On Parrots and Pupae: Bioacoustic Repertoire Diversity at Multiple Scales

A dissertation presented

Michael David Schindlinger

The Department of Organismic and Evolutionary Biology

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

in the subject of

Biology

Harvard University

Cambridge, Massachusetts

December 2008

On Parrots and Pupae: Bioacoustic Repertoire Diversity at Multiple Scales

ABSTRACT

A persistent challenge in understanding the development and expression of animal

acoustic signals has been the reconciliation of categorical counts of a) signal-types in a repertoire with b) variation within those signal-types. This body of work looks at both categorical and continuous variation in two very different animal communication

systems, in studies aimed at holistic descriptions of acoustic behavioral repertoires.

Part A (chapters 1-3) is an examination of simple patterns in the complex acoustic

repertoire wild Yellow-headed Amazon Parrots (Amazona oratrix). I document variation

within and among call types using manual and automated classification, among

repertoires of individuals and pairs, and of populations across the species’ range.

Playback of population-level and genus-level repertoire variants reveal highest response

levels from same and similar repertoires. I present evidence of vocal stability of vocal

units within a population across thirty-four years. Subsong of juveniles (age 3 months)

contain recognizable facsimiles of population-type signals.

Part B (chapters 4-6) explores complex patterns within the simple acoustic system

of larvae and pupae of the Imperial Blue Butterfly, Jalmenus evagoras, under controlled

laboratory conditions. I further developed a suite of tools to record, detect, and compare

individual sound signals to facilitate comparison of entire acoustic repertoires. Such a

complete sampling of signals reveals levels of variation previously unseen in this species.

iii Extended recordings also reveal diurnal patterns of signal production. Measurements of the energetic cost of signal production (measured as respiratory production of carbon dioxide) reveal that on average, calling behavior for isolated pupae accounts for about two percent of the variance in CO2 production, but climbs as high as nearly twenty percent when comparing periods of highest and lowest calling rates. The measurement of costs associated with this species’ mutualistic interactions with Iridomyrmex ants informs wider studies of costs and benefits in symbiotic relationships.

iv Contents

Preface vi Part A Simple Patterns in a Complex Communication Repertoire 1

Chapter 1. Introduction to Acoustic Signaling in the Yellow-Headed Amazon Parrot (Amazona oratrix) 1 2

Chapter 2. Vocal Repertoires of Yellow-Headed Amazon Parrots (Amazona oratrix) at multiple spatial and temporal scales 3939 142

Chapter 3. Vocal Response of Yellow-Headed Amazon Parrots To Population-Level and Species–Level Repertoire Variants 125

______

Part B Complex Patterns in a Simple Communication Repertoire

Chapter 4. Introduction to Acoustic Signaling In Lycaenid-Ant Interactions 150

Chapter 5. Characterization of Acoustic Signaling of Jalmenus evagoras pupae 188

Chapter 6. Physiology of Acoustic Signaling in Jalmenus evagoras Larvae and 225 Pupae: Metabolic Costs and Mechanisms

v Preface

Acknowledgements

Fieldwork assistance was provided by Andrew Stone, Brendan Anderson, and

Jenny Lamont. Additional field work was performed by the three EarthCorps teams of

1996. Logistical and moral support were provided by Ernesto Enkerlin-Hoeflich, and

Jack Clinton-Eitniear of the Center for the Study of Tropical Birds, and Prof. Mario

Vasquez from UAT in Mexico. The work was conducted at Los Colorados ranch, and adjacent Las Arboledas ranch, thanks to the obliging hospitality of Roberto, Carlos, and

Arturo Clynes and their families. José Luis Manzano-Loza, and José Jaime González shared their expertise in the field, and helped with cultural adjustments. Tomas Cuervo

(the ranch cowboy-veterinarian) and all the ranch hands were also graciously tolerant of our presence, and generously helpful when we got ourselves into trouble. Intellectual encouragement and inspiration were provided by Naomi Pierce, David Haig, Richard

Lewontin, Paul Rozin, W. John Smith, Bruce Waldman, and Evan Balaban. Assistance with manuscript preparation, and inspirational renewal, was provided by Irene A. Chen.

The work in Part B was assisted in many ways by many people. Logistical, intellectual, and technical aid was provided by Mark Travassos, Naomi Pierce, David

Haig, Richard Lewontin, William Woods, Robert Stevenson, Tom Bifano, Joe Demuz,

Mark Cornwall, Daniel D’andrea, and Irene Chen. Special appreciation is felt for my mother Monica, and my father Jerry, who are always optimistic and supportive.

Support for this work was provided by an IIS Fulbright Grant (Mexico); NIH-

Harvard Genetics Training Grant; a Sheldon Traveling Fellowship; Earthwatch Institute

vi grant; Fundacion Mexico en Harvard award; James Snitzler Scholarship; a grant from the

Ark Foundation; and Graduate Research Grants from the Harvard Dept. of Organismic &

Evolutionary Biology.

I am deeply grateful for all you have given me.

vii

Part A

Simple Patterns in a Complex Communication Repertoire

Birds from the Crystal Palace Show The Illustrated London News December 4, 1858

viii

Chapter 1

Introduction to Acoustic Signaling in The Yellow-Headed Amazon Parrot (Amazona oratrix)

Birds sometimes exhibit benevolent feelings; they will feed the deserted young ones even of distinct species, but this perhaps ought to be considered as a mistaken instinct. They will feed, as shewn in an earlier part of this work, adult birds of their own species which have become blind. Mr. Buxton gives a curious account of a parrot which took care of a frost-bitten and crippled bird of a distinct species, cleansed her feathers, and defended her from the attacks of the other parrots which roamed freely about his garden. It is a still more curious fact that these birds apparently evince some sympathy for the pleasures of their fellows. When a pair of cockatoos made a nest in an acacia tree, "it was ridiculous to see the extravagant interest taken in the matter by the others of the same species." These parrots, also, evinced unbounded curiosity, and clearly had "the idea of property and possession." They have good memories, for in the Zoological Gardens they have plainly recognized their former masters after an interval of some months. --Charles Darwin, 1871, The Descent of Man.

Evolutionary biology as a discipline is concerned with the origins, extent, and maintenance of the diversity and complexity of life’s many forms. In recent decades, it has become clear that the ability to transmit behaviors through imitative learning - especially vocal imitation - has evolved repeatedly across a wide taxonomic range of mammals and birds. Examples include marine species (Boisseau 2005; Morisaka et al.

2005), bats (Boughman 1997; Boughman 1998), elephants (Poole et al. 2005), and

songbirds, hummingbirds, and parrots (Iwaniuk et al. 2005; Jarvis 2006; Nelson et al.

1995). Such learning could modify variation available for selective evolution, either by

increasing or decreasing signal similarity within an individual, among individuals, among

family groups, and among populations (Mundinger 1982).

The study of vocalizations through acoustic analysis is a popular approach in the

study of behavioral traditions in part because acoustic recordings can be an easily

quantifiable trace of an otherwise complex neuromuscular process, as compared with

descriptions of non-vocal display behaviors (such as Serpell’s description of display

postures in trichoglossine parrots; Serpell 1981). Non-vocal display behaviors contain movement in time and space, while audio recordings contain only a single acoustic signal over time, measured as variations in voltage at the microphone diaphragm. All higher- level descriptions of a sound -- amplitude, frequency, pulse-rate, call-type, etc. -- are derived as scale-appropriate quantitative transformations of this low-dimensional audio time signal. This is as true for a perceiving animal as it is for the eavesdropping researcher. The former takes the vibratory information from surface hair cells, or from stretch receptors in tympanic membranes or muscles, and through the physiological equivalent of a Fourier transformation (as in the standing-wave excitation of the hair cells along the acoustically-tuned basal membrane of the mammalian cochlea) extracts some underlying component signals in the underlying vibration (such as tone), and through

neural circuits derives temporal characteristics from the perceived signal. In more complex animals, this derived set of feature parameters may be compared to a memorized

template set, before a response is selected from a range of possible responses (Smith

1977).

The challenge for an eavesdropping researcher listening to animals talking to each

other is first to perceive the signal correctly, using a set of tools that can properly capture

the appropriate frequency range and timescale of signal variation within the appropriate

modality (Baker and Logue 2003; Hill 2008; Wanker et al. 1998). The second challenge is to properly identify (segmenting, or parsing) the individual communication signals within an audio stream (Boisseau 2005; Cortopassi and Bradbury 2006). The third and perhaps greatest challenge is then to connect the diversity of correctly identified signals to the factors that give rise to this diversity (Smith 1977).

The parrots (Psittaciformes) are one taxonomic group that shows a particularly large influence of learning on vocal behavior, and within this evolutionary branch, one species that is acclaimed for its vocal learning ability is the Yellow-headed Amazon

Parrot, Amazona oratrix1 (Juniper and Parr 1998). How and why such imitative vocal

learning has arisen is a driving question within the study of animal behavior for a broad

spectrum of imitative species. Each specific instance in comparison with other such

instances, within and across taxonomic groupings, adds to our general understanding of

the ecological and social factors which have given rise to open developmental programs

where learning through imitation shapes and influences the expression of vocal behavior

(Bolhuis and Eda-Fujiwara 2003; Cruickshank et al. 1993; Pepperberg 2002; Poole et al.

2005; Slabbekoorn and Smith 2002).

Current theories for the evolution and maintenance of vocal learning are not

necessarily mutually exclusive, but form a mosaic of likely explanations and processes. A

general understanding of the factors that have selected an open developmental program

1 “oratrix” is Latin for “speaker”

over a more narrowly rigid developmental program is yet elusive; “why” questions often require several different ultimate answers, whereas “how” questions can have clearer proximate answers. Parrots as a group generally differ in a few key ways from passerines

For example, lack of physical dimorphism and mate fidelity are the general rule in parrots

(Forshaw 1989). Because vocal learning in parrots and passerines has been posited to have evolved independently (Hackett et al. 2008; Jarvis 2006), the study of parrot vocal behavior is thought to be a means to independently test and verify, or to extend, our current conceptual understanding of the origin and process of vocal learning. However, a recently published phylogeny suggests that the parrots and passerines may actually be sister groups (Hackett et al. 2008). It appears too early to draw detailed conclusions, since within Aves, let alone the multi-species complex to which the focal species here belongs, phylogenetic relationships are not yet certain (Eberhard and Bermingham 2004b; Hackett et al. 2008; Ribas et al. 2007; Russello and Amato 2004; Sibley and Ahlquist 1990).

Cultural and Genetic Evolution

Vocal learning with modification by way of high-fidelity vertical transmission

(each new generation learning from its elders) will lead to clustering of more-similar vocal subgroups. The extent of this clustering and subclustering is dependent on several key variables, namely imitative fidelity (Tchernichovski et al. 2001), migration (Nelson et al. 1995), social and sexual patterns of association (Baker et al. 1987; Searcy and

Yasukawa 1996), and geographic differences in ambient acoustic signal filtering (Hunter and Krebs 1979; Ryan and Brenowitz 1985). In comparing genetic and cultural interdemic variation, it has generally been found that cultural clines are much steeper

than genetic clines (Cavalli-Sforza and Feldman 1981; Lynch et al. 1989; Wright and

Wilkinson 2001), though the two often share boundaries (Baker 1975; Balaban 1988a;

MacDougall-Shackleton and MacDougall-Shackleton 2001). Assortative mating along cultural lines (e.g. female preference for specific male song variants) could thus result in steeper genetic clines, perhaps a first step in the process of speciation (Nottebohm 1970).

Whether such a partitioning of populations is adaptive (as first suggested by (Nottebohm

1972) or is merely epiphenomenal to the process of imprinting has been the subject of

much lively debate (Baker and Mewaldt 1981; Paterson 1985; Petrinovich et al. 1981;

Slabbekoorn and Smith 2002).

We might thus expect to find greater numbers of species in clades where

imitatively-learned signals for social and sexual affiliation are found. Such a correlation

does exist to some extent within the birds, as three of the four most diverse orders are

those in which imitative vocal learning has been identified (Nottebohm 1970). However,

the correlation does not necessarily imply causality, as there are counterexamples within

the passerines (suboscines, e.g. kingbirds). Parrots of the genus Amazona - renowned as

vocalists at approximately thirty species - are the most diverse genus within the

Psittaciformes, and comprise nearly a tenth of all parrot species (Forshaw 1989; Juniper

and Parr 1998). As more studies of the vocal behavior and imitative range of a variety of

parrot species become available, it may be possible to independently test the theory that

vocal learning may accelerate speciation rates while not affecting extinction rates, but for

now the data are suggestive but inconclusive.

Birdsong has long served as a major animal model for the study of both the

process of vocal acquisition, and of the patterns of geographic variation that result from

learning through imitation of conspecifics (Slater 2003). Population-level differences in learned display behaviors have been examined in the context of pre-mating isolation mechanisms early in the process of diversification (Baker and Mewaldt 1981; Petrinovich et al. 1981). However, less clear still is the effect of vocal learning upon species recognition and social behavior, i.e., whether vocal variants can cause, or are a merely a result of, population partitioning within a species.

Much of the work related to bird song has been concentrated within one evolutionary branch of birds: the passerines, or songbirds. More recently (within the past two decades), this experimental program has been extended systematically to include the other branches of the avian tree that demonstrate vocal learning. Three branches among the approximately 27 orders of birds demonstrate learning in the development of typical adult vocal repertoires (Figure 1.1). These groups are: 1) Passeriformes, the songbirds, which accounts for nearly half of all extant bird species (Nottebohm 1970; Nottebohm

1972), 2) Trochiloformes , the hummingbirds (Baptista and Schuchmann 1990; Gaunt et al. 1994; Wiley 1971), and 3) Psittaciformes, the parrots (Hackett et al. 2008; Pfaff et al.

2007; Sibley and Ahlquist 1990).

Figure 1.1. Phylogeny of avian song learning, from (Jarvis 2006).

As mentioned previously, these orders are three out of the four most species-rich

orders of birds (Nottebohm 1972), and they were long considered to form independent

clades (Sibley and Ahlquist 1990), although a recent phylogeny of birds suggests that

passerines and psittacines may be sister-clades (Hackett et al. 2008). In the Piciformes,

the third most species-rich order, there is also one account of interspecific vocal

imitation, but no recorded evidence exists in this case (emerald toucanet; Nottebohm

1972). The correlation between species richness and accounts of vocal learning suggests

two possibilities (Kroodsma 1982): first, it is possible that vocal learning is widespread in

birds, possibly present in all orders, but has merely been described in the species-rich

orders first, due to a sampling bias. Second, and perhaps more intriguing is the possibility

that vocal learning, by generating variation among populations, leads to behavioral biases

with respect to species recognition and/or assortative mating, and thus might promote

population subdivision (Baker 1975; Baker and Mewaldt 1981; Balaban 1988a; Marler and Tamura 1962; Paterson 1985; but see [Slabbekoorn and Smith 2002] for a reinterpretation of this view). For this latter case, species that possess dialects must in general respond differentially to vocal dialects, preferring individuals with their own or

similar dialects as mates or social partners. Some examples have been found in which this

is the case in the context of playback experiments (Baker 1983; Baker et al. 1987;

Balaban 1988b; Bradbury et al. 2001; Moravec et al. 2006; Wright 1996), but other cases

have been found in which other variables may be more important in mate selection under

natural conditions (Chilton et al. 1990), or in which species-invariant qualities of

otherwise variable song are sufficient to elicit choice or response (Becker 1982; Gentner

and Hulse 1998; Weary and Krebs 1992).

Theories of Vocal Learning and Vocal Complexity

A functional approach to the study of repertoire evolution defines specific

vocalizations within a behavioral context. For example, Pepperberg has been able to

teach an African grey parrot a large variety of functional labels in captivity (Pepperberg

2002). However, learning is not necessarily a precursor to the development of a varied

vocal repertoire. For example, Evans et al. (1993) showed that domestic chickens (Gallus

gallus) produced contextually-distinct alarm calls in response to presentation of avian versus ground predators, though it is believed that these alarm signals are not learned

(though the context of their production may be, as has been demonstrated for similar predator-class specific alarm calls in Vervet monkeys; Seyfarth et al. 1980). In fact, non- unitary vocal repertoires are found in nearly every species that produces vocalizations, so that while vocal learning may expand the vocal repertoire, learning per se is not a prerequisite for the presence of a repertoire containing multiple discrete signals. What, then, might be the social or ecological factors that have led to the evolution of learning for the development of vocal display repertoires? Researchers interested in the study of bird song and the development of vocal learning have proposed a number of evolutionary theories to account for learning as a way of enhancing vocal complexity.

A. The monotony threshold hypothesis.

One of the early ideas, which has also proven to be perhaps the most robust, was

proposed by Hartshorne (the ‘monotony threshold’ hypothesis; Hartshorne 1956;

Hartshorne 1973). He proposed that changing signals can overcome dishabituation in the receiver. This hypothesis predicts that vocal variety of any type (improvisational or imitative) will be advantageous. Neural systems tend to filter out continuously present signals but attend to changes in signals, and multiple song syllables could be a way to perpetuate the saliency of the signal.

B. Mate Choice: female preference for song complexity

Sexual selection has been shown to play a role in the evolution of large vocal repertoires, at least for some species. In many songbird species (though not all), it is the male alone who produces elaborate song, and there appears to be a connection between repertoire size, configuration of vocal pathways in the brain, and reproductive success

(Brauth et al. 1994; Buchanan et al. 2004; Catchpole 1980; Pfaff et al. 2007; Searcy and

Yasukawa 1996). Additionally, there is a growing body of evidence pointing to female choice as the mechanism by which a male songbird’s repertoire size or composition affects his reproductive success (Baker 1983; Baker et al. 1987; Chilton and Lein 1996;

Chilton et al. 1990; Gil and Slater 2000). There is also evidence of a physiological effect of male song complexity not only upon a female’s reproductive intention (as evidenced through copulatory displays) but upon the stage of her ovarian development. For example, female canaries presented with complex male song show egg development at a slightly earlier stage than females presented with a smaller range of song syllables

(Kroodsma 1976).

It seems apparent that elaboration of male display has arisen in part due to the process of sexual selection, though it is not yet apparent if a component of this selection

comes in the form of a Fisherian process (without survival components of fitness), or whether there is a correlated non-sexual adaptation underlying vocal complexity, or stated in the language of behavioral ecology, whether male song complexity is an “honest signal” of survival ability (Gil and Slater 2000; Searcy and Yasukawa 1996; Spencer et al. 2005).

In Yellow-headed Amazons, males and females both sing year-round, either individually or as a duetting pair. As pairs are stable from year to year, and only a fraction of pairs initiate nesting behavior in a given year (Enkerlin-Hoeflich and Packard

1993), sexual selection of the type seen in many songbirds seems to be an unlikely explanation for the large, varied vocabulary and year-round vocal behavior of this species

of parrot. While examples of mutual sexual selection have been identified in birds (e.g.,

crest length in crested auklets; Jones and Hunter 1999), the low reproductive rate and

absence of food scarcity seem to point against a strong mutual sexual selection

component in the evolution of large repertoires in Yellow-headed parrots.

C. Territorial Defense 1: male-male competition

Repertoires may also play a key role in intrasexual selection, particularly with

respect to territorial establishment and defense. Krebs et al (1978) first demonstrated the

short-term sufficiency of song in excluding rival males from a territory (Krebs et al.

1978). They posited that multiple songs within a male’s repertoire might function as a

deceptive signal, indicating to other males that a given territory might be more crowded

than it actually is (the “Beau Geste” effect). A large repertoire, such as that of the

Northern mockingbird (Derrickson 1988), which imitates other local species, may serve a

territorial function. This may create the auditory illusion of a larger population and deter

potential intruders by creating the guise of numerous individuals ‘manning the ramparts’.

The name Beau Geste is derived from the title of a movie, in which soldiers’ costumes

were arranged upon the ramparts of an underpopulated fort, in an attempt to fool would- be attackers. However, several lines of evidence seem to contradict this supposition

(Slater 1978). In particular, birds can learn to classify different songs as coming from the same singer (Goldman 1973). This may indicate tonal or formant qualities that are unique to individual singers, or, as an alternative explanation, the birds in Krebs’ experiments

may have been grouping recordings of males not on the tonal quality of the male’s voice

but upon differences in the background noise of field recordings employed in the experiments. The use of laboratory recordings or resynthesized stimulus songs would

likely resolve this issue.

Another line of evidence for the role of song repertoires in inter-male territorial competition comes from the phenomenon of song-matching, in which neighboring males will counter-sing shared elements of their individual repertoires (Beecher et al. 2000;

Beecher et al. 1996).

D. Territorial Defence 2: pair-pair competition

Traditionally, duetting was described as a way for two members of a pair to

cement their bond, similar to other pre-mating rituals or bonding behaviors (e.g., dancing

and neck stretching in grebes). Farabaugh reviewed the literature on passerines in which

both male and female sing together in a collaborative vocal duet, and arrived at several

generalizations that may predict the presence of duetting in a species (Farabaugh 1982).

These are 1) stable pair-bonds across multiple breeding seasons; 2) year-round presence

within a home-range territory; and because of these first two criteria, most tend to be 3)

tropical species. The parrots that form the subject of this paper are in concordance with

these observations, and provide non-passerine support of these generalizations. However,

in a selfish interpretation of a duetting pair, each member of a male-female pair may be

signaling other birds regarding its willingness to seek extra-pair copulations, and the other member of the pair may be negating that advertisement (Langmore 1998; Smith

1994).

E. Territorial Defense 3: resident-nonresident competition

One interesting hypothesis is the ‘resident discrimination’ hypothesis of Craig and

Jenkins (Craig and Jenkins 1982). They surmise that resident birds are in an asymmetrical competition with nonresident territorial intruders, attributable to a resident’s greater age, greater knowledge of a specific resource value (Austad 1983), and/or proven competitive ability. They argue that development of local complex dialects could be an honest signal that is difficult to copy, which would allow birds to distinguish resident holders of territories from drifters, or potential territorial intruders. Residents should aim to reveal this asymmetry, for once revealed, they should be more willing to escalate in an agonistic confrontation with a non-resident. Conversely, a nonresident should, in its own self-

interest, avoid provoking confrontations that it is likely to lose. Craig and Jenkins assert

that geographically distinct complex vocalizations, which may be difficult for an

intruding nonresident to imitate, could be the cue employed by residents in detecting

whether or not a challenge is asymmetric. This model predicts that songs will become

more complex within an area, such that a precise imitation would take time, experience,

exposure, and practice on the part of the learner. Indeed, there is some evidence of song-

matching among neighbors, which would allow territory-holders to concentrate their

aggression on new intruders rather than known, non-threatening neighbors (Beecher et al.

2000; Beecher et al. 1996). Song-matching among neighbors cannot be easily explained

by the other hypotheses. For example, the monotony threshold hypothesis predicts that

individuals might be improvisational, rather than sequestering a large memorized

repertoire.

Nonresidents that are able to evade detection or to conceal competitive asymmetries (disadvantages) through imitation of local song variations may have an advantage over less adept imitators, against whom a resident would more rapidly escalate an aggressive defense. This scenario could set up an evolutionary arms race (Dawkins

1976) between intruder and resident, with selection acting on intruders for greater imitative ability, and on residents for greater song complexity that would hamper imitation. Thus strong concurrent directional selection for greater song complexity and for learning ability would arise.

F. Cross-Modal Acuity: a novel hypothesis for imitative song learning

One of the difficulties in discerning the function of song learning stems from the presupposition that song production has an immediate adaptive social function, as described in the above-mentioned theories. What if accurate song imitation and production provide not only direct benefits through the manipulation of conspecifics, but also indirect, temporally distal fitness benefits, through the training and fine-tuning of

neuromuscular control (similar to the purported function of play behavior), memory, and perceptual discrimination?

Cross-modal transference is the process by which a learned performance skill in a discrimination task using one sensory modality (such as flashing lights, in the visual domain) is readily transferred to an analogous discrimination task in another sensory modality (such as beeping sounds, in the auditory domain). Additionally, cross-modal interactions in shaping perception (as with the McGurk effect; McGurk and Macdonald

1976) indicate a kinesthetic interconnection of sensory modalities. Thus, it is likely that non-mode-specific (“central”) areas of the brain are involved in pattern recognition and discrimination (Dennett 2001; Pepperberg 2002; Pepperberg 2004; Rozin 1976). These higher “cognitive” layers can be represented in neural networks by an added layer of

“perceptrons,” single unit pattern integration and response generators (Minsky and Papert

1969). In cases where researchers can find little or no immediate socially-adaptive function in imitative song production (during subsong, or, e.g. in “affiliative” vocal behavior of duetting species), we posit that the singer may be training the more “central” parts of its brain in the complex tasks of fine motor coordination, pattern recognition, and memory storage and recall. Such singing behavior may then enhance future performance in non-singing tasks that employ those trained areas of the brain. Examples of a correlation between bird vocal performance and overall organismic performance can be found in (Pfaff et al. 2007; Spencer et al. 2004; Spencer et al. 2005). I stress the difference between imitative (or internal template based) and inventive song learning, since in imitative song learning a bird has an exact template against which to precisely

compare and hone its own singing, forming a tight match between stimulus perception

and stimulus production.

Similarly, vocal performance as a display would not be of a strictly Fisherian

nature, to the extent that (for example) a superior vocal memory could correlate with a

superior spatial memory. In this example, vocal performance could serve as an honest

indicator of a variety of non-vocal-specific cognitive functions which do have adaptive

survival value (Pfaff et al. 2007; Searcy and Nowicki 2008). I do not know of any

experiments that have yet tried to determine whether imitative song learning does

enhance pattern-matching abilities or memory in other domains for birds; but Rauscher et al. showed that children who received private piano lessons performed better in tests of

spatio-temporal reasoning than those who received private computer training or no

training (Rauscher et al. 1997). These results lend tentative credence to the “cross-modal

acuity” hypothesis for one function of imitative song learning. Other early support for a

cross-modal acuity model comes from studies of impacts of musical learning in humans

(Hassler et al. 1985; Oerter 2003).

G. Bourgeois/Baroque Hypothesis: A novel theory for cultural elaboration

When selection for a highly-selected trait is relaxed, the proportion of variation in fitness due to the next-most-selected trait increases. Weaker forms of selection may

predominate in the cultural domain for species with low mortality, low fecundity, and low nutritional stress, since many choices or preferences may have little remote genetic fitness impact on the cultural performer. Cultural drift and other evolutionary tendencies

of a vocal system may develop in the cultural domain in the absence of fitness effects on the signaler (Cavalli-Sforza and Feldman 1981).

For long-lived, infrequently breeding parrots, the fitness value of day to day vocal behavior should not be assumed; it would remain to be demonstrated, and under natural conditions this can be a potentially tall order. While cultural models of birdsong evolution may explicitly include neutral patterns of variation (such as random diffusion of cultural elements across geographic space), a more overtly “adaptationist” paradigm has generally prevailed in the study of birdsong. Often preferences in mate choice or signal response are posited to correlate with local adaptations (“good genes complex”), or conversely, inbreeding avoidance, as a theoretical way to connect song learning with some fitness benefit.

Vocal learning in order Psittaciformes

While parrots are well known as vocal imitative learners in captivity, they have been less studied in the wild context. In one example, the extent to which the vocal signals of parrots are labile was demonstrated in a natural experiment (Rowley and

Chapman 1986). Pink cockatoos are able to displace incubating galahs from their nest cavities, as galahs breed 2-3 weeks earlier than pink cockatoos. Two nests were observed in which the pink cockatoos adopted the eggs of the galahs in addition to laying their own

eggs, thus rearing a mixed-species brood. A comparison of the spectrograms of the

contact calls of Galahs, Pink Cockatoos, and the Galah chicks raised by Pink Cockatoo

parents, shows that the calls of the chicks more closely resembles the calls of their

adoptive parents, rather than their own species. Interestingly, the chick begging calls and

the adult alarm calls remained more similar to their own species, indicating that not all

aspects of the vocal repertoire are equally amenable to developmental influence through

learning. These results highlight the remarkable capacity and subtleties of vocal learning

in parrots.

The literature documenting geographic variation of bird vocalizations is extensive. A number of descriptions of geographic vocal variation in non-passerines exists (for reviews see Catchpole and Slater, 1995; Baker and Cunningham, 1985;

Mundinger, 1982; Krebs and Kroodsma, 1980). Several similar analyses have been done for wild parrots (Order Psittaciformes). The very first description is an anecdotal account of observations of different contact calls of the Orange-winged Amazon parrot on parts of

Trinidad, published without sonograms (Nottebohm and Nottebohm, 1969; Nottebohm,

1970, 1972). Also, variation of "contact calls" among roosting areas has been documented (Yellow-naped Amazon parrot; Wright, 1996). Another study suggestive of vocal variation in Short-billed (White-tailed) Black Cockatoo was inconclusive due to small sample size (Saunders, 1983). More recent descriptions of geographic, and presumably cultural, variations in vocalization structure can be found for keas (Bond and

Diamond 2005), Orange-fronted Parakeets (Bradbury et al. 2001), St. Lucia Parrots

(Kleeman and Gilardi 2005), Galahs (Baker 2003), Yellow-naped Amazon Parrots

(Wright and Dorin 2001), and Ring-necked Parrots (Baker 2000).

Parrots have been long renowned for their ability to learn large repertoires, and to retain the ability to acquire new vocalizations into adulthood (Gramza 1970; Nottebohm

1970; Pepperberg 1993; Pepperberg 2002; Todt 1975a; Todt 1975b). This makes parrots,

in light of many differences with passerines in mating tactics, social behavior, and life-

span, ideal for the comparative study of the general process of cultural transmission

(Mundinger 1980).

The Yellow-headed Amazon parrot (Amazona oratrix)

Amazona oratrix is distributed as subspecies oratrix on the eastern and magna on the western coasts of Mexico, subspecies tresmarias on the Tres Marias Islands off the western coast of Mexico, and as belizensis in Belize (Eberhard and Bermingham 2004a;

Eisermann 2003).

