CHAPTER OVERVIEW 11: Behavioral Ecology Learning Objectives Explain Tinbergen's levels of analysis and be able to formulate questions that would be addressed at each level of analysis. Use concepts from optimal foraging models to explain foraging behavior, and compare and contrast solitary versus group foraging. Explain the costs and benefits of migration, and describe how these movements are studied by ecologists. Describe the variety forms and functions of animal communication. Explain direct and indirect and use Hamilton's rule to assess when kin selection could be acting in a population. 11.1: Proximate and Ultimate Causes of Behavior 11.2: Foraging Ecology 11.3: Optimal Foraging Theory 11.4: Movement Ecology 11.5: Animal Communication 11.6: How Does Social Behavior Evolve? 11.7: How Do Social Systems Evolve Summary Behavioral ecology is the study of the evolutionary basis for animal behavior due to ecological pressures. Behavioral ecology seeks to address questions associated with the proximate causes, ontogeny, survival value, and phylogeny of a behavior. The field of behavioral ecology includes a variety of disciplines, including the study of how organisms find food, how they move about the environment, and how they communicate with each other. This field is also interested in studying the evolution of social behaviors and social systems. 11: Behavioral Ecology is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.
11.1: Proximate and Ultimate Causes of Behavior
Behavioral ecology is the study of the evolutionary basis for animal behavior due to ecological pressures (Figure
Behavioral ecology emerged after Nikolaas Tinbergen outlined four questions to address when studying animal behaviors (Figure
) that focused on two levels of answers: What are the ultimate (evolutionary) explanations of behavior, and what are the
proximate (physiological or developmental) explanations of behavior?
: The study of behavioral ecology focuses on the ways organisms behaviorally interact with their physical and social
environment. Here we see penguins huddling in the Antarctic (Ian Duffy), the bee waggle dance communicating information
(Emmanuel Boutet), a frog with inflated vocal sac (Benny Trapp), and a stotting gazelle (Rick Wilhelmsen).
Tinbergen's four questions are complementary categories of explanations for animal behaviour. These are also commonly referred to as levels of analysis (MacDougall-Shackleton, 2011). It suggests that an integrative understanding of behaviour must include: ultimate (evolutionary) explanations, in particular the behaviour (1) adaptive function and (2) phylogenetic history; and the proximate explanations, in particular the (3) underlying physiological mechanisms and (4) ontogenetic/developmental history (Daly & Wilson, 1983). When asked about the purpose of sight in humans and animals, even elementary-school children can answer that animals have vision to help them find food and avoid danger (function/adaptation). Biologists have three additional explanations: sight is caused by a particular series of evolutionary steps (phylogeny), the mechanics of the eye (mechanism/causation), and even the process of an individual's development (ontogeny).
: Summary of Tinbergen's four questions (Dan Wetzel).
Evolutionary (ultimate) explanations 1. Function (adaptation) Darwin's theory of evolution by natural selection is the only scientific explanation for why an animal's behavior is usually well adapted for survival and reproduction in its environment. However, claiming that a particular mechanism is well suited to the present environment is different from claiming that this mechanism was selected for in the past due to its history of being adaptive (Tinbergen, 1963). The literature conceptualizes the relationship between function and evolution in two ways. On the one hand, function and evolution are often presented as separate and distinct explanations of behaviour (Cartwright, 2000, Buss, 2004). On the other hand, the common definition of adaptation, a central concept in evolution, is a trait that was functional to the reproductive success of the organism and that is thus now present due to being selected for; that is, function and evolution are inseparable. However a trait can have a current function that is adaptive without being an adaptation in this sense, if for instance the environment has changed. Imagine an environment in which having a small body suddenly conferred benefit on an organism when previously body size had had no effect on survival (Tinbergen, 1963). A small body's function in the environment would then be adaptive, but it wouldn't become an adaptation until enough generations had passed in which small bodies were advantageous to reproduction for small bodies to be selected for. Given this, it is best to understand that presently functional traits might not all have been produced by natural selection (Tinbergen, 1963). The term "function" is preferable to "adaptation," because adaptation is often construed as implying that it was selected for due to past function. 2. Phylogeny (evolution) Evolution captures both the history of an organism via its phylogeny, and the history of natural selection working on function to produce adaptations (Alcock, 2001; Mayr, 2001). There are several reasons why natural selection may fail to achieve optimal design. One entails random processes such as mutation and environmental events acting on small populations. Another entails the constraints resulting from early evolutionary development. Each organism harbors traits, both anatomical and behavioral, of previous phylogenetic stages, since many traits are retained as populations evolve. Reconstructing the phylogeny of a species often makes it possible to understand the "uniqueness" of recent characteristics: Earlier phylogenetic stages and (pre-) conditions which persist often also determine the form of more modern characteristics. For instance, the vertebrate eye (including the human eye) has a blind spot, whereas octopus eyes do not. In those two lineages, the eye was originally constructed one way or the other. Once the vertebrate eye was constructed, there were no intermediate forms that were both adaptive and would have enabled it to evolve without a blind spot. Proximate explanations 3. Mechanism (causation) In examining living organisms, biologists are confronted with diverse levels of complexity (e.g. chemical, physiological, psychological, social). They therefore investigate causal and functional relations within and between these levels. A biochemist might examine, for instance, the influence of social and ecological conditions on the release of certain neurotransmitters and hormones, and the effects of such releases on behaviour, e.g. stress during birth has a tocolytic (contraction-suppressing) effect. Some prominent classes of causal mechanisms include: The brain: Broca's area, a small section of the human brain, has a critical role in linguistic capability. Hormones: chemicals used to communicate among cells of an individual organism. Testosterone, for instance, stimulates aggressive behaviour in a number of species. Pheromones: chemicals used to communicate among members of the same species. Some species (e.g., dogs and some moths) use pheromones to attract mates. 4. Ontogeny Ontogeny is the process of development of an individual organism from the zygote through the embryo to the adult form.
