Behavioural Ecology

Reinmar Hager. Encyclopedia of Life Sciences: Supplementary Set. Volume 21, Wiley, 2007.

Behavioural ecology investigates how animal behaviour is adapted to the physical and social environment of the species concerned. Both empirical and theoretical studies analyse how evolution has shaped behaviour through the process of natural selection.

What is Behavioural Ecology?

Behavioural ecology is a branch of the biological sciences that deals with questions about the adaptive value of behaviour. Behavioural patterns are viewed as a result of natural selection, which have evolved in relation to the physical and social environment in which animals live.

Tinbergen’s Four Questions About Behaviour

In 1963, Nikolaas Tinbergen published his seminal paper on the aims and methods of ethology in which he defined, based on Julian Huxley’s work, four major questions in biology. These are known as the four ways of asking why an animal behaves in a specific manner: (1) what underlying mechanism causes this behaviour? (proximate questions), (2) what is the survival value to the individual? (ultimate questions), (3) how did this behaviour evolve over time?, and (4) how does such behaviour develop during an individual’s lifetime?

Costs and Benefits of Behaviour

Most studies in behavioural ecology, and in particular early works, have focused on answering ultimate questions. An ideal analysis would involve weighing all costs against benefit associated with a specific trait and investigating the effects on an individual’s reproductive success. The peacock’s excessively long tail, which is used in courtship displays to attract females, is a classic example, which may serve to illustrate the different types of costs and benefits. Fitness costs associated with the expression of this trait are increased predation risk, costs of producing the feathers and reduced mobility, while the key benefit is increased mating success from attracting more females. The advantages gained from expressing this trait outweigh the costs that are entailed and thus selection has favoured such a trait and the associated behaviour.

Proximate and ultimate questions represent different ways of addressing the issue of why an animal behaves in a certain manner and are not mutually exclusive. The migration of birds exemplifies this point. If we asked why birds migrate, the proximate answer might be that the change in daylight triggers migratory behaviour through activation of hormone production. An ultimate answer to this question would focus on the fitness benefits gained from increased food availability and avoidance of adverse weather conditions in northern climates. Such migratory behaviour is evolutionarily advantageous and has been selected for in many bird species.

Constraints on Behaviour

How does knowledge of proximate causes help in understanding behaviour? For instance, in Drosophila larvae are often parasitized by wasps that lay their eggs inside the larvae, which normally die as a consequence. However, they are able to mount an immune response by encapsulating the eggs with haemocytes. Selection experiments for enhanced resistance showed that survival in Drosophila could be increased from 5% to up to 60%. Given the potential for higher resistance—and hence benefits—the question is why did it not evolve in the wild? A candidate ultimate answer may be the low rate of parasitism in such species or an overall low selection pressure on evolving countermeasures (other mortality factors might have a higher impact on survival). However, further experiments demonstrated that there is a significant cost to resistance because of the underlying mechanism. Encapsulation of the parasite’s eggs is achieved by aggregation of haemocytes within the circulatory system, and resistant individuals have more haemocytes in their blood. A higher proportion of haemocytes, in turn, leads to a lower feeding rate, with such individuals losing out in competition with other larvae. This constraint imposed by the mechanism of the immune defence limits parasite resistance. It is therefore crucial to consider constraints to gain a full understanding of animal behaviour.

Key Concepts in Behavioural Ecology

In the 1960s and 1970s, several schools of thought laid the foundations for today’s field of behavioural ecology (Table 1). The pioneering work by Tinbergen and Konrad Lorenz proposed that a behaviour can be taken as characteristic of a species, equivalent to morphological traits, and stressed the need to study animals in the natural habitat to which the species has adapted. Following on this, studies in birds by John Crook and David Lack linked differences in the social structure of related species to differences in the ecological conditions under which they were living. This became known as the comparative approach. The principles of kin selection and concept of fitness were put forward by William Hamilton and George Williams; these had a significant impact on our current view that selection acts at the individual level rather than at a group level. In 1964, Vero Wynne-Edwards proposed the idea that animals behave for the good of the pecies. To some extent, the group selection argument was also advocated by Lorenz and has generally provoked much discussion. Although little empirical and theoretical support was found, this view was attractive to popular scientists. At present, it is agreed that the gene is the unit of replication and that the organism, as the carrier of genes, is the subject on which natural selection acts.

