Download individual variation in mammals

INDIVIDUAL VARIATION IN MAMMALS
JACK
P.
HAYES AND STEPHEN H. JENKINS
Department of Biology, University of Nevada, Reno, NV 89557
The study of individual variation offers an underexploited wealth of opportunities for mammalogists. This paper addresses recent developments in the study of both intra- and interindividual variation. After reviewing several methods (e.g., intraclass correlation, productmoment correlation, and confirmatory factor analysis) for quantifying intra-individual consistency or repeatability, we discuss how these measures of repeatability can serve as guides
for appropriately defining traits and how they may be helpful in ensuring that appropriate
statistical models are used (e.g., in accounting for measurement errors in regression analyses). We discuss three aspects of inter-individual variation; phenotypic selection, alternative individual strategies and phenotypic integration, and quantitative genetic analyses. The
value of these approaches for studying inter-individual variation is illustrated with recent
examples from the literature. Finally, we discuss how many field studies of mammals may
be well poised to exploit the unique insights that can be gained from studying individual
variation.
Key words: evolutionarily stable strategy, individual variation, measurement error, repeatability, phenotypic selection
The study of individual variation has an
illustrious history, forming one of the cornerstones of Darwin's theory of evolution
by natural selection (Darwin, 1958). While
much has been written about individual
variation since Darwin's time (Schmalhausen, 1949; Yablokov, 1974), many biological insights remain to be gained by
studying it. Indeed, stimulated by physiologists (Bennett, 1987), population biologists
(Clutton-Brock, 1988), and behaviorists
(Boake, 1989), the study of individual
variation is undergoing a renaissance, to
which mammalogists are paying increasing
attention (Blank, 1992; Chappell and Bachman, 1995; Chappell et aI., 1995; Hayes and
Chappell, 1990; Hayes et aI., 1992; Horton
and Rowsemitt, 1992; Konarzewski and
Diamond, 1994; Srether and Heim, 1993;
Speakman et al., 1994).
Individual variation can be defined in
many ways but, most broadly defined, it is
synonymous with intraspecific variation.
Accordingly, individual variation would include differences related to group attributes
(e.g., all members of a rodent population ocJournal of Mammalogy, 78(2):274-293, 1997
cupying habitat with dark volcanic soils
might have dark pelage, while their conspecifics occupying pale-colored sands might
all have pale pelage-Benson, 1933). In this
example, as in many comparisons among
populations (Garland and Adolph, 1991),
the variation (i.e., in pelage color) is more
usefully attributed to variation among
groups than to variation among individuals.
Among-group variation is important, but we
believe it is fruitfully considered separately
from variation within populations. Consequently, for this paper, we define individual
variation as differences among individuals
within a population, following Darwin's
(Darwin, 1958:58) individual differences
" ... differences ... in the individuals of the
same species inhabiting the same confined
locality."
Studying individual variation requires
measuring the characteristics of individual
organisms. Some individual characteristics
change with time (e.g., body or antler size),
others do not (e.g., number of vertebrae). A
full appreciation of variation among individuals requires that we not only understand
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
how characters vary among individuals, but
also how individual characteristics change
over time (e.g., with age or season). Hence,
besides differences among individuals
within a population, we consider individual
variation to include intra-individual variation as well.
Why do we think the study of individual
variation is undergoing a renaissance? Our
belief is based on the outpouring of research
triggered by influential papers on interindividual variation (Arnold, 1983; Bennett,
1987; Lande and Arnold, 1983) and on repeatability (Boake, 1989). Before these papers, much of the emphasis on studying adaptation involved comparing populations
from different environments (e.g., testing
the among-population relationship between
urinary concentrating ability and aridity for
rodents). Although across-population analyses have provided powerful insights into
evolutionary processes, an overemphasis on .
them led to a narrow focus on the mean attributes of populations or species and, consequently, to a neglect of the considerable
value of within-population analyses (Bennett, 1987). This neglect of within-population analyses is being remedied by the
recent resurgence in the study of variation
among individuals within populations.
Boake (1989) addressed the significance
of intra-individual variation, specifically the
importance and benefits of understanding
how repeatable traits of individuals are over
time. A central tool for formulating hypotheses about the significance or function of
any trait is examining its morphological,
physiological, ecological, environmental, or
other correlates. The constancy of a trait
over time is implicit in many of these analyses. If a trait is not constant over time, this
raises the issue of whether the trait is really
a single trait, and it complicates the analysis of its correlates (Arnold, 1986, 1990,
1994). Moreover, as Boake (1989) states,
assessing the selective value of a trait requires that we understand how variable it is
both among and within individuals.
Individual variation deserves study for
275
many reasons. Our goals in this paper are
to review recent developments in the study
of individual variation, primarily of quantitative traits, illustrate how these developments are leading to new insights, and suggest some ways in which mammalian studies are well suited to contribute additional
insights.
INTRA-INDIVIDUAL VARIATION
A central issue in the study of intraindividual variation is the consistency or repeatability of traits over time. Traits may
change over time either because gene expression changes, or because an organism
encounters a new environment or a new
specific (i.e., localized) microenvironment
(Falconer, 1989; Henderson, 1990; Hewitt,
1990). Repeatability is an indicator of
changes in traits over time; hence, repeatability has profound ecological and evolutionary ramifications because it affects how
we define traits and how we analyze variation in them. Arnold (1994) briefly discussed ways to study traits depending on
their repeatability (Arnold, 1986, 1990).
Despite Arnold's discussion and Boake' s
(1989) paper, some of the ramifications of
repeatability deserve further explication. In
this section, we discuss how to assess repeatability, how repeatability should influence statistical analyses, and how repeatability affects the definition of traits.
Quantifying repeatability: the intra class
correlation, the product-moment correlation, confirmatory factor analysis, and other
approaches.-The repeatability of a trait
over time can be described in several ways.
