Behavioral Ecology
doi:10.1093/beheco/ari075
Advance Access publication 13 July 2005
Review
Determining the fitness consequences of
antipredation behavior
Johan Lind and Will Cresswell
School of Biology, Bute Building, University of St Andrews, St Andrews, Fife, KY16 9TS, UK
Any animal whose form or behavior facilitates the avoidance of predators or escape when attacked by predators will have a greater
probability of surviving to breed and therefore greater probability of producing offspring (i.e., fitness). Although in theory the
fitness consequences of any antipredation behavior can simply be measured by the resultant probability of survival or death,
determining the functional significance of antipredation behavior presents a surprising problem. In this review we draw attention
to the problem that fitness consequences of antipredation behaviors cannot be determined without considering the potential for
reduction of predation risk, or increased reproductive output, through other compensatory behaviors than the behaviors under
study. We believe we have reached the limits of what we can ever understand about the ecological effects of antipredation
behavior from empirical studies that simply correlate a single behavior with an apparent fitness consequence. Future empirical
studies must involve many behaviors to consider the range of potential compensation to predation risk. This is because antipredation behaviors are a composite of many behaviors that an animal can adjust to accomplish its ends. We show that observed
variation in antipredation behavior does not have to reflect fitness and we demonstrate that few studies can draw unambiguous
conclusions about the fitness consequences of antipredation behavior. Lastly, we provide suggestions of how future research
should best be targeted so that, even in the absence of death rates or changes in reproductive output, reasonable inferences of
the fitness consequences of antipredation behaviors can be made. Key words: antipredation behaviors, fitness, predation risk
compensation, predator-prey interaction. [Behav Ecol 16:945–956 (2005)]
redation is clearly one of the major selection pressures
that determine the form (Endler, 1991) and behavior
of animals (Lima, 1998). For example, almost all populations
of animals suffer major mortality in their first year because of
predation (Newton, 1998). Any animal whose form or behavior facilitates the avoidance of predators or facilitates escape
when attacked by predators will clearly have a greater probability of surviving to breed and therefore greater probability of
producing offspring (i.e., fitness). Although in theory the fitness consequences of any antipredation behavior can simply
be measured by the resultant probability of survival or death,
determining the functional significance of antipredation behavior presents a surprising problem. Put simply, death rates
are difficult to measure either because observing predation
events in natural systems is difficult or because ethical considerations prevent appropriate experiments. Furthermore, conclusions about the adaptive value of a morphological or
behavioral trait can still be ambiguous even if death rates
can be correlated with a particular behavior where all other
things are equal (e.g., in an experiment). That is because in
natural systems there may frequently be many other ways for
animals to compensate because all other things are never
equal, so that the particular behavior can actually be of little
consequence for individual fitness. Such compensation may
also be confounding when using death rates to determine the
fitness consequences of an antipredation behavior in natural
systems because although many antipredation behaviors may
P
Address correspondence to J. Lind. E-mail: johan.lind@st-and.ac.uk.
Received 9 February 2005; revised 21 June 2005; accepted 23 June
2005.
The Author 2005. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved.
For permissions, please e-mail: journals.permissions@oupjournals.org
increase fitness they do so by reducing probability of attack,
but once an animal is attacked, other behaviors may determine probability of capture. Variation in death rate then results from both probability of attack and capture, controlled
by two or more behaviors that are unlikely to be independent
and that probably interact. These points are best illustrated
with an example. Aerodynamic theory and laboratory studies
have demonstrated that fatter birds fly more slowly (reviewed
in Lind et al., in press), hence, it is frequently concluded that
fatter birds of greater mass will be more at risk of capture
when being chased by hawks. Yet, if fatter birds simply adopt
behaviors that reduce the risk of being attacked (Cresswell,
1999) or facilitate escape, then there may be no obvious relationship between the mass of a bird and mortality to predators, one major component of individual fitness (Lind,
2004).
In this review, we draw attention to the problem that fitness
consequences of an antipredation behavior cannot be determined without considering the potential for reduction of predation risk or increased reproductive output through other
compensatory behaviors. In short we make the case that we
have reached the limits of what we can ever understand about
the ecological effects of antipredation behavior from empirical studies that simply correlate a single behavior with an
apparent fitness consequence. Future empirical studies must
involve more behaviors to consider the range of potential
compensation to predation risk. This is because antipredation
behaviors are a composite of many behaviors that an animal
can adjust to accomplish its ends.
First, we will discuss why observed variation in antipredation
behavior does not have to reflect anything about individual
fitness. Second, we outline how antipredation behavior is
typically studied and demonstrate that few studies can draw
946
Behavioral Ecology
unambiguous conclusions about the fitness consequences of
antipredation behavior. Third, we provide suggestions of how
future research should best be targeted so that even in the
absence of death rates or changes in reproductive output reasonable inferences of the fitness consequences of antipredation behaviors can be made. For example, we can consider
multiple behaviors simultaneously, or correlate antipredation
behaviors with indices of fitness, or study prey choice by predators and predator hunting behavior. We do not intend to
review the field of antipredation behavior in animals, but
our aim is simply to explore how we can improve empirical
studies of antipredation behavior and how to avoid pitfalls in
the quantification of their fitness consequences (for relevant
reviews see Bednekoff and Lima, 1998; Brown and Kotler,
2004; Lima, 1998, 2002; Lima and Dill, 1990; Ydenberg and
Dill, 1986).
The relation between antipredation behaviors and fitness
Predation risk is usually a composite of several interacting
factors, and so it is often difficult to quantify simply. Predation
risk (death rate) for an animal is a function of attack frequency and its probability of being caught when attacked.
Attack frequency (attack rate) incorporates the reaction of
predators to the behavior of prey, for example, a functional
and numerical response. All of the behaviors that a prey can
adopt to modify its risk of being targeted and caught when
attacked comprise prey vulnerability. The key variable in
determining predation risk is probably prey vulnerability
because predators that are foraging optimally will select the
prey that give the maximum energy return for energy invested
in capture, that is those individuals of a prey species that are
the easiest to catch (Stephens and Krebs, 1986). Prey vulnerability will also influence relative prey abundance and the
predator’s functional and numerical response because even
a very common prey will not be attacked if it cannot be captured. Theoretically then, measurements of prey vulnerability
should measure predation risk and there is some empirical
evidence to support this (Biro et al., 2003c; Quinn and
Cresswell, 2004; Sinclair and Arcese, 1995).
Vulnerability of prey is determined in its simplest form by
the trade-off between self-maintenance and allocation of time
by the prey individual to antipredation behaviors, for example, feeding versus use of refuge, mass, vigilance, flock spacing, distance from cover, or flock size. In essence, any animal
that maximizes its antipredation behaviors will never be eaten,
but it will of course starve and certainly never have any
reproductive fitness. However, whenever an animal starts to
allocate resources to some other activity than antipredation,
then its vulnerability to predation increases.
This means that even if part of a population of animals
shows relatively poor antipredation behavior, it does not necessarily mean that they have lower fitness. Although they may
be more likely to be depredated, on average, those that survive
may have more resources to allocate to produce young, on
average, so that the fitness of the low vigilance strategy may
be similar to a high vigilance strategy.
Vulnerability of prey in its more realistic form of course
consists of many antipredation behaviors, all expressed to
varying degrees dependent on the trade-off between time
or energy allocated to the behavior or to an alternative
fitness-enhancing behavior (McNamara and Houston, 1986).
Crucially, the various antipredation behaviors may not be independent, so allocation of many resources to a particular
antipredation behavior may allow few resources to be allocated to another, or vice versa, resulting in an equal fitness
(Figure 1). For example, an animal may allocate little time
to antipredation vigilance but may allocate much time to
Figure 1
How allocation of resources from one antipredation behavior to
another may result in equal fitness despite a low allocation to one
of the behaviors. Increasing body reserves do generally come with
a survival cost (e.g., due to increased foraging effort), whereas increasing predator avoidance enhances survival. This graph depicts
how the costs of increasing body reserves can be compensated for
fully if more resources are allocated to an antipredation behavior
resulting in equal fitness as when allocation to body reserves and to
predator avoidance are reduced.
defending a territory, which instead provides safety from predators. This is what we term predation risk compensation,
where an antipredation behavior that is little expressed may
be compensated for by another antipredation behavior that
is strongly expressed. Overall fitness may be a consequence
of many behaviors, rather than just the two illustrated in
Figure 1.
Predation risk compensation may be further complicated
because the fitness consequences of allocating the same
amount of resources into different antipredation behaviors
may differ. We demonstrate this problem with a simple example: if the probability that an animal escapes from a predator
depends on its mass because mass compromises its running
ability, then it seems reasonable to conclude that individuals
with higher mass have a greater predation risk. Yet, if the
probability of escaping from a predator depends more on
early detection of a predator then mass will be relatively
unimportant compared to vigilance (Figure 2). The relative
Figure 2
How allocation of resources to one antipredation behavior may be
more important to fitness than another antipredation behavior.