In the latter half of the twentieth century, social scientists debated whether human behaviour was the product of nature (genes) or nurture (environment in the developmental period, including culture). Many forms of developmental learning have a critical period, for instance, for imprinting among geese and language acquisition among humans. In such cases, genes determine the timing of the environmental impact. Causal relationships
: Diagrammatic explanation of behavioral ecology: the interaction between an organisms behavior, its genes, the
environment, and evolutionary forces (credit: W. Pete Welch, available in the public domain; adopted from Tinbergen, 1963).
The figure shows the causal relationships among the categories of explanations. The left-hand side represents the evolutionary explanations at the species level; the right-hand side represents the proximate explanations at the individual level. In the middle are those processes' end products--genes (i.e., genome) and behaviour, both of which can be analyzed at both levels.
Evolution, which is determined by both function and phylogeny, results in the genes of a population. The genes of an individual interact with its developmental environment, resulting in mechanisms, such as a nervous system. A mechanism (which is also an end-product in its own right) interacts with the individual's immediate environment, resulting in its behaviour.
Here we return to the population level. Over many generations, the success of the species' behaviour in its ancestral environment--or more technically, the environment of evolutionary adaptedness may result in evolution as measured by a change in its genes.
In sum, there are two processes--one at the population level and one at the individual level--which are influenced by environments in three time periods.
Examples Let's look at a couple of examples of explaining a trait or behavior using Tinbergen's four questions: Visual perception Function: to find food and avoid danger. Phylogeny: the vertebrate eye initially developed with a blind spot, but the lack of adaptive intermediate forms prevented the loss of the blind spot. Causation: the lens of the eye focuses light on the retina.
Development: neurons need the stimulation of light to wire the eye to the brain (Moore, 2001, pp. 98-99). Sleep (Bode & Kuula, 2021): Function: energy restoration, metabolic regulation, thermoregulation, boosting immune system, detoxification, brain maturation, circuit reorganization, synaptic optimization, avoiding danger. Phylogeny: sleep exists in invertebrates, lower vertebrates, and higher vertebrates. NREM and REM sleep exist in eutheria, marsupialiformes, and also evolved in birds. Mechanisms: mechanisms regulate wakefulness, sleep onset, and sleep. Specific mechanisms involve neurotransmitters, genes, neural structures, and the circadian rhythm. Ontogeny: sleep manifests differently in babies, infants, children, adolescents, adults, and older adults. Differences include the stages of sleep, sleep duration, and sex differences. References Alcock, J. (2001). Animal behavior: An evolutionary approach (7th ed., p. 492). Sinauer Associates. Bode, A., & Kuula, L. (2021). Romantic love and sleep variations: Potential proximate mechanisms and evolutionary functions. Biology, 10(9), 923. https://doi.org/10.3390/biology10090923 Buss, D. M. (2004). Evolutionary psychology: The new science of the mind (p. 12). Allyn & Bacon. Cartwright, J. (2000). Evolution and human behavior: Darwinian perspectives on human nature (p. 10). MIT Press. Daly, M., & Wilson, M. (1983). Sex, evolution, and behavior. Brooks-Cole. MacDougall-Shackleton, S. A. (2011). The levels of analysis revisited. Philosophical Transactions of the Royal Society B: Biological Sciences, 366(1574), 2076-2085. https://doi.org/10.1098/rstb.2010.0363 Mayr, E. (2001). What evolution is (p. 289). Basic Books. Moore, D. S. (2001). The dependent gene: The fallacy of "nature vs. nurture". Henry Holt. Tinbergen, N. (1963). On aims and methods in ethology. Zeitschrift für Tierpsychologie, 20, 410-433. [p. 411] Contributors and Attributions Modified by Dan Wetzel (University of Pittsburgh) from the following sources: Wikipedia, Behavioral ecology Wikipedia, Tinbergen's four questions. 11.1: Proximate and Ultimate Causes of Behavior is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.
: Grizzly bear (Ursus arctos horribilis) mother and cubs foraging in Denali National Park, Alaska.
Foraging is searching for wild food resources. It affects an animal's fitness because it plays an important role in an animal's ability to survive and reproduce (Danchin et al., 2008). Foraging theory is a branch of behavioral ecology that studies the foraging behavior of animals in response to the environment where the animal lives.
Behavioral ecologists use economic models to understand foraging; many of these models are a type of optimal model. Thus foraging theory is discussed in terms of optimizing a payoff from a foraging decision. The payoff for many of these models is the amount of energy an animal receives per unit time, more specifically, the highest ratio of energetic gain to cost while foraging (Hughes, 1989). Foraging theory predicts that the decisions that maximize energy per unit time and thus deliver the highest payoff will be selected for and persist. Key words used to describe foraging behavior include resources, the elements necessary for survival and reproduction which have a limited supply, predator, any organism that consumes others, prey, an organism that is eaten in part or whole by another, and patches, concentrations of resources (Danchin et al., 2008).