The concept of optimality was introduced in behavioural ecology mainly by Robert MacArthur and Eric Pianka, focusing on the idea that natural selection favours behaviours that ultimately optimize the spread of an individual’s genes. It is often assumed that behaviour serves to optimize a more specific parameter such as mating or feeding rate (see ‘Trade-offs in Individual Decision-making’). Finally, the effects of a behaviour will depend on what others in the population are doing; in other words, the costs and benefits of behaviour are frequency dependent. This was first formally introduced in the behavioural sciences in the 1970s and 1980s by John Maynard Smith with the application of game theory to biological systems (see also section ‘Social Behaviour: Competitive and Cooperative Interactions’). In 1978, John Krebs and Nick Davies published their book entitled ‘Behavioural EcologyAn Evolutionary Approach‘ in which they brought together theoretical and empirical studies that were based on the above-mentioned approaches to study animal behaviour.

Trade-Offs in Individual Decision-Making

Individuals face a basic trade-off in life: resources can be allocated either to growth and maintenance of the body or to reproduction. Ultimately, an individual will be selected for on the basis of lifetime reproductive success. However, at a particular time, investing too many available resources in reproduction rather than body maintenance may result in poor body condition. This, in turn, may lower the lifetime reproductive success.

In a given situation, animals may wish to maximize a more specific parameter such as feeding rate or copulation rate. Behavioural ecologists try to understand behaviour by assuming that this behaviour serves to optimize the net benefit, given certain constraints and costs. For instance, if we wish to analyse bird feeding behaviour we assume that the bird will optimize the rate of food intake or a similar parameter, i.e. maximize the benefits while minimizing the costs.

Table 1 Key concepts in behavioural ecology and examples

Concept Description Example
Comparative approach Relating differences in behaviour between related species to differences in ecological conditions Differences in social organization in African ungulates reflect different dietary requirements depending on the body size. Species range from solitary for aging animals requiring high-quality food to large group-living species eating low-quality food
Kin selection A behaviour will be selected for or against because of the effects on an individual’s fitness and the fitness of relatives Sentinels in ground squirrels are subject to a higher predation risk but help related individuals by warning them of predators
Optimality approach Selection optimizes the spread of an individual’s genes by weighing the costs and benefits associated with a specific behaviour Starlings optimize feeding rate at the nest. The decision on how many prey items to take per round depends on the travel and search time
Frequency dependence and evolutionarily stable strategies (game theory approach) The costs or benefits of a behaviour will depend on what other individuals in the population are doing Competing male dungflies attempt to maximize copulation rate by waiting for varying lengths of time at cowpats for arriving females

Optimizing Feeding Rate in Starlings

The seminal work by Alex Kacelnik on starling behaviour illustrates this point. When starlings feed their young they have to travel repeatedly back and forth to the nest to provide the young with food, in this case leatherjackets. How many prey items should the bird take? The problem for the starling is that as the number of prey items in its beak increases, it will become more difficult to pick up additional ones because the others may fall out again. It will also take longer to find other prey in this patch due to depletion. In other words, the more time the bird spends foraging in this particular patch, the fewer leatherjackets it will find. The other important constraint is the energetic cost incurred in flying to the patch and back to the nest. If the bird’s nest is not far away, it might be better off taking less than the maximum possible number of leatherjackets and returning to the nest rather than spending a lot of time on searching for the last few items. The saved time is better spent on foraging in another patch (assuming that the rate of finding prey is high at the beginning). If we assume that the bird is optimizing delivery rate to the nest, then the number of prey items it should pick up depends on the distance of the food patch to the nest. The solution to the starling’s problem is that as the distance from the nest increases, the bird should spend more time foraging to pick up more prey items per trip, which was confirmed by Kacelnik’s experimental work.

