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Naturwissenschaften (2002) 89:533–541 DOI 10.1007/s00114-002-0379-y REVIEW ARTICLE Lee Alan Dugatkin Animal cooperation among unrelated individuals Published online: 29 November 2002 Springer-Verlag 2002 Abstract The evolution of cooperation has long been a topic near and dear to the hearts of behavioral and evolutionary ecologists. Cooperative behaviors run the gamut from fairly simple to very complicated and there are a myriad of ways to study cooperation. Here I shall focus on three paths that have been delineated in the study of intraspecific cooperation among unrelated individuals: reciprocity, byproduct mutualism, and group selection. In each case, I attempt to delineate the theory underlying each of these paths and then provide examples from the empirical literature. In addition, I shall briefly touch upon some recent work that has attempted to examine (or reexamine) the role of cognition and phylogeny in the study of cooperative behavior. While empirical and theoretical work has made significant strides in the name of better understanding the evolution and maintenance of cooperative behavior in animals, much work remains for the future. From the point of view of the moralist, the animal world is on about the same level as the gladiator’s show. The creatures are fairly well treated, and set to fight; whereby the strongest, the swiftest and the cunningest live to fight another day. The spectator has no need to turn his thumb down, as no quarter is given … the weakest and the stupidest went to the wall, while the toughest and the shrewdest, those who were best fitted to cope with their circumstances, but not the best in any other way, survived. Life was a continuous free fight, and … a war of each against all was the normal state of existence. Introduction To read the opening quote from Thomas Henry Huxley literally would be to present a warped view of animal societies. Huxley, brilliant though he was, simply underestimated the role of cooperation in animal behavior. No doubt the “gladiator show” aspects that Huxley refers to occur in nature, but cooperation, too, is a common occurrence. The literature on intra- and interspecific cooperation in animals is large (Dugatkin 1997) and would be impossible to review in detail here. In particular, the literature on both kin-selected cooperation (Hamilton 1964) and interspecific cooperation (or mutualism) has been reviewed amply and numerous times in other places (see Closing thoughts section) and so here I focus on intraspecific cooperation among unrelated individuals. This is not meant to underplay the significance of either kin-selected or intraspecific cooperation. In fact, most behavioral ecologists would agree that many of the instances of cooperation that occur in nature are, in one way or another, linked to kinship. Such cooperation is well understood, both from a theoretical and empirical standpoint, and so I have opted here to address cooperation among unrelated individuals, an issue that is more of a sticking point than kin-based prosocial behavior. More specifically, I will focus on the following questions that touch on both proximate and ultimate aspects of cooperative behavior: l (Huxley 1888) l l L.A. Dugatkin ()) Department of Biology, University of Louisville, Louisville, KY 40292, USA e-mail: [email protected] Fax: +1-502-8520725 l What three paths to intraspecific cooperation among unrelated individuals have behavioral ecologists identified? What theory lies behind each? What empirical evidence supports each of these paths? How can work on cognition play a role in further understanding the evolution of cooperation? What role does cooperation play in coalition formation? What role does phylogeny play in explaining the distribution of animal cooperation? 534 Three paths to intraspecific cooperation Over the last four decades, behavioral ecologists have developed three paths to the evolution and maintenance of cooperation among unrelated animals: reciprocity (Axelrod 1984), byproduct mutualism (Brown 1983), and group selection (Wilson 1980). Below, I will touch upon all three of these paths, trying to convey an essence of the theory underlying each as well as at least one empirical example that captures the essence of the particular path under consideration. Path 1: Reciprocity Perhaps the best examined path to cooperation is that of reciprocal altruism. In his now classic paper, “The evolution of reciprocal altruism,” Trivers (1971) argued that genes for cooperative and altruistic acts might be selected if individuals differentially distribute such behaviors to others that have already been cooperative and altruistic toward the donor. Trivers frames the evolution of cooperation in terms of a mathematical game called the Prisoner’s Dilemma (Fig. 1). In the Prisoner’s Dilemma game, each of two players can choose to either cooperate or not cooperate (defect). Mutual cooperation provides both players with a payoff labeled R, while mutual defection produces a payoff of P for both players. Should one player cooperate, but its partner defect, the former receives payoff S, while