We use the term repeatability interchangeably with consistency; however, in some
disciplines (e.g., quantitative genetics) repeatability may be used in a more restrictive sense. The classical measure of repeatability taken from the field of quantitative
genetics is the intraclass correlation coefficient (tau, 7). To estimate the intraclass correlation, repeated measurements of a trait
are made on a number of individuals. The
observed mean squares from an analysis of
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JOURNAL OF MAMMALOGY
TABLE l.-How to calculate repeatability as
the intraclass correlation coefficient (T). The observed mean squares from a one-way analysis of
variance by individual are partitioned based on
the expected mean squares. Assume that each of
20 individuals is measured three times and that
the following ANOVA table results. k is the number of times each individual is measured (i.e., 3
in this example), 0; is the error variance (i.e.,
the variance among measurements within individuals), and ~ is the among-individual variance. The repeatability equals ~/(o; + ~). The
among-individual variance component is obtained by setting the observed mean square equal
to the expected mean square, substituting for 0;
(i.e., 0; = 500) andfor k (i.e., k = 3), and solving for ~ (i.e., ~ = (2000 - 500)/3 = 500).
Thus, in this case, the repeatability equals 500/
(500 + 500) or T = 0.5.
Source
df
Observed
mean
square
Among individuals
Error (Le., among
measurements
within individuals)
19
2,000
40
500
Expected
mean
square
a:+K'a;
a e2
variance by individual are then decomposed
to estimate the within- and amongindividual variances (Table 1). The ratio of
the among-individual variance to the total
phenotypic variance is the intraclass correlation coefficient (Becker, 1984; Falconer,
1989; Lessells and Boag, 1987). Tau may
range from zero, indicating that all variation
is within individuals (i.e., the means of the
repeated measures for all individuals are
equal), to one, indicating that all the variation is among individuals (i.e., every time
an individual is measured the same value is
obtained). However, in practice, because
variance components sometimes have large
sampling errors, the among-individual variance component may fall outside its theoretical limits and have a negative value (indicating that the best estimate for repeatability is zero). A misspecified statistical
model (e.g., failure to account for a significant gender or other effect) may also result in erroneous (i.e., negative) estimates
Vol. 78, No.2
of among-individual variance components
(Searle et al., 1992).
The intrac1ass correlation coefficient has
a major potential advantage' as a measure of
repeatability. If repeated measures on individuals represent the same genetic trait,
whose expression is altered only by localized microenvironmental differences experienced by individuals, if the variances of
all the sets of repeated measurements are
equal, and if the genetic and environmental
variances occur in the same proportion for
each repeated measure, then the intrac1ass
correlation sets an upper bound to the
broad-sense heritability (Falconer, 1989).
The broad-sense heritability is the sum of
the additive genetic variance, which is
needed for an evolutionary response to selection, and of the dominance and other
non-additive genetic variances (e.g., epistatic variance). If a trait has zero repeatability, this suggests that it lacks the additive genetic variance required to respond to
selection. If the repeatability is above zero,
then the trait may have the additive genetic
variance required to respond to selection. As
Boake (1989) indicated, it is easier to measure repeatability than heritability, so repeatability may have some utility as a
screening tool to determine whether more
detailed genetic analyses are warranted.
However, because the interpretation of repeatability as an upper bound to heritability
also depends on several, frequently untenable, assumptions, we believe that this use
of repeatability is of questionable value.
A drawback to using the intrac1ass correlation coefficient for assessing repeatability
is that it is sensitive to changes in the means
of repeated measures. For example, suppose
that two measurements are made per individual and the mean of the first measurements of individuals differs from the mean
of the second measurements of individuals.
This difference across repeated measures
will result in increased within-individual
variance, yielding a decreased intraclass
correlation (and possibly a misspecified statistical model, because systematic differ-
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
ences occur from one measurement to the
next). This can be undesirable because the
performance of individuals relative to the
mean of each measurement may be highly
consistent, even if the intraclass correlation
is low.
Problems with interpreting the intraclass
correlation coefficient have led to increasing use of the product-moment correlation
(r) for assessing repeatability (Chappell et
aI., 1995; Garland, 1994; Hayes and Chappell, 1990; Speakman et al., 1994; van Berkum et aI., 1989). The product-moment correlation assesses the consistency of a trait
relative to the mean. Thus, if relative to the
mean, neonatal size is a good indicator of
size of adult, then the product-moment correlation for size could be large and positive,
even if the intraclass correlation was low.
The product-moment correlation can only
be calculated for two repeated measurements per individual at a time, so many
correlations may be needed to describe all
possible pairwise correlations (e.g., six
product-moment correlations are needed to
describe the pairwise relationships among
four repeated measures). Across many repeated measures this can lead to a proliferation of statistics.
A third way to assess repeatability is with
confirmatory factor analysis (Bollen 1989;
Loehlin, 1992). Conceptually, the repeated
measurements of a trait can be treated as observable indicators of an underlying true
factor or latent variable that cannot be measured directly owing to measurement error,
where measurement error includes both
technical errors in measurement and sampling error caused by stochastic intraindividual variation. The variance in the repeated measures can be partitioned into
variance owing to the factor and variance
owing to measurement error. This approach
to assessing repeatability rarely has been
used, but offers promise for some types of
analyses (Hayes and Shonkwiler, 1996).
Besides the intraclass correlation, the
product-moment correlation, and confirmatory factor analysis, numerous other meth-
277
ods sometimes are used to assess repeatability. These other methods include nonparametric analyses such as the Spearmanrank correlation and Kendall's coefficient
of concordance (Bennett, 1980). Other parametric methods used to assess repeatability
include analyses of variance components
based on more complex ANOVAs than the
one-way ANOVA shown in Table 1, analyses of covariance, and other types of linear
models. Good examples of these approaches can be found in Turner and Young
(1969), Mansour et al. (1981), Houck et al.
(1985), and Huey et al. (1990). For discrete,
as opposed to continuous, traits still other
techniques are used (Ducey and Brodie,
1991; Rutledge, 1977).
Effects of measurement error, environment, and ontogeny on repeatability.-AImost all continuous traits are less than perfectly repeatable. Understanding why traits
are less than perfectly repeatable has major
implications for how traits are defined and
for how their relationships with other variables are analyzed. A trait is perfectly repeatable as measured by the intraclass correlation only if it is constant over time. The
treatment of a trait as if it were constant
over time is implicit in many analyses. Constancy of traits should not be assumed, because if this assumption is false, it may lead
to erroneous biological inferences.
