Heritability, covariation and natural selection on 24 traits of common evening primrose (Oenothera biennis) from a field experiment




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Discussion


Our study shows the importance of examining both selection on disparate traits, and the genetic variance and covariance among those traits, in order to understand the evolutionary ecology of plant populations and their interactions with herbivores. Our focus on a wide diversity of functional traits enabled us to account for a large amount of the variation in herbivory (>70%), with both life-history traits and secondary chemistry influencing resistance to an abundant insect herbivore. Although secondary compounds can deter herbivores (Berenbaum et al., 1986; Mauricio, 1998; Agrawal, 2005), a comprehensive characterization of phenolic plant chemistry revealed both positive and negative relationships between plant secondary compounds and herbivory. Natural selection acted on multiple plant traits, which accounted for >95% of the variation in relative fitness among plant genotypes. Because of the nature of this selection, the extensive genetic covariance among traits and the functionally asexual genetic system of O. biennis, we predict that there are strong constraints on adaptive evolution in O. biennis. Therefore individual traits will not respond independently to natural selection and we predict that several maladaptive traits (e.g., susceptibility to herbivores) might be maintained or increase in populations.

Heritability of plant traits


The patterns we observed in heritability estimates from different classes of functional traits were remarkably similar to those patterns reported from a recent review (Geber & Griffen, 2003). Geber and Griffen (2003) found that among 1214 heritability estimates reported in seventy-four studies, mean heritability of chemical traits was more than 2x greater than the mean heritability of morphological, phenological or life-history traits. We also found that the heritability of secondary chemistry was approximately two times greater than other types of traits, and CV values were similarly high (Table 1). Although this pattern could be due to differences in environmental sensitivities (plasticity) among trait types, we believe it is more likely that this pattern reflects greater balancing selection on chemical traits due to the highly variable nature of selection by herbivores, which varies in both space and time (Thompson, 2005). Such variable selection can maintain genetic variation due to genotype-by-environment interactions (Hedrick, 1986) and via frequency-dependent selection (Dybdahl & Lively, 1998). For life-history traits, regardless of whether we treated biomass as a morphological trait (most common in the animal literature) or a life-history trait (most common in the plant literature), we did not find strong evidence for the prediction that life-history traits exhibit the lowest heritabilities due to strong selection that erodes genetic variance (Fisher, 1930; Mousseau & Roff, 1987). Rather, as found by Geber and Griffen (2003), physiological traits exhibited the lowest heritability values (Table 1).

Genetic covariation, evolutionary constraints and correlated evolution


The functional asexuality of O. biennis leads to near complete linkage of the genome, which is predicted to have dramatic effects on a population’s evolution (Holsinger & Ellstrand, 1984). While trait evolution in asexual populations is still a function of the strength of selection and the nature of genetic variances and covariances among traits, these traits cannot evolve independently in finite populations, which results in extensive interference selection among loci (Hill & Robertson, 1966). Essentially, the genotype with the highest relative fitness in asexual populations is favored over all others, leading to the fixation of adaptive and non-adaptive traits (Barton & Turelli, 1989). A corollary of such evolutionary dynamics is that natural selection is expected to drive extensive correlated evolution among traits. Exceptions to this occur in very large populations where mutational variance is sufficient to enable all traits to reach an optimum (Crow & Kimura, 1965), and in small populations where genetic drift leads to greater stochasticity in evolutionary outcomes. The latter may be particularly important in O. biennis as its populations are frequently small (Johnson et al., 2009).

Consistent with the prediction that the genetic system of O. biennis constrains adaptive evolution and leads to substantial correlated evolution, many traits exhibited significant selection differentials, yet relatively few traits had significant selection gradients. Therefore many traits indirectly influenced, or were at least associated with traits controlling fitness (sensu Geber & Griffen, 2003). Based on our genotypic selection analyses, the traits under strong directional selection (biomass, life-history strategy, oenothein A; see Table 3) all exhibited positive genetic covariances (Supplementary Table 1). Therefore, selection should effectively increase the values of these traits, provided that populations are sufficiently large to prevent strong genetic drift (Hartl & Clark, 1997). Selection also favored decreased concentrations of quercetin glucuronide, which exhibited significant positive genetic covariance with plant biomass, and non-significant positive covariances with life-history strategy and oenothein A. Therefore, evolution for reductions in quercetin glucuronide will likely be constrained by the comparatively strong selection on other plant traits.

