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THE YEAR IN EVOLUTIONARY BIOLOGY 2009

The Covariance between Genetic and Environmental Influences across Ecological Gradients Reassessing the Evolutionary Significance of Countergradient and Cogradient Variation David O. Conover, Tara A. Duffy, and Lyndie A. Hice School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York, USA Patterns of phenotypic change across environmental gradients (e.g., latitude, altitude) have long captivated the interest of evolutionary ecologists. The pattern and magnitude of phenotypic change is determined by the covariance between genetic and environmental influences across a gradient. Cogradient variation (CoGV) occurs when covariance is positive: that is, genetic and environmental influences on phenotypic expression are aligned and their joint influence accentuates the change in mean trait value across the gradient. Conversely, countergradient variation (CnGV) occurs when covariance is negative: that is, genetic and environmental influences on phenotypes oppose one another, thereby diminishing the change in mean trait expression across the gradient. CnGV has so far been found in at least 60 species, with most examples coming from fishes, amphibians, and insects across latitudinal or altitudinal gradients. Traits that display CnGV most often involve metabolic compensation, that is, the elevation of various physiological rates processes (development, growth, feeding, metabolism, activity) to counteract the dampening effect of reduced temperature, growing season length, or food supply. Far fewer examples of CoGV have been identified (11 species), and these most often involve morphological characters. Increased knowledge of spatial covariance patterns has furthered our understanding of Bergmann size clines, phenotypic plasticity, species range limits, tradeoffs in juvenile growth rate, and the design of conservation strategies for wild species. Moreover, temporal CnGV explains some cases of an apparent lack of phenotypic response to directional selection and provides a framework for predicting evolutionary responses to climate change. Key words: countergradient variation; cogradient variation; covariance; clines; genetic compensation; adaptation; phenotypic plasticity; Bergmann’s rule; growth rate evolution; species range; conservation biology

Introduction Strong ecological gradients, where environmental factors change systematically with geography, have been a very important resource for

Address for correspondence: David O. Conover, School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000. Voice: 631-632-8187; fax: 631-632-8915. dconover@ notes.cc.sunysb.edu

understanding the patterns and processes responsible for phenotypic change (Endler 1986). Early ecologists observed that within many species, phenotypes tend to change in predictable ways across large-scale gradients such as latitude, altitude, and water depth, leading to a series of ecological “rules.” Examples of such clines in phenotypic variation include Bergmann’s rule, which describes the oftobserved increase in body size with latitude

The Year in Evolutionary Biology 2009: Ann. N.Y. Acad. Sci. 1168: 100–129 (2009). c 2009 New York Academy of Sciences. doi: 10.1111/j.1749-6632.2009.04575.x

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Conover et al.: Evolutionary Significance of Countergradient and Cogradient Variation

(Bergmann 1847; Atkinson & Sibly 1997), and Jordan’s rule, which refers to the increase in vertebral number with latitude (Jordan 1891; Yamahira et al. 2006; McDowall 2008). Whether such geographical patterns reflect genetic variation as opposed to phenotypic plasticity or represent adaptive variation in response to selection gradients as opposed to neutrality with respect to fitness continues to stimulate investigation and debate (Partridge & Coyne 1997; Blanckenhorn & Demont 2004; Ghalambor et al. 2007; Crispo 2008; McDowall 2008). An important development in our understanding of the interplay between environmental and genetic influences on phenotypic variation across ecological gradients emerged from concepts introduced by Richard Levins in 1968. Levins (1968, 1969) pointed out that genotypes within a species may be distributed in nature such that genetic influences on a trait oppose environmental influences leading to reduced phenotypic variation across the gradient. Levins (1969) referred to this pattern as “countergradient variation” (CnGV) and pointed out how it contradicted the then widely held belief that genetic variation, if any, would parallel phenotypic variation (i.e., cogradient variation [CoGV]). His example was from fruit flies inhabiting an altitudinal gradient from sea level to 3,000 ft in the mountains of Puerto Rico. In nature, flies from cooler, high-altitude localities were slightly larger than those from the warmer lowlands. This pattern reversed, however, when flies were reared in a common laboratory environment, thus allowing the genetic component of phenotypic variation to be expressed. Normally, developmental temperature has a strong negative correlation with body size, so genetic variation was partially counteracting the phenotypic effect of temperature, thereby moderating the change in body size with altitude that would have otherwise been expressed. Levins argued that this pattern was adaptive because of selection caused by desiccation. Large body sizes reduce desiccation stress in the hot, dry lowlands; hence, genetic variation for increased

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body size, in opposition to the effect of temperature, is favored at low altitudes. Levins’ insight underscored the importance of understanding the covariance between genetic and plastic influences on phenotypic variation across environmental gradients. His ideas, however, were largely overlooked until a series of reports by Keith Berven on altitudinal variation in frogs of the genus Rana (Berven et al. 1979; Berven 1982a; Berven 1982b). In general, Berven and colleagues showed that in nature larval frogs from high elevations displayed slower growth and development rates and were larger at each larval stage than low-elevation frogs. However, when the frogs were reared in common garden experiments, the relative performance of the two populations with respect to growth and development was reversed; hence, they displayed CnGV. In the decade that followed, Conover and Present (1990) described the occurrence of CnGV in fish from different latitudes, but only a few other cases were reported during this period (see Conover & Schultz 1995). Conover and Schultz (1995) argued that CnGV was probably far more prevalent than suggested by the literature at that time and its evolutionary significance far greater than was then appreciated. They pointed out that because CnGV generates phenotypic similarity, it represents a form of hidden genetic variation that can be revealed only by common garden experiments. Because investigators are more likely to explore the causes of phenotypic change rather than similarity across ecological gradients, many cases of CnGV might be overlooked. Indeed, most of the 18 cases reported from the literature by Conover and Schultz (1995) were not labeled as CnGV by the original authors even though the data in these reports were clearly indicative of it. Hence, lack of familiarity with the concepts of CnGV and CoGV may also have been hindering investigation. In recent years, the number of articles reporting countergradient and cogradient variation has increased greatly. Patterns are

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beginning to emerge such as the observation that CnGV traits involve primarily compensatory physiological characters, whereas CoGV more often tends to involve morphology (Marcil et al. 2006). Also, there are now several well-documented examples of the evolution of CnGV over temporal scales encompassing environmental change (Garant et al. 2004; Merilä et al. 2001; Wilson et al. 2007). Moreover, increased knowledge of CnGV and CoGV has played an important role in our general understanding of a variety of other topics, including the interpretation of observed phenotypic variation (e.g., Conover & Schultz 1995; Merilä et al. 2001; Garant et al. 2004; Ellegren & Sheldon 2008), Bergmann size clines (e.g., Atkinson & Sibly 1997; Arnett & Gotelli 1999; Blanckenhorn & Demont 2004), tradeoffs among life history traits (Arendt 1997; Gotthard et al. 1994; Billerbeck et al. 2001; Lankford et al. 2001; Arnott et al. 2006), the evolution of phenotypic plasticity (Grether 2005; Ghalambor et al. 2007), and the limits to species ranges (Kirkpatrick & Barton 1997; Eckhart et al. 2004; Sanford et al. 2006; Ghalambor et al. 2007). Finally, from the viewpoint of applied evolution, the widespread existence of genotype–environment covariance has crucial significance for conservation biology, including strategies for restoration and/or management of species in the wild and understanding the responses of species to global climate change. Here we review our current understanding of patterns in the covariance between genotypes and environments across ecological gradients in space and time and its evolutionary significance. We begin with definitions of CnGV and CoGV and approaches to detect and interpret the adaptive significance of these patterns in nature. We then report on cases known to us from the literature and highlight a few well-studied taxa that may serve as models. We discuss how knowledge of CnGV and CoGV has influenced our understanding of phenotypic variation in the wild and its implications for species conservation.