Repeatability can be helpful in assessing
and dealing with measurement error in at
least two ways (Martin and Kraemer, 1987).
First, repeatability can be used to evaluate
and possibly guide improvement in measurement procedures. Second, statistical
analyses should account for repeatability,
when repeatability provides an indication of
measurement error (Lynch, 1988). For example, using a trait that is less than perfectly
repeatable (owing to measurement error) as
a predictor in an ordinary regression is inappropriate, because the assumption that the
predictor variable is measured without error is violated (Draper and Smith, 1981;
Fuller, 1987; LaBarbera, 1989; MitchellOlds and Shaw, 1987; Riska, 1991).
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Two major reasons that traits are not perfectly repeatable are that they are affect6d
by age (e.g., fecundity changes with age in
Microtus montanus-Negus and Pinter,
1965) and they are affected by the environment (e.g., camels alter their core body temperatures and rates of water and heat
exchange as a function of water
availability-Schmidt-Nielsen et aI., 1957).
In addition, age by genotype and environment by genotype interactions are probably
common. Ontogenetic or environmental effects on traits should prompt investigators
to reassess how they define traits, so that
their subsequent analyses are biologically
meaningful and statistically valid. A trait
that changes with the environment in which
it is measured or the age at which it is measured needs to be treated differently than a
trait that is constant over time (Arnold,
1990, 1994). Superficially, this seems a
frivolous observation; however, many field
studies characterize traits of individuals at
only a single or a few points in time. Assuming that traits have high repeatabilities
is a dubious proposition, particularly if the
repeatability of interest spans long time periods or diverse environments (Austin and
Shaffer, 1992; Chappell et aI., 1995; DeVera
and Hayes, 1995; Friedman et aI., 1992;
Huey et al., 1990; Semlitsch et aI., 1993;
Speakman et aI., 1994; van Berkum et aI.,
1989).
Environmental and ontogenetic effects on
expression of traits are widespread. For example, many physiological and behavioral
traits differ markedly if measured in the
night versus day, during winter versus summer, under high versus low humidity, in the
presence of a conspecific versus not, and so
on. When expression of a trait varies temporally, this must be accounted for, or else
the relationships among traits or of traits
with other variables may be grossly misleading (Fig. 1; Brodie, 1993).
Repeatability, definition of traits, and
some implications for data analysis.-If a
trait is not repeatable across environments
or ages, then two general approaches are
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possible for analyzing its relationship with
other biological or environmental variables. First, as many age- or environmentspecific traits can be defined as needed to
yield traits that are repeatable at least over
the environment or age for which they
are defined (Eaves et aI., 1990; Hewitt,
1990; Lynch, 1988). For example, one
might redefine body temperature (Tb) as
T b measured during the day versus T b
measured at night. Indeed, Connolly and
Lynch (1981) suggest that for laboratory
mice T b measured at night is heritable,
but T b measured during the day is not.
When defining age- and environmentspecific traits an objective should be to define biologically meaningful and ecologically realistic traits (Crespi, 1990; Henderson, 1990).
Environment- or age-specific traits likely
share some common genetic and environmental influences. Quantitative genetic
analyses of age- or environment-specific
traits can help elucidate the existence of
shared genetic determinants (i.e., genetic
covariances among traits owing to pleiotropic gene action and linkage disequilibrium).
For example, Riska et al. (1984) studied
age-specific size in mice. They showed that
as the time between pairs of age-specific
measures of size increased, genetic and phenotypic correlations decreased (e.g., mass at
age 21 days may be highly genetically and
phenotypically correlated with mass at age
14 or 28 days, but will likely show lower
correlations with mass at age 7 days or 35
days). Thus, age-specific traits measured at
particular ages may be acceptable indicators
of the values of traits at similar ages, but
are less likely to be acceptable indicators at
dissimilar ages.
A problem with traits that change continuously with age or environmental conditions is that adequately redefining them can
require an infinite number of age- or
environment-specific traits (Kirkpatrick,
1988). Defining an infinite number of traits
is less than desirable. However, if changes
in traits across age or environments follow
May 1997
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
10
9
8
X
.....
CO
7
6
~
I-
5
4
3
2
o
1.5
0.5
2
Time
FIG. l.-A simple scenario illustrates how even highly predictable temporal variation, which results in reduced repeatability, can drastically affect inferred associations among traits. Three individuals have values of trait x that vary sinusoidally around means of 5, 6, and 7. The corresponding
y's (not shown) are fixed at 5, 6, and 7. If traits x and y are measured contemporaneously for all
three individuals, then depending on which point in time the measurements are made, the productmoment correlation between trait x and trait y (rxy ) can range from -1 to 0 to + 1. Thus, without
information on repeatability of x an investigator could reach grossly misleading conclusions about
the relationship between x and y. Information on repeatability assessed via the intraclass correlation
could be valuable because it would likely indicate that the underlying dynamics of trait x needed
further examination. This should lead to a better characterization of x and subsequently a better understanding of how to analyze the relationship between x and y. Patterns of variation like the one
depicted for x might be found for traits that show circadian rhythms whose phase varies across individuals.
a repeatable functional form (e.g., growth
over age follows a Gompertz curve, body
temperature over the course of a day follows a sine wave), then a second approach
to the classification of these traits is possible. The parameters of the function can be
treated as traits and the number of dimensions of the trait can be reduced to a more
manageable level (Cock, 1966; Kidwell et
aI., 1979; Kirkpatrick et aI., 1994; McCarthy and Bakker, 1979).
For some traits, it may prove impossible
to redefine them or refine measurement protocols so that single-measurement repeatabilities are high. However, even for these
traits a solution may be available. The repeatability of traits quantified as the mean
of multiple measurements improves upon
the repeatability of traits quantified as
single measurements because as sample size
increases (i.e., the number of measurements
averaged), sampling variance tends to de-
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JOURNAL OF MAMMALOGY
crease (Falconer, 1989). This observation is
based on widely known general principles,
but the implications of it are relatively
poorly appreciated. Arnold's (1994) recent
exposition, of the value of using repeated
measurements to define traits (and reduce
sampling error) may help remedy this. Once
improved measurements are obtained (e.g.,
by defining body mass as the mean of mass
measured on fivCE occasions as opposed to a
single measure) effective analysis of the
newly defined trait may be possible.
