Animal Communication

Fireflies flash, moths spray pheromones, bees dance, fish emit electric pulses, lizards drop dewlaps, frogs croak, birds sing, bats chirp, lions roar, monkeys grunt, apes grimace, and humans speak. These systems of communication, irrespective of sensory modality, are designed to mediate a flow of information between sender and receiver (Hauser 1996).

Early ethologists argued that signals are designed to proffer information to receptive companions, usually of their own species (Tinbergen 1951; Hinde 1981; Smith 1969). When a bird or a monkey gives a "hawk call," for example, this conveys information about a kind of danger. And when a redwing blackbird reveals its red epaulette during territorial disputes, it is conveying information about aggressive intent. Analyses of aggressive interactions, however, revealed only weak correlations between performance of certain displays and the probability of attack as opposed to retreat, leaving the outcome relatively unpredictable (Caryl 1979). Thus, while information transfer is basic to all communication, it is unclear how best to characterize the information exchange, particularly because animals do not always tell the truth.

In contradistinction to the ethologists, a new breed of animal behaviorist -- the behavioral ecologists -- proposed an alternative approach based on an economic cost-benefit analysis. The general argument was made in two moves: (1) selection favors behavioral adaptations that maximize gene propagation; and (2) information exchange cannot be the entire function of communication because it would be easy for a mutant strategy to invade by providing dishonest information about the probability of subsequent actions. This places a premium on recognizing honest signals. Zahavi (1975) suggested a mechanism for this, using the following recipe: signals are honest, if and only if they are costly to produce relative to the signaler's current condition and if the capacity to produce honest signals is heritable. Consider the anti predator stotting displays of ungulates -- an energetically expensive rigid-legged leap. In Thompson's gazelle, only males in good physical condition stot, and stotting males are more likely to escape cheetah attacks than those who do not.

Departing slightly from Zahavi, behavioral ecologists Krebs and Dawkins (1984) proposed that signals are designed not to inform but to manipulate. In response to such manipulation, selection favors skeptical receivers determined to discriminate truths from falsehoods. Such manipulative signaling evolves in situations of resource competition, including access to mates, parental care, and limited food supplies. In cases where sender and receiver must cooperate to achieve a common goal, however, selection favors signals that facilitate the flow of information among cooperators. Thus signals designed to manipulate tend to be loud and costly to produce (yelling, crying with tears), whereas signals designed for cooperation tend to be quiet, subtle, and cheap (whispers).

Turning to ecological constraints, early workers suggested that signal structure was conventional and arbitrary. More in-depth analyses, however, revealed that the physical structure of many signals is closely related to the functions served (Green and Marler 1979; Marler 1955). Thus, several avian and mammalian species use calls for mobbing predators that are loud, short, repetitive, and broad band. Such sounds attract attention and facilitate sound localization. In contrast, alarm calls used to warn companions of an approaching hawk are soft, high-pitched whistles, covering a narrow frequency range, only audible at close range and hard to locate (Marler 1955; Klump and Shalter 1984). The species-typical environment places additional constraints on the detectability of signals and the efficiency of transmission in long-distance communication, selecting for the optimal time of day and sound frequency window (Marten, Quine, and Marler 1977; Morton 1975; Wiley and Richards 1978). To coordinate the movements of groups who are out of sight, elephants and whales use very low frequency sounds that circumvent obstacles and carry over long distances. In contrast, sounds with high frequency and short wavelengths, such as some alarm calls and the biosonar signals used by bats and dolphins for obstacle avoidance and prey capture, attenuate rapidly.

The design of some signals reflects a conflict between natural and sexual selection pressures (Endler 1993). An elegant example is the advertisement call of the male Tungara frog (Ryan and Rand 1993). In its most complete form, one or more introductory whines are followed by chucks. Because females are attracted to the chucks, males who produce these sounds have higher mating success. But because frog-eating bats can localize chucks more readily than whines, frogs producing chucks are more likely to be eaten. They compromise by giving more whines than chucks until a female comes by. There are many such cases in which signal design is closely related to function, reflecting a tightly stitched tapestry of factors that include the sender's production capabilities, habitat structure, climate, time of day, competitors for signal space, the spatiotemporal distribution of intended recipients, and the pressures of predation and mate choice.