The Yellow-headed Amazon is highly prized by the pet trade, and may be “the most popular and sought-after Amazon in the trade” (Collar et al. 1992). This popularity, both in the international market and within its range, has lead to its threatened status in the wild, with most wild populations greatly reduced in number or extirpated completely from parts of the range, and the species is now listed on CITES Appendix I (BirdLife

International 2000; Collar et al. 1992). The great value placed upon these birds is in large part due to their ability and proclivity to mimic human speech. The trade in this species sometimes drives trade in other Mexican Amazona species (Cantú Guzmán and Sánchez

Saldaña 2007), which are occasionally sold deceptively as A. oratrix with their head feathers painted or dyed yellow (Enkerlin-Hoeflich 1995). As there has been a relative paucity of natural history data on parrots (but see Enkerlin-Hoeflich 1995; Rowley 1990;

Snyder et al. 1987 for more comprehensive treatises), parrots are best known as pets, and it may be that people acquire parrots as pets in part to satisfy a deep curiosity about their renowned vocal ability. I hope that this thesis can help to quench that curiosity, to the

extent that a reader might reconsider the purchase of a threatened or endangered parrot species.

The species under study in this thesis, the Yellow-headed Amazon parrot, has a number of distinguishing features in comparison to other animal species. It is regarded by the pet business as one of the best ‘talkers’ in captivity, along with a small number of other talking specialist species. These parrots also have a long lifespan of potentially greater than 50 years. Some of the large parrot species have the longest life spans among the birds, and indeed relative to their body size, live longer than most animals. Like other parrots, Yellow-headed Amazon parrots have zygodactylous feet, and this anatomical feature (two toes pointing forward, two toes pointing backward) allows stability and dexterity when climbing and hanging, and when manipulating objects.

Yellow-headed Amazons form stable pair bonds (Figure 1.2). Many studies have observed the continuation of a psittacine pair bond across breeding seasons, but no one has studied them long enough to know whether this is a lifelong bonding situation

(Snyder et al. 1987). A conservation effort of the Puerto Rican Amazon parrot observed a number of cases where pairs re-mated, or repaired with other individuals after an unsuccessful nesting attempt (Snyder et al. 1987). However, these parrots were from a remnant population of small size, so it is difficult to draw a general conclusion from these cases. Both sexes of the Yellow-headed Amazon vocalize during displays known as duets, and each member of a pair contributes an apparently equivalent effort. This symmetry is different from the situation observed for most songbirds, although duets are observed in many year-round territory-holders, particularly in tropical areas (Farabaugh

1982; Smith 1994).

Figure 1.2. Affiliative grooming behavior in a bonded pair of captive Amazona oratrix; note the variation in head plumage colors. Inset: Cover Parrots of The World (Forshaw 1989), showing depiction of Yellow-headed and Blue-fronted Amazons.

These notable aspects of the phenotype of Yellow-headed Amazon parrots are features that humans also share with these birds. Humans are also regarded as one of the best talkers among the terrestrial mammals. Indeed, until only recently, humans were considered the only imitative vocal learner among terrestrial mammals; in 2005, this belief was initially shaken when elephants were observed to imitate truck sounds (Poole et al. 2005). In any case, learning among terrestrial vocal mammals is extremely rare.

Humans also have a uniquely long lifespan among mammals and form stable pair bonds

(a trait found in only 2% of primates [Fuentes 2002], and in few mammals). Furthermore, both human sexes are equally vocal (Mehl et al. 2007). Finally, human hands are also specialized ability to facilitate fine scale control. Interestingly, researchers have

speculated that manual dexterity, which seems to be correlated with left-right brain

specialization, might also be a precursor of the left-right specialization of the brain that is

observed with speech production (Snyder and Harris 1997a; Snyder and Harris 1997b;

Snyder et al. 1996).

Amazona oratrix oratrix and A. o. magna are distributed along eastern and western coastal Mexico, and two other subspecies, A. o. tresmariae and A. o. belizensis,

are found in the Islas Tres Marias, Mexico, and in northern Belize. Due to poaching for

the pet trade and habitat destruction throughout their range, A. oratrix is currently listed

as threatened (IUCN 2007; Snyder 1999). A. oratrix is part of a multi-species Amazona

ochrocephala complex, which inhabits parts of Central America and the Amazon basin

(Figure 1.3). The ochrocephala complex had been considered a single species until a

revision of the American Ornithologist’s Union in 1986. Indeed, there is still

disagreement about where areas of intergradation and genetic species boundaries occur

(Eberhard and Bermingham 2004b).2 In this case, the naming of species has important conservation value (Eisermann 2003; Ribas et al. 2007; Russello and Amato 2004). When

Amazona oratrix and A. auropalliata were considered as part of a large single species (A.

ochrocephala), they were not given appropriate conservation status because A.

ochrocephala maintained widespread distribution and abundance, masking the scarcity of the two subtypes. When A. oratrix and A. auropalliata were again designated as separate

species, the demographic trends of each species could be considered properly in isolation

2 The authors of this paper refer to the standing of the group as a ‘taxonomic headache’.

of the other A. ochrocephala species, indicating the need for immediate conservation action for both species (Enkerlin-Hoeflich 1995; IUCN 2007; Snyder 1999)3.

Distribution of the Amazona ochrocephala complex (after Juniper and Parr 1998)

J R. E1 E B

Š Eberhard, J. R., and E. Bermingham. 2004a. The Auk 121: 318

Figure 1.3. Range map.

3 In 2004, the IUCN estimated that A. oratrix had suffered a 68% decline in 10 years, declaring: “The Yellow-headed Amazon has suffered one of the most dramatic population declines of all birds in the Americas.”

The species now known as Amazona oratrix occurs as four subspecies throughout

Mexico, northern Belize, and northeastern Guatemala (Eisermann 2003). Historically, its range consisted primarily of coastal areas in the east and west of Mexico, but current populations are restricted to tiny fragments of that historical range (Figure 1.4). In particular, A. oratrix is found on the Tres Marias islands, where the parrots benefit from the protection afforded by the Tres Marias island penal colony, which excludes most people from the island (aside from prisoners and staff). In northeastern Mexico, some remnant populations remain as well (Aragon-Tapia 1986). There, in a number of geographic locations, we were able to find Yellow-headed Amazon parrots and to study them in greater detail.

The northeastern coastal area of Mexico is defined as subtropical in climate. Less than 10% of forest remains in coastal Mexico; most has been cleared for agricultural purposes, particularly cattle ranching. Where forest remains, the common trees of the area are strangler fig, ebony, and coma trees (Enkerlin-Hoeflich 1995; Enkerlin-Hoeflich and Packard 1993). Although the forest at our primary research site had been cleared in the 1960s and 1970s for cattle ranching, the land was unusual in that it had not been fully cleared, but shelter belts, forest remnants, and many of the largest trees had been left in place. As the largest of the Amazon parrots in northern Mexico, A. oratrix requires very large tree cavities for nest brooding. Although these parrots do not excavate cavities, some available tree cavities are excavated by woodpeckers that share their range. A. oratrix seek suitable cavities and then defend them. The loss of large trees throughout their range raises the possibility that even remnant populations seen today may be in the

process of extirpation as they fail to recruit juvenile individuals into the adult populations.

Figure 1.4. Geographic range of Amazona oratrix. (BirdLife International 2000).

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38 Chapter 2

Vocal Repertoires of Yellow-headed Amazon Parrots (Amazona oratrix)

Abstract

In this chapter, I describe our characterization of vocalizations in the Yellow-headed Amazon parrot. A video catalog database was created in order to identify individual parrots based on their plumage. I developed quantitative, objective tools to characterize acoustic signal variation. Using these tools, I documented and described the range of variation of vocal repertoires among wild populations of Yellow-headed Amazon parrots. Despite the ability of A. oratrix to mimic human speech in captivity (Juniper and Parr 1998) and the fact that it lives in the wild alongside two congeneric Amazon species, no interspecific mimicry was noted among these (or other) Amazons species. These parrots show highly differentiated vocal repertoires among distant populations, as well as ‘dialect’ variation within a group of similar repertoires. I found that vocalizations are likely to be stable in an area over a relatively long period of time (by comparing recordings spanning a gap of thirty-four years). To my knowledge, this is the longest documented wild persistence of acoustic memes in a non-human vocal learner. Observational evidence is presented of early acquisition of population- typical signals, by first season juveniles. These results indicate the potential for vocal units to aid in identifying the geographic origin of birds confiscated from poachers. The variation of vocal repertoire may also be useful in reconstructing the natural history of different segregated populations by revealing population connectivity through migration. Evidence of possible color dimorphism for this species is presented.

INTRODUCTION

“It is more remarkable that parrots, belonging to a group distinct from the Insessores [perching birds], and having differently constructed vocal organs, can be taught not only to speak, but to pipe or whistle tunes invented by man, so that they must have some musical capacity. Nevertheless it would be very rash to assume that parrots are descended from some ancient form which was a songster. Many cases could be advanced of organs and instincts originally adapted for one purpose, having been utilized for some distinct purpose.” --Charles Darwin, 1871, The Descent of Man.

Despite our long history of fascination with parrots - more than two and a half millennia of parrots as pets (Boehrer 2004) - only recently have researchers ventured beyond the cage to explore how parrots’ capacity for vocal learning is manifested in the wild. The very first description of geographic variation in the calls of wild parrots is an anecdotal account of Orange-winged Amazons (Amazona amazonica) on parts of

Trinidad (Nottebohm and Nottebohm 1969). More recent descriptions of geographic, and presumably cultural, variations in the calls of wild parrots can be found for keas (Bond and Diamond 2005), Orange-fronted Parakeets (Bradbury et al. 2001), St. Lucia Parrots

(Kleeman and Gilardi 2005), Galahs (Baker 2003), Yellow-naped Amazon Parrots

(Wright and Dorin 2001), and Ring-necked Parrots (Baker 2000). Not all parrot species are reputedly equally vocal. Among those species most highly sought by the pet trade

(Cantú Guzmán and Sánchez Saldaña 2007; Enkerlin-Hoeflich 1995; Juniper and Parr

1998; Snyder 1999; Wright et al. 2001), largely because of their imitative abilities, are

African Grey parrots (Pepperberg 2002; Snyder and Harris 1998; Todt 1975) and the

40 Amazon parrots of the ochrocephala-complex: the Yellow-naped and Yellow-headed and

Yellow-crowned Amazon parrots. Because these species are long-lived, are generally monomorphic, and are often monogamous, they represent an interesting counterpart to

studies of passerine vocal learning and evolution (Farabaugh 1982; Hackett et al. 2008;

Iwaniuk et al. 2005; Marler and Tamura 1962; Nicholls et al. 2006; Searcy and Nowicki

2008; Wright et al. 2005).

Attempts to classify sounds of another species can be problematic, particularly for

those species with large repertoires or complicated patterns of usage, for a number of reasons (Hanser et al. 2004; McCowan et al. 2002; McCowan et al. 1999; Suggs and

Simmons 2005); these include difficulties of sample size required to reveal higher-order syntax, correct categorical labeling of sounds, and appropriate measures of the behavioral salience of the signals.

In this chapter, I measure and characterize vocal variation of wild oratrix parrots at multiple scales and resolutions. These extend from variability of a repertoire within an individual parrot, to comparisons of call-types among individuals, up to population-level variation of repertoires. Vocal syntax was measured within vocal bouts by constructing first-order Markov transition matrices, to reveal the extent of immediate repetition

(variety). As a measure of vocal stability, recordings from 1995 were compared to an archival recording from 1961, and three vocalization-type matches were identified. I also describe the presence of adult-type vocalizations in sub-song of first-season juveniles, indicating early acquisition and expression of population-typical signals. Despite call fidelity among individuals within a population, indicating a keen vocal ability, interspecific mimicry was not observed. This is remarkable because of the annual presence of two other Amazon parrots at the focal field site, namely the Green-cheeked

41 Amazon parrot, A. viridigenalis, and the Red-lored amazon, A. autumnalis, (BirdLife

International 2000; Enkerlin-Hoeflich 1995), and stands in contrast to a literature report of another highly-regarded vocal mimetist, the African Grey Parrot (Psittacus erithacus)

(Cruickshank et al. 1993).

The studies in this chapter of variation in vocal repertoire are structured like the film “Powers of Ten” (Eames and Eames 1978), in which we zoom out from an atom, until we are beyond the galactic scale to reveal structure at multiple scales of matter. I first characterize the repertoire of a single parrot calling in a tree, and then use the methods developed for this characterization to analyze calls of another individual, and then further extend our analysis to examine call variety and usage among local pairs.

Pulling back further allowed us to see differences among populations using the same signal types (among dialects, but within a language group). We broaden our perspective further, and compared language groups across the range of this species in Mexico (and among closely related species in Chapter Three). Further insight is provided by observation of call acquisition by first-season juveniles, and by identification of homologous calls across thirty-four years at one site. Knowledge gained from such a hierarchical multi-scale approach comes at a cost, however, of sample size within levels.

Some of the tools developed or applied herein (automated parsing and classification of recordings) can enable current and future studies to reduce the difficulties of working with large sound catalogs at multiple scales in an objective, quantitative manner (see

Chapters Five and Six on pupal pulse analysis of large (greater than 105) ‘call’ datasets), borrowing in some part from a bioinformatic perspective on the study of acoustic variety

(Boisseau 2005; McCowan et al. 2002; McCowan et al. 1999; Sherwin et al. 2006; Suggs and Simmons 2005). Information about regional vocal variation of this or other learning

42 species can also act as population markers, in showing population fragmentation or

migration (an example of which is shown here). A complete vocal dialect map might

even reveal poaching patterns, where sadly, species such as the Yellow-headed Amazon

are targeted precisely because of their vocal abilities.

METHODS

Species

The Yellow-headed Parrot, Amazona oratrix, is a large Amazon parrot highly

sought in the pet market. It has been extirpated through much of its historical range in

coastal Mexico, and was listed as CITES Appendix 1 in 2000. The species is more fully

described in Chapter One.

Field sites: Audio Recording Samples

I produced audio recordings of Amazona oratrix in Tamaulipas, Mexico from:

- Rancho Los Colorados (LC), the focal field site, near Barra del Tordo from 1993 through 1996 - Rancho El Tecomate (T), 15 km southwest of La Pesca, in 1994 - In the Sierra Madre mountains 20 km north of Ciudad Victoria (V) in 1994 and 1995 - Tepeguajes, 30 km south of El Tecomate in 1994 - North of Casas, near the Presa Vicente Guererro in 1994 and 1995

Additional, recordings were obtained from a Tres Marias Islands population in 1996

(Ernesto Enkerlin), from Rancho Sandoval, Campeche, 1993 (John Sauer of the

Pawtuxent Field Station), and from 20km north of Cd. Victoria I, Tamaulipas, Mexico, achived at the Cornell Library of Natural Sounds (L. Irby Davis, 1961 recording, LNS

8435). See Figure 2.27 for map. These points together represent the species’ range extents in Mexico.

43 I obtained recordings in the field using Dan Gibson EPM-650 parabolic microphones (which enable a higher signal to background ratio than any other microphone type) connected to either a Sony TCD5M recorder, a Sony WMD6C recorder, or a Canon L1A Hi8 video camera. The L1A video camera was fitted with an

EOS adapter and a Tokina 75-300 zoom lens, and a 2x telephoto doubler, which yielded an effective optical magnification of about 60X, enabling individual identification by plumage during calling bouts.

Automated Parsing / Manual Classification

Digitized audio recordings of known parrots at LC and unknown parrots at other sites were parsed into individual vocalizations using the Event Detector module of the sound analysis program Signal (Engineering Design), using an event threshold of -40db, and a pre and post settings of 70 ms and 100 ms. Vocalizations were assigned to categorical classes based on manual inspection of acoustic and/or spectrogram similarity.

Vocalizations which were unique or which were not readily manually classifiable as belonging to a common type were assigned to the category designated “U” (unknown). I analyzed the usage of the six most common call types given by 12 social units of birds

(unpaired individuals, pairs, trios) in Los Colorados. Call counts were then tallied by class among geographic locations. For one geographic locale, calls from 1961 were compared to calls identified as homologous in 1994 and 1995.

In this way I was able to build a library of sounds at several levels of scale, from variation within a single call in a single individual, to variation in call use and form among individuals, up to variation among a group of populations, and within a population across time.

44 Measuring Pulse-count Variation of Pulse-type calls

Calls identified as Pulse-type were analyzed for Los Colorados, and for El

Tecomate, by counting individual pulses in each pulse train. Pulses were randomly selected from each population pool. Calls were amplitude-normalized, and thresholded at

-40db, with a minimum pulse length and pulse gap of .05 seconds using Signal software

(Figure 2.1).

Figure 2.1. Demonstration of method used to count pulses. Bottom: raw wave

form. Middle: signal after application of a logical threshold of the smoothed

absolute magnitude of the waveform. Top: instantaneous pulse rate, calculated by

subtracting .5 from the logical buffer, and counting the zero-crossings per unit

time, with units in pulses per second.

Automated classification and similarity measurement

For subsets of calling bouts parsed into individual calls, pair-wise call similarity matrices were calculated using Matlab as follows. First, I made spectrograms of each sound sample with the following settings: 512 frequency bands, a time window of 512 points, and a windows overlap of 75%. Similarity measures among pairs of sound files were calculated by finding the maximum of the two-dimensional normalized cross-

45 correlation of their spectrograms (using the normxcorr2.m function in Matlab), and the

similarity was measured with respect to the shorter of the two sounds. (For example,

using this algorithm, if a call were to be compared to a fragment of itself, the returned

value would be 1.) This similarity measure is symmetric, resulting in a square matrix with diagonal values (a call and itself) equal to 1 and an axis of symmetry along the diagonal.

Principal components ordination was performed to reduce the dimensionality of the call similarity matrix. Dendrograms were created using Euclidean distances of the similarity matrix, using mean distances for linkage. Automatic cluster labels were generated using linkage information.

Mantel Tests of call categorization and differences of shared calls among parrots

A pool of high-quality recordings of calls of each type were selected from each

pair. A sub-sample was then randomly chosen for each pair, by sorting in a spreadsheet,

coded by a random variable, to avoid any bias in the selection of calls for comparison.

The calls were then cross-correlated using the CORMAT function of the Signal sound

analysis package (Engineering Design) in three ways: by spectrogram, by sound envelope

(time-varying amplitude), and by frequency power-spectrum. Spectrogram cross-

correlations were limited to a frequency window of 400 to 2500 Hz, to focus the

comparisons on the fundamental frequency, to the exclusion of higher harmonics.

Spectrogram granularity was with 256-band frequency resolution, and 200 time-steps,

and signal clipping was set at 40dB below signal peak, to exclude background noise.

Three spectrogram cross-correlations were performed for each vocalization type: with

time-normalization (T-norm) to remove the effects of varying length of the calls; with

frequency-shifting (F-shift), to remove the effects of the call being shifted up or down in

46 frequency; and with both T-norm and F-shift. Amplitude envelope cross-correlations

were performed with a time-margin of 20 ms (for T-norm), again with a cutoff of the

signal at levels 40dB or greater below signal peak. Frequency power-spectrum cross-

correlations were done on spectra smoothed with a 30 Hz window.

The resulting matrix of tabulated pair-wise similarities was then compared to a matrix coded with 1 for intra-pair comparisons, and with 0 for all inter-pair comparisons, using the Mantel test. Probabilities (p-values) for each inter-matrix correlation value (r2) were calculated by performing a Monte Carlo simulation (using Signal and Matlab), which randomly reorders the coding matrix and tallies the correlation between the similarity matrix and the coding matrix, and counts the number of instances in which the inter-matrix correlation equals or exceeds the actual correlation value. Each Monte Carlo simulation was run with 1000 iterations. Results for each comparison type were compared, to determine if differences within a call type among parrot pairs was attributable to frequency shifts of otherwise similar calls, or whether call structure varied from pair to pair (amplitude profile change as well as frequency shifting).

Automated vs. manual classification of calls

Repertoires were also analyzed by automated measures (normalized cross-

correlation of call spectrograms), and automated classifications were compared to manual

classification to evaluate correspondence, in order to assess if computer-derived

categorical parsing can be employed in cases where an observer has little prior

knowledge of the range of the sampled repertoire and its underlying diversity.

Call Syntax / Markov Matrices

47 I created on-time and off-time lists for identified vocalizations within pairs at LC,

and with sample pool at T. Custom scripts allow us to extract a transition matrix (Markov

matrix) by call and by time between calls. Data from LC were pooled for display, and for

comparison with T.

I determined the first-order transition matrix among the seven common call types

(i.e., a Markov process with one-step memory: Figure 2.2), for the Los Colorados

population. I also determined the average time between calls to further characterize the

transitions.

Repertoire analysis

Vocalization i a b c d e f g h i a pq b j c 1st order d e 2nd order f Vocalization Vocalization g etc. h i j

Figure 2.2. Construction of transition matrix between calls (as discussed by (McCowan et al. 1999) in an analysis of bottle-nose dolphins).

Comparing dialect variants of “Yelp” call across 90 km

Calls were examined using Canary software (Cornell Bioacoustics Lab), and the length of the unmodulated mid-section of the yelp call was measured using the on-screen cursor. Yelps were drawn randomly from the Barra del Tordo (Los Colorados; n = 37)

48 and La Pesca (El Tecomate; n=30) population recordings, and differences in population

mean values were evaluated with the Mann-Whitney U test.

Multidimensional scaling plots

Data for MDS plots within calls within an individual, within calls among individuals, within-calls among-pairs, and within-calls among populations were calculated from similarity matrix data (normalized cross-correlation of spectrogram), using SYSTAT and Matlab.

Identification of individuals through plumage variation

I was able to identify birds in the field based on their plumage. For example, one

important descriptor for plumage variation was the extent of yellow covering the head: ¼

(to the eyes), ½ (to the middle of the back of the head), ¾ (encompassing the head), and

full (encompassing the head, down to the neck and shoulders) (Figure 2.3). Our primary

field site was Los Colorados at Barra del Tordo in Tamaulipas, Mexico, a 1,800 hectare

plot of partially cleared forest. Using data from Los Colorados, I created a database of

254 captured images of 54 individuals containing identifiable singletons, and 15 known

pairs and trios (Figure 2.4). In two cases of disagreement out of 56 (inter-observer

reliability > 95%), assignment was decided by both observers together. Twenty eight

pairs (56 individuals) were included for analysis of pair dimorphism. This identification

enabled us to characterize the ranging and calling behavior of particular individuals and

pairs, and then test pairs in playback experiments (Chapter 3).

Figure 2.3. Individuals in the field and on video were scored as shown.

Figure 2.4. Example “mugshots” taken by video telescope. Note differences in yellow head proportions, and in mottling. These individual differences were used to identify resident pairs around the focal field site, Rancho Los Colorados and Rancho Las

Arboledas.

51 RESULTS

Initial characterization of vocal repertoire at Los Colorados

The common sounds that make up the majority of utterances can be categorized

into distinct signal classes based on their spectrographic appearance. The six most

common vocal signals of Los Colorados accounted for 96.8 % of all vocal utterances.

Another class of vocal types (the remaining calls at LC, not otherwise explored here), are rarer songs, whinnies, and numerous highly embellished vocal units, some examples of which are shown in Appendix I. Nearly all of the parrots in Los Colorados use the same vocal repertoire. With one exception, each of the six most-used vocal signals was largely stereotyped, and differences in vocal repertoire, by call type use, among individuals could not be observed in the field. The six most abundant vocal categories are referred to here as Pulse-purrs, Growls, Yelps, Ringbarks, and (onomatopoetically), Hotedo, and Rkrew

(one letter symbols used as well). For exemplars of these call categories, see Figures 2.5-

2.10a.

I unambiguously assigned vocalizations to the highly stereotyped classes manually. Recordings of singleton birds calling alone were initially used, since bouts can be unambiguously parsed into individual vocalizations (overlap in pair duets can obscure call boundaries). To verify the manual categorization, I calculated the two-dimensional normalized cross-correlation of spectrograms in all pair-wise combinations, as shown in the heat map (Figure 2.11). The cross-correlations show similarity measure clustering of the calls within manual categories, for an individual A. oratrix (coded name “LA5”).

Given this strong clustering among vocal categories, cross-correlation coefficients were used to generate subsequent automated classifications in conjunction with manual call- type labels. Figure 2.12a shows the dendrogram generated by this automated clustering

52 for singleton “LA5”; Figure 2.12b shows the corresponding principal components

ordination of six manually classified exemplars of each of six main call types. The

correspondence between manual and automated classification is shown in Figure 2.13.

Variation within each of the manual call categories within a single individual is

depicted in a multidimensional scaling plot for each call category in Figure 2.14. Further

confirmation of automated classification of repertoire subtypes is demonstrated in Figure

2.15, in which automated classes of vocalizations parsed from another calling singleton,

“BadEye” matched manual classification.

Ringbarks and Yelps show structured subcategorical variation (Figure 2.14); other calls show continuous variation within call types. A particularly diverse category of calls is the Pulse-type class. I further determined the variation in pulse counts, as shown

(Figure 2.10b).

53 Figure 2.5. Examples of automatically-parsed, manually categorized

“HOTEDO” calls for singleton parrot LA5

5000

Frequency (Hz) 0 0 0.2 0.4 0.6 Time (s)

54 Figure 2.6. Examples of calls labeled as “YELP” for parrot LA5

5000

Frequency (Hz) 0 0 0.2 0.4 Time (s)

55 Figure 2.7. Examples of calls manually labeled as “RING” for parrot LA5

5000

0 Frequency (Hz) 0 0.2 0.4 Time (s)

56 Figure 2.8. Examples of calls manually labeled as “GROWL” for parrot LA5

5000

Frequency (Hz) 0 0 0.1 0.2 0.3 Time (s)

57 Figure 2.9. Examples of calls manually labeled as “RKREW” for parrot LA5

5000

Frequency (Hz) 0 0 0.2 0.4 Time (s)

58 Figure 2.10a. Examples of calls manually labeled as “PULSE” for parrot LA5. Note differences in length, and number of pulses

0 0.5 0 0.5 0 0.2 0.4

0 0.2 0 0.2 0.4 0 0.2

0 0.2 0 0.2 0.4 0 0.5

0 0.5 0 0.5 1 0 0.5

0 0.5 0 0.5 0 0.5

5000

Frequency (Hz) 0 0 0.2 0.4 0 0.5 0 0.5 Time (s)

59 Continuous variation in the Pulsed call type Number of pulses per vocalization

150 0.3

r o

100 0.2 p

r t

i o

n n

o p

50 0.1 a r

0 0.0 0 10 20 30 NPULSES

NPULSES PULSE RATE (Hz) d-PULSE RATE (Hz)

Minimum 3.000 9.059 -0.008 Maximum 28.000 37.802 0.005 Mean 9.766 19.134 -0.002 Standard Dev 3.945 4.745 0.002

Figure 2.10b. Histogram of pulse counts of 496 PULSED vocalizations drawn from 10 pairs of the Los Colorados population.

60 ‘LA5’ Calls: Similarity Matrix 6 call types, 18 replicates of each

Hotedo 0.9

Growl 0.8

0.7 Rkrew

0.6 Yelp

0.5

Ring 0.4

Pulse 0.3

Hotedo Growl Rkrew Yelp Ring Pulse

Figure 2.11. Variation within and among calls within an individual parrot’s repertoire.

61 54 51 46 48 44 50 49 43 42 53 47 52 45 41 40 39 38 37 36 35 34 23 22 26 33 21 31 29 28 25 24 20 32 30 27 19 107 108 101 103 100 99 106 105 98 97 95 96 94 104 102 93 92 91 69 67 65 70 71 63 62 66 59 68 56 64 61 60 72 57 58 55 13 84 83 82 81 75 80 79 76 77 78 74 73 88 90 89 87 86 85 12 16 15 14 7 6 17 11 10 9 8 5 18 1 4 3 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Figure 2.12a. Variation within and among calls within an individual parrot’s repertoire sample: Clustergram from analysis of similarity matrix in previous figure, and labeled according to 8 forced cluster categories. Yellow = rKrew, dark blue = growl, lime green = pulse, light blue = yelp, aqua green = ringbark, magenta = ringbark, red = hotedo; number of calls used = 108.

62 0.3

0.2

0.1

-0.1 Principle Component 3

-0.2 0.15 0.25 0.1 0.2 0.15 0.05 0.1 0 0.05 -0.05 0 -0.1 -0.05 -0.15 Principle Component 1 -0.1 -0.2 Principle Component 2

Figure 2.12b. Principal components ordination of calls of LA5 singleton; six manual classification categories, eighteen calls of each of six types. Black = hotedo, dark blue = growl, cyan = rKrew, green = yelp, yellow = ringbark, red = pulse.

63 14

6 Cluster number

4 Automated Category

0 Hotedo Growl Rkrew Yelp Ring Pulse

Manual Category

Figure 2.13. Correspondence between manual and automated classification of 108 calls (18 of each of 6 types). For example of sensitivity, refer back to HOTEDO (Figure 2.5, fifth row, first column) and YELP (Figure 2.6, sixth row, third column), to examine single outliers identified above. The Pulse category is also the most variable, in automatic classification. LA5 (108 calls, 18 calls per type). X-axis represents manual classification labels. Y-axis: Automatically-scored clusters forced into 13 categories.

64 Yelps

n 0

D -1

-2 -2 -1 012 Dimension-1

Gro wls Hotedo Ringbarks

2 2 3

1 1

- 1

s n 0 0

D D D -1 -1 -1

-2 -2 -3 -2 -1 0 1 2 -2 -1 0 1 2 -3 -1 1 3 Dimension-1 Dimension-1 Dimension-1

RkRew Pulses

2 3

1 1

n 0

D D -1 -1

-2 -3 -2 -1 0 1 2 -3 -1 1 3 Dimension-1 Dimension-1

Figure 2.14. Multidimensional scaling of variation within call classes for a single individual (LA5). (379 vocalizations)

65 Another individual – parrot “BadEye”)

Figure 2.15. Automatic classifications of calls from “BadEye.” Cluster labels given by title. Note exact correspondence to manual call-classification categories (1=R, 2=P,3=K, 4=G, 5=Y).