It is important for biologists to consider
the repeatability of the traits they study,
however, even when traits are significantly
repeatable, caution must be exercised in defining the repeated measurements of an organism's phenotype as a single trait. For example, consider two behavioral measurements made on successive days. The two
measures may exhibit statistically significant repeatability, however, if the repeatability is less than perfect, then the traits
may be genetically correlated, but nonetheless distinct traits. If the objective is to define traits solely on their genetic bases, then
any set of repeated measurements dermed
as a trait should pass the test that the genetic correlations among the repeated measurements are all equal to, or at least not significantly different from, one. Dohm et aI.
(1996) provide a more detailed discussion
of this issue.
Examples of how intra-individual variation is providing insight into biological
problems.-Although most intra-individual
studies to date have been conducted in the
laboratory (Friedman et al., 1992; Garland
et al., 1990, 1995; Hayes and Chappell,
1990; Perrigo et al., 1991), field studies
also are focusing more attention on intra-individual variation (Blumstein, 1992;
Boonstra and Boag, 1992; Chappell et aI.,
1995; Hansson, 1991; Hayes, 1989). For example, Chappell et al. (1995) studied the repeatability of maximal aerobic performance
(V02 max) in Belding's ground squirrels
(Spermophilus beldingi). V0 2 max is a
physiological variable that sets the upper
Vol. 78, No.2
limit for sustained aerobic activity. It has
been the subject of intense study by physiologists and ecologists because it has manifold effects on organismal biology and because selection on it may have been partly
responsible for the evolution of endothermy
(Bennett and Ruben, 1979; Hayes and Garland, 1995). Product-moment correlations
between residuals of V02 max regressed on
body mass were' significantly correlated for
V02 max measured 2 h apart or 5-18 days
apart (Chappell et al., 1995). Over 1-2
years, product-moment correlations were
significant for V0 2 max residuals measured
with an exercise test, but not significant for
V0 2 max residuals measured via cold exposure. This study showed that even over considerable time spans V0 2 max may be repeatable in nature. Another important finding was that V02 max was not repeatable
between juveniles and adults, so measurements at these two ages should be treated
as distinct characters. This study indicates
that phenotypic selection on V02 max is
possible given that V0 2 max is repeatably
expressed in nature.
Blumstein (1992) studied within-year repeatability of maximal running speeds of
golden marmots (Marmota caudata) in Pakistan. Product-moment correlations between repeated measures of maximal running speeds were low and non-significant.
Blumstein's (1992) analysis of data on type
of substrate, distance run, gender, body
mass, and the inclination of the running path
suggested that the proximate environment
experienced by individuals was a more important determinant of maximal running
speed than among-individual differences.
Blumstein (1992) speculated that perhaps
extant selection forces are not working on
among-individual variation in maximal running speed, but they may be working on behavioral factors that minimize the necessity
of escaping predators. ,Without his data on
repeatability, this interesting hypothesis is
unlikely to have been formulated.
Armitage and colleagues have persistently made the case that individual varia-
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
tion in social behavior of yellow-bellied
mannots (MarmotaJlaviventris) has significant effects on the fitness of these animals.
Svendsen and Armitage (1973) used mirrorimage stimulation (MIS) to classify marmots as aggressive, sociable, or avoiders.
This technique involved capturing animals
in the field, holding them briefly in captivity, observing them for 15 min in an arena
with a mirror at one end, and using factor
analysis to group individuals into the three
categories based on their performance of 22
behaviors. Six juveniles were retested as
yearlings and five of the six were placed in
the same category (i.e., aggressive, sociable, or avoider) as they had been as juveniles (Svendsen, 1974), although Armitage (1986a) reported that only five individuals were retested as yearlings and three
of these five were placed in the same personality category at the two ages. In addition, eight adults and yearlings were retested and all remained in the same category
(Armitage 1986b), although it is unclear
how much time elapsed between multiple
tests of the same individual. These results
suggest that mannots have individual personalities that influence their social interactions. Hence, a more detailed analysis of the
repeatability of behavioral responses in
mannots would likely be of great interest.
Loughry and Lazari (1994) recently described an interesting, but incomplete, approach to the analysis of individual variation in behavior of black-tailed prairie dogs
(Cynomys ludovicianus). They cited several
studies supposedly documenting individual
behavioral differences in sciurids, including those discussed above, and they stated
that "anyone who has watched prairie dogs
for some length of time has the intuitive
impression of individual 'personalities'
among the animals" (Loughry and Lazari,
1994:1284). They asked whether variability
in time budgets among individual prairie
dogs increased or decreased with age. These
different patterns of change are predicted by
alternative general models of the ontogeny
of behavior (Bateson, 1991; Caro and Bate-
281
son, 1986). Based on multi-group discriminant analyses of large amounts of behavioral data, Loughry and Lazari (1994) reported greater variation in time budgets
among juveniles than among yearlings, but
an increase in variation among adults compared to yearlings. This result did not match
their prediction of a unimodal relationship
between behavioral variation and age, and
they speculated that the increased variation
in all classes of adults, except mothers,
might reflect idiosyncratic adaptations of
individuals to their particular environmental circumstances. However, Loughry and
Lazari (1994) did not separate interindividual variation from intra-individual
variation. Thus, lower apparent interindividual variation in one age class (e.g.,
yearlings) might simply be an artifact of
higher intra-individual variation in this
group. Loughry and Lazari (1994) apparently have the data to assess consistency of
behavior within individuals (animals were
dyed for individual recognition; repeated
samples of behavioral data were collected
on the same individuals over the course of
one active season). As for Armitage's
(1991) data on mannots, we suggest that reanalysis of the data on prairie dogs by
Loughry and Lazari (1994) might prove
worthwhile.
Another example of why intra-individual
variation deserves study is based on a recent report describing the daily energy expenditure (DEE) of the pouched mouse
(Saccostomus campestris). DEE was measured with doubly labeled water on 3 consecutive days in the laboratory (Speakman
et aI., 1994). A pooled analysis indicated a
significant product-moment correlation between DEEs on successive days (r = 0.62).
