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First published online May 26, 2006
Journal of Experimental Biology 209, 2344-2361 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02244
Review Article: Phenotypic Plasticity in Evolution |
Phenotypic plasticity and experimental evolution
Department of Biology, University of California, Riverside, Riverside, CA 92521, USA
* Author for correspondence (e-mail: tgarland{at}ucr.edu)
Accepted 29 March 2006
Summary
Natural or artificial selection that favors higher values of a particular trait within a given population should engender an evolutionary response that increases the mean value of the trait. For this prediction to hold, the phenotypic variance of the trait must be caused in part by additive effects of alleles segregating in the population, and also the trait must not be too strongly genetically correlated with other traits that are under selection. Another prediction, rarely discussed in the literature, is that directional selection should favor alleles that increase phenotypic plasticity in the direction of selection, where phenotypic plasticity is defined as the ability of one genotype to produce more than one phenotype when exposed to different environments. This prediction has received relatively little empirical attention. Nonetheless, many laboratory experiments impose selection regimes that could allow for the evolution of enhanced plasticity (e.g. desiccation trials with Drosophila that last for several hours or days). We review one example that involved culturing of Drosophila on lemon for multiple generations and then tested for enhanced plasticity of detoxifying enzymes. We also review an example with vertebrates that involves selective breeding for high voluntary activity levels in house mice, targeting wheel-running behavior on days 5+6 of a 6-day wheel exposure. This selection regime allows for the possibility of wheel running itself or subordinate traits that support such running to increase in plasticity over days 1–4 of wheel access. Indeed, some traits, such as the concentration of the glucose transporter GLUT4 in gastrocnemius muscle, do show enhanced plasticity in the selected lines over a 5–6 day period. In several experiments we have housed mice from both the Selected (S) and Control (C) lines with or without wheel access for several weeks to test for differences in plasticity (training effects). A variety of patterns were observed, including no training effects in either S or C mice, similar changes in both the S and C lines, greater changes in the S lines but in the same direction in the C lines, and even opposite directions of change in the S and C lines. For some of the traits that show a greater training effect in the S lines, but in the same direction as in C lines, the greater effect can be explained statistically by the greater wheel running exhibited by S lines (`more pain, more gain'). For others, however, the differences seem to reflect inherently greater plasticity in the S lines (i.e. for a given amount of stimulus, such as wheel running/day, individuals in the S lines show a greater response as compared with individuals in the C lines). We suggest that any selection experiment in which the selective event is more than instantaneous should explore whether plasticity in the appropriate (adaptive) direction has increased as a component of the response to selection.
Key words: adaptive plasticity, artificial selection, complex traits, environment, exercise, genotype, locomotion, mouse
Introduction
Natural selection tends to act most strongly on aspects of the phenotype
(traits) at relatively high levels of biological organization because they are
the most strongly correlated with Darwinian fitness (e.g. lifetime
reproductive success). Components of life history, behaviors and aspects of
organismal performance (for reviews, see
Ketterson and Nolan, Jr, 1999
;
Irschick and Garland, Jr,
2001; Kingsolver and Huey,
2003; Costa and Sinervo,
2004) are `complex traits' in that they are composed of many
subordinate traits at lower levels of biological organization
(Swallow and Garland, Jr,
2005) (Fig. 1).
Thus, the evolutionary response to selection on such complex traits
necessarily entails associated changes in aspects of morphology, physiology
and biochemical pathways (Ghalambor et
al., 2003; Sinervo and
Calsbeek, 2003). In addition, complex patterns of trade-offs and
constraints are expected to occur, and the genetic architecture underlying
these may itself evolve in response to selection (e.g.
Chippindale et al., 2003;
Rose et al., 2005).
|
;
Kupferberg, 1997). Whenever
they act, the consequences of environmental effects are often termed
developmental or phenotypic plasticity.
Directional natural selection is predicted to have various effects, some
fairly obvious but others less so. If natural selection in a given population
favors individuals with higher values of a particular trait, then the
population mean value of that trait is predicted to increase from generation
to generation (Fig. 2A),
assuming that some additive genetic variance exists and that the trait is not
too strongly genetically correlated with other traits under selection.
Evolutionary biology is replete with empirical examples illustrating the
validity of this prediction (Endler,
1986). Moving from phenotype to genotype, a second prediction is
that alleles with `appropriate' pleiotropic effects will be favored, which
would facilitate the coordinated evolution of components of complex
phenotypes. For example, if selection were to favor individuals that foraged
widely to find food, then alleles that increased motivation for high locomotor
activity might be favored most directly, and the subset of those alleles that
also tended to increase ability for high activity would be particularly
favored. This sort of process, in which the genetic architecture of the traits
involved (especially the additive genetic variance–covariance matrix)
evolves to become more consistent with the prevailing pattern of multivariate
selection, could facilitate further evolution and adaptive radiation (e.g.
Garland, 1994;
Schluter, 1996). A somewhat
more subtle genetic prediction is that directional selection should tend to
favor alleles that exhibit phenotypic dominance in the direction of selection,
and this has also received empirical support (e.g.
Broadhurst and Jinks, 1974;
Henderson, 1981;
Hewitt et al., 1981;
Mather and Jinks, 1982;
Falconer, 1989;
Garland et al., 1990;
Lynch, 1994;
Lynch and Walsh, 1998). For
example, under a selective regime that favored high activity levels, alleles
that promoted high activity and were dominant to alleles with neutral or
negative effects on activity would be the most favored among the spectrum of
`high-activity alleles'.