The optimality approach has been applied to many other problems that animals face when they have to trade off effects of two behaviours. For instance, sticklebacks were found to weigh predation risk against hunger. As foraging and being vigilant are mutually exclusive in this species, hungrier fish were less vigilant than well-fed fish. Other examples include maximizing the rate of matings or minimizing starvation risk. The advantage of this approach is that predictions may be tested experimentally, using only the few key parameters specified in a model. It is crucially important to identify what the animal wishes to maximize, i.e. the currency, and what the trade-off is about. If the experiment fails to confirm model predictions, it may be that the assumptions about currencies and costs of this specific behaviour were incorrect. For instance, it may well turn out that the animal was not making a choice between foraging and travelling but between foraging and vigilance. It could also be that the experimental design was inappropriate to address the question and that other factors were measured.

Social Behaviour: Competitive and Cooperative Interactions

The effects of a specific behaviour on an individual’s fitness will depend not only on the physical environment but also on the social environment. Animals interact with conspecifics in their group or their family and also with other species. A simple optimality approach would fail to analyse such situations because the payoffs of behavioural patterns are not fixed but will vary according to what others in the population are doing. A method to investigate frequency-dependent behaviour, mainly in animal contests, was first put forward by Maynard Smith, using and extending the framework of game theory.

With the aim of analysing human behaviour in economics, John von Neumann and Oskar Morgenstern formally introduced game theory in the early 1950s. One of the key assumptions was that humans behave in their self-interest, which made it applicable to biology when phrasing the game’s assumptions in terms of darwinian fitness.

Male Dungflies Maximize Mating Rate by Taking into Account What Other Males Are Doing

A good example of how the behaviour of others will influence an individual’s best strategy can be found in Geoff Parker’s work on dungflies. Females lay their eggs in fresh cowpats. Males mate with a female and guard her against other males that attempt to push rival males off while she is laying her eggs. As the cowpat gets older, it also becomes more difficult to lay eggs because a crust will form. Therefore, fewer and fewer females will arrive as time after disposal increases. The male has the option to stay at the same cowpat and wait for late-arriving females or to fly and search for a new cowpat where there is a higher chance that more females will arrive. The question at issue is how long should the male wait at a given cowpat, or more precisely what are the expected payoffs of specific waiting times in terms of successful matings? The answer will depend on what the other males in the population do. If, for instance, most males leave the cowpat soon after it was laid, it may pay the focus male to wait for the late-arriving females because there will be fewer competitors around.

The behaviour that yields the highest payoff is called an evolutionarily stable strategy (ESS). The property of an ESS is that if almost all members of the population adopt this strategy, the fitness of these individuals will be greater than that of any other individual adopting a strategy different from the ESS. Thus, a population in which most members play the ESS cannot be invaded by a mutant strategy. Where does the frequency-dependent effect come in? At equilibrium, the ESS yields identical payoffs for varying waiting times. If we assume, for instance, that short waiting times and flying to new cowpats yield the highest reproductive success, then the frequency of this strategy will increase over time because individuals adopting this strategy produce more young than others. However, as the number of individuals playing this strategy increases, the level of competition rises. As a consequence, the payoff of this particular strategy will become smaller as the number of competitors increases. At the same time, the benefit of the alternative strategy (wait longer for late-arriving females) increases because more individuals fly off early, so there will be less competition later. Eventually, the payoff of waiting longer would be greater than that of a shorter waiting time and the frequency of such a strategy would increase.