Selection on life-history and chemical trait variation is also predicted to drive correlated evolution on plant traits, which may impact interactions with herbivores. For example, the positive covariances between herbivory and two traits under positive directional selection (annual reproduction and oenothein A) are predicted to lead to the evolution of increased susceptibility to P. japonica. In fact, several of the traits positively associated with herbivory (Table 2) also positively covaried with biomass (Supplementary Table 2), which further suggests O. biennis populations may evolve increased susceptibility to P. japonica. Although there was no evidence for direct selection by P. japonica, herbivores can have negative fitness consequences on O. biennis (Johnson & Agrawal, 2005), and we predict that selection by herbivores will likely increase if populations evolve greater susceptibility. As discussed above, these conclusions from the multivariate breeder’s equation are most accurate in predicting the outcome of evolution over a single generation (Lande, 1979; Lande & Arnold, 1983).

The extensive positive and negative genetic covariances among secondary compounds allows us to make inferences about the types of genes that cause genetic variation in the production of phenolics. Two related biosynthetic pathways are responsible for the production of phenolics: flavonoid biosynthesis is produced via a combination of acetate-malonate and shikimate (from phenylalanine) pathways, while ellagitannin biosynthesis is produced via the shikimate pathway (from gallic acid) (Winkel-Shirley, 2001; Salminen et al., 2004). Therefore flavonoids and ellagitannins compete for a common precursor, dehydroshikimic acid (Ossipov et al. 2003). The nature of variances and covariances among these chemical compounds sheds light on whether there might be polymorphisms in genes that: a) influence the total amount or rate at which substrates move down these pathways (i.e. flux), versus b) polymorphisms that affect the relative amounts of substrates that move down alternative biosynthetic paths to produce the final compounds. If polymorphisms only influence flux, then we should observe variation in the total concentrations of flavonoids and positive genetic correlations among them (Riska, 1986). Consistent with this interpretation, the total concentrations of flavonoids varied 5-fold among genotypes and flavonoid compounds exhibited only significant positive genetic correlations and non-significant correlations, suggesting there is at least genetic variation in genes that control total flux in the flavonoid pathway.

Alternatively, when two or more enzymes compete for a limiting substrate at branching points within a pathway, genetic variation in the competing enzymes’ concentrations, or substrate affinities/activities, will cause a greater frequency of negative correlations (Riska, 1986). Consistent with this expectation, we found negative genetic correlations among multiple ellagitannins. We also observed positive correlations among some ellagitannins as well as variation in the total concentration of ellagitannins, suggesting that there are polymorphisms in structural or regulatory genes that control branching points within the ellagitannin pathway, as well as polymorphsisms that control total flux.

A wholistic approach to the evolutionary ecology of plants and plant-herbivore interactions


Ever since Fraenkels’ (1959) classic paper suggesting that herbivore defense is the “raison d’etre” of plant secondary compounds, many studies investigating the evolution of resistance have been biased towards testing for the defensive function of plant chemicals and conspicuous physical defenses (e.g. thorns, latex). Recent studies, however, indicate that a greater variety of traits may play a role in reducing herbivory, including phenology (Pilson, 2000; Kursar & Coley, 2003), physiological traits (Agrawal, 2004; Johnson, 2008), and even third trophic-level predators and parasitoids attracted by plant volatiles (Thaler, 1999). These studies suggest that a wholistic approach to the study of resistance may help to explain a greater proportion of the variation in resistance.

Our results show that plant secondary chemistry played a dominant role in affecting resistance to herbivores on O. biennis, yet measurement of multiple types of traits was still beneficial as traits other than plant secondary chemistry (e.g. life-history strategy) also accounted for variation in resistance (Table 2). The effects of secondary chemistry were complex, as different chemicals had either negative or positive effects on herbivory (Table 2) and were often correlated with other types of traits (Fig. 2b). At least one secondary compound negatively affected the amount of herbivory by P. japonica, while others had positive effects. Perhaps surprisingly, variation in relatively minor components of flavonoids (see “Other flavonoids”, Table 2) explained a large proportion of the variation in herbivory. These results support recent conjecture that measures of total concentrations of secondary chemicals of a particular type (e.g., total phenolics) may be a poor indicator of resistance (Salminen et al., 2004).

Our results also provide evidence for intraspecific suites of traits associated with resistance that have the potential to evolve as adaptive syndromes of plant defense against herbivores (Kursar & Coley, 2003; Agrawal & Fishbein, 2006). Specifically, we found that suites of traits covaried with one another to explain variation in herbivory (see PCA results, Table 2; also Fig. 2). Whether the covariance underlying such traits is stable through space and time, allowing for the evolution of clearly defined defensive syndromes, is not yet known and is an avenue for future research (Steppan et al., 2002).

