Oikos 000: 001–012, 2012
doi: 10.1111/j.1600-0706.2012.20339.x
© 2012 The Authors. Oikos © 2012 Nordic Society Oikos
Subject Editor: Matthew Symonds. Accepted 20 January 2012
Adaptive strategies for managing uncertainty may explain
personality-related differences in behavioural plasticity
Kimberley J. Mathot, J. Wright, B. Kempenaers and N. J. Dingemanse
K. J. Mathot ([email protected]) and N. J. Dingemanse, Evolutionary Ecology of Variation Group, Max Planck Institute for Ornithology,
Eberhard Gwinner Strabe 7, DE-82319 Seewiesen, Germany. Njd also at: Behavioural Ecology, Dept Biology II, Ludwig-Maximilians Univ.
of Munich, Grobhadener str. 2, DE-82152 Planegg-Martinsried, Munich, Germany. – J. Wright, Dept of Biology, Norwegian Univ. of
Science and Technology (NTNU), NO-7491 Trondheim, Norway. – B. Kempenaers, Dept of Behavioural Ecology and Evolutionary Genetics,
Max Planck Institute for Ornithology, Eberhard Gwinner Strabe 7, DE-82319 Seewiesen, Germany.
There is growing evidence that individuals within populations show consistent differences in their behaviour across
contexts (personality), and that personality is associated with the extent to which individuals adjust their behaviour as
function of changing conditions (behavioural plasticity). We propose an evolutionary explanation for a link between
personality and plasticity based upon how individuals manage uncertainty. Individuals can employ three categories of
tactics to manage uncertainty. They can 1) gather information (sample) to reduce uncertainty, 2) show strategic (statedependent) preferences for options that differ in their associated variances in rewards (i.e. variance-sensitivity), or
3) invest in insurance to mitigate the consequences of uncertainty. We explicitly outline how individual differences in
the use of any of these tactics can generate personality-related differences in behavioural plasticity. For example, sampling
effort is likely to co-vary with individual activity and exploration behaviours, while simultaneously creating population
variation in reactions to changes in environmental conditions. Individual differences in the use of insurance may be
associated with differences in risk-taking behaviours, such as boldness in the face of predation, thereby influencing the
degree of adaptive plasticity across individuals. Population variation in responsiveness to environmental changes may also
reflect individual differences in variance-sensitivity, because stochastic change in the environment increases variances in
rewards, which may both attract and benefit variance-prone individuals, but not variance-averse individuals. We review
the existing evidence that individual variation in strategies for managing uncertainty exist, and describe how positivefeedbacks between sampling, variance-sensitivity and insurance can maintain and exaggerate even small initial differences
between individuals in the relative use of these tactics. Given the pervasiveness of the problem of uncertainty, alternative
strategies for managing uncertainty may provide a powerful explanation for consistent differences in behaviour and
behavioural plasticity for a wide range of traits.
Behavioural variation has traditionally been studied at the
level of the population, but more recent work has highlighted the fact that within populations there is often marked
repeatable variation between individuals (Réale et al. 2007,
Bell et al. 2009). These consistent individual differences in
behaviour across contexts have become known as ‘animal
personality’ (Sih 2004, Dingemanse et al. 2010) and have
been reported in species ranging from insects to primates.
Understanding how different personality types arise and are
maintained has been an area of major interest in recent years
(Dall et al. 2004, Dingemanse and Wolf 2010, Wolf and
Weissing 2010) and explicit consideration of animal personality has led to new insights concerning the evolution of
behaviour (Wolf and Weissing 2010). Here we argue that
animal personality has an underlying ecological cause, environmental uncertainty, which may have important implications for personality-related differences in behavioural
plasticity.
Although personality is defined as consistent differences
in behaviour across contexts, this does not imply an absence
of behavioural plasticity at the level of the individual
(Dingemanse et al. 2010). Under changing environmental
conditions, individuals can adjust their behaviour while
remaining consistently different from each other. However, the fact that there are consistent differences between
individuals does suggest that plasticity is limited, with any
given individual showing only a subset of the behavioural
diversity present in the whole population (Sih 2004). Given
the potential selective advantage to individuals of being
able to adjust their behaviour to current conditions, it is a
challenge to understand why plasticity should be limited
at all (Wolf et al. 2008). Commonly invoked explanations
include the idea that plasticity, or the ability to be plastic, is costly, and that there are limits to the benefits that
individuals can derive from being plastic (DeWitt et al.
1998). There is also evidence that individuals not only show
1
limited behavioural plasticity, but that the level of plasticity
differs between individuals from the same population
(Dingemanse et al. 2010). Theoretical work has shown that
consistent individual variation in behavioural plasticity can
evolve and be maintained in populations via frequencydependence (Wolf et al. 2008, Dubois et al. 2010) and
via positive feedbacks that reduce the cost of plasticity for
individuals that have previously exhibited plasticity (Wolf
et al. 2008).
Personality-related differences in behavioural plasticity
can take at least two forms. The average level of a given
behaviour may be correlated with the degree of plasticity for the same behaviour (i.e. elevation–slope covariance,
Fig. 1a). For example, aggressive mice do not adjust their
level of aggression as a function of social context, while less
aggressive mice do (Natarajan et al. 2009). Alternatively,
personality-related differences in behavioural plasticity can
arise when consistent individual differences in one behaviour
correlate with the level of plasticity in another behaviour
(Fig. 1b). For example, great tits Parus major scored ‘slow’
(as opposed to ‘fast’) for exploration adjust more rapidly
to changes in the spatial location of food (Verbeek et al.