66 Variation among individuals within a site

Variation of Yelps among pairs at Los Colorados

After parsing and manually classifying calls by pair, I calculated cross-correlation coefficients of spectrograms of Yelp calls from six different pairs from Los Colorados

(Figure 2.16). The overall similarity of all Yelp calls was quite high (>0.5). The proportion of variation among calls that was could be explained by partitioning among pairs was statistically significant but quite small (1-2%; Table 2.1). Nevertheless, differences among pairs were detectable.

67 Similarity Matrix, Los Colorados Pairs, Yelp Call, frequency comparison 1

231 0.95 Coding Matrix 0.9 232 231 0.85 232 0.8 233 233 0.75

239 239 Oratrix Pair Oratrix 0.7 Oratrix Pair Oratrix

0.65 E12 E12 0.6 LA7

0.55 231 232 233 239 E12 LA7 LA7 Oratrix Pair 0.5

231 232 233 239 E12 LA7 Oratrix Pair

Mantel Test, coded by pair: r = .1299, p<.001, Montecarlo 1000 permutations

0.4

0.2

-0.2 Principle Component3 -0.4 0.6 0.4 0.4 0.2 0.2 0 0 -0.2 -0.2 -0.4 Principle Component 2 Principle Component 1

Figure 2.16. Pair-wise similarities calculated between yelp calls, drawn from six pairs of birds (6 calls from each pair) (heat map). A Mantel test showed that coding by pair explains a statistically significant (p<.001) but small (r = .1299) proportion of the variance. Principal components map of calls is shown in bottom figure. (PCO, 6 pairs, 6 yelps each (from frequency differences))

68 Among-pair similarity of call types at Los Colorados: Differences attributable to Envelope vs Spectral Frequency variations

Call Y Among-pair Differences (Mantel test, 6 pairs, 6 calls/pair)

Spectrogram Envelope Spectrum r 0.0992 0.09610.1208 0.1259 0.1267 0.1299 p< 0.005 0.001 0.001 0.001 0.001 0.001 T-norm + + + F-shift + +

Call K Among-pair Differences Mantel test, 6 pairs, 6 calls/pair

Spectrogram Envelope Spectrum r 0.1424 0.13390.1321 0.0143 0.0373 0.1563 p 0.001 0.001 0.001 0.24 0.017 0.001 T-norm + + + F-shift + +

Call R Among-pair Differences Mantel test, 5 pairs, 6 calls/pair

Spectrogram Envelope Spectrum r 0.2530 0.23640.1766 0.1889 0.2290 0.3399 p 0.001 0.001 0.001 0.24 0.017 0.001 T-norm + + + F-shift + +

Table 2.1. Cross-correlation coefficients calculated by spectrogram, amplitude envelope, or frequency spectrum. T-norm: time-normalized. F-shift: frequency-shifted for maximal correlation.

69 Sharing of call types among parrots at Los Colorados

I observed that most birds utilize all call types (Figure 2.17). In aggregation, the frequency of usage of all common call types were roughly similar (i.e., within one order of magnitude).

Figure 2.17. Vocal usage by vocalization category, for 12 parrot social units from Los Colorados (Y-axis labels: Singletons (LA5 and LA7), Pairs (233, 232, A1, CP, E12, SW2, LA7, LA9) and trios (231, P18) sorted by unit sample size (most to least). Last category “U” is the bin for unknown / unclassified sounds.

70 Transitions between call types: syntax

The first-order Markov transition matrix for calls of the parrots of Los Colorados is shown in Table 2.2.

71 Transitions among vocal types (Markov matrix) Los Colorados parrots, (n = 2,823 manually classified vocalizations)

Counts Call g h k P r y t u Type

g 35 8 48 22 17 19 0 6 h 7 145 26 58 27 25 0 13 k 32 30 376 77 64 146 0 6 p 44 30 123 357 38 73 2 13 r 6 36 31 60 126 48 0 7 t* 0 0 0 2 0 0 0 0 y 21 46 114 80 40 244 0 9 u 5 8 8 17 5 10 0 33

Weighted Counts (%) g h k P r y t u Transition matrix, Los Colorados g g 22.58 5.16 30.97 14.19 10.97 12.26 0 3.87 h h 2.33 48.17 8.64 19.27 8.97 8.31 0 4.32 k k 4.38 4.1 51.44 10.53 8.76 19.97 0 0.82 p p 6.47 4.41 18.09 52.5 5.59 10.74 0.29 1.91 r r 1.91 11.46 9.87 19.11 40.13 15.29 0 2.23 Vocal category Vocal y y 3.79 8.3 20.58 14.44 7.22 44.04 0 1.62 t t* 0 0 0 100 0 0 0 0 u u 5.81 9.3 9.3 19.77 5.81 11.63 0 38.37 g h k p r y t u Vocal category

Average times (sec) k p y r H g u k 0.62 3.18 0.82 0.81 0.13 1.34 0.78 p 2.93 2.59 2.95 2.12 1.46 4.38 2.59 y 1.34 2.5 0.59 0.74 0.67 2.85 2.53 r 2.35 2.21 0.94 0.44 0.28 2.13 0.72 h 5.02 5.69 0.47 0.75 0.3 2.68 2.84 g 1.81 4.7 0.43 0.24 0.44 0.75 0.5 u 0.21 2.36 2.93 2.35 0.52 1.4 3.91

Table 2.2. Transitions among calls in the Los Colorados population. Top: by absolute count. Middle: by percentage. Inset shows heat-map plot of transition probabilities, with high probability of immediate repetition. (Note near absence of ‘T’ type calls*). Bottom: first-order transition times.

* El Tecomate-type vocalization recorded at LC; see Fig. 2.20

72 Geographic variation of vocal repertoires

The overall structures of the El Tecomate vocalizations were clearly homologous

to the vocalizations produced at Los Colorados; in particular, the common vocalizations

found in each population have homologs in the other population (Figure 2.18). Most

individuals in each population showed these vocal types. Six of the seven call classes of

Los Colorados and El Tecomate occurred with roughly similar frequencies in both

populations (Figure 2.19). The one exception was the most common vocal element used in El Tecomate (T call; Figure 2.20), which was used only twice in Los Colorados. This vocal type was the most common (21% of 746 vocalizations sampled) at El Tecomate,

and was found in surrounding populations (near Casas and Tepeguaje), but was

essentially absent from the Los Colorados repertoire.

Several examples of the Yelp calls of El Tecomate are shown in Figure 2.21

(compare with Figure 2.6).

73 Homologous Vocalizations among the eastern sites (types H,K,and R)

Tecomate yelps K

Figure 2.18. Spectrograms of H, K, and R calls from Los Colorados and El Tecomate.

El Tecomate Los Colorados

Figure 2.19. Histogram of call types in Los Colorados and El Tecomate, 90 km apart.

0 0.5 0 0.2 0.4 0 0.2 0.4

0 0.2 0.4 0 0.5 0 0.5

0 0.5 0 0.5 0 0.5

0 0.2 0.4 0 0.2 0.4 0 0.2 0.4

0 0.2 0.4 0 0.5 0 0.5 5000

0 Frequency (Hz) 0 0.5 0 0.5 0 0.2 0.4 Time (s)

Figure 2.20. T–type vocalizations from El Tecomate (manually classified).

76 Examples of El Tecomate – type Yelps

0 0.5 0 0.2 0.4 0 0.5

0 0.2 0.4 0 0.2 0.4 0 0.5

0 0.2 0.4 0 0.2 0 0.5

0 0.2 0.4 0 0.2 0.4 0 0.2 0.4

0 0.2 0.4 0 0.5 0 0.5 5000

0 Frequency (Hz) 0 0.2 0.4 0 0.2 0.4 0 0.2 0.4 Time (s)

Figure 2.21. Spectrograms of Yelps from El Tecomate.

77 I found that yelp calls from Los Colorados could be quantitatively distinguished from yelp calls from El Tecomate. One immediately apparent difference between these populations is the length of the unmodulated section of the call (∆tyelp; Figure 2.22). The

population average of ∆tyelp in Los Colorados was 109 ms, while the average ∆tyelp in El

Tecomate was 76 ms. There was little overlap between the distributions of ∆tyelp in these two populations (p < 0.0001).

I used this distinction between the populations of Los Colorados and El Tecomate to assess the likely origin of a juvenile parrot who produced El Tecomate-type yelps at

Los Colorados (Figure 2.22).

To assess the syntax of the El Tecomate calls, the Markov transition matrix of calls from the El Tecomate population was determined (Table 2.3; compare with Table

2.2 of Los Colorados). I also determined the variation in pulse counts in Pulse-type vocalizations from the El Tecomate population (Figure 2.23; compare with Figure 2.10b from Los Colorados).

78 “Dialect” Variation in a Shared Call Type

Figure 2.22. Dialect variation of Yelps between Los Colorados and El Tecomate. The “mystery yelp” from a flying juvenile at Los Colorados closely matches the yelp structure from calls obtained at El Tecomate; this call was followed by T-type calls.

79 El Tecomate Transitions: Markov Matrices

Counts i G H K N P R T Y U G 1 0 5 0 1 2 0 1 0 H 1 39 7 1 12 8 20 8 5 K 0 14 65 0 13 13 16 17 5 j N 1 1 0 3 1 1 1 2 0 P 4 5 22 1 28 14 13 7 4 R 1 8 9 1 16 27 26 17 5 T 1 28 10 3 11 19 67 13 4 Y 0 2 22 1 10 20 9 9 3 U 1 4 4 0 4 6 4 3 9

Percentage Transition matrix, El Tecomate G H K N P R T Y U G G 10 0 50 0 10 20 0 10 0 H H 1 39 7 1 12 8 20 8 5 K K 0 10 45 0 9 9 11 12 4 N N 10 10 0 30 10 10 10 20 0 P P 4 5 22 1 29 14 13 7 4 R

Vocal category R 1 7 8 1 15 25 24 15 5 T T 1 18 6 2 7 12 43 8 3 Y Y 0 3 29 1 13 26 12 12 4 U U 3 11 11 0 11 17 11 9 26 G H K N P R T Y U Vocal category

Average Transition times (sec)

G H K N P R T Y U Mean Min Max G 0.39 0.15 2.62 2.62 1.45 0.15 2.62 H 20.74 0.71 7.18 9.22 0.16 11.78 7.9 12.96 8.83 0.16 20.74 K 2.08 1.53 1.72 1.54 0.05 1.03 0.91 1.27 0.05 2.08 N 1 1.01 37.57 2.6 2.91 9.02 1 37.57 P 3.87 9.86 4.1 15.64 9.14 1.84 5.54 9.62 3.65 7.03 1.84 15.64 R 1.18 33.51 8.86 1.61 3.02 1.52 0.73 8.45 5.36 7.14 0.73 33.51 T 4.89 2.03 1.31 1.03 12.45 2.67 0.33 0.57 0.24 2.84 0.24 12.45 Y 1.56 0.3 2.02 21.07 1.79 16.77 1.82 1.96 5.91 0.3 21.07 U 0.98 0.65 15.92 2.56 2.94 7.78 0.35 0.73 3.99 0.35 15.92

Table 2.3. Transition matrix for calls in El Tecomate (n=740 vocalizations)

80 Variability among Pulsed Vocalizations from El Tecomate (n = 43)

N pulses Pulse Rate/sec mean 5.16 15.26 std 1.34 3.24

min 2 9 max 8 22

0 0.1 0.2 0.3 0 0.2 0.4

0 0.2 0.4 0 0.2 0.4

Figure 2.23. Variation among pulse calls in El Tecomate. Pulse purrs may rise in dominant frequency, or rise and then fall. Frequency modulation, number of pulses, and pulse rate are all highly variable within individuals. Pulses were counted by thresholding the amplitude envelope and counting pulse gap size.

81 Northeastern Dialect Group

I extended the analysis of geographic call variation to more regions of

northeastern Mexico (Casas and Tepeguajes; Figure 2.29 for map). A multidimensional

scaling plot of yelp calls recorded from our four locations in this area is shown in Figure

2.24, and reveals that one of the four subpopulations (Los Colorados) could be

distinguished from the others. This distinction was used to assign the likely origin of a

captive bird, ‘Berry’, of unknown wild origin (see Figure 2.25 for a multidimensional

scaling plot comparing Berry’s yelp calls to Los Colorados and El Tecomate yelp calls;

see Figure 2.26 for clustergram of Berry’s yelp calls to the yelp calls of the four northeastern locations).

I also determined the proportional representation of all common call types from the four northeastern locations (Figure 2.27), revealing the presence of the T-type

vocalization, previously associated with El Tecomate and not Los Colorados, at Casas

and Tepeguajes. Exemplars of common calls from the four northeastern populations are

shown in Figure 2.28, demonstrating call homology.

Figure 2.24. Multidimensional scaling plot, from yelp spectrogram cross- correlation similarity matrix, using yelps drawn from Los Rancho Colorados (black), Tepeguajes (red), Rancho El Tecomate (green), and north of the village of Casas (yellow). Inset shows relative population locations in Tamaulipas, Mexico.

Figure 2.25. Multidimensional scaling plot showing yelp calls from captive bird of unknown wild origin (“Berry”) and calls from Los Colorados and El Tecomate.

LC1 LC4 LC10 LC6 LC9 LC8 LC7 LC3 LC2 LC5 BERRY7 BERRY2 BERRY3 BERRY8 BERRY6 BERRY1 BERRY10 BERRY9 BERRY5 BERRY4 TEC6 TEC3 TEC2 TEC1 TEC7 TEC8 TEC5 TEC9 TEC10 CASAS4 TEC4 TEP1 CASAS1 TEP2 CASAS2 TEP3 TEP4 CASAS3 TEP5 TEP6 0.0 0.1 0.2 0.3 0.4 Distances

Figure 2.26. Clustergram of Berry’s yelp calls and yelp calls from the northeastern dialect group, based on pairwise cross-correlation of spectrograms.

85 Vocal usage rates among four locations in the northeast dialect group

Figure 2.27. Relative rates of call usage among the northeastern dialect group, for calls P, K, Y, H, R, G, and T.

Figure 2.28. Examples of calls drawn from each population. The Los Colorados T vocalization depicted is one of only two recorded there, from one individual, who also produced y-type calls typical of the more northward populations (see also Figure 2.24).

The Victoria language group

Calls from an inland population, Victoria (see Figure 2.29 for map), were also analyzed both manually and by automated cluster analysis. Exemplars of common calls from Victoria are shown in Figure 2.30. There was no overlap of homologous calls in the

Victoria population compared with the northeastern dialect group. A subsample of calls from Victoria was subjected to automated cluster analysis (Figure 2.31) to determine relative counts of each classified type from this unfamiliar repertoire (language). Using automated categorization, I also sorted usage rate by rank order, for a comparison of usage rates of repertoire elements, between Victoria and Los Colorados (Figure 2.32) bouts.

Figure 2.29. Spectrograms of common call types from audio samples of seven locations throughout the range of Amazona oratrix in Mexico.

Figure 2.30. Typical vocalizations from a population in a different language group, from mountains northwest of Cd. Victoria, Mexico. Percentages shown are for analysis of one of four pairs observed and recorded in 1994.

Victoria vocalizations, automatically classified from spectrogram cross-correlation

Figure 2.31. Pair bout of 78 vocalizations. 13 forced categories (shown by color); spectrograms of modal types shown.

Within-bout Variability: Comparing bouts from different language groups

120 25

100 20

80 . 15

60 Count Count 10

5 20

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Cluster number, 15 forced clusters Cluster number, 15 forced clusters

Singleton LA5 Bout from pair in Victoria (n= 390 vocalizations in bout) (n= 78 vocalizations in bout)

Figure 2.32. Sorted histogram of call types (15 forced clusters) from different repertoire groups, showing a similar usage pattern. Left: Los Colorados. Right: Victoria.

Temporal stability of vocal elements

I used an automated clustering algorithm on a recording of a pair of A. oratrix from the Cornell library of natural sounds to classify 36 calls present in this recording

(Figure 2.33). I compared rare calls from Casas visually and acoustically, and identified 3 call types present in the 1994 and 1995 recordings that matched the 1961 recordings

(Figure 2.34).

Cornell archived vocalizations, automatically classified from spectrogram cross-correlation

Figure 2.33. Dendrogram of 36 calls recovered from LNS catalog 8435. Spectrograms of noise-reduced modal call types shown, color- matched to the cluster containing them.

Figure 2.34. Homologous vocalizations of Amazona oratrix: comparison of Cornell archival recordings and our recordings made in 1995 in a similar location, near the village of Casas in Tamaulipas, Mexico.

Juvenile development of stereotyped calls

I observed calls during subsong of juveniles 3 months post-hatching at Los

Colorados and found examples of all six of the common adult call types (Figure 2.35).

Juveniles Parents

Figure 2.35. Wild juvenile A. oratrix vocal development at two-months post-fledging (three months post hatching). Call labels are given in the left column.

Dimorphism in head coloration

I examined dimorphism within pairs of A. oratrix at Los Colorados. Counts of pairs by head fraction of yellow is shown in Table 2.4, revealing an over-representation of dimorphic dyads that had head-fraction differences >1/2.

Pair Observed Expected full-full 2 4 full-3/4 5 6 full-1/2 1 1 full-1/4 10 5 3/4-3/4 3 3 3/4-1/2 2 1 3/4-1/4 5 5 1/2-1/2 0 0 1/2-1/4 0 1 1/4-1/4 0 2

n = 28 pairs (56 paired birds); d.f. = 9 Chi-squared test, p = 0.43 ______

Collapsing categories by differences between birds in a pair

Category N (obs) N(exp) X2 Diff. > 1/2 10 5 0.013618* Diff. <= 1/2 18 23 493 *P < 0.05 Bonferroni-adj. alpha (.025) for 2 repeated measures

Table 2.4. Top: Pair dyad counts, by fraction of yellow on head. Bottom: pooled counts and Chi-squared test.

DISCUSSION

Characterization of vocalizations

I recorded birds within Los Colorados for initial characterization of vocal

repertoire. Many pairs are resident during the day on territories year round (Enkerlin,

1994; personal observations). Like most other birds, vocal activity is generally confined

to morning and evening hours, and most days the parrots are quiet during midday.

Automated classification revealed some categories to be more variable than others

(e.g., Pulse). Manual and automated classification agreed broadly, while category

subtypes that were not classified manually were revealed by automated classification

(though with careful choice of number of forced categories to reveal corresponding splits)

with manual classification boundaries (Figure 2.12). Of course, subtler measures of behavioral response and contextual use associated with these intra-call variances are required in order to confirm that these are salient variations to the birds themselves

(Dooling et al. 1990). Since Pulse-type calls are highly variable compared to other call types, there is a high potential for continuous variation in these signal class to encode subtle gradations of motivation or intention (Morton 1977; Smith 1977).

With a reasonable rate of categorization correspondence between human and programmatic observers, I can begin to understand the levels of variation of large

repertoires of long-lived animals by employing automated-assisted methods. Such

methods are also being developed in other fields, to the extent that models which can identify human musical genres by frequency, rhythm, and timbre analyses are in early- stage working forms (Bagci and Erzin 2007; Cataltepe et al. 2007; Kitahara et al. 2007).

Some examples of the application of feature-based categorization of animal signals using

similar approaches include studies of killer whales (Brown et al. 2006), bull-frogs (Suggs

and Simmons 2005), and bats (Melendez et al. 2006) - but see (Deecke and Janik 2006)

for a cautionary warning about relying exclusively on automated measurement and

categorization systems. By relying on automated-assisted methods of deconstruction of

animal signal repertoires, in conjunction with immense sampling capacity afforded by

modern digital field recorders, we can get to the point were our data analysis rate exceeds

our data collection and interactive playback becomes possible, e.g. recognizing call types

in real time, and responding with playback of signals according to the hidden rules

revealed by Markov-chain analysis to test signal function. Smith distinguishes the

"message" of a signal, the indication of the signaling individual's state and identity, from

its "meaning," the influence of a signal on recipients (Smith 1977).

In an analysis of several hundred pulsed vocalizations, I observed continuous

variation in the number of pulses per vocalization (Figure 2.10b). An interesting possibility is that these calls may represent broad gradations of “meaning” overlaid on a carrier signal of subunit pulses that vary in number, and in emphasized frequency.

The source of variation of the detectable differences within stereotypical call

types among pairs is mostly due to frequency shifts rather than amplitude envelope

differences (Table 2.1). However, these frequency shifts may represent momentary frequency shifts of call structure, e.g., due to motivational state, rather than fixed differences in structure among pairs. For example, I noticed when I approached a tree containing parrots, the calls of the parrots would shift up in pitch, although no alarm- specific calls were identified.

Except for one call each for BE (h) and LA9 (g), each vocal type was found for each recorded territorial social group, showing a large repertoire overlap. Difference in rates of use, or presence / absence may be due in some pairs to precision from uneven sample sizes.

I also analyzed the vocal repertoire by transitions among call types, to understand whether variety in vocalization is immediate or eventual and whether calls cluster in time by type. The most obvious feature of the transition matrices is that the diagonal entries exceed the other entries within a row, indicating that a call of any given type is most likely to be followed by a call of the same type. The diagonal entries also exceed the other entries in each column, indicating that if a call occurs, it was most likely to have been preceded by a call of the same type. This demonstrates eventual, rather an immediate, variety in display of call types. Transitions among call types of El Tecomate were similar to transitions among call types of Los Colorados, with a similarly strong tendency for immediate repetition (compare Table 2.2 and Table 2.3). As in the population at Los Colorados, pulse calls at El Tecomate were also quite variable (Figure

2.23).

Variation in dialects among populations can act as a marker for the origin of a particular bird. Every January, large numbers of juvenile birds arrive at Los Colorados, including fledglings and unpaired birds. The source of these juveniles was unknown, since the breeding rate at the ranch was quite low, with only approximately 10% of pairs attempting to breed each year. While most juveniles produced sounds of consistent with the Los Colorados population, I observed one juvenile at Los Colorados that made unusual calls. Recordings of this individual showed that ∆tyelp was consistent with the

100 calls of El Tecomate but not Los Colorados (Figure 2.22). Indeed, the next vocalization given by this juvenile was the T type, the most common call type at El Tecomate, accounting for about 20% of calls sampled there (out of n = 746 sampled vocalizations from numerous individuals and pairs). However, at Los Colorados this call was only observed twice out of nearly 3000 sampled calls; both examples were given by this juvenile. This suggests that the populations of Los Colorados and El Tecomate communicate through the migration of juveniles.

This call type (T) may represent a signal lost by the Los Colorados population, or a novel signal acquired by the El Tecomate population. Other studies of dialect variation have shown that the population boundaries for all call types within a repertoire usually coincide ((Balaban 1988), (Balaban 1988; Wright 1996)). However, this is a case where there is a striking difference between the population boundaries of most calls and the boundary of the T call, especially given the abundant use the calls in the northern dialect group, excluding Los Colorados.

Characterizing calls automatically, and then rank sorting by usage (as in applications of “Zipf’s law”), gives us a way to compare apples and oranges (calls from different language groups, where the similarity of common calls is too low permit the identification of call homology, as in Figures 2.29) (McCowan et al. 2005).

Development of song in juvenile parrots

I observed a pair of juveniles at Los Colorados three months post hatching (one month after fledging), as they were producing subsong (variations of adult calls produced at low volume during juvenile song development) while the parental trio was away

101

(Figure 2.35). Despite the young age of these juvenile individuals, I were able to categorize their calls into the adult call types. It appears that linguistic variety of the local population may be learned quite early in development, highlighting the likely importance of acquiring these vocal signals. This is also consistent with the hypothesis that juveniles begin to acquire and practice vocal labels prior to dispersal, as suggested by the presence of El Tecomate vocalizations in a juvenile in Los Colorados (see above).

Dimorphism and plumage variation

Amazona oratrix is an extreme among birds with respect to the amount of variation in plumage across the entire body, which enabled us to distinguish individuals

(Figure 2.3-2.4). The parrots vary in the amount of yellow on their heads, with some having full and others having partial yellow heads. Many have spotting or mottling at the point at which the yellow of the head and the green of the body meet. Some have bright red or yellow epaulets; others have yellow on the breast, back, or legs. The extent to which plumage changes with age is unknown, other than that the yellow increases from hatchling to adult; however, some reports indicate that adults may continue to become more yellow as they age.

Individuals of this species show unusually high levels of plumage color variation, often attributed in the breeding and pet community to population of origin; however, the extent of individual plumage variation observed in eastern Mexico make this geographic interpretation of plumage variation suspect (but see (Eberhard and Bermingham 2004a;

Eberhard and Bermingham 2004b; Eisermann 2003; Ribas et al. 2007) for more recent attempts to resolve phylogenetic relationships within this and closely related species ).

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Individuals differ not only in the amount of yellow on head, breast, legs and even back,

but can vary as well from highly visible to almost no red pigmentation at the resting

wing bend (scapular and secondary coverts, often described as “epaulets”). Given the

difficulty of determining the sex of wild individuals parrots (apart from rare mating and

nesting observations), and since most parrots are described as sexually monomorphic, I

was curious about the possibility of plumage dimorphism in pairs of yellow-headed

parrots, i.e., whether parrots chose mates that were similar or different. I did not have sufficient statistical power with the chi-square test to resolve differences among all possible pairs (Table 2.4), since some categorical counts were below 5. However, I collapsed the categories, by classifying pairs as relatively dimorphic (difference of head coloration between pair members > ½), or relatively monomorphic (difference of head coloration ≤ ½. I again performed a chi-square test, Bonferroni-adjusted for multiple measures. This analysis revealed a statistically significant excess of relatively dimorphic

pairs than would be expected if mates were chosen at random. This over-representation of dimorphic pairs could, however, represent age differences among pair members.

Although I was unable to determine the sex of all individuals from long-range observations of mating behavior, sexual dimorphism cannot be ruled out, and remains an intriguing possibility, especially since male/female size ratios appear to be greater in

oratrix than in auropalliata or ochrocephala (Table 2.5, tabulated from published data

(Forshaw 1989)). An analysis of pair-wise calling from the closely related Yellow-naped

Amazon (Wright and Dahlin 2007) revealed sex-specific calls employed in duets.

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Male / female specimen size in three species of the ochrocephala complex: Size dimorphism?

ochrocephala auropalliata oratrix ochrocephala xantholaema nattereri panamensis auropalliata parvipes belizensis oratrix tresmarie

Mean Values wmean 1.017 1.045 1.047 1.005 1.057 1.027 1.043 1.046 1.046 tailmean 1.01 1.01 1.00 1.02 1.01 1.09 1.14 1.04 1.04 exculmean 1.04 1.06 0.99 1.00 1.04 0.99 1.08 1.07 1.06 tarsmean 1.02 1.09 1.05 1.00 1.03 1.03 0.99 1.05 1.07

By Species 1.024 1.034 1.055

Max. wmax 0.976 1.008 0.960 1.019 1.000 1.107 1.101 1.071 1.046 tailmax 0.98 1.01 0.96 1.02 1.00 1.11 1.10 1.07 1.05 exculmax 1.12 1.06 0.97 1.00 1.06 1.00 1.09 1.03 1.06 tarsmax 1.00 1.09 1.00 1.04 1.00 1.08 1.00 1.04 1.08

By Species 1.013 1.044 1.061

Min. wmin 1.055 1.045 1.047 1.005 1.048 1.000 1.054 1.078 0.987 tailmin 1.00 1.01 1.05 1.01 0.96 1.08 1.16 0.99 1.02 exculmin 1.03 1.06 1.00 1.00 1.00 0.94 1.06 1.00 1.03 tarsmin 1.00 1.09 1.08 1.04 1.04 0.96 1.04 1.09 1.04

By Species 1.032 1.004 1.046

Sample size male 10 1 8 8 10 4 3 15 10 female 8 1 7 8 10 2 3 13 4 Table 2.5. Ratios calculated from data (means, maxima, and minima) as reported by (Forshaw 1989) for museum specimens. Column headings are species and subspecies. “By Species” is an index (mean) of all tabulated values for each species. Measures were from wing, tail, exposed culmen, and tarsus lengths. Numbers greater than one indicate larger male size.

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Higher-level geographical variation of call repertoires

While El Tecomate, Los Colorados, Casas, and Tepeguajes comprised a dialect

group, there were still noticeable differences in the same call types. I analyzed cross-

correlations among calls within a call type (e.g., yelp) using multi-dimensional scaling

(Figure 2.24). Multi-dimensional scaling is a data reduction method that operates on

similarity, dissimilarity, or distance matrices, and collapses variance among samples into

a limited number of dimensions, and is useful as a clustering technique. This analysis

revealed that Los Colorados calls are somewhat distinct from the El Tecomate,

Tepeguajes, and Casas locales (Figure 2.24). Parrots within the Los Colorados area, though, still had high categorical overlap with the common repertoire of other members of the eastern dialect group. Furthermore, these northeastern dialects shared similar rates of call usage for the major call types, excepting the T-type call, which was deficient at

Los Colorados, as discussed above (Figure 2.27).

Because I found two dialects of the same vocal repertoire at Los Colorados and El

Tecomate, I investigated whether all populations of A. oratrix shared the same recognizable vocal repertoire. At an inland site, Victoria, I recorded several individuals and pairs. To my surprise, I found that the Victoria population had a non-overlapping, distinct set of vocal signals that could not be assigned as homologous to vocalizations at

Los Colorados or El Tecomate. The three most common vocalizations accounted for more than 87% of the vocalizations recorded from Victoria. Calls of Victoria are qualitatively different from all of the call types from Los Colorados and El Tecomate.

This is best shown in a comparison of the spectrograms of the vocalizations from the different populations (Figure 2.29-2.30; contrast with Figures 2.5-2.10a). While the

105 parrots of Los Colorados and El Tecomate may be said to produce ‘dialects’ of the same

‘language’, the parrots of Victoria appeared to produce a different ‘language’.

Because of my short exposure to the Victoria population, my intuitive knowledge of repertoire call types for this population was not as informed as for the Los Colorados population. Therefore, after validating the method of automated call classification using the Los Colorados population, as described above, I used automated classification and cluster analysis to categorize the Victoria calls (Figure 2.31). This allowed us to count calls of each call type and offers the ability to compare among repertoires that share few to no homologous vocalizations (McCowan et al. 2002; McCowan et al. 1999). Although the calls of Victoria and the northeastern dialect groups were not obviously homologous,

I did observe a similar distribution of relative rates of calling, which is a measure of repertoire diversity (Figure 2.32).

