we suggest that the study of the evolution of animal communication signals needs to integrate traits indicative of signaling behavior (i.e. vocal output) to the classical view of only studying signal elaboration or complexity. In our study,

104 understory birds emit more vocalizations than canopy birds, implying that habitat drives changes in a behavioral trait associated to signaling although signal elaboration per se may not differ between environments. More generally, considering that communication is multimodal, that signals do not act independently, and that many animals communicate by the integration of signals from different sensory modalities

(Hebets and Papaj 2004), it is remarkable that much past research on the evolution of animal communication systems has largely focused on single and isolated signals. An overarching conclusion of our study is that examining multiple communication channels and considering their efficiency in local environments is essential to understand the mechanisms underlying the evolution of communication signals.

Acknowledgments

We thank Chingaza National Park for allowing us to record bird songs. Greg Budney and Matt Medler of the Macaulay Library of Natural Sounds at Cornell University, and Paula Caycedo of the Colección de Sonidos Ambientales of the Instituto

Alexander von Humboldt gave us unrestricted access to the information archived in their collections. We thank all the recordists who have deposited their recordings in these archives and who have made them available through xeno-canto. We are very grateful to Laura Céspedes who made all the field measurements of the background color. We are grateful to Simón Quintero, Ignacio Areta, Mecky Hollzmann, Trevor

Price and Alex Kirschel for asistance in the field. This work was supported by funds from the Facultad de Ciencias at Universidad de los Andes, Bogotá, Colombia and

Fundación Biodiversa-Colombia.

105 Literature Cited

Badyaev, A. V, G. E. Hill, and B. V Weckwort. 2002. Species divergence in sexually selected traits: increase in song elaboration is related to decrease in plumage ornamentation in finches. Evolution 56:412–419.

Cardoso, G. C., and Y. Hu. 2011. Birdsong performance and the evolution of simple

(rather than elaborate) sexual signals. Am. Nat. 178:679–686.

Cardoso, G. C., and P. G. Mota. 2010. Evolution of female carotenoid coloration by sexual constraint in Carduelis finches. BMC Evol. Biol. 10:1471–.

Darwin, C. 1871. The Descent of Man and Selection in Relation to Sex. J. Murray,

London.

Doucet, S. M., D. J. Mennill, and G. E. Hill. 2007. The evolution of signal design in manakin plumage ornaments. Am. Nat. 169:S62–S80.

Endler, J. A. 1992. Signals, signal conditions, and the direction of evolution. Am. Nat.

139:S125–S153.

Endler, J. A., and P. W. Mielke. 2005. Comparing entire colour patterns as birds see them. Biol. J. Linn. Soc. 86:405–431.

Gilliard, T. 1956. Bower ornamentation versus plumage characters in Bowerbirds.

Auk 73:450–451.

Gomez, D., and M. Théry. 2004. Influence of ambient light on the evolution of colour signals: comparative analysis of a Neotropical rainforest bird community. Ecol. Lett.

7:279–284.

106 Gomez, D., and M. Théry. 2007. Simultaneous crypsis and conspicuousness in color patterns: comparative cnalysis of a Neotropical rainforest bird community. Am. Nat.

169:S42–S41.

Gotmark, F., and P. Post. 1996. Prey selection by sparrowhawks, Accipiter nisus: relative predation risk for breeding passerine birds in relation to their size, ecology and behaviour. Philos. Trans. R. Soc. B Biol. Sci. 351:1559–1577.

Hebets, E. A., and D. R. Papaj. 2004. Complex signal function: developing a framework of testable hypotheses. Behav. Ecol. Sociobiol. 57:197–214.

Heuschele, J., M. Mannerla, P. Gienapp, and U. Candolin. 2009. Environment- dependent use of mate choice cues in sticklebacks. Behav. Ecol. 20:1223–1227.

Hill, G. E., and K. J. McGraw. 2006. Bird coloration: function and evolution. Harvard

University Press.

Hilty, S. L., and W. L. Brown. 1986. A guide to the birds of Colombia. Princeton

University Press, New Jersey.