Lind and Cresswell • Antipredation behavior and fitness
fitness consequences of allocation of resources to different
antipredation behaviors may then vary depending on specific
ecological circumstances (McNamara and Houston, 1994) or
an animal’s internal state (Houston et al., 1988; Mangel and
Clark, 1988). For example, contrary to the examples above
(Lind, 2004), if animals forage far away from predatorconcealing cover where attacking predators are always detected before they reach the prey, then endurance, escape
speed, and perhaps maneuverability can be more important
than early predator detection.
Predation risk compensation has a final level of complexity
because alternative behaviors, with different costs and benefits
are not just available simultaneously; there is also a range of
alternative behaviors that can occur at different stages during
the predation event. For example, an animal may choose to
feed in a small group because small groups may be relatively
inconspicuous and so infrequently attacked by predators. Yet,
when a small group is attacked, the capture rate may be high
relative to larger groups because the confusion and dilution
effect is less in smaller groups. The overall fitness consequence of choice of group size will therefore be the consequence of both attack and capture probability that are
affected in opposite ways (see for example, Cresswell, 1994b;
Lindström, 1989; Vine, 1971).
Therefore, any of the many ecological studies that measure
relative vulnerability in terms of a single behavior or at a single
stage during predation and conclude that, for example, animals with lower vigilance rates are more at risk than those with
higher rates are potentially flawed, even if the study measures
death rate as a consequence or reproductive output. This is
because of the potential for compensation in other dimensions such as choice of for example feeding site or group size
(Lank and Ydenberg, 2003). Similarly, any laboratory study
that measures variation in antipredation behavior in terms
of a single behavior, constraining an animal’s choice of alternative behaviors (‘‘all other things being equal’’) cannot draw
any conclusions about the fitness of a particular behavior in
the natural ‘‘multibehavioral’’ system (Irving and Magurran,
1997; Lima and Bednekoff, 1999; Lind, 2004; Wolff and
Davis-Born, 1997).
The complexity of antipredation behavior has been acknowledged before; in one of the classic theoretical analyses
of antipredation vigilance, Lima (1987) concluded (with our
comment in parentheses): ‘‘The model developed herein,
however, suggests that this relationship (between vigilance
and predation risk) may be complex even in seemingly simple
situations. (. . .) It is more fruitful for present purposes, however, to examine potential problems with how we as investigators study the risk of predation and its effects. For instance,
experimental manipulations that purport to examine scanning as it relates to one factor may actually be examining
scanning as it relates to changes in two (or more) factors.’’
Nevertheless in many ecological studies variation in predation
risk is often invoked as an explanation for observed form or
behavior without any measures of death rate or inferences
about reductions in reproductive output or considerations
of possible other compensatory behaviors (for reviews see
Elgar, 1989; Lima and Dill, 1990). In the next section we give
examples of how variation in predation risk has been studied
to demonstrate that despite very many antipredation behavior
studies being carried out relatively few can actually draw firm
conclusions with respect to fitness.
How studies have attempted to relate antipredation
behavior to fitness
There are three ways that studies have been carried out to
determine the fitness consequences of antipredation behav-
947
ior. First, variation in antipredation behaviors can be related
to death rates: this has rarely been done successfully because
predation events are difficult to observe and because death
rates are often a consequence of several interacting behaviors,
it is difficult to make behavior-specific conclusions. Second,
variation in antipredation behavior can be related to reproductive output: this has, to the best of our knowledge, rarely
been done because of the difficulties of measuring antipredation behavior and reproductive output in the same individuals
over a sufficiently long time and large enough scale to draw
meaningful conclusions and because reproductive rates will
be a consequence of several interacting behaviors. Third, variation in antipredation behavior can be related to correlates of
death rate: although this approach is common, this has rarely
been done successfully because few studies have considered
the full potential for compensation in other behaviors that
may change predation risk.
Studies that relate antipredation behavior to death rates
When discussing studies of how effectively an antipredation
behavior reduces measured death rate, it is important to make
the distinction between behaviors that affect the probability of
attack (i.e., occur before attack) and those that affect the
probability of capture (i.e., occur after attack). This is well
illustrated by returning to a previous example, where small
groups may be relatively inconspicuous and so infrequently
attacked by predators, yet be subject to a high capture rate
when attacked. A study that examined the effect of group size
by measuring attack rate might conclude that group size was
positively correlated with death rate, while another study that
examined the effect of group size by instead measuring capture rate might conclude the opposite, that group size was
negatively correlated with death rate. In reality, a more useful
estimate of the fitness consequence of choice of group size
would be the probability of attack multiplied by the probability of capture (see for example, table 3 in Lindström, 1989).
Another important distinction to be made is the one between attack and death rates. Most studies that have tried to
relate antipredation behavior to death rates are flawed because they do not consider attack rate as the most important
determinant of predation risk and instead they measure capture rates. We demonstrate this with another example. Juvenile
redshanks Tringa totanus feeding on a risky salt marsh have a
very low probability of capture for each sparrowhawk Accipiter
nisus attack because of effective antipredation behaviors such
as avoidance and flocking (Cresswell, 1994a,b). However, because juvenile redshanks are forced to feed on the salt marsh
for long periods due to competition (Cresswell, 1994a) and
energy costs (Yasué et al., 2003) they are attacked frequently
during the winter, and their probability of being killed during
the winter is very high (Cresswell and Whitfield, 1994). What
probably determines fitness for redshanks in this system is the
number of days that an individual is forced to feed on the
risky salt marsh rather than its antipredation behavior when
on the salt marsh. This shows unambiguously that one can
easily ascribe erroneous significance to results obtained by
measuring capture rates without taking attack rate into
account.
A further reason to make the distinction between attack and
capture when examining studies that relate antipredation behaviors to death rates is methodological. Studies that measure
how a behavior influences the probability of capture inevitably
measure death rate, and so their results in fitness terms are
relatively unequivocal (at least under the conditions of the
study). However, behaviors that may influence the probability
of attack are less clearly directly related to fitness: for example,
the fitness consequences of a low attack rate through avoiding
a predator may be manifest in low body condition or higher
Behavioral Ecology
948
Table 1
Studies that have related variation in antipredation behavior during attack to death rates
Behaviors
Species
Reference
Escape performance
Common lizard
Atlantic silverside
Redshank
White-tailed deer Odocoileus
virginianus, Mule deer
Odocoileus hemionus
Brook trout Salvelinus fontinalis
Skylark
Skylark
Thomson’s gazelle
Tree frog
Thomson’s gazelle
Wood pigeon
Clobert et al. (2000)
Lankford et al. (2001)
Cresswell (1993)
Lingle and Pellis (2002)
Escape response
Predator recognition
Pursuit-deterrent signals
Predator-induced hatching
Early detection of attack
risks elsewhere. This means that behaviors that influence the
outcome of predator attacks are more likely to be observed,
studied, and indeed published because their results in fitness
terms are clear. This may then mean that behaviors that
reduce death rate through reducing attack probability are
relatively unrepresented in the literature, even though such
behaviors could be theoretically far more important (for
example see, Lind, 2004).
Table 1 gives a list of studies that have related variation in
antipredation behavior during attack to fitness. Interpretation
of the results of these studies seems mostly straightforward:
either a behavior is more or less likely to lead to death on
attack by a predator. It is important to remember, however,
that if the study is at all experimental and has constrained the
predator’s behavior then, although a behavior may affect
probability of capture, it may be of little significance because
the predator in natural circumstances may never attack an
individual that might respond in that way. Hence, experiments that fall under this category should require support
by natural observations for results to be of relevance. Also,
even in natural systems, because individuals may only spend
a small proportion of their time carrying out a risky behavior,
the capture rates associated with a particular behavior may not
give a good indication of how important the behavior is in
determining fitness for most individuals. This will be complicated by the fact that some predators preferentially target
vulnerable individuals of some but not other species (Temple,
1987) and because observers may direct their attention
to situations where predators have a high capture rate, so
making behaviors that occur at a low frequency seem more
significant.
Studies showing that antipredation behaviors accrue fitness
benefits under attack (Table 1) have shown that responding
quickly on attack augments survival (Fitzgibbon, 1989;
Kenward, 1978) and that correct identification of attacking
predators and appropriate responses to the predators are important for reducing the probability of death under attack
(Cresswell, 1993; Lingle and Pellis, 2002; Mirza and Chivers,
2000). Even embryos can benefit from responding rapidly
under attack. Tree frog embryos Agalychnis callidryas increase
their survival by hatching when their arboreal eggs are attacked by snakes and escape by falling into the water below
(Warkentin, 1995). That pursuit-deterrent signals incur fitness
benefits has also been shown for Thomson’s gazelle Gazella
thomsoni (Fitzgibbon and Fanshawe, 1988) and skylarks Alauda
arvensis (Rhisiart, 1989, cited in Cresswell, 1994c; Hasson,
1991). Also growth rate in Atlantic silversides Menidia menidia
appears to be inversely related to survival because the fast-
Mirza and Chivers (2000)
Cresswell (1994c)
Rhisiart, 1989, cited in Hasson (1991)
Fitzgibbon and Fanshawe (1988)
Warkentin (1995)
Fitzgibbon (1989)
Kenward (1978)
growing and sated fish suffer from impaired predator evasion
as compared to slow-growing and hungry fish (Billerbeck
et al., 2001; Lankford et al., 2001). Similarly, locomotor endurance in juvenile common lizards Lacerta vivipara seems to
be connected to growth rate and hence incur an increased
predation risk, as measured by reduced survival (Clobert et al.,
2000; see also Le Galliard et al., 2004). This is somewhat
counterintuitive because one could argue that larger individuals should have a relatively higher locomotor performance
and would be more adept at escaping predators.