Annals of the New York Academy of Sciences

Countergradient and Cogradient Variation The sources of phenotypic variation in a quantitative character (VP ) can be partitioned as VP = VG + VE + VG ×E + 2Cov(G,E) (Falconer & Mackay 1996; Conover & Schultz 1995). VG is variance due to genotypic effects, whereas VE is variance due to environmental influences, also known as the norm of reaction or plasticity of the phenotype. Presence of the interaction term VG ×E indicates that reaction norms are nonparallel among genotypes. The interaction term can be quite important in understanding the geography of locally adapted genotypes in nature. For instance, it is common to find that genotypes from a particular locality have higher fitness under environmental conditions closest to those most frequently encountered in the wild (e.g., Lonsdale & Levinton 1985; Bronikowski 2000; Caley & Schwarzkopf 2004; Belk et al. 2005). The subject of this report is the covariance term. Cov(G,E) exists when genotypes that influence phenotypic expression are nonrandomly distributed among environments that influence the same phenotypic traits (Falconer & Mackay 1996). This term can be either positive or negative depending on whether genetic and environmental effects reinforce or oppose each other. If genotypes are distributed randomly, then the change in mean phenotypic value across the gradient will reflect only the plasticity induced by the environment (Fig. 1A). However, when genotypes that shift trait expression in a particular direction are found in environments that also shift trait expression in the same direction, then Cov(G,E) is positive and VP is increased; such cases are referred to as cogradient variation (CoGV) (Fig. 1B). For example, this occurs when genotypes that code for a particular body form (e.g., fusiformshaped fish) are more common in habitats that also induce similar characteristics (e.g., limnetic waters; Day et al. 1994; Robinson & Wilson 1996). Conversely, when genotypes that influence trait expression in one direction are


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Figure 1. Phenotypic patterns of variation that would result from (A) phenotypic plasticity, (B) cogradient variation, (C) countergradient variation, and (D) genetic assimilation. The various components of phenotypic variation are represented as follows: thick, solid line is the norm of reaction, which represents the environmental influences on phenotype; the dashed line and the shaded region is the genetic component; and the dotted line is the phenotype expressed (the sum of the environmental and genetic influences). Where two components of variation are identical, they are depicted side by side (panels A and D). In panel A, phenotypic change across the gradient is caused only by environmental effects, whereas in panel B it is accentuated by the additive effect of environmental and genotypic influences. In panel C, the plastic effect of the environment on phenotypic expression is exactly counteracted by the distribution of genotypes in the environment such that phenotypes in nature appear identical across the gradient. In panel D, genetic influences have assimilated some of the environmental influence on phenotypic expression across the gradient, leading to a reduction in plasticity.

commonly found in environments that do the opposite, then Cov(G,E) is negative and VP is reduced; such cases are called countergradient variation (CnGV) (Fig. 1C). For example, genotypes that code for fast growth are often found in habitats that would otherwise cause slow growth, perhaps because of low temperature or food availability (Blanckenhorn 1991;

Arnett & Gotelli 1999; Jonassen et al. 2000; Ficetola & De Bernardi 2005, 2006). The inflation of VP across the gradient is the feature that distinguishes CoGV from the related concept of genetic assimilation (Grether 2005; Pigliucci et al. 2006; Crispo 2007), which is said to occur when genetic effects on phenotypic expression replace plasticity rather than

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adding to it. In contrast with CoGV, genetic assimilation involves a reduction in plasticity through canalization of phenotypic expression (Flatt 2005), resulting in little or no change in VP (Fig. 1D). As a practical matter, however, if a species has expanded its range across a gradient it may be difficult to distinguish between these two alternatives unless the ancestral norm of reaction is known. Geographical or temporal patterns of Cov(G,E) would be of little interest from an evolutionary perspective if countergradient or cogradient distributions of genotypes in nature were driven purely by chance, as with genetic drift, rather than selection. However, we now know that many cases of CoGV and CnGV involve clinal variation across broad environmental gradients in physical factors such as altitude, latitude, and aridity–humidity. The driving force in such cases must be some form of natural selection (Endler 1986). CoGV should arise when selection and environmental influences act in the same direction on a trait (termed synergistic selection by Falconer 1990) across the gradient, and CnGV should evolve when the environment induces phenotypes that are the opposite of those favored by selection (i.e., antagonistic selection; Falconer 1990). Hence, the agents of selection and how they influence the fitness of phenotypes induced by environmental differences across ecological gradients are a crucial component of the evolution of CoGV and CnGV. There has been some confusion in the literature about the distinction between countergradient selection as a process and CnGV as the adaptive response (discussed later). The former occurs whenever environmental influences on trait expression push the phenotype away from its optimum in a given location, thereby reducing its fitness. CnGV is a form of local adaptation that may evolve to counteract such detrimental influences on fitness. Conversely, cogradient selection occurs when phenotypes induced by the environment have higher fitness, which may result in the evolution of CoGV.

Disentangling the Genetic and Environmental Components of Cov(G ,E ) The starting point of many investigations is the observation of some pattern in phenotypic variation in nature. When phenotypes are observed to differ between locations or at different times, field biologists want to know why, so they formulate hypotheses. Is it caused by phenotypic plasticity? Does it have a genetic basis? Does it represent local adaptation? However, the covariance term in the preceding equation greatly complicates the answers to such questions and requires not only our attention to explaining phenotypic change but also similarity. Because Cov(G,E) can either increase or decrease VP across environmental gradients, the magnitude of observed VP in nature can be highly deceptive (Conover & Schultz 1995; Ellegren & Sheldon 2008). In particular, CnGV requires us to mistrust our intuition because the phenotypic pattern observed in nature may be the opposite of what is favored by natural selection. Phenotypic change across a gradient does not by itself signify the direction or magnitude of phenotypic plasticity (VE ), the underlying pattern of genetic variation (VG ), or even the phenotypes favored by selection. Depending on the magnitude of CnGV, three different patterns of phenotypic variation can emerge. First, when strong CnGV overcompensates for plasticity caused by environmental differences, then the observed phenotypic pattern is in the same direction as genetic differences and the selection differential across the gradient (Fig. 2A), although the true magnitude of genotypic change would be much greater than implied by the change in phenotypes. Second, when CnGV exactly compensates for phenotypic plasticity, then there is no apparent change in phenotypes across the gradient (Fig. 2B). Hence, phenotypic similarity in plastic traits across environmental gradients ought to motivate our curiosity because it implies the existence of hidden genetic variation. Finally, CnGV may only partially compensate


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Figure 2. Reaction norms and the phenotype expressed for different environments. (A) overcompensating CnGV, (B) perfectly compensating CnGV, (C) partially compensating CnGV, (D) CoGV, (E) G × E interaction, and (F) a combination of G × E interaction and undercompensating CnGV. Solid circles (N1 and N2) represent phenotypes that each genotype expresses in its native environment. All other components are the same as in Figure 1.

for environmental effects on the phenotype (Fig. 2C). This can be confusing because here the residual pattern in phenotypic variation is the opposite of what may be favored by selection. CoGV in trait expression can also be deceptive. In this case, the observed patterns in nature are consistent with what might be predicted from plasticity alone. However, the change in

mean phenotype across the gradient actually exceeds what could be accounted for by plasticity (Fig. 2D). The genetic component of trait variation can be revealed only after correcting for phenotypic plasticity. If strongly positive or negative values of Cov(G,E) are common in nature, then the implications for the interpretation of phenotypic variation as observed in nature are profound.

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Our understanding of natural selection and adaptation depends greatly upon correct interpretation of the causes of phenotypic patterns in the wild. Moreover, CnGV and CoGV may occur not only across the spatial domain but also over temporal scales. For example, CnGV may evolve over time as climate changes at a given location (Skelly et al. 2007), thereby maintaining phenotypic similarity but through hidden shifts in genetic variation (Merilä et al. 2001; Garant et al. 2004; Wilson et al. 2007; Ellegren & Sheldon 2008). Either the maintenance of trait similarity or its rapid divergence over a period of environmental change may reflect the evolution of CnGV or CoGV, respectively. Hence, rates of evolution calculated from measures of phenotypic change could be positively or negatively biased by the Cov(G,E) term. Finally, spatial patterns in phenotypic variation play a major role in defining the population structure of a species and hence strategies for conservation. Given such potential importance, we next discuss approaches for detecting and interpreting CnGV and CoGV. Methods for Detecting Countergradient and Cogradient Variation The potential for positive or negative covariance between genetic and environmental influences across an ecological gradient can be deduced initially from three pieces of information: (1) careful measurement of the pattern of change in a trait across a spatially or temporally varying environmental gradient, (2) the norm of reaction for the trait in response to the key environmental factors, and (3) a measure of the magnitude of environmental change across the gradient. In some North American temperate fishes, for example, size at the end of the first growing season changes little with increasing latitude despite a threefold decline in length of the growing season (Conover 1990). From this observation, the likelihood of CnGV in growth rate was predicted and now has been verified