|
;
Wilson and Franklin, 2002;
Gabriel, 2005) on the
population. More specifically, plasticity of the trait under selection or of
subordinate traits that contribute to that trait should increase in the
direction that would be adaptive (tend to confer higher organismal performance
and/or higher Darwinian fitness) under the prevailing selective regime
(Fig. 2B). To clarify this
hypothesis, we need to distinguish between selective agents that (1) impose
selective events that are virtually instantaneous relative to the time course
of possible plastic responses and (2) impose relatively prolonged selective
events. As an example of an `instantaneous' selective event, a cheetah
(selective agent) chases an antelope (selective event) and the outcome (life
or death for the antelope) occurs so quickly (a matter of seconds) that the
exercise physiology of the antelope has no chance to `train'. In addition to
its own behavior, the motivation and abilities of the cheetah, and some
element of chance (e.g. tripping over a rock), what determines the outcome of
the selective event are the `innate' (constitutive or intrinsic) exercise
abilities of the antelope at the instant the cheetah began its pursuit. As an
example of a `prolonged' selective event, global warming trends that occur
over years would allow long-lived organisms to acclimatize to the higher
temperatures in a way that might be beneficial (e.g. upregulation of
heat-shock proteins). Thus, the Darwinian fitness of individuals in a
crocodilian population experiencing environmental warming might depend on both
innate and induced components of heat tolerance. Of course, the distinction
between instantaneous and prolonged selective events is not always clean. For
example, an antelope might learn from a failed close encounter with a cheetah
and became better at avoiding capture in subsequent encounters by altering its
own behavior. The classic selective event of an ice storm that killed some
(but not all) sparrows in a fortuitously collected sample
(Bumpus, 1899;
Lande and Arnold, 1983;
Endler, 1986) lasted
overnight, such that individual sparrows may have varied in thermal tolerance
because of both innate and induced individual variation in components of heat
tolerance.
The actual mechanisms for the evolution of increased plasticity could be
several, of which we will mention two. First, referring to
Fig. 2A, in addition to
individuals whose phenotypes are intrinsically high, individuals that exhibit
plasticity in the direction favored by selection will tend to be among the
`survivors' each generation; thus, appropriately plastic individuals will be
favored by phenotypic selection [see p. 67
(Falconer, 1990)]. If
plasticity is heritable, then it will evolve in response to such selection
(assuming genetic correlations with other traits under selection are not too
strong). Second, the genes that affect the constitutive value of the phenotype
might also have pleiotropic effects (in the appropriate direction) on the
plastic response of that phenotype when the organism experiences chronic (more
than instantaneous) exposure to the selective agent (e.g. gradual warming,
repeated encounters with predators). The genetics of plasticity are discussed
further elsewhere (Scheiner,
1993; Pigliucci,
2005). Here it is also worth noting that the evolution of
plasticity is related to the concept of genetic assimilation, a process in
which environmentally induced phenotypic variation that is favored by
selection (natural or artificial) gradually (across many generations) comes to
be constitutively produced [reviewed elsewhere in this issue
(Pigliucci et al., 2006)].
The foregoing ideas about evolutionary processes would seem to be implied
by the `beneficial acclimation hypothesis' (see below), but to our knowledge
they have not been discussed so explicitly in the literature. In any case, we
hypothesize that the mean plasticity of a population under directional
selection (Fig. 2A) should
evolve from being neutral (or possibly deleterious) to being beneficial or
adaptive (Fig. 2B). This
evolutionary hypothesis has received little direct empirical attention (but
see Falconer, 1990;
Scheiner, 2002). Nonetheless,
as discussed below, many laboratory experiments impose selective regimes that
could allow for the evolution of enhanced plasticity (e.g. desiccation trials
with Drosophila that last for several days), and the few that have
tested for evolutionary changes in plasticity have found some evidence for
it.
When behavioral performance traits are the subject of directional
selection, the role of phenotypic plasticity in evolutionary response may be
particularly interesting [other perspectives on the importance of behavior
have been discussed elsewhere (Huey et
al., 2003; Price et al.,
2003; Price,
2006)]. The term `self-induced adaptive plasticity' was proposed
(Swallow et al., 2005) for
situations in which a behavior induces plastic changes in morphological or
physiological traits that in turn enhance the ability to perform the behavior.
For example, animals that migrate altitudinally might make `trial runs' that
would cause cardiovascular, pulmonary or metabolic changes that would improve
their ability to function at high altitude. Similarly, animals that begin
feeding on a new type of food may experience changes in digestive enzymes that
increase efficiency of nutrient extraction and/or detoxification [examples of
related effects of diet are reported elsewhere, including references therein
(Geiser et al., 1997;
Kupferberg, 1997)].
The first purpose of this paper is to provide a brief introduction to
phenotypic plasticity from an ecological and evolutionary perspective [see
also elsewhere in this issue (Fordyce,
2006; Pigliucci et al.,
2006; Price,
2006)]. Second, we discuss how the evolution of plasticity can be
studied, with an emphasis on the experimental evolution approach. Finally, we
review some results from a study on the experimental evolution of high
voluntary activity levels in house mice, including examples of self-induced
adaptive plasticity.
Defining phenotypic plasticity
From the perspective of evolutionary biology, classic and dramatic examples
of phenotypic plasticity in animals include wing polymorphisms in some
insects, the timing of metamorphosis in amphibians, and alternative
reproductive tactics in male vertebrates – all of which exhibit complex
neuro-endocrine control mechanisms that are sensitive to various environmental
factors (Ketterson and Nolan, Jr,
1999; Sinervo and Calsbeek,
2003; Boorse and Denver,
2004; Knapp, 2004;
Zera, 2004). From the
biomedical perspective, well-known examples of plasticity include effects of
intentional physical conditioning (exercise training)
(Flück, 2006) such as
weight lifting, on human morphology and physiology. Various biomedical
subfields use additional terminology, such as `metabolic plasticity' or
`cardiac remodeling', and the molecular mechanisms underlying such processes
as muscular and neuronal plasticity are the subject of intensive study [for
reviews, see other articles in this issue
(Flück, 2006;
Hood et al., 2006;
Johnston, 2006;
Magistretti, 2006;
Swynghedauw, 2006)]. (Many
environmental insults, e.g. excessive alcohol consumption, smoking, inhalation
of coal dust, can lead to `plastic' changes in organs and organ systems, but
when such changes are clearly pathological they are not typically included
under the rubric of phenotypic plasticity.) In plants, basic growth form is
notoriously plastic, and many readers will be familiar with the differences
between dandelions growing in shade versus sun [although genetic
differences among clones may also be involved
(Collier and Rogstad,
2004)].
As with the term `adaptation' (see below), phenotypic plasticity can refer
both to a process and to the outcome of that process. Phenotypic plasticity
can be defined formally as the ability of one genotype to produce more than
one phenotype when exposed to different environments, as the modification of
developmental events by the environment, or as the ability of an individual
organism to alter its phenotype in response to changes in environmental
conditions (Gordon, 1992;
Scheiner, 1993;
Via et al., 1995;
Futuyma, 1998;
Freeman and Herron, 2004;
Pigliucci, 2005;
Rezende et al., 2005;
Stearns and Hoekstra, 2005;
Pigliucci et al., 2006). The
range of phenotypes that a given genotype (possessed by an individual organism
or by an entire clone or inbred line) may produce when exposed to a range of
environmental conditions is termed its norm of reaction, and non-parallel
reaction norms of different genotypes indicate the presence of
genotype-by-environment interaction.