the latter receives T. The payoffs of this game are set up as follows: T >R >P >S (another inequality, 2R >T+S, is often tacked on to this list). The dilemma in this game centers around the fact that, regardless of what a player’s opponent does, it receives a higher payoff when defecting (as T >R and P >S). This is true for both players in a game and hence both individuals should defect and end up with a payoff of P. Yet both players would had received a higher payoff if they had both cooperated (as R >P) (Poundstone 1992). On a single play of the Prisoner’s Dilemma, then, rationality as well as natural selection leads players toward a suboptimal solution. Axelrod and Hamilton (1981), using both analytical techniques and computer simulations, examined the success of an array of behavioral strategies in the iterated Prisoner’s Dilemma game, where an iterated game is one that is played over and over between the same individuals [in Axelrod and Hamilton’s (1981) case, the exact endpoint of the game is unknown]. When analyzing the iterated Prisoner’s Dilemma, Axelrod and Hamilton (1981) searched for the evolutionarily stable strategy (ESS) to this game (Maynard Smith 1982), where an ESS is a strategy that once adopted by individuals in a population can be “invaded” and replaced by any other strategy. Axelrod and Hamilton (1981) demonstrated that if the probability of meeting a given partner in the future was above some critical level, then in addition to the success of a simple strategy of “always defect” (ALLD), a Fig. 1 The Prisoner’s Dilemma. In this game, players 1 and 2 can each choose to cooperate or defect (not cooperate). For the game to qualify as a Prisoner’s Dilemma, T >R >P >S (and 2R >T+S) conditionally cooperative strategy called tit-for-tat (tft) was a robust solution to the iterated Prisoner’s Dilemma. tft is a strategy that cooperates on the initial encounter with a new partner, and subsequently copies its partner’s last move. Axelrod (1984) argued that tft’s success is a function of three defining characteristics: (1) “Niceness” – tft is never the first to defect, (2) Swift “retaliation” – tft immediately defects on a defecting partner, and (3) “Forgiving” – tft remembers only one move back in time. As such, tft forgives prior defection if a partner is currently cooperating (i.e., it does not hold grudges). Since the original models of tft have appeared, scores of variants of this strategy have been analyzed (Dugatkin 1997), with most sharing many of the essential characteristics we have just examined. A well-cited example of reciprocity is Wilkinson’s (1984) work on blood sharing among vampire bats. Vampire bats (Desmodus rotundus) typically live in groups composed largely of females, with a low average coefficient of relatedness (between 0.02 and 0.11; Wilkinson 1984, 1990). Somewhat remarkably, females in a nest of vampire bats regurgitate blood meals to others that have failed to obtain food in the recent past (Wilkinson 1984, 1985). This sort of food sharing can literally be a matter of life or death, as individuals may starve if they do not receive a blood meal every 60 h. While relatedness does play some role in understanding reciprocity and food sharing in vampires, Wilkinson created an “index of opportunity for reciprocity” that examined the role of reciprocity per se in shaping food exchange. When analyzing the vampire data with this sort of index, Wilkinson suggests three lines of evidence that reciprocity, above and beyond an effects of relatedness, is important in vampire blood sharing: (1) the probability of future interaction between female bats is high as predicted by tft models, (2) the blood meal obtained is critical, while the cost of giving up some blood may not be that great, thus satisfying one of the conditions stipulated by Triver’s model of reciprocal altruism, and (3) vampires are able to recognize one another and are more likely to give blood to those that have donated in the past. While it is clear that vampires do not play a strict version of tft, 535 they are employing some sort of reciprocal altruistic strategy when it comes to blood sharing. Path 2: Byproduct mutualism A second path to the evolution of cooperation has been dubbed “byproduct mutualism” (Brown 1983; Connor 1995; also see West-Eberhard 1975; Rothstein and Pirotti 1987). My colleagues and I have argued that for byproduct mutualism to operate, a boomerang effect – any uncertainty that increases the probability that a defector will be the victim of its own cheating – must be in play (Mesterton-Gibbons and Dugatkin 1992; Dugatkin 1997). That is, byproduct mutualism occurs when an individual pays an immediate cost for not acting cooperatively, such that the immediate net benefit of cooperating outweighs that of cheating. Byproduct mutualism is favored in “harsh” environments” where cheating is costly. In “mild” environments, where cheating is not as costly, byproduct mutualism fares poorly. So, for example, if two individuals each do better by hunting prey cooperatively than either would do trying to hunt such prey alone, then no incentive for cheating occurs, and byproduct mutualism