in many species (discussed later). A CoGV example involves Jordan’s rule. Low temperature during development tends to induce more vertebrae in many species, so the increase with latitude could be attributed solely to phenotypic plasticity. But for some species, the increase with latitude is far greater than could possibly be explained by temperature, suggesting the existence of CoGV (Billerbeck et al. 1997). Hence, careful field work and analysis can reveal a great deal about the potential underlying patterns of CoGV or CnGV and thereby guide the development of hypotheses. We recommend that investigators develop a clear understanding of phenotypic patterns in nature, as described earlier, before designing experiments to evaluate causal mechanisms. To reveal the underlying pattern of genotypic variation and/or the possibility that the norm of reaction itself might differ geographically or temporally will generally require common garden experiments wherein genotypes from different populations are reared in the same controlled environment. Even in species that are easy to culture, these are not trivial experiments to conduct. First, to verify that the genetic component of the change in mean phenotype is statistically correlated with the gradient of interest, it requires substantially more than just two sites to be compared. Second, because the plasticity of a trait might also vary among populations, such common garden experiments should involve multiple environmental conditions chosen to bracket the range of variation encountered in nature for the parameter of interest. For latitudinal or altitude gradients, for example, this might involve a wide range of temperatures (e.g., Dehnel 1955; Jonassen et al. 2000; Yamahira et al. 2007). Comparing a wide range of values for a given parameter in common garden experiments is also necessary to distinguish between VG ×E and Cov(G,E) (Yamahira & Conover 2002; Yamahira et al. 2007). Local adaptation across environmental gradients may occur in either term, or a combination of both, and the distinction is crucial (see examples in the following).


When local adaptation occurs via VG ×E , local genotypes will display highest fitness under conditions that mimic those from the native habitat and lower fitness elsewhere. When genotypes from different localities are compared, the norms of reaction cross, reflecting a change in the rank order of performance as a function of environmental conditions (Fig. 2E; e.g., Bronikowski 2000; Belk et al. 2005; Caley & Schwarzkopf 2004). Norms of reaction that are parallel and lie above or below one another in trait value represent either CnGV (Fig. 2A,B,C) or CoGV (Fig. 2D). Both VG ×E and Cov(G,E) act simultaneously when norms of reaction are nonparallel (i.e., they differ in slope) but nonetheless do not cross over a wide range of environmental conditions (Fig. 2F). Noncrossing reaction norms that display CnGV suggest that some genotypes display trait values with higher fitness in all environments (Fig. 2F). For traits that are expected to be positively correlated with fitness, such as fecundity, growth rate, or body size (i.e., more is better in terms of fitness), this represents a paradox for life history evolution (Conover & Schultz 1995), a topic that will be further discussed later. Another method for detecting CnGV and CoGV is reciprocal transplants, in which individuals from different localities are transported and placed together in each of the original habitats (Arnett & Gotelli 1999; Trussell 2000; Byars et al. 2007). Such experiments provide the best means to test for overall differences in fitness across multiple, naturally varying parameters in nature. Of course, to avoid introgression such experiments should be conducted only with organisms where unintended escapes to the wild of alien genotypes can be prevented. Both common garden and reciprocal transplant experiments must contend with the confounding effect of environmental experience prior to the experiment on test organisms. Both the maternal and abiotic environment experienced during development can have lasting effects later in life that could be erroneously

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interpreted as genetic differences (Falconer & Mackay 1996). To minimize this potential bias, test subjects should be drawn from parents that have themselves been reared in a common environment, or transplanted to experimental field sites, for one or more generations. For many organisms, logistic difficulties of rearing would make this impossible. Even so, common garden experiments may still be useful with the recognition that phenotypic differences among treatments may not be purely genetic. Having identified the phenotypic patterns in nature, quantified environmental differences across the gradient, and measured the VE , VG ×E , and Cov(G,E) components of VP , one’s final task is to identify the agents of selection responsible for such patterns. Here it will be necessary to identify the potential drivers of environmental change and test for differences in fitness as a function of selection differentials across the gradient. For example, Schultz and Conover (1999) demonstrated that a gradient in size-selective winter mortality was probably responsible for CnGV in growth of a fish, and Walsh et al. (2006) then used selection experiments to show that growth rate and related physiological traits evolved rapidly in response to this single agent of selection. Another approach is to compare adaptive responses across multiple gradients that may differ in the agents of selection, especially when these have a known colonization history. Such approaches have proven useful in studies of fish (Reznick 1996; Ghalambor et al. 2004; Arendt & Reznick 2005), flies (Huey et al. 2000, Gilchrist et al. 2001), and plants (Byars et al. 2007). Countergradient and Cogradient Variation in the Literature We compiled examples of CnGV and CoGV from the literature with the following criteria. First, we list only those cases where the pattern of trait variation across an ecological gradient

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in nature has been identified and the plasticity of the trait with respect to the primary driver of environmental variation was known from experiments or could be deduced. Second, we looked for strong evidence of a genetic component to phenotypic variation that was based either on a common garden experiment, a fieldbased transplant experiment, or data sufficient to allow a correction for plasticity in the pattern observed in the field. Doing so forced us to eliminate several studies where there was clearly a spatial trend in some mean trait value across a gradient but insufficient evidence of a genetic component (e.g., Ashton 2001; Malo & Baonza 2002). In other cases, there was a measure of the genetic component of phenotypic variation but there was no measure of the norm of reaction because the common garden experiments were conducted under only one standardized environmental regime (e.g., Telfer & Hassall 1999; Karan et al. 2000). We also excluded studies that discussed CnGV or CoGV at the interspecific level (e.g., Van Doorslaer & Stoks 2005; Lusk et al. 2008) unless they involved very closely related species pairs (e.g., as in Gasterostueus spp. that occupy the same lake; Day et al. 1994). We included species where only two locations were compared even though such studies are insufficient to prove the existence of a statistically significant correlation with the gradient of interest. We found a total of 62 species where existing studies provided strong evidence of CnGV or CoGV or both (Tables 1 and 2). In all but seven species, the confirming data were obtained from either common garden experiments or transplants across field locations. Of the seven species based on in situ field analyses, three involved CnGV in time and the others were sufficiently convincing, on the basis of the contrast between measured phenotypic trait variation and that predicted from plasticity, to warrant inclusion. CnGV has been identified in 60 species (Table 1), 90% of which are ectotherms. CnGV has been found more often in fishes (21 species) than any other taxon (35% of all cases). The


next most common taxa were insects with 13 examples, followed by mollusks (nine), amphibians (four), reptiles (three), crabs (three), plants (three), birds (three), and mammals (one). Physiological traits were by far the most common to display CnGV. Of the 60 species with CnGV, all but eight displayed CnGV in growth and/or development rate (87%). Metabolic (respiration) rate displayed CnGV in 12% of species. Traits representing reproductive output such as fecundity and egg size displayed CnGV in only four species, although there were relatively few studies of such characters (Conover 1992; Kinnison et al. 2001; Kokita 2003, 2004; Eckhart et al. 2004). A variety of other characters also displayed CnGV at a low prevalence including behavioral (5%), morphological (3%), and sexually selected (3%) traits. There were 28 cases where multiple traits displayed CnGV within the same species, with the maximum being Menidia menidia and Rana temporaria with nine and 10 traits, respectively. In contrast, only 11 species have been reported to show conclusive evidence of CoGV (Table 2). As with CnGV, the fishes are the taxon with the most cases of CoGV (five species), with the rest spread among the insects (two), mollusks (two), and plants (two). In nine of the 11 species, the trait(s) displaying CoGV was morphological. Only four species displayed CoGV in physiological traits. Hence, by comparison with CnGV, the premise that CoGV tends to involve primarily morphological traits is supported. However, CoGV in physiological traits such as growth rate does also occur (e.g., Arendt & Reznick 2005). Although the reported cases of CoGV are relatively few, we caution against drawing any conclusions on this score. We suspect that there are many other cases that have not been recognized or reported. In the insects, for example, we found many common garden studies showing positive or negative correlations between body size and latitude, but because the plasticity of body size with respect to temperature was not reported, we cannot classify these trends as CoGV versus CnGV (Brennan & Fairbairn