1994). There is a growing body of empirical evidence for
both types of personality-related differences in behavioural
What is uncertainty, and how can animals
respond to it?
Behaviour A
(a)
Environmental gradient
Behaviour B
(b)
Environmental gradient
Figure 1. Personality-related differences in behavioural plasticity
illustrated using a behavioural reaction norm approach. In (a)
individuals differ in elevation (personality) and slope (plasticity),
with elevation–slope covariance for the same behaviour. In (b) individuals differ in elevation (personality) for behaviour A, as
indicated by the type of line (solid  fast explorer, dashed  slow
explorer). The lines indicate individual behavioural plasticity for
behaviour B. Individual differences in behaviour A are correlated
with individual differences in slope (plasticity) in behaviour B.
2
plasticity (Table 1). Just as limited behavioural plasticity
and individual variation in behavioural plasticity require
explanation, so does the observation that plasticity differs
between types of individuals. Although a variety of explanations have been put forth (Table 1), a general framework for
understanding the underlying causes of personality-related
differences in behavioural plasticity is lacking.
Here, we propose that differences between individuals
in how they behave when faced with environmental uncertainty may provide an explanation for personality-related
differences in behavioural plasticity. We begin with a brief
overview of what we mean by uncertainty, and review
the alternative tactics available for managing uncertainty.
We then outline how individual variation in the use of any
of these tactics could generate both consistent differences
in behaviour (i.e. personality) and consistent differences in
behavioural plasticity, and review the existing evidence that
such individual differences in strategies for managing environmental uncertainty exist. We argue that more explicit
consideration of the mechanisms underlying the linkage
between personality and behavioural plasticity may provide
important insights into the evolutionary potential of both
personality and plasticity. Finally, we outline promising
avenues for future research.
Animals are constantly faced with decisions, such as
where, when and how long to forage, with whom to mate,
where to breed, etc. While classical optimization models
assume that animals are omniscient, in reality decisionmaking is fraught with uncertainty (Dall and Johnstone
2002). Uncertainty exists because animals cannot know
the true state of all alternative options at all times. There
are limitations to the amount of time and energy that animals can invest in gathering and storing information about
alternative options, information ‘sampling’ may be imperfect and involve perceptual errors, and the environment
determining the pay-offs of alternative options often varies
stochastically and may therefore change rapidly and unpredictably (Stephens and Krebs 1986). Uncertainty can
have profound consequences when it leads animals to
make the wrong fitness-relevant decisions, and animals
have therefore developed various solutions for coping with
uncertainty. Although the problem of uncertainty applies to
almost every decision an animal faces, we focus our review
on uncertainty in a foraging context, if only because most
previous work on the problem of uncertainty has been
conducted in this field. Additionally, while empirical data
on behavioural reactions norms (i.e. behavioural plasticity)
are more commonly available for behavioural traits that are
frequently studied within the animal personality paradigm
(i.e. aggression, activity, and exploration), we believe that
alternative strategies for managing uncertainty may underlie
individual variation in a wide array of behavioural traits.
Tactics for managing uncertainty can be broadly classified into three categories: sampling, variance-sensitivity,
and insurance. Sampling (i.e. gathering and storing) information concerning relevant features of the environment
allows individuals to reduce uncertainty (Stephens 1987,
3
yes
yes
yes
yes/no4
yes
yes3
yes
yes
Δ provisioning rate
Δ provisioning rate
Δ proportion of time
spent vigilant
Δ aggressiveness
Δ stress response
Δ aggressiveness
Δ aggressiveness
temperature
experience
experience
social context
stress
response5
aggressiveness
aggressiveness
proportion of
time spent
vigilant
aggressiveness
provisioning
rate
exploration
nestling age
partners
provisioning
rate
predation
danger
social
experience
time
Δ exploration
Personality-related
differences in
plasticity
yes3
no
no
yes
water
temperature
Measure(s) of
plasticity1
Δ boldness,
Δ activity,
Δ aggression
Δ boldness
boldness,
activity,
aggression
boldness
Environmental
gradient
(P) personality-related differences in brain neural
networks
none postulated
(P) personality-related differences in attention
to stimuli
(P) state-dependent constraints on trait expression
(P) allometric relationship between nestling size and
demand, (P) differences in the ability to detect
changes in offspring demand, or (P) individual
differences in maximum provisioning rates
(U) state-dependent benefits for anti-predator
behaviour
(U) personality-related differences in the benefit
of plasticity
none postulated
none postulated
Postulated explanation (U or P)2
2U  ultimate,
(delta) refers to change.
P  proximate.
3Elevation slope covariance was non-linear: extreme activity types showed low plasticity, intermediate types showed high plasticity.
4Elevation slope covariance found across one predation danger gradient, but not another. Negative elevation-slope covariance interpreted as a numerical constraint.
5Measured as a categorical variable: shy, timid, aggressive.
1Δ
Tree swallows
Tachycineta bicolor
Yellow-eyed penguins
Magadyptes antipodes
Mammals
Golden hamster
Mesocricetus auratus
House mouse
Mus musculus domesticus
Red knots
Calidris canutus
Rainbow trout
Oncorhynchus mykiss
Three-spined stickleback
Gasterosteus aculeatus
Birds
House sparrows
Passer domesticus
Fish
Damselfish
(Pomacentrus spp.)