is in operation. This might be the case if prey were particularly large. In this case, the size of the prey creates the harsh environment needed for byproduct mutualism to operate. If, however, hunters can do well by simply catching smaller prey on their own, byproduct mutualism fails. Here, the smaller prey size creates a “milder” environment where cheating is favored. If groups of two or more unrelated individuals form a group, either temporarily of permanently, in response to harsh environments, and then cooperate in such harsh environments, then byproduct mutualism is at play. An interesting case of byproduct mutualism has been uncovered by Clements and Stephens (1995), who tested pairs of bluejays (Cyanocitta cristata) in a laboratory apparatus that allowed each individual in a pair to peck at one of two keys – the “cooperate key” or the “defect key.” Two payoff matrices (Fig. 2) were used. The first was a Prisoner’s Dilemma (P matrix), while the second was a byproduct mutualism matrix in a harsh environment (M matrix). In the Prisoner’s Dilemma game, if both birds hit the “cooperate” key, they would each receive 3 units of food, while if one bird hit the cooperate key and its partner hit the defect key, the former would receive 0 units of food, while the latter would receive 5. As is characteristic of the Prisoner Dilemma games, if both birds employ a tit-for-tat, they do better than had each adopted an “always defect” strategy. In the byproduct mutualism matrix, cooperation was clearly the best choice regardless of what the other player did, no matter how many times the game was played (Fig. 2). Birds were initially exposed to the P matrix, then to the M matrix, and finally to the P matrix once again. What Clements and Stephens (1995) found was that, regardless of whether the jays could see each other or not, birds defected in the first Prisoner’s Dilemma matrix, cooper- Fig. 2 The two matrices used in Clements and Stephens’ (1995) work on foraging in bluejays ated in the M matrix, and reverted to defection the second time they encountered the Prisoner’s Dilemma matrix. As such, bluejays cooperate via byproduct mutualism and not reciprocity. House sparrows’ (Passer domesticus) “chirrup” calls (Summers-Smith 1963) appear to be another instance of byproduct mutualism at play in bird foraging. Using the roofs of Cambridge University as his testing grounds, Elgar (1986) examined whether chirrup vocalizations brought conspecifics to a newly discovered food source, and if so, under what conditions. To tackle this question, Elgar (1986) recorded chirrup calls at artificial feeders containing pieces of bread. The patches contained either bread that was divisible among sparrows, or bread that was just enough food for a single bird. To begin with, Elgar found evidence that those sparrows arriving at a patch of food first were the most likely to produce chirrup calls. Furthermore, chirrup call rates were higher when the food resource was divisible. When food items were small enough that sparrows could pick them up and fly away, that is just what the sparrows did, and in the process, they produced no chirrup calls. Chirrup calls were, however, emitted when larger food items – those that were too big to remove from the experimental area – were found at feeders. Why cooperative chirrup calls when larger, but not smaller items, are uncovered? One possibility is that when larger food items were placed at feeders, sparrows needed to remain in the vicinity of feeders for long periods of time. This, however, was dangerous, as long-term foraging conflicts 536 with predator vigilance activities. But, the more other sparrows at a feeder (via chirrup calls), the less each bird needs to scan for predators. Given this, it may be safer to remain near feeders when in the company of other sparrows, and as such, the benefits associated with predator detection outweigh the costs of inviting other foragers to your food at the site. If this proves to be the case, then chirrup calls are most easily understood in the context of byproduct mutualism. Path 3: Trait group selection At the heart of “modern” or trait-group selection models is the contention that natural selection operates at two levels: within- and between-trait groups (see Wilson 1983; Wilson and Sober 1994; Sober and Wilson 1998 for reviews), where a trait-group is defined as all individuals that affect each other’s fitness (Wilson 1980). Withingroup selection is very similar to standard “selfish gene” thinking. That is, within-group selection acts against cooperators, since in trait group models, cooperators, by definition, take on some cost that others do not. Defectors are always favored by within-group selection since they receive any benefits that accrue because of the actions of cooperators, but pay none of the costs (as in the Prisoner’s Dilemma). As opposed to within-group selection, between-group selection favors cooperation, if groups with more cooperators outproduce other groups. For example, when individuals in a group give alarm calls, they may pay a cost within groups (as they may be the most obvious target of a predator honing in on such