1995; Blanckenhorn & Fairbairn 1995; Telfer & Hassall 1999). Eight species display both CoGV and CnGV in different traits. Usually this involves CnGV variation in a physiological trait coupled with CoGV in a morphological trait (e.g., Menidia spp., Drosophila melanogaster, Bembicium vittatum, Littorina obtusata; Tables 1 and 2). The occurrence of both covariance patterns in the same species suggests that genetic constraints do not prevent the evolution of clines representing both CoGV and CnGV in the same species across the same ecological gradient. The ecological gradients most often associated with CnGV and CoGV were latitude (42 species) and altitude (nine species), and often the geographic scale of detectable variation is very broad, stretching across much of a species’ range. We caution, however, that a large fraction of species (31%) involve comparisons of only two populations, which is an insufficient sample size to prove a correlation with a gradient. Several studies have demonstrated that CnGV also occurs on microgeographic scales. Ficetola and De Bernardi (2005) found CnGV among populations of Rana latastei only 50 km apart, and Byars et al. (2007) reported CoGV across altitudinal transects in Poa hiemata only 1 km apart. Skelly (2004) reported CnGV on spatial scales of tens to hundreds of meters among populations of Rana sylvatica that differed microgeographically in thermal regime as a function of canopy cover. Pardo and Johnson (2005) used transplant experiments to reveal CnGV in growth among ecotypes of an intertidal gastropod Littorina saxatilis no more than 100 m apart, although more work to confirm that the differences are genetic is necessary. Such fine-scale geographic structuring of local adaptive genetic variation suggests that countergradient selection can mold adaptive genetic variation even without isolation by distance. It is evident from Tables 1 and 2 that three groups of species—fishes, amphibians, and insects—have been especially well stud-

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ied, and each of these groups has contributed uniquely to our understanding of CoGV and CnGV. We discuss findings from each of these taxa next. Frogs Members of the genus Rana have provided key insights into the adaptive significance of CnGV. The classic reports by Berven and colleagues on the green frog Rana clamitans and the wood frog, R. sylvatica, were the first to rigorously demonstrate CnGV in a vertebrate (Berven et al. 1979; Berven 1982a; Berven 1982b) and were the stimulus for many of the studies that followed. R. sylvatica exhibits CnGV in development and growth rates across latitudinal (Riha & Berven 1991) and altitudinal (Berven 1982b) gradients and among nearby local ponds that differ in thermal regime as a function of tree canopy cover (Freidenburg & Skelly 2004; Skelly 2004). Another well-studied ranid is the Scandinavian species R. temporaria. A suite of physiological, morphological, and behavioral traits display CnGV (Table 1) across a 1,600-km latitudinal gradient in this species. Initially, Merilä et al. (2000) showed that growth rate and age at metamorphosis were consistently higher in northern populations and concluded that a short growing season combined with colder temperatures in the north had selected for faster growth and development. Faster growth is achieved by higher food conversion efficiency in northern populations (Lindgren & Laurila 2005). Further common garden and field experiments illustrated that seasonality, frog density, and pond hydroperiod all generate selection acting on tadpole development rate, age, and size at metamorphosis, and body shape (Laurila et al. 2002, Laugen et al. 2003, Laurila et al. 2008). Palo et al. (2003) compared neutral molecular genetic (F st ) to quantitative trait variation (Q st ) across these same R. temporaria populations. Divergence in quantitative traits that displayed CnGV was far greater than for neutral traits, indicating that natural

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TABLE 1. Examples of Countergradient Variation in the Literature Taxon Plantae Carex aquatilis

Popsa

Gradient

Methodb

Reference

5

Shoot height, growth

Latitude

RT

5

Phosphate uptake

Soil phosphate level Geographic

RT

Chapin & Chapin 1981 Chapin & Oechel 1983

T

Eckhart et al. 2004

Successional stage

CG, RT

Thompson et al. 1991a; Thompson et al. 1991b

Ciliary activity, growth rate

Latitude

T

Dittman 1997; Dittman et al. 1998

Latitude Wave action Latitude Latitude Latitude Latitude Latitude

T RT CG CG CG CG RT, F

Parsons 1997 Johnson & Black 1998 Ament 1979 Ament 1979 Dehnel 1955 Dehnel 1955 Trussell 2000

Wave action

CG

Trussell 2002

Intertidal zone Latitude

RT CG

Pardo & Johnson 2005 Dehnel 1955 Vernberg & Costlow 1966 Sanford et al. 2006 Vernberg 1959

Clarkia xantiana

18

Spartina anglica

3–10

Bivalvia Crassostrea virginica

2–3

Gastropoda Bembicium vittatum

Trait

Littorina saxatilis Thais emarginata Malacostraca Uca pugilator

4 2

Growth rate Shell shape Growth rate Growth rate Larval growth rate Larval growth rate Shell mass and thickness growth Tissue and shell growth Growth rate Larval growth rate

3

Respiration rate

Latitude

CG

Uca pugnax Uca rapax Insecta Aedes albopictus

5 2

Development time Respiration rate

Latitude Latitude

CG CG

9

Latitude

CG

Armbruster & Conn 2006

Aquarius remigis

6

Larval growth, pupal mass Preadult developmental period Development time

Latitude

CG

Body size Egg and larval development rate, metabolic rate, growth rate, growth efficiency

Altitude Latitude

CG CG

Growth rate, metabolic rate Developmental time

Latitude

CG

Blanckenhorn & Fairbairn 1995 Levins 1969 James & Partridge 1995; Berrigan & Partridge 1997; De Moed et al. 1998; Robinson & Partridge 2001 De Block et al. 2008

Temp

RT, CG

Blanckenhorn 1991

Crepidula convexa Crepidula fornicata Crepidula nummaria Lacuna carinata Littorina obtusata

3 3 2 2 2 2 2–25

Flower development time Flowering time Growth, survival

2

Drosophila melanogaster

5 4–20

Enallagma cyathigerum

3

Gerris remigis

2

Continued

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Conover et al.: Evolutionary Significance of Countergradient and Cogradient Variation TABLE 1. Continued Taxon

Popsa

Lasiommata petropolitana

3

Leptinotarsa decemlineata Myrmeleon immaculatus

4 4

Omocestus viridulus

Reference

CG

Gotthard 2004

Latitude Latitude

CG RT, CG

Boman et al. 2008 Arnett & Gotelli 1999

Altitude

CG

Berner et al. 2004

Latitude

CG

Gotthard et al. 1994

Latitude

CG

Latitude

CG

Schutze & Clarke 2008 Nygren et al. 2008

Latitude

CG

Blanckenhorn & Demont 2004

16

Growth rate

Latitude

F

3

Growth rate

Latitude

F

Power & McKinley 1997 Lombardi-Carlson et al. 2003

Larval growth rate Growth rate, embryo development

Latitude Latitude

F CG

11

Paropsis atomaria

2

Polyommatus icarus

3

Scathophaga stercoraria

5

Osteichthyes Cynoscion nebulosus Fundulus heteroclitus

Methodb

Latitude

2

Sphyrna tiburo

Gradient

Larval development rate, growth rate, pupae weight Development rate Growth rate, adult body size, development time Embryonic and juvenile development rate Larval development rate, growth rate, pupae weight Body size, development time Body mass, development rate, growth rate Development rate

Pararge aegeria

Chondrichthyes Acipenser fulvescens

Trait

3 2–5

Smith et al. 2008 Schultz et al. 1996; DiMichele & Westerman 1997 Purchase & Brown 2001; Salvanes et al. 2004 Marcil et al. 2006 Jonassen et al. 2000

Gadus morhua

2

Growth rate, food conversion efficiency

Latitude

CG

Hippoglossus hippoglossus

2 3

Thermal Latitude

CG CG

Lepomis gibbosus

6

Body shape Growth rate, growth efficiency Growth rate, cranial ossification

CG

Arendt & Wilson 1997, 1999, 2000

Menidia menidia

2–5

Densitydependent competition Latitude

CG

Conover & Present 1990; Conover 1992; Present & Conover 1992; Billerbeck et al. 2000, 2001; Lankford et al. 2001; Munch & Conover 2003; Arnott et al. 2006; Chiba et al. 2007

Growth rate, food consumption rate, egg size, fecundity, growth efficiency, metabolic rate, swimming performance, predator vulnerability, foraging behavior

Continued

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TABLE 1. Continued Taxon Menidia peninsulae