When signals are produced or perceived, complex processing by the sense organs and the central nervous system is engaged. Songbirds have a set of interconnected forebrain nuclei specialized for song learning and production. Nuclei vary widely in size between the sexes, between species and even between individuals of the same sex, though there are significant exceptions to these generalizations. Variations appear to correlate, not only with the commitment to singing behavior, but also with the size of the song repertoire (Arnold 1992; Nottebohm 1989). Some aspects of song learning are analogous to those documented for human speech, including involvement of particular brain areas, local dialects, categorical perception, innate learning preferences, and a motor theory-like system for coordinating articulatory production and feature perception (Nelson and Marler 1989).

For most animals, the acoustic morphology of the vocal repertoire appears to be innately specified, with experience playing little to no role in altering call structure during development. In contrast, the ontogeny of call usage and comprehension is stongly influenced by experience in several nonhuman primates, and possibly in some birds (Cheney and Seyfarth 1990; Hauser 1996; Marler 1991), with benefits accruing to individuals that can learn to use call types and subtypes in new ways. Generally speaking, however, the number of discrete signals in animal repertoires seems to be limited (Green and Marler 1979; Moynihan 1970), although reliable repertoire estimates are hard to come by, especially when signals intergrade extensively. Explosive expansion of the repertoire becomes possible if elements of the repertoire can be recombined into new, meaningful utterances, as they are in human speech.

Empirical studies documenting the decomposability of speech into smaller units, themselves meaningless, prepared the groundwork for the Chomskyan revolution in linguistics. The human brain takes our repertoire of phonemes and recombines them into an infinite variety of utterances with distinct meanings. There is no known case of animals using this combinatorial mechanism. Some birds create large learned song repertoires by recombination, but like human music, birdsongs are primarily affective signals, lacking the kind of referential meaning that has been attributed to primate vocalizations and chicken calls. Thus it appears that the songbirds' repertoire expansion serves more to alleviate habituation than to enrich meaning. The same is almost certainly true of animals with innate repertoires that engage in a more limited degree of recombination, although some researchers have reported evidence of syntactical organization (Hailman et al. 1987). More detailed analyses of the production and perception of vocal signals are required before we can reach any comprehensive conclusions on the developmental plasticity of animal communication systems.

At least one bird (the domestic chicken) and a few primates (ring-tailed lemurs, rhesus and diana monkeys, vervets) produce vocalizations that are functionally referential, telling others about specific objects and events (food, predators). Use of such calls is often contingent on the presence and nature of a social audience (e.g., allies or enemies). Vocalizing animals will, for example, withhold alarm calling in response to a predator if no audience is present, and in other cases, will use vocalizations to actively falsify information (Cheney and Seyfarth 1990; Evans and Marler 1991; Marler, Karakashian, and Gyger 1991; reviewed in Hauser 1996). While there is no evidence that animal signals are guided by awareness of beliefs, desires, and intentions, essential to human linguistic behavior (see PRIMATE COGNITION), there is a clear need for researchers in call semantics and cognition to work closely together to elucidate the mental states of animals while communicating.

See also

Additional links

-- Marc Hauser and Peter Marler


Arnold, A. P. (1992). Developmental plasticity in neural circuits controlling birdsong: Sexual differentiation and the neural basis of learning. Journal of Neurobiology 23:1506-1528.

Caryl, P. G. (1979). Communication by agonistic displays: What can games theory contribute to ethology? Behaviour 68:136-169.

Cheney, D. L., and R. M. Seyfarth. (1990). How Monkeys See the World: Inside the Mind of Another Species. Chicago: Chicago University Press.

Endler, J. (1993). Some general comments on the evolution and design of animal communication systems. Proceedings of the Royal Society, London 340:215-225.

Evans, C. S., and P. Marler. (1991). On the use of video images as social stimuli in birds: Audience effects on alarm calling. Animal Behaviour 41:17-26.