Table 2 gives a list of studies that have related antipredation
behaviors that occur prior to attack to death rates. Some of
the best evidence for a clear link between antipredation behavior and fitness comes from studies that relate overall activity to death rates. This approach sidesteps the main problem
identified in this review, the presence of behavioral compensation confounding any single behavior relationship because
activity is a composite of many antipredation behaviors. That
a reduction of activity levels in response to increased predation risk incurs fitness benefits has been documented in various taxa (e.g., Schwarzkopf and Shine, 1992; Sih, 1986; Skelly,
1994; e.g., Anholt and Werner, 1995, 1998; Biro et al., 2003a,b,
2004; Downes, 2002; Persons et al., 2001, 2002; Wisenden
et al., 1999). Although these studies show that high activity
incurs an increased predation risk in general, most studies
investigating the consequence of activity level on survival have
not investigated antipredation behavior per se but have instead used predation risk treatments to investigate questions
of general ecological significance. These studies have for
example led to insights about how behavioral responses to
variation in predation risk can affect population dynamics (e.g.,
Anholt and Werner, 1995; Biro et al., 2003a,b). Measurement
of activity levels is thus a reliable and important method to
reach conclusions about how vulnerable individuals are relative to others. However, the combination of the diverse nature
of antipredation behaviors and the impression that animals
modify every aspect of their behavioral repertoires to variation
in predation risk (reviewed in Lima, 1998) suggests that measuring only activity levels, rather than constituent behaviors,
will not allow us to understand these constituent behaviors.
Another problem with studying activity levels is that many
animals are far too large and elusive for this to be a possible
approach.
Studies measuring the effect of antipredation behaviors
(other than activity) on attack rate that can draw conclusions
about consequent death rates are rare, mainly because observing predation rates is difficult. Here we report studies where
the prey was free to compensate behaviorally and where the
Lind and Cresswell • Antipredation behavior and fitness
949
Table 2
Studies that have measured the consequences of antipredation behaviors before attack in terms of death rate
Behaviors
Species
Reference
Activity
Tadpole Rana catesbeiana
Tadpole Rana sylvatica
Common garden skink Lampropholis guichenoti
Wolf spider Pardosa milvina
Wolf spider Pardosa milvina
Mosquito larvae Culex pipiens
Amphipod Gammarus minus
Rainbow trout Oncorhynchus mykiss
Skink Eulamprus tympanum
Butterflies
Redshanks
Ocean skaters
White-nosed coatis
Wood pigeons
Web-building spiders
Northern bobwhites
Thomson’s gazelle
Redshank
Blue tit, Great tit
White-tailed deer, mule deer
Anholt and Werner (1995)
Anholt and Werner (1998) and Skelly (1994)
Downes (2002)
Persons et al. (2001)
Persons et al. (2002)
Sih (1986)
Wisenden et al. (1999)
Biro et al. (2003a,b, 2004)
Schwarzkopf and Shine (1992)
Burger and Gochfeld (2001)
Cresswell (1994b) and Whitfield (2003)
Foster and Treherne (1981)
Hass and Valenzuela (2002)
Kenward (1978)
Uetz et al. (2002)
Williams et al. (2003)
Fitzgibbon (1989)
Cresswell (1994a) and Whitfield (2003)
Hinsley et al. (1995)
Lingle (2002) and Lingle and Pellis (2002)
Activity, habitat use
Activity, cryptic behavior
Group benefits
Vigilance
Habitat choice
predator’s choice was not constrained so that attack rates were
biologically meaningful (also see Table 1). We have also excluded studies where there was a confounding effect of the
focal behavior on capture rate during an attack. That antipredation benefits from reduced attack rates that accrue from
flocking have been shown for ocean skaters Halobates robustus
(Foster and Treherne, 1981), butterflies Lepidoptera (Burger
and Gochfeld, 2001), web-building spiders Meteperia incrassata
(Uetz et al., 2002), northern bobwhites Colinus virginianus
(Williams et al., 2003), wood pigeons Columba palumbus
(Kenward, 1978), redshanks (Cresswell, 1994b), and whitenosed coatis Nasua narica (Hass and Valenzuela, 2002).
Choice of safe habitats when foraging is also important to
reduce attack rates because high attack sites incur survival
costs in great tits Parus major, blue tits Parus caeruleus (Hinsley
et al., 1995), and redshanks (Cresswell, 1994a; Whitfield,
2003). However, this effect was not found in brambling flocks
Fringilla montifringilla foraging during migratory stopover
(Lindström, 1990). In Thompson’s gazelles individual vigilance has also been shown to be a determinant of mortality
with less vigilant individuals being more vulnerable to attack
by cheetahs Acinonyx jubatus (Fitzgibbon, 1989).
Studies that relate antipredation behaviors to reproductive output
Variation in antipredation behavior has rarely been related to
reproductive output, except in studies that directly examine
behaviors that result in defense of young. Many studies have
been carried out, particularly with respect to nest defense
in birds (Byrkjedal, 1987; see review in Montgomerie and
Weatherhead, 1988). Other studies have examined how antipredation behavior is directly related to increasing survival of
young (e.g., Laundré et al., 2001). We do not consider further
studies into parental antipredation behaviors directed towards
nest predators (e.g., nest defense) or defense of young that
have measured juvenile (or clutch) survival because the results of these studies are relatively easy to interpret in terms
of fitness benefits but do not necessarily tell us anything about
the fitness consequences of general antipredation behavior
when an animal is not directly defending its young.
This leaves few studies because of the logistical difficulties
of measuring antipredation behavior and reproductive output
in the same individuals over a sufficiently long time and large
enough scale to draw meaningful conclusions. Consequently,
most studies of antipredation behavior use nonbreeding animals. In any case, reproductive rates are a consequence of
several interacting behaviors such as parental quality, territory
quality, life-history trade-offs, phenotypic limitations, and so
on. Allocation of time to antipredation activities is just one of
several alternative strategies that will affect reproductive output, and because individuals that allocate more time to antipredation activities either before or during reproduction are
likely to make life-history trade-offs, it may be necessary to
monitor lifetime reproductive output. This is the holy grail
of antipredation studies and has up to now only been done
successfully in one species of spider and in three species of
stream-dwelling insects: being able to relate time or resources
allocated to antipredation behavior to lifetime reproductive
output.
Results from an experimental study on the wolf spider Pardosa milvina suggest that an allocation in predator avoidance
behaviors can be coupled to fitness costs in terms of lighter egg
sacs that also contain fewer eggs (Persons et al., 2002). This
effect was probably due to the fact that individuals spending
more time avoiding predators consumed less prey, resulting in
less energy to invest into the egg sacs. In addition, studies
examining the costs of antipredation behaviors in insects that
have nonfeeding adult life stages have shown, for example,
that reduced feeding due to predator avoidance translates into
negative fitness consequences (Ball and Baker, 1996; Peckarsky
et al., 1993; Scrimgeour and Culp, 1994; see also Peckarsky and
McIntosh, 1998) often, but not always (Ball and Baker, 1995),
mediated through reduced fecundity. Even though we failed in
finding any studies on vertebrates relating antipredation behaviors to reproductive output, we would like to point out that
perceived predation risk, a probable major determinant of
antipredation behaviors, has been shown to have a profound
impact on reproductive rate. Animals can probably adjust their
reproductive output to perceived predation risk, and graysided voles Clethrionomys rufocanus might even suppress their
breeding when exposed to the odor of weasels Mustela nivalis
(Fuelling and Halle, 2004). We believe that no field study to
date has provided any link between individual benefits of antipredation behavior and reproductive success.
Behavioral Ecology
950
Table 3
Studies that have considered more than one dimension of antipredation behavior
Species
Reference
Caridean shrimp Tozeuma carolinense
Salamander larvae Ambystoma barbouri
Lizard Eumeces laticeps
Main (1987)
Sih et al. (2003)
Cooper et al. (1990) and
Schwarzkopf and Shine (1992)
Pérez-Tris et al. (2004)
Williams et al. (2003)
Ydenberg et al. (2002)
Wahungu et al. (2001)
Tellerı́a et al. (2001)
Kotler et al. (2004)
Lizard Psammodromus algirus
Northern bobwhites
Western sandpiper
Red-necked pademelon
Blue tits
Allenby’s gerbil
Studies that have considered multiple antipredation behaviors
Variation in antipredation behavior has often been related to
single correlates of death rate (Lima, 1998): although this
approach is common, this has rarely led to robust conclusions
about fitness because few studies have considered the full
potential for compensation in other behaviors that may
change predation risk. Predation risk varies with a plethora
of single abiotic (e.g., moonlight and temperature) and biotic
(e.g., information about predation risk) factors. Single factors
governing antipredation behaviors may be relatively straightforward, such as the avoidance of diurnal predators by seabirds returning to their colonies at night (Watanuki, 1986) or
that cryptic prey animals can reduce the risk of being detected
by choosing microhabitats that resembles their coloration
(Edmunds, 1974). Further examples include studies where
information about prevailing predation risk helps animals to
recognize and avoid predators effectively (for comprehensive
reviews see Chivers and Smith, 1998; Kats and Dill, 1998). This
has been documented in, for example, branchiopods, especially Daphnia (Lampert, 1993), amphipods Gammarus minus
(Wisenden et al., 1999), thrips Frankliniella occidentalis, wolf
spiders P. milvina (Persons et al., 2002), and in fish (e.g., Mirza
and Chivers, 2001; Smith, 1992). However, the measure of the
behavior will only be an index of fitness unless death rates, or
decreases in reproductive output are measured, and knowledge of the system is complete enough to rule out the effects
of other compensating behaviors.