The populations of Rancho El Tecomate and Rancho Los Colorados appear to be part of a large, wide-ranging eastern dialect group, as revealed by repertoire similarity. I wondered whether any dialect boundaries existed between Victoria and the coastal populations (Los Colorados and El Tecomate). I located remnant populations of yellow- headed amazon parrot populations at intermediate locations: Casas, approximately halfway between Victoria and El Tecomate, and Tepeguajes, approximately halfway between El Tecomate and Los Colorados. The recordings made at these intermediate populations showed that the vocal repertoire of these intermediate populations most resembled the El Tecomate population, indicating that Casas and Tepeguajes are part of the eastern dialect group (Figure 2.27-2.28), and are distinct from Victoria.

106

It is likely that there is occasional interchange of juveniles or other non-resident

individuals among these Los Colorados and locations are El Tecomate, and possibly meme flow of vocal elements with them as well, since juvenile acquisition of local labels before dispersal was observed. The population near Victoria is likely insular to the other

northeastern populations discussed here, and may be a remnant population living at a

lower density than at the former locales (Aragon-Tapia, 1986; Schindlinger, pers. obs.

based on interviews with local residents). The vocal repertoire of the Victoria population

possesses no easily identifiable homologous calls with populations in the eastern dialect

group.

I further sampled calls from of the Yellow-headed Amazon parrot from two more

distant locations: Tres Marias Islands (recordings provided by Ernesto Enkerlin), and

Rancho Sandoval, Campeche, Mexico (From John Sauer at Pawtuxet Wildlife Research

Station). The most common calls from the Tres Marias, Sandoval, and Victoria

population recording samples are shown in Figure 2.29, and do not appear to be

homologous in form with any of the previous populations' calls or with each other.

However, without information about clinal gradients of call types between populations

(perhaps unlikely for the geographically isolated Tres Marias population), I cannot rule

out the possibility that they are not in fact evolutionary variants of these same calls. For

examples of similar call types from other Amazon parrot species, see Appendix to this

chapter.

The geographic variation of the eastern dialect calls has a potential utility for

identifying the location of origin for a particular bird. One tourist from Wisconsin, who

visited our field site, had an old Yellow-headed Amazon parrot that produced sounds that

107 she believed to be wild in origin. She suggested that the vocalizations of her bird

(“Berry”) were similar to the calls heard at Los Colorados, but with higher pitch and with a different “accent”. Indeed, multidimensional scaling of recordings of the yelp call of her captive bird showed that this parrot’s calls clustered with the El Tecomate/ Casas/

Tepeguajes dialect group (Figure 2.25-2.26), rather than with the Los Colorados group.

Within the El Tecomate/ Casas/ Tepeguajes dialect group, Berry’s calls cluster more closely with El Tecomate than with the other dialects, suggesting the most likely region of origin. This suggests the possibility of using call similarity to assign population of origin to birds that have been in captivity for 30 or more years.

Geographic variation of acoustic signals has been documented in many species.

Within the twenty-nine orders of birds, there is yet little documentation of such variation in the orders other than Passeriformes. In one such account of a non-passerine,

Nottebohm (1970, 1972) describes differences in flight calls of Amazona amazonica between populations on the island of Trinidad, yet without published sonographs. Other than this flight call, there is no mention of the scope of this variation with respect to the rest of that species' repertoire. Similarly, Wright (1996) notes variation in a contact-calls and duets among roost areas in A. auropalliata in Costa Rica. Saunders (1983) presents several sonographs of the Wy-lah call of Short-billed Glossy-black Cockatoos recorded in Australia from two locations separated by 400 km, six from one population and three from the other. One of these latter three sonagraphs closely matches a vocalization from the other population, and the other two vocalizations, though similar to each other, differ from those reported for the other population. In this case there is some evidence for geographic variation, but as Saunders points out, the evidence is inconclusive. More

108

conclusive data for wild parrot dialects can be found for keas (Bond and Diamond 2005),

Orange-fronted Parakeets (Bradbury et al. 2001), St. Lucia Parrots (Kleeman and Gilardi

2005), Galahs (Baker 2003), Yellow-naped Amazon Parrots (Wright and Dorin 2001),

and Ring-necked Parrots (Baker 2000). Here I presented evidence of geographic

variation for Amazona oratrix. Two populations sampled (LC and T) had slightly

different repertoires with significant overlap of vocal elements. A third population (V)

differed from the other two. Recordings of the Yellow-crowned Amazon parrot (A.

ochrocephala) from Peru and Venezuela show marked differences in repertoire as well

(pers. obs.). Thus it is clear that in the Psittaciformes geographic variation of acquired

vocalizations occurs, and may be widespread. It will likely be found in other parrot

species as well as more are recorded in the field. How such variation may influence social

behavior is examined further in Chapter 3.

Environmental factors influencing vocal variation

Wiley and Richards (1978) suggest that geographic song variants may arise which represent acoustic optima for maximizing broadcast range, given the physical sound

transmission properties of different environments. While this might explain some of the

differences between the Victoria and eastern dialect group of A. oratrix, it is also

plausible that the variation found for these parrot vocalizations represents the

evolutionary divergence of culturally inherited traits. The eastern dialect group locations

(LC and T) appeared to have a much higher density of parrots than the Victoria location.

For culturally transmitted traits like vocalizations it is likely that a larger, more densely

spaced population can maintain a larger repertoire (or "meme pool") than a smaller, more

109 dispersed population (Williams and Slater, 1990; Boyd and Richerson, 1986).

Interchange within the coastal population, in conjunction with social pressure to conform, may explain the remarkable persistence of learned vocal elements in this species, despite the vocal lability in captive conditions for which they are renowned.

Interspecific mimicry has been noted in wild African Grey parrots (Cruickshank et al. 1993). Nottebohm and Nottebohm (1969) did not note any interspecific mimicry by

A. amazonica, nor have I noted it in A. oratrix, or in two sympatric parrot species, A. viridigenalis or A. autumnalis, possibly indicating a different function for vocal learning in these species than in African Greys. The imitative vocal proficiency for which A. oratrix is famous appears to reduce inter-individual variation within a population.

Not yet well understood is whether vocal units within a repertoire can follow independent evolutionary trajectories, or conversely whether all repertoire units will possess the same geographic boundaries (as (Wright 1996) reports for call and duet repertoires of the Yellow-naped parrot among roost areas in Costa Rica). An evolutionary independence of vocal units (analogous to independent assortment) would strengthen the analogy between genes and memes (Dawkins 1976), and would also strengthen the hypothesis that repertoire variation may be due to processes other than geographic isolation-by-distance. The presence of the T-type vocalization at high frequency in El

Tecomate, and its near-absence in Los Colorados, despite sharing all other common calls, indicates that the evolution of specific calls may indeed follow independent trajectories.

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Temporal Stability

One recording of A. oratrix, recorded near the Rio Corona (approximately 35 km northeast of Victoria) in June 1961 was obtained from the Cornell Library of Natural

Sounds (recordist L. Irby Davis). I heard and recorded vocalizations similar to those on this tape within 10 km of the original site in December 1994 and March 1995 at a large roost of predominantly eastern repertoire-type oratrix. There were only a handful of birds out of at least 100 pairs at this site making these 1961-like vocalizations. These were compared acoustically and by sonogram, and were categorized by both manual assignment and verified by automatic classification (Figure 2.33).

The recordings of A. oratrix from 1961 were compared to similar vocalizations I recorded in 1994 and 1995. Spectrograms of the homologous recordings from both time periods are shown in Figure 2.34. There has been little change in these vocal elements in thirty-four years, and what differences can be seen may be due to an inexact match of the locations where the recordings were made.

The fact that vocalizations appear to be temporally stable within an area, yet vary across geographic space, may make it possible to use vocalizations as population markers. Recordings taken from within each area reveal a high level of stereotypy within most common vocalization types among the local population. This raises the possibility of using vocal repertoire as a conservation tool in establishing whether populations at different sites are insular (as suspected for Victoria relative to Los Colorados), or are two samples from a large, widely dispersed population (as suspected for Los Colorados and

El Tecomate). It may also reveal the origin of specific individuals during migration or in law enforcement when animals poached from the wild for the pet trade are confiscated.

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These possibilities hinge on the assumption that dialects are stable within areas over long periods of time. Preliminary evidence, in the form of observations spanning thirty-four years, indicates that they are.

112

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APPENDIX

118

Five minute solo song bout, showing calling rate at multiple scales A. oratrix ‘Female 233’ 5000

0 30 35 40 45 50 55 60 5000

0 60 65 70 75 80 85 90 5000

0 90 95 100 105 110 115 120 5000

0 120 125 130 135 140 145 150 5000

0 150 155 160 165 170 175 180 5000

0 180 185 190 195 200 205 210 5000

0 210 215 220 225 230 235 240 5000

0 240 245 250 255 260 265 270 5000

0 270 275 280 285 290 295 300 Time (sec)

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Calls in Context: Duets from Mountains above Ciudad Victoria

Duet of six Victoria vocalizations, ending with a pulse-type vocalization.

Another Victoria pair duetting

Another duet

120

6000

5000

4000

3000 Frequency (Hz)

2000

1000

1.5 2 2.5 3 3.5 Time (sec) A. oratrix nestling “ick ack ack ack ack” begging call

121

A. oratrix nestling, begging call bout (24 sec):

0.4 0.2 0 -0.2 -0.4

6000

4000

2000 Frequency (Hz) Frequency

0 0 5 10 15 20 Time (sec)

122

Musical duets from Amazona ochrocephala, recorded Yupakari, Rupununni, Guyana, 2007

Highly embellished common calls, from Amazona ochrocephala, recorded Yupakari, Rupununni, Guyana, 2007

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Pulse-type calls from Amazona ochrocephala, recorded Yupakari, Rupununni, Guyana, 2007

Duet from Blue-fronted Amazon (Amazona aestiva), Brazil, with call similar in structure to oratrix calls

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Chapter 3

Vocal Response of Yellow-headed Amazon Parrots to Population-level and Species-level Repertoire Variants

ABSTRACT

I investigated the vocal response of wild Amazona oratrix parrots to playback of calls recorded from multiple populations of this species, and from the two sister-species of the ochrocephala species group (A. ochrocephala and A. auropalliata) to determine the role of shared vocal dialect in determining a response, in this exemplary vocal species. I performed playback experiments to determine whether these repertoire differences had functional consequences for the behavior of listening individuals, using recordings from each of three populations in Tamaulipas, northeastern Mexico (Rancho Los Colorados, near Barra del Tordo; Rancho el Tecomate, near La Pesca; and Victoria, in the Sierra Madre Oriental, 14 km northwest of Ciudad Victoria). A second set of experimental playbacks recordings of sister-species to A. oratrix (A. auropalliata and A ochrocephala) provides some indication that they do not distinguish between unfamiliar repertoires of their own species, and those of other closely-related species of parrot. The trend of biased listener response to vocalizations along a gradient of similarity to the listener’s own vocal repertoire (self-same preference) in this and other species of vocal learner is discussed as a mechanism driving imitative learning, and creating population-level repertoire variation.

INTRODUCTION

The study of the evolution of vocally complex, open-learning communication systems in animals is seen as a comparative counterpart to historical human linguistic

studies, in the attempt to explain the rise of exemplary vocal and linguistic skills (such as

our own, and those of cetaceans and many bird species). In particular, biased responses to

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familiar sounds may arise from a learning-based system, based on perceptual (sensory)

bias and on memory constraints of the listener, and lead to a heightening of the variance

of vocal signals among populations, compared to within-population variance. In species

where such biased responses in social interactions lead to differences in mate preference

(Paterson 1985), or prejudice in directed aggression (Beecher et al. 1994; Krebs et al.

1978; Nelson and Soha 2004), among-population vocal differences could act as barriers

to dispersal (isolation mechanism) among populations. Alternatively, for species which

retain the ability to acquire vocal signals beyond the dispersal stage, the presence of such

vocal response biases may act as the underlying social impetus for immigrating

individuals to imitate local vocal types (Baker and Mewaldt 1981; Lynch et al. 1989).

Research into the ecological and social conditions favoring vocal learning has

profited greatly through the use of comparative methods (e.g., (Baker 1975; Baptista and

Schuchmann 1990; Catchpole 1980; Farabaugh 1982; Kroodsma 1977; Nelson et al.

1995; Nottebohm 1976)). By expanding the taxonomic range of species under consideration we hope to uncover broader generalities about the factors selecting for vocal learning. In many bird species, though not all, the degree to which a bird will respond in playback is in proportion to the similarity of the stimulus recording to the

bird's own vocalizations(e.g., as first demonstrated for chaffinches (Hinde 1958), and

later (Baker 1983; Baker et al. 1987; Beecher et al. 2000; Beecher et al. 1994; Beecher et

al. 1996)). This finding has been further extended to another species in the order

Psittaciformes, and matches the general trend found in this order (Vehrencamp et al.

2003; Wright 1996; Wright and Dorin 2001), and to species in the Passeriformes (e.g.

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swamp sparrows (Balaban 1988); but see (Chilton and Lein 1996) for typical a counter- example based on mate attraction, rather than territorial defense).

In this research, Istudied the vocal response of Amazona oratrix to recordings of vocal signals from conspecifics of the same or different population, as well as vocal signals from a congeneric species. Results from this study are discussed in light of the growing body of evidence of self-similarity- or familiarity-based responses to vocalizations in parrots and other vocal-learning species.

METHODS

Identifying locations for playback to unique pairs and trios

Iidentified 11 reliable “pair-points” within Los Colorados ranch (LC; Figure 3.1), where a unique pair or trio responded to a playback of vocal recordings. Eleven "pair- points" were located at LC by playing back locally recorded vocalizations at various locations throughout the ranch when A. oratrix were present. If a pair responded by flying directly overhead with loud vocalizations and landed nearby, that location was deemed to have territorial significance for that pair. In this manner eleven such points were located, each corresponding to a different pair (or trio, in two cases). Repeated trials prior to experimentation indicated that the same pair or trio responded at each point (n =

14 pairs).

In this pilot study of playback response, two stimulus tapes were used to identify pair- points. One stimulus recording was from a pair interactively vocalizing, using common call-types. The other recording used was of a female producing “song,” characterized by an abundance of rare complex vocalizations not common in the call repertoire, but heard

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occasionally from many of the focal parrots. Response was scored according to the

reaction to playback as follows:

0: No apparent response from the subject pair, either vocally or with movement

1: Pair flies directly overhead during playback

2: Pair approaches and lands nearby (less than 100 m away), while vocalizing

vigorously

3: Pair rapidly lands nearby vocalizing vigorously, and moves around the playback

speaker in an apparent attempt to locate the source of the calls.

Each pair or trio that responded with a score of two or more were identified for

subsequent playback experiments, since for that pair, locally-recorded calls played at that

geographic location elicited a vocal response. Unique pairs and individuals were identified by plumage variation, as described in Chapter Two.

Construction of playback stimulus recordings

Tapes from three different parrot pairs were made for each treatment category,

and were selected from bouts of unique pairs producing loud broadcast vocalizations for

at least sixty seconds. Samples to be played back were chosen solely on the basis of the

quality and clarity of the recording, not on the presence or absence of any specific

vocalizations. Each playback presentation consisted of sixty seconds of

vocalizations, repeated three times with ten second spaces in between. Eleven pairs were

tested with each of the treatments. So that each of the four stimulus classes would be

presented without order bias, a 12 x 12 Latin square was constructed (since each

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playback tape was used again 3 times), where every tape occurred in every playback position. Data for the 12th pair of parrots tested in the field was lost.

Figure 3.1 – Location in Tamaulipas, Mexico of source oratrix recordings used for playback at LC.

Playback equipment

Playback was done with a Sony Xr6200 or Sanyo M7024H cassette player through a Realistic Super Powerhorn at 100 dB SPL at peak (A-weighting) at one meter, as measured on an analog Realistic SPL meter. This is approximately the calculated sound output (96 dB) of vocalizing oratrix parrots, measured from known distances in the field (unpublished data).

Criteria for initiation of playback experiment

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The context in which a playback was presented was controlled in order to reduce extraneous influences. A playback session would begin if a pair was seen and identified at no more than 150 meters from its playback point, and was not engaged in a vocal exchange with another nearby pair. Four tapes were played at each pair-point, one from each treatment class, for a total of 44 presentations. Each pair was tested not more than once with a playback in a morning or evening observation period; no two pairs with adjacent home ranges were tested in the same observation period. LC recordings presented to each pair were not from that pair, or from a pair with an adjacent homerange, and were from different pairs than those used in defining the pair-points prior to experimentation (those used in the female song versus pair calls experiment). Playback experiments targeting the same pair-point were separated by a minimum of six hours, up to four months.

Call counting

Calls were recorded using an 18” parabolic dish microphone (Dan Gibson EPM-

650) connected to a Sony TCD-5M cassette recorded or Canon L1 video camera.

Iexamined digitized sounds acoustically and visually. Calls were counted manually (pulse trains were counted as a single vocalization), both by eye and ear using audio-visualization software (Canary, CoolEdit), in order to distinguish overlapping calls given by pairs, and to obtain an accurate count of the total number of calls produced by a pair. Call counts were compared among treatments using the Kruskal-Wallis test. Where means were statistically different, a Multiple Comparisons Test (Zar 1974) was done to determine the significance of pair-wise differences among treatments.

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Experiment 1. Geographic Variation: behavioral response

Once the geographic pair-points were identified for subsequent playback, Iset up the experiment with four treatment classes of stimuli:

1) LC- Same repertoire, same population (but neither from the same pair nor an

adjacent-homerange pair): Rancho Los Colorados the home population

2) T - Same repertoire, different population: Rancho El Tecomate, located

approximately 160 km due north of LC, and which shared vocal elements with

the LC population

3) V - Different repertoire, different population: Victoria, approximately 240 km

northeast of LC, and which did not share vocal elements with LC or T

4) C - Different congeneric species (A. viridigenalis): a sympatric Amazon

parrot at Rancho Los Colorados, as a control for the experimental conditions

(such as presence of the experimenter)

This allowed us to determine the response to a continuum of similarity of vocalizations, from their same population down to a different Amazon parrot species.

When the criteria for initiation of a playback experiment were met, Iinitiated the experiment (Figure 3.2). The pair was recorded for ninety seconds (pretest) to obtain baseline vocal activity, thereby enabling the control of pair-wise differences in background vocal rates. This was followed by playback of the first three minutes of the stimulus tape. The stimulus tape was then stopped, and the subject pair was again recorded for ninety seconds (intertest), followed by three minutes of the playback. A final recording of the pair was made for 90 seconds (posttest). This back-and-forth exchange

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of playback and silence is similar to the natural exchange I observed between different pairs in the wild.

record play record play record

Figure 3.2 Structure of playback experiments (1 & 2).

This design permitted three controlled comparisons:

1) Pre-test calling rates can be compared among treatments before

playback presentation to ensure that initial vocal activity level did not

differ among treatment groups, and permit normalization in response

rates among pairs;

2) Vocalization rates can be compared among treatments within

stages; and

3) Within treatments, changes in vocalization rate can be measured

against activity prior to playback.

Response level was measured by counting the total number of discrete vocalizations given by a pair during the pre-, inter-, and post-test, for each of 11 pairs in response to each of 4 stimuli (44 playback experiments). Because some pairs may be more vocally active than others, response rates were also normalized by pair relative to

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their own maximal response to the four playback treatments (LC, T, V, C) (e.g., the

greatest response of a given pair to any stimulus would be normalized to ‘1’). This

analysis should eliminate noise due to background differences in vocal activity among

pairs.

Experiment 2. Response to playback of closely related species within the

ochrochephala complex, and to unfamiliar repertoire of conspecifics.

The stimulus tapes used in this experiment all represented vocal repertoires unfamiliar to the subject parrots. The first recording was again from Victoria, while the second and third treatments were recordings of the Yellow-crowned Amazon, recorded at the Tambopata River in Peru in 1993, and A. auropalliata, the Yellow-Naped Amazon, recorded in southwestern Guatemala in 1993 ( at Finca El Caobana, Esquintla) (Figure

3.3). The methodology employed was identical to that of Experiment 1, and again, three tapes of three unique vocalizing pairs of parrots were used for each treatment. A total of

23 playbacks was performed (n = 7, 8, and 8 for Victoria, ochrocephala, and auropalliata playback presentations, respectively).

Figure 3.3. The Yellow-crowned, Yellow-headed, and Yellow- naped Amazons (A. ochrocephala, A. oratrix, and A. auropalliata). See also range map for these species in Chapter One (Figure 1.3).

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Experiment 3. Playback stimulus: song vs. call

A post-hoc analysis of response to each of the stimulus tapes was performed.

These stimulus tapes were: 1) a solo female engaged in elaborate song of rare syllable types (played to n=8 responding pairs); and 2) a pair of parrots calling loudly using common, stereotyped syllables (played to n=13 pairs). Responses scaled by the

behavioral score above were compared using the nonparametric Kruskal-Wallis test to

determine if pairs responded differentially to the two recordings. All pairs included for

analysis had a non-zero response.

RESULTS

Experiment 1. Geographic Variation: behavioral response

Before playback during the pretest, there were no significant differences in the

number of calls given among treatments (n = 11 pairs, p = 0.249, Kruskal-Wallis test),

indicating that baseline vocal activity was similar among treatments.

During the intertest interval after 3 min of playback, Iobserved two levels of call

response, high and low. The difference was statistically significant (p < .018) (Figures 3.4 and 3.5, left). The high level of response (approximately 40-45 calls) was elicited by stimuli of recordings of their home population (Los Colorados) and recordings of a similar repertoire of a different population with a similar vocal repertoire (El Tecomate)

(see Chapter 2). A low level of vocal response (approximately 10-20 calls) was elicited by stimuli of recordings of the different repertoire of the farther population (Victoria) and recordings of a different species (A. viridigenalis). However, after the second presentation of playback (a total of 6 minutes of stimulus), the LC parrots were

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distinguishing among all four treatments (p = .004; non-parametric Multiple Comparisons

Test (Zar 1974); Figure 3.4 and 3.5, right). There is thus a graded response of the subject

parrots corresponding to the similarity of the stimulus to their home population, in the

following order (most to least): LC, T, V, Control. In particular, the response to the home

population is significantly greater than the response to the similar dialect.

After normalizing responses within pairs, we again observe significant differences among stimulus tapes (Figure 3.5). After 3 minutes of playback, a high level of response was given to the home population and similar repertoire (LC and T), and a low level of response was given to the different repertoire and different species (V and C). Again, a graded response among treatments was observed after 6 minutes, with response to the home population significantly greater than any other stimulus class.

The response call rate to the sympatric congeneric species (Control: A. viridigenalis) after 6 minutes of playback was very close to zero. Icompared the subject parrots’ response to calls recorded in Victoria and their low level of response to A. viridigenalis. This difference was not significant after three minutes of playback, but it was significant after six minutes.

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Figure 3.4. Response to playback of calls from home population (Los Colorados), similar population (El Tecomate), different- sounding population (Victoria), and a control (green-cheeked Amazon parrot, recorded at Los Colorados).

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Figure 3.5. Response to playbacks, same data as Figure 3.1, but normalized with respect to maximum response of each pair.

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Experiment 2. Playback of ochrocephala complex and species recognition

Differences in response levels of LC A. oratrix to the three treatments (unfamiliar

A. oratrix dialect, A. auropalliata, A. ochrocephala) were not significant (Figure 3.6).

The response levels to all treatments in this experiment were less than the response to T or LC vocalizations (their own dialect group) in the previous experiment.

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▼ ▼

Figure 3.6. Response to playback of congeneric sister species (A. ochrocephala and A. auropalliata) compared to unfamiliar conspecific vocalizations.

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Experiment 3. Playback stimulus: song vs. call

The context of the recordings (solo female song versus stereotypical pair calling)

used to identify pair-points did not affect the response of the listeners (Table 3.1). The

difference of scored responses to the two tapes was not significant (Kruskal-Wallis

statistic = 0.994; p = .331).

Behavioral Response Index 3

0: No apparent response from the subject r

f pair, either vocally or with movement

1: Pair flies directly overhead during s

playback o 2

2: Pair approaches and lands nearby (less s

than 100 m away), while vocalizing e

R vigorously

3: Pair rapidly lands nearby vocalizing e x 1

vigorously, and moves around the e

playback speaker in an apparent attempt to d

n locate the source of the calls. I

0 p233 p1 Stimulus Tape

Stimulus Tape N Mean (non-zero) Rank Sum (playbacks) response score Female song (‘p233’) 8 2.188 101 Pair calls (‘p1’) 13 1.885 130

Table 3.1. Index of response to female song vs. pair calls, in pilot test of responses to playback. Mann-Whitney U test statistic = 39.0; p = 0.331; Chi-square approximation = 0.944 with 1 df

140

DISCUSSION

In the work described in this thesis, telescopic video equipment and long range parabolic microphones gave us the ability to observe closely the behaviors of these

parrots while keeping a long distance. Idetermined approximate home-ranges (pair-

points, where a resident pair responded to playback) so that known parrots could be

reliably followed from week to week, and in some cases, year to year.1

Playback experiments to territorial pairs of parrots reveal that they respond most

strongly to vocalizations recorded within their home population area (Los Colorados),

and less strongly to same-repertoire (language) / different-dialect recordings made 90

kilometers north (El Tecomate). The focal parrots responded only very weakly to

recordings from their own species recorded 200 kilometers northwest, which exhibited a

highly different vocal repertoire (Victoria) (Figure 3.4-3.5). In analogy to human

vocalizations, “language” may be an appropriate analogous term for this higher level of

difference, as compared to “dialect,” which generally refers to lesser variation within a

language. Even within a repertoire group, response to playback varied in proportion to

similarity of the stimulus to the subject's own repertoire. In order to maximize the social

potency of vocal signals, it seems that acquiring the local dialect would be important to a

parrot (see (Beecher et al. 1994) for a passerine example of young birds conforming to

microgeographic vocal variations). This discrimination may be one important selective

1 When I was first forming an idea of a graduate thesis, I suggested studying wild parrot social

behavior to an associate professor in OEB, Steve Austad, and he remarked that although this was a

fascinating project, it was impractical because individuals would simply fly away and be lost to follow-up observation. Although this project was indeed technically difficult, it did prove to be possible.

141

factor in the evolution of imitative learning in parrots, and perhaps in other species as

well.

As expected, Amazona oratrix responded least to the recordings of vocalizations

of a congeneric species, A. viridigenalis. This is consistent with our understanding of

their behavior. Though they are often seen in the same food trees, A. oratrix and A.

viridigenalis have not been observed to generally interact, except perhaps at the

beginning of the breeding season when prospecting for nest-hole cavities (though they do

roost in the same forest patch in LC (Enkerlin-Hoeflich 1995) with another Amazon

parrot, A. autumnalis).

After short playback times (3 minutes), the subject parrots did not distinguish

between LC and T, or between V and the control (A. viridigenalis)), as indicated by their

response. This could indicate that LC parrots do not recognize V recordings as

originating from conspecifics (or possibly as from conspecific juveniles, not adults).

However, given long playback times (6 minutes), the subject parrots at Los Colorados

were able to distinguish distant conspecifics (Victoria) from A. viridigenalis. Therefore,

Iwondered whether they could also distinguish more closely related sister taxa from distant conspecifics. In order to examine more closely the weak response of LC parrots to the calls of the different-sounding Victoria population, and to more clearly determine whether the differential response to Victoria vocalizations versus A. viridigenalis was indeed a difference in species recognition (i.e., to test if species recognition could take place in the absence of population-specific vocalizations), a subsequent playback experiment was performed. Icompared the responses of the test parrots to more closely- related species from the ochrocephala-complex against their responses to conspecifics of

142

an unfamiliar dialect population (V), and failed to show a different level of response

(Figure 3.6). (Recent attempts to clearly disentangle this phylogenic taxonomy can be found in (Eberhard and Bermingham 2004; Ribas et al. 2007). This suggests that A. oratrix from Los Colorados are no more responsive to the calls of a distant population of their own species, with whom they do not share a common repertoire, than they are to the calls of another parrot species, and points to the importance of learned sound shapes corresponding to local dialects, irrespective of species-specific voice characteristics.

A post-hoc analysis of response to each of the stimulus tapes used to initially identify known responsive pairs indicate that there was no significant difference in response to the two very different vocal modes exhibited in the playback tapes used to initially identify pair-points, solo song from a female (see [Langmore 1998] for a discussion of female bird song) and common calls from a pair (Table 3.1).

Unfortunately, it is not yet experimentally clear what is being communicated by many wild parrot vocal signals (but see [Martella and Bucher 1990; Pidgeon 1980; Power

1966; Saunders 1982] for attempts at functionally characterizing the repertoires of various wild psittacines). For A. oratrix parrots, vocalizations in response to playback may serve a territorial function, as response was elicited only from resident pairs, and only within the area of their residence, and seemingly irrespective of the calling mode of the playback source (elaborate female song versus stereotypical male-female pair calling). While certain vocalizations are used by wild A. oratrix in predictable contexts

(e.g. pre-flight, pair-separation contact - unpublished data), much of the variability with a vocal bout, in terms of having a low repetition number (Kroodsma 1982), occurs in long, song-like sequences or in duets, without apparent reference to external objects or

143

events. This also appears to be the case for two closely related species, A. auropalliata

(Wright and Dahlin 2007; Wright and Dorin 2001; Wright et al. 2005) and A. ochrocephala (personal observations).

In summary, I found that variability in vocal repertoire among different A. oratrix populations and among different Amazona species leads to a reduced response of A. oratrix when the presented dialect differs greatly from the subjects' own dialect. The behavioral consequence of vocal variation was a clinal response to playback corresponding to repertoire similarity. Where this rule of stronger responses to familiar or self-same sounds is in effect, birds which do not learn the local dialect may pay a premium with the loss of signal efficacy. For birds that are broadcast-calling in order to maximize the behavioral response of intended listeners, they can do so best by reproducing sounds from the listeners’ own repertoire.