Huxley, J. S. 1938. Darwin’s theory of sexual selection and the data subsumed by it, in the light of recent research. Am. Nat. 72:416–433.

Jetz, W., G. H. Thomas, J. B. Joy, K. Hartmann, and a O. Mooers. 2012. The global diversity of birds in space and time. Nature 491:444–448.

Jønsson, K. A., P. Fabre, S. A. Fritz, R. S. Etienne, R. E. Ricklefs, and T. B.

Jørgensen. 2012. Ecological and evolutionary determinants for the adaptive radiation of the Madagascan vangas. Proc. Natl. Acad. Sci. 109:6620–6625.

107 Kirschel, A. N. G., D. T. Blumstein, R. E. Cohen, W. Buermann, T. B. Smith, and H.

Slabbekoorn. 2009a. Birdsong tuned to the environment: green hylia song varies with elevation, tree cover, and noise. Behav. Ecol. 20:1089–1095.

Kirschel, A. N. G., D. T. Blumstein, and T. B. Smith. 2009b. Character displacement of song and morphology in African tinkerbirds. Proc. Natl. Acad. Sci. U. S. A.

106:8256–61.

Kroodsma, D. 2007. The Singing Life of Birds: The art and science of listening to birdsong. Houghton Mifflin Harcourt.

Kroodsma, D. E. 1984. Songs of the Alder flycatcher (Empidonax alnorum) and

Willow flycatcher (Empidonax traillii) are innate. Auk 101:13–24.

Kusmierski, R., G. Borgia, A. C. Uy, and R. H. Crozier. 1997. Labile evolution of display traits in bowerbirds indicates reduced effects of phylogenetic constraint. Proc.

Biol. Sci. 264:307–313.

Maia, R., C. M. Eliason, P.-P. Bitton, S. M. Doucet, and M. D. Shawkey. 2013. Pavo : an R package for the analysis, visualization and organization of spectral data.

Methods Ecol. Evol. 4:906–913.

Marler, P. 1955. Characteristics of some animal calls. Nature 176:6–7.

Mason, N. A., A. J. Shultz, and K. J. Burns. 2014. Elaborate visual and acoustic signals evolve independently in a large, phenotypically diverse radiation of songbirds.

Proc. R. Soc. B Biol. Sci. 281:1–10.

108 Mcnaught, M. K., and I. Owens. 2002. Interspecific variation in plumage colour among birds: species recognition or light environment? J. Evol. Biol. 15:505–514.

Morton, E. S. 1975. Ecological sources of selection on avian sounds. The American

Naturalist. Am. Nat. 109:17–43.

Nemeth, E., H. Winkler, and T. Dabelsteen. 2001. Differential degradation of antbird songs in a neotropical rainforest: adaptation to perch height? J. Acoust. Soc. Am.

110:3263–3274.

Nowicki, S., S. Peters, and J. Podos. 1998. Song Learning , Early Nutrition and

Sexual Selection in Songbirds 1. Comp. Gen. Pharmacol. 190:179–190.

Odeen, A., and O. Håstad. 2009. New primers for the avian SWS1 pigment opsin gene reveal new amino acid configurations in spectral sensitivity tuning sites. J.

Hered. 100:784–9.

Odom, K. J., M. L. Hall, K. Riebel, K. E. Omland, and N. E. Langmore. 2014. Female song is widespread and ancestral in songbirds. Nat. Commun. 5:3379.

Orme, D., R. Freckleton, G. Thomas, T. Petzoldt, S. Fritz, N. Isaac, and W. Pearse.

2012. Caper: comparative analyses of phylogenetics and evolution in R. R package version 0.5. http://CRAN.R-project.org/package=caper.

Ornelas, J. F., C. González, and A. Espinosa de los Monteros. 2009. Uncorrelated evolution between vocal and plumage coloration traits in the trogons: a comparative study. J. Evol. Biol. 22:471–484.

109 Podos, J. 2001. Correlated evolution of morphology and vocal signal structure in

Darwin ’ s finches. Nature 409:185–188.

Price, J. J. 2004. Patterns of song evolution and sexual selection in the oropendolas and caciques. Behav. Ecol. 15:485–497.