a call), but groups with many alarm callers may outproduce groups with fewer cooperators. The relative strength of within- and between-group forces then determines whether cooperation evolves in any particular situation (Wilson 1980; Wilson and Sober 1994; Sober and Wilson 1998). Two interesting putative cases of trait-group selected cooperation have been documented during cooperative colony foundation in ants (see Nonacs 1988; Strassmann 1989; Hlldobler and Wilson 1990; Keller 1991, 1995; Bernasconi and Strassmann 1999 for reviews). In ants, as opposed to other social insects, cooperating cofoundress queens are not closely related (Bartz and Hlldobler 1982; Tschinkel and Howard 1983; Rissing and Pollock 1986, 1988; Rissing et al. 1989). Cooperative colony foundation has been well studied in the desert seed harvester ant Messor pergandei. In this species, adult colonies are very territorial (Wheeler and Rissing 1975; Ryti and Case 1984). Early in a colony’s life, “brood raiding,” in which brood captured after some act of between-group aggression are raised within the victorious nests and “loser” colonies die, occurs. In M. pergandei, all queens produce workers, and what is particularly intriguing is that until workers emerge, queens do not fight and no dominance hierarchy exists. That is, (unrelated) queens cooperate with one another in the production of workers. From a between-group perspective, there exists a positive correlation between the number of cooperating foundresses in a colony and the number of initial workers produced by that colony (Rissing and Pollock 1991). Colonies with more workers are then more likely to win brood raids (Rissing and Pollock 1987). In M. pergandei we see a case where within-group selection clearly favors “cheating” (not cooperating with other queens and expending resources only on one’s own offspring), but selection between groups favors cooperation, as groups with many cooperators survive brood raiding. In this instance, betweengroup selection forces seem to be strong enough to keep queen–queen cooperation in existence. Here trait-group selection can be distinguished from byproduct mutualism by asking what would happen if there was only a single group in play. If byproduct mutualism was driving this system, and the environment were harsh (as it is), cooperation would be favored. If, however, a single group existed, cooperation could never take hold via group selection in the ant case (or, for that matter, in any case). It is important to note that the idea that trait-group evolution drives cooperative colony foundation in M. pergandei (as argued in Wilson 1990; Dugatkin et al. 1992; Mesterton-Gibbons and Dugatkin 1992; Dugatkin 1997) has recently been challenged. Pfennig (1995) constructed a field experiment contrasting single and double foundress associations in M. pergandei. Not only did two-queen nests not outlive one-queen nests in Pfennig’s experiment, but no brood raiding at all was observed. Another putative case of trait-group selection in ants comes from Rissing et al.’s (1989) work on Acromyrmex versicolor. In A. versicolor many nests have multiple foundresses, no dominance hierarchy exists among queens, all queens produce workers, and brood raiding among starting nests appears to be common. Furthermore, the probability of the nest surviving the brood-raiding period is again a function of the numbers of workers produced. A. versicolor differs from M. pergandei in that A. versicolor queens forage after colony foundation. Foraging behavior, however, is a very dangerous activity, as a result of high predation pressure. Yet once a queen assumes the role of colony forager, she remains in that role, and shares all the food brought into her nest with her cofounders. That is, foragers assume the risks of foraging and obtain the benefits, while other queens simply obtain the benefits, but pay no costs. Once again we have a case where within-group selection favors cheating (not being the forager), while between-group selection favors cooperation, as cooperation on the part of a specialized forager appears to lead to increased productivity in the form of new workers. This in turn affects the probability that a given nest will be the one to survive the period of brood raiding, thus providing the between-group component necessary for cooperation to evolve (Seger 1989; Rissing et al. 1989). 537 Cognition and cooperation It is important to recognize that the three paths to cooperation mentioned above do not all necessarily require the same sort of cognitive abilities. In particular, we shall be focus on two aspects of cognition, namely memory and individual recognition. The rationale behind narrowing our discussion of cognition to these two components of a complex array of possible cognitive functions is as follows. Memory and individual recognition play a prominent role in the literature on cooperation, most notably, in the case of tft, which as we have already seen, and will discuss in more depth later, often requires that individuals recognize opponents and remember their prior actions (Dugatkin and Alfieri 2001). Before examining cognition and our three