144

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148 Part B

Complex Patterns in a Simple Communication Repertoire

Illustration by John Tenniel (1820-1914)

149 Chapter 4

Introduction to Acoustic Signaling In Lycaenoid-Ant Interactions

Caterpillars of many species in the Lycaenoidea (the group containing the butterfly families Riodinidae plus Lycaenidae) are found in symbiotic or obligate association with numerous ant species. Sound production in the Lepidoptera bears all the hallmarks of other well-studied insect communication systems, with signals evolving in response to selection on sexual or defensive traits (Ahlen 2006; Lapshin and Vorontsov

2007; Murillo-Hiller 2006). Juvenile sound production in the Lycaenoidea has an important additional function in mediating associations with ants. Certain lepidopteran pupae, often assumed to be in a somnolent stage of development, respond to touch by producing a series of rapid, tremulous abdominal contractions. Some larvae also produce sounds when disturbed, rubbing body parts together or against a substrate such as a leaf, as was recently shown for other lepidopteran families as well, such as for the hook-tipped moth (Drepanidae (Fletcher et al. 2006); and in the common silkmoth caterpillar

(Bombycoidea; Brown et al. 2007). Research to date has suggested that such sounds are a simple startle defensive mechanism, like those produced by some adults (Downey and

Allyn 1978; Hoegh-Guldberg 1972), except in the Lycaenoidea. In this group, sound production may play a role in mediating communication between caterpillars and pupae

150 and their protective ant guard (DeVries 1991; Travassos et al. 2002; Travassos and Pierce

2000).

The Lycaenidae and Riodinidae are close sister taxa, and caterpillars in both families associate with ants, but there are interesting morphological and behavioral differences between them. Caterpillars in the Riodinidae have composed vibrational songs that they use to entice certain ants to attend and protect them. The ultimate goal in these interactions is survival, but the path to this end is an intricate combination of chemical, secretory, and acoustic cues. Sound production in immatures of the

Lycaenidae is not as well understood, but suggests a framework just as elaborate. Here, we summarize what is known about sound production in juveniles of these two groups.

Much of what is known comes from natural history observations, although recent experimental work has lent insight into the context and function of these sounds. We conclude by discussing what is known more specifically about acoustic signaling between the Australian lycaenid, Jalmenus evagoras and its attendant ants in the genus

Iridomyrmex, and we use this as a framework for describing some of the main goals of this thesis.

PUPAL SOUND PRODUCTION

Mechanisms and Distribution Hinton (1948) divided the mechanisms of pupal sound production into four classes, two of which include members of the Lycaenidae. The first one consists of pupae that hammer their abdomens against the substrate on which they rest. These pupae lack specialized structures for hammering, and instead generate sound by knocking against a substrate such as a leaf or twig (Bell 1925). Pupae produce these sounds when disturbed, suggesting a defensive function. As Jackson (1937, p.214) observed of

151 Argiolaus silas crawshayi, "When alarmed the pupa will hammer rapidly on the leaf with its headcase, making quite sufficient noise to frighten away a small predator." No experiments have been done to examine the functions of these sounds. The second class of sound production that includes lycaenids is also the most widespread among lepidopteran pupae: stridulation, the act of grating a file lined with teeth against a hardened plate (Downey 1966). This file-and-scraper organ is found in the intersegmental region of at least one of several possible abdominal segments: 4-5, 5- 6, or 6-7. Pupae from seven superfamilies have all been observed to produce calls in this manner (Table I). Interestingly, despite finding a pupal stridulatory organ in members of the Lycaenoidae, including the Riodinidae, Downey did not find evidence of such an organ in specimens from the families Papilionidae, Nymphalidae, or Pieridae. Hinton (1948), however, had found stridulatory organs in several papilionid pupae, and De Nicéville (in Tutt 1900) had observed that when disturbed, a Troides amphrysus pupa produced a loud noise by moving one abdominal segment over the other. Another butterfly pupa, the common monarch, Danaus plexippus, is capable of generating a clicking sound, but the mechanism of production has not been explored (Downey 1966).

Origins All of the mechanisms of pupal sound production described by Hinton (1948) require varying degrees of abdominal movement. Downey (1966) hypothesized that abdominal flexion is ancestral in the Lepidoptera: ease of movement in the pupal stage is important for moths as they emerge from stiffened silk cocoons. Butterfly pupae have lost most of this movement as abdominal segments fused; lacking cocoons, they no longer required flexibility. Tutt (1900) noted that papilionids retain movement only for abdominal segments four through seven, while nymphalids and pierids have lost all antero-posterior movement and are only capable of lateral flexibility. The retention of

152 limited abdominal movement in pupae in the Riodinidae and the Lycaenidae, Downey (1966) suggested, reflects a functional need.

Riodinid pupal sound production There is only one record noting sound production by a riodinid pupa. Downey (1966) observed that pupae of Apodemia mormo deserti produce audible sounds. In contrast, DeVries (1991a) surveyed 26 species of pupae in two riodinid subfamilies and nine tribes and found no evidence of pupal sound production. He did not examine Apodemia mormo, but he found no evidence of sound production in pupae of another member of the Emesini, Emesis lucinda. Distribution: All ten species of riodinid pupae that have been examined possess two sets of file-and-scraper stridulatory organs, located between the fourth and fifth abdominal segments and also between the fifth and sixth abdominal segments (Table I). This includes members of the subfamily Hamearinae and five tribes of the Riodinidae. Only one of the pupae examined, Lemonias caliginea, is tended by ants. Function: A simple set of experiments conducted by Ross (1966) on L. caliginea suggests that sound production in riodinid pupae may play a role in attracting ants. In addition to a pair of stridulatory organs, a L. caliginea pupa possesses a pair of glands on the metathoracic segment that may produce a chemical attractive to ants (Ross 1964). To examine the role of the stridulatory organ and the paired thoracic organs in the pupa’s interactions with its attendant ant Camponotus abdominalis, Ross used a fast-drying lacquer to occlude pupal organs in each of three different treatments: (1) the paired metathoracic organs, (2) the stridulatory organs, (3) both the stridulatory and metathoracic organs. In the first two treatments, ants continued to attend the pupae, while in the last treatment, the attendant ants abandoned the pupa after 48 hours. From

153 this, Ross hypothesized that the stridulatory organs and metathoracic glands work in concert to attract and maintain attendant ants.

Lycaenid pupal sound production We have known for over two centuries that lycaenid pupae produce sounds, and the mechanism of pupal call production has been extensively studied (Kleeman 1774). The lycaenid file-and-plate mechanism is found in the region between the fifth and sixth abdominal segments of a pupa (Prell 1913; Downey 1966). The file, found on the anterior margin of the sixth abdominal segment, consists of rows of teeth that oppose a rough plate located on the posterior end of the fifth abdominal segment. A pair of longitudinal muscles originating on the anterior region of the third abdominal segment attaches to this intersegmental region halfway between the spiracle and the pupa’s midline, while late-stage pupae possess a short ligament connecting the adult integument with the intersegmental membrane bearing the stridulatory organ (Downey 1966). There is no antagonistic set of muscles; fluid pressure within the pupa most likely returns the file to a resting position after a call has been produced. Downey (1966) and Downey and Allyn (1978) described species-specific variations in the stridulatory organ. Call Characteristics: Descriptions of lycaenid pupal calls vary. Dodd (1916) described the calls of Ogyris as a “tick,” of a “pleasing musical nature,” unlike anything he had heard from moth pupae. Bell (1919) ascribed to pupae of Jamides celeno a “thin creaking noise,” while Downey and Allyn (1978, p.13) described "machine-gun like" clicks common to several lycaenid pupae. Close examination of lycaenid calls has revealed three distinct signals (Hoegh-Guldberg 1972, Downey and Allyn 1978): a primary signal detectable without amplification, often produced by stimulation of the pupa; a secondary signal of lower amplitude and consisting of a set of clicks produced in bursts, sometimes interspersed between primary signals; and tertiary signals that have only been detected in the largest species of pupae and consist of low-amplitude

154 background clicking sounds. All three signals have a wide frequency range, with an airborne-sound component estimated by Downey and Allyn (1978) to encompass the span between 400 and 5000 Hz. Using a particle-velocity microphone attached to a recording stage, DeVries (1991a) examined the substrate-borne vibrations of three species of lycaenid pupae. These pupae had mean dominant frequencies between 1970 Hz and 2300 Hz and pulse rates ranging from 7.5 pulses per second to 19 pulses per second. Using an accelerometer taped to wooden sticks that were sites of pupation, Travassos and Pierce (2000) found that the vibrational components of primary signals of pupae of Jalmenus evagoras had a mean dominant frequency of 849.2 Hz and a pulse rate of 1.76 pulses per second, and secondary signals had a frequency of 772.6 Hz and a pulse rate of 9.24 pulses per second. The primary signal was 5.9 dB louder than the secondary signal. Distribution: A pupal stridulatory organ has been found in every lycaenid pupa that has been examined (Table I). This includes members of the Miletinae, Curetinae, and all five tribes of the Lycaeninae. Pupae from the subfamily Poritiinae have not been examined for the presence of a stridulatory organ. Sound production has been noted in both tended and untended pupae and has been observed in all biogeographic regions. Factors Influencing Calling: Several characteristics about a lycaenid pupa may indicate its likelihood to stridulate. As pupae age, most researchers have observed an increase in sound production. Downey (1966) and Brakefield et al. (1992) heard pupae stridulate just before eclosion, and Hoegh-Guldberg (1972) noted a large volume of pupal calls in the minute before emergence. However, Downey and Allyn (1978) noted that the number of pulses produced each day by pupae of Chlorostrymon simaethis decreased as they got older. Travassos and Pierce (2000) found that pupae of Jalmenus evagoras produced a similar quantity of primary signals in the presence of the attendant ant Iridomyrmex anceps regardless of age, though older pupae produced fewer secondary

155 signals than did their younger counterparts. During diapause, pupae stridulate less frequently (Downey 1966) or not at all (Hiruma et al. 1997). Several other factors are also predictors of a pupa's likelihood to produce calls. Hypochrysops digglesii pupae stridulate more frequently at night (De Baar 1983). Downey and Allyn (1978) noted that Lycaena hyllus females produce primary pulses of longer duration than those of males, but concluded that this intersex difference was insignificant. Likewise, Hoegh-Guldberg (1972) and Travassos and Pierce (2000) determined that there were no sex differences in pupal call production. Pupal size plays a role in the amplitude of a signal. Tertiary signals have only been detected in large species of lycaenid pupae (Downey and Allyn 1978). While lycaenid pupae may produce calls spontaneously (Hoegh-Guldberg 1972, Downey and Allyn 1978, Chapter 5 of this dissertation), they also stridulate in response to certain stimuli, although responses vary between species. Researchers have noted pupal sound production most often in response to a disturbance, such as being touched (Bell 1919, Schlosz 1991, Fiedler et al. 1994) or being pinched with a pair of forceps (DeVries 1991a, Travassos and Pierce 2000). Hoegh-Guldberg (1972) found Aricia pupae remain silent in response to several stimuli, including loud sounds, sudden exposure to light, or unusual odors (Hoegh-Guldberg 1972). Thorn (1924) observed that Ogyris pupae call when exposed to a strong light. Interestingly, individual pupae of J. evagoras, a lycaenid whose juveniles form aggregations, increased call production in the presence of a larval conspecific (Travassos and Pierce 2000). As the temperature rises, pupae in the genus Aricia increase their call production (Hoegh-Guldberg 1972), whereas Strymon melinus and Lycaena hyllus actually lower call production (Downey and Allyn 1978). Callophrys henrici produces primary signals in response to low level sound between 100-3000 Hz; however, pupae remain silent when exposed to sounds of 200 Hz, 2000 Hz, and frequencies greater than 3000 Hz (Downey and Allyn 1978). No work has been done investigating habituation of pupae to any of these different stimuli.

156 Clusters of lycaenid pupae may synchronize their calls. Dodd noted that when one Ogyris pupa begins to call, “others in the vicinity as a rule follow its example” (Bethune-Baker 1905). Upon disturbing a group of pupae in the genus Arhopala, he observed “a perfect outburst of harmony” (Dodd 1916). The mechanisms underlying this chorusing are unknown. Function: Observing the rapid calling response induced in pupae when disturbed, most researchers have concluded that calls are produced for defensive purposes. The ease with which pupae call when touched drew the attention of early naturalists. Upon touching a Rathinda amor pupa, Bell (1919) observed that “it gives vent to a little knocking noise; even, sometimes, when blown upon.” Dodd (1916) and Downey (1966) proposed a different role for lycaenid pupal sounds, suggesting that stridulation may be important to lycaenids in maintaining a symbiotic relationship with ants, a character found in at least half of all extant family members (Pierce et al. 2002). Observations of call production of pupae in the Australian lycaenid genus Ogyris support this notion. Ogyris pupae stridulate in the presence of ants; when the ants are removed, the pupae become silent (Bethune-Baker 1905). Likewise, Eastwood and King (1998) noted that Arhopala wildei pupae produced sound only in the presence of the attendant ant Polyrachis queenslandica and, interestingly, they suggested that such sounds may share the same frequency as the tapping produced by P. queenslandica ants when alarmed. Hoegh-Guldberg (1972) found that Aricia pupae call when in contact with some insects, including certain ants. Prell (1913) suggested an alternative function of lycaenid pupal calls. Focusing on another characteristic of lycaenids, the formation of juvenile aggregations on host plants, he suggested that calling may be a means of attracting conspecifics to these clusters. Downey and Allyn (1978) found that pupal calls have a wide frequency range, lacking the narrow bandwidths of calls that might be attractive to ants and instead resembling random noise. They concluded that calls act primarily as a deterrent to

157 predators and parasites. Most researchers have accepted this conclusion (DeVries 1990; Elfferich 1988). However, Brakefield et al. (1992, p.114) have suggested that pupal sounds may “simultaneously alarm and pacify attendant ants,” thereby putting them in an agitated state to defend the pupa. In at least one instance, Travassos and Pierce (2000) studied pairs of pupae of J. evagoras in which one member was experimentally muted and found that calling pupae attract and maintain a higher ant guard than their silent counterparts. The results of this experiment, coupled with the fact that J. evagoras pupae significantly increase sound production in the presence of their attendant ants, indicate that calling is an important component of this lycaenid pupa-ant mutualism. Because J. evagoras pupae have an unusually high level of ant attendance among lycaenids, however, this result may not be widely generalizable to other taxa. The rapid tremors of the cuticle associated with calling in lycaenids may also be important in directly fending off parasites, as has been observed in the Nymphalidae. When touched by the antennae or tarsus of an ichneumonid fly attempting to deposit its eggs within its skin, the nymphalid pupa Aglais urticae trembles so violently that it shakes off the parasite (Cole 1959).

158 Table 1. Juvenile sound production in the Lycaenoidea.

Classificationa Speciesb Subtribe Myrmecophilous?c Larval sound- Reference Pupal sound- Reference producing organ?d producing organ?e RIODINIDAE Hamearinae Hamearis lucina No (see Malicky 1969) Silent Schurian and Fiedler Yes Downey and Allyn 1973 1991 Riodininae Eurybiini Eurybia lysica Yes Heard DeVries 1991a Silent DeVries 1991a Eurybia patrona persona Yes Heard DeVries 1991a Eurybia elvina Yes Heard DeVries 1991a Yes Yes; Heard & Travassos et al. Observed 2002 Eurybia sp. Yes Heard DeVries 1991a Riodiniini Calephelis wrighti No Yes Downey 1966 C. nemesis No Yes Downey and Allyn 1973 C. perditalis No Yes Downey and Allyn 1973 C. rawsoni No Yes Downey and Allyn 1973 C. virginiensis No Yes Downey and Allyn 1973 Caria ino Probably not - check Yes Downey and Allyn 1973 Melanis pixie No Yes Downey and Allyn 1973 Emesini Apodemia mormo virgulti Probably not - check Yes Downey 1966 A. m. deserti Probably not - check Yes; Heard Downey 1966

Lemoniini Lemonias caliginea Yes Yes Ross 1964 Yes Downey 1966 Thisbe irenea Yes Yes; Heard DeVries 1988; Silent DeVries 1991a DeVries 1991a Synargis mycone Yes Yes; Heard DeVries 1991a Silent DeVries 1991a S. brennus Yes Yes Callaghan 1986 S. gela Yes Yes; Heard DeVries 1991a Silent DeVries 1991a Juditha molpe Yes Yes; Heard DeVries 1991a Silent DeVries 1991a Audre (Hamearis) susanae Yes Yes Bourquin 1953 A. (H.) epulus signatus Yes Yes Bruch 1926 A. (H.) erostratus Yes Yes? Schremmer 1978 Nymphidiini Menander menander Yes Yes; Heard DeVries 1991a Menander felsina Yes Yes Callaghan 1977 Calospila cilissa Yes Yes; Heard DeVries 1991a Silent DeVries 1991a C. emylus Yes Yes; Heard DeVries 1991a Silent DeVries 1991a Theope nr thestias Yes Yes; Heard DeVries 1991a Silent DeVries 1991a T. nr matuta Yes Yes; Heard DeVries 1991a Silent DeVries 1991a T. virgilius Yes Yes; Heard DeVries 1991a Silent DeVries 1991a T. nr decorata Yes Yes; Heard DeVries 1991a Silent DeVries 1991a Theope sp. Yes Yes; Heard DeVries 1991a Nymphidium mantus Yes Yes; Heard DeVries 1991a Silent DeVries 1991a Nymphidium sp. Yes Yes; Heard DeVries 1991a

159 N. haematostictum Yes Yes; Silent DeVries 1991a N. azanoides occidentalis Yes Yes; Silent DeVries 1991a N. cachrus Yes Yes; Silent DeVries 1991a N. chione onaeum Yes Yes; Silent DeVries 1991a N. caricae Yes Yes; Silent DeVries 1991a LYCAENIDAE Poritiinae Miletinae Miletini Allotinus horsfieldi Miletini Myrmecoxenous Heard Roepke 1918 A. unicolor Miletini Myrmecoxenous Heard I Fiedler 1994a Feniseca tarquinius Spalgini Myrmecoxenous Heard Mathew et al. 2008 Yes Downey 1966 Miletus sp. (possibly) Myrmecoxenous Heard Dodd 1916 Curetinae Curetis thetis Heard Bell 1919 C. acuta paracuta Yes Downey and Allyn 1973 C. bulis Heard Fiedler et al. 1995 Yes; Heard Fiedler et al. 1995 C. santana Heard Fiedler et al. 1995 Yes; Heard Fiedler et al. 1995 Lycaeninae Aphnaeini Phasis thero Yes Yes Downey and Allyn 1973 Chrysoritis (Poecilmitis) Yes Yes Downey and Allyn 1973 thysbe C. (P.) brooksi Yes Heard Schlosz and Schlosz 1990; Schlosz 1991 C. (P.) nigricans Yes Heard Schlosz 1991 C. dicksoni Yes Heard Heath 1998 Spindasis vulcanus Yes Yes Downey and Allyn 1973 Lycaenini Lycaena phlaeas Yes Heard DeVries 1991a Yes, Heard Downey 1966 L. gorgon Yes Downey and Allyn 1973 L. helle Yes; Heard Downey and Allyn 1973; Elfferich 1988 L. hyllus Heard Downey and Allyn 1978 L. thoe Yes; Heard Downey 1966 L. helloides Yes Downey 1966 L. virgaureae Yes Downey 1966 L. arota Yes Downey 1966 L. (Chrysophanus) tityrus Heard Schurian and Fiedler Yes; Heard Fiedler 1988; Elfferich 1988 1991 L. (Thersamonia) dispar Yes Yes; Heard Downey 1967; Elfferich 1988 L. alciphron Heard Elfferich 1988 L. ochimus Uncertain Silent Schurian and Fiedler Yes; Heard Schurian and Fiedler 1996 1996 Palaeochrysophanus Yes Heard DeVries 1991a Yes; Heard Downey 1967; Elfferich 1988 hippothoe Theclini

160 Thecla legytha Thecliti Yes Heard DeVries 1991a Thecla betulae Thecliti Yes Yes Jans 1980? (in Elfferich 1988) T. (Thereus) pedusa Eumaeiti Yes Heard DeVries 1991a Heard DeVries 1991a T. (Thereus)nr enenia Eumaeiti Yes Heard DeVries 1991a Quercusia (Thecla) Thecliti Yes Yes; Heard Prell 1913 quercus Habrodais grunus Thecliti Yes Downey 1966 Hypaurotis crysalus Thecliti Heard Brown in Clench 1961 Pseudodipsas sp. Luciiti Yes Heard Dodd 1916 Heard Dodd 1916 Hypochrysops ignita Luciiti Yes Yes Downey and Allyn 1973 H. delicia delos Luciiti Yes Heard Dunn 1983 H. digglesii Luciiti Yes Heard De Baar 1983; Quick 1984 H. theon Luciiti Yes Heard Valentine 1984 H. apelles Luciiti Yes Heard Valentine 1984 H. narcissus Luciiti Yes Heard Valentine 1984 Jalmenus evagoras Zesiiti Yes Heard Pierce et al. 1987 Yes; Heard Downey 1966; Pierce et al. 1987 J. ictinus Zesiiti Yes Heard Costa and Pierce Yes Downey 1966 1997 J. pseudictinus Zesiiti Yes Heard Valentine 1984 J. icilius Zesiiti Yes Heard Costa and Pierce 1997 Arhopala sp. Arhopaliti Yes Heard Dodd 1916 Heard Dodd 1916 A. centaurus Arhopaliti Yes Heard Valentine 1984 Yes; Heard I Common and Waterhouse 1981; Hinton 1948 A. pseudocentaurus Arhopaliti Yes Heard Norman 1949 A. madytus Arhopaliti Yes Yes; Heard Hill 1993 Yes; Heard Hill 1993 A. eupolis Arhopaliti Yes Heard Dodd 1916 A. wildei Arhopaliti Yes Heard I, IV Eastwood and King 1998 Surendra florimel Arhopaliti Yes Heard Fiedler 1992a Narathura araxes eupolis Arhopaliti Heard Dodd 1916 N. centaurus Arhopaliti Heard Bell 1919 Semanga superba Arhopaliti Yes Heard Fiedler and Seufert Heard Fiedler and Seufert 1995 1995 Ogyris sp. Ogyriti Yes Heard Dodd 1916 Heard Dodd 1916 O. genoveva Ogyriti Yes Yes; Heard Downey 1966; Dodd 1916 O. amaryllis hewitsoni Ogyriti Yes Heard Bethune-Baker 1905 O. ianthis Ogyriti Yes Heard De Baar 1984 Heard Valentine 1984 O. olane Ogyriti Yes Heard Thorn 1924 O. oroetes Ogyriti Yes Heard Bethune-Baker 1905 O. zosine Ogyriti Yes Heard Valentine 1984 Heard Bethune-Baker 1905 O. abrota Ogyriti Yes Heard Common and Waterhouse 1981 Eumaeini Arawacus lincoides Eumaeiti Yes Heard DeVries 1991a

161 Olynthus narbal Eumaeiti Yes Heard DeVries 1991a Callophrys rubi Eumaeiti No Fiedler et al. 1992; Yes Heard Fiedler et al. 1992 Yes; Heard Prell 1913; Kleeman 1774 Brakefield et al. 1992 C. sheridani Eumaeiti Yes Downey 1966 C. sheridanii (Chech Eumaeitii Heard Hiruma et al. 1997 Bridges) C. viridis Eumaeiti Yes Downey 1966 C. augustinus Eumaeiti Yes Downey and Allyn 1973 C. dumetorum Eumaeiti Yes Downey and Allyn 1973 C. eryphon Eumaeiti Yes Downey and Allyn 1973 C. fotis Eumaeiti Yes Downey 1966 C. hadros Eumaeiti Yes Downey and Allyn 1973 C. henrici Eumaeiti Yes; Heard Downey 1966 C. gryneus Eumaeiti Yes; Heard Downey 1966 C. hesseli Eumaeiti Yes Downey 1966 C. iroides Eumaeiti Yes Downey and Allyn 1973 C. johnsoni Eumaeiti Yes; Heard Downey 1966 C. liparops Eumaeiti Yes Downey and Allyn 1973 C.loki Eumaeiti Yes Downey 1966 C. miserabilis Eumaeiti Yes Downey and Allyn 1973 C. nelsoni Eumaeiti Yes; Heard Downey 1966 C. niphon Eumaeiti Yes Downey and Allyn 1973 C. siva Eumaeiti Yes Downey and Allyn 1973 C. spinetorum Eumaeiti Yes Downey 1966 C. xami Eumaeiti Yes Downey and Allyn 1973 C. macfarlandi Eumaeiti Panthiades bitias Eumaeiti Yes Heard DeVries 1991a Phaeostrymon alcestis Eumaeiti Yes Downey and Allyn 1978 oslari Rekoa palegon Eumaeiti Yes Heard DeVries 1991a Chlorostrymon simaethis Eumaeiti Yes Heard DeVries 1991a Yes; Heard Downey and Allyn 1973; DeVries 1991a Strymon yojoa Eumaeiti Yes Heard DeVries 1991a S. bazochii Eumaeiti Yes Downey and Allyn 1973 S. columella istapa Eumaeiti Yes Downey and Allyn 1973 S. melinus Eumaeiti Yes; Heard Brown in Clench 1961 Fixsenia (Strymonidia) Eumaeiti Observed? Eltringham 1921 Yes Downey 1966 pruni Tmolus echion Eumaeiti Yes Heard DeVries 1991a Yes Downey 1966 T. azia Eumaeiti Yes Downey and Allyn 1973 Micandra platyptera Eumaeiti Yes Heard DeVries 1991a Atlides halesus Eumaeiti Yes; Heard Downey 1966 Nordmannia ilicis Eumaeiti Yes Downey and Allyn 1973 Calycopis beon Eumaeiti Yes; Heard Downey 1966 C. cercrops Eumaeiti Yes Downey and Allyn 1973 Dolymorpha jada Eumaeiti Yes Downey 1966

162 Eumaeus debora Eumaeiti Yes Downey 1966 E. minyas Eumaeiti Yes Downey 1966 Parrhasius (Eupsyche) m- Eumaeiti Yes; Heard Downey 1966; Clench 1961 album Euristrymon ontario Eumaeiti Yes Downey and Allyn 1973 autolycus Satyrium acadica Eumaeiti Yes Downey 1966 S. adenostomatis Eumaeiti Yes Downey 1966 S. auretorum Eumaeiti Yes Downey 1966 S. behrii Eumaeiti Yes Downey 1966 S. calanus falacer Eumaeiti Yes Downey and Allyn 1973 S. saepium Eumaeiti Yes Downey 1966 S. sylvinus Eumaeiti Yes Downey 1966 S. (Strymon) dryope Eumaeiti Yes Downey and Allyn 1973 S. (S.) w-album Eumaeiti Yes; Heard Downey 1966; Carter 1952 S. (Strymonidia) acaciae Eumaeiti Yes; Heard Downey 1966; Elfferich 1988 S. (S.) spini Eumaeiti Yes; Heard Prell 1913; Downey 1966 Harkenclenus Eumaeiti Heard Brown in Clench 1961 (Chrysophanus) titus Iolaus silas Iolaiti Heard I Jackson 1937 I. (Tanuetheira) timon Iolaiti Heard Farquharson 1921 I. (Argiolaus) sp. Iolaiti Heard Farquharson 1921 I. (A.) silas crawshayi Iolaiti Heard I Jackson 1937 I. (Epamera) sp. Iolaiti Heard Farquharson 1921 I. (E.) farquharsoni Iolaiti Heard Farquharson 1921 I. alienus Iolaiti Heard I Clark & Dickson, in Downey 1966 I. glaucus jordanus Iolaiti Yes Downey and Allyn 1973 I. mimosae Iolaiti Heard I Clark & Dickson, in Downey 1966 I. sidus Iolaiti Heard I Jackson 1937 Pratapa blanka argentea Iolaiti Heard Bell 1919 P. deva Iolaiti Heard Bell 1919 Hemiolaus sp. (tentative) Iolaiti Heard DeVries 1991a Hypolycaena sp. Hypolycaeniti Yes Heard Dodd 1916 Heard Dodd 1916 H. erylus Hypolycaeniti Yes Heard DeVries 1991a H. othona Hypolycaeniti Yes Heard Fiedler 1992b Yes; Heard Fiedler 1992b H. philippus Hypolycaeniti Yes Heard I Clark & Dickson, in Downey 1966 Eooxylides tharis Loxuriti Yes Heard Fiedler 1994a Yasoda pita Loxuriti ? Heard Fiedler 1994a Rapala lankana Deudorigiti Heard De Niceville 1900 R. manea schistacea Deudorigiti Yes Heard Bell 1920 R. varuna Deudorigiti No Heard Bell 1920 Deudorix epijarbas diovis Deudorigiti No Heard De Baar 1984 Bindahara phocides Deudorigiti No Heard Valentine 1984