The sequence of events involved in phenotypic plasticity often includes the
following components: (1) something in the environment changes; (2) the
organism senses that change; (3) the organism alters gene expression; and (4),
usually, the altered gene expression yields additional observable phenotypes
[e.g. see fig. 8 in Flück's paper in this issue
(Flück, 2006)]. Several
aspects of this scenario require amplification. With respect to (1), we may
attempt to draw a distinction between environmental factors that are external
or internal to an organism. Changes in ambient temperature, humidity or oxygen
concentration would constitute external environmental factors, and many
organisms respond to these with phenotypic plasticity that involves multiple
organ systems and multiple levels of biological organization. Mechanical
overload of the heart is an example of an environmental change that occurs
within an organism, and it leads mainly to organ-specific changes that
necessarily involve fewer levels of biological organization
(Swynghedauw, 2006). Of
course, external environmental `stresses' can also lead to tissue-specific
responses (e.g. Cossins et al.,
2006). Nonetheless, we may predict that, in general, external
environmental changes will lead to more and more pervasive plastic responses
as compared with internal changes. With respect to (2), some changes may occur
without any formal sensing by the organism, e.g. as a result of direct (and
possibly differential) effects of temperature on the rates of ongoing
biochemical and physiological processes. With respect to (3), it is important
to note that some plastic responses need not involve changes in gene
expression (transcription) but instead could occur via
phosphorylation of existing proteins, changes in protein levels caused by
variation in protein ubiquitination, or stimulation of existing microRNAs
(Nelson et al., 2003;
Schratt et al., 2006). For
point (4) we emphasize the word `usually' because it is possible that lower
level traits might change in offsetting ways such that higher level traits
could show little or no apparent change. For example, it would be
theoretically possible (though perhaps unlikely) for exercise training to
cause an increase in maximal heart rate but a reduction in stroke volume such
that cardiac output was unchanged.
Acclimation and acclimatization
(Wilson and Franklin, 2002),
as well as learning and memory (e.g.
Magistretti, 2006), are
encompassed by the most inclusive definitions of phenotypic plasticity.
Therefore, environmentally induced changes may or may not be reversible,
depending on the organism, trait, and when in the lifecycle and for how long
the environmental exposure occurs (Hatle,
2004; Johnston,
2006). If the capacity for change is more-or-less fully
reversible, then it may be termed phenotypic flexibility
(Piersma and Lindstrom,
1997).
Whether reversible or not, it is generally assumed that environmentally
induced modifications are adaptive in the sense that they improve organismal
function and/or enhance Darwinian fitness of the individual organisms that
exhibit such effects (Nunney and Cheung,
1997). In fact, this may or may not be true, and the claim that
such changes will aid the organism has been termed the beneficial acclimation
hypothesis (Leroi et al.,
1994; Huey and Berrigan,
1996; Huey et al.,
1999; Wilson and Franklin,
2002). In some cases, behavioral plasticity can shield lower level
traits from selection (Huey et al.,
2003; Price et al.,
2003). At the population level, phenotypic plasticity in behavior
and other traits can facilitate invasions of new habitats
(Price et al., 2003;
Price, 2006;
Pigliucci et al., 2006). As
reviewed elsewhere in this issue (Fordyce,
2006), many ecological (cross-species) interactions are mediated
by the phenotypically plastic responses of one or more species involved in the
interaction. Some of these ecological interactions can be quite complex and
difficult to predict, as when an herbivore induces a plant phenotype that in
turn affects the performance of other herbivores
(Fordyce, 2006)
At this point it is worth remembering that the word adaptation has numerous
meanings in biology (Garland and Carter,
1994; Bennett,
1997). Most generally, we should keep in mind the distinction
between what is often called `physiological adaptation' (environmentally
induced changes that occur within individual organisms during their lifetimes,
including acclimation and acclimatization) and `evolutionary adaptation'
(cross-generational changes in the genetic composition of a population in
response to natural selection). Physiological adaptation is one type of
phenotypic plasticity, but the ability to be plastic for any particular trait
may also be an evolutionary adaptation whose details vary among organisms.
As noted above, although biologists have usually assumed that physiological
adaptation is adaptive in the evolutionary sense, this is not always a safe
assumption because some changes will be simply the result of activation of
control systems designed to do something else, and they can even be
maladaptive, including various human pathologies
(Nesse, 2005;
Swynghedauw, 2006). In
general, non-adaptive plasticity might be expected to occur any time that an
organism is exposed to environmental conditions with which it is `unfamiliar'
in terms of its evolutionary history. This follows from the general
evolutionary principle that organisms gradually lose abilities and traits that
are no longer under positive selection, well-illustrated by things like blind
cave fish or flightless birds on islands that lack predators
(Diamond, 1986). Thus, imagine
a species that has inhabited low-elevation environments for millions of years,
adapting evolutionarily to function (reasonably) well in `normal' levels of
atmospheric oxygen (
21%). If one were to expose individuals of this
species to high altitude, then they might be expected to exhibit inappropriate
physiological responses to reduced atmospheric oxygen. The literature on human
physiological responses to high altitude, both acute and chronic, is
interesting in this context because it offers conflicting views on whether and
to what extent various changes are adaptive versus maladaptive, and
whether long-term, high-altitude native populations exhibit evolutionary
adaptations to hypoxia (e.g. Winslow et
al., 1989; Beall,
2001; Brutsaert et al.,
2005; Norcliffe et al.,
2005; Wu et al.,
2005). More generally, it is worth noting that the environment
that many human beings experience (including aspects of nutrition, sanitation,
medicine and the so-called built environment) has changed very rapidly
relative to our generation time. Concomitantly, average lifespan has increased
in many countries and diseases associated with aging have become much more
common (e.g. Swynghedauw,
2006). Therefore, it may be expected that at least some aspects of
our phenotypic plasticity may not be adaptive.
To be or not to be: when should plasticity evolve?