paths to cooperation, a brief discussion of the relationship between the two components of cognition on which we shall focus – individual recognition and memory of specific events – might prove helpful. One important aspect of this relationship is that individual recognition can exist in the absence of memory of specific events, and vice versa. I may, for example, recognize you by some composite measure of your facial features (individual recognition), but not necessarily remember anything about what you have done to me in the past (memory). That is, I can recognize an individual, but not have explicit memory of any actions that individual has undertaken. In one sense, this recognition requires memory – of facial features, etc. – but the key is that it does not require memory with respect to behavioral interactions per se. Conversely, I may be able to remember that someone did x to me, without remembering who it was that actually did x. In relatively rare circumstances, individuals involved in reciprocal exchanges will have the same partner for very long periods of time. This might, for example, be a result of individuals being somehow physically linked. Another scenario that might produce long-term exchanges is a scarcity of potential partners, where individuals are not physically bound together, but the lack of potential new partners, in effect, creates long-term interactions (Lima 1989). When interactants are in fact engaged in a long-term series of interactions with one partner, all that is required to play tft is memory of a specific event (cooperation or cheating), and individual recognition is not necessary, although it may very well be present. To see this, imagine a study of individual recognition and cooperation in a population where partners are difficult to find, and hence when they are encountered, very longterm partnerships develop. If the probability of interaction with the same partner is very high, then each individual need only remember the action the other undertook, but not their identity, as identity is virtually a constant in such a system. If you were to then experimentally remove partner 2 and place another individual in its place, partner 1 may not recognize that it is now interacting with a stranger, for there would be little selection pressure favoring such abilities in the system as we described it. Individuals involved in reciprocal exchanges are typically free to change partners and do so readily (Dugatkin 1997). When tft is being employed under such conditions, individuals need to remember specific events (cooperation or cheating) and to recognize other individuals. To play tft when partners are swapped, you must do what your partner did on the last move, and that means an individual must recognize who it is paired with at any given moment as well as what behavior its partner just undertook. Animals as simple as aquatic polychaete worms seem capable of playing tft when trapped in a Prisoner’s Dilemma with numerous partners (Sella 1985, 1988; see Dugatkin 1997 for a review). For example, Ophryotrocha diadema is a simultaneously hermaphroditic worm that appears to engage in the “egg-swapping” that is seen in many deep-sea fish (Fischer 1988). Individual worms pair up and take turns contributing eggs and sperm to matings. How these worms keep track of one another’s behavior remains unknown, yet “cheating” (failing to provide eggs at the appropriate turn) is rare. Approximately 8% of interactions involved cheating – so low a figure that the worms often appear to fail to detect cheating when it occurs (Sella and Lorenzi 2000). The fact that polychaete worms are capable of such behavior suggests that reciprocity and partner fidelity may be deeply rooted in evolutionary time. Group-selected cooperation requires neither recognition nor memory. Some forms of cooperation can evolve by group selection if groups are formed randomly and individuals have no memory or recognition abilities. That being said, group-selected cooperation is certainly more strongly favored if cooperators are able to identify and interact with other cooperators (Eshel and Cavalli-Sforza 1982; Peck 1993; Wilson and Dugatkin 1997; Roberts and Sherratt 1998). In-group biasing, in which individuals show a strong tendency to favor those in their own group (Tajfel 1978), is one possible outgrowth of group-selected cooperation, when some type of categorization (individual i is a group-member or a foreigner) is possible. In-group biasing can be extremely powerful in humans, even with respect to what appear to be trivial decision-making processes. Consider Tajfel’s (1978) examination of in-group biasing in English teenagers. Sixty-four males from a school were asked to estimate the number of dots flashed on a wall. The boys were then informed that they fell into one of two categories – those that overestimated the number of dots on the wall, and those that underestimated the number of dots. Subsequent to this, each subject was placed in a room by themselves with a series of forms. The forms posed a series of questions about how the subjects would divide up monetary rewards and penalties between two other youngsters involved in the study. The two other youngsters were either both from the subject’s own group (dot overestimators or underestimators), both from the group to which the subject did not belong, or one from each of these groups (one overestimator and one underestimator). 