163

Polyommatini Leptotes cassius Polyommatiti Yes Heard DeVries 1991a Yes; Heard Downey and Allyn 1973; Downey and Allyn 1978 L. marina Polyommatiti Yes Downey 1966 Cupido minimus Polyommatiti Yes Heard DeVries 1991a Yes Downey 1967 Cupido (Rathinda) amor Polyommatiti Heard Bell 1919 Maculinea arion Polyommatiti Yes Heard DeVries 1991a M. nausithaus Polyommatiti Yes Heard DeVries 1991a Yes; Heard Downey and Allyn 1973; Elfferich 1988 M. teleius Polyommatiti Yes Heard DeVries 1991a Yes Downey 1967 M. rebeli Polyommatiti Yes Heard DeVries 1991a M. alcon Polyommatiti Yes Heard DeVries 1991a Yes; Heard Downey 1967; DeVries 1991a Danis hymetus taygetus Polyommatiti Yes Downey 1966 Glaucopsyche alexis Polyommatiti Yes Heard Fiedler et al. 1992 Yes Downey and Allyn 1973 G. lygdamus Polyommatiti Yes Heard Fiedler et al. 1992 Yes Downey and Allyn 1973 Celastrina argiolus Polyommatiti Yes Yes; Heard Downey 1967; Hoegh- Guldberg 1972 Philotes mohave Polyommatiti Yes Downey and Allyn 1973 P. rita Polyommatiti Yes Downey and Allyn 1973 P. speciosa Polyommatiti Yes Ultraaricia (Aricia) Polyommatiti Yes Heard Schurian 1995 Yes; Heard Schurian 1995 anteros Aricia artaxerxes Polyommatiti Yes Yes; Heard Hoegh-Guldberg 1972 A. agestis Polyommatiti Yes Heard Schurian and Fiedler Yes; Heard Hoegh-Guldberg 1972 1994 Pseudoaricia hyacinthus Polyommatiti Heard Schurian and Fiedler 1991 Polyommatus (Lysandra) Polyommatiti Yes Heard Fiedler et al. 1992 Yes; Heard Fiedler 1988; Elfferich 1988 coridon P. (L.) bellargus Polyommatiti Yes Heard Fiedler et al. 1992 P. (L.) dezinus Polyommatiti Likely Heard Schurian and Fiedler Yes; Heard Schurian and Fiedler 1994 1994 P. icarus Polyommatiti Yes Heard DeVries 1991a Yes; Heard Downey 1967: Hoegh- Guldberg 1972 P. candalus Polyommatiti Yes Heard Fiedler et al. 1994 Heard Fiedler et al. 1994 P. daphnis Polyommatiti Yes Heard Fiedler et al. 1994 P. escheri Polyommatiti Yes Heard Fiedler et al. 1994 P. damon Polyommatiti Yes Heard Fiedler et al. 1994 P. thersites Polyommatiti Yes Heard Fiedler et al. 1994 Heard Elfferich 1988 P. semiargus Polyommatiti Yes Heard Fiedler et al. 1994 Plebulina emigdionis Polyommatiti Yes Heard DeVries 1991a Lycaeides melissa samuelis Polyommatiti Yes Heard DeVries 1991a L. idas Polyommatiti Yes Yes; Heard Downey and Allyn 1973; Elfferich 1988

164 Lysandra coridon Polyommatiti Yes Heard DeVries 1991a Yes; Heard Downey 1966; Elfferich 1988 L. hispana Polyommatiti Yes Heard DeVries 1991a L. bellargus Polyommatiti Yes Heard DeVries 1991a Yes Downey and Allyn 1973 L. thersites Polyommatiti Yes Downey 1966 L. (Meleageria) ossmar Polyommatiti Heard Schurian and Fiedler 1996 Meleageria daphnis Polyommatiti Yes Heard Schurian and Fiedler Yes Downey and Allyn 1973 1991 Prosotas dubiosa Polyommatiti No Eastwood and Fraser; Yes Heard Fiedler 1992b Yes Downey and Allyn 1973 Fiedler 1991 P. nora auletes Polyommatiti No Yes Downey and Allyn 1973 Agrodiaetus damon Polyommatiti Yes; Heard Downey and Allyn 1973; Elfferich 1988 Caleta roxus Polyommatiti No Heard Fiedler 1994b Equivocal Fiedler 1994b C. manovus Polyommatiti No Heard Fiedler et al 1992 Brephidium exilis Polyommatiti Yes Downey 1966 Everes argiades Polyommatiti Yes Yes Downey 1966 E. comyntas Polyommatiti Yes; Heard Downey 1966 E. alcetas Polyommatiti Heard Elfferich 1988 Nacaduba beroe gythion Polyommatiti Yes Downey and Allyn 1973 N. biocellata Polyommatiti Yes Yes Downey and Allyn 1973 N. pactolus cela Polyommatiti Yes Yes Downey and Allyn 1973 Lampides boeticus Polyommatiti Yes Yes; Heard Elfferich 1988 Plebejus acmon Polyommatiti ? Yes; Heard Downey 1966 P. icarioides Polyommatiti Yes; Heard Downey 1966 P.(Lycaeides) Polyommatiti ? Yes; Heard Downey 1966 argyrognomon P. melissa Polyommatiti Yes Heard Fiedler et al. 1992 Yes Downey 1966 P. argus Polyommatiti Yes Yes; Heard Downey 1966; Hoegh- Guldberg 1972 P. saepiolus Polyommatiti Yes; Heard Downey 1966 P. glandon Polyommatiti Yes Downey 1966 Jamides malaccanus Polyommatiti Yes Heard Fiedler et al. 1992 J. bochus Polyommatiti Yes Yes Downey and Allyn J. celeno Polyommatiti Yes Yes; Heard Downey and Allyn 1973; Bell 1919 Plebicula amanda Polyommatiti Yes Downey and Allyn 1973 P. dorylas Polyommatiti Yes; Heard Downey and Allyn 1973; Elfferich 1988 Zizeeria labradus Polyommatiti Yes Downey 1966 Chilades lajus Polyommatiti Yes Yes Downey and Allyn 1973 Hemiargus ceraunus Polyommatiti Yes Yes Downey and Allyn 1973 zacheinus H. isola alce Polyommatiti Yes Downey and Allyn 1973 H. thomasi Poyommatiti Heard Downey and Allyn 1978

165 Scolitantides orion Polyommatiti Yes Yes; Heard Downey and Allyn 1973; Elfferich 1988 Cyaniris semiargus Polyommatiti Yes Yes; Heard Downey 1967; Elfferich 1988 Syntarucus pirithous Polyommatiti Heard Elfferich 1988 Zizina labradus labradus Polyommatiti Yes Yes Common and Waterhouse 1981 Theclinesthes onycha Polyommatiti Yes Heard Common and Waterhouse 1981 Anthene emolus Lycaenesthiti Yes Heard Fiedler et al. 1992

Eumaeini? Drupadia theda Cheritriti Yes Heard Fiedler et al. 1992 Eumaeini? D. ravindra Cheritriti Heard Fiedler 1994a Eumaeini? Cheritra freja Cheritriti Myrmecoxenous Heard Fiedler et al. 1992 aClassification of superfamilies based on Nielson and Common (1991) and Kristensen (1976). Classification of the Lycaenidae based bLycaenid genera drawn from Bridges (1988). cMyrmecophily based on Fiedler (1991), Eastwood and Fraser (1999), Brakefield et al. (1992), Malicky (1969), and sound references themselves. d"Heard" indicates that sound was noted, but an organ was not necessarily located. "Silent" indicates that the organism was tested for sound production, but no sounds were detected. e"Heard" indicates that sound was noted, but an organ was not necessarily located. "Silent" indicates that the organism was tested for sound production, but no sounds were detected. Roman numerals indicate the kind of pupal sound produced, as per Hinton (1948): I refers to a sound produced by a pupa knocking against a substrate, II refers to a sound produced by the proboscis scraping against ridges on the abdomen, and IV refers to sound produced by a stridulatory organ. A "Yes" without a roman numeral indicates that a stridulatory organ has been found.

166

LYCAENOID LARVAL SOUND PRODUCTION

Riodinidae Bruch (1926) first noted the presence of a pair of vibrating appendages on a riodinid caterpillar. Ross (1964) termed these structures "vibratory papillae" and, noting Bourquin's (1953) observation that they drew the attention of the attendant ants of Audre susanae, suggested that they may have a connection to a larva's myrmecophilous organs. DeVries (1988) observed and explicated call production in a riodinid caterpillar, Thisbe irenea. A pair of these vibratory papillae is found on the distal edge of the prothorax, each one bearing concentric grooves along its length. When a caterpillar oscillates its head, epicranial granulations on the head slide across these grooves, producing low amplitude calls that travel solely through the substrate, unlike the airborne songs of insects such as crickets. Sound production rates can vary with the frequency of head oscillations and are produced most often in a range of different conditions: while walking, when stressed, during the first contact with ants after a period of separation, and when they are traveling to a feeding area from rest. DeVries (1990) compared the ant attendance levels between caterpillars that had their papillae removed and those that remained intact, and found that calling T. irenea caterpillars were tended by more ants, indicating that one role of the calls is ant attraction. Call production plays an important role in the “enticement and binding” process proposed by DeVries (1988) to explain how riodinid caterpillars attract ants. When in need of an ant guard, a larva will call by producing substrate-borne vibrations. Upon contact with ants, it then produces attractive and nutritious secretions, and everts its tentacle organs, thereby mimicking ant pheromones. The intermittent release of sound production, secretions, and chemical signals allows a caterpillar to maintain this myrmecophilous association over time.

167 Call Characteristics: (DeVries and Penz 2000; DeVries and Penz 2002), and

Travassos et al. (2002) have studied riodinid caterpillar calls, analyzing their substrate-

borne signals with a particle velocity microphone attached to a sound stage. From a

survey of fourteen species of riodinids that produced calls with vibratory papillae,

DeVries (1991) found that the mean dominant frequency ranged from 900 Hz to 2550

Hz, with a mean of 1877.21 Hz, while the pulse rate ranged from 6 to 48 calls per second,

with a mean of 23.36 calls per second. (DeVries and Penz 2000) measured calls of Alesa

amesis (Riodinidae), and later found a likely mechanism for sound production: grating

cervical membrane “teeth” against hemispherical protuberances on the surface of the

head, as (Travassos et al. 2002) had found for Eurybia elvina; they also describe similar

structures of the same type for A. amesis and E. lycisca, E. patrona, E. lycisca, E. nr.

nicaeus and E. elvin, and suggest that the function is likely for sound, since they oppose epicranial granulations known to function this way in several species.

Distribution: Vibratory papillae represent a synapomorphy in the riodinid tribes Lemoniini and Nymphidiini. All myrmecophilous caterpillars surveyed by DeVries (1991a) in these two groups produced calls with the exception of several members of the genus Nymphidium, which possess vibratory papillae but have reduced epicranial granulations that are covered by a dense set of hairs. Eurybia caterpillars, members of the tribe Eurybiini, also produce calls, but lack vibratory papillae and highly developed epicranial granulations, suggesting that call production may have evolved twice in the Riodinidae, with the second mechanism of call production as yet unknown (DeVries 1991a). When producing calls, Eurybia caterpillars do not exhibit the head oscillations necessary for call production in other riodinids.

168 Lycaenidae Dodd (1916, p.13) first observed sound production in lycaenid caterpillars, noting that in certain ant-associated species within the Lycaenidae, larvae emit audible sounds “when bunched up for moulting, but seem to lose it after, and again when undergoing pupation - even in their soft state preceding it and immediately after.” This is also the first suggestion of a link between sound production in butterfly caterpillars and ant association. Farquharson (1921, p.376), in a 1917 letter published after his death, made the first observation of the substrate-borne component of lycaenid caterpillar songs. In handling a lycaenid caterpillar in Nigeria, he describes an unusual phenomenon: “I suddenly was conscious of a curious sensation in my finger and thumb which is very difficult of description. As near as possible it reminded me of a very faint electric shock, not accompanied by a prickly sensation but rather as if one were being tickled by a tiny brush of slightly strong bristles.” He noted that the sensation was “not continuous but rapidly intermittent” (Farquharson 1921, p.376). His Nigerian servant confirmed this observation: "I got my boy to hold one and to say if he felt anything. He replied in good 'pidgin' - 'he scratch my hand,' by which I think he meant tickles" (Farquharson 1921, p. 376). Farquharson concluded that the tickling was caused by an internal, electric mechanism. Call Characteristics: DeVries (1990; 1991a) has extensively surveyed the sounds produced by larvae of the Lycaenidae, vividly describing the calls themselves. Strymon yojoa, for example, produces a call similar to the “metallic fluttering of the tongue,” while Thereus pedusa sounds like “a one-cylinder engine that fires only occasionally” (DeVries 1991a, p.17). Like riodinids, lycaenids produce calls beginning in the third instar, when other myrmecophilous organs develop. Calls of different species vary, but in general resemble a slow “drumming,” compared to the faster chirps of the riodinids. They travel primarily through the substrate, though they also have an airborne component (DeVries 1991a; Schurian and Fiedler 1991). DeVries (1991a) first noted

169 that some lycaenid calls may have two signals: a low background sound accompanied by a constant pulse. Polyommatus candalus superimposes “snarling or quacking pulses” on a background call when disturbed (Fiedler et al. 1994). Under similar conditions, Polyommatus anteros produces either a short “croaking” or a longer “crackling” (Schurian 1995). Travassos and Pierce (2000) have found that J. evagoras larvae produce three signals that differ in acoustic properties and amplitudes: the grunt, drum, and hiss. In a survey of twenty-one species of lycaenid caterpillars, DeVries (1991) found that the mean dominant frequency ranged from 500 Hz to 1700 Hz, with a mean of 1085.24 Hz, and the pulse rate varied from 4 to 36 pulses per second, with a mean of 13.76. Riodinid caterpillars surveyed had a significantly higher dominant frequency and pulse rate. Travassos and Pierce (2000) found that the grunt of J. evagoras had a mean dominant frequency of 754.4 Hz, while the drum and hiss had a dominant frequency of 471.7 and 444.1 Hz, respectively. The grunt was produced with a pulse rate of 2.01 pulses per second, while the drum and hiss had pulse rates of 8.29 and 6.39 pulses per second, respectively. The grunt, the loudest call, was 2.7 dB louder than the drum and 9.8 dB louder than the hiss. Interestingly, the grunt shared the same mean dominant frequency, pulse rate, pulse length, and bandwidth as the pupal primary signal. Fiedler (1992b) estimated that the larval calls of Hypolycaena othona were well below 1 kHz and had a pulse rate of 1-3 pulses per second. Function: Little work has been done examining the function of calls in lycaenid caterpillars. Pointing to the correlation between myrmecophily and the ability to produce calls, DeVries (1991a) suggested that lycaenid calls, like riodinid calls, attract ants. Travassos and Pierce (2000) have found that two calls produced by larvae of J. evagoras, the grunt and hiss, are produced significantly more in the presence of attendant ants, indicating that these calls likely play a role in ant interactions.

170 The calls of Maculinea larvae and the stridulations of the Myrmica ants they parasitize share the same pulse length and dominant frequency, suggesting a general convergence of caterpillar calls upon those produced by their host ants (DeVries et al. 1993). Ants from the subfamilies Myrmicinae, Dolichoderinae, and Formicinae tend lycaenid juveniles (Cottrell 1984). Of these ants, only myrmicines produce calls via stridulation, while some dolichoderine and formicine ants produce vibratory signals by drumming body parts on the substrate (Markl 1971; Hölldobler and Wilson 1990). Outside of the Maculinea-Myrmica system, no other comparisons between caterpillar and attendant ant calls have been made. Fiedler (1992b) and DeVries (1991b) have both observed that lycaenid larvae produce sounds when disturbed. Indeed, DeVries (1991b) has surveyed sound production in lycaenid larvae by stimulating subjects with a pair of forceps and recording any sounds produced. Fiedler et al. (1994) has likewise induced larvae in the genus Polyommatus to produce substrate-borne vibrations by pinching them with forceps. Such work suggests that larval sound production may have a defensive function in addition to a context within ant associations. Mechanism: Examining Farquharson's larval specimens, Eltringham (1921) could not isolate an organ capable of producing an electrical discharge. However, noting the thick, rough cuticle shared by several lycaenid caterpillars and the “shivering” exhibited by Fixsenia pruni larvae under a dissecting microscope, he suggested that a caterpillar's rapid muscular contractions might be sufficient to produce a sensation akin to an electrical impulse upon contact. DeVries (1991a) was also unable to find a sound- producing organ, but likewise noted this shivering in several lycaenids, concluding that it must somehow be important to lycaenid call production. Hill (1993) localized this rapid trembling to abdominal segments five and six of Arhopala madytus and described a file of teeth found on the posterior margin of the fifth abdominal segment that rubs against an opposing plate on the anterior margin of the sixth abdominal segment. An A. madytus

171 larva thus produces calls with a stridulatory organ similar to that found in a lycaenid pupa, though the position of the file and scraper are reversed. Schurian and Fiedler (1994) observed that larvae in the genus Polyommatus appear to use dorsal, longitudinal, and lateral muscles on abdominal segments four through seven when producing calls. However, following cannibalistic attacks that damaged muscles on abdominal segments four through six, P. icarus, P. agestis, and Jamides malaccanus larvae did not produce calls, suggesting that they may use a smaller set of muscles for sound production than some Polyommatus species. Fiedler et al. (1992) suggested that these muscles may be exempt from exploitation by parasitic wasps. Lycaenid larvae surveyed continued to produce sounds for several days after being parasitized by braconid wasps. Distribution: Since the organ for sound production in lycaenids has not been well-documented, we cannot determine how widespread the organ is within the family, considering that there may be more than one kind of sound-producing organ present. Larval sound production itself is widespread phenomenon, found in the Curetinae and all five tribes of the Lycaeninae (sensu Fiedler 1991). This includes caterpillars in all biogeographic regions. In addition, sound production may also be present in the subfamily Miletinae. Dodd (1916) observed sound production in a caterpillar he only describes as being in the genus Miletus, which may or may not be refer to what was then a member of the Miletinae. No larvae in the Poritiinae have been examined for sound production. In his sound production survey, DeVries (1991a) found that only lycaenids that were myrmecophilous produced calls. Several non-ant tended members of the Eumaeini he examined were silent. These results led DeVries to suggest that calling may be important in lycaenid-ant symbioses. However, since then, several non-myrmecophilous lycaenids have been observed to produce sound (Table I). This includes Deudorix epijarbas, Caleta roxus, C. manovus, and Cheritra freja. Callophrys rubi, Curetis bulis,

172 and Curetis Santana, that also produce sound, but accounts of whether ants attend these species conflict (see Fiedler et al. 1992 v. Brakefield et al. 1992; Fiedler et al. v. DeVries 1984). From these results, Fiedler (1992b) has suggested that the ability to produce calls may be universal in the Lycaenidae. While non-ant tended lycaenids produce simple calls in response to a disturbance, he hypothesized, ant-tended lycaenids have calls of a greater complexity that are produced more often.

Current directions: Symbiosis and acoustic signaling in Jalmenus evagoras

Jalmenus evagoras, the Imperial Blue butterfly, is a model system whose symbiosis with ant species in the Iridomyrmex genus has been extensively studied (see

Pierce and Nash 1999 for a review). These lycaenids are found in southeastern Australia and along the eastern coast as far north as Gladstone, and feed on as many as 23 different species of Acacia, which grows in semiarid regions, particularly in areas of disturbance and secondary growth. A larva of J. evagoras has numerous adaptations to communicate with Iridomyrmex ants, including: numerous perforated cupola organs (PCOs), found scattered in the epidermis, which are thought to secrete substances that appease attendant

ants, including amino acids and as yet unidentified compounds; a pair of eversible

tentacle organs (TOs), found on the 8th abdominal segment, which are probably involved

in the release of volatile signals that may startle or alarm ants; a dorsal nectary organ

(DNO) on the 7th abdominal segment that secretes sugars (at a concentration of about

10%) and amino acids, particularly serine (ranging from about 20-40 mM during the day); and adaptations for acoustic signal production, intersegmental rasp and files that appear to be located between every segment (Pierce et al., 2002).

173 Caterpillars of J. evagoras appear to ‘pay’ for protection from ants in the form of nutritious secretions (Pierce 1987). Larval secretions have been shown to result in net colony growth of attendant ants (Nash 1989), although the conditions under which the relationship is truly mutualistic for both parties are still being explored (Mark Cornwall, pers. comm.). For the caterpillars, the benefits of ant attendance include protection from pathogens, parasitoids and predators due to both the grooming behavior of the ants and their vigorous defense of the caterpillar, as well as the ability to feed freely on the acacias in what has been described as “enemy free space” (Atsatt 1981).

However, ant attendance also appears to be costly to the caterpillars. Previous work (Axen and Pierce 1998) has shown that caterpillars in aggregations produce fewer droplets per unit time than isolated caterpillars, indicating an attempt to minimize the cost of droplet production in an aggregation. Elgar and Pierce (1985) showed a correlation between male size at emergence and mating success, as well as a correlation between female size at emergence and number of eggs laid, indicating that there is positive selection for size. This creates an economic cost/benefit situation for pupae, since they are unable to feed during metamorphosis but do expend energy in vigorous bodily contractions for acoustic signals, possibly as an alternative to costly secretions for attracting ants.

Caterpillars are responsive to a variety of stimuli, such as brushing with a paintbrush, which creates a complex mixture of stimuli, including tactile and acoustic signals (Travassos and Pierce 2000); the caterpillars respond with an increased calling rate. Leimar and Axén (1993) showed that caterpillars of Polyommatus icarus that have been pinched with forceps are more likely to produce a droplet from the dorsal nectar

174 organ. Moreover as ant attendance increased, the number of droplets secreted by caterpillars of J. evagoras also increased, for both solitary and aggregated larvae.

However, larval secretion rate also varied with group size: a larva in an aggregation produced fewer droplets per capita than it did when it was solitary. This indicated not only that aggregation might serve as a cost reducing strategy for the larvae, but also that larvae must have some means of detecting whether they are in an aggregation (Axen and

Pierce 1998). A mechanism that could be used for group size assessment in J. evagoras is unknown. However, previously discussed hypotheses for the function of caterpillar stridulations include signaling to conspecifics and signaling to attendant ants. Acoustic signals could play an important role in mediating the cost of nectar production for caterpillars. Importantly, acoustic signals may have a better targetted active space than pheromonal signals, since sounds are conducted through the plant and are thus not subject to the effect of wind direction. Substrate-borne signals may also be more difficult for unintended receivers (such as flying parasitoids) to exploit.

Ants do have the capacity to perceive acoustic signals. Previous work at the turn of 20th century showed that ants are deaf except through substrate-borne vibrations, and that ants respond maximally to the striking of piano keys in the range of 200-2000 Hz

(Fielde and Parker 1904). Indeed, although Iridomyrmex ants have not been documented to stridulate themselves, they do appear to respond to surface borne vibrations, and ants that tend other species of Lycaenidae are known to stridulate. The calls produced by stridulating ants and their symbiotic lycaenid caterpillars were compared by DeVries

(1990), who found an overlap in the acoustic spaces of each partner, possibly indicating the presence of sensory bias characteristics in caterpillar calls. Ants typically stridulate in

175 order to communicate with other ants (e.g. as alarm signals); thus, as discussed above, the lycaenid caterpillars may be exploiting the responses of ants that have evolved for intraspecific communication of alarm or foraging signals.

Travassos and Pierce (2000) found that J. evagoras larvae produce three signals that differ in acoustic properties and amplitudes: the grunt, drum, and hiss (Figure 4.1).

The spectral characteristics of these calls fall within the sensitivity range of their attendant ants as described by Fielde and Parker (1904). How each call type influences ant behavior is still a question open to testing by playback through a substrate.

176 Figure 4.1. Call types as described in (Travassos and Pierce 2000) of J. evagoras caterpillars. Left: Frequency spectrum (amplitude vs. frequency). Bandwidth measurements (vertical lines) are calculated from the inflection points in the smoothed frequency spectrum. Right: spectrogram (frequency vs. time).

177

CONCLUSIONS

Most examples of sound production in lepidopteran caterpillars and pupae appear to have a simple defensive function: the squeaks or chirps emitted by a juvenile may startle a would-be predator or parasite. This does not appear to be true of riodinids and lycaenids. Lycaenid pupae are extremely responsive to their external environment, producing acoustic signals in response to touch, light exposure, and changes in the temperature. Experiments by Ross (1966), DeVries (1990) and Travassos and Pierce

(2000) have indicated that juvenile sound production may play a role in attracting and maintaining associations with ants. How larvae and/or pupae use sound to do this, why such signals are attractive to ants, and whether sound serves other functions such as defense or communicating with conspecifics remain unknown.

Riodinid and lycaenid larval sound production share several similarities. Like other lepidopteran larvae that produce sound, these caterpillars call when disturbed, possibly as a means of deterring predators or parasites. However, the riodinid T. irenea and the lycaenid J. evagoras also produce sound in the presence of their attendant ants, indicating that such calls may have an important inter- or intraspecific communicative function. Sound-producing larvae are found lycaenids whose associations with ants range from the facultative (e.g. Polyommatus icarus) to the obligate (e.g. Ogyris zosine,

Arhopala madytus) to the parasitic (e.g. species of the lycaenid genus Maculinea

(DeVries et al. 1993) and species in the riodinid genus Setabis (DeVries and Penz 2000)).

How these caterpillars use sounds to regulate such ant interactions is poorly known, as is the communicative value of such signals to the ants themselves.

178 Sound production in lycaenid juveniles has not been as well-studied as adult

lepidopteran calls. In part, this is due to the fact that many juvenile calls are only faintly

audible or primarily vibrational and, as such, are easily overlooked. As Markl (1983,

p.332) indicated, “if a signal can hardly be perceived by ourselves, if it can be measured

only with considerable difficulty even with sensitive instruments, and if it finally, as a

rule, works only over a few centimeters if not only on direct contact, [the common

perception is that] it must be of minor significance.” Work by DeVries (1990) shows that

vibratory communication is important in the Riodinidae and suggests that it may be

important in lycaenid butterfly-ant mutualisms. With many insect signals, as noted by

Updike (1997, p.108), “impressive amounts of information are packed into virtually

indistinguishable sounds.” Whether there is more to the brief clicks and chirps issued by

so many lycaenid juveniles remains to be seen.

In the work presented in this thesis, we expand upon these observations of pupae, through the development of methodologies to categorize thousands of calls in an unbiased, automated fashion (as compared to manual inspection of dozens of calls), which enables the detection of rarer calls and their rigorous categorization. We also examine multiple dimensions of variation in the call repertoire of pupae, including changes in repertoire over the course of pupation, and study the behavioral response of caterpillars and ants to acoustic signals.

The acoustic signaling of J. evagoras represents a simple system for the investigation of acoustic signal repertoires, enabling experimentation in a controlled environment and a quantitative approach to revealing behavioral features. In particular, the pupal stage of J. evagoras is immobile, permitting observation in a low noise

179 environment and eliminating the complication of sounds produced by movement. In contrast, caterpillars move and create other vibratory noise that cause difficulties for computer assisted analysis. Like caterpillars, pupae attract and maintain ant attendance, and produce attractive secretions. The presence of ants can also be carefully controlled in the laboratory environment. In terms of the study of acoustic signaling in general, this simple system allows the development of computer based sensors and analysis, establishing a processing system that could be used for characterizing more complex acoustic repertoire catalogs in an automated fashion.

In Chapter 5 of this thesis, I describe work extending our understanding of the acoustic signal variety of J. evagoras pupae, including evidence for previously undescribed call classes, changes in calling behavior during development, and variation over the diurnal cycle. I also describe a study of the anatomy of sound production in pupae using video differencing methods, and in caterpillars using laser Doppler vibrometry. While previous work has measured costs and benefits of some aspects of the symbiosis, the energetic expenditure into the acoustic component of the behavioral repertoire of J. evagoras has not been characterized. Therefore, in Chapter 6, I describe studies of the metabolic cost of acoustic signaling in these caterpillars by measuring production of carbon dioxide and its relationship to calling behavior. Chapter 6 also provides initial evidence for the responsiveness of caterpillars and of ants to acoustic signals presented in the absence of other cues, suggesting both an intraspecific and an interspecific function to these signals.

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186 Ross, G. N. 1966. Life history studies on Mexican butterflies. IV. The ecology and ethology of Anatole rossi, a myrmecophilous metalmark (Lepidoptera: Riodinidae). Annals of the Entomological Society of America 59: 985-1004.

Schlosz, M. 1991. The call of pupae. Metamorphosis 2: 19-20.

Schlosz, M. & Schlosz, P. 1990. A 'sound' observation. Metamorphosis 1: 24-25.

Schremmer, F. 1978. On the bionomy and morphology of the myrmecophilous larva and pupa of the neotropical butterfly species Hamearis erostratus (Lepidoptera: Riodinidae). Entomologica Germanica 4: 113-121.

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Schurian, K. G. & Fiedler, K. 1991. Einfache Methoden zur Schwallwahrnehmung bei Bläulings-Larven (Lepidoptera: Lycaenidae). Entomologische Zeitschrift 101: 393-412.

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187 Chapter 5

Characterization of Acoustic Signaling in Jalmenus evagoras

ABSTRACT

The pupae of J. evagoras make a variety of acoustic signals, and indeed this appears to comprise the majority of their behavioral repertoire while they undergo metamorphosis. By characterizing the acoustic repertoire of pupae in an automated, multidimensional fashion, we were able to quantitatively characterize the ‘grammar’ of their calls. This led to the discovery that the pupae may produce more calls than the two signals previously described. We further characterized the diurnal variation in calling rate, and the variation in calls at different developmental stages, as well as the frequency filtering characteristics of the host plants. Pupae were observed to increase their calling rate significantly in the morning hours, and possible reasons for this are discussed. Finally, we show that acoustic signals alone are sufficient to maintain increased ant presence. These results support the hypothesis that the acoustic signaling of J. evagoras plays an important role in inter-specific communication in their symbiotic relationship with ants.

INTRODUCTION

Acoustic repertoire of pupal Jalmenus evagoras

In contrast to systems with a high degree of complexity of vocal repertoire, such

as Amazona oratrix, the acoustic signals of J. evagoras pupae are simple enough to be

studied systematically and exhaustively. An extensive sampling regime is necessary to

determine the total frequencies of usage of different signals, to discover rare signals, and to capture contextual information. I developed methods to study the diversity of the acoustic signal repertoire in this simple system. In particular, our goal was an unbiased characterization of a behavioral repertoire using continuous quantitative measurements,

188 thus avoiding predetermined behavioral categories that are the hallmark of the

construction of ethograms. I attempted to find natural breakpoints and clusters in the data

to define categorical boundaries, behavioral events, and behavioral states, thus

minimizing human interpretation and a priori bias in the description of those labels.

What is the simplest definition of a behavioral dimension? The behavior of a

quantitative signal may be described as difference in state when measured between two

or more time points. Using this simple definition, we can characterize the behavior of

potentially complex behavioral systems such as the variation in calling rate through time

merely as a difference function in parameter values across time. An appropriate timescale

is required to reveal system dynamics for any given parameter varying over time. To the

extent that ethogram categories are used, one should be able to quantify the parameters

such that events may be defined objectively from the observational data.