Green, S., and P. Marler. (1979). The analysis of animal communication. In P. Marler and J. Vandenbergh, Eds., Handbook of Behavioral Neurobiology, vol. 3, Social Behavior and Communication. New York: Plenum Press, pp. 73-158.

Hailman, J. P., M. S. Ficken, and R. W. Ficken. (1987). Constraints on the structure of combinatorial "chick-a-dee" calls. Ethology 75:62-80.

Hauser, M. D. (1996). The Evolution of Communication. Cambridge, MA: MIT Press.

Hinde, R. A. (1981). Animal signals: Ethological and games-theory approaches are not incompatible. Animal Behaviour 29:535-542.

Klump, G. M., and M. D. Shalter. (1984). Acoustic behaviour of birds and mammals in the predator context: 1. Factors affecting the structure of alarm signals. 2. The functional significance and evolution of alarm signals. Zeitschrift für Tierpsychologie 66:189-226.

Krebs, J. R., and R. Dawkins. (1984). Animal signals: Mind-reading and manipulation. In J.R. Krebs and N.B. Davies, Eds., Behavioural Ecology: an Evolutionary Approach. Sunderland, MA: Sinauer Associates Inc., pp. 380-402.

Marler, P. (1955). Characteristics of some animal calls. Nature 176:6-7.

Marler, P. (1991). Differences in behavioural development in closely related species: Birdsong. In P. Bateson, Ed., The Development and Integration of Behaviour. Cambridge: Cambridge University Press, pp. 41-70.

Marler, P., S. Karakashian, and M. Gyger. (1991). Do animals have the option of withholding signals when communication is inappropriate? The audience effect. In C. Ristau, Ed., Cognitive Ethology: The Minds of Other Animals. Hillsdale, NJ: Erlbaum, pp. 135-186.

Marten, K., D. B. Quine, and P. Marler. (1977). Sound transmission and its significance for animal vocalization. 2. Tropical habitats. Behavioral Ecology and Sociobiology 2:291-302.

Morton, E. S. (1975). Ecological sources of selection on avian sounds. American Naturalist 109:17-34.

Moynihan, M. (1970). The control, suppression, decay, disappearance, and replacement of displays. Journal of Theoretical Biology 29:85-112.

Nelson, D. A., and P. Marler. (1989). Categorical perception of a natural stimulus continuum: Birdsong. Science 244:976-978.

Nottebohm, F. (1989). From bird song to neurogenesis. Scientific American 260(2):74-79.

Ryan, M. J., and A. S. Rand. (1993). Phylogenetic patterns of behavioral mate recognition systems in the Physalaemus pustulosus species group (Anura: Leptodactylidae): The role of ancestral and derived characters and sensory exploitation. In D. Lees and D. Edwards, Eds., Evolutionary Patterns and Processes. London: Academic Press, pp. 251-267.

Smith, W. J. (1969). Messages of vertebrate communication. Science 165:145-150.

Tinbergen, N. (1952). Derived activities: Their causation, biological significance, origin and emancipation during evolution. Quarterly Review of Biology 27:1-32.

Wiley, R. H., and D. G. Richards. (1978). Physical constraints on acoustic communication in the atmosphere: Implications for the evolution of animal vocalizations. Behavioral Ecology and Sociobiology 3:69-94.

Zahavi, A. (1975). Mate selection: a selection for a handicap. Journal of Theoretical Biology 53:205-214.

Further Readings

Andersson, M. (1982). Female choice selects for extreme tail length in a widowbird. Nature 299:818-820.

Andersson, M. (1994). Sexual Selection. Princeton, NJ: Princeton University Press.

Brenowitz, E. A., and A. P. Arnold. (1986). Interspecific comparisons of the size of neural song control regions and song complexity in duetting birds: Evolutionary implications. The Journal of Neuroscience 6:2875-2879.

Brown, C., and P. Waser. (1988). Environmental influences on the structure of primate vocalizations. In D. Todt, P. Goedeking, and D. Symmes, Eds., Primate Vocal Communication. Berlin: Springer, pp. 51-68.