538 Results were striking. When asked to divide up rewards and punishments between two individuals from the same group – either the group the subject belonged to or the one that he did not – rewards were split fairly. If, however, the choice was between someone from the subject’s own group vs. an individual from the other group, subjects consistently favored members of their own group. This despite the subject having no information about the actual identity of the individuals between whom they were choosing. Simply being informed, for example, that others overestimated dots like you did, even if you never meet such dot overestimators, was sufficient information to cause an unequal distribution of monetary rewards. It should be noted that a preference for those in your own group is not restricted to primates. A preference for familiar individuals independent of relatedness has been demonstrated in several species of centrachid fish (Brown and Colgan 1986; Dugatkin and Wilson 1992) and guppies (Magurran et al. 1994; Griffiths and Magurran 1999). Byproduct mutualism adds a new twist to what cognitive prerequisites are needed for different forms of cooperation to evolve. Under byproduct mutualism, neither memory nor individual recognition is necessary. Rather, a form of categorical recognition is needed, but it is not the same form of categorical recognition as in other types of cooperation (where the categories are usually “cheater” and “cooperator”). When byproduct mutualism is in play, if individuals need to categorize anything, it is the environment they are in as “harsh” or “mild,” rather than categorizing some specific attribute of another individual. Under byproduct mutualism, if the environment is categorized as harsh, all individuals should cooperate. If the environment is categorized as mild, no one is predicted to cooperate. As such, selection should have operated strongly on an individual’s ability to distinguish between harsh and mild environments. This appears to be the case, for example, in lions, where we can envision large prey as representing a harsh environment for lion hunters, and smaller prey a mild environment of sorts. Lions typically hunt in cooperative groups when stalking large prey, but hunt alone when going after small prey (Scheel and Packer 1991). Cooperation and coalitions So far we have primarily been examining cooperation in the context of pairwise (dyadic) interactions. Cooperation, however, can also occur in interactions that involve more than two individuals. One example of polyadic cooperative is coalition behavior, where at least two individuals join together in an action against a third. When such coalitions exist for extended periods of time, they are referred to as alliances (Harcourt and de Waal 1992). Such cooperative coalitions and alliances have been documented in numerous primate species (Harcourt and de Waal 1992; Chapais 1992), hyenas (Crocuta crocuta, Zabel et al. 1992), wolves (Canis lupus, Fentress and Ryon 1986), lions (Panthera leo, Packer and Pusey 1982), cheetahs (Acinonyx jubatus, Caro 1994), coatis (Nasura narica, Russell 1983) and dolphins (Tursiops trucatus, Connor et al. 1992a, 1992b, 1999). Connor and his colleagues have uncovered an interesting case of cooperative alliance formation in bottleneck dolphins (Tursiops truncatus, Connor et al. 1992a, 1992b, 1999). By long-term observation of pairs and trios of males in Shark Bay, Western Australia, Connor et al. (1992a, 1999) found not one, but two different types of alliances between male dolphins. Both forms of cooperative alliances uncovered focus on males “herding” reproductive females. Connor’s “first order” alliances are comprise pairs or trios of males acting in a coordinated fashion to keep females by their side, ostensibly for the purposes of mating. Males in these alliances stay very close to one another and alliances remain stable for many years. When females herded by an alliance attempt to swim away (which they often do), males act in a coordinated and aggressive manner to prevent the females from leaving and associating with another male (Connor et al. 1992b). What makes the alliance in dolphins unique is that first-order alliances joined together to form “secondorder” superalliances that aggressively attacked and stole females from other such superalliances. On two occasions, a defending alliance was assisted by another alliance in its attempts to maintain the female it was herding, creating a battle of second-order alliances. Second-order alliances have been documented in only one other species – humans – and Connor et al. (1992a) argue that the complex social interactions inherent in dolphin superalliances, as well as other aspects of dolphin society, may explain the evolution of large brain size in the delphinids. Phylogeny and cooperative breeding in birds Phylogenetic analysis allows