Intuitively, plasticity might be good or bad, depending on the amount of
spatial heterogeneity in the environment, the speed of temporal environmental
changes, the predictability of spatial and temporal heterogeneity, and the
size or duration of heterogeneity relative to an organism's mobility and
lifespan. From a formal theoretical perspective, the evolution of plasticity
has been studied with optimality models, quantitative genetic models, and
gametic models (Scheiner,
1993). Generally, all of these models suggest that adaptive
plasticity will evolve when environmental heterogeneity exists, environmental
cues about that heterogeneity are somewhat reliable, plastic responses confer
a net fitness benefit, and the population contains some additive genetic
variance for the plastic response (Berrigan
and Scheiner, 2004). With regard to spatial variability,
optimality, quantitative genetic and gametic models all predict further that
plasticity is most favored when (1) inter-habitat variability is high, (2) all
habitats are equally regular, (3) selection acts equally strongly across
habitats, (4) the environmental cue-dependent phenotype is correlated with the
environment of selection, (5) habitat selection is correlated with trait
plasticity [for specific references, see elsewhere
(Scheiner, 1993)].
Phenotypic plasticity is typically induced by environmental heterogeneity
or environmental stress (Harshman et al.,
1999; Wilson and Franklin,
2002; Berrigan and Scheiner,
2004; Gabriel,
2005). In this context, `stress' is generally taken to mean
anything that threatens physiological homeostasis (e.g.
Sapolsky, 2005) and/or
reduces Darwinian fitness. Environmental stress can be categorized into biotic
(e.g. predator presence) versus abiotic (e.g. ambient temperature),
and either type may cause changes in behavior, morphology, and/or physiology
(Gabriel, 2005). If the mean
fitness of individuals with plastic strategies exceeds the mean fitness of
those with fixed strategies, then phenotypic plasticity or flexibility will
tend to evolve (Scheiner,
1993; Berrigan and Scheiner,
2004; Gabriel,
2005). Environment tolerance curves have been defined as `the
response of a genotype's total fitness over an environmental gradient'
(Lynch and Gabriel, 1987),
distinguishing this as a special case of the norm of reaction, and using them
to predict when irreversible plasticity will tend to evolve.
However, as noted elsewhere, `If stress periods are short compared to
the life-time of an organism, then irreversible phenotypic plasticity is
unlikely to be a favorable response'
(Gabriel, 2005). Therefore,
Gabriel proposed models predicting the selective advantage of reversible
plasticity (phenotypic flexibility)
(Gabriel, 1999;
Gabriel, 2005). He concluded
(Gabriel, 2005) that `...
reversible phenotypic plasticity would be expected for all organisms under the
following conditions: they are exposed to stress periods that last shorter
than life span; stress appears in the long run with some regularity so that
natural selection can shape non-induced and induced values of adaptive plastic
traits.' In these models, he assumed that plasticity was not costly, with
the rationale that `Plasticity costs would usually enter as constant
factors that do not alter the optimal values of mode and breadth' [see p.
875 (Gabriel, 2005)]. He added
the caveat that `if plasticity costs depend significantly on the amount of
performed phenotypic change, then costs might become a function of the
environmental state during stress in a way that the optimal values of mode and
breadth are affected' (p. 875), but concluded by arguing that `given
the predicted huge fitness advantages, the cost of plasticity would have to be
unexpectedly high in order to counteract selection for reversible phenotypic
plasticity' (pp. 880–881). Thus, it is important to remember
Pigliucci's point on p. 483 (Pigliucci,
2005) that `Research of costs of plasticity is still in its
infancy, but is both theoretically important and empirically challenging, and
should become a major area of future inquiry.'
Studying the evolution of plasticity
As discussed above, natural selection ought to affect plasticity, and
organisms ought to vary in plasticity. How can we test such theoretical
predictions? In general, the same way that we may seek to study adaptation in
any sort of trait. Four general approaches to studying adaptation are commonly
used by evolutionary biologists (e.g. see
Huey and Kingsolver, 1993;
Garland and Carter, 1994;
Bennett, 1997;
Futuyma, 1998;
Schlichting and Pigliucci,
1998; Feder et al.,
2000; Orzack and Sober,
2001; Pigliucci,
2001; Freeman and Herron,
2004; Stearns and Hoekstra,
2005). First, as outlined in the previous section, real organisms
can be compared with predictions of theoretical models, such as those based on
optimality (e.g. Garland,
1998; Orzack and Sober,
2001). Second, examinations of the biology of natural populations
can determine what sorts of traits vary, are heritable, and are currently
under sexual or natural selection (e.g.
Young et al., 2004).
Experimental manipulations of putatively adaptive traits are often employed in
such studies (e.g. Sinervo and Basolo,
1996; Ketterson and Nolan, Jr,
1999) and pp. 224-229 (Costa
and Sinervo, 2004). Although several studies have attempted to
quantify how natural selection acts on plasticity in the field (e.g.
Trussell, 1997;
Donohue et al., 2000;
Nussey et al., 2005), this
area of investigation will not be covered here. Third, one can compare species
(or populations) that vary with respect to ecological factors that might cause
variation in how selection `views' plasticity [overviews of studying
adaptation via `the comparative method' and with a phylogenetic
perspective have been published previously
(Garland and Adolph, 1994;
Garland et al., 2005)]. In the
following subsection, we provide a brief summary of some comparative studies
of plasticity. Finally, selection experiments
(Bennett, 2003;
Garland, 2003;
Swallow and Garland, 2005)
can be used to study adaptation, and this is our main focus, with emphasis on
those that would qualify as `experimental evolution' (e.g.
Rose et al., 1996;
Rose et al., 2004;
Ebert, 1998;
Bennett, 2002;
Bennett, 2003;
Swallow and Garland, 2005)
(http://en.wikipedia.org/wiki/Experimental_evolution).
Comparative studies
Vertebrate morphology and physiology provide dramatic examples of both
inter-specific and inter-trait variation in plasticity [plasticity of the
water barrier in vertebrate integument is reviewed elsewhere
(Lillywhite, 2004)]. With
respect to variation among traits, vertebrate skeletal muscle
(Flück, 2006) and the
gastrointestinal tract (Secor,
2005) are very responsive to use and disuse (`training' and
`detraining' effects). Bone size, shape and architecture also change in
response to variation in loading conditions, but to a much smaller extent than
for muscle [for example, compare
(Houle-Leroy et al., 2000)
with (Kelly et al., 2006)].