Price, J. J. 2015. Rethinking our assumptions about the evolution of bird song and other sexually dimorphic signals. Front. Ecol. Evol. 3:1–6.

Price, J. J., and S. M. Lanyon. 2002. Reconstructing the evolution of complex bird song in the oropendolas. Evolution 56:1514–29.

Price, J. J., and L. M. Whalen. 2009. Plumage evolution in the oropendolas and caciques: different divergence rates in polygynous and monogamous taxa. Evolution

63:2985–2998.

Ricklefs, R. E. 2002. Splendid isolation: historical ecology of the South American passerine fauna. J. Avian Biol. 33:207–211.

Ryan, M. J., and E. A. Brenowitz. 1985. The role of body size, phylogeny, and ambient noise in the evolution of bird song. Am. Nat. 126:87–100.

Seddon, N. 2005. Ecological adaptation and species recognition drives vocal evolution in neotropical suboscine birds. Evolution 59:200–15.

Seddon, N., R. M. Merrill, and J. A. Tobias. 2008. Sexually selected traits predict patterns of species richness in a diverse clade of suboscine birds. Am. Nat. 171:620–

31.

110 Seddon, N., and J. A. Tobias. 2010. Character displacement from the receiver’s perspective: species and mate recognition despite convergent signals in suboscine birds. Proc. Biol. Sci. 277:2475–83.

Seehausen, O., Y. Terai, I. S. Magalhaes, K. L. Carleton, H. D. J. Mrosso, R. Miyagi,

I. van der Sluijs, M. V Schneider, M. E. Maan, H. Tachida, H. Imai, and N. Okada.

2008. Speciation through sensory drive in cichlid fish. Nature 455:620–6.

Shutler, D. 2011. Sexual selection: when to expect trade-offs. Biol. Lett. 7:101–104.

Shutler, D., and P. J. Weatherhead. 1990. Targets of sexual selection: song and plumage of wood warblers. Evolution 44:1967–1977.

Slabbekoorn, H., J. Ellers, and T. Smith. 2002. Birdsong and sound transmission: the benefits of reverberations. Condor 104:564–573.

Slabbekoorn, H., and T. B. Smith. 2002. Habitat-dependent song divergence in the little greenbul: an analysis of environmental selection pressures on acoustic signals.

Evolution 56:1849–58.

Smith, T. B., and L. Bernatchez. 2008. Evolutionary change in human-altered environments. Mol. Ecol. 17:1–8.

Stiles, F. G., and L. Rosselli. 1998. Inventario de las aves de un bosque altoandino:

Comparación de dos métodos. Caldasia 20:29–43.

Stoddard, M. C., and R. O. Prum. 2008. Evolution of avian plumage color in a tetrahedral color space: a phylogenetic analysis of new world buntings. Am. Nat.

171:755–76.

111 Thompson, N. S., K. LeDoux, and K. Moody. 1994. A system for describing bird song units. Bioacoustics 5:267–279.

Tobias, J. A., J. Aben, R. T. Brumfield, E. P. Derryberry, W. Halfwerk, H.

Slabbekoorn, and N. Seddon. 2010. Song divergence by sensory drive in Amazonian birds. Evolution 64:2820–39.

Tobias, J. A., and N. Seddon. 2009. Signal design and perception in Hypocnemis antbirds : evidence for convergent evolution via social selection. Evolution 63:3168–

3189.

Touchton, J. M., N. Seddon, and J. a. Tobias. 2014. Captive rearing experiments confirm song development without learning in a tracheophone suboscine bird. PLoS

One 9.

Vargas, O., and P. Pedraza. 2004. Parque Nacional Natural Chingaza. Gente Nueva

Editorial, Bogotá, D. C. 165 p.

Vorobyev, M., D. Osorio, A. T. Bennett, N. J. Marshall, and I. C. Cuthill. 1998.

Tetrachromacy, oil droplets and bird plumage colours. J. Comp. Physiol. A. 183:621–

33.