behavioral and evolutionary ecologists to examine whether a trait may be common in a group of animal species as a result of common descent per se. Cooperative breeding in birds is an ideal test case for examining the role of phylogeny in the distribution of cooperative behavior, as this form of cooperation is very common (Brown 1987). Early studies of cooperative breeding in birds were quite different from the current adapatationist approach (Brown 1987; Stacey and Koenig 1990) and gave much more weight to phylogeny’s role in cooperative breeding (Davis 1942; Hardy 1961; Brown 1974; Fry 1977). With the advent of new, very powerful phylogenetic tools, Edwards and Naeem (1993), reexamined cooperative breeding from a phylogenetic perspective. Using 166 species of cooperatively-breeding passerine birds in 97 genera (Brown 1987), Edwards and Naeem (1993) began their work by testing whether the distribu- 539 tion of cooperatively breeding species was random. To do this, they created a computer simulation to predict what the distribution of cooperative breeding species would be if they distributed into genera simply based on the number of species in that genera. They found that the distribution in nature differed significantly from the random distributions generated by the computer simulations, with some genera having too many cooperatively breeding species, and others too few. Edwards and Naeem (1993) followed this analysis by using already published phylogenetic trees to examine the distribution of cooperative breeding. For example, their phylogenetic analysis of jays, Australian songbirds, Australian treecreepers and New World wrens suggests that cooperative breeding may have arisen a limited number of times in some common ancestor(s) to modern day species, and has simply been lost by those species that do not cooperate today. The phylogenetic approach does not necessarily conflict with an adaptationist view of cooperative breeding, although it does emphasize the importance of history (phylogeny) in our understanding of the distribution of cooperative breeding. Arnold and Owens (1998, 1999), for example, addressed the question of whether ecological factors can help us explain why cooperative breeding is not randomly distributed across bird families. They found that increases in the level of cooperative breeding were correlated with decreases in annual adult mortality and clutch size. The low rate of mortality often seen in cooperatively-breeding birds is associated with increasing sedentariness, lower latitudes, and decreased environmental fluctuation. Arnold and Owens (1998, 1999) then argue that “low annual mortality is the key factor that predisposes avian lineages to cooperative breeding; then ecological changes, such as becoming sedentary, further slow population turnover and reduce opportunities for independent breeding.” Closing thoughts Ever since Darwin (1859) mused on the problems that cooperation posed for his theory of natural selection, evolutionarily oriented biologists have been fascinated by this type of behavior. The fascination is no less powerful today than 150 years ago, but we have made some progress in understanding both proximate and ultimate aspects of cooperation. In this review we have focused on intraspecific cooperation, but huge strides have also been made in understanding interspecific cooperation (Boucher 1985; Kawanabe et al. 1993; Bronstein 1994, 1999; Frank 1994; Connor 1995; Nee 2000; Stachowicz 2001). With respect to intraspecific cooperation, we have touched on a few of the many ways that behavioral and evolutionary ecologists tackle this subject, by enumerating three paths to cooperation and then examining the role that cognition and phylogeny may play in cooperative behavior. Three recent developments also bode well for the future of studies on cooperative behavior in animals. Reeve and his colleagues (Keller and Reeve 1994; Reeve 1998, 2000; Reeve and Keller 2001), following early work by Emlen (1982) and Vehrencamp (1979, 1983), have developed numerous “transactional models” of social behavior that can be applied to cooperation (also see; Johnstone 2000; Johnstone et al. 2000). These models incorporate genetics, ecology and behavioral dynamics (such as aggressive interactions) under one theoretical umbrella, and show much promise for helping unravel some of the mysteries of cooperation. In addition, recent work has focused on the role of punishment in the evolution and maintenance of cooperation (Clutton-Brock and Parker 1995; Boyd and Richerson 1992; Heinrich and Boyd 2001; Nesse 2002). It turns out that animals are in fact capable of punishing one another for violating established “rules” and that such punishment is a powerful force in securing cooperative behavior. Lastly, a number of sophisticated computer simulation models have tackled the complex question of what role population dynamics plays in the evolution of cooperation (Pollock 1989a, 1989b; Nowak and May 1992; Nowak and Sigmund 1992, 1998; Wilson et al. 1992; Hutson and Vickers 1995; Roberts and Sherratt 1998; Ferriere and Michod 1995). 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