Although the correlation was significant,
the study showed that much of the variation
in daily energy expenditure measured with
doubly labeled water was attributable to
intra-individual variation, despite the fact
that the mice were housed in laboratory
cages that presumably provided few behavioral options. The doubly labeled water
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technique is sensitive to assumptions about
how body mass and total body water change
over the interval during which metabolism
is being estimated, to assumptions about
how isotopic labels enter and leave the
body, to errors in measuring the enrichment
of hydrogen and oxygen isotopes, and to
other factors (Nagy, 1980; Speakman,
1987). Thus, even in the absence of real
intra-individual biological variation, technical errors are likely to render repeatability
of estimated metabolic rates less than perfect. The study of Speakman et al. (1994)
suggests to us that estimates of DEE measured with doubly labeled water should not
be used as predictors in regression models
unless measurement error (i.e., intraindividual variability) is appropriately accounted for (Fuller, 1987; LaBarbera,
1989). This has rarely been done (Hayes
and Shonkwiler, 1996). The role of intraindividual variation as measurement error in
inter-individual analyses is an area that deserves far greater attention than it currently
receives.
INTER-INDIVIDUAL VARIATION
Many approaches to studying the adaptive and functional significance of traits are
possible, including macroevolutionary studies (e.g., those using phylogenetic ally based
comparative methods like Sparti, 1992),
among-population studies (Benson, 1933),
and studies of individual variation within
populations. Several advantages are gained
by looking at individual variation instead of
variation among populations or species.
First, individual variation is clearly linked
to the study of microevolutionary change
caused by selection on genetically based
differences in phenotypes (Arnold, 1983;
Lande and Arnold, 1983). Microevolutionary changes occur on time scales that are at
least potentially observable by biologists.
Second, inter-individual analyses can show
how alternative strategies affect individuals
(Clutton-Brock, 1988; Van Valen, 1965). By
examining the success of alternative strategies or phenotypes, studies of inter-
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individual variation can help elucidate the
mechanisms by which selection operates.
Understanding how selection operates is an
important component of any study of natural selection (Endler, 1986). Hence, it is important to recognize that phenotypes should
not be viewed as a set of independent traits
(Lande and Arnold, 1983). The phenotype
comprises a set of interrelated and integrated traits that form a functioning whole
organism. Analysis of phenotypic correlations should prove valuable in understanding phenotypic integration, so that the relative value of alternative strategies (or phenotypes) can be assessed. Thus, the study
of alternative strategies leads to more thorough analysis of phenotypic integration so
that the costs and benefits of alternatives
can be measured. Third, studies of inter-individual variation can be used to estimate quantitative genetic parameters and to
test hypotheses about genetic constraints
(Boake, 1994).
Phenotypic selection.-The interplay between phenotypes and environments determines how selection acts on phenotypes
(i.e., differential survival and reproduction).
Understanding how selection acts on phenotypes is one of the most important issues
in evolutionary biology and ecology (Arnold, 1983). When selection operates on
traits for which heritable genetic variation
exists, then across~generation evolutionary
change may occur. There have been relatively few convincing studies of natural selection in mammals (Endler, 1986). One
reason for this is that evolution by natural
selection requires inter-individual phenotypic variation, differences in fitness among
individuals (i.e., phenotypic selection), and
a heritable genetic basis for those differences (Endler, 1986). Demonstrating all
three of these conditions is an extremely
difficult task.
Even when genetic studies are intractable,
an enormous amount can be learned from
studying how fitness covaries with other aspects of the phenotype (Arnold, 1983; Bennett and Huey, 1990; Lande and Arnold,
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
1983). This idea has roots that precede even
Darwin, but it receives less attention than it
deserves. Explicitly linking phenotypic
variation to survival and reproduction is a
more powerful approach than linking phenotypic variation with proxies of fitness like
size or energy, because the use of such proxies leaves open the question of how well
they really predict fitness. Herpetologists
have been making major contributions in
this area (Brodie, 1992; Jayne and Bennett,
1990). Mammalogists could as well, particularly because mammalogists have traditionally made major contributions to population biology.
Illius et al. (1995) provide an excellent
example of how informative studying phenotypic selection can be. They studied
an isolated population of sheep on the island of St. Kilda off the coast of Scotland.
This population crashes every 3-4 years
(Clutton-Brock et aI., 1992). The population
increases in size until the vegetation is
severely depleted, which is followed by
exceptionally high, over-winter mortality
(Clutton-Brock et aI., 1991). Illius et al.
(1995) hypothesized that during population
crashes there should be selection for increased foraging efficiency and resistance
to gastrointestinal parasites, because these
parasites interfere with nutrient assimilation. Finding that breadth of the incisor arcade is closely related to rate of food intake,
they evaluated the relationship between
breadth of the incisor arcade and survival
during a popUlation crash. They also evaluated the relationship between survival and
burden of gastrointestinal parasites, estimated from counts of parasite eggs in fecal
samples. Breadth of the incisor arcade was
significantly positively associated with survival. Fecal counts of eggs were significantly negatively associated with survival.
Thus, Illius et al. (1995) were able to test
hypotheses about how two traits might be
adaptive. It is of interest that even if additional work on the genetics of this population proves intractable, the system studied
by Illius et al. (1995) should be amenable
283
to experimental manipulation. For example,
in a year during which a population crash
was expected, some sheep could be fed increased loads of parasites while others
could be treated to reduce burdens of parasites.
Another provocative analysis of phenotypic selection is the work of Ritchie (1988,
1990, 1991) on optimal foraging by Columbian ground squirrels (Spermophilus columbianus). Despite abundant theoretical and
empirical work on optimal foraging, few investigators have tested the fundamental assumption of optimal foraging theory; that
foraging behaviors in natural populations
should maximize fitness because they have
been molded by natural selection (Brown,
1993; Schoener, 1987; Stephens and Krebs,
1986). Instead, researchers have tested
models based on optimization of surrogates
of fitness; e.g., maximization of daily energy intake or minimization of daily feeding time (Hixon, 1982; Schoener, 1971).