Popsa

Trait

Gradient

Methodb

5

Growth rate

Latitude

CG

Micropterus salmoides Morone saxatilis

2 4–6

Growth rate Growth rate

Latitude Latitude

CG CG

Notropis atherinoides Oncorhynchus keta

9 2–3

Growth rate Body shape

Latitude Season of reproduction

F CG

Oncorhynchus nerka

2

Breeding color

CG

Oncorhynchus tshawytscha

2

Ovarian mass

Carotenoid availability Migration distance Latitude

Oryzias latipes

3–12

Poecilia reticulata

6

Pomacentrus coelestis

3

Salmo salar

2–6

Salmo trutta

6

Scophthalmus maximus Amphibia Rana clamitans Rana latastei

Rana sylvatica

3–4

4 5

4–8

3 6–12 Rana temporaria

2–8

Growth rate

CG CG

CG

Reference Yamahira & Conover 2002 Philipp & Whitt 1991 Conover et al. 1997; Brown et al. 1998; Secor et al. 2000 Pegg & Pierce 2001 Tallman 1986; Tallman & Healey 1991 Craig & Foote 2001; Craig et al. 2005 Kinnison et al. 2001 Yamahira et al. 2007; Yamahira & Takeshi 2008 Grether et al. 2005

Sexual body coloration Clutch size, egg size, egg production rate Digestion rate, growth rate

Carotenoid availability Latitude

CG

Latitude

CG

Standard metabolic rate, swimming performance Growth rate, growth efficiency

Thermal

CG

Latitude

CG

Development rate Larval growth and developmental rate, leg length, jumping performance Development rate, larval growth rate (body size) Larval growth rate Thermal preference, development rate Age and size at metamorphosis, development rate, growth rate, growth efficiency, relative gut length, activity and foraging rates

Altitude Altitude

CG CG

Berven et al. 1979 Ficetola & De Bernardi 2005; Ficetola & De Bernardi 2006

Altitude

CG, RT

Berven 1982b

Latitude Thermal (tree canopy) Latitude

CG CG

Riha & Berven 1991 Freidenburg & Skelly 2004; Skelly 2004 Merilä et al. 2000; Laugen et al. 2003; Palo et al. 2003; Lindgren & Laurila 2005; Laurila et al. 2008

CG

Kokita 2003; Kokita 2004 Nicieza et al. 1994a; Nicieza et al. 1994b; Finstad & Forseth 2006 ´ Alvarez et al. 2006; Cano & Nicieza 2006 Imsland et al. 2000; Imsland et al. 2001

Continued

113

Conover et al.: Evolutionary Significance of Countergradient and Cogradient Variation TABLE 1. Continued Taxon

Popsa 9

Trait

Gradient

Methodb

Reference

Altitude

CG

Marquis & Miaud 2008

8

Embryonic development rate, hatch size, effect of UV exposure on larval growth Development rate

Densitydependent competition

CG

Loman 2003

Reptilia Eulamprus quoyii

2

Juvenile growth rate

Latitude

CG

Sceloporus jarrovi

2

Growth rate

Altitude, food availability Latitude

CG

Caley & Schwarzkopf 2004 Smith et al. 1994

Sceloporus undulatus

2–5

Growth rate, growth efficiency, hatch rate, respiration rate, yoke assimilation rate

Aves Ficedula albicollis

1c

Relative body weight

Junco hyemalis Parus major

2 1d

Male weight Fledgling mass

1c

Body size

Mammalia Ovis aries

Temporal environmental change Altitude Temporal climate change Temporal environmental change

CG

Ferguson & Talent 1993; Oufiero & Angilletta 2006; Storm & Angilletta 2007

F

Merilä et al. 2001

CG F

Bears et al. 2008 Garant et al. 2004

F

Wilson et al. 2007

a Number of populations that were compared across the gradient of interest. Where more than one study was done, minimum and maximum populations studied is reported. b Research method used. CG, common garden experiment; F, field-based study; RT, reciprocal transplant; T, transplant experiment. c A single population was studied over a 20-year period. d A single population was studied over a 36-year period.

selection was by far the dominant evolutionary force structuring genetic variation in this species. CnGV in growth also influences activity and foraging rates, antipredator behavior, and morphological defense mechanisms such that fast-growing R. temporaria larvae are more vulnerable to predators (Laurila et al. 2008). These findings reveal tradeoffs between growth and other traits related to defense, which we will discuss further in the following.

Fishes Fish have contributed more cases of CnGV and CoGV than any other taxon (Tables 1 and 2). By far the most common trait to display CnGV in fishes is growth rate (15 species) and the most common gradient is latitude (15 cases). But examples from the fishes have also enriched our understanding of three other issues: (1) how clusters of traits associated with growth evolve;

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TABLE 2. Examples of Cogradient Variation in the Literature Taxon Plantae Clarkia xantiana

Poa hiemata Gastropoda Bembicium vittatum Littorina obtusata Insecta Drosophila melanogaster

Drosophila subobscura

Popsa 18

9

Trait Node of first flower, relative growth rate Leaf length, plant circumference

Gradient

Methodb

Geographic

T

Eckhart et al. 2004

Altitude

RT, CG

Byars et al. 2007

3 2–25

Shell shape Body growth, shell length growth

Wave action Latitude

T RT, F

Parsons 1997 Trussell 2000

10–30

Thorax length, wing area, egg size, wing:thorax size ratio, body size, wing size Wing size

Latitude

CG

Latitude

CG

James et al. 1995; Azevedo et al. 1996; Azevedo et al. 1998; Van’t Land et al. 1999 Gilchrist & Huey 2004; Gilchrist et al. 2004

Prey type

CG

Day et al. 1994

CG CG

Robinson & Wilson 1996 Billerbeck et al. 1997; Yamahira et al. 2006 Yamahira et al. 2006 Arendt & Reznick 2005; Ghalambor et al. 2007

20

Osteichthyes Gasterosteus spp.

2

Lepomis gibbosus

2

Gape width, gill raker length, head depth, snout length Body shape

Menidia menidia

4

Vertebral number

Limnetic/benthic habitat Latitude

Menidia peninsulae

4

Vertebral number

Latitude

CG

Growth rate, offspring size

Resource availability/predator density

CG, T

Poecilia reticulata

Reference

10

a Number of populations that were compared across the gradient of interest. Where more than one study was done, minimum and maximum populations studied is reported. b Research method used. CG, common garden experiment; F, field-based study; RT, reciprocal transplant; T, transplant experiment.

(2) how gradients in other factors such as competition, predation, and diet can lead to CnGV; and (3) the existence of CnGV in sexually selected traits. Also, the fish literature provides several key examples of CoGV in morphology. CnGV in growth has been studied most extensively in silverside fishes. In Menidia menidia, growth rate increases with latitude along the North American east coast such that it

almost exactly counteracts the threefold decrease in length of the growing season (Conover & Present 1990). Hence, adult body size is nearly the same at all latitudes (this is an annual species, so individuals experience only one growing season). The principal agent of selection is size-selective winter mortality, which generates strong directional selection favoring large body sizes in northern populations


(Schultz & Conover 1997; Schultz et al. 1998; Munch et al. 2003). Faster growth is enabled by a cluster of covarying traits that together maximize energy acquisition, including increased standard metabolism (Billerbeck et al. 2000; Arnott et al. 2006), food consumption and conversion efficiency (Present & Conover 1992), and foraging activity (Chiba et al. 2007). M. menidia also displays CnGV in egg production rates (Conover 1992). Selection experiments on captive populations have demonstrated that size-selective adult mortality causes evolutionary changes in a trait cluster that mimics those observed in nature (Walsh et al. 2006). Such high rates of tissue synthesis, however, are costly for northern fish. Fast growth is negatively correlated with swimming speed (Billerbeck et al. 2001; Munch & Conover 2004) and vulnerability to predation (Lankford et al. 2001), which is probably more intense in southern latitudes. Hence, the lessening of time constraints caused by a longer growing season, the relaxation of size-selective winter mortality, and costs of fast growth combine to favor genotypes that acquire and process energy at lower rates in the south. Whether the adaptive significance of CnGV in M. menidia applies to other fishes with CnGV in growth is unknown. However, sizedependent mortality in the first winter of life is very common in temperate species (see review by Hurst 2007), and tradeoffs with growth rate ´ are now becoming well known (e.g., Alvarez & Metcalfe 2007; Royle et al. 2006; Biro et al. 2006; Alonso-Alvarez et al. 2007), so it is certainly plausible that the pattern and mode of adaptive divergence in silversides is of general significance. Fishes also illustrate that ecological gradients where CnGV has been found are not limited to physical factors such as those associated with climate. Arendt and Wilson (1997, 1999) have shown that variation in the intensity of intraspecific competition for food can lead to CnGV in juvenile growth of pumpkinseed sunfish Lepomis gibbosus. Spawning migration distance has led to CnGV in ovarian mass in chinook salmon Oncorhynchus tshawytscha