Because antipredation behaviors may not be independent
and instead interact frequently, the fitness consequences of
variation in any one behavior can be difficult to predict (Sih
et al., 2003). To date, there have been few studies that have
specifically measured more than one or two concurrent antipredation behaviors, so the possibility exists that most studies
are confounded by predation risk compensation. We list studies that have considered more than one antipredation behavior in Table 3. The cost of reduced locomotor performance,
and thus impaired predator evasion, in gravid lizards does not
have to be very important for individual vulnerability because
of behavioral compensation (Cooper et al., 1990; Pérez-Tris
et al., 2004; Schwarzkopf and Shine, 1992). Williams et al.
(2003) used field and aviary experiments to study optimal
group size in Northern bobwhites and found that an intermediate group size was beneficial to individuals due to its effect
on for example group persistence, movement, feeding efficiency, predator detection, and individual survival. Changes
in antipredation behavior can also be affected by landscape
changes and how they translate into differences in group composition (Tellerı́a et al., 2001). Considering multiple antipredation behaviors has also led to insights that one cannot
simply classify habitat quality based on the rate of foraging
gain because antipredation behaviors also affect habitat
choice. For example, Western sandpipers Calidris mauri appear to choose stopover sites based on their internal state as
well as the risk and profitability connected to a specific patch
(Ydenberg et al., 2002). Similarly, red-necked pademelons
Thylogale thetis compensate for predation risk during foraging
by altering vigilance, group size choice, and distance to cover
(Wahungu et al., 2001). Considering multiple behaviors also
makes it possible to pinpoint which behaviors are important
for individual survival. For example, Main (1987) found that
otherwise rare behaviors became increasingly common under
predation and contributed significantly to individual survival.
Sih et al. (2003) have also showed elegantly how antipredation
behaviors, previously thought to be maladaptive, are better
understood when other behaviors were incorporated in their
study. Lastly, Kotler et al. (2004) used indirect measurements
of apprehension and time allocation to illustrate how predation risk varies with energetic state and habitat characteristics
in Allenby’s gerbil Gerbillus a. allenbyi.
Future research to link antipredation behavior to fitness
We conclude, surprisingly, that although we know much about
how predation risk affects individual decision making, we
know relatively little about how individual behavioral modifications translate into fitness. Studies on vertebrates that have
been successful in determining fitness consequences of antipredator behavior are often painstaking field studies and
when the natural systems being observed studies are not constrained by ethical issues (Huntingford, 1984). In contrast,
behavioral ecology experiments in the study of predation,
conducted under sound ethical guidelines, are constantly
forced to base conclusions and analyses on more or less
well-established proxies of fitness. So what is the way forward?
We propose three ways in which studies of the functional
significance of antipredation behavior should be carried
out: (1) studies where multiple alternative compensatory behaviors, and the constraints operating on alternative behaviors
are considered; (2) studies that relate resources allocated to
antipredation behavior to indices of reproductive output; and
(3) studies of how predators choose prey to make inferences
about prey vulnerability.
Multibehavioral studies
If we are to draw conclusions with respect to fitness from
measures of antipredation behavior, we need to know the
following from any empirical study: (1) the range of possible
compensatory behaviors, (2) the degree to which compensatory behaviors are independent, and (3) the ecological constraints or physiological and phenotypic limitations operating
on the expression of each antipredation behavior.
Lind and Cresswell • Antipredation behavior and fitness
First, we need to know the range of possible compensation
behaviors. Clearly, in some systems there may be no alternatives: for example, animals may be restricted to feed in a single
location and cannot vary group size. Here individual vigilance
rate may then give an index of predation risk, but only if
vigilance is a reliable estimate of predator detection (cf. Kaby
and Lind, 2003; Lima and Bednekoff, 1999). More typically,
there will be a range of antipredation options. For example,
birds can compensate to increased predation risk by changing
their mass by avoiding the higher risk area and by spending
more time in antipredation behaviors (Lima, 1998; Lima and
Dill, 1990; Witter and Cuthill, 1993). Moreover, animals may
compensate for increased predation risk by decreasing their
mass because reducing mass might increase their ability to
escape from predators (Kullberg et al., 1996; Lind et al.,
1999; McLachlan et al., 2003; Roitberg et al., 2003; Witter
et al., 1994) or simply avoid the predator spatially (Biro
et al., 2004; Durant, 2000; Rettie and Messier, 2001) or temporally (Biro et al., 2004; Creel and Winnie, 2005; Watanuki,
1986) or allocate more time to antipredation behaviors (Elgar
et al., 1986; Whitfield, 1988; Wolff and Van Horne, 2003). The
exact number or type of important related or compensatory
behaviors that comprise the antipredation behaviors of an
animal will probably be species specific, although some taxonomic generalizations can be made. For example, mass regulation may be important only to flying, climbing, and cursorial
animals, while avoidance will not be an option for any sessile
or dormant animal.
Second, we need to determine the degree to which compensatory behaviors are independent. This is because if there
is a range of compensatory mechanisms that are not independent of each other, then there may be multiple values of the
antipredation behaviors that compensate adequately to any
level of predation risk. However, if one particular way of compensation is the best strategy, then compensation mechanisms
may be independent because compensation in one behavior
may occur fully before it is expressed in a second behavior. For
example, under increased predation risk animals may lose
mass first until all possible mass is lost and then start to compensate by another behavior such as avoidance. This means,
hypothetically, that a single measure such as mass could then
provide a measure of predation risk.
Third, we need to take into account the possibility that
observed behavioral changes may be unrelated to predation
risk due to constraints. Animals may be constrained if factors
other than predation constrain fitness. For example, both
theoretical and empirical studies have shown that time constraints can cause habitat shifts (e.g., a shift from an aquatic to
a terrestrial habitat in metamorphosing insects) and increased
risk taking by prey, even though the expected mortality
and growth rate stay the same (Johansson and Rowe, 1999;
Johansson et al., 2001; Rowe and Ludwig, 1991; Werner and
Anholt, 1993). Animals may also be constrained when other
sources of mortality outweigh that of predation risk, such as
risk of starvation, and this will vary according to ecological
conditions such as predictability of foraging opportunities
(Houston and McNamara, 1993; Ydenberg and Dill, 1986).
For example, the capacity for mass loss may vary between
species (Rogers, 1987) and between seasons within a species
(Cresswell, 1998). In midwinter, mass loss capacity in blackbirds Turdus merula is greater than at the end of the winter as
fat reserves are highest midwinter when starvation risk is high
(Cresswell, 1998). Similarly, redshanks may feed in low predation risk areas until increased energy demands and declining food supply during midwinter forces them to feed in highrisk areas (Yasué et al., 2003). In short, it would be wrong to
conclude that no variation in an antipredation behavior indicated that the behavior did not have fitness consequences
951
because all individuals may be forced to allocate all their
resources away from the antipredation behavior because of a
constraint.
Constraints do not operate equally across a population.
We therefore need to measure the degree to which the compensatory mechanisms are available to all individuals in the
population. There may be age-related differences in how
risky behaviors are; for example, young Thomson’s gazelles
take a higher risk when inspecting predators than do adults
(Fitzgibbon, 1994). Therefore, different individuals will have
different solutions to the trade-off between promoting selfmaintenance or reproduction versus minimizing predation
risk. This can depend on differences in their initial state
(e.g., Godfrey and Bryant, 2000) or competitive ability (e.g.,
Cresswell, 2003), such as juveniles and females having less
access to food because of dominance (Pravosudov and Grubb,
1997). For example, in ground squirrels, lower vigilance of
juveniles is due, at least in part, to the greater nutritional
needs of young animals with consequent increases in foraging, which is largely incompatible with vigilance (Arenz and
Leger, 2000; Bachman, 1993). Similarly, only sated killifish
Fundulus diaphanus can afford to join shoals for antipredation
reasons because of increased foraging competition there
(Hensor et al., 2003).