Behavioral ecologists first tackled this topic in the 1960s and 1970s. Their goal was to quantify and formalize a set of models to test their null hypothesis that animals forage randomly. Important contributions to foraging theory have been made by:
Eric Charnov, who developed the marginal value theorem to predict the behavior of foragers using patches; Sir John Krebs, with work on the optimal diet model in relation to tits and chickadees; John Goss-Custard, who first tested the optimal diet model against behavior in the field, using redshank, and then proceeded to an extensive study of foraging in the common pied oystercatcher
Factors influencing foraging behavior Several factors affect an animal's ability to forage and acquire profitable resources.
Learning Learning is defined as an adaptive change or modification of a behavior based on a previous experience (Raine & Chittka, 2008). Since an animal's environment is constantly changing, the ability to adjust foraging behavior is essential for maximization of fitness. Studies in social insects have shown that there is a significant correlation between learning and foraging performance (Raine & Chittka, 2008).
In nonhuman primates, young individuals learn foraging behavior from their peers and elders by watching other group members
forage and by copying their behavior (Rapaport & Brown, 2008). Observing and learning from other members of the group ensure
that the younger members of the group learn what is safe to eat and become proficient foragers (Figure
: A troop of olive baboons (Papio anubis) foraging in Laikipia, Kenya. Young primates learn from elders in their group about proper foraging.
One measure of learning is 'foraging innovation'--an animal consuming new food, or using a new foraging technique in response to their dynamic living environment (Dugatkin, 2004). Foraging innovation is considered learning because it involves behavioral plasticity on the animal's part. The animal recognizes the need to come up with a new foraging strategy and introduce something it has never used before to maximize his or her fitness (survival). Forebrain size has been associated with learning behavior. Animals with larger brain sizes are expected to learn better (Dugatkin, 2004). A higher ability to innovate has been linked to larger forebrain sizes in North American and British Isle birds according to Lefebvre et al. (1997). In this study, bird orders that contained individuals with larger forebrain sizes displayed a higher amount of foraging innovation. Examples of innovations recorded in birds include following tractors and eating frogs or other insects killed by it and using swaying trees to catch their prey (Dugatkin, 2004).
Another measure of learning is spatio-temporal learning (also called time-place learning), which refers to an individual's ability to associate the time of an event with the place of that event (Murphy & Breed, 2008). This type of learning has been documented in the foraging behaviors of individuals of the stingless bee species Trigona fulviventris (Murphy & Breed, 2008). Studies showed that T. fulviventris individuals learned the locations and times of feeding events, and arrived to those locations up to thirty minutes before the feeding event in anticipation of the food reward (Murphy & Breed, 2008).
: A European honey bee extracts nectar. According to Hunt (2007), two genes have been associated with the sugar concentration of the nectar honey bees collect.
Foraging behavior can also be influenced by genetics. The genes associated with foraging behavior have been widely studied in
honeybees with reference to the following; onset of foraging behavior, task division between foragers and workers, and bias in
foraging for either pollen or nectar (Dugatkin, 2004; Hunt et al., 2007). Honey bee foraging activity occurs both inside and outside
the hive for either pollen or nectar. Similar behavior is seen in many social wasps, such as the species Apoica flavissima. Studies
using quantitative trait loci (QTL) mapping have associated the following loci with the matched functions; Pln-1 and Pln-4 with
onset of foraging age, Pln-1 and 2 with the size of the pollen loads collected by workers, and Pln-2 and pln-3 were shown to
influence the sugar concentration of the nectar collected (Figure
Predators and parasites The presence of predators while a (prey) animal is foraging affects its behaviour. In general, foragers balance the risk of predation with their needs, thus deviating from the foraging behaviour that would be expected in the absence of predators (Roch et al., 2018). Similarly, parasitism can affect the way in which animals forage. Parasitism can affect foraging at several levels. Animals might simply avoid food items that increase their risk of being parasitized, as when the prey items are intermediate hosts of parasites. Animals might also avoid areas that would expose them to a high risk of parasitism. Finally, animals might effectively selfmedicate, either prophylactically or therapeutically (Hutchings et al., 2008).
Types of foraging Foraging can be categorized into two main types. The first is solitary foraging, when animals forage by themselves. The second is group foraging.
Solitary foraging Solitary foraging includes the variety of foraging in which animals find, capture and consume their prey alone. Individuals can manually exploit patches or they can use tools to exploit their prey. For example, Bolas spiders attack their prey by luring them with a scent identical to the female moth's sex pheromones (Foraging strategies, nd). Animals may choose to forage on their own when the resources are abundant, which can occur when the habitat is rich or when the number of conspecifics foraging are few. In these cases there may be no need for group foraging (Riedman, 1990). In addition, foraging alone can result in less interaction with other foragers, which can decrease the amount of competition and dominance interactions an animal deals with. It will also ensure that a solitary forager is less conspicuous to predators (e Roux et al., 2009). Solitary foraging strategies characterize many of the phocids (the true seals) such as the elephant and harbor seals. An example of an exclusive solitary forager is the South American species of the harvester ant, Pogonomyrmex vermiculatus (Torres-Contreras et al., 2007; About Forest to Food, 2023). The theory scientists use to understand solitary foraging is called optimal foraging theory, which predicts that foragers alter their behavior (e.g., when to move to the next foraging area) to maximize energy intake. See the book section on "Optimal Foraging Theory" for more information.
Group foraging is when animals find, capture and consume prey in the presence of other individuals. In other words, it is foraging
when success depends not only on your own foraging behaviors but the behaviors of others as well (Stephens et al., 2007). An
important note here is that group foraging can emerge in two types of situations. The first situation is frequently thought of and
occurs when foraging in a group is beneficial and brings greater rewards known as an aggregation economy. The second situation
occurs when a group of animals forage together but it may not be in an animal's best interest to do so known as a dispersion
economy. Think of a cardinal at a bird feeder for the dispersion economy. We might see a group of birds foraging at that bird feeder
but it is not in the best interest of the cardinal for any of the other birds to be there too (Figure
cardinal can get from that bird feeder depends on how much it can take from the bird feeder but also depends on how much the
: A male northern cardinal at a bird feeder. Birds feeding at a bird feeder is an example of a dispersion economy. This is when it may not be in an animal's best interest to forage in a group.