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(Kinnison et al. 2001). Natural gradients in carotenoid availability in the environment have led to CnGV in two cases. In the guppy Poecilia reticulata, the concentration of drosopterins varies genetically as a function of carotenoid availability to maintain a constant orange hue in males (Grether et al. 2005). In the sockeye salmon Oncorhynchus nerka the ability of males to extract carotenoid pigments in the diet varies inversely with its availability in nature (Craig & Foote 2001; Craig et al. 2005). In both cases, coloration is a sexually selected trait that increases male reproductive success. Male sexual coloration is conserved in nature only because CnGV compensates for environmental differences in carotenoid availability. Fishes also provide several important examples of CoGV and CnGV in morphology. In Menidia spp., Billerbeck et al. (1997) and Yamahira et al. (2006) showed that Jordan’s rule is a form of CoGV: that is, both thermal plasticity and genetic variation are responsible for the increase in vertebral number with latitude. In freshwater fishes with distinct benthic and pelagic morphs such as L. gibbosus, both genetic and environmental influences combine to augment morphological divergence (Robinson & Wilson 1996). Morphological divergence in closely related pairs of Gasterosteus spp. within the same lake also represents genetic variation reinforced by plasticity induced by limnetic versus benthic habitat differences (Day et al. 1994). In contrast, Marcil et al. (2006) showed that similarity in body shape across cod populations (Gadus morhua) is maintained by CnGV, which compensates for the effect of temperature during the early life history. They argued that in other species where morphological form is conserved across environments, cryptic CnGV may be responsible for the maintenance of such similarity. Insects As with amphibians and fishes, traits that display CnGV among insect species involve primarily physiological rates such as

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development time, growth rate, and metabolic rate (Table 1). Studies of Drosophila have proven to be especially illuminating. This genus has provided not only the original discovery of CnGV by Levins (1968, 1969) but also an understanding of rates and modes of evolution across multiple ecological gradients that were based on both laboratory selection experiments and the spread of introduced species across ecological gradients on continental scales. Examples of both CnGV and CoGV in various traits are common in Drosophila. In studies spanning a 2,600-km latitudinal transect in Australia, D. melanogaster from high latitudes display higher rates of larval development (James & Partridge 1995) and growth efficiency (Robinson & Partridge 2001) and higher rates of adult metabolism and activity (Berrigan & Partridge 1997), which compensated for thermal effects of lower temperature on physiological rates. Parallel patterns of physiological evolution were observed in laboratory populations maintained for many generations at low and high temperatures (James et al. 1995; Azevedo et al. 1996, 1998). The selection experiments confirm that temperature is at least one agent of selection driving these evolutionary changes with latitude. On the other hand, morphological traits such as egg size, body size, wing size, and the wing size:body size ratio display CoGV with latitude (Table 2). Flies from any given latitude display higher trait values for these characters when reared at lower temperatures, and flies originating from high latitudes where it is colder have higher trait values at any given temperature than those from lower latitudes (James et al. 1995; Azevedo et al. 1996, 1998). Hence, this finding implies that overcompensating CnGV in growth is responsible for CoGV in body size. Moreover, similar latitudinal patterns in body size and wing size have evolved within two decades after the colonization of both South and North America by D. subobscura from Europe (Gilchrist et al. 2001, 2004; Gilchrist & Huey 2004). These studies demonstrate that CoGV and CnGV patterns


evolve rapidly and therefore must be under intense selection across latitudinal gradients. Summary The similarity in CnGV and CoGV trait evolution in organisms as disparate as amphibians, fishes, insects, and the other species in Tables 1 and 2 is striking. A very strong recurring pattern is the existence of CnGV in traits that involve energy acquisition, processing, and tissue synthesis. Nearly all these examples are ectotherms that use CnGV to compensate either for the direct effects of temperature on metabolism or for the reduced length of the growing season in species that reproduce only once per year. CoGV more often involves morphological traits where the effects of plasticity and selection are in synchrony across ecological gradients. We next discuss the effect that CnGV and CoGV have had on other problems in evolutionary biology.

Implications Our emerging knowledge of Cov(G,E) has sharpened our understanding of several themes within evolutionary biology and has crucial implications for conservation biology, especially for the translocation of organisms from one locale to another. Because CnGV, in particular, is now known to be much more common than previously recognized, these findings take on added importance. The Interpretation of Observed Phenotypic Variation Because CnGV generates phenotypic similarity across ecological gradients, it represents a cryptic, or hidden, form of genetic variation (Conover & Schultz 1995; Ellegren & Sheldon 2008; Schlichting 2008). Its concealment occurs for two reasons. First, human intellectual curiosity is drawn more toward the explanation


of change than it is toward stasis. If phenotypes of a given species look identical between two locales, for example, we suspect that most ecologists would not be motivated to ask why. Second, CnGV cannot be revealed without standardizing the environment in some fashion (see earlier) and that requires either common garden experiments or field transplants, either of which are laborious and may be impossible logistically in many species. This is the likely reason why few examples of CnGV were known prior to 1995 (Conover & Schultz 1995). The ubiquity of CnGV greatly complicates the interpretation of observed phenotypic divergence across ecological gradients. A systematic change in trait value or lack thereof across a gradient may reflect any one of the six circumstances depicted in Figure 2. This means that we can determine little about the evolutionary significance of a spatial phenotypic pattern or lack thereof in nature purely from field observations. Hence, the fallacy of focusing so much attention on adaptive explanations for broad-scale phenotypic patterns such as Bergmann size clines or Jordan’s rule is twofold. Not only might the observed cline be the inverse of that favored by selection, but traits that display no pattern at all ought to be of equal interest. The difficulty of interpreting spatial variation in phenotypes also applies to temporal scales. There are now several well-documented cases of the evolution of CnGV on decadal time scales (Merilä et al. 2001; Garant et al. 2004; Wilson et al. 2007; Ellegren & Sheldon 2008). Rates of evolution over time are often calculated from the magnitude of mean phenotypic divergence, but CnGV undermines the direct correspondence between the magnitude of phenotypic and genotypic change. A systematic shift in traits or lack thereof over a historic period of environmental change may reflect any of the six evolutionary alternatives in Figure 2, but these may be difficult to detect over temporal scales. Long-term periods of phenotypic stasis in the historical record despite a changing environment may reflect considerable evolution

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in the form of CnGV. For example, CnGV in response to a changing environment appears to explain several puzzling cases where heritable phenotypic traits failed to show evidence of evolution despite strong directional selection (Merilä et al. 2001; Garant et al. 2004; Wilson et al. 2007; Ellegren & Sheldon 2008). In these cases, countergradient genetic responses to selection were being masked by environmental changes. Understanding such processes will be of obvious importance in predicting how organisms might adapt to climate change. Bergmann’s Rule The causes of Bergmann’s rule and its adaptive significance have long been debated (see review by Blanckenhorn & Demont 2004). Originally defined as the increase in body size with latitude displayed by endotherms, its applicability to ectotherms (Mousseau 1997; Belk & Houston 2002) and its overall adaptive significance (Van Voorhies 1996; but see Partridge & Coyne 1997) have been questioned. Indeed, in some phyla there are many cases of converse Bergmann clines (Blanckenhorn & Demont 2004). The knowledge that countergradient or cogradient variation underlies many of these cases has provided a unifying conceptual framework for explaining latitudinal patterns in body size. Blanckenhorn and Demont (2004) point out that Bergmann’s rule and its converse in ectotherms can be explained as cases of overcompensating or undercompensating CnGV, respectively. Selection driven by the effect of length of the growing season on body size causes the evolution of CnGV in growth, and the degree to which CnGV can compensate determines whether body size increases, decreases, or is uniform with latitude (Fig. 2). The ant lion, Myrmeleon immaculatus, for example, displays Bergmann’s rule as a result of CnGV in growth overcompensating for the reduction in growth that is caused by lower temperatures and food levels, and a shorter growing season in the north (Arnett & Gotelli 1999). In

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silverside fishes, CnGV in growth almost perfectly compensates for the decline in length of the growing season such that adult body size changes little with latitude (Conover & Present 1990). Belk and Houston (2002) speculated that the general lack of latitudinal trends in size at age of North American freshwater fishes probably reflects the existence of CnGV in growth. In the water strider, Aquarius remigis, a converse Bergmann cline exists because of undercompensating CnGV in growth, which is too weak to counteract fully for environmental effects on body size (Blanckenhorn & Fairbairn 1995). Similarly, De Block et al. (2008) found that undercompensating CnGV in growth was associated with a converse Bergmann cline in the damselfly Enallagma cyathigerum. Cogradient variation, on the other hand, also contributes to converse Bergmann clines as described in at least two species of Drosophila (Table 1) and possibly many more (Blanckenhorn & Demont 2004). The literature on CnGV illuminates a plausible explanation for the adaptive significance of Bergmann’s rule in ectotherms. In silverside fish, for example, it is well established that CnGV evolves in part because large size is positively correlated with winter survival and the intensity of such directional selection on size increases with latitude (Schultz & Conover 1997; Schultz et al. 1998; Munch et al. 2003). This occurs because larger fish store more lipids and have lower per capita rates of metabolism. Hence, large fish can better endure winter starvation without running out of energy. Such patterns of size-selective overwintering mortality are quite common in temperate fishes (Hurst 2007) and could provide an explanation for Bergmann’s rule in other ectotherms. Correspondingly, large size at high latitudes also confers survival advantages during periods of starvation in ant lions (Arnett & Gotelli 2003). It is not clear, however, why low-latitude animals would not also benefit from fast growth and large size caused by other components of fitness such as fecundity or defense from predators, a topic we discuss next.