Cheney, D. L., and R. M. Seyfarth. (1988). Assessment of meaning and the detection of unreliable signals by vervet monkeys. Animal Behaviour 36:477-486.

Cleveland, J., and C. T. Snowdon. (1981). The complex vocal repertoire of the adult cotton-top tamarin, Saguinus oedipus oedipus.Zeitschrift für Tierpsychologie 58:231-270.

DeVoogd, T. J., J. R. Krebs, S. D. Healy, and A. Purvis. (1993). Relations between song repertoire size and the volume of brain nuclei related to song: Comparative evolutionary analyses amongst oscine birds. Proceedings of the Royal Society, London 254:75-82.

Endler, J. A. (1987). Predation, light intensity, and courtship behaviour in Poecilia reticulata.Animal Behaviour 35:1376-1385.

FitzGibbon, C. D., and J. W. Fanshawe. (1988). Stotting in Thompson's gazelles: An honest signal of condition. Behavioral Ecology and Sociobiology 23:69-74.

Hauser, M. D., and P. Marler. (1993). Food-associated calls in rhesus macaques (Macaca mulatta). 1. Socioecological factors influencing call production. Behavioral Ecology 4:194-205.

Klump, G. M., E. Kretzschmar, and E. Curio. (1986). The hearing of an avian predator and its avian prey. Behavioral Ecology and Sociobiology 18:317-323.

Langbauer, W. R., Jr., K. Payne, R. Charif, E. Rapaport, and F. Osborn. (1991). African elephants respond to distant playbacks of low-frequency conspecific calls. Journal of Experimental Biology 157:35-46.

Marler, P. (1961). The logical analysis of animal communication. Journal of Theoretical Biology 1:295-317.

Marler, P. (1976). Social organization, communication and graded signals: The chimpanzee and the gorilla. In P. P. G. Bateson and R. A. Hinde, Eds., Growing Points in Ethology. Cambridge: Cambridge University Press, pp. 239-280.

Marler, P. (1978). Primate vocalizations: Affective or symbolic? In G. Bourne, Ed., Progress in Ape Research. New York: Academic Press, pp. 85-96.

Marler, P. (1984). Song learning: Innate species differences in the learning process. In P. Marler and H. S. Terrace, Eds., The Biology of Learning. Berlin: Springer, pp. 289-309.

Marler, P., A. Dufty, and R. Pickert. (1986). Vocal communication in the domestic chicken: 2. Is a sender sensitive to the presence and nature of a receiver? Animal Behaviour 34:194-198.

Nordeen, E. J., and K. W. Nordeen. (1990). Neurogenesis and sensitive periods in avian song learning. Trends in Neurosciences 13:31-36.

Nottebohm, F. (1981). A brain for all seasons: Cyclical anatomical changes in song control nuclei of the canary brain. Science 214:1368-1370.

Robinson, J. G. (1979). An analysis of the organization of vocal communication in the titi monkey, Callicebus moloch. Zeitschrift für Tierpsychologie 49:381-405.

Robinson, J. G. (1984). Syntactic structures in the vocalizations of wedge-capped capuchin monkeys, Cebus nigrivittatus. Behaviour 90:46-79.

Ryan, M. J., and A. S. Rand. (1995). Female responses to ancestral advertisement calls in Tungara frogs. Science 269:390-392.

Ryan, M. J., and W. Wilczynski. (1988). Coevolution of sender and receiver: Effect on local mate preference in cricket frogs. Science 240:1786-1788.

Seyfarth, R. M., and D. L. Cheney. (1997). Some general features of vocal development in nonhuman primates. In C. T. Snowdon and M. Hausberger, Eds., Social Influences on Vocal Development. Cambridge: Cambridge University Press, pp. 249-273.

Suga, N. (1988). Auditory neuroethology and speech processing: Complex sound processing by combination-sensitive neurons. In G. M. Edelman, W. E. Gall, and W. M. Cowan, Eds., Auditory Function. New York: Wiley-Liss Press, pp. 679-720.

Williams, H., and F. Nottebohm. (1985). Auditory responses in avian vocal motor neurons: A motor theory for song perception in birds. Science 229:279-282.