It seems likely that animals may use any combination of
antipredation compensation depending on ecological conditions, especially when conditions increase costs of allocating
resources away from antipredation behavior. It might be expected that in natural systems under a reasonably constant
moderate predation risk, animals that show compensation to
predation risk in one dimension should show less compensation to predation risk in another, that is, a negative correlation
between behaviors. However, if predation risk increases,
changes in any one behavior to compensate, such as by decreasing mass, may not affect the values of another behavior,
such as avoidance of predators, because an animal may aim to
keep its predation risk at a fixed level. After compensation has
occurred to increased predation risk, any negative correlation
between different behaviors involved in predation compensation may remain (although at a different value) or may be lost
if all animals in the population have fully compensated with
respect to one behavior. For example, if all individuals in
a population lose as much mass as possible in response to
increased predation risk, there can be little or no variation
in mass with respect to further compensation to predation
risk. This is demonstrated by the negative correlations between antipredation behavior and antipredation related morphology in snails, intraspecifically and interspecifically. Snails
with a stronger shell, which provides efficient protection from
crabs, show little predator avoidance behavior (Cotton et al.,
2004; Rundle and Brönmark, 2001).
To conclude, it should be possible to demonstrate that a single antipredation behavior has important fitness consequences
if it is shown that there is only one main antipredation behavior or that the antipredation behavior operates independently
of other antipredation behaviors. A study cannot conclude
that an antipredation behavior does not have a fitness consequence in the absence of variation of the behavior with respect
to predation risk unless it can be demonstrated that there are
no constraints acting on its expression. In practical terms this
probably means a uniformity of lack of response across individuals varying in age, sex, or competitive ability. This has important implications for the validity of experimental studies
under artificial conditions. We would like to emphasize the
importance of taking multiple antipredation behaviors into
account with a final example. Even though reproduction is
often inferred as being costly in terms of increased risk
of predation due to body mass increase (Lee et al., 1996;
952
Magnhagen, 1991), gravid female skinks Eulamprus tympanum
do not suffer from increased mortality from predators
(Schwarzkopf and Shine, 1992). Despite the fact that gravid
females suffered from reduced escape speed, they compensated for this by behaving cryptically, implying that vulnerability in this species is determined primarily by avoiding detection
rather than escaping swiftly (see also Pérez-Tris et al., 2004).
An alternative approach that might prove fruitful to estimate
fitness consequences of foraging under predation risk is to
measure giving-up densities in controlled food patches, that
is, the amount of resources remaining in a patch when the
foraging individual leaves (Brown, 1988). This assumes that
a forager leaves a patch when the benefits of staying are less
than the combined energetic, predation, and missed opportunity costs of going elsewhere (Brown, 1988). This methodology
is successful because it combines several important aspects of
antipredation behavior (Brown, 1999), for example, time allocation and vigilance (Kotler et al., 2004). One reason the
giving-up density works well for certain questions in predation
is that it is a multivariate statistic that distils an entire vector of
behaviors and decisions into a single scalar output that can be
measured under diverse conditions and with animals with different states (see Olsson et al., 2002). However, future studies
using this approach need to design studies where the individual is the statistical unit, which will require validation of the
underlying assumptions as a prerequisite for the possibility of
drawing sound conclusions (Price and Correll, 2001).
Finally, negative results in multibehavioral studies may tell
us as much as positive results. If compensation occurs in a
variety of behaviors it is unlikely that any one antipredation
behavior will correlate with a fitness surrogate. Any study that
investigated how, for example, spacing affected the probability
of capture might find no relationship because more spaced
individuals compensated by being more vigilant. Such a negative result would be less likely to be published and would
certainly not be published in an influential journal. If however
the study also measured vigilance, the lack of a relationship
between spacing and vigilance and fitness would make reasonable sense: two negative results would greatly increase our
understanding of the system and would indeed be much more
publishable. One consequence of this suggestion is that there
are many unpublished nonsignificant relationships that apparently show no effect of variation in an antipredation behavior. If such studies could be viewed together, they could tell
us much about alternative antipredation strategies and predation risk compensation.
Correlating resources allocated to antipredation behavior to
measures that predict reproductive output
Animals that express an antipredation behavior at a low level
may simply be allocating more resources to another fitnessenhancing activity. For example, a bird feeding to gain resources to produce eggs may reduce vigilance to a very low level, so
that its food intake rate and corresponding clutch size is high.
Although on average those individuals that do this may have
a greater death rate, this may be more than offset by the increased fecundity. This type of trade-off and the predictions
that arise from it have been explored within the theoretical
discussion of state-dependent foraging, where an individual’s
internal state will determine the optimal allocation of time
or energy to antipredation behavior (Houston et al., 1988;
Mangel and Clark, 1988). Nevertheless, as we have argued
above, there are almost no empirical studies that have measured these types of trade-offs in the context of their effects on
actual reproductive rates.
Correlating antipredation behavior to actual reproductive
measures will be outside the bounds of most studies because
measuring vigilance behavior in the nonbreeding season and
Behavioral Ecology
the number of offspring produced by the same individual in
the breeding season is very difficult. One way forward, however, is to establish which behaviors correlate with both antipredation behaviors and reproductive output. A candidate
behavior linking antipredation behavior to reproductive output is likely to be competitive foraging ability (Sutherland,
1996). That intake rate might be the best indicator of reproductive fitness is not certain. Nevertheless, it seems likely that
the intake rate should at least correlate well with reproductive
fitness. It should be made clear though that by intake rate we
mean intake rate in terms of absolute foraging rate and susceptibility to interference competition (e.g., Sutherland and
Parker, 1985). In other words, what the feeding rate of an
individual is in the absence of competitors and how this
declines on addition of competitors. An individual that has
a high absolute foraging rate and low decline in intake rate on
addition of competitors will be a good competitor and is likely
to be able to allocate more resources to antipredation behavior or reproduction. In the nonbreeding season, the amount of
resources that can be allocated from self-maintenance to antipredation behaviors will depend on the intake rate, and
similarly in the breeding season the amount of resources
that can be allocated from self-maintenance to offspring
production also will depend on the intake rate.
To put this idea simply, we can discount future reproductive
trade-offs when the overall effects of an antipredation behavior on intake rate are measured. For example, a vigilance
reduction that does not increase energy intake is unlikely to
increase future reproductive output. In this case we might be
able to reasonably conclude that vigilance did indicate predation risk (in the absence of other potential compensation
behaviors as outlined above). We would also learn much about
the fitness consequences of antipredation behaviors by measuring variation in antipredation behavior with respect to individual variation in intake rate. If those individuals with the
greatest potential intake rate (or competitive ability) allocate
more resources to antipredation behavior, then we would
predict strong positive correlation between the degree of expression of antipredation behaviors that affect fitness and
competitive ability. Practically this may mean that dominant
sex and age classes may show the highest level of an antipredation behavior. In effect this is the same as looking at variation in the expression of an antipredation behavior as the cost
of expression decreases (less cost for individuals of higher
competitive ability). Therefore, those antipredation behaviors
that are shown only, or to the highest degree, by individuals of
highest competitive ability are likely to be those that have the
greatest fitness consequences.
Predator preferences and hunting behavior—the difficult shortcut
Studying the hunting behavior of predators, especially vertebrate predators hunting vertebrates, is usually considered difficult, and this is often used as a justification for drawing
indirect conclusions about fitness consequences from behavioral observations of prey animals. However, although counterintuitive, studying the predators instead of the prey could
actually be a more efficient way to understand the fitness
consequences of prey behavior than studying the behavior
of the prey themselves, especially if the goal is to understand
prey vulnerability. As mentioned above, prey vulnerability may
well be the key variable in determining predation risk because
predators that are foraging optimally should be selective and
hunt to gain the maximum energy return for energy invested
during foraging (i.e., the predator should select those individuals that are the easiest to catch within a prey species). Consequently, if we understand the characteristics of the prey
a predator prefers to attack, or the proximate mechanism
behind the predator’s choice (Peckarsky and Penton, 1989),
Lind and Cresswell • Antipredation behavior and fitness
we can then draw reasonable conclusions about what makes
prey vulnerable to predation and hence the benefits accrued
from antipredation behaviors. For example, determining
whether sparrowhawks attack the least vigilant of two mounts
that vary in the state of alert (they do not—see Cresswell et al.,
2003) or the smaller of two flocks (they do—see Cresswell and
Quinn, 2004) suggests that choice of flock is more important
than relative vigilance rate within a flock in determining
attack probability. In addition, contradictory results regarding
group size benefits in shoaling fish will be better understood
when predator hunting behavior is accounted for (Turesson
and Brönmark, 2004). Pioneering work has also been done on
the hunting preferences of bats to determine the vulnerability
of calling frogs (e.g., Tuttle and Ryan, 1982), and a recent
playback study provides empirical evidence of increased predation risk associated with contact calling in crested tits Parus
cristatus (Krams, 2001). However, one should bear in mind
that conclusions drawn from indirect measures based on
either the predator or prey alone should be accepted only
with caution (Blomberg and Shine, 2000). There is of course
an additional level of complexity that needs to be mentioned,
which is added by the fact that prey species are generally
hunted by more than one predator species. The emergent
effects of multiple predators can result either in risk reduction
or risk enhancement for the prey, but not enough is yet
known to draw general conclusions about when either of
these two effects are expected (Sih et al., 1998).
It is interesting to note that because it is necessary to show
that the predator is responding to the prey in order to establish that pursuit deterrence is actually occurring, studies on
pursuit-deterrent systems have resulted in collection of clear
data on the fitness consequences of antipredation behaviors.