In order to characterize the acoustic properties of the signal repertoire of these

pupae, I created an acoustic isolation recording booth (see Methods). Recordings made

without a pupa were compared with recordings made with a pupa present. I obtained

excellent signal to noise ratios from pupae (80-90 dB headroom), which allowed an

examination a much broader magnitude of signal amplitude variation compared to

previous studies (~40 dB headroom) that relied on tape recordings (DeVries 1991;

Travassos and Pierce 2000). Digital recording offers not only greater dynamic range, but

enables a recording length limited by storage; some of the later recordings here are analyses of 24 continuous hours of sound. The rate of analysis as reported here is no

longer limited by the time investment of the researcher. This opens up new possibilities

189 for the study of communication, since we can see large scale, as well as finer, details of a

system in meaningful ways.

METHODS

Caterpillar rearing

J. evagoras egg masses were collected from under loose bark of the host plants,

Acacia filicifolia and A. melanoxylon in Ebor, New South Wales, Australia in 1995, and

were hatched in test tubes in the laboratory. The larvae were transferred to Acacia

irrorata and A. melanoxylon plants grown in greenhouses at Harvard. Caterpillars used in

playback experiments were 4th and 5th instars.

All experiments were conducted between 20 and 25 degrees C ambient room

temperature.

Ant maintenance

Colonies of the attendant ant species, Iridomyrmex gracilis were collected in the

Toohey Forest, Nathan, Queensland. Colonies were maintained in plastic nest boxes

equipped with light-shielded test tubes containing

water and cotton. Ants were fed Bhatkar diet ad

libitum (Bhatkar and Whitcomb 1970).

Acoustic isolation and recording of pupae

The pupae were contained within a glass jar

attached to a Knowles accelerometer (BU-3173).

This jar was housed inside a second glass jar Figure 5.1. Pupa inside double jar padded with cotton (Figure 5.1). The apparatus was acoustic isolation chamber

190 then placed on top of foam set on a granite counter or table. This effectively damped both ambient air-borne environmental noises and substrate vibrations. Acoustic signals from the accelerometer were preamplified through a Velleman kit stereo preamplifier (+40 dB), and the signals were sent to a computer for digitization and characterization.

Recordings were obtained using “CoolEdit 2000” software, and were sampled at 8000

Hz, 16 bits.

Automated acoustic analysis

I developed a system for automated pulse detection and measurement given acoustic signal data. Scripts written for MATLAB were used to identify pulses within selected amplitudes, either within specific frequency bands or across the whole frequency spectrum. This was done by feeding the raw acoustic wave data into the script, convolving against an appropriate filter to smooth high frequency noise, and then using a logical threshold to identify calls above a specified amplitude (Figure 5.2). Once the on- and off- times of each pulse were determined, each pulse was then characterized with respect to frequency, amplitude, length, and power spectral peaks. This allowed a well- defined, automated, quantitative examination of long (up to 24 hours here) audio samples recorded from caterpillars and pupae.

Pulse detection was accomplished by first establishing a signal/noise threshold level, either manually or by examining the first two seconds of a sound file and setting the threshold to at least twice the median absolute signal value. The signal was processed through a low-pass filter, and the threshold was applied to identify regions containing pulse information. Onset and offset of individual pulses were stored and used as indices

191 for subsequent pulse characterization. This automated analysis was completely unsupervised, and inexpensive digital storage makes possible a large, indexed array of sounds within very long sound files (a day or more or continuous recording) for subsequent retrieval and analysis. Three hundred hours of recorded pupae sound was processed in this way in ~3.5 hours, yielding more than 100,000 pulses for analysis.

0.06

0.04

0.02

0 Volts -0.02

-0.04

-0.06

1.5 1.55 1.6 1.65 1.7 1.75 1.8 Sample number (8000 samples/sec) 5 x 10

Figure 5.2. Pulse detection to identify sound events in a pupal recording. The blue line is the raw digitized acoustic signal (oscillogram); the red line is the absolute value of the signal after high-frequency filtering; the green lines represent the boundaries of the pulse, as detected by applying a threshold to the filtered signal. Pulse-on and pulse-off indices within the original sound file are extracted, so pulses can be retrieved for subsequent analyses.

192 1. Pulse Diversity

a. Pulse Detection

Sounds were opened in Matlab in 300 second segments. The 8kHz digitized sound was transformed to absolute magnitude, and then smoothed with a 200-point moving average window (low-pass filtering). The signal was then thresholded to yield a logical buffer (ones and zeros). On- and off-indices were then identified by differentiating the logic buffer, which yields spikes of value 1 at pulse onset, and -1 at offset).

b. Pulse Characteristics

I studied three parameters to characterize calls: pulse amplitude, dominant frequency, and length.

i. Amplitude / power of pulses

Amplitude of each pulse was measured by finding the maximum value in decibels of each pulse’s power spectrum (using 512 bins – see below).

ii. Length

Pulse lengths were determined by subtracting pulse on-time from pulse off-time for each pulse detected in a given recording.

iii. Spectral frequency content

Once pulses were indexed, each pulse was individually retrieved and Fourier transformation applied. Dominant frequency was characterized as the frequency of the spectral peak of the Fourier transform (512 bins). Maximum amplitude in decibels was determined from the magnitude of the spectral peak

iv. Multi-dimensional parameter projection

193 XY plots were constructed by plotting pulse parameters (Amplitude versus

Dominant Frequency, Amplitude versus Call Length) to generate “voice prints” for

individual pupal recordings.

c. Plant transmission and spectral filtering of sound

A small (~ 35 cm tall) Acacia irrorata plant was acoustically coupled to a

RadioShack Optimus 4040 speaker, by way of a 0.5 mm thick steel wire with hot glue at each end. Playback sources were a) a 20 – 20,000 Hz tone sweep, b) white noise (with

equal energy in each frequency band), and c) pink noise (equal energy in each octave).

Surface vibrations were recorded with a ceramic-transduction phonograph cartridge, and

digitized by computer at 44.1 kHz. Playback signals were recorded at the plant’s base

near the point of contact with the speaker-coupled wire, and 25 cm up the plant. Power

spectra of the recorded signals were calculated and plotted in Matlab, with 128 frequency band bins (see (Michelsen et al. 1982) for a similar measure of plant transmission characteristics, and (Cocroft et al. 2006) as well for a more thorough exploration of plant transmission acoustics, and vibrational playback methods).

2. Temporal Patterns of Acoustic Usage

a. Diurnal pattern of sound production

For pupal recordings of length greater than 12 hours (n = 10), calls were summed

within each hour, and all recordings were scaled to a common time-of-day x-axis.

Daylight calling rates were compared to night time calling rates by visual inspection.

b. Calls through development

For two pupae recorded on multiple days, voiceprints of calls from each day were constructed (amplitude vs. pulse length), and compared by visual inspection.

194 c. Inter-pulse intervals: revealing the temporal grammar in acoustic signals1

Inter-pulse interval was calculated as (onset time of pulse n+1) - (offset time of pulse n). Data were then log-transformed, and histograms were created using 60 bins.

d. Return map analysis of call repetition and syntax

Return maps scatter plots were constructed for 28 pupal recordings, by plotting each pulse value against the next pulse’s value, for the following pulse characteristics: dominant frequency, maximum amplitude, and interpulse interval, in order to further reveal general patterns of usage of multiple signal types, and to make evident differences among pupae.

3. Functional significance of sounds to ants

a. Ant attendance on closed vials containing caterpillars

To study ant behavior in response to acoustic stimuli, I placed three caterpillars in an opaque, airtight vial. The reason for using groups of three caterpillars is that I had previously observed that aggregation appears to stimulate calling (see also Travassos and

Pierce 2000). I verified that acoustic signals were detectable through the vial, and that these signals were similar to signals measured by conduction through an Acacia plant

(i.e., detectable at a similar amplitude). To construct a choice test for the ants, the opaque caterpillar vial was mounted on one end of a cardboard dish, and an opaque empty control vial was at the opposite end. The dish was set atop a cork, on the floor of a colony container of ants (containing several hundred ants), so as to maximize the likelihood that the ants away from brood-mates encountering the vials were foraging. To establish a

1 The Merriam-Webster dictionary defines grammar as follows: “2 a: the characteristic system of inflections and syntax of a language b: a system of rules that defines the grammatical structure of a language.” American heritage dictionary offers the following definition for language: “6. the means of communication used by animals: the language of birds.” I am referring to production rules, not reception, similar to Chomsky’s

195 sampling interval period that would be less susceptible to non-independence of successive video frames, we timed individual focal ants with a stopwatch once they touched a vial (caterpillar vials, n= 13, control vials n = 12) and determined mean residence time on the vials. A video snapshot was taken every 10 seconds for a period of

10 minutes, and I recorded the number of ants present in each frame. Our measure of ant attendance was the total number of ants recorded in the experiment (60 frames).

We observed 9 separate test sessions, each with a different set of caterpillars and a different ant colony. We summed ant attendance over all experiments, to yield an estimate analogous to “man-hours”. This metric does not determine the rate of turnover of the ants (i.e., whether the same or different ants are present in each frame). In other words, this metric does not distinguish between an increase in the number of ants entering versus a decrease in the number of ants exiting. However, measurements were taken at 10 second spaced intervals, so there is a low probability that ants observed in any two measurements are the same. Attendance rates at the stimulus and control vials were compared using One-way Chi-square test (df=1) on pooled attendance rate, and by paired t-test on individual trial rates.

Vials were wiped with ethanol and dried with Kimwipes between trials, to reduce the possibility of chemical cues.

b. Simple Acoustic Playback to Ants

We connected cardboard dishes to the speaker cone of a Radio Shack 4040 speaker via 28 gauge piano wire. The dishes were suspended ~ ½ cm off the floor of the plastic colony container. Ants were able to access the dishes using a ‘bridge’ made of cellophane, which was touching the floor of the colony container. The experimental dish

196 was connected to a speaker playing recordings of a caterpillar aggregation; the control dish was connected to a silent speaker. 5 minutes of playback through the left speaker was followed by 5 minutes of silence, and 5 minutes of playback through the right speaker. Data collected were ants in attendance on playback vs. control, every 10 seconds, summed over the experiment (10 minutes).

I then wished to determine whether ants would respond similarly to artificial playback, since it was possible that the ants were detecting other stimuli created by the presence of caterpillars, such as temperature fluctuations, mass, or minute amounts of organic material on the exterior of the vial. If the ants would respond to artificial acoustic signals, this could be a straightforward method for studying ant response to well- controlled acoustic stimuli.

I connected two cardboard dishes to different speakers using piano wire. The dishes were suspended in the ant colony container and connected to the floor of the container by cellophane. I then played caterpillar calls through one speaker, while the control speaker was silent. Video data were collected and ants on each dish were counted to estimate ant attendance. We then reversed left-right after 10 minutes, and continued after a five minute break for 10 minutes. This controlled for any bias between the left and right speaker setups.

197 RESULTS

1. Pulse Diversity

a. Pulse Detection

A total of 149 hours of digitized sound from 28 pupal recordings yielded a total of

63,679 pulses for subsequent measurements reported below. This detection and indexing

of all pulses required less than two hours of computer time for retrieval and processing.

b. Pulse Characteristics

The histogram of pulse power revealed contrasts and similarities among pupae

(Figures 5.3 -5.5).

# of calls

ln (power (volts))

Figure 5.3. Histogram of pulse power. In this case, the power of each of 7,647 calls was recorded for 24 hours for a single pupa (SJ080706).

198

033104B H080106 060904b 040504A 5 50 100 5

0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0 I073006 061004a C072006 I080106 50 100 200 50 50 100 0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0 061404a C072806 I080806 061704a 1000 200 5 200 100 500 0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0 D071906 J080706 062104a E072306 4 1000 200 2000 2 500 0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0 K080606 062204a E072406 L081006

1000 50 200 100 500 100 0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0 062304b E072706 060804a 062404a 40 500 100 200 100 20 0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0 F080206 060804b G073106 060904a 40 100 50 20 20 50 10 0 0 0 0 -10 -5 0 -10 -5 0 -10 -5 0 -10 -5 0

Figure 5.4. Variation in pulse power among pupae. Each box shows a histogram of pulse power (# calls vs. ln(maximum power)), for different pupal recordings. Subtitles indicate a unique pupal designation (initial letters) and the date of recording (MMDDYY). This convention is used for the subsequent figures.

199 Pulse length variation

SC072006 SC072806 SD071906 150 150 15

100 100 10

50 50 5

0 0 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0 -4 -3 -2 -1 SE072406 SE072706 SF080206 100 400 150

100 50 200 50

0 0 0 -3 -2.5 -2 -1.5 -1 -0.5 -4 -3 -2 -1 0 -4 -3 -2 -1 SG073106 SH080106 SI073006 150 100 150

100 100 50 50 50

0 0 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0 -3.5 -3 -2.5 -2 -1.5 -1 SI080106 SI080806 SJ080706 300 600 600

200 400 400

100 200 200

0 0 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0 SK080606 SL081006 1000 150

100 500 50

0 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0

Figure 5.5. Histogram of pulse length, for individual pupal recordings. The horizontal axis is the natural log of pulse length in seconds; the vertical axis is the number of calls.

200 Multi-dimensional parameter projection

I plotted pulse length against pulse power (Figure 5.6) or amplitude (Figure 5.7).

There are several pupal call clusters. In particular, there are actually two clusters

corresponding to a power around -4.4: one cluster characterized by a call length of ~500

ms, and one cluster with a call length of ~1100 ms (Figure 5.6). These clusters could not

be separated using the data for pulse power alone. Therefore, we also plotted pulse

amplitude against frequency (Figure 5.8) for different individuals (Figure 5.9). These

three measures are illustrated for a single individual in Figure 5.10.

)) volts ( ower p maximum ( ln

Call length (ms)

Figure 5.6. Pulse length vs. maximum power of pulse, for pupa SJ, recorded on 08/07/06 (7647 calls). Note the presence of two clusters of different length at ln(maximum power) around -4.4.

201 Pupa: a071706 Min=25 n=102 Pupa: c071706 Min=60 n=223 0 0 -20 -20 -40 -40 -60 -60 -80 -80 0 0.05 0.1 0.15 0 0.05 0.1 0.15 Pupa: d071906 Min=20 n=130 Pupa: a071806 Min=20 n=74 0 0 -20 -20 -40 -40 -60 -60 -80 -80 0 0.05 0.1 0.15 0 0.05 0.1 0.15 Pupa: c071906 Min=20 n=75 Pupa: d072106 Min=20 n=82 0 0 -20 -20 -40 -40 -60 -60 -80 -80 0 0.05 0.1 0.15 0 0.05 0.1 0.15 Pupa: a071906 Min=20 n=34 Pupa: c072106 Min=20 n=35 0 0 -20 -20 -40 -40 -60 -60 -80 -80 0 0.05 0.1 0.15 0 0.05 0.1 0.15 Pupa: a072106 Min=20 n=15 Pupa: d071706 Min=22 n=16

) 0 0 dB

( -20 -20 -40 -40

litude -60 -60 p -80 -80 Am 0 0.05 0.1 0.15 0 0.05 0.1 0.15

Pulse length in seconds

Figure 5.7. Diversity of calls. Each box shows maximum amplitude (dB) vs. call length (s), for different pupal recordings. Subtitles include pupal identification, date, length of recording (min), and number of pulses (n). Recordings were made during daylight hours (see diurnal patterns of behavior, below).

202 Spectral frequency content

) dB (

litude p

Maximum am

Frequency (Hz)

Figure 5.8. Maximum amplitude vs. frequency for calls from an individual pupa. The quietist, lowest category may be noise due to movement, or other non-communicative sounds from physiological processes, since these pulses are quite soft.

203

Figure 5.9. Diversity of calls among individuals (“voiceprints”). Each box shows maximum amplitude (dB) vs. dominant frequency (Hz), for a different pupal recording. Subtitles indicate the pupa’s identity, date of recording, number of calls (N), and recording length (hours).

204

Figure 5.10. Three dimensions of analysis for one individual, pupa SM, recorded 08/16/06. Top: histogram of call amplitude (number of calls vs. amplitude (dB)). Second: histogram of call length (ln(length in seconds)). Third: histogram of dominant frequency (Hz). ______

c. Plant transmission and filtering of vibrational sound

We found that at 1000 Hz, signals are attenuated by about 20 dB at the top versus bottom (30 cm) of the small plant (stem diameter < 1 cm). At low frequencies (less than

200 Hz), attenuation is minimal (Figure 5.11), and may even be amplified at the lowest frequencies.

205

Figure 5.11. Audio frequency filtering through plants. The light gray line indicates the power spectrum of the original source. The dark blue was recorded at the base of the plant near the speaker. The light blue line was recorded at the top of the plant approximately 25 cm from the speaker.

2. Temporal Patterns of Acoustic Use

a. Diurnal pattern of sound production

In the course of these observations, we noticed that the calling rate was significantly influenced by the diurnal cycle. Calling rate was close to zero during the night, and increased sharply around 8 am. This is a pronounced effect, with some pupae

206 having a daytime calling rate 100 times higher than the night-time calling rate. All pupae

exhibited this previously unknown “dawn chorus” effect. Figure 5.12 shows the diurnal

effect, as observed in 10 different individuals.

SL081206 SI073006 400 300

200 200 100 0 0 16 20 0 4 8 12 16 10 13 16 19 22 1 4 SE072306 SE072706

100 150 100 50 50 0 0 19 22 1 4 7 10 13 18 21 0 3 6 9 12 SM081606 SL081006

100 200

100 50

0 0 22 1 3 6 9 11 14 18 21 23 2 5 7 10 SG073106 SJ080706

40 200

20 100

0 0 17 20 22 1 4 6 9 17 20 22 1 4 6 9 SC072006 SK080606 400 200

200 100

0 0 22 0 2 4 6 8 10 22 0 2 4 6 8 10

Figure 5.12. Diurnal pattern of pupal pulsing: nocturnal quiescence and “dawn chorus.” Each box represents a recording from a single pupa. Vertical axes: calls per hour, counting all call types. Horizontal axes: time of day (24 hr notation). Arrows indicate 6:30 am. Plot subtitles indicate pupa ID and recording date (MMDDYY).

______

b. Calls through development

The observed diversity in calls of different individuals could have several underlying causes, including pupal size, genetics, or developmental stage. The

‘voiceprint’ did change during development, as seen in a series of plots of amplitude vs.

207 call length (Figure 5.13). In particular, call length of the loudest signal class appeared to decrease over time. Our sample of available pupae did not allow us sufficient resolution to determine the influence of sex and developmental stage in metamorphosis (but is the focus of ongoing research).

208

Pupa A Pupa C

Pupa: a071706 Min=25 n=102 0 Pupa: c071706 Min=60 n=223 -20 0

-40 -20 -60 -40 -80 0 0.05 0.1 0.15 -60 Pupa: a071806 Min=20 n=74 -80 0 0 0.05 0.1 0.15 -20 Pupa: c071906 Min=20 n=75 -40 0

-60 -20 -80 0 0.05 0.1 0.15 -40 Pupa: a071906 Min=20 n=34 -60 0 -20 -80 0 0.05 0.1 0.15 -40 Pupa: c072106 Min=20 n=35 -60 0 -80 0 0.05 0.1 0.15 -20

Pupa: a072106 Min=20 n=15 -40 0 -20 -60

-40 -80 0 0.05 0.1 0.15 -60 -80 0 0.05 0.1 0.15

Figure 5.13. Change in call characteristics during pupal development. Recordings were made from a single pupa (pupa ‘A’ on left, and pupa ‘C’ on right), on different days as indicated. Each plot shows amplitude (dB) vs. call length (s). Subtitles include length of recording (min) and number of pulses detected (n)

209 c. Inter-pulse intervals (IPI)

We observed peaks in the IPI histogram corresponding to scales of 10-2 sec, 1 sec, and 100 sec (Figures 5.14 - 5.15).

Figure 5.14. Histograms of inter-pulse interval for selected pupae, showing three apparent categories of call interval. Vertical axis is number of calls; horizontal axis is time in seconds between pulses.

210

Figure 5.15. Histograms of inter-pulse intervals for different pupal recordings. Vertical axis: number of calls. Horizontal axis: ln (time (s)). Subtitles include length of recording (hrs) and total number of pulses (NumPulses).

d. Return map analysis of repetition and syntax

A large variety among pupal recordings (and many obvious similarities) in one- step call transitions can be discerned in Figure 5.16.

211

Maximum amplitude (dB) Dominant frequency (Hz) IPI of pulses (seconds, log scale)

Figure 5.16. One-step return maps (similar to Markov matrices), showing plots of each parameter Pt versus Pt+1, for all pulses of each pupa. Total number of pulse transitions plotted = 63,651.

212 3. Functional significance of sounds to ants

a. Vials test – Focal ant residence time

The mean residence time for foraging ants in pre-trials was not significantly different on the vial with caterpillars and without (Box 5.1). Mean residence time was approximately ten seconds, as shown in the table beneath the graph.

CONTROL CATERPILLAR VIAL VIAL

10 Seconds on Vial Seconds on Vial

0 15 10 5 0 5 10 15 Count Count

Group N Mean (seconds) SD control 12 10.417 7.549 experimental 13 9.692 2.626

Analysis of Variance

Source Sum-of-Squares df Mean-Square F-ratio P

LEFTRIGHT$ 4.967 1 4.967 0.155 0.698 EXPERMT$ 1.255 1 1.255 0.039 0.845

Error 704.719 22 32.033

Box 5.1. Residence times of focal ants upon vials with and without caterpillars

213 Vials test – Ant attendance

We found that there was significantly greater ant attendance on the opaque,

airtight vial containing caterpillars, if each ant is considered an independent trial (Figure

5.17; n = 902 vs. n = 697; X2 = 26.02, p <.0001). To control better for the true non-

independence, we calculated statistics by trial. Results of a paired t-test, with each trial’s data sum, appears below (Table 5.1).

______

Trial Xa Xb Xa - Xb 1 154 87 67 2 57 65 -8 3 44 73 -29 4 257 251 6 5 83 44 39 6 77 71 6 7 125 38 87 8 69 43 26 9 36 25 11

Summary Values

Values Xa Xb Xa - Xb n9 9 9 sum 902 697 205 mean 100.2222 77.4444 22.7778 st. dev. 69.8655 68.0443 36.5437 Standard deviations are calculated with denominator = n-1.

MeanA - MeanB t df 22.7778 1.87 8 one-tailed 0.0492105* P two-tailed 0.098421 ______

Table 5.1. Paired-t-test for each trial (n=9 trials); Xa = vial with caterpillars inside, Xb = empty vial

214

Figure 5.17. Vibrational cues of caterpillars increase ant attendance. Background photo: experimental setup for choice test; left vial contained three caterpillars, right vial was empty in this trial. Graph: comparison of ant attendance on caterpillar vs. control vial. Vertical axis is sum total ant attendance score over nine tests (data above).

215 b. Playback of recorded caterpillar sounds to whole colony

After 10 minutes, we counted 107 ants at the stimulus side, and 23 at the control. Overall sums, and also the time-series, are shown in Figure 5.18.

5 Silent 4 Playback 3

0 0 10 20 30 40 50 60 Figure 5.18. TOP: Increased ant attendance in response to acoustic playback of caterpillar calls. Background photo: experimental setup of playback. Graph: total ant attendance at playback (n = 107) vs. control dish (n = 23); BOTTOM: ant counts, 10 sec apart for 10 minutes; X2 = 43.26, p <.0001)

216 DISCUSSION

Pulse detection

Previous studies of insect signal diversity were limited by the size of sampled recordings that could be reasonably measured by eye, by ear, and human hand. By developing signal processing scripts in Matlab to detect and measure pulse properties, this bottleneck is removed. How much we “hear” in animal signals may be a function only of how much we “listen.” With inexpensive storage of very long sound files, we can hear much more than we can actually listen to. Two other notable projects with similar aims of automating the process of signal detection and analysis are xbat from the Cornell

Laboratory of Ornithology1, and Sound Analysis Pro, from the laboratory of Ofer

Tchernichovski.2

Pulse variety

We can also reveal multiple call types within a single recording by looking at

several dimensions of variation. Some clusters have more variation along a different

dimension, and some clusters may overlap. For example, by plotting maximum amplitude

vs. dominant frequency, we see at least three apparent categories (Figure 5.8). However,

we also observed substantial variation among pupae (Figure 5.9) for this pair of dimensions. Most pupal ‘voiceprints’ fill similar regions of phenotypic space, but some

are clearly more clustered than others, and some may not have multiple clusters.

Thus, there may be a need for a broad range of descriptors to capture true range of

variety in phenotypic space (Figure 5.10). In particular, these data show that the size of

the call repertoire of these pupae was previously underestimated (Travassos and Pierce

1 http://xbat.org/home.html 2 http://ofer.sci.ccny.cuny.edu/html/sound_analysis.html

217 2000). While collecting the initial data, we noticed that individual pupae showed diverse

patterns of call repertoire, involving a number of distributions, including unimodal and

multimodal examples (Figure 5.4-5.5).

Whether calls defined by feature boundaries in multi-modal distributions

represent behaviorally distinct categories (e.g., as a precursor to droplet release), and

whether they trigger different behavioral responses in ants or caterpillars, or even other

pupae, has yet to be shown. Further study now underway, with an expanded sample pool

of sexed individuals, will likely reveal whether differences observed among pupae carry information about sex, size, or condition. However, the natural categories observed here

provide a quantitative basis for subsequent playback experiments (e.g., differential response of ants to calls of different parameter values, or sex-specific differences in call

structure or usage pattern).

The grammar of pupae calls: inter-pulse intervals

We are also interested in understanding the temporal aspect of calling behavior, in part to determine whether a central pattern generator in the pupal ganglia is producing species specific signals similar to those that have been characterized for crickets (Hedwig

2000) and other song-producing insects (Ewing 1989; Faure and Hoy 2000; Hill 2001a;

Pires and Hoy 1992). The grammar of pupae calls can be captured by the transition between calls, as shown by analyzing the lengths of all inter-pulse intervals. Interestingly, histograms of inter-pulse intervals also differ among pupae (Figure 5.15). Variation of respiration rate has been described throughout metamorphosis, yielding a U-shaped curve over time (Nash 1990; Pierce and Nash 1999); these differences in inter-pulse interval

218 may represent different developmental stages, perhaps reflecting changes in the

musculature of the 5th-6th intersegmental space, or perhaps different sex.

Despite the obvious variation among pupae, many do show similar patterns of

inter-pulse interval usage. For example, many pupae have three peaks in their inter-pulse

interval usage (Figure 5.14), suggesting that there is a shared pattern to call usage.

Furthermore, if many pupal recordings are averaged together, we can see 3 clear peaks in

inter-pulse interval, centered at 0.05 sec, 2 sec, and 90 sec (Figure 5.14). This indicates

the internal tendencies of the endogenous “grammar” of call production, whereby metamorphosing pupae express multiple signal types, in patterns revealed by both inter- pulse interval plots (Figures 5.14-5.15) and by one-step return maps (Figure 5.16), in isolation from external environmental cues. Whether these different pulses and timing tendencies are the result of different physical or neural anatomies of the producing structures remains an open question.

Acoustic signaling and ant attendance

The role of acoustic signaling in this two-species mutualistic system has been suggested through several lines of experimental evidence. Caterpillars of the riodinid butterfly, Thisbe irenea, attract fewer attendant ants when their vibratory papillae are removed (DeVries 1991), and pupae of J. evagoras attract fewer attendant ants when their stridulatory organs have been silenced (Travassos and Pierce 2000). In this study,

we wanted to determine whether the acoustic signals of J. evagoras caterpillars were

alone sufficient to increase ant attendance in the absence of other cues such as nutritious

secretions. The playback of caterpillar calls was indeed attractive. There was significantly greater ant attendance at the dish connected to the speaker playing

219 caterpillar sounds (Figure 5.17, p < 0.0001), indicating that acoustic signals produced by

J. evagoras are sufficient to increase attendance of Iridomyrmex ants. Further studies are required to determine whether the ants distinguish between calls of different species of lycaenids, and whether they respond differently to the different call types of J. evagoras immatures [see (Cocroft 2001) for a review of multiple contextual functions in treehoppers (membracids) and other insects, such as group formation during movement, or juvenile response to parental alarm signals, and (Cocroft 1996) for a demonstration of alarm signals].

Interestingly, the magnitude of the relative increase in ant attendance compared with the control was different between the experiments. There are two possible explanations for why there might be relatively greater ant attendance in the control of the first experiment. In the first experiment, the caps of the vials were affixed to the same butter dish using putty. This might transmit some acoustic vibrations to the other vial.

Also, food was placed in the middle of the dish on previous experiment, which increased the total number of ant visits to both vials (this was necessary to attract a reliably countable number of ants to the butter dish, which appeared to be relatively difficult for ants to access). In the second experiment, no food bait was necessary because the tape provided an ant ‘bridge’ to the dishes, and the two dishes were not acoustically coupled except through cellophane to the colony container itself.

Influence of plant filtering on caterpillar signals transmission

Communication bandwidth, both in terms of acoustic spectral power and temporal

pulse resolution, is a function of the channel through which the signal is transmitted and

thus transformed (see Hill 2001b and Cocroft and Rodríguez (2005) for a review of

220 vibration in animal communication systems). For organisms using plants as acoustic

transmission channels (Cocroft et al. 2006), high frequencies will be more greatly

attenuated with distance than low frequencies, particularly for surface, or bending, waves

(Michelsen et al. 1982). To place the spectral characteristics of Jalmenus evagoras

stridulations in the context of the constraints of plant filtering, we took sample

measurements of the spectral attenuation of a small (~40 cm tall) Acacia plant (see

Methods).

Signals transmitted through a small plant indicate that over a distance 30 cm,

sounds in the frequency range of 1000Hz will be attenuated, while at lower frequencies

(less than 200 Hz), attenuation is minimal (Figure 5.11), and signals may even be

amplified at the lowest frequencies through resonance with the plant substrate. Therefore,

caterpillars focusing their calls in the lower frequencies (<200 Hz) will be detectable

across a greater distance. Calls employed for near range communication would be

expected in the higher frequencies, and this difference between call types may indicate

the intended range of functional consequence, and remains an area for further

investigation.