Lemon (1993) showed that choice of
patches yielding different rates of energy
gain by members of a laboratory population
of zebra finches (Taeniopygia guttata) was
heritable, although there was substantial
variation within, as well as among, individuals. He also showed that lifetime fitness was positively related to net rate of
energy intake, consistent with the fundamental assumption of optimal-foraging
theory (Lemon, 1991). However, field studies on ground squirrels provide the best evidence for a mammal, and one of the best
cases for any taxon, that fitness is related
to optimal foraging (Ritchie, 1988, 1990,
1991).
Ritchie (1988) used a linear-programming model to predict optimal diets of
109 free-ranging Columbian ground squirrels during a 4-year period. A unique prediction of an optimal diet to maximize daily
energy intake was calculated for each subject based on its body mass, total activity
time, and rates of harvesting monocots and
dicots. Optimal diets were predicted and
compared to estimates of observed propor-
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JOURNAL OF MAMMALOGY
tions of monocots and dicots in diets of each
individual based on microhistological examination of feces.
Ritchie (1988) assessed repeatability of
the absolute value of the difference between
predicted proportion of monocots in the optimal diet and observed proportion of monocots in the feces by testing 22 individuals
in enclosures 4-6 weeks after their foraging behavior had been observed under freeranging conditions in the field. The absolute
values of deviations were significantly positively correlated (r = 0.79) in the two tests
(Ritchie, 1988).
Ritchie (1990) classified ground squirrels as optimal foragers if their observed
diets did not differ significantly from
their optimal diets predicted by a linearprogramming model of energy maximization, and as deviators if observed and optimal diets did differ significantly. Individual
growth rates, yearly survival probabilities
(of females), and litter sizes of females were
greater for optimal foragers than for deviators, such that optimal females were estimated to have six times the reproductive
success of deviators by age three. This indicates a striking difference in fitness based
on selection of diet, in support of one of the
fundamental assumptions of optimalforaging theory. Furthermore, deviations of
observed from optimal diets for offspring
were significantly correlated with deviations for their mothers, although the relative
roles of genetic and other effects (such as
learning from observing the mother) in determining similarity between mothers and
young were unclear (Ritchie, 1991). Ritchie's work is an excellent example of how
inter-individual variation can be studied to
learn about phenotypic selection on ecologically relevant traits.
Alternative strategies and phenotypic integration.-A common path of progress in
research is to identify the major factors affecting a study system and then to develop
simple models to describe its properties. As
knowledge of the system increases, exceptions to the simple models are found and
Vol. 78. No.2
more complex models must be constructed.
Analysis of inter-individual variation can be
a powerful aid in progressing from simple
models to more biologically realistic ones.
Since the work of Lande and Arnold (1983)
on measuring selection on correlated characters, there has been an increasing appreciation that the phenotype cannot be viewed
as a set of independent characters. The phenotypic correlations among characters must
be studied to understand how selection operates on the whole organism (Crespi, 1990;
Crespi and Bookstein, 1989; Kingsolver
and Schemske, 1992; Schluter and Nychka,
1994). Information on phenotypic correlations among characters helps in understanding how the phenotype is integrated and
may provide clues to the action of selection.
By studying inter-individual variation, we
can discern whether there is a single best
strategy for an individual in a population to
adopt or whether there are several viable alternatives (Bemus et aI., 1991). Populations
of rodents, for example, may be composed
of some individuals whose reproductive
competency declines during short photoperiod (photoresponsive) and some whose reproductive competency does not (nonphotoresponsive; Desjardins et aI., 1986; Heideman and Bronson, 1991; Kerbeshian et aI.,
1994). Thus, winter populations contain a
mix of individuals, some of which may produce offspring and others that cannot. The
capacity for successfully producing offspring when winter conditions are favorable
is a clear advantage to being photoresponsive. However, functional links may exist
between photoresponsiveness and seasonal
thermoregulatory adjustment, and between
photoresponsiveness and body mass, such
that being able to breed in winter incurs a
thermoregulatory cost or a cost associated
with maintaining a summer-like body mass
(Blank and Ruf, 1992; Moffat et aI., 1993).
Consequently, which strategy (photoresponsive or not) is more successful may depend
on environmental conditions. There is convincing evidence for genetic varIatIOn in
photoresponsiveness in both Peromyscus
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
and Microtus, and several populations appear to be able to respond rapidly to selection on photoresponsiveness (Desjardins et
aI., 1986; Heideman and Bronson, 1991;
Kerbeshian et aI., 1994; Spears and Clarke,
1988). Work on the photoresponsiveness of
reproduction is just one area in which interindividual studies are providing a better understanding of phenotypic integration so
that insightful hypotheses about the action
of selection on alternative strategies can be
formulated.
Recent discussion of inter-individual
variation by behavioral ecologists has emphasized alternative mating strategies. Gadgil (1972) provided one of the first theoretical treatments of this topic, and used a
mammalian example to illustrate his arguments. He suggested that polymorphism in
morphology and behaviors associated with
reproduction in males might persist as a result of frequency-dependent selection. For.
example, males with highly developed secondary sex characteristics might have
greater mating success but shorter life
spans, and males with poorly developed
secondary sex characteristics might have
lower mating success but longer life spans.
He used the report of Darling (1937) on the
persistence of anterless males in populations of red deer (Cervus elaphus) as a potential example of this phenomenon, although relative fitnesses of deer with and
without antlers were not known. A more recent study of red deer focusing on lifetime
reproductive success did not discuss such a
polymorphism (Clutton-Brock et aI., 1982).
A similar example apparently exists in
Asian elephants (Elaphus maximus), with
tusked and tuskless males coexisting at different frequencies in various populations
(Gadgil and Taylor, 1975; Sukumar, 1992).
Despite this promising beginning, much
of the theoretical and empirical progress
since 1972 in understanding alternative
mating strategies has been based on taxa
other than mammals. This means that mammalogists should be well placed to contribute to broader and deeper understanding of
285
this key phenomenon at the interface of behavior, ecology, and evolution (Travis et al.,
1995). We will discuss several general issues that need to be considered by students
of alternative mating strategies or other
forms of inter-individual variation in social
behavior.
