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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,
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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
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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).
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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). Such models often uncover new and
fascinating heretofore-unthought-of twists and turns in
our understanding of cooperative behavior.
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