Tradeoffs and the Evolution of Juvenile Growth Rate The widespread existence of CnGV in growth (Table 1) has provided model systems in which to understand the tradeoffs associated with juvenile growth rate. Until recently, scant attention was focused on the evolution of juvenile growth rate, largely because life history theory predicted that juveniles should nearly always maximize growth (Arendt 1997). In species displaying CnGV, however, juvenile growth rate is clearly not maximized in general by selection because many locations along the gradient display submaximal growth. With the presumed benefits of large size to fitness that should prevail in all environments (e.g., overall viability, reproductive success), why does submaximal growth evolve in the low-latitude or low-altitude forms of the many species in Table 1 that display CnGV in growth? Moreover, faster growth in these species is generally associated with higher food conversion (growth) efficiency, and this pattern holds for flies (De Moed et al. 1998; Robinson & Partridge 2001), fishes (Present & Conover 1992), frogs (Lindgren & Laurila 2005), and lizards (Oufiero & Angilletta 2006). If food resources are generally limiting in nature, why would submaximal food conversion efficiency evolve? Equally perplexing are those cases where CnGV in reproductive output has been documented (Conover 1992; Kokita 2003; Kokita 2004). In answering such questions, it is important to draw a distinction between two different kinds of tradeoffs: (1) those where performance in a given environment trades off against performance in some other environment and (2) those where performance or expression of a given trait trades off against expression of some other trait (Yamahira & Conover 2002; Yamahira et al. 2007). The first case represents G × E interaction and occurs when adaptation of growth to a given thermal regime is negatively correlated with growth rate at alternative temperatures. This would occur, for example, if optimization of growth to a local


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thermal regime results in reduced growth rate at temperatures prevailing elsewhere. For instance, there are many examples where lowlatitude genotypes grow faster than those from high latitude at high temperatures, but with the rank order reversed at low temperatures (Lonsdale & Levinton 1985; Bronikowski 2000; Caley & Schwarzkopf 2004; Belk et al. 2005). Prior to the discovery of CnGV, this form of thermal evolution was presumed to be the principal means of adapting to the change in climate with latitude (Lonsdale & Levinton 1985; Cossins & Bowler 1987). CnGV, on the other hand, involves faster growth in some populations (e.g., those at higher latitudes) across the full range of temperatures experienced normally in nature (Fig. 3). It evolves when there is a time constraint on reaching a given size by the end of the growing season (Conover & Present 1990; Laugen et al. 2003; Blanckenhorn & Demont 2004). However, if growth rate and fitness are positively correlated in general, this begs the question of why low-latitude forms would evolve submaximal growth even when no time constraint exists. The answer now emerging is that, in fact, there are a variety of tradeoffs against fast growth. These include negative correlations between growth and starvation endurance (Gotthard et al. 1994), locomotory ability (Billerbeck et al. 2001; Munch & Conover 2004; Ficetola & De Bernardi 2006; Cano & Nicieza 2006), predation vulnerability (Lankford et al. 2001; Laurila et al. 2008), and immune function (De Block et al. 2008).

Figure 3. Three examples of CnGV with respect to temperature in fishes. The slope of each line represents the norm of reaction (phenotypic plasticity) for each species. The solid line represents the population from the highest latitude, the dotted line is a midlatitude population, and the dashed line is the lowest

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− latitude population studied. Species displayed are (A) Atlantic silverside, Menidia menidia (from Conover & Present 1990); (B) mummichog, Fundulus heteroclitus (from Schultz et al. 1996); and (C) Atlantic halibut, Hippoglossus hippoglossus (from Jonassen et al. 2000). Examples are redrawn from tables or figures from the aforementioned references. In all three species, CnGV in growth (elevated lines at higher latitudes) and G × E interaction (differences in slope) are apparent.

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Arnott et al. (2006) further demonstrated that the higher metabolic costs associated with energy acquisition and tissue synthesis reduce the metabolic scope available for activity. Hence, the fraction of energy invested in juvenile growth versus other functions that influence survival is an allocation problem much like the classic life history tradeoff between growth and reproduction. Species with CnGV in growth have been the key to revealing such tradeoffs. Plasticity and the Evolution of Species Range The concept of covariance has important implications for our understanding of adaptive phenotypic plasticity (DeWitt et al. 1998; Grether 2005; Ghalambor et al. 2007; Crispo 2008) and the factors that influence the geographic range limits of a species (Eckhart et al. 2004). Cogradient selection occurs when phenotypes induced by environmental influences in a given locale have higher fitness. If the phenotype is at its fitness optimum purely as a result of plasticity, then there is no directional selection acting on genetic variation associated with the trait and therefore no likelihood for a CoGV pattern to evolve (Ghalambor et al. 2007). However, if environmental influences produce phenotypes that lie below their optimum, then directional selection may act on genetic variation such that the norm of reaction shifts in the direction of the optimal phenotype. CoGV could therefore evolve by a shift in mean trait value toward the optimum, an increase in plasticity (i.e., a steeper norm of reaction), or both. Hence, CoGV represents a form of adaptive phenotypic plasticity that evolves through cogradient selection acting on a given trait. CoGV would therefore be expected to facilitate the expansion of the geographic limit of a species (Eckhart et al. 2004; Ghalambor et al. 2007). Migrants at the edge of the range would express a phenotype that is at least partially favored in its new habitat and possess genetic variation that could further shift the phenotype toward its fitness optimum (Sanford et al. 2006).

Conversely, it is tempting to think of CnGV as maladaptive phenotypic plasticity (Eckhart et al. 2004; Crispo 2008). Here trait values favored by countergradient selection are the opposite of those induced by the environment: plastic responses shift the trait value away from the optimum. By analogy with CoGV, there are in theory two ways to remedy this problem. The first is to evolve higher mean trait values to compensate for the plastic response, a process that Grether (2005) calls genetic compensation but is essentially synonymous with the evolution of CnGV. The second is to reduce phenotypic plasticity. However, if the optimal mean trait value is moving further away from that induced by the environment (as it does in some cases of countergradient selection), merely eliminating plasticity still leaves the phenotype well below its optimum. To track the optimum by altering plasticity alone, the norm of reaction would have to reverse (i.e., the slope must change sign), which is probably impossible in most cases. For example, physiological constraints probably prohibit ectotherms from reversing the overall decrease in development or growth rate that generally occurs with lower temperature. Hence, CnGV is a necessary form of genetic compensation for plasticity that is seemingly maladaptive. We caution, however, that plasticity per se is not necessarily maladaptive in traits displaying CnGV (Ghalambor et al. 2007). In ectotherms, for example, the temperature dependence of metabolism is highly beneficial over an annual cycle because it reduces energy demands during winter when food resources are low. In fact, rather than reducing plasticity to overcome the temperature dependence of growth, adaptation to high-latitude climates in some species involves CnGV combined with increased plasticity (Fig. 3). In fishes, for example, high-latitude genotypes not only display faster growth than low latitude forms across all temperatures but also accelerate growth rate more rapidly with increasing temperature to attain large size during the brief growing season (Fig. 3). Hence, when confronted with a time constraint on