In red harvester ants, the foraging process is divided between three different types of workers: nest patrollers, trail patrollers, and foragers. These workers can utilize many different methods of communicating while foraging in a group, such as guiding flights, scent paths, and "jostling runs", as seen in the eusocial bee Melipona scutellaris (Hrncir et al., 2000).
Chimpanzees in the Taï Forest in Côte d'Ivoire also engage in foraging for meats when they can, which is achieved through group foraging. Positive correlation has been observed between the success of the hunt and the size of the foraging group. The chimps have also been observed implying rules with their foraging, where there is a benefit to becoming involved through allowing successful hunters first access to their kills (Boesch, 1994; Gomes & Boesch, 2009; Gomes & Boesch, 2011).
: Female lions make foraging decisions and more specifically decisions about hunting group size with protection of their cubs and territory defense in mind.(Packer et al., 1990)
As already mentioned, group foraging brings both costs and benefits to the members of that group. Some of the benefits of group foraging include being able to capture larger prey, being able to create aggregations of prey, being able to capture prey that are difficult or dangerous and most importantly reduction of predation threat (Packer et al., 1990; Benoit-Bird & Whitlow, 2009; Stephens et al., 2007). With regard to costs, however, group foraging results in competition for available resources by other group members. Competition for resources can be characterized by either scramble competition whereby each individual strives to get a portion of the shared resource, or by interference competition whereby the presence of competitors prevents a forager's accessibility to resources (Danchin et al., 2008). Group foraging can thus reduce an animal's foraging payoff (Stephens et al., 2007).
Group foraging may be influenced by the size of a group. In some species like lions and wild dogs, foraging success increases with
an increase in group size then declines once the optimal size is exceeded. A myriad number of factors affect the group sizes in
different species. For example, lionesses (female lions) do not make decisions about foraging in a vacuum (Figure
make decisions that reflect a balance between obtaining food, defending their territory and protecting their young. In fact, we see
that lion foraging behavior does not maximize their energy gain. They are not behaving optimally with respect to foraging because
they have to defend their territory and protect young so they hunt in small groups to reduce the risk of being caught alone (Packer
et al., 1990). Another factor that may influence group size is the cost of hunting. To understand the behavior of wild dogs and the
average group size we must incorporate the distance the dogs run (Creel & Creel, 1995).
References About Forest to Food. (2023, June 5). Harvesting Nature's Bounty, One Step at a Time. Retrieved June 29, 2023, from https://foresttofood.org/ Benoit-Bird, K., & Whitlow, W. L. (2009). Cooperative prey herding by the pelagic dolphin, Stenella longirostris. The Journal of the Acoustical Society of America, 125(1), 125-137. https://doi.org/10.1121/1.2967480 Boesch, C. (1994). Cooperative hunting in wild chimpanzees. Animal Behaviour, 48(3), 653-667. https://doi.org/10.1006/anbe.1994.1285 Creel, S., & Creel, N. M. (1995). Communal hunting and pack size in African wild dogs, Lycaon pictus. Animal Behaviour, 50(5), 1325-1339. https://doi.org/10.1016/0003-3472(95)80048-4 Danchin, E., Giraldeau, L. A., & Cézilly, F. (2008). Behavioural ecology. Oxford University Press. Dugatkin, L. A. (2004). Principles of animal behavior. W. W. Norton & Company. Foraging strategies. (n.d.). Encyclopedia.com. https://www.encyclopedia.com Gomes, C. M., & Boesch, C. (2009). Wild chimpanzees exchange meat for sex on a long term basis. PLOS ONE, 4(4), e5116. https://doi.org/10.1371/journal.pone.0005116 Gomes, C. M., & Boesch, C. (2011). Reciprocity and trades in wild West African chimpanzees. Behavioral Ecology and Sociobiology, 65(11), 2183-2196. https://doi.org/10.1007/s00265-011-1227-x
Hrncir, M., Jarau, S., Zucchi, R., & Barth, F. G. (2000). Recruitment behavior in stingless bees, Melipona scutellaris and M. quadrifasciata. II. Possible mechanisms of communication. Apidologie, 31(1), 93-113. https://doi.org/10.1051/apido:2000109 Hughes, R. N. (Ed.). (1989). Behavioural mechanisms of food selection (p. v). Springer-Verlag. Hunt, G. J., et al. (2007). Behavioral genomics of honeybee foraging and nest defense. Naturwissenschaften, 94(4), 247-267. https://doi.org/10.1007/s00114-006-0183-1 Hutchings, M. R., Athanasiadou, S., Kyriazakis, I., & Gordon, I. J. (2008). Can animals use foraging behaviour to combat parasites? Proceedings of the Nutrition Society, 62(2), 361-370. https://doi.org/10.1079/PNS2003243 le Roux, A., Cherry, M. I., & Gygax, L. (2009). Vigilance behaviour and fitness consequences: Comparing a solitary foraging and an obligate group-foraging mammal. Behavioral Ecology and Sociobiology, 63(8), 1097-1107. https://doi.org/10.1007/s00265-0090762-1 Lefebvre, L., Whittle, P., Lascaris, E., & Finkelstein, A. (1997). Feeding innovations and forebrain size in birds. Animal Behaviour, 53(3), 549-560. https://doi.org/10.1006/anbe.1996.0330 Murphy, C. M., & Breed, M. D. (2008). Time-place learning in a neotropical stingless bee, Trigona fulviventris Guérin (Hymenoptera: Apidae). Journal of the Kansas Entomological Society, 81(1), 