Our approach illustrates that natural selection acts on multiple heritable plant traits, and perhaps suites of covarying traits (Table 3). As might be expected, directional selection was strongest on life-history traits, but moderately strong selection also acted on specific secondary compounds. Overall, we show that a broad trait-based approach can lead to a better understanding of the evolutionary ecology of species interactions and the processes and constraints that influence adaptive evolution.


Acknowledgements


We thank K. Mooney, J. Parker, and M. Stastny for logistical support in the field. J. Barrows, A. McDowell, and A. Tuccillo helped with data collection. S. Cook, A. Hastings, R. Lande, S. McArt, A. Parachnowitsch, and M. Rausher provided helpful comments on the paper. This research was supported by NSF-DEB 0447550 to AAA, NSERC Canada and North Carolina State University to MTJJ, and from the Academy of Finland to JPS (grant no. 119659).

References

Ackerly, D.D. 2004. Adaptation, niche conservatism, and convergence: Comparative studies of leaf evolution in the California Chaparral. Am. Nat. 163: 654-671.

Agrawal, A.A. 2004. Plant defense and density dependence in the population growth of herbivores. Am. Nat. 164: 113-120.

Agrawal, A.A. 2005. Natural selection on common milkweed (Asclepias syriaca) by a community of specialized insect herbivores. Evol. Ecol. Res. 7: 651-667.

Agrawal, A.A. & Fishbein, M. 2006. Plant defense syndromes. Ecology 87: S132-S149.

Barton, N.H., Turelli, M. 1989. Evolutionary quantitative genetics: how little do we know? Ann. Rev. Gen. 23: 337-370.

Berenbaum, M.R., Zangerl, A.R. & Nitao, J.K. 1986. Constraints on chemical coevolution: wild parsnips and the parsnip webworm. Evolution 40: 1215-1228.

Caruso, C.M., Maherali, H., Mikulyuk, A., Carlson, K. & Jackson, R.B. 2005. Genetic variance and covariance for physiological traits in Lobelia: are there constraints on adaptive evolution? Evolution 59: 826-837.

Chapin, F.S.I., Autumn, K. & Pugnaire, F. 1993. Evolution of suites of traits in response to environmental stress. Am. Nat. 142: S78-S92.

Cleland, R.E. 1972. Oenothera: Cytogenetics and Evolution. New York, NY, USA: Academic Press.

Conner, J.K., Franks, R. & Stewart, C. 2003. Expression of additive genetic variances and covariances for wild radish floral traits: comparison between field and greenhouse environments. Evolution 57: 387-495.

Conner, J.K. & Hartl, D.L. 2004. A Primer of Ecological Genetics. Sinauer Associates, Sunderland, MA, USA.

Crow, J.F. & Kimura, M. 1965. Evolution in sexual and asexual populations. Am. Nat. 99: 439-450.

Dethier, V.G. 1941. Chemical factors determining the choice of food plants by Papilio larvae. Am. Nat. 75: 61-73.

Dietrich, W., Wagner, W.L. & Raven, P.H. 1997. Systematics of Oenothera section Oenothera subsection Oenothera (Onagraceae). Syst. Bot. Mon. 50: 1-234.

Dybdahl, M.F. & Lively, C.M. 1998. Host-parasite coevolution: Evidence for rare advantage and time-lagged selection in a natural population. Evolution 52: 1057-1066.

Falconer, D.S. & Mackay, T.F.C. 1996. Introduction to Quantitative Genetics. 4th edn., Longman, Essex, UK.

Fenster, C.B., Armbruster, W.S., Wilson, P., Dudash, M.R. & Thomson, J.D. 2004. Pollination syndromes and floral specialization. Ann Rev Ecol Evol Syst 35: 375-403.

Fisher, R.A. 1930. The Genetical Theory of Natural Selection. Oxford, UK: Oxford University Press.

Fraenkel, G.S. 1959. The raison d'être of secondary plant substances. Science 129: 1466-1470.

Geber, M.A. & Griffen, L.R. 2003. Inheritance and natural selection on functional traits. Int. J. Plant Sci. 164: S21-S42.

Grime, J.P. 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat. 111: 1169-1194.

Hartl, D.L. & Clark, A.G. 1997. Principles of Population Genetics. 3rd edn. Sinauer Associates, Sunderland, MA, USA.

Hedrick, P.W. 1986. Genetic polymorphism in heterogeneous environments: a decade later. Ann Rev Ecol Syst 17: 535-566.

Hemsley, A.R. & Poole, I. 2004. The Evolution of Plant Physiology: From Whole Plants to Ecosystems. Elsevier Academic Press, London, UK.