Adult vertebrate lung also seems to have relatively low plasticity (e.g.
Hoppeler et al., 1995;
Weibel, 2000;
Hsia, 2001;
Henderson et al., 2002). In
plants, one study shows that aspects of gas exchange may be more plastic than
structural traits (Valladares et al.,
2000).
Interspecific variation in plasticity has also been documented. In
vertebrates, for example, attempts at aerobic exercise training (to improve
cardiopulmonary and/or muscular function) of lizards have generally not been
successful, even when patterned after those that cause large changes in
mammals (Garland and Else,
1987; Conley et al.,
1995) (A. Szucsik, personal communication). In amphibians and
squamates, species differences in gut plasticity seem to be related to their
feeding ecology, in particular the frequency and/or regularity of feeding
(Secor, 2005). In Burmese
pythons, ventricular mass can increase 40% within 48 h after feeding, a change
that is fully reversible (Andersen et al.,
2005). Among species of fishes, carp and goldfish seem to be
especially plastic (Cossins et al.,
2006; Johnston,
2006). In plants, a common-garden study of 16 shrubs in the genus
Psychotria showed that species found in the understory, where light
is less variable, showed less plasticity for a variety of traits as compared
with species that generally occur in forest gaps, where light is more variable
(Valladares et al., 2000).
Population differences in plasticity have also received considerable attention
in plants, with several studies suggesting that the they are indeed adaptive
(e.g. Cook and Johnson, 1968;
Donohue et al., 2000).
Selection experiments and experimental evolution
Selection experiments have provided valuable insights into central
questions surrounding the evolution of phenotypic plasticity (see
Scheiner, 2002). At their
most basic, they have demonstrated that the plasticity of a trait is often
heritable, capable of responding rapidly to selection, and determined by
multiple genetic loci. [A genetic basis for the response to aerobic exercise
training has also been demonstrated in human twin studies (references in
Koch et al., 2005).] In
addition, selection experiments have shown that plasticity (environmental
sensitivity) of a given trait can evolve independently of the population mean
value for that trait. More specifically, experiments with plants and
invertebrates have shown plasticity to evolve in response to selection (1)
directly on the reaction norm, (2) on a single trait in one environment, and
(3) on a single trait across multiple environments. With respect to
vertebrates, although many selection experiments have been performed, very few
have focused on phenotypic plasticity as a component of the response to
selection (Falconer, 1990;
Scheiner, 2002).
The reaction norm has been directly selected upon in Drosophila
melanogaster, the butterfly Bicyclus anynana, and the tobacco
plant Nicotina rustica
(Scheiner, 2002). We will
only highlight the experiment performed by Scheiner and Lyman
(Scheiner and Lyman, 1991),
as it appears to be the most comprehensive and has also been reviewed in
detail (Scheiner, 2002).
The stated purpose in Scheiner and Lyman's experiment
(Scheiner and Lyman, 1991)
was to determine if plasticity could respond to selection that was imposed
under controlled and reproducible conditions. They began by capturing 301
individual D. melanogaster from the wild. These flies founded a stock
that was maintained in the laboratory by mass culture at 21°C for
2–3 months (several generations), thus establishing a genetically
heterogeneous base population. They then used 50 randomly chosen pairs to
establish each of 14 separate experimental lines, which comprised two
replicates of each of six selection regimes (increased thorax size at
19°C, decreased thorax size at 19°C, increased thorax size at
25°C, decreased thorax size at 25°C, increased plasticity, decreased
plasticity) plus a control line that was not intentionally selected. To impose
selection, plasticity was defined as the difference in average thorax length
for sets of full-sibs raised at 19°C and 25°C. Plasticity did indeed
respond to selection, but with a rather low realized heritability of
0.088±0.027 (mean ± s.e.m.). The authors concluded that the
plasticity was not the result of overdominance, but rather a genetic
interaction among multiple loci.
As noted above, the evolution of phenotypic plasticity has also been
examined as a correlated response to selection on a specific trait in a single
environment. For example, Harshman et al.
(Harshman et al., 1991)
studied detoxification enzymes in D. melanogaster. After establishing
a base population from wild-caught flies, three Control (C) lines were reared
on standard medium and three Selected (S) lines on lemon for 20 generations.
For the lemon-cultured lines, the selection process was as follows: (1) flies
were placed in bottles with freshly cut lemon (10 g, pesticide free) at room
temperature for 7–10 days; (2) approximately 50% mortality occurred; (3)
survivors were placed into a new bottle of freshly cut lemon (30 g) and
vermiculite to produce the next generation. According to Harshman et al., the
50% mortality (during the lemon selection episodes) may have been caused by
natural insecticide activity in lemons, or by toxins produced by bacteria
growing on the fruit (Harshman et al.,
1991). Flies (35–70) were randomly mated to produce the
subsequent generation in all six lines. In the Control lines, flies were
transferred to fresh medium for mating.
After 20 generations, all flies to be tested were reared on ordinary medium
for one generation to standardize environmental conditions. They were then
transferred to either lemon (which may induce the expression of detoxification
enzymes) or fresh medium (to allow determination of enzyme activities under
baseline conditions) for 24 h prior to sacrifice. Activities of epoxide
hydrolases and glutathione S-tranferase (GST) were then measured. For
GST measured using trans-stilbene oxide (TSO) as a substrate, S and C
lines showed no significant difference for the sample exposed to fresh medium
for 24 h, but the S lines showed substantially higher enzyme activities than C
lines when lemon-exposed for 24 h (Fig.
3). Harshman et al. concluded
(Harshman et al., 1991):
`After 20 generations on lemon there was a pronounced change in
environment-dependent expression... The response appeared independently in all
three lines on lemon.' They also noted that: `In the present study
the culturing regime used was ostensibly continuous, unless the process of
lemon rotting every generation constitutes temporal variation. Normally, one
would anticipate selection for change in environment-dependent enzyme
expression to occur in variable environments but the results of the present
study suggest it can evolve in a relatively constant regime.' Harshman et
al. give additional examples in which the plasticity of an enzyme activity
seems to have evolved as a correlated response
(Harshman et al., 1999).