73-76. https://doi.org/10.2317/JKES-704.23.1 Packer, C., Scheel, D., & Pusey, A. E. (1990). Why lions form groups: Food is not enough. American Naturalist, 136, 1-19. https://doi.org/10.1086/285079 Raine, N. E., & Chittka, L. (2008). The correlation of learning speed and natural foraging success in bumble-bees. Proceedings of the Royal Society B: Biological Sciences, 275(1636), 803-808. https://doi.org/10.1098/rspb.2007.1652 Rapaport, L. G., & Brown, G. R. (2008). Social influences on foraging behavior in young nonhuman primates: Learning what, where and how to eat. Evolutionary Anthropology, 17(4), 189-201. https://doi.org/10.1002/evan.20180 Riedman, M. (1990). The pinnipeds: Seals, sea lions, and walruses. University of California Press. Roch, S., von Ammon, L., Geist, J., & Brinker, A. (2018). Foraging habits of invasive three-spined sticklebacks (Gasterosteus aculeatus) - Impacts on fisheries yield in Upper Lake Constance. Fisheries Research, 204, 172-180. https://doi.org/10.1016/j.fishres.2018.02.014 Stephens, D. W., Brown, J. S., & Ydenberg, R. C. (2007). Foraging: Behavior and ecology. University of Chicago Press. Torres-Contreras, H., Olivares-Donoso, R., & Niemeyer, H. M. (2007). Solitary foraging in the ancestral South American ant, Pogonomyrmex vermiculatus: Is it due to constraints in the production or perception of trail pheromones? Journal of Chemical Ecology, 33(2), 435-440. https://doi.org/10.1007/s10886-006-9240-7 Contributors and Attributions Modified by Dan Wetzel (University of Pittsburgh) and Natasha Gownaris (Gettysburg College) from the following sources: Wikipedia, the free Encyclopedia, article on Foraging 11.2: Foraging Ecology is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.
11.3: Optimal Foraging Theory Optimal foraging theory
: Worker bees forage nectar not only for themselves, but for their whole hive community. Optimal foraging theory predicts that this bee will forage in a way that will maximize its hive's net yield of energy.
Optimal foraging theory (OFT) is a behavioral ecology model that helps predict how an animal behaves when searching for food.
Although obtaining food provides the animal with energy, searching for and capturing the food require both energy and time. To
maximize fitness, an animal adopts a foraging strategy that provides the most benefit (energy) for the lowest cost, maximizing the
). OFT helps predict the best strategy that an animal can use to achieve this goal.
OFT is an ecological application of the optimality model. This theory assumes that the most economically advantageous foraging pattern will be selected for in a species through natural selection (Werner & Hall, 1974). When using OFT to model foraging behavior, organisms are said to be maximizing a variable known as the currency, such as the most food per unit time. In addition, the constraints of the environment are other variables that must be considered. Constraints are defined as factors that can limit the forager's ability to maximize the currency. The optimal decision rule, or the organism's best foraging strategy, is defined as the decision that maximizes the currency under the constraints of the environment. Identifying the optimal decision rule is the primary goal of the OFT (Sinervo, 1997).
Building an optimal foraging model An optimal foraging model generates quantitative predictions of how animals maximize their fitness while they forage. The model building process involves identifying the currency, constraints, and appropriate decision rule for the forager (Sinervo, 1997; Stephens & Krebs, 1986). Currency is defined as the unit that is optimized by the animal. It is also a hypothesis of the costs and benefits that are imposed on that animal (Krebs & Davies, 1989). For example, a certain forager gains energy from food, but incurs the cost of searching for the food: the time and energy spent searching could have been used instead on other endeavors, such as finding mates or protecting young. It would be in the animal's best interest to maximize its benefits at the lowest cost. Thus, the currency in this situation could be defined as net energy gain per unit time (Sinervo, 1997). However, for a different forager, the time it takes to digest the food after eating could be a more significant cost than the time and energy spent looking for food. In this case, the currency could be defined as net energy gain per digestive turnover time instead of net energy gain per unit time (Verlinden & Wiley, 1989). Furthermore, benefits and costs can depend on a forager's community. For example, a forager living in a hive would most likely forage in a manner that would maximize efficiency for its colony rather than itself (Krebs & Davies, 1989). By identifying the currency, one can construct a hypothesis about which benefits and costs are important to the forager in question. Constraints are hypotheses about the limitations that are placed on an animal (Krebs & Davies, 1989). These limitations can be due to features of the environment or the physiology of the animal and could limit their foraging efficiency. The time that it takes for the forager to travel from the nesting site to the foraging site is an example of a constraint. The maximum number of food items
a forager is able to carry back to its nesting site is another example of a constraint. There could also be cognitive constraints on animals, such as limits to learning and memory (Sinervo, 1997). The more constraints that one is able to identify in a given system, the more predictive power the model will have (Krebs & Davies, 1989).