112

a b 12 12 8 8 6 6 4 Visual Signals Visual 4 Acoustic Signals Acoustic 2

Canopy Understory Understory Canopy 1.0 1.0 c d 0.5 0.5 0.0 0.0 -0.5 -0.5 Visual Signals Visual Visual Signals Visual -1.0 -1.0

-1.0 -0.5 0.0 0.5 1.0

-1.0 -0.5 0.0 0.5 1.0

Acoustic Signals Acoustic Signals

1.0 e 0.5 0.0 -0.5 Visual Signals Visual -1.0

-1.0 -0.5 0.0 0.5 1.0

Acoustic Signals

Figure 1. Predictions of the sensory drive and transfer hypotheses proposed to explain signal evolution. In our montane forest study site, the sensory drive hypothesis predicts that (a) visual signals should be most elaborate in the canopy where light availability is greater, and that (b) acoustic signals should be most elaborate in the understory where obstacles do not favor visual signals. The transfer hypothesis predicts (c and d) negative relationships between communication signals even in species from the same habitat. The hypothesis that sensory drive determines the negative relationships observed between communication signals predicts these relationships exist only when considering species from different habitats (e).

113 Vocal output: ARUs Vocal output: Sound Collections Song Rate 2

5 * 1 2.5 4 0 3 1.5 -1 Log Songs/m Log 2 -2 0.5 Residuals Collections/Area Residuals Log Number of Recordings Number Log 1

Canopy Understory Canopy Understory Canopy Understory

Song Length Syllable Variety Syllable Diversity 6 2 3.0 5 1 4 2.0 3 0 Log Secs Log 2 1.0 -1 No. Syllable Types No. Syllable Syllable Variety/Sec Syllable 1 0.0

Canopy Understory Canopy Understory Canopy Understory

Figure 2. Variation in acoustic communication strategies between understory and canopy bird species from Chingaza National Park (see sample sizes in Table 1).

Boxplots show two measures of vocal output (recordings counted in autonomous recording units ARUs and in three sound collections) and four measures of song elaboration: song rate, song length, syllable variety and syllable diversity. Analyses indicate that understory birds sing more frequently than canopy birds in the recordings collected in ARUs as predicted by sensory drive, but the numbers in sound collections were not statistically significant. There were not differences between habitats in any measure of song elaboration. Significance of statistical results of the phylogenetic

ANOVAs after Bonferroni correction: * p<0.05.

114

Figure 3. Variation in visual communication strategies between understory and canopy bird species from Chingaza National Park (see sample sizes in Table 2).

Boxplots show measures of the contrasts of patches to background, color conspicuousness and hue disparity. Analyses indicate that understory canopy birds contrast more to the background than understory birds, although the crown, throat and rump were not different after Bonferroni correction. Hue disparity was not different between strata, but variation was greater in the canopy. Chromatic contrasts were measured as just noticeable differences (JNDs, see text). Significance of statistical results of the phylogenetic ANOVAs: * p<0.05.

Crown Chromatic Contrast Back Chromatic Contrast Rump Chromatic Contrast 25 25 * 25 20 20 20 15 15 15 JNDs JNDs JNDs 10 10 10 5 5 5 0 0 0

Canopy Understory Canopy Understory Canopy Understory

Breast Chromatic Contrast Color Conspicuousness Hue Disparity 25 25 * * 1.0 20 20 0.8 15 15 0.6 JNDs JNDs 10 10 0.4 Disparity 5 5 0.2 0 0

Canopy Understory Canopy Understory Canopy Understory

115 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 Hue Disparity Hue Disparity Hue 0.2 0.2

0 50 100 150 200 250 300 0 50 100 150 200 250 300

Vocal output: No. of recordings in ARUs Vocal output: No. of recordings in Collections 20 20 15 15 10 10 5 5 Color Conspicuousness JNDs Conspicuousness Color JNDs Conspicuousness Color 0 50 100 150 200 250 300 0 50 100 150 200 250 300

Vocal output: No. of recordings in ARUs Vocal output: No. of recordings in Collections

Figure 4. Relation between two variables describing investment in plumage coloration

(i.e. hue disparity and color conspicuousness) and vocal output (i.e. number of recordings in ARUS and number of recordings found in sound collections).