This contrasts with the study of most other antipredation behaviors where the focus of study is the prey only rather than
the response of the predator to changes in prey behavior. This
insight leads us to an important conclusion for the future:
if antipredation behaviors can be shown to affect predator
behavior, then they are likely to have important fitness
consequences, even if variation in death rates cannot be measured. Finally, by studying the predators, the former ‘‘fixedrisk’’ paradigm in the study of predator-prey interactions
will be abandoned, which will tell us much about how predators themselves respond to prey. To acknowledge that predators are themselves optimal foragers that move, wait, and
search for prey can lead to unexpected results concerning
how effective prey decision making is in reducing predation
(Lima, 2002).
CONCLUSIONS
We believe that there is little point in continuing the study of
antipredation behavior by measuring the variation in an antipredation behavior or by correlating the occurrence of one
antipredation behavior with another in the absence of attack
and capture rate data. We think that it is possible, however, to
draw firm conclusions regarding how behaviors or potential
strategies contribute to individual fitness. It can be done by
measuring how animals may compensate for potentially risky
behaviors and bodily states, then measuring to what degree
the compensations are available to the individuals in the
population, and finally by determining whether the compensations are independent. This could also be done by measuring variation in antipredation behavior with respect to an
index of fitness such as competitive ability. Finally, another
alternative is to study which prey predators choose or how
predator behavior is affected by antipredation behavior. These
approaches can also be more fruitful because it has become
well established that individuals differ consistently in how they
953
cope with a variety of circumstances (reviewed in Koolhaas
et al., 1999). Hence, we think that the knowledge of how
antipredation behavior arises and how risk is managed within
and between individuals would benefit from fully incorporating the potential for compensation into the study of antipredation behavior. When we consider multiple antipredation
behaviors, we can learn more about how and why individuals
differ in their risk management, whether there are consistent
individual strategies, and how personality traits may affect how
individuals manage their risks.
To apply our ideas simply, future empirical studies could
concentrate on measuring antipredation behaviors in to the
context of individual energy budgets. This approach ensures
that the full range of antipredation behaviors is considered
and the relative importance of each in terms of cost can be
assessed. A vital component of energy budgets, and in perhaps
many cases a reasonable index for energy budget as well, are
time budgets. These are straightforward to measure in natural
systems. Therefore, we believe time budgets may be the easiest
way to measure the full range of antipredation behaviors and
the potential for compensation and so discount future reproductive trade-offs. Time and/or energy budgets are probably
essential to study the fitness consequences of antipredation
behavior in any case. This is because the costs of an antipredation behavior can only be assessed by determining how
much energy or time is being allocated away from feeding
and/or reproductive investment. And the costs of an antipredation behavior must be known if we are to understand the
constraints operating on its expression, its occurrence in the
population, and indeed whether an alternative antipredation
behavior is more likely. So instead of measuring the costs of
not showing an antipredation behavior (i.e., death rates), we
can more easily measure the costs of showing the behavior
(i.e., time budgets). Where costs of an antipredation behavior
are high, and not compensated for by other behaviors, then
it would seem reasonable to conclude that the antipredation
behavior affects predation risk and fitness.
Although the best insights into the constraints and adaptations to predation risk are gained from elegant, often
laboratory-based, experimental work, perhaps the only way
to understand the fitness consequences of antipredation behavior is through time budgets in natural systems. The last
generation of natural historians had the right approach to
understand predation, and we need to get back into painstaking study of individuals in natural systems. This does not mean
that we should not experiment; rather it means that we should
conduct experiments within natural systems that are well understood from observation, where the animals have the opportunity to use their full repertoire of behaviors to manage
predation risk. Such experiments will inevitably show a behavior accounting for a lower proportion of variance, because all
other things are not held equal, but will allow that behavior’s
proportional contribution to overall fitness to be assessed
properly. Also, such experiments will likely show no consequence of variation in a behavior much more often than in
laboratory experiments, but negative results in the context of
a natural system tell us much about the ecological or other
constraints acting on the system, and therefore how natural
selection and evolution operates. The evolution of antipredation behavior can only really be understood by considering
the whole system at the same time: interactions between behaviorally complex predators hunting behaviorally complex
prey in a heterogeneous environment cannot be meaningfully
studied out of context of the whole system.
We thank Joel Brown for constructive criticism and Anne Magurran
for giving comments on an early draft of the manuscript. We would
also like to thank two anonymous reviewers who helped us improve
954
the manuscript. W.C. is a Royal Society University Research Fellow
and J.L. was supported by the Swedish Research Council.
REFERENCES
Anholt BR, Werner EE, 1995. Interaction between food availability
and predation mortality mediated by adaptive behavior. Ecology
76:2230–2234.
Anholt BR, Werner EE, 1998. Predictable changes in predation mortality as a consequence of changes in food availability and predation
risk. Evol Ecol 12:729–738.
Arenz CL, Leger DW, 2000. Antipredator vigilance of juvenile and
adult thirteen-lined ground squirrels and the role of nutritional
need. Anim Behav 59:535–541.
Bachman GC, 1993. The effect of body condition on the trade-off
between vigilance and foraging in Belding ground-squirrels. Anim
Behav 46:233–244.
Ball SL, Baker RL, 1995. The non-lethal effects of predators and the
influence of food availability on life history of adult Chironomus
tentans (Diptera: Chironomidae). Freshw Biol 34:1–12.
Ball SL, Baker RL, 1996. Predator-induced life history changes: antipredator behavior costs of facultative life history shifts? Ecology
77:1116–1124.
Bednekoff PA, Lima SL, 1998. Randomness, chaos and confusion in
the study of antipredator vigilance. Trends Ecol Evol 13:284–287.
Billerbeck JM, Lankford TE Jr, Conover DO, 2001. Evolution of
intrinsic growth and energy acquisition rates. I. Trade-offs with
swimming performance in Menidia menidia. Evolution 55:
1863–1872.
Biro PA, Abrahams MV, Post JR, Parkinson EA, 2004. Predators select
against high growth rates and risk-taking behaviour in domestic
trout populations. Proc R Soc Lond B 271:2233–2237.
Biro PA, Post JR, Parkinson EA, 2003a. Density-dependent mortality is
mediated by foraging activity for prey in whole-lake experiments.
J Anim Ecol 72:546–555.
Biro PA, Post JR, Parkinson EA, 2003b. From individuals to populations: prey fish risk-taking mediates mortality in whole system experiments. Ecology 84:2419–2431.
Biro PA, Post JR, Parkinson EA, 2003c. Population consequences of
a predator-induced habitat shift by trout in whole-lake experiments.
Ecology 84:691–700.
Blomberg SP, Shine R, 2000. Size-based predation by kookaburras
(Dacelo novaeguineae) on lizards (Eulamprus tympanum: Scincidae):
what determines prey vulnerability? Behav Ecol Sociobiol 48:
484–489.
Brown JS, 1988. Patch use as an indicator of habitat preference,
predation risk, and competition. Behav Ecol Sociobiol 22:37–47.
Brown JS, 1999. Vigilance, patch use and habitat selection: foraging
under predation risk. Evol Ecol Res 1:49–71.
Brown JS, Kotler BP, 2004. Hazardous duty pay and the foraging cost
of predation. Ecol Lett 7:999–1014.
Burger J, Gochfeld M, 2001. Smooth-billed ani (Crotophaga ani) predation on butterflies in Mato Grosso, Brazil: risk decreases with
increased group size. Behav Ecol Sociobiol 49:482–492.
Byrkjedal I, 1987. Antipredator behavior and breeding success in
greater golden-plover and Eurasian dotterel. Condor 89:40–47.
Chivers DP, Smith RJF, 1998. Chemical alarm signalling in aquatic
predator-prey systems: a review and prospectus. Ecoscience 5:
338–352.
Clobert J, Oppliger A, Sorci G, Ernande B, Swallow JG, Garland T Jr,
2000. Trade-offs in phenotypic traits: endurance at birth, growth,
survival, predation and susceptibility to parasitism in a lizard, Lacerta
vivipara. Funct Ecol 14:675–684.
Cooper WE Jr, Vitt LJ, Hedges R, Huey RB, 1990. Locomotor impairment and defense in gravid lizards (Eumeces laticeps): behavioral
shift in activity may offset costs of reproduction in an active forager.
Behav Ecol Sociobiol 27:153–157.
Cotton PA, Rundle SH, Smith KE, 2004. Trait compensation in marine
gastropods: shell shape, avoidance behaviour, and susceptibility to
predation. Ecology 85:1581–1584.
Creel S, Winnie JJA, 2005. Responses of elk herd size to fine-scale
spatial and temporal variation in the risk of predation by wolves.
Anim Behav 69:1181–1189.
Cresswell W, 1993. Escape responses by redshanks, Tringa totanus, on
attack by avian predators. Anim Behav 46:609–611.
Behavioral Ecology
Cresswell W, 1994a. Age-dependent choice of redshank (Tringa totanus) feeding location: profitability or risk? J Anim Ecol 63:589–600.