attainable size, high-latitude fish increase both the mean growth rate and its plasticity to maximize growth (as in Fig. 2F). The plasticity of growth with respect to temperature is both the problem (it is too cold to grow during much of the year) and solution (grow as fast as possible during the brief interval when it is warm) to survival in high-latitude environments. This example illustrates two important points: (1) that adaptive variation may involve both G × E interaction and Cov(G,E) (Fig. 2F) and (2) that adaptive phenotypic plasticity may be associated with both CoGV and CnGV (Crispo 2008). Eckhart et al. (2004) argued that the consequences of CnGV for species range expansion are the opposite of those of CoGV. “Countergradient variation, in contrast, would be expected to restrict adaptation in marginal or novel environments and thus restrict range expansion, because plasticity, in this case, moves phenotypes away from character optima” (Eckhart et al. 2004). This is a point with which we disagree. The issue here is to distinguish between countergradient selection as a process and CnGV as the adaptive genetic response. Although it is true that the existence of countergradient selection probably restricts range expansion, countergradient variation is the adaptive response to such selection that allows species to occupy habitats that would otherwise cause extinction. The northern range limit of many temperate fishes, for example, would probably be at a much lower latitude were it not for CnGV in growth rate. Munch and Conover (2002) developed bioenergetic models showing that slow-growing southern silversides would go extinct if transplanted to a northern climate because they could not grow large enough to survive the winter. Sanford et al. (2006) showed that larval fiddler crabs at the extreme northern edge of their range in the Gulf of Maine displayed CnGV in growth compared with those from farther south. They speculated that ongoing directional selection maintains CnGV in growth at the range limit despite extensive gene flow from populations farther south. Hence,

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countergradient selection at the boundary facilitates range expansion. Conservation One of the tools sometimes used in efforts to restore or rehabilitate declining populations is translocation of individuals from one population to another (Fischer & Lindenmayer 2000). Such plans are often based on phenotypic similarity between populations coupled with evidence of gene flow either from studies of dispersal or from phylogeographic analyses of genetic structure that are based on molecular genetic surveys of markers neutral to selection (Storfer 1999). With the widespread occurrence of CnGV on both micro- and macrogeographic scales, however, the use of phenotypic similarity as a guide for identifying genetic similarity is highly questionable unless the necessary common garden experiments have been performed. Moreover, there are many cases where the adaptive genetic variation exists over small spatial scales despite gene flow (Skelly 2004; Conover et al. 2006 and references therein). To avoid translocation of genotypes that may turn out to be poorly adapted to their new environs, and to avoid the possibility of outbreeding depression, it is important to account for CoGV and CnGV. This problem is particularly urgent because of the pronounced worldwide decline of amphibians (Semlitsch 2002). Amphibian populations are generally small, highly genetically differentiated, and increasingly subject to local extinction (Sagvik et al. 2005). Translocation of animals is a common method of bolstering populations in decline or reestablishing those that have gone extinct (Semlitsch 2002). One guiding principle of translocation has been to introduce nearest neighbors and thereby reduce the likelihood of genetic mixing. However, studies such as those of Skelly (2004) and Freidenburg and Skelly (2004), who observed CnGV in response to different thermal regimes among populations of Rana sylvatica only tens of meters apart, call this principle into question. The

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plasticity of traits and the potential for CnGV and CoGV patterns across environmental gradients must be considered if reintroductions are to be successful (Semlitsch 2002; Morrison & Hero, 2003; Sagvik et al. 2005; Ficetola & De Bernardi 2005). Because of the widespread occurrence of CnGV in fishes, the issue of protecting adaptive genetic variation is important also in fishery management and conservation (reviewed by Conover et al. 2006). Here the issues are not only the problem of translocation but also the effect of size-selective fishing on life history evolution. Prior to the knowledge that CnGV in growth in fishes was widespread and evolves in response to a gradient in size-selective natural mortality, there was little evidence that growth was an evolvable trait in marine fishes. However, mounting evidence (Table 1) suggests that fishing induces evolutionary changes in growth rate and other life history traits of wildharvested populations (e.g., Olsen et al. 2005; Kuparinen & Merilä 2007; Swain et al. 2007). Because CoGV and CnGV occur most commonly across temperature gradients (Table 1), an understanding of the evolution of these patterns will greatly assist in predicting the consequences of climate change (Deutsch et al. 2008). Evolution of CnGV on scales encompassing contemporary environmental change has been documented in great tits by Garant et al. (2004), in collared flycatchers by Merilä et al. (2001), and in Soay sheep by Wilson et al. (2007). Knowledge of the agents of countergradient or cogradient selection, the plasticity of traits, and the resulting cogradient and countergradient patterns of genetic variation can be used to predict how species might adapt physiologically to a warmer (or cooler) climate. For populations residing in a given location, for example, countergradient adjustments to a warmer environment might include changes in physiological rate processes that would reduce metabolism, feeding, digestion, foraging, and growth rates, thereby maintaining a similar body size over time. Such knowledge of the physiological bases of CoGV and


CnGV could then be used to predict changes in the geographic range of a species on altitudinal or latitudinal scales in response to climate change. Summary and Future Directions Our understanding of CoGV and CnGV patterns in nature on both spatial and temporal scales has advanced rapidly over the past decade or so. Not only have many more cases been identified but also we are beginning to achieve a broader understanding of the agents of selection that drive these patterns, at least in a few taxa, and knowledge of Cov(G,E) is contributing to our knowledge of several longstanding issues in evolutionary biology. The pace of these advances suggests that new discoveries will continue to illuminate our understanding of the covariance between genetic and environmental influences on phenotypic variation in nature. The evolution of CnGV in physiological rate processes that compensate for temperature and seasonality differences among habitats is now well established. In many species, however, only one or a few traits have been studied (Table 1). The extensive work on Rana, Menidia, Drosophila, and others suggest that often it is a diverse suite of traits that coevolve, including not only physiology but also behavior and morphology. Tradeoffs among these traits, such as foraging/feeding rates and predation risk, are key elements of the selective landscape. In this regard, the concept of covariance provides a new framework for thinking about life history evolution, one that places tradeoffs in resource allocation into an explicit geographic context. CoGV, CnGV, and G × E are not mutually exclusive evolutionary responses but act in concert in adapting to the selective landscape. Only by conducting carefully designed common garden experiments over multiple environmental conditions for a series of populations transecting steep environmental gradients


can one tease apart these components of VP . Such studies should be coupled with genomics approaches that allow for the estimation of gene flow across the same gradients and the measurement of selection at the level of the gene (Ellegren & Sheldon 2008). Studies of planned or accidental introductions of species to novel environments have been especially illuminating with regard to the tempo and mode of covariance evolution (Reznick 1996; Ghalambor et al. 2007; Gilchrist et al. 2001, 2004; Gilchrist & Huey 2004) and should be pursued more broadly in other species with known colonization histories. No doubt many more cases of CnGV and CoGV remain to be found. Of particular importance is to expand our knowledge of the diversity of traits that display CnGV across environments beyond the physiological. CnGV may be masterfully deceptive, as evidenced by its nearly invisible role in sexual selection and reproductive isolation in salmon (Craig & Foote 2001; Craig et al. 2005) and guppies (Grether et al. 2005). This is an especially exciting avenue for further research in additional species. Another problem that deserves more attention is the likelihood that CnGV may be a widespread mechanism for producing apparent uniformity in morphology across various environments both spatially (Marcil et al. 2006) and temporally (Merilä et al. 2001; Garant et al. 2004; Wilson et al. 2007; Ellegren & Sheldon 2008). Finally, the spatial and temporal scales of CnGV and CoGV need further definition. Most investigations have tended to focus on broad geographic patterns, but the recent examples of microgeographic variation demonstrate that long distances are not a requirement for CnGV or CoGV to exist (Ficetola and De Bernardi 2005; Byars et al. 2007; Skelly 2004; Pardo & Johnson 2005), nor are long periods required for the evolution of such covariance patterns (Merilä et al. 2001; Garant et al. 2004; Wilson et al. 2007). The consequences of these fine-scale patterns of spatial and temporal variation for conservation of living resources are

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just now beginning to be appreciated (Conover et al. 2006). Clearly, we will not be able to fully comprehend the evolutionary significance of phenotypic patterns in nature, nor will we be able to conserve biodiversity in the wild, without understanding the covariance between genetic and environmental influences across ecological gradients in space and time. Acknowledgments

The authors wish to thank the U.S. National Science Foundation for many years of support of our research (currently via grant OCE-0425830) and two anonymous reviewers for their helpful comments on the manuscript. Conflicts of Interest

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