Species
Behavioural
trait(s)
lab
lab
field
field
lab
field
lab
lab
lab
Setting
Natarajan et al. 2009
David et al. 2004
Ellenberg et al. 2009
Betini and Norris 2012
Mathot et al. 2011
Westneat et al. 2011
Dingemanse et al. 2012
Frost et al. 2007
Biro et al. 2010
Refs
Table 1a. Review of studies on personality-related differences in behavioural plasticity: individual variation in elevation and slope of a behavioural reaction norm for a given behaviour with elevation
slope covariance (illustrated in Fig. 1a).
4
Fish
Convict cichlid
Amatitlania nigrofasciata
starvation risk (time
of day) and
perceived
predation risk
experience
exploration
aggression
maze orientation or
presence of spatial
cues
instantaneous change
in food availability
exploration
boldness
instantaneous change
in location of food
exploration
predation danger
activity
social context
experience
boldness
exploration
experience
exploration4
experience
instantaneous change
in predation danger
Environmental
gradient
number of errors before
solving maze
yes3
yes3
yes
Δ proportion of time on
more profitable feeder
time to learn avoidance
of aposematic prey
yes3
yes
yes
yes3/no5
yes3
yes3
no
yes
yes
no
yes3
distance travelled to
locate new food
time to learn acoustic
discrimination task
ability to solve visual
discrimination task
type of response (freeze/
escape) and latency to
resume pre-stimulus
behaviours
time to locate ‘good’ food
patch
time to locate ‘good’ food
patch
Δ flight initiation distance
following simulated
predator intrusions
time to react to predator
Measure of plasticity1
Personality-related
differences in
plasticity
(P) HPA axis underlying variation in
both aggression and learning
none
(P) personality-related differences in
sampling behaviour and/or
sensitivity to stimuli
(P) personality-related differences
in value of past and current
information
(U) personality-related differences in
life history strategies
none postulated
lab
(P) HPA axis underlying variation in
both boldness and learning
(P) personality-related differences in
vulnerability to predation and/or
differences in detection ability
lab
lab
Benus et al. 1991
Exnerová et al. 2010
Quinn et al. 2012
van Overveld and Matthysen 2010
field
field
Verbeek et al. 1994
Marchetti and Drent 2000
Quinn and Cresswell 2005
Arnold et al. 2007
Guillette et al. 2009, 2011
Rodriguez-Prieto et al. 2011
Jones and Godin 2010
Refs
lab
lab
lab
lab
lab
lab
Setting
none postulated
(P) personality-related differences in
exposure to stimuli and attention
to stimuli
(U) personality-related differences in
food/safety tradeoff and/or
(P) differences in perceptual
constraints
Postulated explanation (U or P)2
2Type
(delta) refers to change.
of explanation (U)  ultimate, (P)  proximate.
3Evidence for personality-related differences in plasticity should be considered with caution because the measure of plasticity could be interpreted as a personality trait rather than plasticity.
4Note that the exploration score used differed from the conventional exploration score for small passerines, and repeatability of exploration score was not tested.
5Significant personality-related differences in type of response to gradients in predation danger (crouching vs escape flights), but not in latency to resume activity post-hawk encounter.
1Δ
Mammals
House mouse
Mus musculus domesticus
Great tit
Parus major
Birds
Black-capped chickadee
Poecile atricapilius
Blue tit
Cyanistes caeuleus
Chaffinch
Fingilla coelebs
boldness
exploration
sociability
neophobia
exploration
Species
Reptiles
Iberian wall lizard
Podarcis hispanica
Behavioural
trait(s)
Table 1b. Reviews of studies on personality-related differences in behavioural plasticity: individual differences in elevation of the behavioural reaction norm (BRN) for behaviour A are correlated with
the BRN slope for behaviour B (illustrated in Fig. 1b).
Fitness
(a)
Benefit
Cost
Reward
(b)
Benefit
Fitness
Dall and Johnstone 2002). By sampling alternative options
regularly, an individual can track environmental change
and benefit from exploiting options that are the most
profitable and avoiding options that are not. While uncertainty can be reduced via sampling, it cannot be eliminated
entirely. Some degree of uncertainty always remains, because
of stochastic variation in the environment, and because of
limits to perception and storage of information (Stephens
and Krebs 1986, Stephens et al. 2007).
Individuals can respond adaptively to the uncertainty
generated by any unpredictable stochastic variation in
resources by strategically adjusting their preference or
aversion for variance (i.e. variance-sensitivity). This has previously been referred to as risk-sensitivity (risk-prone and
risk-averse), but the variance sensitivity terminology used
here is now considered to be more appropriate (Stephens
et al. 2007). Variance-sensitive decisions are expected
whenever animals have non-linear utility functions – that
is, whenever fitness does not increase linearly with increasing resource value (Stephens and Krebs 1986, McNamara
et al. 1991, Stephens et al. 2007). An individual that prefers the more variable option is said to be ‘variance-prone’,
while an individual preferring the less variable option is
‘variance-averse’. By choosing the variable option, an animal
has the chance of obtaining a reward that is greater than
the mean. However, the animal also has a chance of experiencing a reward that is less than the mean. An individual
with a concave utility function (Fig. 2a) should be varianceprone, because the fitness benefit of obtaining a reward
greater than the mean exceeds the fitness cost of a obtaining
a reward below the mean (Stephens 1981, Stephens and
Krebs 1986). Conversely, an individual with a convex utility function (Fig. 2b) should be variance-averse, because
the fitness cost of obtaining a reward smaller than the mean
outweighs the potential fitness benefit of obtaining a reward
above the mean (Stephens 1981, Stephens and Krebs 1986).