Cresswell W, 1994b. Flocking is an effective anti-predation strategy in
redshanks, Tringa totanus. Anim Behav 47:433–442.
Cresswell W, 1994c. Song as a pursuit-deterrent signal, and its occurrence relative to other anti-predation behaviours of skylark (Alauda
arvensis) on attack by merlins (Falco columbarius). Behav Ecol Sociobiol 34:217–223.
Cresswell W, 1998. Diurnal and seasonal mass variation in blackbirds
Turdus merula: consequences for mass-dependent predation risk.
J Anim Ecol 67:78–90.
Cresswell W, 1999. Travel distance and mass gain in wintering blackbirds. Anim Behav 58:1109–1116.
Cresswell W, 2003. Testing the mass-dependent predation hypothesis:
in European blackbirds poor foragers have higher overwinter body
reserves. Anim Behav 65:1035–1044.
Cresswell W, Lind J, Kaby U, Quinn JL, Jakobsson S, 2003. Do opportunistic predators preferentially attack non-vigilant prey? Anim
Behav 66:643–648.
Cresswell W, Quinn JL, 2004. Faced with a choice, sparrowhawks more
often attack the more vulnerable prey group. Oikos 104:71–76.
Cresswell W, Whitfield DP, 1994. The effects of raptor predation on
wintering wader populations at the Tyninghame estuary, southeast
Scotland. Ibis 136:223–232.
Downes SJ, 2002. Does responsiveness to predator scent affect lizard
survivorship? Behav Ecol Sociobiol 52:38–42.
Durant SM, 2000. Living with the enemy: avoidance of hyenas and
lions by cheetahs in the Serengeti. Behav Ecol 11:624–632.
Edmunds M, 1974. Defence in animals: a survey of anti-predator
defences. Harlow: Longman.
Elgar MA, 1989. Predator vigilance and group size in mammals and
birds: a critical review of the empirical evidence. Biol Rev 64:13–33.
Elgar MA, McKay H, Woon P, 1986. Scanning, pecking and alarm
flights in house sparrows. Anim Behav 34:1892–1894.
Endler JA, 1991. Interactions between predators and prey. In: Behavioural ecology: an evolutionary approach (Krebs JR, Davies NB,
eds). Oxford: Blackwell; 169–196.
Fitzgibbon CD, 1989. A cost to individuals with reduced vigilance in
groups of Thomson’s gazelles hunted by cheetahs. Anim Behav
37:508–510.
Fitzgibbon CD, 1994. The costs and benefits of predator inspection
behavior in Thomson gazelles. Behav Ecol Sociobiol 34:139–148.
Fitzgibbon CD, Fanshawe JH, 1988. Stotting in Thomson’s gazelles: an
honest signal of condition. Behav Ecol Sociobiol 23:69–74.
Foster WA, Treherne JE, 1981. Evidence for the dilution effect in the
selfish herd from a fish predation on a marine insect. Nature
293:466–467.
Fuelling O, Halle S, 2004. Breeding suppression in free-ranging grey
sided voles under the influence of predator odour. Oecologia
138:151–159.
Godfrey JD, Bryant DM, 2000. State-dependent behaviour and energy
expenditure: an experimental study of European robins on winter
territories. J Anim Ecol 69:301–313.
Hass CC, Valenzuela D, 2002. Anti-predator benefits of group living in
white-nosed coatis (Nasua narica). Behav Ecol Sociobiol 51:570–578.
Hasson O, 1991. Pursuit-deterrent signals: communication between
prey and predator. Trends Ecol Evol 6:325–329.
Hensor EMA, Godin JJ, Hoare DJ, Krause J, 2003. Effects of nutritional state on the shoaling tendency of banded killifish, Fundulus
diaphanus, in the field. Anim Behav 65:663–669.
Hinsley SA, Bellamy PE, Moss D, 1995. Sparrowhawk Accipiter nisus
predation and feeding site selection by tits. Ibis 137:418–420.
Houston AI, Clark CW, McNamara JM, Mangel M, 1988. Dynamic
models in behavioural and evolutionary ecology. Nature 332:29–34.
Houston AI, McNamara JM, 1993. A theoretical investigation of the fat
reserves and mortality levels of small birds in winter. Ornis Scand
24:205–219.
Huntingford FA, 1984. Some ethical issues raised by studies of predation and aggression. Anim Behav 32:210–215.
Irving PW, Magurran AE, 1997. Context-dependent fright reactions in
captive European minnows: the importance of naturalness in laboratory experiment. Anim Behav 53:1193–1201.
Johansson F, Rowe L, 1999. Life history and behavioral responses to
time constraints in a damselfly. Ecology 80:1242–1252.
Lind and Cresswell • Antipredation behavior and fitness
Johansson F, Stoks R, Rowe L, De Block M, 2001. Life history plasticity
in a damselfly: effects of combined time and biotic constraints.
Ecology 82:1857–1869.
Kaby U, Lind J, 2003. What limits predator detection in blue tits
(Parus caeruleus): posture, task or orientation? Behav Ecol Sociobiol
54:534–538.
Kats LB, Dill LM, 1998. The scent of death: chemosensory assessment
of predation risk by prey animals. Ecoscience 5:361–394.
Kenward RE, 1978. Hawks and doves: factors affecting success and
selection in goshawk attacks on woodpigeons. J Anim Ecol
47:449–460.
Koolhaas JM, Korte SM, De Boer SF, Van Der Vegt BJ, Van Reenen CG,
Hopster H, De Jong IC, Ruis MAW, Blokhuis HJ, 1999. Coping styles
in animals: current status in behavior and stress-physiology. Neurosci Biobehav Rev 23:925–935.
Kotler BP, Brown JS, Bouskila A, 2004. Apprehension and time allocation in gerbils: the effects of predatory risk and energetic state.
Ecology 85:917–922.
Krams I, 2001. Communication in crested tits and the risk of predation. Anim Behav 61:1065–1068.
Kullberg C, Fransson T, Jakobsson S, 1996. Impaired predator evasion in fat blackcaps (Sylvia atricapilla). Proc R Soc Lond B 263:
1671–1675.
Lampert W, 1993. Ultimate causes of diel vertical migration of zooplankton: new evidence for the predator-avoidance hypothesis.
Arch Hydrobiol Beih Ergebn Limnol 39:79–88.
Lank DB, Ydenberg RC, 2003. Death and danger at migratory stopovers: problems with ‘‘predation risk’’. J Avian Biol 34:225–228.
Lankford TE Jr, Billerbeck JM, Conover DO, 2001. Evolution of
intrinsic growth and energy acquisition rates. II Trade-offs with
vulnerability to predation in Menidia menidia. Evolution 55:
1873–1881.
Laundré JW, Hernández L, Altendorf KB, 2001. Wolves, elk, and
bison: reestablishing the ‘‘landscape of fear’’ in Yellowstone National
Park, USA. Can J Zool 79:1401–1409.
Le Galliard J-F, Clobert J, Ferrière R, 2004. Physical performance and
Darwinian fitness in lizards. Nature 432:502–505.
Lee SJ, Witter MS, Cuthill IC, Goldsmith AR, 1996. Reduction in
escape performance as a cost of reproduction in gravid starlings,
Sturnus vulgaris. Proc R Soc Lond B 263:619–624.
Lima SL, 1987. Vigilance while feeding and its relation to the risk of
predation. J Theor Biol 124:303–316.
Lima SL, 1998. Stress and decision making under the risk of predation: recent developments from behavioral, reproductive, and ecological perspectives. Adv Study Behav 27:215–290.
Lima SL, 2002. Putting predators back into behavioral predator-prey
interactions. Trends Ecol Evol 17:70–75.
Lima SL, Bednekoff PA, 1999. Back to the basics of antipredatory
vigilance: can nonvigilant animals detect attack? Anim Behav
58:537–543.
Lima SL, Dill LM, 1990. Behavioral decisions made under the risk of
predation: a review and prospectus. Can J Zool 68:619–640.
Lind J, 2004. What determines probability of surviving predator
attacks in bird migration? The relative importance of vigilance and
fuel load. J Theor Biol 231:223–227.
Lind J, Fransson T, Jakobsson S, Kullberg C, 1999. Reduced take-off
ability in robins (Erithacus rubecula) due to migratory fuel load.
Behav Ecol Sociobiol 46:65–70.
Lind J, Jakobsson S, Kullberg C, in press. Impaired predator evasion in
the life-history of birds; behavioral and physiological adaptations to
reduced flight ability. Curr Ornithol.
Lindström Å, 1989. Finch flock size and risk of hawk predation at
a migratory stopover site. Auk 106:225–232.
Lindström Å, 1990. The role of predation risk in stopover habitat
selection in migrating bramblings, Fringilla montifringilla. Behav
Ecol 1:102–105.
Lingle S, 2002. Coyote predation and habitat segregation of whitetailed deer and mule deer. Ecology 83:2037–2048.
Lingle S, Pellis SM, 2002. Fight or flight? Antipredator behavior
and the escalation of coyote encounters with deer. Oecologia 131:
154–164.
Magnhagen C, 1991. Predation risk as a cost of reproduction. Trends
Ecol Evol 6:183–186.