First, traits may vary continuously or discretely, and appropriate methods of analysis will differ for these two types of variation (Austad, 1984; Clark and Ehlinger,
1987; Dunbar, 1982). The term alternative
mating strategies typically implies discontinuous variation; i.e., two or more distinct
forms of behavior, perhaps having distinct
morphological characteristics associated
with each (Caro and Bateson, 1986). For
continuously varying traits, the microevolutionary perspective discussed in the last section will ordinarily prove most useful. Several interesting evolutionary hypotheses
may account for the existence and maintenance of discrete, alternative behaviors. In
any case, the decision about whether variation in a trait is continuous or discontinuous may not always be obvious, and deserves careful attention.
Second, if a behavioral polymorphism exists in a population, it may reflect any combination of intra-individual and interindividual variation, and the evolutionary
consequences of the polymorphism will differ depending on its cause. One extreme in
the continuum from zero repeatability to
100% repeatability is a situation in which
individuals are completely opportunistic in
performance of a behavior. For example, a
territorial male might attempt to maintain
exclusive access to one or more females on
his territory, but also attempt to obtain
sneaky copUlations with females on neighboring territories. Using DNA fingerprinting and related molecular techniques, researchers have found examples of this pattern in many species of birds (Westneat et
al., 1990). An intermediate situation is one
in which males adopt one strategy when
they are young, small, or socially subordinate and another strategy when they are
286
JOURNAL OF MAMMALOGY
older, larger, or dominant. This is one form
of "making the best of a bad job" (Dawkins,
1980:344). In this case, short-term repeatability of the behavior would be high, but
long-term repeatability would be low, so the
different expressions of the behavior might
best be thought of as different traits rather
than alternative forms of the same trait. Examples of ~trategies changing over time
can be found in taxa as diverse as elephant
seals (Mirounga angustirostris-Haley et
at, 1994; Mesnick and Le Boeuf, 1991),
mountain sheep (Ovis canadensis-Hogg,
1984), dwarf mongooses (Helogale parvula-Keane et at, 1994), and gray squirrels (Sciurus carolinensis-Koprowski,
1993). Finally, some individuals may adopt
alternative mating strategies for life, either
because of genetic differences or irreversible ontogenetic processes. Two remarkable
examples of this pattern in vertebrates occur in ruffs (Philomachus pugnax-HoganWarburg, 1966; Shepard, 1975; van Rhijn,
1973) and Coho salmon (Oncorhynchus
kisutch-Gross, 1985). We know of no similarly well-documented patterns in mammals, although the case of Asian elephants
mentioned above is an intriguing possibility (it will undoubtedly be more difficult to
measure long-term fitness consequences of
alternative strategies in elephants than in
ruffs or salmon, however). This last pattern
of consistent inter-individual differences in
mating strategies (or other aspects of social
behavior) may be the most interesting, because the evolutionary causes of this pattern
are least obvious.
Third, elaborating on the last point, there
are several potential evolutionary explanations for the within-population maintenance
of alternative mating strategies that are relatively fixed within individuals. Relative
success of alternative strategies might depend on population density or other factors
that vary temporally or spatially, such that
no one strategy is best for a long enough
time or over a large enough area to reach
100% frequency in a population. Coexistence of alternative strategies might repre-
Vol. 78, No.2
sent a non-equilibrium situation due to recent anthropogenic change in the habitat of
a species. Individuals might adopt alternative mating strategies that differ in lifetime
fitness consequences regardless of environmental conditions because of morphological
limitations that constrain their reproductive
options. For example, it may be fruitless for
small males to try to compete for mating opportunities associated with high rank in a
dominance hierarchy. If variation in body
size has a genetic basis or is determined
early in ontogeny, then small males might
opt out of agonistic interactions with larger
males and instead adopt a lifetime strategy
of attempting sneaky copulations. This is
another example of making the best of a bad
job, in which alternative strategies might
persist in a population despite having consistently unequal fitnesses (Stockley et aI.,
1994). It also illustrates the possibility that
continuous variation in one trait (e.g., body
size) may be translated into discrete variation in another (e.g., reproductive behavior).
Finally, alternative mating strategies of
males have been of primary concern in studies of inter-individual variation in behavior,
but they are not the only interesting kinds
of behavioral variation. Two recent mammalian examples of other kinds of behavioral strategies illustrate this point. Gubernick et ai. (1994) demonstrated a dimorphism in factors that induce paternal behavior and inhibit infanticide in monogamous
California mice, Peromyscus californicus.
The majority of males in experimental trials ignored or attacked unfamiliar juveniles
until after birth of their own young, but ca.
35% of males showed paternal behavior and
failed to attack unfamiliar juveniles as early
as 24 h after copulation and cohabitation
with a female partner. Apparently these two
groups of males switched from aggressive
behavior toward young mice to paternal behavior in response to different stimuli. The
possible adaptive significance of these alternative strategies will be an exciting topic for
further research. Heinsohn and Packer
(1995) reported repeatable differences
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HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
among female African lions (Panthera leo)
in aggressiveness toward real and simulated
(by playback) territorial intruders. Some females consistently led approaches to the
loudspeaker, others consistently followed.
Some evidence from real encounters suggested that leading was dangerous, yet leaders tolerated followers as pridemates. The
complexities of this system as described by
Heinsohn and Packer (1995) suggest that it
will be a most interesting one to consider
in the larger context of lifetime fitnesses of
females showing alternative behavior patterns.
Quantitative genetic analyses.-The field
of quantitative genetics is based in large
part on studying inter-individual variation.
Quantitative genetics has historically focused on animal and plant breeding (Falconer, 1989), although it has long been of
interest to evolutionary biologists (Fisher,
1930). Increased interest in studying natural selection (Brodie et ai., 1995), improved
tools for quantitative genetic analyses (e.g.,
Shaw, 1987), and a broader appreciation
that an understanding of genetics is critical
for tackling some ecological and evolutionary questions (Boake, 1994) have led to a
renewed interest in quantitative genetics.
Arnold (1994) gives an excellent, concise
introduction to quantitative genetic methods
(Brodie and Garland, 1993).
