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Special Issue
An overview of the evolutionary causes and consequences of behavioural
plasticity
Emilie C. Snell-Rood
*
Department of Ecology, Evolution and Behaviour, University of Minnesota, Twin Cities, MN, U.S.A.
article info
Article history:
Received 8 August 2012
Initial acceptance 8 October 2012
Final acceptance 19 December 2012
Available online 5 March 2013
MS. number: ASI-12-00615
Keywords:
cognition
human-induced rapid environmental
change (HIREC)
life history
niche construction
phenotypic plasticity
variable environment
I outline how understanding the mechanism of behavioural plasticity is important for predicting how
organisms will respond to rapidly changing and novel environments. I define two major forms of
behavioural plasticity: developmental and activational. Developmental plasticity refers to the capacity of
a genotype to adopt different developmental trajectories in different environments. Activational plasti-
city refers to differential activation of an underlying network in different environments such that an
individual expresses various phenotypes throughout their lifetime. I suggest that the costs and benefits
of these two forms of behavioural plasticity may differ: developmental plasticity is slow, but results in
a wider range of more integrated responses. Furthermore, the neural costs associated with activational
plasticity may be greater because large neural networks must be maintained past an initial sampling and
learning phase. While the benefits of plasticity are realized in variable environments, I argue that fine-
grained and coarse-grained variation may differentially select for activational and developmental plas-
ticity, respectively. Because environmental variation experienced by an organism is largely determined
by behaviour, developmental plasticity may still evolve in fine-grained environments if niche choice
results in coarse-grained ‘realized’variation. Behavioural plasticity should impact evolution in novel
environments because it increases the chances of survival in these environments. Developmental
behavioural plasticity may be particularly important for diversification in novel environments because it
can impact not only survival, but also the development of signals and preferences important in mate
choice. Future areas of research on behavioural plasticity and rapid environmental change include stress
as a mechanism underlying rapid integrated responses and life history perspectives on predicting
developmental versus evolutionary responses.
Ó2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
Biologists have long been fascinated with understanding
organismal responses to novel and variable environments. Differ-
ences in how species survive in the face of glacial advances,
changing atmospheric composition, faunal interchanges and cata-
strophic events such as meteor impacts have shaped the compo-
sition of modern ecosystems. Today, organisms are presented with
novel and rapidly changing environments with the spread of
invasive species, climate change, pollutants, habitat destruction,
and conversion to agriculture and urban areas. A major topical
question is understanding how species respond to such rapid
human-induced environmental change (Sih et al. 2011;Tuomainen
& Candolin 2011).
Many populations show evolutionary responses that track such
novel and changing environments, such as the evolution of
antibiotic resistance in bacteria and pesticide resistance in insects
(Palumbi 2001). However, scientists have surmised that the rate of
current environmental change exceeds the evolutionary response
rate of many populations (Bell & Collins 2008;Chevin et al. 2010;
Hoffmann & Sgro 2011). In particular, for many species, the repro-
ductive rates of species are too slow, the generation time too long,
or the population size is too small to result in rapid evolutionary
responses (Lande 1998;Reznick & Ghalambor 2001;Bell &
Gonzalez 2009). Thus, more and more biologists have turned to
studying developmental responses, in particular, adaptive pheno-
typic plasticity, to understand how organisms will respond to
rapidly changing environments (Price et al. 2003;Ghalambor et al.
2007;Lande 2009).
Adaptive phenotypic plasticity, the ability of a genotype to vary
its phenotype across environments, and thus maintain high per-
formance across that environmental gradient, is important for
survival in variable environments (Schlichting & Pigliucci 1998;
West-Eberhard 2003;Bateson & Gluckman 2011). In particular,
behavioural plasticity should be important in novel and variable
environments given that the development and expression of
*Correspondence: E. C. Snell-Rood, Department of Ecology, Evolution and
Behaviour, University of Minnesota, 1987 Upper Buford Circle, Ecology 100, St Paul,
MN 55108, U.S.A.
E-mail address: emilies@umn.edu.
Contents lists available at SciVerse ScienceDirect
Animal Behaviour
journal homepage: www.elsevier.com/locate/anbehav
0003-3472/$38.00 Ó2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.anbehav.2012.12.031
Animal Behaviour 85 (2013) 1004e1011
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
behaviour is remarkably sensitive to environmental conditions.
However, a comprehensive framework for understanding the ori-
gins and consequences of behavioural plasticity remains elusive,
largely because what we mean by ‘plasticity’encompasses a broad
range of mechanisms. In this review, I argue that distinguishing
between types of behavioural plasticity, and outlining the under-
lying mechanism, is important for understanding how behavioural
plasticity will impact survival in novel and variable environments.
BEHAVIOURAL PLASTICITY: AN OVERVIEW OF TYPES
Distinguishing between Developmental and Activational
Behavioural Plasticity
Behavioural plasticity can be broadly classified into two
types: developmental and activational. Developmental behav-
ioural plasticity corresponds to the traditional definition of
phenotypic plasticity, where a genotype expresses different
behavioural phenotypes in different environments as a result of
different developmental trajectories triggered by those envi-
ronments. Developmental behavioural plasticity encompasses all
of what is generally defined as ‘learning’, or any change in the
nervous system as a result of experience. However, devel-
opmental behavioural plasticity also includes developmental
changes in morphology and physiology relevant to a particular
behaviour, such as changes in muscles, limbs or bones that in-
fluence foraging or locomotion (e.g. Wainwright et al. 1991;
Losos et al. 2000;Young&Badyaev2010).
Developmental behavioural plasticity is different from ‘activa-
tional’behavioural plasticity, referred to elsewhere as ‘behaviour as
plasticity’(Dukas 1998)or‘innate’behavioural plasticity (Mery &
Burns 2010). Here, the external context results in the expression
of a particular behaviour (Stamps & Groothuis 2010) such that an
individual expresses different behaviours as it encounters different
environments or conditions. Activational plasticity refers to the
differential activation of an underlying network.
To emphasize the distinction further, imagine a neural network
underlying behaviour, with a sensory (input) layer, a processing
layer and a motor (output) layer (Fig. 1). Activational behavioural
plasticity represents the differential activation of this underlying
network in different environments (Fig.1a). In this way, this form of
behavioural plasticity is analogous to physiology, where underlying
metabolic networks result in different enzymes being expressed
under different nutritional conditions. In contrast, developmental
behavioural plasticity is the development of different neural net-
works in different environments (Fig. 1b). Such plasticity thus in-
volves learning and neural plasticity, but may also encompass
developmental changes in associated sensory systems and mor-
phologies. As an example, activational plasticity could refer to
hiding or foraging behaviour of an individual in the presence or
absence of predators, whereas developmental plasticity could refer
to individuals developing into relatively more shy or bold behav-
ioural types in the presence or absence of predators. Some behav-
ioural phenomena include aspects of both activational and
developmental behavioural plasticity. For instance, listening to
conspecific song may result in activational plasticity in the short
term (such as territory defence) and developmental plasticity in the
long term (such as learning neighbour songs or changing testos-
terone expression). This review argues that distinguishing between
activational and developmental behavioural plasticity (or under-
standing the relative contribution of either type of plasticity) is
critical for understanding the costs and consequences of plasticity.
