Foraging, the search for food, is a fundamental part of behavior. All animals, from the simplest invertebrates to primates, have to take in food. Because appropriate food may be more abundant at some times and places than others, an animal that can learn about the characteristics of its food supply is likely to be able to forage more efficiently than one that cannot learn. Indeed, the need for efficient foraging creates a strong selection pressure for the evolution of learning and memory.
Since the late twentieth century, the study of foraging behavior has been guided by optimal foraging theory, a body of mathematical models specifying how animals should behave so as to maximize foraging efficiency. After briefly introducing this framework, this entry describes some of the ways in which animals use learning and memory in foraging.
Optimal Foraging Theory
Optimal foraging theory is a topic in behavioral ecology, the field of biology dealing with how behavior contributes to an animal's reproductive success or fitness. Many aspects of foraging can be understood by assuming that animals have evolved to maximize the rate at which they take in energy while foraging. An animal that can forage efficiently will have more time for other important activities like finding a mate or defending a territory. If the economics of a particular foraging situation are understood well enough, it is possible to make a mathematical model that specifies what the animal should do in order to maximize its energy intake while foraging. Stephens and Krebs (1986) describe this approach in detail. However, some examples are easy to understand intuitively without any mathematics.
Consider a small bird in the spring collecting food to bring back to the young in its nest. To feed the hungry nestlings it must spend a good part of the day searching for food and carrying it back home. How far should it travel and how much should it collect on each trip? It might seem obvious that the bird should load up as much as it can each time, but this suggestion overlooks the fact that as the bird loads its beak with food like grubs or caterpillars, increasing the load becomes harder and harder. In addition, more energy is needed to fly back to the nest with more prey items. On the other hand, if the bird has had to fly some distance from the nest in order to find suitable prey, it is worth its while to collect as many items as possible. This informal argument suggests that there should be a direct relationship between the size of the bird's load and the distance it has traveled: Bigger loads should be collected when the bird is farther from the nest.
Kacelnik and Cuthill (1987) studied this problem of central-place foraging with starlings nesting around a farm. They trained the birds to visit a feeder and collect mealworms that the experimenter dropped down a pipe. By placing the feeder at different distances from the starlings' nests while keeping constant the rate of dropping mealworms, Kacelnik and Cuthill were able to obtain clear evidence of the predicted relationship. With further experiments in both the field and the laboratory they were able to account for many details of what the birds learn and remember.
Implicit in this example are a number of uses of learning and memory. To return straight home with its prey, a starling had to learn the location of its nest. On each trip it had to remember where it was in relation to the nest. The birds also had to learn how often worms were available at the experimental feeder and how valuable they were.
An animal encountering a potential prey item may accept it or go on searching for alternative prey. What it should do to maximize its rate of energy in-take can be understood intuitively. If it can expect to find a bigger or more quickly consumed item soon enough, the forager should reject the item at hand and go on searching; otherwise it should take the encountered item. To select prey as efficiently as they do, animals must learn about their value and their abundance and adjust their behavior as the environment changes. Many of the studies of prey selection and other aspects of foraging reviewed by Shettleworth (1998) have emphasized how the learning mechanisms animals use in foraging are the same as those revealed in experiments on operant conditioning.
Learning to Find Cryptic Prey
Many animals that are potential prey for other animals have evolved to look like their surroundings so they are hard for predators to see. For example, moths may be speckled black and gray like the bark of the trees where they rest, and caterpillars that live on green plants may be green. In turn, predators have evolved the ability to learn how to discriminate such cryptic prey from their backgrounds. Laboratory studies using bluejays, chicks, and pigeons searching for grains or for images on slides under controlled conditions have provided evidence that predators may form a specific search image for, or "learn to see," cryptic prey. When a bird encounters several cryptic prey items in a row, it becomes better at detecting them. It may be paying more attention to subtle details that differentiate the prey items from their background, or it may be learning to search more slowly when prey are difficult to detect. Both kinds of learning probably contribute to improving foraging efficiency. Using a computerized "virtual ecology" in which bluejays search for moths, Kamil and Bond (2001) have shown how the birds' learning can contribute to the evolution of one prey type rather than another.