Hill, W.G. & Robertson, A. 1966. The effect of linkage on limits to artifical selection. Gen. Res. 8: 269-294.

Holsinger, K.E. & Ellstrand, N.C. 1984. The evolution and ecology of permanent translocation heterozygotes. Am. Nat. 124: 48-71.

Houle, D. 1992. Comparing evolvability and variability of quantitative traits. Genetics 130: 195-204.

Howard, G.Z. & Mabry, T.J. 1972. Distribution of flavonoids in twenty-one species of Oenothera. Phytochemistry 11: 289-291.

Johnson, M.T.J. 2007. Genotype-by-environment interactions lead to variable selection on life-history strategy in Common Evening Primrose (Oenothera biennis). J. Evol. Biol. 20: 190-200.

Johnson, M.T.J. 2008. Bottom-up effects of plant genotype on aphids, ants, and predators. Ecology 89: 145-154.

Johnson, M.T.J. & Agrawal, A.A. 2005. Plant genotype and environment interact to shape a diverse arthropod community on Evening Primrose (Oenothera biennis). Ecology 86: 874-885.

Johnson, M.T.J., Vellend, M. & Stinchcombe, J.R. 2009. Evolution in plant populations as a driver of ecological changes in arthropod communities. Phil. Trans. R. Soc. Lond. B, In press

Karban, R. & Baldwin, I.T. 1997. Induced Responses to Herbivory. University of Chicago Press, Chicago, IL, USA.

Kinsman, S. 1982. Herbivore Responses to Oenoethera biennis (Onagraceae): Effects of the Host Plant's Size, Genotype, and Resistance of Conspecific Neighbors. Ph.D. Dissertation. Cornell University, Ithaca, NY, USA.

Kursar, T.A. & Coley, P.D. 2003. Convergence in defense syndromes of young leaves in tropical rainforests. Biochem. Syst. Ecol. 31: 929-949.

Lande, R. 1979. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33: 402-416.

Lande, R. & Arnold, S.J. 1983. The measurement of selection on correlated characters. Evolution 37: 1210-1226.

Larson, E.L., Bogdanowicz, S.M., Agrawal, A.A., Johnson, M.T.J. & Harrison, R.G. 2008. Isolation and characterization of polymorphic microsatellite loci in common evening primrose (Oenothera biennis). Mol. Ecol. Res. 8: 434-436.

Legendre P. & Legendre L. 1998. Numerical Ecology, 2nd edn. Amsterdam: Elsevier.

Littell, R.C., Milliken, G.A., Stroup, W.W. & Wolfinger, R.D. 1996. SAS System for Mixed Models. SAS Institute, Cary, NC, USA.

Lynch, M. & Walsh, B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Sunderland, MA, USA.

Mauricio, R. 1998. Costs of resistance to natural enemies in field populations of the annual plant Arabidopsis thaliana. Am. Nat. 151: 20-28.

Mitchell-Olds, T. & Shaw, R.H. 1987. Regression analysis of natural selection: statistical inference and biological interpretation. Evolution 41: 1149-1161.

Mousseau, T.A. & Roff, D.A. 1987. Natural selection and the heritability of fitness components. Heredity 59: 181-197.

Ossipov, V., Salminen, J.-P., Ossipova, S., Haukioja, E. & Pihlaja, K. 2003. Gallic acid and hydrolysable tannins are formed in birch leaves from an intermediate compound of the shikimate pathway. Biochem. Syst. Ecol. 31: 3-16.

Otto, S.P. & Lenormand, T. 2002. Resolving the paradox of sex and recombination. Nat. Rev. Gen. 3: 252-261.

Pilson, D. 2000. Herbivory and natural selection on flowering phenology in wild sunflower, Helianthus annuus. Oecologia 122: 72-82.

Price, G.R. 1970. Selection and covariance. Nature 227: 520-521.

Rausher, M.D. 1992. The measurement of selection on quantitative traits: biases due to environmental covariances between traits and fitness. Evolution 46: 616-626.

Riska, B. 1986. Some models for development, growth, and morphometric correlation. Evolution 40: 1303-1311.

Roff, D.A. 1992. The Evolution of Life Histories: Theory and Analysis. Chapman & Hall, New York, NY, USA.

Salminen, J.-P., Roslin, T., Karonen, M., Sinkkonen, J., Pihlaja, K. & Pulkkinen, P. 2004. Seasonal variation in the content of hydrolyzable tannins, flavonoid glycosides, and proanthocyanidins in oak leaves. J. Chem. Ecol. 30: 1693-1711.