Trade-off between Response Time and Phenotypic Integration
One of the major differences between activational and devel-
opmental behavioural plasticity comes as a trade-off between
response time and phenotypic integration. Activational behavioural
plasticity is an immediate response to the environment. The un-
derlying neural networks are present, and it is only a matter of
activating different neurons and muscles. Developmental behav-
ioural plasticity, by definition, requires developmental changes
Environment 1 Motor output 1
Motor output 2
Development in environment 1 Development in environment 2
Environment 2
(a)
(b)
Figure 1. A neural network illustration of the two forms of behavioural plasticity. (a) Activational behavioural plasticity refers to differential activation of an underlying network by
different environments. Thus, different motor outputs result in different environments. (b) Developmental behavioural plasticity refers to the differential development of neural
networks in different environments such as a change in synaptic weights as a result of experience.
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e1011 1005
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
such as neuron or muscle growth. These processes take time. For
example, even long-term memory formation in Drosophila takes at
least 5 h (Margulies et al. 2005).
Developmental behavioural plasticity, while a slower process,
has the potential to result in a much wider range of highly inte-
grated behavioural phenotypes. Cues on the environmental state
received early in development can influence the subsequent
development of many traits (Fig. 2). There are constraints on
altering phenotypes once developed, an idea broadly referred to as
the epiphenotype problem (DeWitt et al. 1998). This idea has been
put forward to explain why cues received earlier in development
(relative to those received later) can result in a greater range and
degree of integration of adult phenotypes (Wheeler 1986;Bruni
et al. 1996;Zera & Denno 1997;Hoverman & Relyea 2007). Thus,
developmental behavioural plasticity has a much greater chance of
resulting in differences in the neural pathways, morphology and
physiology underlying a behaviour. For example, developing in
different resource environments may result in the development of
not only different foraging and food handling behaviour (Burghardt
& Krause 1999;Slagsvold & Wiebe 2007;Woo et al. 2008), but also
different food handling morphology, such as mouthpart shape
(Wainwright et al. 1991;Wimberger 1991;Adams et al. 2003) and
digestive enzyme activity (Cahu et al. 1998;Sabat et al. 2005;Brzek
et al. 2009).
Distinguishing between activational and developmental
behavioural plasticity in this way can give important insights into
common questions that arise in the animal personality and
behavioural syndrome literature (Sih et al. 2004;Bell 2007). Why
do we often observe inflexibility in individual behaviour given that
environment-specific expression of behaviour is often advanta-
geous? In many cases, the physiology, morphology and behaviour
of an individual may be an integrated suite of traits (Duckworth
2010) that developed to ‘match’to a particular environment,
resource or starting phenotypic environment such as body size
or social environment. This is similar to the idea that initial dif-
ferences in individual state (such as body size) may be amplified
over development as individuals adopt different strategies best
suited to their differences in state (Dingemanse & Wolf 2010).
While it is possible that differences in the environment may drive
the development of different behavioural types or personalities,
studying the development of behavioural syndromes is still an
open area of research (Stamps & Groothuis 2010).
WHY DOES BEHAVIOURAL PLASTICITY VARY AMONG
INDIVIDUALS?
Costs of Developmental Behavioural Plasticity
Biologists have long been interested in understanding why
phenotypic plasticity varies within and between species, a question
which often comes down to understanding costs and benefits of
plasticity. The costs of developmental behavioural plasticity stem
from the fact that much of this form of plasticity is underlain by
‘trial-and-error’or developmental selection processes. Devel-
opmental or somatic selection involves both sampling of a range of
phenotypes and environmental feedback on which phenotypes
perform well in current conditions (West-Eberhard 2003;Snell-
Rood 2012). Such selective processes in development are costly in
time, energy and exposure.
While costs associated with all forms of phenotypic plasticity
remain elusive (Van Buskirk & Steiner 2009), costs associated with
developmental behavioural plasticity, in particular learning, are
clearer (e.g. Mery & Kawecki 2003,2004,2005). Sampling, whether
exploring resources, the environment or a range of phenotypes,
requires time, energy and necessitates making mistakes (Laverty &
Plowright 1988;Ericsson et al. 1993;Janz & Nylin 1997;Byers et al.
2005), referred to as the cost of naiveté (Dukas 1998), or the
exploration-exploitation trade-off (Kaelbling et al. 1996). Increasing
the breadth of possible learned phenotypes necessitates an
increase in neural network sensory input (Hampson 1991;Zohary
1992;Eurich et al. 1997;Huerta et al. 2004) and motor output
(Hopfield 1982;Aboitiz 1996;Sporns et al. 2000), which is signif-
icant because neural tissue is one of the most metabolically
expensive tissues (Laughlin et al. 1998;Attwell & Laughlin 2001;
Niven et al. 2007).
Studies show that variation in learning and cognition is often
correlated with variation in neural investment, both within and
across species (Sherry et al. 1992;Lefebvre & Sol 2008;Roth &
Pravosudov 2009;Snell-Rood et al. 2009), and as a product of
artificial selection (Herman & Nagy 1977;Jensen & Fuller 1978). The
costs associated with exploration and neural tissue can explainwhy
species and genotypes with increased investment in learning and
neural tissue tend to have delays in reproduction and reduced
fecundity (Mace & Eisenberg 1982;Iwaniuk & Nelson 2003;
Lefebvre et al. 2006;Barrickman et al. 2008;Snell-Rood et al. 2011).
Thus, the costs of learning, and any developmental selection pro-
cess more generally, should result in a shift, along the continuum of
life history strategies, towards more K-selected or ‘slow’life history
traits (Snell-Rood 2012). This could occur because the costs of
learning select against ‘fast’life history traits or because species
with ‘slower’life history traits can afford the costs of learning.
Costs of Activational versus Developmental Behavioural Plasticity
Different mechanisms underlying plastic responses generally
come with different costs and constraints (Snell-Rood et al. 2010;
Snell-Rood 2012). It is likely that the two major categories of
behavioural plasticity also come with different costs. Increasing
activational behavioural plasticity would correspond to an increase
in the possible motor responses to a large range of different envi-
ronmental states. To respond to finer variation in environmental
state, a nervous system must have an increase in sensory input
(Fig. 3). For example, increased sensory perception often corre-
sponds to larger regions of dedicated neural space (Catania & Kaas
1997;Glendenning & Masterton 1998;Hutcheon et al. 2002).
