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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 274 May 1997 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 276 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- May 1997 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). 278 JOURNAL OF MAMMALOGY 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 Vol. 78, No.2 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 279 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- 280 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- May 1997 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 282 JOURNAL OF MAMMALOGY 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- Vol. 78, No.2 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, May 1997 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- 284 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 May 1997 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 May 1997 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. 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