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In numerous other selection experiments where plasticity could potentially
evolve (because the selection regime is more than instantaneous), plasticity
of the selected trait or of potentially related or subordinate traits does not
appear to have been examined. In one such example
(Bubliy and Loeschcke, 2005),
correlated responses to selection for stress resistance and longevity in a
laboratory population of D. melanogaster were examined. Several
selection regimes were imposed in this experiment: cold-shock resistance
selection, heat-shock resistance selection, heat knockdown resistance
selection, desiccation resistance selection, starvation resistance selection
and longevity selection. Here we discuss the cold-shock resistance selection
line, for which selection was imposed for 21 generations. The selection regime
was as follows. Flies were maintained on standard medium for 5 days at
11°C for acclimation, then placed in empty vials and exposed to 0.5°C
for 27–50 h with relative humidity near 100%. Surviving flies were
allowed to recover for 24 h at 25°C in vials with standard medium, then
allowed to reproduce. We would argue that plasticity of traits that may
support cold resistance in D. melanogaster could potentially be
altered during the acclimation phase of this selection protocol. Furthermore,
plasticity of traits that may support cold resistance may be evolving across
generations. Although phenotypic plasticity apparently has not been
investigated in these flies, we contend that selection experiments of this
type should explore whether plasticity has increased as a component of the
response to selection. In the following section, we discuss an ongoing
experiment with house mice that has begun to examine plasticity in various
traits as a potential correlated response to artificial selection for high
voluntary wheel running, as expressed during days 5 and 6 of a 6-day exposure
to wheels.
Selective breeding for high voluntary wheel running in house mice
Since 1993 our laboratory has been conducting a replicated selection experiment for high voluntary wheel-running behavior on days 5+6 of a 6-day wheel exposure. By housing mice from each of the four replicate S lines and from each of the four replicate C lines with or without wheel access for several days or weeks, we can test for differences in plasticity (training effects) in various traits. As outlined in the remainder of this section, we have found several traits that show greater differences between S and C lines when they are housed with wheel access than when they are housed without wheel access (or, in some cases, housed with access to wheels that are locked to prevent rotation). For some of these traits, the greater differences can be explained statistically by the greater wheel running exhibited by mice from S lines. For others, however, the differences seem to reflect greater plasticity in the S lines [i.e. for a given amount of stimulus (wheel running/day), individuals in the S lines show a greater response than in the C lines].
Animals and experimental protocol
The original progenitors (founding population) were outbred, genetically
variable house mice (Mus domesticus) of the Hsd:ICR strain
(Harlan-Sprague-Dawley, Indianapolis, IN, USA). After purchase from HSD, mice
were randomly mated for two generations, paired, and then assigned randomly to
eight closed lines (10 pairs in each). Four of these lines have been
designated to experience selective breeding for high voluntary activity (lab
designation, lines 3, 6, 7, 8) and four serve as controls (lines 1, 2, 4,
5).
The selection protocol has been described in detail elsewhere
(Swallow et al., 1998a). In
brief, when each generation of mice are 6–8 weeks old, they are housed
individually with access to running wheels (circumference=1.12 m) for 6 days.
Daily wheel-running activity is monitored with photocell counters linked to a
computer-automated system. Wheel activity is recorded in 1-min bins for
23–24 h of each of the 6 days of wheel access. For purposes of
selection, wheel running is quantified as the total number of revolutions on
days 5 and 6 of the 6-day test. After accounting statistically for any
variation related to measurement block, age, wheel resistance, and sex,
breeders are chosen. In the four S lines, the highest running male and female
are chosen from each family as breeders to propagate the lines of the next
generation. Within-family selection is performed to increase the effective
population size (Ne), while reducing maternal and
environmental variances, including effects of genotype-environment
interactions (Henderson,
1989). In the four C lines, breeders are randomly chosen from each
family. Within all lines, sibling matings are disallowed.
By generation 16, the high-activity lines exhibited a 170% increase in
total revolutions/day as compared with the C lines. This was caused primarily
by S mice running faster rather than for more minutes each day, but the
relative importance of the two components differs between the sexes, with
females from the S lines typically showing little or no increase in amount of
time running whereas males do show an increase in time running
(Swallow et al., 1998a;
Koteja et al., 1999a;
Koteja et al., 1999b;
Rhodes et al., 2000;
Girard et al., 2001). This
increase in wheel running greatly exceeds that of wild house mice born and
raised under the same conditions (Dohm et
al., 1994), and comes close to spanning the range of variation
that has been reported among 13 species of wild murid rodents
(Garland, 2003). Therefore, it
seems that we have an evolutionarily `important' amount of divergence in wheel
running between the S and C mice. Additionally, based on high-speed video
analyses, estimates of instantaneous running speeds have shown that S line
females run twice as fast as C line females, as well as more intermittently
(Girard et al., 2001).
However, since approximately generation 16, the differential in wheel-running
distances has remained relatively constant, indicating that a selection limit
or plateau may have been attained.
Plasticity of wheel running
Because our wheel-testing protocol is prolonged (6 days) rather than
instantaneous (e.g. a few minutes), it is possible that the S lines may have
evolved greater plasticity in this behavior. In other words, as compared with
the C lines, mice from S lines might now exhibit a greater increase in wheel
running across the 6 days of wheel access, given that only their performance
on days 5+6 affects their probability of reproducing.
Fig. 4 shows some hypothetical
examples of how plasticity might be greater in the S lines. In
Fig. 4A, Control lines show
constancy of wheel running, whereas S lines increase monotonically across days
1–6; clearly, plasticity is greater in S lines. In
Fig. 4B, wheel running is
identical and increases gradually in both S and C lines over the first four
days of testing. Selected lines then show a much greater increase between days
4 and 5, thus indicating greater plasticity during this time period. In these
two cases, the greater plasticity of S as compared with C lines is reflected
in the ratio of S/C (see Fig.
4A,B).
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In the case of Fig. 4C, the interpretation is more complicated. Both S and C lines increase monotonically across days 1–6. On an absolute basis, S lines increase more (2000) than C lines (667) on each day. Relative to their own starting values, however, S and C lines increase by the same percentage each day, although this increase becomes smaller each day (40, 29, 22, 18 and 15%, respectively). As a result, the S/C ratio is a constant. Thus, whether one considers the S and C lines to differ in plasticity depends on whether absolute or relative values are considered.