Another means of managing uncertainty is to act in a way
that minimizes its potential consequences, called ‘insurance’
(Dall and Johnstone 2002, Dall 2010). For example, by
maintaining fat stores, an individual can buffer itself against
the negative fitness consequences of unpredictably occurring
poor foraging periods (Dall 2010).
Several extrinsic factors have been identified that shape
the adaptive use of sampling behaviour (Stephens 1987),
variance-sensitivity (Stephens 1981, Bateson 2002) and
insurance (Houston and McNamara 1993). However, it is
important to note that flexibility in tactic use does not preclude the possibility that individuals differ consistently from
one another. For example, investment in sampling tends to
increase with increasing environmental stochasticity, but
individuals that have a relatively high use of sampling compared with congeners when the environment is stable may
also have a relatively high use of sampling when the environment is more stochastic. In this article, we focus on the implications of consistent differences in the use of these alternative
tactics in the face of uncertainty by individuals from the same
population. We outline how individual differences in the relative use of these alternative tactics could generate personalityrelated differences in behavioural plasticity, and review
the evidence that such individual differences in response
to uncertainty exist in nature within single populations.
Cost
Reward
Figure 2. Examples of non-linear utility functions. Dots with
horizontal error bars show how a given deviation from the mean
reward value differentially affects fitness when the deviation is
positive (benefit illustrated by the blue line) versus negative (cost
illustrated by the red line), depending on the shape of the utility
function. (a) Illustrates an individual with a concave utility
function, where for a given deviation from the mean reward
benefit  cost. The individual should therefore be variance prone.
(b) Illustrates an individual with a convex utility function, where for
a given deviation from the mean reward benefit  cost. The individual should therefore be variance averse.
Sampling behaviour and personality-related
differences in behavioural plasticity
Sampling is an investment of time and/or energy in gathering and storing information concerning relevant features
of the environment. Sampling allows individuals to track
changes in the environment and reduces uncertainty
(Stephens 1987, Dall and Johnstone 2002). Hence, indivi­
duals that invest relatively more (time, energy, etc.) in sampling or that are relatively better at sampling, for example
because they make fewer perceptual errors, will perceive changes in the state of alternative foraging options
sooner, and so might be able to adjust their behaviour
more rapidly following a change in the state of a relevant
environmental parameter. Depending on the spatial distribution of resources, and the conspicuousness of cues regarding resource quality, greater sampling may have particular
associations with activity and/or exploration behaviour.
Activity and exploration are two behavioural traits for which
individuals are known to differ consistently over a range of
environmental gradients, where activity is a measure of the
5
speed with which an individual moves through a familiar
environment, and exploration is a measure of the speed
with which an individual moves through a novel environment (Réale et al. 2007). Several studies have found
a positive correlation between activity and exploration,
even after controlling for the effect of activity on exploration scores (Sih 2004). Thus, such associations represent a
correlation between two distinct behaviours measured in
different behavioural assays. Furthermore, the strength of
the positive relationship between activity and exploration
can vary between populations according to the adaptive
context (Dingemanse et al. 2007).
When resources are widely dispersed and cues to their
presence are easily detected, individuals that have a higher
sampling rate may move through the environment more
quickly (i.e. high activity and/or fast explorers) and may also
be quicker to adjust their behaviour in response to changes
in the distribution of resources. Conversely, when resources
are spatially clumped and the cues for them are less
conspicuous, sampling will require an investment of time
(Dall and Johnstone 2002) and may also require that animals stop moving in order to process stimuli (Kramer
and McLaughlin 2001). In this case, individuals that sample more thoroughly may be those that move through the
environment more slowly than individuals that sample only
superficially (Verbeek et al. 1994, Wilson and Godin 2010).
In this latter case, we might predict that if individuals differ in their relative investment in sampling, individuals that
sample more thoroughly would have lower exploration/
activity scores, but exhibit greater plasticity. This predicted
relationship between exploration/activity and plasticity is
consistent with patterns previously observed in great tits
and convict cichlids Amatitlania nigrofasciata: individuals
with slow exploration scores are quicker to adjust to changes
in the distribution of known food patches (Verbeek et al.
1994) and the presence of predators in familiar environments (Jones and Godin 2010), respectively.
Although studies of sampling behaviour have traditionally asked whether the behaviour of the average individual
conforms to predictions from optimal sampling models, a
few studies have reported consistent individual differences
in sampling behaviour. In one study, great tits were able to
choose between two food patches of differing quality, and
individuals differed markedly in the number of sampling
events made before choosing a food patch (Krebs et al. 1978).