Understory species are shown in blue and canopy species in red. Notice that understory birds exhibit more variation and reach higher values in vocal output, whereas canopy birds have more variation and higher values in the two color variables studied.

116

0 20 **

-1 15 Strata Strata Canopy Canopy Understory Understory 10 -2 Hue Disparity Hue

5 -3 Color conspicuousness JNDs conspicuousness Color Oscine Suboscine Oscine Suboscine

2 ** 5 * 1 Strata4 Strata 0 Canopy Canopy 3Understory Understory -1 2

-2 output: ARUs Vocal 1

Vocal output: Sound Collections output: Sound Vocal Oscines Suboscines Oscines Suboscines

Figure 5. Boxplots showing variation in vocal output (measured using ARUs and the number of recordings in sound collections corrected by area of distributional ranges), hue disparity and color conspicuousness in understory and canopy birds belonging two taxonomic groups. In oscines (n=27 species) investment in acoustic and visual signals differs between habitats as predicted by sensory drive (birds are less conspicuous and sing more often in the understory than in the canopy), but this pattern was not present in suboscines. (n=17 species). Due to small sample sizes, non- passerines we excluded from this analysis. Statistical results of a t-student test * p<0.05, ** p<0.001.

117 Table 1. Phylogenetic analyses of variance of vocal variables with strata as a predictor. Statistical differences were found in variables related to vocal output as predicted by the sensory drive hypothesis (i.e. number of recordings counted in ARUs and number or recordings in sound collections/size of distributional range), but not in variables related to song elaboration. Significant differences after Bonferroni correction are shown in bold.

Variable F p N

Recordings in ARUs 5.97 0.05 45

Res. Sound collections/Area 4.01 0.08 52

Song Rate 2.92 0.24 39

Song Length 3.16 0.24 37

Syllable Variety 0.21 0.64 37

118 Table 2. Phylogenetic analyses of variances for the color variables with habitat as a categorical predictor. Statistical differences were found in the contrast to background of two patches (i.e. back and breast) and color conspicuousness as predicted by the sensory drive hypothesis. The color of throat, rump, crown and hue disparity were not statistically different between understory and canopy birds. Significant differences after Bonferroni correction are shown in bold.

Variable F p N

Crown contrast to BG 3.58 0.12 52

Back contrast to BG 5.52 0.04 52

Rump contrast to BG 3.87 0.15 52

Breast contrast to BG 5.22 0.04 52

Throat contrast to BG 0.03 0.96 52

Color Conspicuousness 4.624 0.05 52

Hue Disparity 0.67 0.82 52

119

Table 3. Phylogenetic generalized least squares multiple regressions of color conspicuousness on a set of predictors related to vocal output and song elaboration (F

= 3.34, R2 = 0.25, p = 0.01. AIC=24.03, N=28). Color conspicuousness was negatively related to the number of recordings in ARUs: birds with colors contrasting less against the background sing more frequently, as predicted by the transfer hypothesis. Significant difference is shown in bold.

β t-value p

ARUs -1.32 -2.55 0.01

Syllable Variety 0.79 0.97 0.34

Song Length 0.52 1.24 0.22

Song Rate 0.54 1.35 0.18

120

Table 4. Phylogenetic generalized least squares multiple regressions of hue disparity on a set of predictors related to vocal output and song elaboration (F = 3.32, R2 =

0.24, p = 0.02, AIC = 46.98, N=28). Song length was negatively related to hue disparity: birds with lower diversity of colors produce longer songs as predicted by the transfer hypothesis. Significant difference is shown in bold.

β t-value p

ARUs -0.15 -1.78 0.08

Syllable Variety 0.05 0.41 0.68

Song Length -0.17 -2.46 0.02

Song Rate 0.05 0.83 0.41

121 Table 5. Lineal regressions and PGLS of hue disparity and color conspicuousness and our two measures of vocal output. Results of linear models with hue disparity as response variable supported our hypothesis that negative relationships between signals are observed when considering birds from the understory and canopy due to the expected differences in communication strategies between habitats. PGLS models partially supported this hypothesis, but also the negative and significant relationships found within the understory supported the transfer hypothesis. All the significant relationships marked with an asterisk were negative.