Fig. 5 shows example data
from our selection experiment, and the pattern resembles the one shown in
Fig. 4C. As reported elsewhere
(Belter et al., 2004), 48
female mice from generation 23 were studied. As shown in
Table 1, S lines ran
significantly more than C on every day. A repeated-measures ANOVA (SAS Proc
Mixed with autoregressive error structure) indicated highly significant
effects of day (P<0.0001) and line type (P=0.0001), but
no significant day-by-line type interaction (P=0.7184). The foregoing
results suggest that S lines do not exhibit a greater plasticity in wheel
running.
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On the other hand, the difference between total revolutions on day 6 and day 1 was considerably higher, on average, for S lines (5658) than for C lines (2112). This greater absolute increase in wheel running across 6 days is not statistically significant (P=0.1047), but becomes significant (P=0.0325) when one outlier is removed. This was an S individual whose wheel running declined anomalously from 14 375 on day 1 to 7603 on day 6, the greatest decline for any mouse in the sample of 48. This may represent a real phenomenon, or it might indicate a problem with the wheel on day 6. We intend to explore the plasticity of wheel running more in future studies, with larger sample sizes. In any case, we believe that the greater increase in wheel running across the 6-day trial may well have biological significance, and may well have required coadaptational changes in one or more subordinate traits that support wheel running.
Apparent exercise adaptations in the high-activity lines
A main goal of the selection experiment was to identify traits that have
evolved in concert with increased activity levels and that may be necessary
for them, i.e. evolutionary adaptations for the high wheel running. Several
considerations make this goal non-trivial. First, exercise physiology is
complicated, and we have not examined all possible subordinate traits that
could be key in terms of allowing high wheel running. Second, of those traits
that have been examined, not all have been examined in the same generation.
Some adaptations may have occurred in earlier generations and others in later
ones, and indeed those occurring in later generations may even have supplanted
some that occurred earlier. Third, adaptations may only exist, or at least be
more developed, around the age at which wheel testing normally occurs, which
is 6–8 weeks of age. Fourth, adaptations may only exist on days 5 and 6
of wheel testing, i.e. they require some days of wheel access to develop.
Fifth, adaptations may to some extent be sex-specific, especially given that
females in the S lines have increased total activity almost entirely by
running faster, whereas males also show an increase in amount of time spent
running. Given that we have not studied both sexes, at all ages, under all
possible housing conditions (e.g. with or without wheel access), let alone in
every generation, we may well have missed some key adaptations. With those
cautions in mind, we have discovered a number of traits that seem to represent
adaptations for high wheel running in the S lines. We review the motivational
basis for high wheel running elsewhere
(Rhodes et al., 2005).
Mice from the selected lines have higher maximal oxygen consumption during
forced treadmill exercise (VO2max), especially
in males (Swallow et al.,
1998b; Rezende et al.,
2006a; Rezende et al.,
2006b) and higher insulin-stimulated glucose uptake in the
extensor digitorum longus muscle [located in the hindlimb
(Dumke et al., 2001)]. Mice
from S lines have larger femoral heads and more symmetrical hindlimb bone
lengths (Garland and Freeman,
2005; Kelly et al.,
2006). Interestingly, S lines exhibit reduced hindlimb muscle
mass, especially in two lines that contain a Mendelian recessive allele that
halves hindlimb muscle mass while increasing mass-specific aerobic capacity
and having a variety of other pleiotropic effects
(Garland et al., 2002;
Houle-Leroy et al., 2003;
Swallow et al., 2005;
Syme et al., 2005;
Kelly et al., 2006). The S and
C lines differ with respect to many other traits as well, such as higher
plasma corticosterone levels (Girard and
Garland, 2002) and reduced body fat in S lines
(Swallow et al., 2001;
Dumke et al., 2001). We are
currently attempting to determine which of these are adaptations that enhance
wheel-running ability, as opposed to non-adaptive (and possibly even
maladaptive) correlated responses.
Plasticity of exercise-related traits
Many traits (e.g. heart mass, VO2max) that
one might expect to evolve as a correlated response to selection for high
activity levels are also known to respond to the amount of exercise that an
individual organism exhibits. Indeed, the literature on mammalian training
effects is immense, in part because of our interest in competitive athletics
but also because many exercise-related traits are known or thought to be
important in promoting physical and/or psychological health
(Booth et al., 2002;
Castaneda et al., 2005)
(Health Activity Center:
www.cvm.missouri.edu/hac/index.html).
Given that mice from the S lines run more than C when given wheel access, they
might also be expected to exhibit greater training responses (physical
conditioning) over a given period of time, such as several weeks.
Imagine that groups of both S and C mice were housed either without
(sedentary group) or with (active group) access to a running wheel for 8 weeks
(e.g. Swallow et al., 2005).
Imagine further that for mice housed without wheels, we observed no difference
in some phenotype, such as hematocrit. For the mice housed with running
wheels, consider a hypothetical phenotype for which values are higher in the S
lines (Fig. 6A); this can be
explained, statistically at least, by their higher wheel running: a single
regression line adequately describes the relation. In this case, we would
interpret the data as indicating that mice from S and C lines are equally
plastic: it seems to be a simple case of `more pain, more gain.' One real
example of this pattern involves the level of brain-derived neurotrophic
factor (BDNF) in the hippocampus of S and C mice after one week of access to
running wheels [see fig. 2 (Johnson et
al., 2003)].
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Fig. 6B shows a different situation. When housed with wheel access, mice from S lines again have higher values for the phenotype, but we see no relation with the amount of running within either group. If we imagine further that S and C mice housed without wheels showed no difference (or at least values similar to those of C mice housed with wheels), then the S mice seem to be more responsive to wheel exposure, i.e. they are more plastic. For a given amount of exercise (wheel running), S mice experience a greater training response. Remember also that phenotypic differences between genotypes (e.g. S versus C mice) that appear only in some environments are termed genotype-by-environment interactions.
As in the hypothetical scenarios just discussed, we have published several
papers that involved groups of both S and C mice housed with or without access
to functional wheels for several weeks. We have studied various traits,
including body mass, VO2max, organ masses, bone
properties and enzyme activities (Swallow
et al., 1999; Houle-Leroy et
al., 2000; Thomson et al.,
2002; Belter et al.,
2004; Swallow et al.,
2005; Kelly et al.,
2006). We found a variety of responses in these phenotypes,
including some that differ between the sexes. Some traits do not differ
between S and C mice regardless of housing conditions [e.g. tail length,
adjusted for variation in body mass, in both sexes
(Swallow et al., 2005)]. Some
traits were found to differ between S and C mice regardless of housing
conditions [e.g. S mice are smaller in body mass but have relatively larger
kidneys (Swallow et al.,
2001; Swallow et al.,
2005; Kelly et al.,
2006)]. Others showed a difference between S and C lines when
housed with wheels but not when housed without [e.g. hematocrit and blood
hemoglobin content (Swallow et al.,
2005)].