In a second study, the sampling behaviour of white king
pigeons (a breed of rock pigeon, Columba livia) was
recorded when faced with a food patch, the quality of which
fluctuated randomly between good and poor. Individuals differed not only in how frequently they sampled the
variable food patch, but also in how quickly they switched
to exploiting it as it became profitable (Shettleworth
et al. 1988). Additionally, individual differences in intermittent locomotion, characterized by movement interspersed
with pauses, have been documented in several studies
(McLaughlin and Grant 2001, Trouilloud et al. 2004,
Wilson and Godin 2010), and this may also reflect individual differences in sampling behaviour if pauses during
locomotion facilitate the perception of stimuli (Kramer and
McLaughlin 2001).
6
While these studies documented individual variation
in sampling behaviour in a solitary context, individual differences in sampling behaviour may be even more marked
in social contexts, due to frequency-dependent payoffs
for sampling, and the decision to invest in sampling or not
can be seen as a producer-scrounger game (Giraldeau and
Caraco 2000, Dall 2005). Individuals that invest in sampling can act as sources of social information for congeners
regarding the location of resources, without requiring congeners to sample the environment themselves (Giraldeau
and Caraco 2000, Dall 2005). As more individuals in a
group invest in sampling (producers), the payoff for sampling decreases, because individuals also have the option to
glean information from congeners via social information
(scroungers) (Giraldeau and Caraco 2000). Because the
payoffs to individual sampling are frequency-dependent,
individual differences in the relative investment in sampling
can be maintained within a social group or population. A
recent study in nutmeg mannikins Lonchura punctulata
explicitly tested whether individuals differ in their sampling
behaviour in two social contexts: a patch choice game
and a social foraging game (Morand-Ferron et al. 2011).
They found that within each context, individuals differed
consistently in their sampling frequency, but sampling
behaviour was not correlated across the two games. Other
studies have reported that individual differences in the propensity to acquire information personally (producer) versus
socially (scrounger) are linked to both individual variation
in behavioural (Marchetti and Drent 2000, Kurvers et al.
2010a, b) and physiological traits (basal metabolic rate:
Mathot et al. 2009).
Variance-sensitivity and personality-related
differences in behavioural plasticity
Variance-sensitivity is a measure of the extent to which
individuals use observed differences in stochastic variation
in the value of resources to increase fitness. Differences in
variance-sensitivity may generate consistent individual differences in behaviour, because the types of options that
will be exploited by variance-prone versus variance-averse
individuals will differ. It has previously been suggested that
personality-related differences in plasticity may be the result
of personality-related differences in sensitivity to environmental stimuli (Verbeek et al. 1994), and we suggest that
individual differences in variance-sensitivity may provide a
parsimonious explanation for both individual differences in
average behaviour (animal personality) and personalityrelated differences in sensitivity to stimuli. Imagine individuals feeding in a food patch that experiences a sudden
drop in patch quality. An individual that adjusts its behaviour immediately following the drop in quality, for example
by leaving to feed at an alternative food patch, would be
said to be more sensitive to environmental stimuli and to
have a higher behavioural plasticity compared with an
individual that requires multiple sampling events before
adjusting its behaviour. We hypothesize that differences in
the tendency to be variance-prone or variance-averse could
generate differences in how individuals respond to changing
stimuli from the environment. This is because as the state
True resource value
of the environment changes, animals can update their estimate of a particular environmental parameter in a variety of
ways. All past events may receive equal weight, more recent
events may be given greater weight, or animals may only
consider events within a fixed memory window (Stephens
and Krebs 1986, McNamara and Houston 1987). Importantly, regardless of how an individual updates its estimate
of an environmental parameter, a change in the experienced
value can result in a change in both the estimated mean
value and the estimated variance of pay-offs expected, as
illustrated in Fig. 3. If individuals differ in their variancesensitivity, variance-averse individuals would be expected
to respond to changes in the state of the environment
sooner compared with variance-prone individuals, resulting
in a greater apparent sensitivity to stimuli and behavioural
plasticity.
Theoretical studies have identified several factors that
may lead to individual differences in the shape of the utility function, which would in turn generate individual differences in preference or aversion to variance (McNamara
and Houston 1992). These include the level of reserves an
individual carries and the biological significance of energy
for the individual (i.e. starvation avoidance, reproduction,
or growth) (McNamara and Houston 1992). While we are
not aware of any study that has directly tested for individual differences in utility functions, there are a few studies
to date that have considered whether individuals differ consistently in their preference or aversion to variance. In one
study in Siberian jays Perisoreus infaustus, breeders tended
to be variance-prone while subordinates tended to be
variance-averse (Ratikainen et al. 2010). Another study
reported that female green woodhoopoes Phoeniculus
purpureus were more variance-averse than males (Wright
and Radford 2010). These differences in preference or
aversion for variance were attributed to differences in
energetic requirements between classes of individuals (subordinates vs dominants, and males vs females respectively),
which ultimately affect the shape of the utility function
(Ratikainen et al. 2010, Wright and Radford 2010).