Lineal Regressions PGLS models

All Canopy Understory All Canopy Understory

Birds Birds Birds Birds Birds Birds

Hue Disp. ~ Rec. in ARUs * NS NS ** NS *

Hue Disp. ~ Rec. In Collections * NS NS NS NS *

Color Consp. ~ Rec. In ARUs NS NS NS NS NS NS

Color Consp. ~ Rec. In NS NS NS NS NS NS Collections

* p<0.05, **p<0.01

122

Table S1. Number of recordings of avian vocalizations gathered using ARUs by month and elevation in Chingaza National Park from February to August 2013.

ARU Geographical February April May July August Total Elevation (m) No Coordinates

1 248 248 4°41’47’’; 73°50’53’’ 2950

2 121 138 92 351 4°41’54’’; 73°50’50’’ 2960

3 158 158 4°42’08’’; 73°50’50’’ 3015

4 233 121 246 246 846 4°41’44’’; 73°50’39’’ 3030

5 88 38 126 4°41’27’’; 73°50’24’’ 3015

6 131 132 150 50 463 4°42’42’’; 73°50’21’’ 3170

Total 760 357 383 396 296 2192

123

Tabla S2. Data on habitat, area of distribution, hue disparity, color conspicuousness and vocal elaboration and output for 52 species present in the Palacio sector in

Chingaza National Park.

124 Area Hue Color Song rate Song Syllable Family Species Strata Crown Back Rump Throat Breast ARUs XC CSA MLS (Km2) Disparity Conspicuousness (songs/min) Length Variety