So how long do we expect male dungflies to wait? Game theory shows that the ESS for this situation does not predict a fixed waiting time (since this could be exploited by other males) but to behave unpredictably, i.e. sometimes wait longer, sometimes leave earlier. It should be noted that for the ESS analysis, it does not matter whether one individual adopts a random strategy and changes waiting times or whether every member of the population waits a fixed time, as long as the distribution of waiting times in the population as a whole follows a random distribution. As Parker could show, this is exactly what dungflies do. The number of males at a cowpat exponentially declines with time after deposition, and the mating success of males with different waiting times is equal.

Cooperative Interactions Between Individuals

The dungfly example serves to illustrate competitive interactions between individuals. However, many interactions in animal groups are of a cooperative nature. Of particular interest are behaviours that seemingly lower an individual’s fitness but benefit others. For instance, in Belding’s ground squirrels, Spermophilus beldingi, some individuals give alarm calls when predators are spotted. The sentinel is therefore more conspicuous and they suffer from a much higher predation risk. However, the other group members, thus warned, can escape safely into their burrows. Why does the calling individual expose itself to such a high risk when the chances of being killed are higher than for the other group members? Several field studies have shown that females are more likely to give alarm calls than males. Females are also more closely related to other group members than males because of male dispersal. It was concluded that the sentinel benefits from the increased survival chances of relatives, thus outweighing costs associated with higher predation risk. Such cooperative behaviour has most likely evolved through kin selection, a process by which individuals may incur a fitness cost to themselves but which is compensated for by beneficial effects on the fitness of their own offspring and other related individuals.

Processes by Which Cooperation Can Evolve: Kin Selection and Reciprocal Altruism

The formal analysis of the evolution of cooperation goes back to Hamilton, who, in 1964, published a paper in which he defined the conditions under which a behaviour will be selected for through kin selection, known as Hamilton’s rule: rBC > 0, where r is the coefficient of relatedness between two individuals (0 < r < 1), B is the benefit to the recipient of the behaviour (in our example, the benefit of reduced predation risk to the other ground squirrels) and C is the cost of the behaviour to the donor of this specific behaviour (the higher predation risk to the sentinel). In other words, a cooperative behaviour will spread if the benefits to the recipient weighed by the coefficient of relatedness are greater than the costs to the donor.

However, cooperation can also evolve between unrelated individuals. In such reciprocal interactions, the costs to the actor should be less than the benefits to the recipient. When the individuals meet again, the act should be reciprocated so that each gains a benefit from a cooperative interaction. The problem for the evolution of such behaviour is that it is prone to cheating because one individual might not reciprocate later. In cases where the benefit immediately accrues to all, such as in communal hunting in lions, this problem does not arise. For the evolution of reciprocity, however, repeated interactions are necessary. In a computer-simulated contest of different behavioural strategies, Hamilton and Axelrod showed in 1981 that the ESS is a strategy called ‘Tit for Tat’. Properties of this strategy are that it cooperates in the first move and then does what the opponent strategy did in the previous move. For example, if in the first move Tit for Tat cooperates and the opponent strategy defects, then Tit for Tat will defect in the next move but will return to cooperation after that if the opponent strategy cooperates in the second move. Given that the number of future encounters between two strategies or players is large enough to be unpredictable (because otherwise it would pay to exploit ‘Tit for Tat’ in the last encounter, the last but one, etc.), no other strategy could beat ‘Tit for Tat’. The reason for its success is that it always tried to establish cooperation after just one act of retaliation, since cooperation yields a higher payoff than mutual defection. In addition, ‘Tit for Tat’ discourages other strategies from attempts to exploit because it retaliates in the next move. These rather simple rules proved to be more successful than all other strategies, some of which were much more complex.

Evolutionary Antagonism

Evolutionary antagonism occurs between species (e.g. parasite-host or predator-prey), within a species between separate individuals (e.g. sexual conflict) and within individuals (intra- and intergenomic conflict). Such antagonism may result in arms races where one side evolves mechanisms to avoid predation or parasitism, which is then countered by more sophisticated armament or tactics to overcome the newly evolved defences.