There are other, more subtle, sources of variation in
energy thresholds between individuals that may affect
variance-sensitivity. For example, inter-individual variation
in metabolic rates is well documented, often exceeding
two-fold levels of variation among individuals from the
same population (Speakman et al. 2004). Because differences
in metabolic rates reflect differences in minimum energy
requirements, an individual with a higher metabolic rate
will have a higher probability of experiencing an energetic
25
20
15
10
5
0
(a)
0
10
20
30
40
Time
Estimated value ± CV
Long-term averaging
Memory window
Bayesian
25
20
15
10
5
0
0
(d)
(c)
(b)
10
20
30
40 0
10
20
30
40 0
10
20
30
40
Time
Figure 3. Illustration of how changes in patch quality lead to a change in the estimated value of the patch, as well as the variance or
uncertainty associated with that estimate. (a) Illustrates hypothetical fluctuations in resource value (solid line). The resource value experienced at a given time is indicated by solid circles. Changes in the resource value result in changes in both the estimated patch quality and
the estimated variance according to the different rules used to update information: (b) long-term averaging, (c) restricted memory window,
and (d) Bayesian updating (weighted averages). In (c), the memory window is 5 time units. In (d), the most recently sampled value is
weighted 3 greater than all previous samples. Variance estimates are shown as the coefficient of variation (CV), which controls for the
effect of the mean on any error estimates.
7
shortfall, and consequently should be variance-averse early
in a foraging bout and variance-prone late in a foraging
bout (Mathot et al. 2009). Individual differences in foraging tactic use in zebra finches Taeniopygia guttata are consistent with this prediction (Mathot et al. 2009), although it
should be noted that explanations other than metabolic
rate-related differences in variance-aversion may also
account for this pattern (Mathot et al. 2009). If variation
in metabolic rates is associated with differences in varianceaversion (or preference), this could have important and
long-lasting consequences for individual differences in
response to uncertainty, since metabolic rates are known to
have significant heritability in several systems (Biro et al.
2010). Associations between metabolic rate and personality have previously been hypothesized by several authors
(Stamps 2007, Biro and Stamps 2008, Careau et al. 2008,
Biro et al. 2010, Houston 2010), however the implications
of inter-individual variation in metabolic rates for variancesensitive behaviour and behavioural plasticity have yet to
be explored.
Insurance and personality-related differences
in behavioural plasticity
Individuals can also adopt tactics that mitigate the
fitness consequences of irreducible uncertainty. Tactics
that provide a buffer against worst-case scenarios are called
‘insurance’, and individual variation in the use of insurance
may be an important factor underlying animal personality.
Maintaining extra energy stores is a classic example of insurance against stochastic variation in food availability (Dall
2010). However, possessing greater energy stores alters the
tradeoff between acquiring food and avoiding predation,
and individuals possessing greater reserves are expected to
expose themselves less to the chance of being predated and
consequently, be less bold (Houston and McNamara 1993,
Clark 1994, Dall et al. 2004). In addition to being associated with differences in average behaviour, such as boldness, individual differences in the tendency to maintain fat
reserves as a form of insurance may also be expected to be
associated with individual differences in behavioural plasticity. Individuals with greater energy reserves are expected
to show stronger anti-predator responses across gradients of
increasing predation danger (i.e. greater behavioural plasticity), both because they are better able to afford a reduction
in feeding rate and because they have greater assets to protect
(Clark 1994, Luttbeg and Sih 2010).
Theoretical work shows that even stochastic individual
differences in energy reserves can generate long-lasting individual differences in response to predation risk via positive
feedbacks between state and behaviour, and as a result, even
chance differences in energy reserves can persist over time
(Rands et al. 2003, 2008, Luttbeg and Sih 2010). There is
substantial empirical evidence from insects, birds and mammals indicating that not only do individuals differ in their
tendency to maintain energy reserves, but that this variation is often repeatable, and in some cases, may be heritable
(Merilä et al. 2001, Schulte-Hostedde et al. 2005).
In addition, we predict that individual variation in
the use of insurance may be linked with both sampling
behaviour and variance-sensitivity, with fat-insurers having
8
higher sampling rates and being more variance-averse.
Previous theoretical work has shown that individuals that
have greater amounts of energy reserves will have greater
opportunity to invest in sampling (Dall and Johnstone
2002), with resultant consequences for personality-related
differences in behavioural plasticity as outlined above.
Similarly, variation in energy reserves will influence an
individual’s tendency to be variance-prone versus varianceaverse, with increased energy reserves generally favouring variance-averse behaviour (Stephens and Krebs 1986,
Bateson 2002). Although theoretical studies suggest that
associations between insurance, sampling and variancesensitivity are likely, empirical tests of these predicted associations are currently lacking. Positive associations between
alternative tactics for managing uncertainty warrant explicit
investigation, as they could generate suites of correlated
behaviours, also known as behavioural syndromes, and may
also result in individuals being more (or less) plastic across
suites of behaviours.
Another form of insurance against stochasticity in
foraging conditions is to maintain a range of options (i.e.
to generalise) (Dall 2010). An individual that generalises
on a broad array of prey types is much less likely to suffer
catastrophic reductions in food intake and starvation during episodes of bad foraging conditions. Individual differences in the tendency to generalise as a form of insurance,
by definition, will generate consistent individual differences in behaviour because generalists will adopt different
behavioural tactics as compared to specialists. For example,
generalists may exploit a broader range of prey, resulting in
differences in foraging behaviours, types of habitats occupied and/or home-range size. Furthermore, differences in the
tendency to generalise or specialise are likely to be associated with individual variation in plasticity because generalist
foragers (i.e. insurers) may temporarily specialize on subsets
of their repertoire according to current conditions, thus
appearing more behaviourally plastic (van Tienderen 1997,
Dall 2010). There is already evidence that generalists and
specialists can coexist in populations (Wilson and Yoshimura
1994), and we would argue that in some cases, consistent
individual differences in behaviour (i.e. animal personality)
may reflect individual variation in this form of insurance.