Tinamidae Nothocercus julius Understory 9.3 0.184 9.6 9.4 7.1 13.7 2.0 8.4 32.0 24.0 19.0 4.0 73.1 0.3 1.0 Accipitridae Accipiter striatus Canopy 24.7 0.123 11.5 11.8 11.7 9.9 10.3 11.0 3.0 3.0 3.0 1.0 1.7 NA NA Cracidae Penelope montagnii Understory 30.3 0.202 14.2 9.0 5.0 13.9 7.2 9.8 16.0 20.0 41.0 42.0 25.4 NA NA Columbidae Patagioenas fasciata Canopy 323.1 0.099 12.1 11.5 11.5 11.7 11.5 11.7 NA 31.0 30.0 17.0 NA 0.6 1.3 Psittacidae Pyrrhura calliptera Canopy 2.1 0.410 9.3 5.9 5.0 10.1 13.9 8.9 2.0 4.0 15.0 0.1 NA NA NA Strigidae Glaucidium jardinii Canopy 16.8 0.104 10.8 11.1 10.7 10.6 10.2 10.7 7.0 26.0 19.0 24.0 1.0 6.7 4.5 Ramphastidae Andigena nigrirostris Canopy 9.0 0.262 12.2 10.9 7.6 10.4 13.6 10.9 33.0 33.0 29.0 7.0 6.6 0.7 1.7 Picidae Colaptes rivolii Canopy 37.9 0.411 11.7 18.1 16.8 10.9 10.2 13.5 7.0 8.0 37.0 18.0 1.7 NA NA Xiphocolaptes 398.3 0.191 10.6 6.6 2.8 11.4 5.2 7.3 19.0 3.6 5.0 6.3 Dendrocolaptidae promeropirhynchus Understory 54.0 33.0 66.0 Furnaridae Hellmayrea gularis Understory 13.2 0.207 5.2 2.6 3.8 16.7 4.0 6.5 8.0 13.0 8.0 22.0 3.5 1.7 5.1 Furnaridae Margarornis squamiger Understory 38.6 0.287 7.9 4.3 4.2 15.2 13.1 8.9 3.0 10.0 5.0 10.0 8.3 0.9 3.2 Pseudocolaptes 32.6 0.234 12.9 7.5 2.1 14.8 10.8 9.6 NA NA 3.2 5.2 Furnaridae boissonneautii Understory 11.0 7.0 23.0 Grallaridae Grallaria rufula Understory 19.3 0.070 5.7 4.3 5.2 2.9 3.4 4.3 111.0 139.0 35.0 51.0 1.3 10.3 1.8 Rhinocryptidae Acropternis orthonyx Understory 9.2 0.219 10.7 11.5 2.8 2.2 8.6 7.1 6.0 51.0 29.0 26.0 14.7 0.4 1.0 Rhinocryptidae Scytalopus griseicollis Understory 0.6 0.070 17.9 16.2 17.4 17.2 16.1 NA 55.0 38.0 40.0 3.0 2.0 NA NA Tyrannidae Phyllomyias nigrocapillus Canopy 20.8 0.133 10.9 8.4 8.6 9.9 8.7 9.3 21.0 16.0 21.0 11.0 1.6 1.7 4.3 Tyrannidae Mecocerculus leucophrys Canopy 73.1 0.116 10.3 9.6 9.3 11.6 9.1 10.0 36.0 40.0 84.0 72.0 3.7 1.5 4.9 Tyrannidae Mecocerculus stictopterus Understory 21.9 0.134 10.8 9.1 9.2 11.1 10.2 10.1 55.0 21.0 17.0 43.0 7.4 1.5 20.0 Tyrannidae Anairetes agilis Understory 4.3 0.135 14.4 10.8 9.7 16.0 12.8 12.7 13.0 16.0 9.0 13.0 17.8 0.6 3.4 Tyrannidae Pyrrhomyias cinnamomeus Canopy 44.7 0.193 6.4 9.7 3.7 5.2 2.8 5.6 6.0 51.0 87.0 65.0 8.5 0.9 3.1 Tyrannidae Ochthoeca rufipectoralis Understory 27.5 0.200 15.9 10.7 12.4 16.2 4.3 11.9 6.0 12.0 6.0 23.0 2.0 0.5 3.0 Tyrannidae Ochthoeca diadema Understory 10.7 0.111 13.5 10.5 10.1 12.6 10.1 11.4 67.0 12.0 15.0 10.0 2.3 NA NA Ochthoeca 25.6 0.267 17.4 18.4 18.4 17.3 3.3 15.0 6.0 14.8 5.1 4.5 Tyrannidae cinnamomeiventris Understory 24.0 18.0 21.0 Tyrannidae Myiotheretes striaticollis Canopy 52.9 0.173 10.2 10.1 11.5 10.5 12.2 10.9 7.0 11.0 14.0 15.0 10.7 0.4 1.1 Tyrannidae Myiotheretes fumigatus Canopy 16.6 0.086 10.6 10.0 10.1 10.2 9.7 10.1 5.0 13.0 19.0 19.0 28.4 3.0 2.1 Corvidae Cyanolyca armillata Canopy 7.9 0.291 22.5 22.3 20.1 24.6 22.5 22.4 NA 12.0 45.0 9.0 NA NA NA Trogloditidae Cinnycerthia unirufa Understory 12.5 0.097 4.3 1.7 3.6 4.5 2.2 3.2 29.0 22.0 69.0 23.0 3.5 1.1 9.7 Trogloditidae Henicorhina leucophrys Understory 59.3 0.245 14.9 6.2 6.2 17.5 14.2 11.8 303.0 288.0 380.0 182.0 14.5 NA NA Turdidae Turdus fuscater Canopy 33.8 0.053 10.9 10.6 10.9 10.5 10.3 10.6 17.0 32.0 95.0 65.0 7.0 58.0 28.6 Parulidae Myioborus ornatus Canopy 7.4 0.229 15.1 10.9 10.5 12.6 12.5 12.3 147.0 62.0 98.0 39.0 2.9 5.4 16.7 Parulidae Basileuterus nigrocristatus Understory 15.8 0.170 17.0 8.7 8.1 6.1 5.6 9.1 109.0 79.0 105.0 63.0 4.7 NA NA