955
Main KL, 1987. Predator avoidance in seagrass meadows: prey behavior, microhabitat selection and cryptic coloration. Ecology 68:
170–180.
Mangel M, Clark CW, 1988. Dynamic modelling in behavioural ecology. Princeton: Princeton University Press.
McLachlan A, Ladle R, Crompton B, 2003. Predator-prey interactions
on the wing: aerobatics and body size among dance flies and
midges. Anim Behav 66:911–915.
McNamara JM, Houston AI, 1986. The common currency for behavioural decisions. Am Nat 127:358–378.
McNamara JM, Houston AI, 1994. The effect of a change in foraging
options on intake rate and predation rate. Am Nat 144:978–1000.
Mirza RS, Chivers DP, 2000. Predator-recognition training enhances
survival of brook trout: evidence from laboratory and field-enclosure
studies. Can J Zool 78:2198–2208.
Mirza RS, Chivers DP, 2001. Chemical alarm signals enhance survival
of brook charr (Salvelinus fontinalis) during encounters with predatory chain pickerel (Esox niger). Ethology 107:989–1005.
Montgomerie RD, Weatherhead PJ, 1988. Risks and rewards of nest
defence by parent birds. Q Rev Biol 63:167–187.
Newton I, 1998. Population limitations in birds. New York: Academic
Press.
Olsson O, Brown JS, Smith HG, 2002. Long- and short-term statedependent foraging under predation risk: an indication of habitat
quality. Anim Behav 63:981–989.
Peckarsky BL, Cowan CA, Penton MA, Anderson C, 1993. Sublethal
consequences of stream-dwelling predatory stoneflies on mayfly
growth and fecundity. Ecology 74:1836–1846.
Peckarsky BL, McIntosh AR, 1998. Fitness and community consequences of avoiding multiple predators. Oecologia 113:565–576.
Peckarsky BL, Penton MA, 1989. Mechanisms of prey selection by
stream-dwelling stoneflies. Ecology 70:1203–1218.
Pérez-Tris J, Dı́az JA, Tellerı́a JL, 2004. The loss of body mass under
risk of predation: cost of anti-predatory behaviour or adaptive
fit-for-escape? Anim Behav 67:511–521.
Persons MH, Walker SE, Rypstra AL, 2002. Fitness costs and benefits
of antipredator behavior mediated by chemotactile cues in the
wolf spider Pardosa milvina (Araneae: Lycosidae). Behav Ecol 13:
386–392.
Persons MH, Walker SE, Rypstra AL, Marshall SD, 2001. Wolf spider
predator avoidance tactics and survival in the presence of dietassociated predator cues (Araneae: Lycosidae). Anim Behav 61:
43–51.
Pravosudov VV, Grubb TC, 1997. Energy management in passerine
birds during the non-breeding season: a review. Curr Ornithol
14:189–234.
Price MV, Correll RA, 2001. Depletion of seed patches by Merriam’s
kangaroo rats: are GUD assumptions met? Ecol Lett 4:334–343.
Quinn JL, Cresswell W, 2004. Predator hunting behaviour and prey
vulnerability. J Anim Ecol 73:143–154.
Rettie WJ, Messier F, 2001. Range use and movement rates of woodland caribou in Saskatchewan. Can J Zool 79:1933–1940.
Rogers CM, 1987. Predation risk and fasting capacity: do wintering
birds maintain optimal body mass? Ecology 68:1051–1061.
Roitberg BD, Mondor EB, Tyerman JGA, 2003. Pouncing spider, flying
mosquito: blood acquisition increases predation risk in mosquitoes.
Behav Ecol 14:736–740.
Rowe L, Ludwig D, 1991. Size and timing of metamorphosis in
complex life cycles: time constraints and variation. Ecology 72:
413–427.
Rundle HD, Brönmark C, 2001. Inter- and intraspecific trait compensation of defence mechanisms in freshwater snails. Proc R Soc Lond
B 268:1463–1468.
Schwarzkopf L, Shine R, 1992. Costs of reproduction in lizards: escape
tactics and susceptibility to predation. Behav Ecol Sociobiol 31:17–25.
Scrimgeour GJ, Culp JM, 1994. Feeding while evading predators by
a lotic mayfly: linking short-term foraging behaviours to long-term
fitness consequences. Oecologia 100:128–134.
Sih A, 1986. Antipredator responses and the perception of danger by
mosquito larvae. Ecology 67:434–441.
Sih A, Englund G, Wooster DE, 1998. Emergent impacts of multiple
predators on prey. Trends Ecol Evol 13:350–355.
Sih A, Kats LB, Maurer EF, 2003. Behavioural correlations across situations and the evolution of antipredator behaviour in a sunfishsalamander system. Anim Behav 65:29–44.
956
Sinclair ARE, Arcese P, 1995. Population consequences of predationsensitive foraging: the Serengeti wildebeest. Ecology 76:882–891.
Skelly DK, 1994. Activity level and the susceptibility of anuran larvae to
predation. Anim Behav 47:465–468.
Smith RJF, 1992. Alarm signals in fishes. Rev Fish Biol Fish 2:33–63.
Stephens DW, Krebs JR, 1986. Foraging theory. Princeton: Princeton
University Press.
Sutherland WJ, 1996. From individual behaviour to population ecology. Oxford: Oxford University Press.
Sutherland WJ, Parker GA, 1985. Distribution of unequal competitors.
In: Behavioural ecology (Sibly RM, Smith RH, eds). Oxford: Blackwell Scientific Publications; 255–273.
Tellerı́a JL, Virgós E, Carbonell R, Pérez-Tris J, Santos T, 2001. Behavioural responses to changing landscapes: flock structure and antipredator strategies of tits wintering in fragmented forests. Oikos
95:253–264.
Temple SA, 1987. Do predators always capture substandard individuals
disproportionately from prey populations? Ecology 68:669–674.
Turesson H, Brönmark C, 2004. Foraging behaviour and capture success in perch, pikeperch and pike and the effects of prey density.
J Fish Biol 65:363–375.
Tuttle MD, Ryan MJ, 1982. The role of synchronized calling, ambient
light, and ambient noise, in anti-bat-predator behavior of a treefrog.
Behav Ecol Sociobiol 11:125–131.
Uetz GW, Boyle J, Hieber CS, Wilcox RS, 2002. Antipredator benefits
of group living in colonial web-building spiders: the ‘early warning’
effect. Anim Behav 63:445–452.
Vine I, 1971. Risk of visual detection and pursuit by a predator and the
selective advantage of flocking behaviour. J Theor Biol 30:405–422.
Wahungu GM, Catterall CP, Olsen MF, 2001. Predator avoidance,
feeding and habitat use in the red-necked pademelon, Thylogale
thetis, at rainforest edges. Aust J Zool 49:45–48.
Warkentin KM, 1995. Adaptive plasticity in hatching age: a response to predation risk trade-off. Proc Natl Acad Sci USA 92:
3507–3510.
Behavioral Ecology
Watanuki Y, 1986. Moonlight avoidance behavior in Leach’s stormpetrels as a defense against slaty-backed gulls. Auk 103:14–22.
Werner EE, Anholt BR, 1993. Ecological consequences of the trade-off
between growth and mortality rates mediated by foraging activity.
Am Nat 142:242–272.
Whitfield DP, 1988. Sparrowhawks Accipiter nisus affect the spacing
behaviour of wintering turnstone Arenaria interpres and redshank
Tringa totanus. Ibis 130:284–287.
Whitfield DP, 2003. Redshank Tringa totanus flocking behaviour, distance from cover and vulnerability to sparrowhawk Accipiter nisus
predation. J Avian Biol 34:163–169.
Williams CK, Lutz RS, Applegate RD, 2003. Optimal group size and
northern bobwhite coveys. Anim Behav 66:377–387.
Wisenden BD, Cline A, Sparkes TC, 1999. Survival benefit to antipredator behavior in the amphipod Gammarus minus (Crustacea: Amphipoda) in response to injury-released chemical cues from conspecifics
and heterospecifics. Ethology 105:407–414.
Witter MS, Cuthill IC, 1993. The ecological costs of avian fat storage.
Phil Trans R Soc Lond B 340:73–92.
Witter MS, Cuthill IC, Bonser RHC, 1994. Experimental investigations
of mass-dependent predation risk in the European starling, Sturnus
vulgaris. Anim Behav 48:201–222.
Wolff JO, Davis-Born R, 1997. Response of gray-tailed voles to odours
of a mustelid predator: a field test. Oikos 79:543–548.
Wolff JO, Van Horne T, 2003. Vigilance and foraging patterns of
American elk during the rut in habitats with and without predators.
Can J Zool 81:266–271.
Yasué M, Quinn JL, Cresswell W, 2003. Multiple effects of weather on
the starvation and predation risk trade-off in choice of feeding
location in redshanks. Funct Ecol 17:727–736.
Ydenberg RC, Butler RW, Lank DB, Guglielmo CG, Lemon M, Wolf N,
2002. Trade-offs, condition dependence and stopover site selection
by migrating sandpipers. J Avian Biol 33:47–55.
Ydenberg RC, Dill LM, 1986. The economics of fleeing from predators. Adv Study Behav 16:229–249.