For example, differences in tendency to maintain a range
of alternative foraging options (e.g. use of a range of foraging patches) may be reflected in differences between individuals in their activity and exploration behaviours.
How do individual differences in responses
to uncertainty arise and persist?
Although animal personality and behavioural plasticity
are often thought of as traits that are under direct selection,
several recent works have called into question the validity of this assumption (McNamara and Houston 2009,
Dochtermann 2011, Dingemanse et al. 2012, Fawcett
et al. in press). In this article, we have described how both
variation in personality and plasticity may arise as a result
of individual differences in the use of tactics for managing
uncertainty. Individual differences in sampling behaviour,
insurance and/or variance-sensitivity may come about in
a variety of ways, and following McNamara and Houston
(A) State-dependence
(c) ‘G’
B
B
Tactic use
Tactic use
(a) ‘PE’
(B) Alternative strategies
A
C
A
Early environment value
Genotype
B
(d) ‘G × E’
Trait A
(b) ‘PE × E’
A
Trait A
C
B
A
C
C
Environmental gradient
Environmental gradient
B
(e) ‘I × E’
Trait A
A
C
Environmental gradient
Figure 4. Schematic overview illustrating sources of variation in tactic use (sampling, insurance, and/or variance-sensitivity) and the slope
of a behavioural reaction norm (BRN or ‘I  E’ in quantitative genetics terms, as illustrated in Fig. 1a and b). (A) State-dependence:
individual variation in tactic use arises when individual-specific variation in early environmental conditions affects the phenotype (called
permanent environment variance, or ‘PE’, Fig. 4a, circles represent individuals). Note that ‘PE’ does not imply that the trait becomes
fixed in the early environment, but only that the early environment produces lasting effects on the trait. This leads to non-heritable
individual variation in tactic use, which in turn produces non-heritable variation in the slopes of a BRN for tactic use, shown by the
interaction between ‘PE’ and the environment interaction (‘PE  E’, Fig. 4b, lines represent individuals exhibiting different levels of
tactic use as illustrated in Fig. 4a). (B) Alternative strategies: Individual variation in tactic use arises from allelic variation in genes affecting
the expression of tactic (genetic variance, ‘G’, Fig. 4c, circles represent individuals). This induces heritable variation in the slopes of a
BRN for ‘trait A’, shown by the interaction between the genetic variance and the average environment (‘G  E’, Fig. 4d, lines represent
individuals exhibiting different values of tactic use as illustrated in Fig. 4c). Note that ‘I  E’ is a product of both ‘PE  E’ and ‘G  E’.
(2009) and Fawcett et al. (in press), we argue that explicit
consideration of the decision rules underlying this behavioural variation is warranted, because it may provide crucial
insights into the genetic underpinnings of individual
variation in behavioural plasticity (Fig. 4). For example,
populations may be monomorphic for state-dependent
decision-rules that shape the use of these alternative tactics.
Individual variation in expressed tactic use may then arise
due to between-individual differences in state (Houston and
McNamara 1999), leading to ‘permanent environment’ (PE)
effects in quantitative genetics terms (Nussey et al. 2007,
Dingemanse et al. 2010), and these differences in tactic use
may subsequently be enforced via positive-feedbacks. Under
this scenario, personality-related differences in plasticity
would not reflect genetic variation in the propensity for
plasticity (Dingemanse et al. 2012). For example, having
greater energy reserves may free up individuals to investmore heavily in sampling (Dall and Johnstone 2002). If
better information about the state of relevant environmental parameters influences foraging success, then individuals that sample more may be better able to acquire and/or
maintain energy reserves. Being averse to variance implies
that the cost of uncertainty outweighs the potential benefits (Fig. 2). This suggests that individuals that are averse
to variance may both invest more in sampling and in insurance in order to mitigate the potential costs of uncertainty.
Possessing greater energy reserves in turn tends to favour
variance-averse behaviour (Stephens and Krebs 1986).
Positive feedbacks between alternative tactics for managing
uncertainty imply that even small chance variation between
9
individuals in state can lead to significant and lasting differences in behaviour. The importance of positive feedbacks
has previously been suggested as a potent explanation for
consistent individual variation in personality (Dall et al.
2004, Luttbeg and Sih 2010) and plasticity (Wolf et al.
2008, 2011, Wolf and Weissing 2010).
Alternatively, individual variation in sampling behaviour, insurance and/or variance-sensitivity may arise if
alternative strategies that shape the use of these tactics
co-exist within populations. Alternative strategies can be
maintained within populations as a mixed evolutionarily
stable strategy because they perform equally well on average. In this case, personality-related differences in plasticity would reflect underlying genetic variation, and would
consequently suggest that personality-related differences
in behavioural plasticity have the potential to evolve. The
co-existence of alternative strategies is already well
documented in a range of contexts (Brockmann 2001).
Additionally, alternative strategies may be maintained in
populations via fluctuating selection. Any adaptive advantage provided by sampling, variance-sensitivity or insurance depends crucially upon the temporal and spatial
distribution of environmental uncertainty. Since different
times and places may experience different means and variances in conditions by chance, we would expect selection
pressures on strategies that determine sampling, insurance,
and variance-sensitive behaviour to fluctuate. Importantly,
even if different strategies are favoured by different combinations of environmental conditions, fluctuating selection
could contribute to the maintenance of alternative strategies within the same population so that at any point in
time in a given population, individuals are present that
differ in the extent of their sampling, insurance and/or
variance-sensitivity.