123 Parulidae Conirostrum rufum Canopy 3.2 0.418 10.0 10.5 10.3 14.8 15.2 12.2 NA 10.0 3.0 0.1 NA 5.1 6.5 Parulidae Conirostrum sitticolor Canopy 24.7 0.411 11.6 19.4 19.1 11.5 13.0 14.9 NA 9.0 14.0 12.0 NA 4.0 11.3 Icteridae Cacicus chrysonotus Canopy 17.9 0.185 12.2 12.5 20.5 12.4 12.0 13.9 NA 3.0 66.0 7.0 NA 5.3 6.8 Thraupidae Sericossypha albocristata Canopy 6.5 0.700 10.6 13.9 16.4 17.8 11.6 14.1 23.0 10.0 43.0 18.0 NA 16.9 5.7 Thraupidae Anisognathus igniventris Canopy 23.4 0.610 12.1 12.3 20.5 12.9 31.8 17.9 10.0 54.0 24.0 39.0 1.3 3.8 5.9 Thraupidae Dubusia taeniata Canopy 23.0 0.594 11.9 14.4 14.6 10.0 17.2 13.6 11.0 34.0 22.0 26.0 7.4 1.7 2.4 Thraupidae Buthraupis montana Canopy 24.6 0.706 11.1 23.6 21.5 10.7 15.1 16.4 3.0 16.0 12.0 29.0 3.0 2.7 3.3 Thraupidae Buthraupis eximia Canopy 4.3 1.022 24.7 7.7 20.6 11.7 16.7 16.3 3.0 9.0 0.0 2.0 8.5 NA NA Thraupidae Hemispingus verticalis Understory 3.7 0.306 9.7 11.3 11.8 11.2 11.0 11.0 1.0 5.0 0.0 5.0 NA 6.4 7.4 Thraupidae Hemispingus superciliaris Understory 22.3 0.486 12.6 9.1 9.5 8.2 8.1 9.5 38.0 23.0 10.0 18.0 3.8 2.8 3.1 Thraupidae Hemispingus atropileus Understory 15.7 0.455 10.8 9.8 9.0 11.5 12.3 10.7 NA 9.0 0.0 29.0 NA NA NA Thraupidae Catamblyrhynchus diadema Canopy 29.2 0.377 20.3 13.0 12.4 14.1 15.0 15.0 NA 19.0 2.0 8.0 NA NA NA Thraupidae Diglossa cyanea Canopy 64.4 0.284 21.1 19.7 19.0 17.7 18.7 19.2 10.0 43.0 31.0 46.0 7.7 3.1 7.3 Thraupidae Diglossa caerulescens Canopy 21.9 0.117 14.5 15.6 15.3 14.5 13.9 14.8 36.0 18.0 12.0 16.0 6.4 2.4 7.3 Thraupidae Diglossa albilatera Canopy 19.6 0.262 10.6 11.1 11.4 11.2 11.3 11.1 1.0 40.0 22.0 24.0 6.0 1.3 1.9 Thraupidae Diglossa humeralis Canopy 12.7 0.371 13.3 13.5 13.6 12.1 12.9 13.1 4.0 12.0 9.0 4.0 1.3 1.5 10.4 Thraupidae Diglossa lafresnayii Canopy 8.9 0.523 12.7 14.9 13.4 11.7 11.9 12.9 NA 38.0 11.0 32.0 NA NA NA Emberizidae Atlapetes schistaceus Understory 13.2 0.259 3.5 17.6 17.4 16.4 17.2 14.4 26.0 35.0 66.0 21.0 21.2 1.1 2.2 Emberizidae Atlapetes pallidinucha Understory 8.0 0.235 5.7 16.2 16.0 7.2 7.3 10.5 13.0 32.0 27.0 16.0 1.3 NA NA Emberizidae Arremon brunneinucha Understory 51.2 0.310 4.6 10.9 11.2 17.1 16.5 12.1 26.0 69.0 77.0 56.0 16.3 1.6 5.3 Emberizidae Arremon torquatus Understory 37.3 0.211 4.6 10.9 11.2 17.1 16.5 12.1 66.0 29.0 87.0 21.0 NA NA NA

124

125