Likewise, a greater or finer range of motor responses, such as
manual dexterity or behavioural repertoire size, also corresponds to
an increase in neural investment (Iwaniuk et al. 1999;Changizi
2003). Thus, an increase in activational behavioural plasticity
should correspond to an increase in neuron number and overall
brain size. Given the costs associated with neural tissue (Laughlin
et al. 1998;Attwell & Laughlin 2001;Niven et al. 2007), this is
likely a substantial cost.
Increasing developmental behavioural plasticity should also
correspond to increased neural investment, but only early in
Cells making up different traits
Zygote
Cue 2
Cue 1
Developmental time
Figure 2. Developmental plasticity can result in a wide range of integrated adult
phenotypes. A cue received early in development (cue 1) can more easily affect the
development of all subsequent traits than a cue received later in development (cue 2),
which must be independently communicated to traits that have already differentiated.
This is referred to as the epiphenotype problem (DeWitt et al. 1998).
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e1011100 6
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
development. To develop appropriate motor responses to a range of
possible environments or resources, an individual must initially
sense a broad range of inputs and express a broad range of outputs.
Such trial-and-error learning includes a broad initial sampling
period, followed by reinforcement and consolidation of high-
performing phenotypes. At the neural level, this developmental
process corresponds to very high numbers of neurons early in
development, which are reduced and refined with experience
(Huttenlocher 1979;Oppenheim 1991;Purves et al. 1996;Luo &
O’Leary 2005). Because larger neural networks are necessary to
produce more rearrangements and ultimately a better match to
local conditions (Aboitiz 1996), the initial neural costs associated
with activational behavioural plasticity and developmental
behavioural plasticity should be comparable. However, the overall
costs are likely lower for developmental behavioural plasticity due
to pruning and refinement of neural networks over developmental
time.
Environmental Variation Selects for Plasticity
The benefits of phenotypic plasticity are realized in variable and
novel environments. The plasticity literature has shown that one of
the most important selective factors for phenotypic plasticity is
environmental variation, in particular coarse-grained variation
(Levins 1968;Moran 1992;Stephens 1992). Coarse-grained envi-
ronmental variation refers to situations where the environment
varies between generations, but remains relatively constant within
generations (Fig. 4). Models of developmental plasticity, where
reversibility of phenotype is difficult or absent, generally suggest
that coarse-grained environmental variation is most favourable for
the evolution of plasticity. In coarse-grained environments, the
benefits of plasticity are maximized, while the costs of plasticity are
minimized. In particular, if the environment remains constant
within a generation, the costs of phenotypeeenvironment mis-
match are minimized (Padilla & Adolph 1996;DeWitt et al. 1998),
along with any costs of relearning a behavioural phenotype or
altering a morphological phenotype.
Fine-grained variation, where the environment varies within
the lifetime of an individual (Fig. 4), should select for reversible
phenotypic plasticity to avoid costly mismatches. In the case of
behaviour, activational behavioural plasticity should be favoured in
the presence of fine-grained environmental variation such that
different behaviours are expressed in the different environments
encountered by an individual. Fine-grained environmental varia-
tion should be particularly high for very long-lived species, which
encounter multiple environments over their lifetime. Such varia-
tion will also be pronounced for nonmigratory, temperate species
that do not diapause or hibernate. The ‘cognitive buffer hypothesis’,
which states that increased brain size offers benefits for environ-
mental change within the lifetime of an individual (Sol 2009),
formalizes the link between activational behavioural plasticity and
fine-grained environmental variation. This can explain why longer-
lived species and those that experience more seasonality tend to
have larger neural investment (Sol et al. 2007;Roth & Pravosudov
2009;Gonzalez-Lagos et al. 2010;Roth et al. 2011).
The Importance of Niche Choice in Determining Realized
Environmental Variation
Environmental variation is often considered as external to an
organism. However, all students of animal behaviour know the
importance of behaviour in structuring environmental variation.
For example, individuals may experience more environmental
variation through dispersal between environments or through
No variation:
No plasticity
Coarse-grained
variation:
Developmental
plasticity
Fine-grained
variation:
Activational plasticity
Lifetime of individual
Environment
Phenotype Phenotype Phenotype
Individua
l
Genotype
Genotype
or individual
AB
AB
AB
AA
AB
AB
Figure 4. Environmental variation and behavioural plasticity. No environmental var-
iation selects for fixed behaviour, both for individual behaviour over their lifetime and
for the capacity of a genotype to develop differently in different environments. Coarse-
grained variation, where the environment varies more between than within genera-
tions, should select for developmental plasticity, where a genotype develops differ-
ently based on the environment. Fine-grained variation, where the environment varies
within an individual lifetime, should select for activational plasticity, where an un-
derlying network (e.g. neural network or metabolic network) is differentially activated
in different environments, resulting in an individual varying its behaviour across
environments.
Lower activational behavioural plasticity
Enviro 1 Output 1
Output 2
Enviro 2
Enviro 1 Output 1
Output 2
Output 3
Output 4
Enviro 2
Enviro 3
Enviro 4
Higher activational behavioural plasticity
Figure 3. Neural costs of increasing activational plasticity. Higher activational plasti-
city corresponds to an increase in the range of environments that an individual can
detect and in the range of motor responses that the individual can match to these
different environments. Theoretically, this should correspond to an increase in the size
of the neural network underlying the response, although there may be network de-
signs (e.g. parallel structure) that minimize increasing costs.
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e1011 1007
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
neophilia, exploring a wider range of objects or resources within
their environment (Greenberg 1983,1989;Mettke-Hofmann et al.
2002,2009). Individuals also have many mechanisms to reduce
‘realized’environmental variation. Habitat and resource prefer-
ences are ubiquitous among animals. In particular, animals often
prefer habitats that are similar to those they have experienced
before, through natal habitat preference induction (Stamps 1995;
Davis & Stamps 2004;Mabry & Stamps 2008) or phenotypee
habitat matching (Edelaar et al. 2008;Karpestam et al. 2012).
Realized environmental variation may also be reduced through
diapause or hibernation (Tauber & Tauber 1976), habitat modifi-
cations (Frederickson et al. 2005), or sensory biases that result in
ignoring stimuli (Wcislo & Tierney 2009).
The various methods that reduce environmental variation could
potentially turn fine-grained variation into coarse-grained varia-
tion, favouring the evolution of developmental plasticity (or no
variation, favouring the evolution of specialization). Indeed, the
reduced neural costs and increased range of phenotypes associated
with developmental behavioural plasticity (see above) may result
in selection for such plasticity through mechanisms that decrease
lifetime environmental variation. In this way, many ‘generalist’
species that cope with a wide range of environments may be
composed of individual specialists that have developed behavioural
and morphological traits suited for particular resources or micro-
environments (Fox & Morrow 1981;Bolnick et al. 2003).
A niche choice or niche construction perspective also reveals the
possibility for complex evolutionary feedbacks (Kylafis & Loreau
2008). For instance, an increase in learning or cognition may lead
to exploring and surviving in a wider range of environments,
resulting in an increase in realized environmental variation,
selecting for greater plasticity. This positive feedback loop would
eventually be offset by the costs of plasticity (Fig. 5).