A well-studied system of coevolutionary arms races is cuckoos and their hosts. These brood parasites replace a host’s egg with their own. Once hatched, the young cuckoo ejects all the other host eggs from the nest and the host will feed it until fledging. Obviously, losing a brood and investing in a cuckoo chick incurs fitness costs, and hosts that suffer from high rates of parasitism have evolved good discriminative abilities and show higher rejection rates than hosts that are subject to lower rates of parasitism. In turn, cuckoo egg colouring and patterns reflect rejection rates by their hosts, with production of highly mimetic eggs when the hosts are more discriminative.

Sexual Conflict

A major field of research in behavioural ecology is concerned with evolutionary antagonism between the sexes and its consequences. Sexual conflict is essentially seen as a result of differences in the reproductive potential. Males are defined as the sex that produce a large number of small gametes (sperm) while females produce few large gametes (eggs). This has the effect that the potential reproductive success of males is much higher than that of females, and that males generally invest much less in offspring than females. Therefore, male reproductive success will often be limited by access to females. By contrast, female reproductive success is limited by access to resources. From this basic difference, many aspects of reproductive behaviour have been explained. For instance, females are expected to be selective in their mate choice. If a female mates with a low-quality male that does not provide a good territory or access to vital resources, she wastes a large investment not only in terms of resources invested in offspring but also in terms of lost time that she otherwise could have spent searching for a high-quality mate. Males, on the other hand, normally invest little in their offspring and they do not lose much if they mate with any female, being much less choosy.

Table 2 Types of evolutionary antagonism with examples

Type of antagonism Description Example
Between different species (interspecific) Predator-prey Improved search image is countered by better camouflage, e.g. crypsis in butterflies
Between different species (interspecific) Parasite-host Production of highly mimetic eggs by cuckoos is countered by better discrimination in host
Within same species, different individuals (intraspecific) Conflicts between members of a family: parent-offspring conflict, sexual conflict, sibling competition In mammals, males favour greater maternal investment in their offspring than is optimal for the female and offspring will demand more from parents than parents are selected to give
Within same species, same individual (intragenomic) Selfish genetic elements Meiotic drive genes increase their rate of transmission at the expense of the other genes in the organism
Within same species, different individuals (intergenomic) Conflict between different loci in the same genome Coevolution of genes whose effects will mutually influence each other’s rate of transmission by their effects on the fitness of the bearer, e.g. toxicity levels in male Drosophila seminal fluids

Family Conflicts

Evolutionary antagonism is also reflected in the conflict between members of a family. As outlined above, there is a conflict between males and females over the amount of parental investment. In addition, there is also a conflict between parents and offspring over how much should be invested in young, where young will demand more than is optimal for the parents. Young value their own fitness higher than that of their siblings, while the parent values all offspring equally. The reason for this is that offspring are 100% related to themselves, but only 50% to their parents and their siblings. Finally, offspring will compete over who obtains how much parental investment. This will be in the form of conflict either between members of the same brood (intrasibling competition) or between offspring of different generations (intersibling competition). Family conflicts can be observed in many species, for instance, in begging birds or piglets that fight for access to the most profitable teats. Research in mice by Reinmar Hager and Rufus Johnstone investigated into who in a family decides how family conflicts are resolved. Their results surprisingly demonstrated that fathers determine litter size presumably by imprinted genes expressed in the young that prevent resorption of embryos in utero. Producing larger litters is costly to the female because she has to provide resources during gestation and lactation. It showed that females who produce larger litters due to the paternal effect on litter size compensate for the increased costs by providing less resources to their young, demonstrating antagonistic coevolution of maternal and paternal effects on distinct life history traits.