: Energy gain per cost (E) for adopting foraging strategy x. Adapted from Parker & Smith (1990).[34]
Given the hypotheses about the currency and the constraints, the optimal decision rule is the model's prediction of what the
animal's best foraging strategy should be (Sinervo, 1997). Possible examples of optimal decision rules could be the optimal number
of food items that an animal should carry back to its nesting site or the optimal size of a food item that an animal should feed on.
shows an example of how an optimal decision rule could be determined from a graphical model (Parker & Smith,
1990). The curve represents the energy gain per cost (E) for adopting foraging strategy x. Energy gain per cost is the currency being
optimized. The constraints of the system determine the shape of this curve. The optimal decision rule (x*) is the strategy for which
the currency, energy gain per costs, is the greatest. Optimal foraging models can look very different and become very complex,
depending on the nature of the currency and the number of constraints considered. However, the general principles of currency,
constraints, and optimal decision rule remain the same for all models.
To test a model, one can compare the predicted strategy to the animal's actual foraging behavior. If the model fits the observed data well, then the hypotheses about the currency and constraints are supported. If the model doesn't fit the data well, then it is possible that either the currency or a particular constraint has been incorrectly identified (Krebs & Davies, 1989).
Different feeding systems and classes of predators Optimal foraging theory is widely applicable to feeding systems throughout the animal kingdom. Under the OFT, any organism of interest can be viewed as a predator that forages prey. There are different classes of predators that organisms fall into and each class has distinct foraging and predation strategies. True predators attack large numbers of prey throughout their life. They kill their prey either immediately or shortly after the attack. They may eat all or only part of their prey. True predators include tigers, lions, whales, sharks, seed-eating birds, ants (Cortés & Gruber, 1990). Grazers eat only a portion of their prey. They harm the prey, but rarely kill it. Grazers include antelope, cattle, and mosquitoes. Parasites, like grazers, eat only a part of their prey (host), but rarely the entire organism. They spend all or large portions of their life cycle living in/on a single host. This intimate relationship is typical of tapeworms, liver flukes, and plant parasites, such as the potato blight. Parasitoids are mainly typical of wasps (order Hymenoptera), and some flies (order Diptera). Eggs are laid inside the larvae of other arthropods which hatch and consume the host from the inside, killing it. This unusual predator-host relationship is typical of about 10% of all insects (Godfray, 1994). Many viruses that attack single-celled organisms (such as bacteriophages) are also parasitoids; they reproduce inside a single host that is inevitably killed by the association. The optimization of these different foraging and predation strategies can be explained by the optimal foraging theory. In each case, there are costs, benefits, and limitations that ultimately determine the optimal decision rule that the predator should follow.
The optimal diet model One classical version of the optimal foraging theory is the optimal diet model, which is also known as the prey choice model or the contingency model. In this model, the predator encounters different prey items and decides whether to eat what it has or search
for a more profitable prey item. The model predicts that foragers should ignore low profitability prey items when more profitable items are present and abundant (Stephens et al., 2007). The profitability of a prey item is dependent on several ecological variables. E is the amount of energy (calories) that a prey item provides the predator. Handling time (h) is the amount of time it takes the predator to handle the food, beginning from the time the predator finds the prey item to the time the prey item is eaten. The profitability of a prey item is then defined as E/h. Additionally, search time (S) is the amount of time it takes the predator to find a prey item and is dependent on the abundance of the food and the ease of locating it (Sinervo, 1997). In this model, the currency is energy intake per unit time and the constraints include the actual values of E, h, and S, as well as the fact that prey items are encountered sequentially.
Model of choice between big and small prey Using these variables, the optimal diet model can predict how predators choose between two prey types: big prey1 with energy value E1 and handling time h1, and small prey2 with energy value E2 and handling time h2. In order to maximize its overall rate of energy gain, a predator must consider the profitability of the two prey types. If it is assumed that big prey1 is more profitable than small prey2, then E1/h1 > E2/h2. Thus, if the predator encounters prey1, it should always choose to eat it, because of its higher profitability. It should never bother to go searching for prey2. However, if the animal encounters prey2, it should reject it to look for a more profitable prey1, unless the time it would take to find prey1 is too long and costly for it to be worth it. Thus, the animal should eat prey2 only if E2/h2 > E1/(h1+S1), where S1 is the search time for prey1. Since it is always favorable to choose to eat prey1, the choice to eat prey1 is not dependent on the abundance of prey2. But since the length of S1 (i.e. how difficult it is to find prey1) is logically dependent on the density of prey1, the choice to eat prey2 is dependent on the abundance of prey1 (Krebs & Davies, 1989).
Generalist and specialist diets The optimal diet model also predicts that different types of animals should adopt different diets based on variations in search time. This idea is an extension of the model of prey choice that was discussed above. The equation, E2/h2 > E1/(h1+S1), can be rearranged to give: S1 > [(E1h2)/E2] - h1. This rearranged form gives the threshold for how long S1 must be for an animal to choose to eat both prey1 and prey2 (Krebs & Davies, 1989). Animals that have S1's that reach the threshold are defined as generalists. In nature, generalists include a wide range of prey items in their diet (Pulliam, 1974). An example of a generalist is a mouse, which consumes a large variety of seeds, grains, and nuts (Adler & Wilson 1987). In contrast, predators with relatively short S1's are still better off choosing to eat only prey1. These types of animals are defined as specialists and have very exclusive diets in nature (Pulliam, 1974). An example of a specialist is the koala, which solely consumes eucalyptus leaves (Shipley et al., 2009). In general, different animals across the four functional classes of predators exhibit strategies ranging across a continuum between being a generalist and a specialist. Additionally, since the choice to eat prey2 is dependent on the abundance of prey1 (as discussed earlier), if prey1 becomes so scarce that S1 reaches the threshold, then the animal should switch from exclusively eating prey1 to eating both prey1 and prey2 (Krebs & Davies, 1989). In other words, if the food within a specialist's diet becomes very scarce, a specialist can sometimes switch to being a generalist.