CONSEQUENCES OF BEHAVIOURAL PLASTICITY IN NOVEL
ENVIRONMENTS
Both types of behavioural plasticity impact evolution in novel
environments by increasing the probability of survival in that
environment. Developmental behavioural plasticity is particularly
relevant to survival in novel environments because trial-and-error
processes such as learning, which include both phenotype sam-
pling and environmental feedback, have the ability to immediately
shift an entire population close to a novel adaptive peak (Hinton &
Nolan 1987;Snell-Rood 2012). Such plasticity can result in the
development of alternate phenotypes that differ in morphology,
behaviour and physiology. For instance, sticklebacks develop dif-
ferences in not only behaviour, but also morphology, when reared
in benthic and limnetic conditions (Shaw et al. 2007;Wund et al.
2008,2012).
Activational behavioural plasticity is also important for survival
in new environments because it can allow immediate adjustment
to novel environments. For example, stringent preferences for
mates or resources are often adjusted when resources or mates are
less common. Mate preferences are often relaxed when organisms
are reared in low-density environments (Bailey & Zuk 2008;
Fowler-Finn & Rodriguez 2012), which might facilitate survival of
populations in new environments where population density is low.
Similarly, butterflies in an environment without innately preferred
host plants will start exploring (and accepting) alternate hosts
(Bergstrom et al. 2004;Snell-Rood & Papaj 2009). Thus, plasticity in
acceptance thresholds based on environmental conditions repre-
sents differential development of activational behavioural plasticity
that may facilitate survival in new environments.
The importance of both forms of behavioural plasticity for sur-
vival in new environments is emphasized by the range of studies
linking brain size to survival in cities and after invasions (reviewed
in Wright et al. 2010). Both birds and mammals with greater neural
investment (and behavioural innovation rate) do better after
introduction into novel environments (Sol et al. 2005,2008). Sim-
ilarly, bird species with relatively larger forebrains are more likely
to thrive in urban environments or rural environments with
increasing human presence (Carrete & Tella 2011;Maklakov et al.
2011;Pocock 2011). Behavioural plasticity may facilitate novel,
innovative behaviour that allows survival in novel environments
(Sol et al. 2002;Møller 2009), such as learning to avoid or ignore
novel predators (Levey et al. 2009) or adjusting signals in the face of
noise (Halfwerk & Slabbekoorn 2009).
Behavioural plasticity, in particular developmental plasticity,
also has major impacts on evolutionary diversification (Price et al.
2003;West-Eberhard 2003;Lande 2009;Pfennig et al. 2010).
Plastic populations have the potential to immediately shift to new
adaptive peaks, especially if phenotypes develop through learning-
like processes that incorporate both sampling and environmental
feedback (Hinton & Nolan 1987;Frank 1996,2011;Snell-Rood
2012). Once on these new selective peaks, the costs of plasticity,
which are especially pronounced for learning and similar mecha-
nisms of plasticity, should select for more efficient production of
the phenotype; in other words, reduced plasticity. Alternatively, if
the environment is still variable, there may be selection for habitat
choice, followed by reduced plasticity. Either way, genetic assim-
ilation may result, where an initially environmentally induced
phenotype is constitutively produced, potentially leading to
diversification (see Pigliucci & Murren 2003;West-Eberhard 2003;
Pfennig et al. 2010;Bateson & Gluckman 2011).
Because developmental plasticity can simultaneously affect the
development of behaviour, ornaments and sensory systems, it may
be especially important for diversification because it can lead to
immediate prezygotic isolation. It is well established that the
environment affects the development of signals: song structure is
often learned (Marler 1997;Doupe & Kuhl 1999) and then adjusted
in noisy environments (Gross et al. 2010;Luther & Derryberry
2012), and the development of ornamentation often depends on
diet (Hill 1992;Toomey & McGraw 2009). We also know that the
development of sensory systems depends on the environment. For
instance, visual systems develop quite differently in different
lighting conditions (Cronin et al. 2001;Fuller et al. 2010). This
suggests that there may be simultaneous developmental shifts in
traits that affect survival in a novel environment and traits that
affect mate choice. Thus, there is potential for immediate
Niche choice
Environmental variation
Costs
Behavioural plasticity
Survival in more environments
Figure 5. Feedbacks in selection on behavioural plasticity. Environmental variation
tends to select for behavioural plasticity while the costs select against behavioural
plasticity. Given that plasticity increases survival in a range of environments, increases
in plasticity may correspond to increases in realized environmental variation, selecting
further for plasticity in a positive feedback loop. Niche choice may offset this feedback
loop through a decrease in realized environmental variation, for instance through
preference for habitats and resources experienced earlier in life.
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e1011100 8
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
prezygotic isolation from populations using ancestral environ-
ments (Price 1998;Slabbekoorn & Smith 2002).
CONCLUSIONS AND FUTURE DIRECTIONS
In conclusion, this review has discussed how distinguishing
between different forms of behavioural plasticity can yield insights
into the costs and consequences of plasticity. The general discus-
sion has highlighted several exciting and open areas of current
research. First, the neural costs associated with developmental and
activational plasticity are hypothesized to be different, but we have
no strict tests of this idea. The most straightforward way to initially
test this idea would be through neural network models that also
incorporated costs. But it could also be informative to contrast
behavioural plasticity of genotypes (or species) that differ in the
maintenance of neural investment from early in life to later in life.
Second, developmental plasticity was highlighted as important
for integrated and potentially broad responses to novel environ-
ments. What are the physiological mechanisms that underlie such
extreme and integrated responses? It is likely that stress will play
a major role in plastic responses to novel environments given that
stress responses are incredibly environmentally sensitive and affect
the activation and development of a suite of traits. Understanding
the intersection between stress and plasticity in the context of
novel and rapidly changing environments is an exciting area of
current and future research (McEwen & Wingfield 2003;Wingfield
2003;Badyaev 2005).
Third, discussions throughout this review have highlighted
the importance of habitat choice and niche construction in the
evolution of plasticity. In particular, habitat choice may be one
mechanism by which organisms reduce the costs of plasticity and
maximize the benefits of developmental plasticity. This hypoth-
esis predicts that many generalist species may actually be acting
as specialists at the level of individuals, in terms of behavioural,
morphological and physiological differentiation. Incorporating
niche construction into models of plasticity, diversification and
personality is also an area ripe for research (Stamps & Groothuis
2010). Studies of behaviour are poised to add empirical views to
theoretical studies linking niche construction to the evolution of
cognition (Kerr & Feldman 2003;Kerr 2007)andthemain-
tenance of variation within populations (Hui & Yue 2005;Han
et al. 2006).