Genetic Conflicts and Genomic Imprinting

Some of the underlying mechanisms of family conflict resolution have been elucidated by a number of studies in mice. These studies demonstrated a role for differentially expressed genes in the same individual. These so-called imprinted genes are expressed depending on the sex from which they were inherited (i.e. mono-allelic expression). Imprinted genes are passed on in a mendelian fashion like any other gene but, for instance, paternally expressed genes are expressed only when they were inherited from a male and the maternally inherited copy is silenced. Many imprinted genes were found to be vital in nutrient transfer between embryo and mother in utero. Studies in mice showed that a paternally expressed gene positively influences maternal investment. Pups that carried a deactivated copy of the Peg3 (paternally expressed gene3) were growth retarded compared with the wild type. Among the first imprinted genes found were Igf-2 (insulin-like growth factor) and Igf-2R (the receptor to Igf-2). The paternally expressed Igf-2 gene increases the amount of resources embryos receive in utero, whereas the maternally expressed receptor reduces this effect by binding to the gene product of Igf-2. This genetic conflict within the same individual was interpreted as the result of an evolutionary arms race, where paternally expressed genes serve to exploit maternal resources while maternally expressed genes attempt to reduce the deleterious fitness effects to the female. Although paternally and maternally expressed genes have the same mode of inheritance, selection is expected to favour the evolution of uniparental expression whenever the effects are asymmetrical for males and females. Studies on the effects of imprinted genes and their role in family conflict resolution will thus be at the start of exciting future empirical and theoretical work.

Intragenomic and Intergenomic Conflict

Other examples of evolutionary antagonism can be found within the same individual (intragenomic conflict). Such conflicts arise between genes and selfish genetic elements because of differences in their mode of transmission. Selfish genetic elements attempt to increase their rate of transmission, often with overall adverse effects on the organism and the other genes, hence the conflict. Among these elements are ‘meiotic drive genes’ and transposons. Meiotic drive genes are found on both sex chromosomes and autosomes and work as segregation distorters, in that they increase their chance of transmission during meiosis at the expense of the other allele. Normally, both alleles have equal chances of transmission during meiosis. The t gene in mice, however, manages to reduce sperm mobility of the wild-type allele so that more than 90% of a heterozygous male’s offspring carry the t gene. Since meiotic drive genes cause widespread damage to the other genes, many suppressors of meiotic drive genes are found throughout the genome as a counteradaptation. Another class of selfish genetic elements are transposons, which are able to jump within the genome and change their position (e.g. Drosophila P)or which replicate using the original element as a template and insert the replicate at a different position (e.g. Drosophila gypsy). Mutations that are caused in this manner often have deleterious effects on the organism.

In addition to a conflict within the same individual (intragenomic conflict), there is evolutionary antagonism between loci within the gene pool of a species (intergenomic conflict). If the gene product of a specific gene (a) in one individual is used in contest interactions with another individual that reacts in a certain way because of the gene product of a different gene (b), then this can lead to antagonistic coevolution between these loci. The result is that the probability of transmission into the next generation, i.e. the fitness, of gene a will depend on gene b, and we expect to see adaptation and counteradaptation between these loci. Studies in Drosophila demonstrated the existence of such a process by allowing males to evolve more toxic seminal fluids but experimentally preventing females from counteradaptation. Seminal fluids reduce the chances of other males to fertilize eggs, but have adverse side effects for females. Bill Rice could show that female fitness is significantly reduced when they were prevented from evolving counteradaptations.

Future studies in behavioural ecology will increasingly address proximate and ultimate questions in conjunction and apply methods from other fields such as molecular and population genetics, physiology or mathematics. In particular, an interesting avenue of research will investigate how conflicts of interest between individuals are resolved. What role do genetic and epigenetic effects on lower levels (within one individual) play in how individuals behave towards one another? Studies on the coevolution of traits, such as coadaptation between mother and offspring or male and female behaviour, require a more extended empirical analysis and could yield interesting new insights into what shapes the behaviour of animals.