Estimates of the genetic variance and covariance among traits are essential ingredients for predicting evolutionary change in
quantitative traits. When additive genetic
variances and covariances are known, they
can be combined with estimates of the
forces of selection on the phenotype (i.e.,
the selection gradients) to predict acrossgeneration change (Arnold, 1994; Lande
and Arnold, 1983). There are many caveats
associated with predicting evolutionary
change (Deng and Kibota, 1995; MitchellOlds and Shaw, 1987; Rausher, 1992;
Schluter and Nychka, 1994; Wade and Kalisz, 1990) and the sheer effort required to
obtain all the necessary parameter estimates
ensures that these types of studies will be
287
rare despite their value. Quantitative genetic
studies are, however, also of value in
searching for genetic constraints and in better understanding organismal integration.
Few mammalogists have contributed as
much to our understanding of the quantitative genetics of ecologically important traits
as C. Lynch. Lynch (1994) studied the quantitative genetic bases of nest building and
of body size in Mus domesticus. Her study
populations represented a clinal range that
spanned from cold northern climates to
warmer southern ones. She and her coworkers showed that inter-individual variation for nest building and body mass had a
heritable basis and that both traits responded to artificial selection in the laboratory. Populations of mice from progressively colder locales were larger and built
larger nests, as would be predicted given
their environments. Moreover, in northern
popUlations, the evolution of a large proportion of brown adipose tissue (BAT),
tissue specialized for high rates of heat
production, may be constrained by a negative genetic correlation with body mass.
That is, selection for increased BAT, a probable advantage in northern populations,
also would tend to result in a decrease in
body mass, a disadvantage in northern
populations. Knowledge of this type of genetic constraint may be helpful in understanding the types of evolutionary options
available to populations, at least over the
short-term.
Other recent work on quantitative genetics by mammalogists includes attempts to
look for genetic covariances between locomotor speed and endurance and between
basal and maximal aerobic metabolic rates.
One might expect that a mammal could not
both be fast and have high endurance (Garland, 1994). One might also expect that
maintaining the capacity for a high maximal aerobic metabolic rate would only be
possible if there was a correlated elevation
of basal metabolic rate (Bennett and Ruben,
1979). If such associations are ineluctable
consequences of their underlying physi-
288
JOURNAL OF MAMMALOGY
ological determinants, then these phenotypic
correlations may indicate underlying genetic
correlations as well. Dohm et al. (1996, pers.
comm.) used a randombred strain of Mus domesticus to test for these genetic correlations, which would represent constraints on
short-term evolutionary change. They found
weak evidence for a negative genetic covariance between speed and endurance and no
support for a genetic covariance between
basal and maximal aerobic metabolic rates.
Future examination of other potential genetic constraints would be interesting.
PROMISING AVENUES FOR MAMMALOGISTS
TO CONTRIBUTE TO STUDYING INDIVIDUAL
VARIATION
Field studies of the natural history, demography, or ecology of one or a few populations of mammals are common. Mammalogists conducting these field studies are
well poised to advance our understanding of
both inter- and intra-individual variation.
They can do this in several ways.
A reemphasis can be placed on examining inter-individual variation within populations as opposed to among popUlation
variation. Focusing on inter-individual
variation is likely to be as rewarding to
mammalogists as it has been to herpetologists and behavioral ecologists (Bennett,
1987; Bennett and Huey, 1990; Boake,
1994). Inter-individual studies can provide
valuable clues to how different parts of the
phenotype are integrated (Moffat et al.,
1993; Ruf and Heldmaier, 1992; Zelditch,
1987). For example, Chappell and Bachman
(1995) recently found a correlation between
resting oxygen consumption and maximal
aerobic performance during exercise in wild
Belding's ground squirrels, even after adjusting statistically for the effects of mass.
This provides support for the notion that
these two physiological components of the
phenotype are inherently coupled (Bennett
and Ruben, 1979; Hayes and Garland,
1995).
Longitudinal field studies can be even
more productive that they have in the past.
Vol. 78, No.2
If investigators collecting longitudinal demographic data collaborate with morphologists, behaviorists, and physiologists, then
the effects of morphological, behavioral,
and physiological variation on other aspects
of the ecology of the mammal (e.g., reproduction and survival) can be discerned. It
also should be possible to estimate the
forces of natural selection (sensu Lande and
Arnold, 1983) on these traits (Kingsolver
and Smith, 1995). These kinds of studies
have been relatively rare, but our understanding of the ecology and evolution of
mammals will be improved if they receive
more attention.
Longitudinal field studies are also an
excellent source of data for studying intra-individual variation. Data on intraindividual variation can help mammalogists
in deciding how best to define the characters that interest them. For example, mammalogists have long known that it is informative to dissect fecundity into age-specific
fecundities or, at least, first-year versus
subsequent-year fecundity. Longitudinal
studies of intra-individual variation can help
us explore whether body mass, digestive efficiency, size of home range, or other traits
of interest also should be dissected into ageor environment-specific traits. Can, for example, energetically based models of size of
home range rely on point estimates of mass
or digestive efficiency? Or are more complex models necessary? Studying intraindividual variation can guide us to answers
to these questions.
Studies of intra-individual variation can
provide valuable clues to the kinds of measurement errors that mammalogists should
attend to in their data analyses. Some kinds
of measurement error are difficult to estimate and hence account for in the analyses
(Riska, 1991). Obtaining data on intraindividual variation can, however, provide
estimates for some types of measurement
errors. This will allow more cogent analysis of inter-individual variation. Markrecapture plots used to study small rodents
may be a plentiful source for such data. We
May 1997
HAYES AND JENKINS: SPECIAL FEATURE: INDIVIDUAL VARIATION
encourage mammalogists to consider the
many ways in which their research may
help advance our understanding of individual variation.
ACKNOWLEDGMENTS
We thank J. Berger for providing an important
reference, M. Dohm for discussions about repeatability, and M. Dohm, T. Garland, Jr., and
an anonymous reviewer for comments on the
manuscript. J. P. Hayes was supported by National Science Foundation (NSF) grant IBN
9410693 and by National Institutes of Health
Grant AI36418-01 (to S. C. St. Jeor et al.), and
S. H. Jenkins was supported by NSF grant IBN
9211752.
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