For traits that differ more between S and C lines when they are housed with
wheel access, we can examine statistically which of the competing patterns
shown in Fig. 6A,B better
describes the data (see also Swallow et
al., 2005). The general strategy is as follows. First, we identify
a trait that shows a statistical interaction between the effects of line type
(S versus C lines) and wheel access (the environmental factor). For
these analyses, we use SAS Proc Mixed to implement a mixed-model, nested ANOVA
(or ANCOVA if such covariates as age or body mass are included in the model),
in which replicate line is a random effect nested within line type (S or C).
Degrees of freedom for testing the effect of line type, the effect of wheel
access, and the line type x wheel access interaction are all 1 and 6.
With this type of analysis, one trait that showed a statistically significant
interaction is hematocrit in a sample of 81 female mice housed with or without
wheels for 8 weeks (Swallow et al.,
2005), as repeated here in
Table 2.
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Second, we examine the mean values for the four subgroups. In the case of
hematocrit in females, adjusted means (SAS Proc Mixed) were 48.51, 48.76,
48.66, and 50.90 for Control Sedentary, Control Active, Selected Sedentary and
Selected Active, respectively [see table 3
(Swallow et al., 2005)].
Thus, the line type effect is greater when mice are housed with wheel access.
Indeed, separate ANCOVAs reveal no significant effect of line type
(P=0.8502) for sedentary mice but a significant effect
(P=0.0472) for the active group. Third, within the active group
(Fig. 7), we can ask whether
the data are better fit by a model that does or does not include the amount of
wheel running as an additional covariate. For hematocrit in females,
Table 2 shows that the ln
likelihood of the nested ANCOVA model without wheel running (–75.7) is
larger (less negative, in this case) than for the model with wheel running
(–83.7). As the latter model contains one additional parameter
(estimating the effect of wheel running), twice the difference in ln
likelihoods (16.0, in this case) can be compared with a 
2
distribution with one degree of freedom, for which the critical value for
P=0.05 is 3.841. Therefore, the model with wheel running as an
additional covariate yields a significantly worse fit to the data, and we
conclude that the difference in hematocrit between S and C mice when housed
with wheel access is not best explained as a simple function of the greater
running by S mice. Instead, the greater training effect experienced by S mice
seems to indicate that they have greater plasticity for this trait when given
wheel access. Results are similar for blood hemoglobin content.
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We are currently in the process of examining or reexamining a variety of
traits, from several different published and unpublished studies, by the
approach outlined in the previous paragraph.
Table 2 shows some additional
examples for the female mice studied elsewhere
(Houle-Leroy et al., 2000;
Swallow et al., 2005). We are
finding a number of traits that fit the pattern described for hematocrit in
females, including hemoglobin content of the blood as well as cytochrome
c oxidase and pyruvate dehydrogenase activity in mixed hindlimb
muscle in females. Thus, several traits seem to show greater plasticity in
response to wheel access in S lines as compared with C lines. In addition, the
studies by Houle-Leroy et al. and Swallow et al.
(Houle-Leroy et al., 2000;
Swallow et al., 2005) housed
the `sedentary' mice with access to locked wheels (unable to rotate), and a
subsequent study revealed that mice from S lines climb more than those from C
lines in locked wheels. Therefore, the relative magnitude of training effects
in S and C lines might differ if `sedentary' mice were housed in ordinary
cages with no access to even locked wheels.
The results shown in Table 2
are for (female) mice housed with or without access to a functional wheel for
8 weeks. However, the selection regime involves only 6 days of wheel access,
so it will be crucial in future studies to see if a similar pattern emerges
for shorter periods of wheel access. In fact, we have already found that, for
some traits, the effects of wheel access can be dramatic even over a matter of
days. For example, the amount of GLUT-4 glucose transporter in gastrocnemius
muscle did not differ between S and C females when they were housed without
wheels (Gomes et al., 2004).
After 5 days, both groups exhibited an increase in GLUT-4, but the increase
was much greater in S mice, such that S and C showed no overlap in values.
When the amount of GLUT-4 was plotted against the amount of wheel running on
day 5, the relation was similar to that shown in
Fig. 6B. Thus, the difference
between S and C lines in gastrocnemius GLUT-4 expression is not a simple
linear function of the amount of wheel running; rather, mice from the S lines
seem to have greater plasticity for this trait, and this greater plasticity
can have large effects even in as few as 5 days.
Some traits show altered plasticity in the S lines, but in a complicated
way. For example, the amount of neurogenesis in the hippocampus [see fig. 2D
(Rhodes et al., 2003)] shows
a relation similar to that depicted in Fig.
8. Mice from C lines (gray squares) exhibit a positive and
quantitative relation with the amount of wheel running exhibited over several
weeks, but this relation is lost in the S lines (black circles). Finally, we
have also observed some traits that show an actual reversal of the direction
of plasticity in S lines. For instance, relative ovary mass was found to be
larger in S mice than in C when both were housed without wheels, but the
opposite was true for mice housed with wheels for 8 weeks
(Swallow et al., 2005).
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Evolutionary change versus phenotypic plasticity
As noted above, some traits do not show a significant interaction between
line type and wheel access. For these traits, the magnitude of any S
versus C difference is relatively constant, regardless of housing
conditions, and the magnitude of any training effect is similar in both S and
C. Therefore, we can compare these two effects in a straightforward way
plotting one versus the other.
Kelly et al. report hindlimb bone properties for male mice from generation
21 that were given wheel access for 8 weeks and compared with counterparts
housed in ordinary cages with no wheels
(Kelly et al., 2006). As shown
in Fig. 9, bone lengths were
not affected by either selective breeding or chronic wheel access. Diameters,
in contrast, tended to be increased by both factors, with the magnitude of the
evolutionary effect being somewhat greater than the training effect. In spite
of the fact that mice from S lines ran considerably more than C, the magnitude
of training effects was similar in S and C lines, indicating no
genotype-by-environment interactions (see
Kelly et al., 2006