Finally, predicting how species will respond to novel and rapidly
changing environments is an important focus of future research,
not just for conservation reasons, but also for understanding gen-
eral evolutionary responses of populations to major environmental
changes. The costs of learning suggest that species with pro-
nounced developmental or plastic responses to novel environ-
ments may have more ‘slow’or K-selected life history traits. On the
other hand, species with short generation times and high fecundity
are thought to have rapid evolutionary responses to novel envi-
ronments (Lande 1998;Reznick & Ghalambor 2001;Bromham
2011). These interactions between plastic and evolutionary re-
sponses suggest that both empirical and theoretical work on re-
sponses to rapid environmental change should more explicitly
incorporate a life history perspective into predictions of which
species will thrive in these novel environments (e.g. Sol 2013).
Acknowledgments
Many thanks to Susan Foster and Andy Sih for organizing the
symposia linking behavioural plasticity to evolution and coping
with rapid human-induced environmental change. Thanks to the
Animal Behavior Society and the National Science Foundation for
funding the symposium. This manuscript was improved based on
discussions with Reuven Dukas and Daniel Sol and comments from
two anonymous referees.
References
Aboitiz, F. 1996. Does bigger mean better? Evolutionary determinants of brain size
and structure. Brain Behavior and Evolution,47, 225e245.
Adams, C. E., Woltering, C. & Alexander, G. 2003. Epigenetic regulation of trophic
morphology through feeding behavior in Arctic charr, Salvelinus alpinus.Bio-
logical Journal of the Linnean Society,78,43e49.
Attwell, D. & Laughlin, S. B. 2001. An energy budget for signaling in the grey
matter of the brain. Journal of Cerebral Blood Flow and Metabolism,21,1133e
1145.
Badyaev, A. V. 2005. Stress-induced variation in evolution: from behavioral plas-
ticity to genetic assimilation. Proceedings of the Royal Society B,272, 877e886.
Bailey, N. W. & Zuk, M. 2008. Acoustic experience shapes female mate choice in
field crickets. Proceedings of the Royal Society B,275, 2645e2650.
Barrickman, N. L., Bastian, M. L., Isler, K. & van Schaik, C. P. 2008. Life history
costs and benefits of encephalization: a comparative test using data from long-
term studies of primates in the wild. Journal of Human Evolution,54, 568e590.
Bateson, P. & Gluckman, P. 2011. Plasticity, Robustness, Development and Evolution.
Cambridge: Cambridge University Press.
Bell, A. M. 2007. Future directions in behavioral syndromes research. Proceedings of
the Royal Society B,274, 755e761.
Bell, G. & Collins, S. 2008. Adaptation, extinction and global change. Evolutionary
Applications,1,3e16.
Bell, G. & Gonzalez, A. 2009. Evolutionary rescue can prevent extinction following
environmental change. Ecology Letters,12, 942e948.
Bergstrom, A., Nylin, S. & Nygren, G. H. 2004. Conservative resource utilization in
the common blue butterfly: evidence for low costs of accepting absent host
plants? Oikos,107,345e351.
Bolnick, D. I., Svanback, R., Fordyce, J. A., Yang, L. H., Davis, J. M., Hulsey, C. D. &
Forister, M. L. 2003. The ecology of individuals: incidence and implications of
individual specialization. American Naturalist,161,1e28.
Bromham, L. 2011. The genome as a life-history character: why rate of molecular
evolution varies between mammal species. Philosophical Transactions of the
Royal Society B,366, 2503e2513.
Bruni, N. C., Young, J. P. & Dengler, N. G. 1996. Leaf developmental plasticity of
Ranunculus flabellaris in response to terrestrial and submerged environments.
Canadian Journal of Botany-Revue Canadienne de Botanique,74, 823e837.
Brzek, P., Kohl, K., Caviedes-Vidal, E. & Karasov, W. H. 2009. Developmental ad-
justments of house sparrow (Passer domesticus) nestlings to diet composition.
Journal of Experimental Biology,212, 1284e1293.
Burghardt, G. M. & Krause, M. A. 1999. Plasticity of foraging behavior in garter
snakes (Thamnophis sirtalis) reared on different diets. Journal of Comparative
Psychology,113,277e285.
Byers, J. A., Wiseman, P. A., Jones, L. & Roffe, T. J. 2005. A large cost of female mate
sampling in pronghorn. American Naturalist,166 ,661e668.
Cahu, C. L., Infante, J. L. Z., Peres, A., Quazuguel, P. & Le Gall, M. M. 1998. Algal
addition in sea bass (Dicentrarchus labrax) larvae rearing: effect on digestive
enzymes. Aquaculture,161,479e489.
Carrete, M. & Tella, J. L. 2011. Inter-individual variability in fear of humans and
relative brain size of the species are related to contemporary urban invasion in
birds. PLoS One,6,8.
Catania, K. C. & Kaas, J. H. 1997. Somatosensory fovea in the star-nosed mole:
behavioral use of the star in relation to innervation patterns and cortical rep-
resentation. Journal of Comparative Neurology,387,215e233.
Changizi, M. A. 2003. Relationship between number of muscles, behavioral rep-
ertoire size, and encephalization in mammals. Journal of Theoretical Biology,
220,1
57e168 .
Chevin, L. M., Lande, R. & Mace, G. M. 2010. Adaptation, plasticity, and extinction in
a changing environment: towards a predictive theory. PLoS Biology,8,8.
Cronin, T. W., Caldwell, R. L. & Marshall, J. 2001. Sensory adaptation: tunable
colour vision in a mantis shrimp. Nature,411,547e548.
Davis, J. M. & Stamps, J. A. 2004. The effect of natal experience on habitat pref-
erences. Trends in Ecology & Evolution,19,411e416.
DeWitt, T. J., Sih, A. & Wilson, D. S. 1998. Costs and limits of phenotypic plasticity.
Trends in Ecology & Evolution,13,77e81.
Dingemanse, N. J. & Wolf, M. 2010. Recent models for adaptive personality dif-
ferences: a review. Philosophical Transactions of the Royal Society B,365, 3947e
3958.
Doupe, A. J. & Kuhl, P. K.1999. Birdsong and human speech: common themes and
mechanisms. Annual Review of Neuroscience,22, 567e631.
Duckworth, R. A. 2010. Evolution of personality: developmental constraints on
behavioral flexibility. Auk,127, 752e758.
Dukas, R. 1998. Evolutionary ecology of learning. In: Cognitive Ecology: the Evolu-
tionary Ecology of Information Processing and Decision Making (Ed. by R. Dukas),
pp. 129e174. Chicago: University of Chicago Press.
Edelaar, P., Siepielski, A. M. & Clobert, J. 2008. Matching habitat choice causes
directed gene flor: a neglected dimension in evolution and ecology. Evolution,
62, 2462e2472.
Ericsson, K. A., Krampe, R. T. & Teschromer, C. 1993. The role of deliberate practice
in the acquisition of expert performance. Psychological Review,100 , 363e406.