As previously mentioned, the amount of time it takes to search for a prey item depends on the density of the prey. Functional
response curves show the rate of prey capture as a function of food density and can be used in conjunction with the optimal diet
theory to predict foraging behavior of predators. There are three different types of functional response curves (Figure
: Three types of functional response curves. Adapted from Staddon (1983).
For a Type I functional response curve, the rate of prey capture increases linearly with food density. At low prey densities, the search time is long. Since the predator spends most of its time searching, it eats every prey item it finds. As prey density increases, the predator is able to capture the prey faster and faster. At a certain point, the rate of prey capture is so high, that the predator doesn't have to eat every prey item it encounters. After this point, the predator should choose only the prey items with the highest E/h (Jeschke et al., 2002).
For a Type II functional response curve, the rate of prey capture negatively accelerates as it increases with food density (Staddon, 1983). This is because it assumes that the predator is limited by its capacity to process food. In other words, as the food density increases, handling time increases. At the beginning of the curve, rate of prey capture increases nearly linearly with prey density and there is almost no handling time. As prey density increases, the predator spends less and less time searching for prey and more and more time handling the prey. The rate of prey capture increases less and less, until it finally plateaus. The high number of prey basically "swamps" the predator (Jeschke et al., 2002).
A Type III functional response curve is a sigmoid curve. The rate of prey capture increases at first with prey density at a positively accelerated rate, but then at high densities changes to the negatively accelerated form, similar to that of the Type II curve (Staddon, 1983). At high prey densities (the top of the curve), each new prey item is caught almost immediately. The predator is able to be choosy and doesn't eat every item it finds. So, assuming that there are two prey types with different profitabilities that are both at high abundance, the predator will choose the item with the higher E/h. However, at low prey densities (the bottom of the curve) the rate of prey capture increases faster than linearly. This means that as the predator feeds and the prey type with the higher E/h becomes less abundant, the predator will start to switch its preference to the prey type with the lower E/h, because that type will be relatively more abundant. This phenomenon is known as prey switching (Staddon, 1983).
Predator-prey interaction Predator-prey coevolution often makes it unfavorable for a predator to consume certain prey items, since many anti-predator defenses increase handling time (Boulding, 1984). Examples include porcupine quills, the palatability and digestibility of the poison dart frog, crypsis, and other predator avoidance behaviors. In addition, because toxins may be present in many prey types, predators include a lot of variability in their diets to prevent any one toxin from reaching dangerous levels. Thus, it is possible that an approach focusing only on energy intake may not fully explain an animal's foraging behavior in these situations.
The marginal value theorem and optimal foraging
The marginal value theorem is a type of optimality model that is often applied to optimal foraging. This theorem is used to
describe a situation in which an organism searching for food in a patch must decide when it is economically favorable to leave.
While the animal is within a patch, it experiences the law of diminishing returns, where it becomes harder and harder to find prey
as time goes on. This may be because the prey is being depleted, the prey begins to take evasive action and becomes harder to
catch, or the predator starts crossing its own path more as it searches (Krebs & Davies, 1989). This law of diminishing returns can
be shown as a curve of energy gain per time spent in a patch (Figure
). The curve starts off with a steep slope and gradually
levels off as prey becomes harder to find. Another important cost to consider is the traveling time between different patches and the
nesting site. An animal loses foraging time while it travels and expends energy through its locomotion (Sinervo, 1997).
In this model, the currency being optimized is usually net energy gain per unit time. The constraints are the travel time and the
shape of the curve of diminishing returns. Graphically, the currency (net energy gain per unit time) is given by the slope of a
diagonal line that starts at the beginning of traveling time and intersects the curve of diminishing returns (Figure
maximize the currency, one wants the line with the greatest slope that still touches the curve (the tangent line). The place that this
line touches the curve provides the optimal decision rule of the amount of time that the animal should spend in a patch before
: Marginal value theorem shown graphically.
Examples of optimal foraging models in animals
: Right-shifted mussel profitability curve. Adapted from Meire & Ervynck (1986).
Oystercatcher mussel feeding provides an example of how the optimal diet model can be utilized. Oystercatchers forage on mussels
and crack them open with their bills. The constraints on these birds are the characteristics of the different mussel sizes. While large
mussels provide more energy than small mussels, large mussels are harder to crack open due to their thicker shells. This means that
while large mussels have a higher energy content (E), they also have a longer handling time (h). The profitability of any mussel is
calculated as E/h. The oystercatchers must decide which mussel size will provide enough nutrition to outweigh the cost and energy
required to open it (Sinervo, 1997). In their study, Meire and Ervynck tried to model this decision by graphing the relative
profitabilities of different sized mussels. They came up with a bell-shaped curve, indicating that moderately sized mussels were the
most profitable. However, they observed that if an oystercatcher rejected too many small mussels, the time it took to search for the
next suitable mussel greatly increased. This observation shifted their bell-curve to the right (Figure
model predicted that oystercatchers should prefer mussels of 50-55 mm, the observed data showed that oystercatchers actually
prefer mussels of 30-45 mm. Meire and Ervynk then realized the preference of mussel size did not depend only on the profitability
of the prey, but also on the prey density. After this was accounted for, they found a good agreement between the model's prediction
and the observed data (Meire & Ervynck, 1986).
: If starlings are maximizing net rate of energy gain, longer traveling time results in larger optimum load. Adapted from Krebs and Davies (1989).