Shaw, R.G., Platenkamp, A.J., Shaw, F.H. & Pdolsky, R.H. 1995. Quantitative genetics of response to competitors in Nemophtla menziesii: a field experiment. Genetics 139: 397-406.

Stearns, S.C. 1992. The Evolution of Life Histories. Oxford University Press, New York, NY, USA.

Steppan, SJ, Phillips, PC & Houle, D. 2002. Comparative quantitative genetics: evolution of the G matrix. Trends Ecol. Evol. 17: 320-327.

Stinchcombe, J.R., Agrawal, A.F., Hohenlohe, P.A., Arnold, S.J. & Blows, M.W. 2008. Estimating nonlinear selection gradients using quadratic regression coefficients: double or nothing? Evolution 62: 2435-2440.

Thaler, J.S. 1999. Jasmonate-inducible plant defences cause increased parasitism of herbivores. Nature 399: 686-688.

Thompson, J.N. 2005. The Geographic Mosaic of Coevolution. University of Chicago Press, Chicago, IL, USA.

Ward, J.H. 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58: 236-244.

Westoby, M., Falster, D.S., Moles, A.T., Vesk, P.A. & Wright, I.J. 2002. Ecological strategies: some leading dimensions of variation between species. Ann Rev Ecol Evol Syst 33: 125-159.

Wilkinson, L. (1999) SYSTAT vers. 9. Chicago, IL, USA.

Winkel-Shirley, B. 2001. Flavonoid biosynthesis. A colorful model for genetics, biochemistry, cell biology, and biotechnology. Plant Phys. 126: 485-493.

Zar J.H. 1996. Biostatistical Analysis. Upper Saddle River, NJ, USA: Prentice Hall.

Zinsmeister, H.D. & Bartl, S. 1971. The phenolic compounds of Oenothera. Phytochemistry 10: 3129-3132.





Table 1. Mean, range, genetic variance (Vg), broad-sense heritability (H2) and coefficient of genetic variance (CV) for twenty-four O. biennis traits and lifetime fitness from a field experiment. There were 5-11 replicate plants from each of 39 plant genotypes. Bolded numbers in parentheses denote peaks from HPLC (see Fig. 1)


Trait

Mean

Range

Vg

H2

CV (%)

Herbivory
















Proportion of leaves damaged

0.06

0-0.88

0.004***

0.19

97.9

Foliar traits
















C:N

15.90

11.2-24.8

2.97***

0.40

10.8

SLA (mm2/mg)

17.62

10.9-28.0

2.21***

0.21

8.4

% water content

72.74

55.5-84.9

3.15**

0.18

2.4

trichome density (trichomes/cm2)

343.18

4-989

10832***

0.43

30.3

Phenolics (mg/g dry tissue)
















total phenolics

189.08

97.7-314.6

1148.91***

0.63

17.9

caffeoyl tartaric acid (2)

1.43

0.5-3.4

0.24***

0.77

34.5

Ellagitannins
















ellagitannin (1)

3.66

0.7-20.5

6.88***

0.73

71.7

oenothein B (3)

52.36

0.6-101.4

531.81***

0.78

44.0

ellagitannin (4)

2.91

1.3-6.3

0.33***

0.41

19.6

oenothein A (5 and 6)

13.51

1.8-96.62

232.27***

0.91

112.8

ellagitannin (7)

3.25

1.2-5.5

0.44***

0.51

20.3

other ellagitannins

20.11

6.3-55.0

72.50***

0.86

42.3

total ellagitannins

95.81

38.7-158.3

222.63***

0.40

15.6

Flavonoids
















quercetin glucuronide (8)

2.05

0.6-5.2

0.41***

0.61

31.0

flavonoid glycoside (9)

0.17

0-0.4

0.004***

0.71

39.0

kaempferol glucuronide (10)

2.19

0.6-5.2

0.68***

0.73

37.5

flavonoid glycoside (11)

0.17

0-0.57

0.009***

0.69

54.5

other flavonoids

0.23

0-0.5

0.005***

0.61

30.3

total flavonoids

4.82

2.0-9.9

1.05***

0.50

21.3

Life-history and phenology
















LH strategy (rosette-0/flower-1)

0.56

0 or 1

5.22***

0.05(0.52)a

408.0

bolting date (days since Feb 1)

168.27

146-246

147.72***

0.46

7.2

flowering date (days since Feb 1)

199.40

139-246

111.78***

0.38

5.3

Biomass (g)

48.43

0-402.3

2293.40***

0.54

98.9

Fitness
















Fruits

115.45

0-800

10597***

0.38

89.2
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