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e1011 1009
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
Eurich, C. W., Schwegler, H. & Woesler, R. 1997. Coarse coding: applications to the
visual system of salamanders. Biological Cybernetics,77,41e47.
Fowler-Finn, K. D. & Rodriguez, R. L. 2012. Experience-mediated plasticity in
mate preferences: mating assurance in a variable environment. Evolution,66,
459e468.
Fox, L. R. & Morrow, P. A. 1981. Specialization: species property or local phe-
nomenon. Science,211, 887e893.
Frank, S. 1996. The design of natural and artificial adaptive systems. In: Adaptation
(Ed. by M. Rose & G. Lauder), pp. 451e505. New York: Academic Press.
Frank, S. A. 2011. Natural selection. II. Developmental variability and evolutionary
rate. Journal of Evolutionary Biology,24,2310e2320.
Frederickson, M. E., Greene, M. J. & Gordon, D. M. 2005. ‘Devil’s gardens’
bedevilled by ants. Nature,437, 495e496.
Fuller, R. C., Noa, L. A. & Strellner, R. S. 2010. Teasing apart the many effects of
lighting environment on opsin expression and foraging preference in bluefin
killifish. American Naturalist,176,1e13.
Ghalambor, C. K., McKay, J. K., Carrol, S. P. & Reznick, D. N. 2007. Adaptive versus
non-adaptive phenotypic plasticity and the potential for contemporary adap-
tation in new environments. Functional Ecology,21, 394e407.
Glendenning, K. K. & Masterton, R. B. 1998. Comparative morphometry of mam-
malian central auditory systems: variation in nuclei and form of the ascending
system. Brain Behavior and Evolution,51,59e89.
Gonzalez-Lagos, C., Sol, D. & Reader, S. M. 2010. Large-brained mammals live
longer. Journal of Evolutionary Biology,23,1064e1074.
Greenberg, R. 1983. The role of neophobia in determining the degree of foraging
specialization in some migrant warblers. American Naturalist,122,444e453.
Greenberg, R. 1989. Neophobia, aversion to open space, and ecological plasticity in
song and swamp sparrows. Canadian Journal of Zoology-Revue Canadienne de
Zoologie,67,1194e1199.
Gross, K., Pasinelli, G. & Kunc, H. P. 2010. Behavioral plasticity allows short-term
adjustment to a novel environment. American Naturalist,176 , 456e464.
Halfwerk, W. & Slabbekoorn, H. 2009. A behavioral mechanism explaining noise-
dependent frequency use in urban birdsong. Animal Behaviour,78, 1301e1307.
Hampson, S. 1991. Generalization and specialization in artificial neural networks.
Progress in Neurobiology,37, 383e431.
Han, X. Z., Li, Z. Z., Hui, C. & Zhang, F. 2006. Polymorphism maintenance in
a spatially structured population: a two-locus genetic model of niche con-
struction. Ecological Modelling,192,160e174.
Herman, B. H. & Nagy, Z. M. 1977. Development of learning and emmory in mice
genetically selected for differences in brain weight. Developmental Psychobiol-
ogy,10,65e75.
Hill, G. E. 1992. Proximate basis of variation in carotenoid pigmentation in male
house finches. Auk,109 ,1e12.
Hinton, G. & Nolan, S. 1987. How learning can guide evolution. Complex Systems,1,
495e502.
Hoffmann, A. A. & Sgro, C. M. 2011. Climate change and evolutionary adaptation.
Nature,470,479e485.
Hopfield, J. J. 1982. Neural networks and physical systems with emergent collective
computational abilities. Proceedings of the National Academy of Sciences, U.S.A.,
79,2554e2558.
Hoverman, J. T. & Relyea, R. A. 2007. How flexible is phenotypic plasticity?
Developmental windows for trait induction and reversal. Ecology,88, 693e705.
Huerta, R., Nowotny, T., Garcia-Sanchez, M., Abarbanel, H. D. I. &
Rabinovich, M. I. 2004. Learning classification in the olfactory system of in-
sects. Neural Computation,16, 1601e1640.
Hui, C. & Yue, D. X. 2005. Niche construction and polymorphism maintenance in
metapopulations. Ecological Research,20,115e119 .
Hutcheon, J. M., Kirsch, J. W. & Garland, T. 2002. A comparative analysis of brain
size in relation to foraging ecology and phylogeny in the chiroptera. Brain
Behavior and Evolution,60,165e180.
Huttenlocher, P. R. 1979. Synaptic density in human frontal cortex: developmental
changes and effects of aging. Brain Research,163,195e205.
Iwaniuk, A. N. & Nelson, J. E. 2003. Developmental differences are correlated with
relative brain size in birds: a comparative analysis. Canadian Journal of Zoology-
Revue Canadienne De Zoologie,81,1913e1928.
Iwaniuk, A. N., Nelson, J. E. & Whishaw, I. Q. 1999. The relationships between
brain regions and forelimb dexterity in marsupials (Marsupialia): a comparative
test of the principle of proper mass. Australian Journal of Zoology,48,99e110 .
Janz, N. & Nylin, S. 1997. The role of female search behavior in determining host
plant range in plant feeding insects: a test of the information processing hy-
pothesis. Proceedings of the Royal Society B,264,701e707.
Jensen, C. & Fuller, J. L. 1978. Learning performance varies with brain weight in
heterogenous mouse lines. Journal of Comparative and Physiological Psychology,
92, 830e836.
Kaelbling, L. P., Littman, M. L. & Moore, A. W. 1996. Reinforcement learning:
a survey. Journal of Artificial Intelligence Research,4,2
37e285.
Karpestam, E., Wennersten, L. & Forsman, A. 2012. Matching habitat choice by
experimentally mismatched phenotypes. Evolutionary Ecology,26, 893e907.
Kerr, B. 2007. Niche construction and cognitive evolution. Biological Theory,2,
250e262.
Kerr, B. & Feldman, M. W. 2003. Carving the cognitive niche: optimal learning
strategies in homogeneous and heterogeneous environments. Journal of Theo-
retical Biology,220,169e188.
Kylafis, G. & Loreau, M. 2008. Ecological and evolutionary consequences of niche
construction for its agent. Ecology Letters,11, 1072e1081.
Lande, R. 1998. Anthropogenic, ecological and genetic factors in extinction and
conservation. Researches on Population Ecology,40, 259e269.
Lande, R. 2009. Adaptation to an extraordinary environment by evolution of phe-
notypic plasticity and genetic assimilation. Journal of Evolutionary Biology,22,
1435e1446.
Laughlin, S. B., van Steveninck, R. R. D. & Anderson, J. C. 1998. The metabolic cost
of neural information. Nature Neuroscience,1,36e41.
Laverty, T. M. & Plowright, R. C. 1988. Flower handling by bumblebees: a com-
parison of specialists and generalists. Animal Behaviour,36, 733e740.
Lefebvre, L. & Sol, D. 2008. Brains, lifestyles and cognition: are there general
trends? Brain Behavior and Evolution,72,135e144.