Learning about Patches of Food
Not only do animals have to detect and capture prey efficiently, they have to learn where prey can be found. Food generally occurs in patches. For example, a freshly watered lawn is a good place for a robin to look for worms, but lawns may be separated by roads and sidewalks that do not provide good foraging for a robin. Clearly, it is best to be in the patch with the most abundant prey—foraging in the freshly watered lawn with worms close to the surface is preferable to foraging in the dried-up lawn next door. An efficient forager needs good spatial learning abilities so it can navigate from one part of the environment to another. Information about location of suitable foraging areas and the density of prey in each has to be constantly updated as the environment changes. Thus animals should sample the environment, sometimes exploring new patches or patches that were not good the last time they were tried, in order to discover whether they have changed for the better.
One aspect of patch choice that has been studied extensively is how animals should respond to depletion of foraging patches. In our example, as the robin hops around the lawn finding worms, its own foraging activity (and perhaps that of other birds) reduces the density of worms in the patch. Some are eaten and others burrow down into the soil at the birds' approach. When should the robin leave this patch and look for another? The foraging theorist's answer to this question takes into account several factors other than the average density of prey in the current patch. These factors include the density of prey in other patches, how far away the patches are, and whether prey are constant or variable in size or frequency. Experiments reviewed by Shettleworth (1998) have shown how animals learn about and respond to all these variables.
Some Special Problems for Foragers: Nectar-Feeding and Food-Storing
Learning where to search for prey and detecting and selecting it once a suitable patch is found are problems for virtually any forager. Some animals also face special problems that may require specialized learning and memory abilities. One set of problems, related to the patch-depletion problem just discussed, is faced by bees, bats, and hummingbirds that suck nectar from flowers. A flower that has been depleted of nectar produces more nectar at a rate that depends on factors like what kind of flower it is. The efficient forager will time its visits so as to return at long enough intervals to find the flower full, but not so long that some other forager will have depleted the flower. One way to ensure this is to travel a fixed route among a number of plants. Some nectar-feeding animals do forage in this way. For example, Gould (1982) describes how bees learn the features of flowers and the times and places at which nectar can be found. As reviewed by Shettleworth (1998), an exquisite sensitivity to times of day and time intervals between foraging opportunities is evident in the foraging behavior of many other species.
Another specialized foraging problem requiring memory is faced by some birds that spend the winter in a harsh climate. To have enough to eat at such times, birds such as chickadees, nuthatches, and jays store food when it is abundant. The Clark's nutcracker, a bird of the American Southwest, buries thousands of pine seeds in the late summer and recovers them up to six months later. Because each cache is in a different place, the birds must use memory to recover the food. Experiments in the laboratory with nut-crackers and chickadees, described by VanderWall (1990), have shown that these birds can indeed remember the locations of their stores and do not need to use other cues. Some research reviewed by Shettle-worth (1998) suggests that they have evolved a better spatial memory than birds that do not store food. This suggestion is supported by evidence that relative to body size, food-storing birds have a larger hippocampus (the brain area necessary for spatial memory) than other birds.
Considering what and how animals must learn and remember to forage efficiently is one of the best illustrations of how observations and theories about behavior in the wild have been integrated with the study of animals' cognitive processes. This analysis is at the core of cognitive ecology, a growing interdisciplinary area of animal behavior research discussed by Healy and Braithwaite (2001).
Gould, J. L. (1982). Ethology. New York: W. W. Norton.
Healy, S. D., and Braithwaite, V. (2001). Cognitive ecology: A field of substance? Trends in Ecology and Evolution 15, 22-26.
Kacelnik, A., and Cuthill, I. C. (1987). Starlings and optimal foraging theory: Modelling in a fractal world. In A. C. Kamil, J. R. Krebs, and H. R. Pulliam, eds., Foraging behavior, pp. 303-333. New York: Plenum Press.
Kamil, A. C., and Bond, A. B. (2001). The evolution of virtual ecology. In L. A. Dugatkin, ed., Model systems in behavioral ecology, pp. 288-310.
Stephens, D. W., and Krebs, J. R. (1986). Foraging theory. Princeton, NJ: Princeton University Press.
VanderWall, S. B. (1990). Food hoarding in animals. Chicago: University of Chicago Press.