The foraging behavior of the European starling, Sturnus vulgaris, provides an example of how marginal value theorem is used to
model optimal foraging. Starlings leave their nests and travel to food patches in search for larval leatherjackets to bring back to
their young. The starlings must determine the optimal number of prey items to take back in one trip (i.e. the optimal load size).
While the starlings forage within a patch, they experience diminishing returns: the starling is able to hold only so many
leatherjackets in its bill, so the speed with which the parent picks up larvae decreases with the number of larvae that it already has
in its bill. Thus, the constraints are the shape of the curve of diminishing returns and the travel time (the time it takes to make a
round trip from the nest to a patch and back). In addition, the currency is hypothesized to be net energy gain per unit time (Krebs
& Davies, 1989). Using this currency and the constraints, the optimal load can be predicted by drawing a line tangent to the curve
of diminishing returns, as discussed previously (Figure
Kacelnik et al. wanted to determine if this species does indeed optimize net energy gain per unit time as hypothesized (1984). They
designed an experiment in which the starlings were trained to collect mealworms from an artificial feeder at different distances
from the nest. The researchers artificially generated a fixed curve of diminishing returns for the birds by dropping mealworms at
successively longer and longer intervals. The birds continued to collect mealworms as they were presented, until they reached an
shows, if the starlings were maximizing net energy gain per unit time, a short travel
time would predict a small optimal load and a long travel time would predict a larger optimal load. In agreement with these
predictions, Kacelnik found that the longer the distance between the nest and the artificial feeder, the larger the load size. In
addition, the observed load sizes quantitatively corresponded very closely to the model's predictions. Other models based on
different currencies, such as energy gained per energy spent (i.e. energy efficiency), failed to predict the observed load sizes as
accurately. Thus, Kacelnik concluded that starlings maximize net energy gain per unit time. This conclusion was not disproved in
later experiments (Bautista et al., 1998; Bautista et al., 2001).
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Godfray, H. C. J. (1994). Parasitoids: Behavioral and evolutionary ecology. Princeton University Press. Jeschke, J. M., Kopp, M., & Tollrian, R. (2002). Predator functional responses: Discriminating between handling and digesting prey. Ecological Monographs, 72(1), 95-112. https://doi.org/10.1890/0012-9615(20...FRDBH]2.0.CO;2 Kacelnik, A. (1984). Central place foraging in starlings (Sturnus vulgaris). I. Patch residence time. Journal of Animal Ecology, 53(1), 283-299. https://doi.org/10.2307/4357 Krebs, J. R., & Davies, N. B. (1989). An introduction to behavioral ecology (4th ed.). Blackwell Scientific Publications. Meire, P. M., & Ervynck, A. (1986). Are oystercatchers (Haematopus ostralegus) selecting the most profitable mussels (Mytilus edulis)? Animal Behaviour, 34(5), 1427-1435. https://doi.org/10.1016/S0003-3472(86)80213-5 Parker, G. A., & Smith, J. M. (1990). Optimality theory in evolutionary biology. Nature, 348(6296), 27-33. https://doi.org/10.1038/348027a0 Pulliam, H. R. (1974). On the theory of optimal diets. The American Naturalist, 108(959), 59-74. https://doi.org/10.1086/282885 Shipley, L. A., Forbey, J. S., & Moore, B. D. (2009). Revisiting the dietary niche: When is a mammalian herbivore a specialist? Integrative and Comparative Biology, 49(3), 274-290. https://doi.org/10.1093/icb/icp051 Sinervo, B. (1997). Optimal foraging theory: Constraints and cognitive processes. In Behavioral ecology (pp. 105-130). University of California, Santa Cruz. [Archived 23 November 2015] Staddon, J. E. R. (1983). Foraging and behavioral ecology. In Adaptive behavior and learning (1st ed.). Cambridge University Press. Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton University Press. Stephens, D. W., Brown, J. S., & Ydenberg, R. C. (2007). Foraging: Behavior and ecology. University of Chicago Press. Verlinden, C., & Wiley, R. H. (1989). The constraints of digestive rate: An alternative model of diet selection. Evolutionary Ecology, 3(3), 264-272. https://doi.org/10.1007/BF02270727 Werner, E. E., & Hall, D. J. (1974). Optimal foraging and the size selection of prey by the bluegill sunfish (Lepomis macrochirus). Ecology, 55(5), 1042-1052. https://doi.org/10.2307/1940354 Contributors and Attributions Modified by Dan Wetzel (University of Pittsburgh) and Natasha Gownaris (Gettysburg College) from the following sources: Wikipedia, the free encyclopedia 11.3: Optimal Foraging Theory is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.
11.4: Movement Ecology The content for this subtopic is found in an external page. Please click the link below to access this information. Animal Migration Nebel, S. (2010) Animal Migration. Nature Education Knowledge 3(10):77 11.4: Movement Ecology is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.
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11.7: How Do Social Systems Evolve The content for this subtopic is found in an external page. Please click the link below to access this information. Cooperation, Conflict, and the Evolution of Complex Animal Societies Rubenstein, D. & Kealey, J. (2010) Cooperation, Conflict, and the Evolution of Complex Animal Societies. Nature Education Knowledge 3(10):78 Chapter summary Behavioral ecology is the study of the evolutionary basis for animal behavior due to ecological pressures. Behavioral ecology seeks to address questions associated with the proximate causes, ontogeny, survival value, and phylogeny of a behavior. The field of behavioral ecology includes a variety of disciplines, including the study of how organisms find food, how they move about the environment, and how they communicate with each other. This field is also interested in studying the evolution of social behaviors and social systems. 11.7: How Do Social Systems Evolve is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.