SUMMARY

The problem of characterizing diversity in a heterogeneous sample is common to ecologists who study incompletely characterized mixtures (e.g., a multispecies sample in which the species have not yet been defined, or a phenotypically diverse sample of the same species). We used objective criteria to identify call types of pupae of Jalmenus evagoras, and we used automated analysis to obtain large sample sizes (up to 24 hr

221 continuous recordings, yielding 100-1000 times more pulses for analysis than in previous

studies) and to minimize human interpretive error. This larger sampling allows fine

characterization the acoustic repertoire of J. evagoras pupae along multiple dimensions,

including pulse power or amplitude, frequency, and length. The pupae display

considerably greater acoustic signal variation than previously described, by uncovering

acoustic variation among pupae (which could be due to differences in developmental

stage, sex and size). Pupae showed diurnal variation in calling rate, and a “dawn chorus”

effect. We uncovered a common pattern to inter-pulse intervals in the absence of ants or

other external cues, suggesting the presence of an endogenous central pattern generator

(or generators), functioning across multiple time scales. We also noted similarities and

differences in call usage pattern from return maps (which are essentially syntax maps) of

call parameters. Thus, continuous monitoring of pupae through long time periods

facilitated by development of custom automated acoustic analysis programs revealed

possible elements of temporal construction rules, as well as diurnal patterns in production

of signals. In short, the complex use-patterns of simple signal sets can be revealed.

We also performed playback experiments to ants using caterpillar recordings, and found evidence that acoustic signals may be sufficient to maintain attendance in absence of other signals (e.g., chemical or behavioral cues) in this model symbiotic relationship.

It seems likely that acoustic signals could also function as intra-specific signals to conspecifics, used in regulating juvenile aggregation and dispersal, though this has not

yet been thoroughly tested.

There are two sides of an act of communication: the signal and its variations as

produced by the signaler, and the signal and its variations as perceived by the receiver.

222 Signal construction rules are in effect in the signaler; reception and response rules that

function on signal diversity in the receiver may act on only portions of the emitted signal.

This study has revealed the former, in order to take the next step of uncovering the latter: the “meaning” of this signal variation in a functional context, when perceived by caterpillars, pupae, and imagoes, and by their symbiotic ant associates.

REFERENCES

Bhatkar, A., and W. H. Whitcomb. 1970. Artificial Diet for Rearing Various Species of

Ants. Florida Entomologist 53:229-232.

Cocroft, R. B. 1996. Insect vibrational defence signals. Nature 382:679-680.

—. 2001. Vibrational communication and the ecology of group-living, herbivorous

insects. American Zoologist 41:1215-1221.

Cocroft, R. B., and R. L. Rodríguez. 2005. The behavioral ecology of insect vibrational

communication. BioScience 55:323-334.

Cocroft, R. B., H. J. Shugart, K. T. Konrad, and K. Tibbs. 2006. Variation in plant

substrates and its consequences for insect vibrational communication. Ethology

112:779-789.

DeVries, P. 1991. Call production by myrmecophilous riodinid and lycaenid butterfly

caterpillars (Lepidoptera): morphological, acoustical, functional, and evolutionary

patterns. Am. Mus. Nov. 3025:1–23.

Ewing, A. W. 1989. Arthropod Bioacoustics: Neurobiology and Behavior. Ithaca, Cornell

University Press.

223 Faure, P. A., and R. R. Hoy. 2000. The sounds of silence: cessation of singing and song

pausing are ultrasound-induced acoustic startle behaviors in the katydid

Neoconocephalus ensiger (Orthoptera; Tettigoniidae). Journal of Comparative

Physiology A - Sensory Neural and Behavioral Physiology 186:129-142.

Hedwig, B. 2000. Control of cricket stridulation by a command neuron: Efficacy depends

on the behavioral state. Journal of Neurophysiology 83:712-722.

Hill, P. S. M. 2001a. Vibration and Animal Communication: A Review. American

Zoologist 41:1135–1142.

—. 2001b. Vibration and Animal Communication: A Review1. American

Zoologist:1135-1142.

Michelsen, A., F. Fink, M. Gogala, and D. Traue. 1982. Plants as transmission channels

for insect vibrational songs. Behavioral Ecology and Sociobiology 11:269-281.

Nash, D. 1990. Cost-benefit analysis of a mutualism between lycaenid butterflies and

ants, PhD thesis. Oxford Univ.

Pierce, N. E., and D. R. Nash. 1999. The imperial blue, Jalmenus evagoras (Lycaenidae).

Monographs on Australian Lepidoptera 6:279-315.

Pires, A., and R. R. Hoy. 1992. Temperature Coupling in Cricket Acoustic

Communication .2. Localization of Temperature Effects on Song Production and

Recognition Networks in Gryllus firmus. Journal of Comparative Physiology a-

Sensory Neural and Behavioral Physiology 171:79-92.

Travassos, M. A., and N. E. Pierce. 2000. Acoustics, context and function of vibrational

signaling in a lycaenid butterfly-ant mutualism. Anim Behav 60:13-26.

224 Chapter 6

Physiology of Acoustic Signaling in Larvae and Pupae of the Imperial Blue Butterfly, Jalmenus evagoras: Metabolic Costs and Mechanisms

ABSTRACT

To determine the respiratory cost of the acoustic signals produced by immatures of the Australian lycaenid butterfly, J. evagoras, respiration levels were measured as CO2 production in parallel with acoustic recordings. In this species, there is a strong correlation between weight at eclosion and measures of subsequent reproductive fitness for both sexes (Elgar and Pierce 1988). Thus, any investment of energetic stores by caterpillars and pupae in the production of communication signals to attendant ants in the form of grunts, pulses, and drummings will directly reduce the subsequent fitness of the imago. This reduction in fitness should be less than the benefits derived for this system to evolve and persist; thus we studied a measure of fitness, defined by caloric expenditure, for these behaviors as a component of overall fitness of traits that comprise the total costs of mutualistic associations of these larvae with their attendant ants. Pupae inhibit carbon dioxide release in response to disturbance, concurrent with response pulsing. We also characterize the anatomical displacements correlated with sound production, and demonstrate maximal displacement between the 5th and 6th abdominal segments by using a video differencing technique for pupae, and using a laser Doppler vibrometer for caterpillars.

INTRODUCTION

Metabolic cost of acoustic signaling

The mutualistic relationship of J. evagoras immatures and Iridomyrmex anceps

ants is actively maintained through the secretion of nutrient-rich rewards by the

caterpillars and pupae. The cost of nectar secretion appears to be biologically significant,

since J. evagoras individuals appear to have cost-reducing strategies such as the

reduction of per capita secretion production by caterpillars in aggregations compared

with single caterpillars (Axen and Pierce 1998). Such signaling may also be costly,

225 especially given the strong correlation between weight at eclosion and reproductive

success (Elgar and Pierce 1988). For example, in the tok-tok beetle (Psammodes striatus), substrate communication has been shown to entail a substantial metabolic cost, in which O2 consumption rises by 50-100% when beetles are tapping (Lighton 1987). On

the other hand, in an avian species (the pied flycatcher), for which singing costs were measured, singing was shown not to be metabolically costly (Ward et al. 2004).To understand the relative cost of stridulation to total metabolic budget, we measured the correlation between respiration and acoustic signaling in J. evagoras pupae, and estimate that during periods of high signal production, about one fifth of their CO2 production is

due to calling effort. Overall (through periods of calling and quiescence), calling effort

accounted for about 2% of metabolic expenditures. Such a measure of investment in

calling, as a proportion of total metabolic expenditure, can serve as a proxy measure of

this fitness component of their overall investment in mutualistic association.

METHODS

Caterpillar rearing

J. evagoras egg masses were collected from under loose bark of the host plants,

Acacia filicifolia and A. melanoxylon in Ebor, New South Wales, Australia in 1995, and

were hatched in test tubes in the laboratory. The larvae were transferred to Acacia

irrorata and A. melanoxylon plants grown in greenhouses at Harvard. Caterpillars used in

playback experiments were 4th and 5th instars.

All experiments were conducted between 20 and 25 degrees C (ambient room

temperature), in natural and fluorescent light.

226

Respirometry of pupae

Pupae were placed in a container with a volume of approximately 50 cm3. Pupae were affixed to an accelerometer (Knowles BU-3173) on the ventral thorax with a non- toxic glue-stick. Using mass flow-controllers and software from Sable Systems, medical- grade air was passed across the pupa at a rate of 50cc/min, and gaseous CO2

concentration in parts per million (ppm) was measured every ten seconds with a Li-Cor

LI-6262 respirometry system.

Acoustic recording and sound analysis

The respiration chamber was placed inside another container as further acoustic

isolation from room sounds, with no direct atmospheric coupling (Figure 6.1). This

permitted high bandwidth, low-noise recordings. The signal from a Knowles

accelerometer signal was preamplified (Velleman universal stereo preamplifier K2572)

and digitized at 8 kHz, 32 bit, using CoolEdit 2000 software (Syntrillium).

Call rates were measured by smoothed summed amplitude (summed absolute

value of audio amplitude per 10 sec interval). Acoustic pulses were indexed, and

measured by computer with scripts developed in Matlab (see Methods, Chapters 2 and 5).

Pulses were detected by empirically establishing both a smoothing window width shorter

than pupal pulse signal, by which the absolute value of the audio signal was convolved

temporally, and a cut-off amplitude threshold. On-time and off-time (indexed by audio

sample number) were thus collected for each pulse for each pupal recording.

227 Pulses identified in the original signal were measured to extract maximum

amplitude, peak frequency (calculated with 512 frequency bands), and the Shannon-

Wiener entropy of the frequency power-spectrum of the pulse (a measure of frequency

pureness versus broad noise). Further calculations from these features provide call

lengths (off-time(i) - on-time(i)), rates (pulse counts per time-unit bin), and inter-pulse

interval (IPI) distributions (on-time (i+1) minus off-time(i). These features were used to help identify acoustic signal clusters.

Figure 6.1. Chamber for acoustic recording and respirometry. Pupa is affixed to accelerometer. Air-flow is streamed through the 50cc glass tube across the pupae (left to right) for CO2 measurement.

Pupal acoustic and respiratory response to stimulation by tapping (Figure 6.1)

Every 5 minutes, and then every 2 minutes, I tapped on the glass chamber 3 times

in rapid succession (~250 ms between taps). Pupal calls were counted in the 30 sec time

window after each tap and in the other time windows. To examine CO2 release response

228 during pupal stridulation (including spiracle contraction with interruption of CO2 release), audio and respiratory signals were synchronously recorded while calls were elicited by tapping on the 50cc glass chamber .

Correlation between respiration and calling energy

Respiration was measured as CO2 level in parts per million (ppm) every 10 seconds, with an averaging window of 1 second, using the Li-Cor 6262 monitor. These data were smoothed with a window of 300 seconds (30 time points) before further processing. Data were converted to z-scores for each individual recording, for scaled display with acoustic energy in units of standard deviation

Acoustic energy was measured by collecting the accelerometer waveform data

(amplitude vs. time) and integrating the absolute value of the amplitude over 10 second windows, to match the time base of the simultaneously recorded respiration trace. These data were smoothed with a window of 300 seconds (30 time points) before further processing. Data were converted to z-scores for plot scaling.

To reduce the noise generated by periods of relative inactivity (e.g., night-time) and discontinuous ventilation resulting from closure of spiracles, I excluded respiration/acoustic energy time points for which z < -1 in the respiration data.

Correlations were calculated by fitting data with least squares linear regression.

Calculating the metabolic cost of calling

To quantify the correlation, data were fit by linear regression, and the slope (m) and squared correlation coefficient (r2) were determined (Table 6.1). To determine the

229 approximate ratio between respiration levels during high and low levels of calling, the slope of the correlation (in standard deviations of CO2 per dB sound level) was used to

compare values 50 dB apart (see low and high ranges in Figure 6.4) in summed call

energy per 10 second window. We analyzed 19 recordings, representing over 207 hours

of pupal stridulation and respiration.

Anatomy of Calling: call production loci

Imaging of pupal flexion by video differencing

Images were captured at 740 x 480 pixel resolution at 30 fps and converted to

grayscale. Frames were subtracted with an offset of 3 frames to determine the absolute

magnitude of the intensity difference. Image processing was performed in Matlab using

custom scripts. We assessed J. evagoras pupae by filming flexion during sound

production and calculating pixel displacement by successive image subtraction. Video

frame pixel differencing as a behavioral measure has been previously used to analyze ant

colony activity hotspots (Cole 1991).

Filmed pulses lasted approximately 0.2 seconds. Successive images were

subtracted using a three frame offset in order to generate a plot showing maximal

difference (pupal displacement) between successive frames.

Measurement of caterpillar sound production by laser Doppler vibrometer

Caterpillars were mounted for measurement as depicted in Figure 6.2. Caterpillars

were affixed to the index card using “Hard as Nails” clear nail polish along the ventral

230 side. Vibrations were detected by a Polytec OFV-303 laser Doppler vibrometer1. Signals were digitized as sound waves in CoolEdit 2000 (Syntrillium Software) and processed as acoustic samples in Matlab (see above). In order to compare amplitude differences within call type between sampled points along the caterpillar, calls were classified by cluster analysis of the first two dimensions after principal components analysis, using amplitude, frequency, spectral entropy, and call length values. The call class containing the largest number of pulses (the “modal” call class) was compared by amplitude at each of 4 sampling point per caterpillar.

Figure 6.2. Setup used to measure anatomy of sound production. Points along length of caterpillar (four segments) were sampled, by raising the jack stand.

1 Generously made available by Prof. Thomas Bifano, BU Photonics lab.

231 RESULTS

Pupal acoustic and respiratory response to stimulation by tapping

Pupae responded by producing high amplitude acoustic signals within 30 seconds,

indicating that they are still reactive to acoustic signals even though they are undergoing metamorphosis (Figure 6.3). For short timescales, we observed that immediately after the taps, CO2 release drops precipitously during pulsing, and recovers over approximately one minute (Figure 6.3).

232

CO2 (ppm/s)

Time (min)

Amplitude (audio)

Tap response

Figure 6.3. Pupal response to tapping on respirometry chamber. Top: respiration trace. Each vertical line (T) marks a manual tap. Note the decrease in CO2 concentration immediately following each tap and the subsequent recovery. Middle: audio recording of pupa simulated by tapping, aligned with respiration trace, as pupa restricts CO2 release during acoustic response. The tall lines correspond to the manual taps. The pupal response can be seen immediately following the tap. This is best seen in the bottom panel, which is a magnification of a tap (one tap produces multiple vibrations due to the undamped properties of the chamber), followed by pupal response (in this case, 7 pulses).

233 Metabolic cost of calling

We examined the correlation between CO2 consumption and calling rate to

determine the proportion of respiratory variance that is due to variation in calling rate.

Each CO2 trace and acoustic energy trace were internally normalized and converted to z- score for visualization (Figure 6.4). A correlation between these traces can be seen by eye; for example, see Figure 6.4b, in which high calling energies are clearly simultaneous with high CO2 production.

The correlation was statistically significant in all but two cases. On average, r2 was approximately 2% (range 0.0015- 0.126), indicating that overall, 2% of the variance in CO2 production could be attributed to calling activity (see Table 6.1). On average, the

slope was positive (0.04 ppm of CO2 per decibel of acoustic energy), indicating that

increasing acoustic energy requires increased metabolic expenditure (the maximum

measured value was 0.185 ppm CO2 / decibel of acoustic energy). Since the calling activity index covers a range of approximately -40 dB at low rates of calling, to 10 dB at high rates of calling (encompassing a span of 50 dB; see Figure 6.4c), we estimate that approximately 2 ppm of additional CO2 during periods of high acoustic activity is

attributable to calling. Since mean overall CO2 expenditure is around 9 ppm (range 4.6 –

18.6; Table 6.1), during times of peak calling about 20% of metabolic expenditure may be from calling behavior (std=34.24).

234

S060804B S060904a S061004A S061404A 4 8 6 2 2 2 4 2 0 0 0 0 -2 -2 -2 -2 2 4 2 4 1 2 3 4 5 1 2 3 4 5 S061504A S061704A S062104A S062204A

4 4 2 2 0 2 2 -2 0 -4 0 0 -6 -2 -8 -2 -2 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 2 4 6 8 10 S062304B S062404A SC072006 SC072806 10 4 2 2 2 5 0 0 0

-2 -2 -2 0 1 2 3 4 5 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 SD071806 SD071906 SD072206 SE072406 4 8 4 2 4 6 0 2 2 -2 4 0 0 -4 2 -2 -2 -6 0 -4 5 10 15 5 10 15 5 10 15 20 5 10 15 SI080806 SK080606 SM081606 15 4 4 10 2 2 0 5 0 -2 0 -2 2 4 6 8 2 4 6 8 10 5 10 15

Figure 6.4a. Respiration traces and simultaneous acoustic energy for 19 pupal recordings. Graph titles indicate the identity of the pupa and recording date, using previously described convention. Vertical axis: red trace represents CO2 level; green trace represents acoustic energy. Both signals were internally normalized and are plotted in units of z-score. Horizontal axis: time in hours. Note: SC072806, SD072206 and SI080806 eclosed toward the end of the recording, leading to a spike in both sound and respiration signals.

235

1 Z-score

Acoustic energy 0

-1 Respiration

-2

-3 0 1 2 3 4 5 6

Hours

Figure 6.4b. Respiration (z-score, red) and acoustic energy (z-score, green) versus time (hours), for S061404A shown in Figure 6.4a. Note the tracking between the two signals at both short and long timescales.

236

Figure 6.4c. CO2 level versus acoustic energy, for 19 pupal recordings. The corresponding time series are shown in Figure 6.4a. Titles indicate pupal identity. Vertical axis: CO2 level (z-score, calculated separately for each recording). Horizontal axis: acoustic energy (dB of z-score). Green lines: linear regression with positive slope; red line: linear regression with negative slope. All points where zrespiration > -1 were included in the analysis (see Methods).

237 σ, approx. % CO2 p- [CO2] m m <[CO2]> increase at high 2 Pupa r value (ppm) (σ /dB) (CO2/dB) (ppm) calling rate Notes

S060804B 0.0066 0.0015 1.18 0.0071 0.0084 6.58 6.4 S060904a 0.0218 <0.001 1.03 0.0071 0.0073 4.63 7.9 S061004A 0.003 0.0156 1.30 0.0169 0.0219 6.68 16.4 S061404A 0.0173 <0.001 1.87 0.0113 0.0212 14.32 7.4 S061504A 0.0031 0.018 0.66 0.0122 0.0081 5.85 6.9 S061704A 0.0172 <0.001 0.83 0.0045 0.0037 12.37 1.5 S062104A 0.0086 <0.001 1.58 0.0118 0.0187 6.77 13.8 S062204A 0.019 <0.001 1.23 0.0098 0.0121 5.51 10.9 S062304B 0.0005 0.3192 1.53 0.0145 0.0221 8.09 13.7 N.S. S062404A 0.0015 0.0152 1.56 -0.0023 -0.0036 7.40 -2.4 SC072006 0.0262 <0.001 0.88 0.0038 0.0033 7.13 2.3 SC072806 0.3886 <0.001 21.33 0.015 0.3199 21.58 74.1 eclosed SD071806 0.126 <0.001 2.32 0.0796 0.1847 18.55 49.8 SD071906 0.0029 0.0001 2.83 0.0446 0.1262 16.71 37.8 SD072206 0.6972 <0.001 16.74 0.0044 0.0737 21.54 17.1 eclosed SE072406 0.0114 <0.001 1.30 0.1413 0.1839 7.15 128.6 SI080806 0.3424 <0.001 16.23 0.0109 0.1769 32.66 27.1 eclosed SK080606 0.0008 0.0971 1.77 0.0832 0.1474 8.64 85.3 N.S. SM081606 0.0017 0.0033 2.69 0.0033 0.0089 17.30 2.6

Average 0.0178 0.0403 9.13 19.3%

Table 6.1. Correlation between respiration level and sound production. Correlations were calculated for recordings shown in Figure 6.4c. “σ, [CO2] (ppm)” is the standard deviation of CO2 levels for each pupal recording. ‘m’ = the slope of the linear regression; N.S. = not significant. Recordings with p-value >0.05, and recordings of pupae that eclosed during recording were excluded from calculation of average values. Pupae that were included in the average are listed in bold type.

238 Anatomy of sound production

We carried out a preliminary investigation of the anatomy of sound production in pupae of J. evagoras using video differencing (to estimate force). Acoustic signals coincided with increased magnitude of video difference (Figures 6.5); an example of pupal audio response is shown in Figure 6.6. Image analysis localized the site of vibrational production to the 5th and 6th abdominal segments (Figure 6.7).

We also examined the anatomy of sound production in caterpillars, using laser

Doppler vibrometry. This technique measures the displacement of vibrating objects targeted by an infrared laser. Caterpillars were affixed to a card and vibrational displacement was measured at various points of along their length (see Methods; Figure

6.8).

239 Difference (intensity units)

Amplitude (volts)

Figure 6.5. Pupal flexion during sound production, detected using video differencing. Top: video difference (grayscale intensity difference between frame ‘n+3’ and frame ‘n’), vs. frame number (30 frames/sec, spanning 15 seconds). Bottom: corresponding audio signal (smoothed over 0.1 second intervals) vs. sample number, spanning 15 seconds. Note that the peaks of video difference correspond to the peaks in acoustic signal production.

240

Audio signal

Frame 0 Frame 3 0 sec 0.1 sec 0.2 sec

Figure 6.6. Audio waveform of a single pupal pulse elicited by manual tap. Note the mirrored symmetric shape of first and second half of the waveform.

241

Figure 6.7. Video differencing to identify anatomical displacement in pupae during calling. Top left: example of one frame of video, showing the abdomen and developing wing. Top right: Difference between two frames during an acoustic pulse (frames were 0.1 seconds apart, showing the maximum difference). A threshold was applied to generate the black and white image. Bottom: overlay of the video image and the difference, showing that maximum anatomical displacement appears to occur at the 5th- 6th abdominal segments.

242

-5

-10

-15 Volts (dB) Volts

-20

-25

Segment T3A1 A3A4 A5A6 A7A8 Mean (dB) Segment

T3A1 40 -17.43 20

0 -30 -25 -20 -15 -10 -5 0 A3A4 -13.34 20

0 -30 -25 -20 -15 -10 -5 0 A5A6 -13.62

0 -30 -25 -20 -15 -10 -5 0 A7A8 20 -12.22 10 Count 0 -30 -25 -20 -15 -10 -5 0 Pulse Amplitude (dB) Figure 6.8a. Anatomy of sound production in caterpillars assessed by laser Doppler vibrometry (caterpillar 1). Top: red dots indicate positions measured. Middle: box plots showing the distribution of pulse amplitudes for all calls at the measured segments: T3A1 (n = 285), A3A4 (n=304), A5A6 (n=113), A7A8 (n=169). Red: median; blue: quartiles; black: range. Superimposed blue line plots represent the mean and quartiles calculated for the modal call cluster (n=270, 299,109,163, respectively). Bottom: histograms showing the distribution of pulse amplitude for the modal call cluster; means are given on the right.

243

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-8

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Segment 40

20 T2T3 0 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

A2A3 0 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 50

A5A6

0 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 3 2 1 A7A8 Count 0 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 Maximum pulse amplitude

Figure 6.8b. Anatomy of sound production in caterpillars (caterpillar 2). Box plots also include some outliers (red). For the modal calls: n = 269, 386, 238, 9.

244

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0 T2T3 -30 -25 -20 -15 -10 -5 0

5 A2A3 0 -30 -25 -20 -15 -10 -5 0 5

A5A6 0 -30 -25 -20 -15 -10 -5 0

Count 0 A7A8 -30 -25 -20 -15 -10 -5 0 Maximum pulse amplitude

Figure 6.8c. Anatomy of sound production in caterpillars (caterpillar 3). For the modal calls: n = 177, 98, 36, 72.

245 DISCUSSION

Pupae perceive and respond to disturbances by modulating respiration and call rates, shown here as pupal pulse bursts elicited by tapping on the recording chamber.

Coincident with these pupal signals was a drop in CO2, consistent with patterns of

discontinuous gas exchange as a defensive response, mediated by closure of spiracles

(Lighton 1994; Quinlan and Gibbs 2006). Ventilation and carbon dioxide release returned to high values after about a minute (Figure 6.3). The anti-correlation imposed by this lag required the application of smoothing filters and time-offset in order to reveal the relationship between calling rate and metabolic expenditure. We showed that acoustic signaling is costly to pupae and measured CO2 production as an indirect proxy of caloric

expenditure on calling rate. For some pupae, calling rate accounted for a major

proportion of the variation in their metabolic budget, lending credence to the previous

conjecture that pupal calling may be an honest signal since pupae compete for the

attention of ants (Travassos and Pierce 2000). Interestingly, caterpillars in aggregations

reduce the number of secretion droplets they produce from the dorsal nectary organ

(DNO) when their companions are relatively larger and older, indicating that individuals

are able to determine the size / age of their companions, possibly through acoustic

signaling. However, it remains to be seen whether calling also represents a relatively

‘cheap’ alternative for maintaining an ant guard, compared to nectar provisioning.

A conservative measure of the cost of active acoustic signaling was estimated to

be about 20%; overall mean investment in calling was measured at 2% of total

metabolism. This latter measure may be low, since night-time hours, when calling rates

are low (see Chapter 5), were included in our analysis. During the night, respiration

246 levels fluctuate for reasons unrelated to calling. Better correlations could likely be

obtained by excluding night-time hours (the magnitude of the effect as measured by

correlation coefficient is strongly influenced by other processes affecting respiration rate). Overall, these results indicate a strong connection between respiration and acoustic

signaling, and indicate a physiologically relevant metabolic burden imposed by calling.

Anatomy of sound production

The organ that produces the acoustic signal is known to be located between the 5th and 6th abdominal segments in lycaenid pupae, and was described in (Downey 1966.;

Downey and Allyn 1973). As expected, peaks of video displacement closely mirrored

peaks in sound production (Figure 6.5). In addition, the areas exhibiting maximum displacement on the video corresponded to the 5th and 6th abdominal segments of the

pupa (Figure 6.7). It is also apparent from the raw oscillogram (Figure 6.6) that the latter half of signal appears to be the mirror image of the former half of signal, indicating that sound is produced by symmetric flexion and relaxation.

For caterpillars, there appears to be an increase in displacement toward the caudal end, at abdominal segments 5-8 for the three caterpillars tested (Figure 6.8). However, it is unclear whether this increase at the caudal end of the caterpillar reflects the true source of signal, or whether this is due to a pivoting effect in the artificially immobilized caterpillar; future investigation is required for the precise description of the anatomical locus of call production in these caterpillars.

247 Summary and future directions

We found that pupae invest up to 20% of metabolism in calling, and that about

2% of the overall variance in metabolic rate can be explained by changes in acoustic energy production. This appears to signify a substantial investment in calling behavior, suggesting that calling may serve an important function, either through ant attraction

(Travassos and Pierce 2000) and/ or cost minimization by attracting conspecifics (Axen and Pierce 1998). Future research is needed to understand the relative costs of nectar production and acoustic signaling, as well as any tradeoff between these modes of attraction. This measure of investment in acoustic signal production serves as a proxy measure of a fitness component related to calling behavior, providing a broad measure of the importance of this mode of signaling. With tight measures of secretion and sound signal production, and with coupled playback experiments of sound with nectar provisioning, it may be possible to partition fitness components (using metabolic investment costs) along multiple behavioral dimensions related to ant attraction.

Further research is needed to explore the parameters of pupal vibratory perception. Given that acoustic signals represent a respiratory cost to the pupae, and that caterpillars regulate secretion production according to aggregation size in the presence of ants, it will also be interesting to determine the role of acoustic stimuli in modulating secretion rate from the dorsal nectary organ.

We also found evidence of discontinuous ventilation patterns in J. evagoras pupae, which has not been previously described in lycaenids, and developed a technique for characterizing the anatomy of calling. These findings suggest the possibility for

248 further research into the neuromuscular basis for acoustic signaling, particularly with regard to the change of calling behavior across pupation (Consoulas et al. 2000).

249 References

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Consoulas, C., C. Duch, R. J. Bayline, and R. B. Levine. 2000. Behavioral transformations during metamorphosis: Remodeling of neural and motor systems. Brain Research Bulletin 53:571-583.

Downey, J. C. 1966. Sound production in pupae of lycaenidae. Journal of the Lepidopterists' Society 20:129-155.

Downey, J. C., and A. C. Allyn. 1973. Butterfly ultrastructure: 1. Sound production and associated abdominal structures in pupae of Lycaenidae and Riodinidae. Bulletin of the Allyn Museum 14:1-47.

Elgar, M. A., and N. E. Pierce. 1988. Mating success and fecundity in an ant-tended Lycaenid butterfly, Pages 59-75 in T. Clutton-Brock, ed. Reproductive Success: Studies of selection and adaptation in contrasting breeding systems. Chicago, University of Chicago Press.

Lighton, J. R. B. 1987. Cost of tokking: the energetics of substrate communication in the tok-tok beetle, Psammodes striatus. Journal of Comparative Physiology B: Biochemical, Systemic, and Environmental Physiology 157:11-20.

—. 1994. Discontinuous ventilation in terrestrial insects. Physiological Zoology 67,:142– 162.

Quinlan, M. C., and A. G. Gibbs. 2006. Discontinuous gas exchange in insects. Respiratory Physiology & Neurobiology 154:18-29.

Travassos, M. A., and N. E. Pierce. 2000. Acoustics, context and function of vibrational signalling in a lycaenid butterfly-ant mutualism. Anim Behav 60:13-26.

Ward, S., H. M. Lampe, and P. J. B. Slater. 2004. Singing is not energetically demanding for pied flycatchers, Ficedula hypoleuca. Behav. Ecol. 15:477-484.

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