The majority of models for adaptive personality differences explore how differences in state can give rise to
consistent individual differences in behaviour or suites of
behaviours (Dingemanse and Wolf 2010). More recent
work shows that frequency-dependent payoffs can give rise
to genetic polymorphisms that may also lead to consistent
individual differences in behaviour (Wolf et al. 2011). Our
suggestion that between-individual differences in the types
of tactics used to manage uncertainty may reflect adaptive
state-dependent behaviour and/or genetic polymorphisms
do not differ from current models for adaptive personality
differences in this regard. However, individual differences in
response to uncertainty may provide a more parsimonious
explanation for behavioural variation, as they can account
for consistent individual variation in both behaviour (personality) and behavioural plasticity.
The problem of uncertainty has broad applicability
Although many of the examples of individual variation in
sampling, insurance and variance-sensitivity cited here are
from a foraging context, the suggested mechanisms underpinning links between personality and plasticity are not
restricted to the realm of foraging behaviour. Individuals
sample the environment to assess predation danger, and
sample potential breeding sites and/or mates. Individuals can
also insure against the cost of potential encounters with
10
predators by investing in defensive morphologies (Dall
2010). Females can insure against uncertainty regarding
the fertility or genetic quality of their social mate by
engaging in extra-pair matings (Kempenaers and Dhondt
1993). Variance-sensitivity is also not restricted to foraging
contexts. Variance-sensitivity is expected whenever the fitness value of each additional unit of a given resource is
not linear, and has been suggested to play a role in group
formation decisions (Caraco et al. 1995) and mate choice
(Wiegmann et al. 1999).
There are some indications that consistent individual
variation in sampling, insurance and variance-sensitivity
are indeed present in non-foraging contexts. For example,
in the great reed warbler Acrocephalus arundinaceus, females
differ in the number of males visited before choosing a mate
(Bensch and Hasselquist 1992). In White’s skink Egernia
whitii, there is significant variation between females in
the proportion of young resulting from extra-pair matings
(While et al. 2009). In song sparrows Melospiza melodia
variation in female extra-pair paternity rates is heritable
(Reid et al. 2011), and variation in extra-pair behaviour
in zebra finches is heritable in both males and females
(Forstmeier et al. 2011). Although it remains to be tested
explicitly, if individual differences in sampling, insurance or
variance-sensitivity outside of a foraging context are repeatable, they too may reflect individual variation in strategies
for managing uncertainty.
Conclusions and future directions
There is increasing evidence that in many systems personality and behavioural plasticity are linked (Table 1).
In this article, we have argued that individual differences
in response to environmental uncertainty may provide a
parsimonious explanation for personality-related differences in behavioural plasticity, a point which has previously
been recognized by a few behavioural ecologists (Verbeek
et al. 1994, van Tienderen 1997, Dall 2010). We recognize
that individual differences in response to uncertainty are
not the only possible explanation for personality-related
differences in behavioural plasticity. Indeed, individual
variation in responses to environmental uncertainty likely
cannot account for some of the examples of personalityrelated differences in plasticity listed in Table 1. For example, it is unclear how differences in sampling, insurance
or variance-sensitivity could account for the finding that
bold rainbow trout Onchorhyncus mykiss reduce their
boldness in response to a losing experience with a conspecific, while shy rainbow trout increase their boldness (measured as latency to approach a novel object) (Frost et al.
2007). Other factors, including frequency-dependent payoffs for plasticity, have previously been shown to generate
personality-related differences in plasticity in social contexts
(Wolf et al. 2011), and we suggest such effects might be in
operation here.
Given the pervasiveness of the problem of uncertainty
(Dall and Johnstone 2002), alternative strategies for managing uncertainty may provide a powerful explanation
for consistent differences in behaviour and in behavioural
plasticity for a wide range of behavioural traits. The importance of environmental uncertainty in generating consistent
individual differences in behaviour (personality) has been
suggested previously (McElreath and Strimling 2006,
Chapman et al. 2010). Our thesis offers a broad adaptive
explanation for both personality and personality-related
differences in plasticity which can be underpinned by both
state- and frequency-dependent payoffs. We have reviewed
empirical studies from a range of taxa that support our
assertion that individuals differ in how they invest in
sampling behaviour (Krebs et al. 1978, Shettleworth et al.
1988, Morand-Ferron et al. 2011), in their tendency to be
variance-prone versus variance-averse (Mathot et al. 2009,
Ratikainen et al. 2010, Wright and Radford 2010), and in
their relative use of insurance (Merilä et al. 2001, SchulteHostedde et al. 2005, While et al. 2009, Reid et al. 2011).
Studies are now needed that explicitly test whether individuals differ consistently in their use of alternative tactics for
managing uncertainty, and whether such individual differences yield the predicted relationships between alternative
tactics for managing uncertainty, personality and plasticity.
Acknowledgements – KJM was supported by the Max-Planck
Institute for Ornithology and an Alexander von Humboldt
post-doctoral fellowship. NJD and BK are supported by the
Max Planck Society (MPG). We are grateful to Dave Westneat for
valuable comments on an earlier version of the manuscript.
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