Lefebvre, L., Marino, L., Sol, D., Lemieux-Lefebvre, S. & Arshad, S. 20 06. Large
brains and lengthened life history periods in odontocetes. Brain Behaviour and
Evolution,68,218e228.
Levey, D. J., Londono, G. A., Ungvari-Martin, J., Hiersoux, M. R., Jankowski, J. E.,
Poulsen, J. R., Stracey, C. M. & Robinson, S. K. 2009. Urban mockingbirds
quickly learn to identify individual humans. Proceedings of the National Academy
of Sciences, U.S.A.,106 , 8959e8962.
Levins, R. 1968. Evolution in Changing Environments: Some Theoretical Explorations.
Princeton, New Jersey: Princeton University Press.
Losos, J. B., Creer, D. A., Glossip, D., Goellner, R., Hampton, A., Roberts, G.,
Haskell, N., Taylor, P. & Ettling, J. 2000. Evolutionary implications of phenotypic
plasticity in the hindlimb of the lizard Anolis sagrei.Evolution,54,301e305.
Luo, L. Q. & O’Leary, D. D. M. 2005. Axon retraction and degeneration in devel-
opment and disease. Annual Review of Neuroscience,28,127e156.
Luther, D. A. & Derryberry, E. P. 2012. Birdsongs keep pace with city life: changes
in song over time in an urban songbird affects communication. Animal Behav-
iour,83, 1059e106 6.
Mabry, K. E. & Stamps, J. A. 2008. Dispersing brush mice prefer habitat like home.
Proceedings of the Royal Society B,275, 543e548.
Mace, G. M. & Eisenberg, J. F. 1982. Competition, niche specialization and the
evolution of brain size in the genus Peromyscus.Biological Journal of the Linnean
Society,17, 243e257.
McEwen, B. S. & Wingfield, J. C. 2003. The concept of allostasis in biology and
biomedicine. Hormones and Behavior,43,2e15.
Maklakov, A. A., Immler, S., Gonzalez-Voyer, A., Ronn, J. & Kolm, N. 2011. Brains
and the city: big-brained passerine birds succeed in urban environments.
Biology Letters,7,730e732.
Margulies, C., Tully, T. & Dubnau, J. 2005. Deconstructing memory in Drosophila.
Current Biology,15, R700eR713.
Marler, P. 1997. Three models of song learning: evidence from behavior. Journal of
Neurobiology,33,5
01e516.
Mery, F. & Burns, J. G. 2010. Behavioral plasticity: an interaction between evolution
and experience. Evolutionary Ecology,24, 571e583.
Mery, F. & Kawecki, T. J. 2003. A fitness cost of learning ability in Drosophila
melanogaster.Proceedings of the Royal Society B,270, 2465e2469.
Mery, F. & Kawecki, T. J. 2004. An operating cost of learning in Drosophila mela-
nogaster.Animal Behaviour,68, 589e598.
Mery, F. & Kawecki, T. J. 2005. A cost of long-term memory in. Drosophila. Science,
308, 1148.
Mettke-Hofmann, C., Winkler, H. & Leisler, B. 2002. The significance of ecological
factors for exploration and neophobia in parrots. Ethology,108 , 249e272.
Mettke-Hofmann, C., Lorentzen, S., Schlicht, E., Schneider, J. & Werner, F. 2009.
Spatial neophilia and spatial neophobia in resident and migratory warblers
(Sylvia). Ethology,115,482e492.
Møller, A. P. 2009. Successful city dwellers: a comparative study of the ecological
characteristics of urban birds in the Western Palearctic.Oecologia,159,849e858.
Moran, N. A . 1992. The evolutionary maintenance of alternative phenotypes.
American Naturalist,139, 971e989.
Niven, J. E., Anderson, J. C. & Laughlin, S. B. 2007. Fly photoreceptors demonstrate
energy-information trade-offs in neural coding. PLoS Biology,5, 828e840.
Oppenheim, R. W. 1991. Cell death during development of the nervous system.
Annual Review of Neuroscience,14, 453e501.
Padilla, D. K. & Adolph, S. C. 1996. Plastic inducible morphologies are not always
adaptive: the importance of time delays in a stochastic environment. Evolu-
tionary Ecology,10,105e117.
Palumbi, S. R. 2001. Evolution: humans as the world’s greatest evolutionary force.
Science,293, 1786e1790.
Pfennig, D. W., Wund, M. A., Snell-Rood, E. C., Cruickshank, T., Schlichting, C. D.
& Moczek, A. P. 2010. Phenotypic plasticity’s impacts on diversification and
speciation. Trends in Ecology & Evolution,25, 459e467.
Pigliucci, M. & Murren, C. J. 2003. Genetic assimilation and a possible evolutionary
paradox: can macroevolution sometimes be so fast as to pass us by? Evolution,
57, 1455e1464.
Price, T. 1998. Sexual selection and natural selection in bird speciation. Philosophical
Transactions of the Royal Society of London, Series B,353,251e260.
Price, T. D., Qvarnström, A. & Irwin, D. E. 2003. The role of phenotypic plasticity in
driving genetic evolution. Proceedings of the Royal Society B,270, 1433e1440.
Pocock, M. J. O. 2011. Can traits predict species’vulnerability? A test with farmland
passerines in two continents. Proceedings of the Royal Society B,278, 1532e1538.
Purves, D., White, L. E. & Riddle, D. R. 1996. Is neural development Darwinian?
Trends in Neurosciences,19, 460e464.
Reznick, D. N. & Ghalambor, C. K. 2001. The population ecology of contemporary
adaptations: what empirical studies reveal about the conditions that promote
adaptative evolution. Genetica,112 ,183e198.
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e10111010
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
Roth, T. C. & Pravosudov, V. V. 2009. Hippocampal volumes and neuron numbers
increase along a gradient of environmental harshness: a large-scale compari-
son. Proceedings of the Royal Society B,276,401e405.
Roth, T. C., LaDage, L. D. & Pravosudov, V. V. 2011. Variation in hippocampal
morphology along an environmental gradient: controlling for the effects of day
length. Proceedings of the Royal Society B,278,2662e2667.
Sabat, P., Riverosa, J. M. & Lopez-Pinto, C. 2005. Phenotypic flexibility in the in-
testinal enzymes of the African clawed frog Xenopus laevis.Comparative Bio-
chemistry and Physiology A: Molecular & Integrative Physiology,140,135e139.
Schlichting, C. D. & Pigliucci, M. 1998. Phenotypic Evolution: a Reaction Norm
Perspective. Sunderland, Massachusetts: Sinauer.
Shaw, K. A., Scotti, M. L. & Foster, S. A. 2007. Ancestral plasticity and the evolu-
tionary diversification of courtship behaviour in threespine sticklebacks. Animal
Behaviour,73,415e472.
Sherry, D. F., Jacobs, L. F. & Gaulin, S. J. C. 1992. Spatial memory and adaptive
specialization of the hippocampus. Trends in Neurosciences,15, 298e303.
Sih, A., Bell, A. & Johnson, J. C. 2004. Behavioral syndromes: an ecological and
evolutionary overview. Trends in Ecology & Evolution,19,372e378.
Sih,A.,Ferrari,M.C.O.&Harris,D.J.2011. Evolution and behavioral responses to
human-inducedrapid environmentalchange.EvolutionaryApplications,4,367e387.
Slabbekoorn, H. & Smith, T. B. 2002. Bird song, ecology and speciation. Philo-
sophical Transactions of the Royal Society of London Series B,357, 493e503.
Slagsvold, T. & Wiebe, K. L. 2007. Learning the ecological niche. Proceedings of the
Royal Society B,274,19e23.
Snell-Rood, E. 2012. Selective processes in development: implications for the costs
and benefits of phenotypic plasticity. Integrative and Comparative Biology,52,
31e42.
Snell-Rood, E. C. & Papaj, D. R. 2009. Patterns of phenotypic plasticity in common
and rare environments: a study of host use and colour learning in the cabbage
white butterflyPieris rapae.American Naturalist,173,615e631.
Snell-Rood, E. C., Papaj, D. R. & Gronenberg, W. 2009. Brain size: a global or
induced cost of learning? Brain Behavior and Evolution,73, 111 e128.
Snell-Rood, E., Van Dyken, J. D., Cruickshank, T., Wade, M. & Moczek, A. 2010.
Toward a population genetic framework of developmental evolution: costs,
limits, and consequences of phenotypic plasticity. BioEssays,32,71e81.
Snell-Rood, E. C., Davidowitz, G. & Papaj, D. R. 2011. Reproductive tradeoffs of
learning in a butterfly. Behavioral Ecology,22,291e302.
Sol, D. 2009. Revisiting the cognitive buffer hypothesis for the evolution of large
brains. Biology Letters,5,130e133.
Sol, D. 2013. Behaviouralflexibility fora life in the city. AnimalBehaviour,85,1101e1112.
Sol, D., Timmermans, S. & Lefebvre, L. 2002. Behavioural flexibility and invasion
success in birds. Animal Behaviour,63,495e502.
Sol, D., Duncan, R. P., Blackburn, T. M., Cassey, P. & Lefebvre, L. 2005. Big brains,
enhanced cognition, and response of birds to novel environments. Proceedings
of the National Academy of Sciences, U.S.A.,102 , 5460e5465.
Sol, D., Szekely, T., Liker, A. & Lefebvre, L. 2007. Big-brained birds survive better in
nature. Proceedings of the Royal Society B,274,763e769.
Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. 2008. Brain size predicts the success
of mammal species introduced into novel environments. American Naturalist,
Supplement,172 , S63eS71.
Sporns, O., Tononi, G. & Edelman, G. M. 2000. Connectivity and complexity: the
relationship between neuroanatomy and brain dynamics. Neural Networks,13,
909e922.
Stamps, J. 1995. Motor learning and the value of familiar space. American Naturalist,
146 ,41e58.
Stamps, J. A. & Groothuis, T. G. G. 2010. Developmental perspectives on person-
ality: implications for ecological and evolutionary studies of individual differ-
ences. Philosophical Transactions of the Royal Society B,365, 4029e4041.
Stephens, D. 1992. Learning and behavioral ecology: incomplete information
and environmental predictability. In: Insect Learning : Ecological and Evolu-
tionary Perspectives (Ed. by D. Papaj & A. Lewis), pp. 195e217. New York:
Chapman & Hall.
Tauber,M.J.&Tauber,C.A.1976. Insect seasonality: diapause maintenance,
termination, and postdiapause development. Annual Review of Entomology,21,
81e107.
Toomey, M. B. & McGraw, K. J. 2009. Seasonal, sexual, and quality related variation
in retinal carotenoid accumulation in the house finch (Carpodacus mexicanus).
Functional Ecology,23,321e329.
Tuomainen, U. & Candolin, U. 2011. Behavioral responses to human-induced
environmental change. Biological Reviews,86, 640e657.
Van Buskirk, J. & Steiner, U. K. 2009. The fitness costs of developmental canal-
ization and plasticity. Journal of Evolutionary Biology,22, 852e860.
Wainwright, P. C., Osenberg, C. W. & Mittelbach, G. G. 1991. Trophic polymor-
phism in the pumpkinseed sunfish (Lepomis gibbosus Linneaus): effects of
environment on ontogeny. Functional Ecology,5,40
e55.
Wcislo, W. T. & Tierney, S. M. 2009. Behavioral environments and niche con-
struction: the evolution of dim-light foraging in bees. Biological Reviews,84,
19e37.
West-Eberhard, M. J. 2003. Developmental Plasticity and Evolution. New York: Ox-
ford University Press.
Wheeler, D. E. 1986. Developmental and physiological determinants of case in
social hymenoptera: evolutionary implications. American Naturalist,128,13e34.
Wimberger, P. H. 1991. Plasticity of jaw and skull morphology in the neotropical
cichlids Geophagus brasiliensis and G. steindachneri.Evolution,45, 1545e1563.
Wingfield, J. C. 2003. Control of behavioural strategies for capricious environments.
Animal Behaviour,66,807e815.
Woo, K. J., Elliott, K. H., Davidson, M., Gaston, A. J. & Davoren, G. K. 2008. Indi-
vidual specialization in diet by a generalist marine predator reflects special-
ization in foraging behavior. Journal of Animal Ecology,77, 1082e1091.
Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L. & Russello, M. A. 2010.
Behavioral flexibility and species invasion: the adaptive flexibility hypothesis.
Ethology Ecology & Evolution,22, 393e404.
Wund, M. A., Baker, J. A., Clancy, B., Golub, J. L. & Foster, S. A. 2008. A test of the
‘Flexible stem’model of evolution: ancestral plasticity, genetic accommodation,
and morphological divergence in the threespine stickleback radiation. American
Naturalist,172 ,449e462.
Wund,M. A., Valena, S., Wood,S. & Baker, J.A. 2012. Ancestralplasticity and allometry
in threespine stickleback reveal phenotypes associated with derived, freshwater
ecotypes. Biological Journal of the Linnean Society,105,573e583.
Young, R. L. & Badyaev, A. V. 2010. Developmental plasticity links local adaptation
and evolutionary diversification in foraging morphology. Journal of Experimental
Zoology B,314, 434e444.
Zera, A. J. & Denno, R. F. 1997. Physiology and ecology of dispersal polymorphism in
insects. Annual Review of Entomology,42, 207e230.
Zohary, E. 1992. Population coding of visual stimuli by coritcal neurons tuned to
more than one dimension. Biological Cybernetics,66, 265e272.
E. C. Snell-Rood / Animal Behaviour 85 (2013) 1004e1011 1011
SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION
