BRIEF COMMUNICATION

doi:10.1111/j.1558-5646.2008.00458.x

ON QUANTIFYING TOLERANCE OF HERBIVORYFOR COMPARATIVE ANALYSESMichael J. Wise1,2 and David E. Carr3,4

1Department of Biology, Bucknell University, Lewisburg, Pennsylvania 178372E-mail: mwise@bucknell.edu

3The Blandy Experimental Farm, University of Virginia, 400 Blandy Farm Lane, Boyce Virginia 226204E-mail: dec5z@virginia.edu

Received January 8, 2008

Accepted June 19, 2008

As the evolutionary importance of plant tolerance of herbivory is increasingly appreciated, more and more studies are not just

measuring a plant’s tolerance, but are comparing tolerance among plant genotypes, populations, species, and environments. Here,

we suggest that caution must be taken in such comparative studies in the choice of measurement scales (and data transformations)

for damage levels and plant performance. We demonstrate with a simple scenario of two plant groups of equal tolerance how the

choice of scales can lead one to infer that the first group is more tolerant, the second group is more tolerant, or the two groups are

equally tolerant—using the identical dataset. We conclude that to make reliable, logically consistent inferences when comparing

tolerances among groups of plants, damage and performance should both be on an additive scale or both on a multiplicative scale.

KEY WORDS: G × E, logarithmic transformation, norm of reaction, plasticity, tolerance of herbivory.

The definition of tolerance of herbivory as the relative degree to

which a plant’s performance is affected by herbivore damage has

been around for many decades (Painter 1958). However, the sig-

nificance of tolerance as a genetically controlled trait that is sub-

ject to natural selection has only relatively recently been widely

appreciated (reviewed by Strauss and Agrawal 1999; Stowe et al.

2000; Fornoni et al. 2003a). With this appreciation, evolutionary

ecologists interested in tolerance have increasingly geared their

efforts toward comparative studies. For instance, tolerance has

been compared among plant species (van der Meijden et al. 1988;

Rosenthal and Welter 1995), among individuals of a species grow-

ing in different geographic locations (Paige 1999; Fornoni et al.

2003b), among individuals growing at different resource levels

(reviewed by Hawkes and Sullivan 2001; Wise and Abrahamson

2005, 2007), and among families within a population (i.e., ge-

netic variation for tolerance) (Stowe 1998; Tiffin and Rausher

1999; Strauss et al. 2003).

Several different methods have been used to measure and

compare tolerance, reflecting the variety of plant and herbi-

vore natural histories and particular interests of the investigators

(Strauss and Agrawal 1999). Although each method has its own

merits, and investigators generally take care to meet the assump-

tions of statistical tests, not all comparative analyses have been

consistent with an intuitive concept of what it means to differ

in tolerance. In particular, an inappropriate choice of scales for

analyzing tolerance differences among plant groups can lead to

inferences that may be statistically sound but logically misleading.

In this article, we discuss the types of scales used to measure

plant damage and fitness and how they can be used for analyz-

ing tolerance of herbivory. We then present a simple scenario

of two equally tolerant but unequally vigorous plant genotypes.

We use analysis of variances (ANOVAs) of simulated data on

these genotypes to demonstrate the four ways tolerance can be

estimated by combining two types of scales for damage and fit-

ness measurements. When the question of interest is whether the

genotypes differ in tolerance of herbivory, two of these ways give

the logically correct answer of equal tolerance, and the other two

give incorrect answers in opposite directions. Finally, we provide

2 4 2 9C© 2008 The Author(s). Journal compilation C© 2008 The Society for the Study of Evolution.Evolution 62-9: 2429–2434

BRIEF COMMUNICATION

guidance on the appropriate combinations of damage- and fitness-

measurement scales in comparative studies of tolerance.

QUANTIFYING HERBIVORE DAMAGE

Although there are many ways to quantify herbivore damage, a

useful dichotomy for types of scales is absolute versus relative

(or additive versus multiplicative). An absolute scale may involve

counts of damaged leaves, flowers, or fruits, or the total area

of leaves eaten. Absolute damage could also be estimated by the

number of herbivores feeding on a plant, or the number of feeding

structures, such as mines or galls. Although using herbivore counts

carries the caveat that individuals may consume different amounts

of tissue, counts may be necessary for insects such as xylem or

phloem feeders, whose damage is difficult if not impossible to

measure precisely (Meyer 1993). Because such measurements

involve a sum of tissues damaged or individuals present, it is

also useful to refer to absolute damage as being measured on an

“additive” scale.

In studies of plant defense, herbivore damage is more often

quantified on a scale relative to the size of the plant (e.g., the

proportion of a plant’s total leaves, flowers, or fruits that are dam-

aged, or the percentage of a plant’s leaf area that has been eaten)

because a plant’s size is a large determinant of how much tissue

can potentially be consumed. As such, size-dependent measure-

ments of damage better represent what is intuitively meant by

resistance (the defense traits that reduce herbivore damage). In

fact, resistance is often defined operationally as one minus the

proportion of leaves (or leaf area) damaged (Simms and Triplett

1994; Stinchcombe and Rausher 2001). It is also useful to refer

to such proportional, or relative-damage measures as “multiplica-

tive” because they are calculated as the amount of tissue damaged

multiplied by the inverse of how much tissue was available.

QUANTIFYING PLANT-FITNESS IMPACTS

The effect of herbivore damage on plant fitness can also be quan-

tified using either additive or multiplicative scales. On an additive

scale, damage may cost a plant in terms of a reduction in the num-

ber of seeds, fruits, rhizomes produced, or any other performance

metric. Thinking of the fitness impact on an additive scale is

straightforward and logical, and this scale is generally employed

as a default. The main reason for departing from the additive scale

is not because of the biology of the system, but for the purposes

of statistical tests of the data.

Because means and variances are commonly positively cor-

related, if undamaged and damaged plants differ in mean fit-

ness, they are also likely to differ in terms of the variance in

their fitness measurements. Such heteroscedasticity is a violation

of parametric analysis of variance. In an analysis in which fit-

ness data are the dependent variable, researchers often perform a

log-transformation of fitness data to redress this violation. Log-

transformation has the effect of putting fitness on a multiplicative

scale, which greatly alters the relative differences among groups

along the scale. For instance, on an additive scale, a plant that pro-

duces 100 seeds is 10 times as fit as a plant producing 10 seeds;

on a logarithmic scale, however, it appears only twice as fit. We

will use the shorthand in this article of referring to the log scale

as the multiplicative scale to demonstrate that it is parallel to the

multiplicative (size-relative) damage scale.

QUANTIFYING TOLERANCE: PUTTING DAMAGE AND

FITNESS TOGETHER

Tolerance of herbivore damage is most often calculated as the

slope of a regression of plant fitness (y-axis) on damage level

(x-axis). Thus, tolerance is often envisioned as a norm of reaction

of plant fitness expressed over a range of “damage environments”

(Abrahamson and Weis 1997; Mauricio et al. 1997; Strauss and

Agrawal 1999). Using the slope is a very convenient way to ex-

press a plant’s tolerance as a single number. However, the scales

(additive or multiplicative) used for damage and fitness measure-

ments will affect the magnitude of the slope. We will demonstrate

that it is important to avoid mixing additive and multiplicative

scales when comparing tolerances between plants: Such mixing

can lead to misleading inferences about their relative tolerances.

Although achieving consistency in scales would seem to be a

basic objective, the importance of this consistency in damage and

fitness measures does not appear to have been adequately appreci-

ated when the goal has been to compare relative tolerances among

groups of plants. In fact, studies involving tolerance comparisons

have often used a multiplicative scale for damage with an additive

measure for fitness (e.g., Welter and Steggall 1993; Simms and

Triplett 1994; Mauricio et al. 1997; Shen and Bach 1997; Stowe

1998; Agrawal et al. 1999; Tiffin and Rausher 1999; Hochwender

et al. 2000). Below we illustrate with a hypothetical scenario how

a mixing of additive and multiplicative scales can provide infer-

ences regarding relative tolerances that are misleading as judged

by the intended meaning of tolerance of herbivory. In particular,

we show that the common practice of using a multiplicative mea-

sure of damage with an additive measure of fitness may lead to an

underestimation of the tolerance of more vigorous plants relative

to less vigorous plants.

MethodsSCENARIO: TWO GROUPS OF PLANTS WITH EQUAL

TOLERANCE

Consider two groups of plants (Genotypes A and B) that are sus-

ceptible to losing up to half of their leaves to herbivores, and that

are equally tolerant of leaf herbivory. Although we consider geno-

types, the plant types could just as easily represent two different

populations of plants, two sets of environmental conditions, two

2 4 3 0 EVOLUTION SEPTEMBER 2008

BRIEF COMMUNICATION

different species of plants, or any other categories. Without her-

bivore damage, plants of Genotype A are more vigorous and have

a mean of 40 leaves, and plants of Genotype B have a mean of

20 leaves. For simplicity, all leaves are assumed to be of the same

size for both genotypes. Each leaf produces enough carbohydrates

to mature one seed, so undamaged plants of Genotype A produce

40 seeds, undamaged plants of Genotype B produce 20 seeds, and

the loss of each leaf leads to a reduction of one seed. Because

this relationship between leaf loss and seed-production loss is the

same for both genotypes, the two genotypes are logically judged

to have equal tolerance.

SIMULATIONS

With this scenario, we illustrate the four ways one could combine

additive and multiplicative scales for the damage and fitness axes

to compare the tolerance of the two genotypes. Two combinations

0.0 0.1 0.2 0.3 0.4 0.5 0.6

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25

Se

ed

s p

er

pla

nt

5

10

15

20

25

30

35

40

45

Proportion of leaves eaten

0.0 0.1 0.2 0.3 0.4 0.5 0.6

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Leaves eaten per plant

0 5 10 15 20 25

Na

tura

l lo

g o

f se

ed

s p

er

pla

nt

1.5

2.0

2.5

3.0

3.5

4.0

4.5

A

B

C

D

Figure 1. Tolerance slopes for two plant genotypes in four different combinations of damage and fitness scales. The circles represent

means of simulated data, and the error bars represent ± 1 SEM. Genotype A is shown as solid circles and lines, and Genotype B is

shown as open circles and dashed lines. (A) Additive damage scale and additive fitness scale. The two genotypes have equal tolerance

(damage-by-genotype interaction: F1,76 = 0.18, P = 0.67). (B) Additive damage scale and multiplicative fitness scale. Genotype A has

greater tolerance than Genotype B (F1,76 = 5.03, P = 0.028). (C) Multiplicative damage scale and additive fitness scale. Genotype B has

greater tolerance than Genotype A (F1,76 = 12.26, P = 0.0008). (D) Multiplicative damage scale and multiplicative fitness scale. The two

genotypes have equal tolerance (F1,76 = 0.02, P = 0.88).

will provide the “correct” answer of equal tolerance, and two will

provide a “wrong” answer. To analyze the scenario, we simulated

a dataset of 80 individuals: 20 damaged and 20 undamaged for

each plant genotype. Undamaged individuals of Genotypes A and

B were assigned a parametric mean 40 and 20 seeds, respectively.

The mean damage level was set to half of each plant’s leaves, so

damaged plants of Genotypes A and B were assigned a parametric

mean of 20 and 10 seeds, respectively. Such a scenario could be

run on multiple genotypes (or other groupings of plants), or on

additional damage levels, or a different range of seed production.

However, this simple two-genotype case scenario allows for ease

of interpretation and generality for application to more complex

scenarios.

For each of our four schemes of comparing tolerance, we

show a graph of mean plant fitness (y-axis) versus leaf damage

(x-axis) for the simulated dataset (Fig. 1). The slopes of this

EVOLUTION SEPTEMBER 2008 2 4 3 1

BRIEF COMMUNICATION

relationship indicate the tolerance of the genotypes: the steeper

the slope, the less tolerant the plants. For each method, we also

ran a two-way factorial ANOVA on the dataset. The main ef-

fects of the ANOVA were genotype and damage, with genotype

considered a class effect and damage considered a continuous ef-

fect. A significant genotype × damage interaction would indicate

that the two genotypes have different tolerances. Because the two

genotypes were set up to have the same tolerance, a significant

interaction signifies a problem with the inference.

The simulations and ANOVAs were run in two different

ways: (1) assuming equal variance among groups, and (2) as-

suming a linear increase in variance with the mean. In the first

simulation, the variance in seed production for all treatments was

set at σ2 = 50. In the second simulation, the population variance

in seed production for damaged Genotype B plants (the group

with the lowest mean seed production) was set at σ2 = 16, and

variance was permitted to increase linearly with an increase in

mean seed production such that the population variance for un-

damaged Genotype A plants was σ2 = 100. For each ANOVA,

the homogeneity of variance assumption was tested using Brown

and Forsythe’s test (Olejnik and Algina 1987), and, as expected,

in simulation scenario 2 only log-transformed data met the homo-

geneity of variance assumption. Nevertheless, in no case was the

inference regarding the relative tolerances of the two genotypes

affected by whether the assumption of homogeneity of variance

was met, and so only results from simulation scenario 1 are re-

ported here.

ResultsMETHOD 1—ADDITIVE DAMAGE, ADDITIVE FITNESS

When both damage levels (number of leaves) and fitness (number

of seeds) are shown on additive scales, the equality of the tol-

erance of the two genotypes is illustrated clearly by the parallel

slopes (Fig. 1A). The ANOVA using additive damage and fitness

scales corroborates this conclusion, as the interaction term is not

significant (F1,76 = 0.18, P = 0.67).

METHOD 2—ADDITIVE DAMAGE, MULTIPLICATIVE

FITNESS

The same damage and fitness data are plotted in Figure 1B except

that the y-axis is now the natural logarithm of the number of seeds.

Fitness is often log-transformed prior to ANOVA to improve the

normality of the residuals. However, a log-transformation of fit-

ness not only affects the distribution of residuals, but it can change

the relative slopes. In Figure 1B, the slope for Genotype B is more

than twice as steep as for Genotype A (interaction term F1,76 =5.03, P = 0.028). Thus, one would conclude that Genotype A

is more than twice as tolerant as Genotype B, which contra-

dicts our logical interpretation that the genotypes have the same

tolerance.

Why did the log-transformation change the conclusion? The

genotype × damage interaction in the ANOVA evaluates the ad-

ditivity of the genotype and damage effects. In our scenario, the

effect of damage on seed production is a purely additive process

on an arithmetic scale for both genotypes (i.e., for each genotype

the loss of one leaf, on average, results in the loss of one seed).

Seed production in Genotypes A and B differs by an average of

20 seeds across the entire range of leaf loss, and Method 1 verifies

that the rate of seed reduction is identical in the two genotypes.

Method 2 expresses part of this linear process on a nonlinear

(logarithmic) scale. The differences between points along the y-

axis are now relative rather than absolute. In an undamaged state,

Genotype A is twice as fit as Genotype B, but when 10 leaves are

removed from each, Genotype A is three times as fit (30 seeds

vs. 10 seeds). This change in the differences between the fitness

of Genotypes A and B is revealed by the unequal slopes in Fig-

ure 1B, and the ANOVA detects this as a significant genotype ×damage interaction.

METHOD 3—MULTIPLICATIVE DAMAGE, ADDITIVE

FITNESS

As discussed above, damage is commonly measured on a multi-

plicative scale in resistance studies. In our scenario, if fitness on

an additive scale is plotted against the same damage levels, but

with damage represented on a multiplicative scale (in terms of

percentage of a plant’s leaves consumed), the tolerance slope for

Genotype A would be twice as steep as for Genotype B (Fig. 1C).

As in Method 2, the ANOVA of data on these scales reveals a

significant genotype × damage interaction (F1,76 = 12.26, P =0.0008), indicating a difference in tolerance. The conclusion, how-

ever, is the opposite to that in Method 2: Genotype B would be

regarded as twice as tolerant as Genotype A using Method 3.

The differences between the conclusions drawn from Meth-

ods 1 and 3 are due to a change from an additive scale to a

multiplicative scale in the measurement of damage. Undamaged

plants of Genotypes A and B differ by an average of 20 seeds, but

plants experiencing 50% damage differ by only 10 seeds. This

nonadditivity of genotypic and damage effects is revealed by the

significant interaction.

The difference in conclusions drawn from Methods 2 and 3

are due to the fact that Method 2 uses multiplicative scale for plant

fitness, whereas Method 3 uses a multiplicative scale for herbivore

damage. Genotype A shows a smaller proportional drop in fitness

relative to Genotype B for each leaf lost to herbivory (Fig. 1B).

Genotype A, however, loses two seeds for every 5% defoliation

increment as compared to the loss of only one seed by Genotype B

(Fig. 1C).

2 4 3 2 EVOLUTION SEPTEMBER 2008

BRIEF COMMUNICATION

METHOD 4—MULTIPLICATIVE DAMAGE,

MULTIPLICATIVE FITNESS

If damage is represented on a multiplicative scale and fitness is

log-transformed, then the fitness-damage slopes of the two Geno-

types are again parallel (Fig. 1D). In addition, the interaction

term in the ANOVA is not significant (F1,76 = 0.02, P = 0.88),

indicating that the genotypes are equally tolerant. This conclu-

sion agrees with intuition: if two plants that lose proportionately

the same amount of tissue have proportionately the same loss in

fitness, the two plants are equally tolerant.

This conclusion is the same as that in Method 1 because

both damage and fitness are again represented on the same type

of scale. In Method 1, there is a linear relationship between leaf

loss and seed production for both genotypes. In Method 4, this

linear process on an arithmetic scale is translated into a log-linear

process. Just as Figure 1A shows an equal drop in seed production

for each leaf lost, Figure 1D shows an equal drop in proportional

seed production of the two genotypes for each proportional loss

of leaves.

DiscussionEmpirical studies comparing tolerance often use an ANOVA in

one of the manners illustrated above. For instance, a study looking

for genetic variation among families in tolerance would focus on

the damage-by-family interaction term (Simms and Triplett 1994;

Fineblum and Rausher 1995; Mauricio et al. 1997; Agrawal et al.

1999; Tiffin and Rausher 1999; Stinchcombe and Rausher 2002).

Similarly, a test of whether resource levels affect tolerance of

herbivory would focus on the interaction between damage and

resource treatment (Wise and Abrahamson 2005, 2007). As the

above scenario illustrates, the choice of scales for damage and

fitness is critical to the significance of the interaction terms, and

thus the choice can affect the validity of the inferences regarding

the presence of genetic variation for tolerance or the influence of

resource levels on tolerance.

Specifically, if groups of plant genotypes (or families, or

populations) differ in fitness regardless of damage (i.e., they dif-

fer in vigor), then an ANOVA will underestimate the tolerance

of the more vigorous genotypes and overestimate the tolerance

of the less vigorous genotypes if fitness is measured on an addi-

tive scale and damage is measured on a multiplicative scale (cf

Fig. 1C). Conversely, an ANOVA of fitness on a multiplicative

(or logarithmic) scale when damage is measured on an additive

scale will overestimate the tolerance of the more vigorous and un-

derestimate the tolerance of the less vigorous genotypes (cf Fig.

1B). In either case, erroneous conclusions regarding the presence

of genetic variation for tolerance are more likely the greater the

difference in vigor among genotypes. Similarly, if two groups of

plants are equally tolerant of herbivory but differ greatly in fit-

ness between environments, then an ANOVA that mixes additive

and multiplicative scales for damage and fitness is likely to con-

clude erroneously that tolerance of herbivory differs between the

environments.

Although an experimenter has some latitude in choosing the

scale of damage and fitness measurements, the particular details

of the study system will often dictate these choices. The choice

of the scale for damage measurements is often dictated by the

natural history of the herbivore or the nature of its damage (e.g.,

leaf chewing, phloem sucking, stem boring, etc.). In some exper-

iments, damage treatments are carefully imposed by the experi-

menter, and thus the choice of scale may be flexible. For instance,

one may choose to damage the same number of leaves on each

plant (an additive scale), or one may damage the same proportion

of each plant’s total leaf area (a multiplicative scale).

When deciding on what plant-performance measure to use

for plant fitness, the primary concern is not what type of scale,

but which performance variable is the closest to true fitness—the

potential for genetic contribution through progeny. This variable

is usually measured on an additive scale (e.g., number of seeds,

biomass, or number of nodes surviving). Unlike damage, fitness

is always a response variable—it is not something that the ex-

perimenter can control with predetermined treatments. Thus the

choice of scale for fitness comes into play not in the measurement

phase, but in the analysis phase, and the choice generally takes

the form of whether or not to transform the data.

We do not suggest that there is any inherently best way to

measure herbivore damage or plant performance. Furthermore, the

preferred method to quantify tolerance will depend on the type of

question being addressed. We suggest only that when analyzing

the relative tolerance of different groups of plants, the type of scale

used to quantify both damage and plant performance should be the

same (i.e., both additive or both multiplicative). If this scale com-

patibility cannot be accomplished at the stage of measurement,

then it can likely be accomplished by data transformation prior to

the stage of the analysis that compares the relative tolerances of

the plant groups. For instance, if damage measurements are on a

multiplicative scale (e.g., proportion of leaf area lost), then plant

fitness should also be on a multiplicative scale. This compatibility

may require a log-transformation of the fitness variable. Alterna-

tively, if damage must be measured on an additive scale, then the

fitness scale should also be additive (i.e., not log-transformed). If

distributional assumptions of the statistical tests employed require

that the fitness variable should be log-transformed, then compat-

ibility of scales may be achieved through a log-transformation of

the additive damage measurement. In the end, the best solution

in many cases may be to perform and present the analyses more

than one way (e.g., Stinchcombe and Rausher 2002) to ensure

that the statistical choices do not obscure the biological meaning

of tolerance.

EVOLUTION SEPTEMBER 2008 2 4 3 3

BRIEF COMMUNICATION

These analyses and the advice that they generated were de-

veloped in the context of our research on the evolutionary ecology

of herbivory tolerance. We intend the advice to be prescriptive for

future studies (rather than a criticism of past studies), as com-

parative analyses of tolerance continue to gain momentum in the

fields of plant-herbivore ecology and evolution. Nevertheless, it

is important to note that the study of tolerance of herbivory is

just a specific case under the general topic of the study of en-

vironmentally induced phenotypic plasticity. Our advice about

choosing compatible scales is relevant to any such studies of

plasticity, particularly those studies in which the x-axis (the envi-

ronmental variable) has a continuous scale (e.g., fertilizer level,

salinity,%shade, etc.). In a recent paper on phenotypic plastic-

ity, Stanton and Thiede (2005) provided similar cautionary ad-

vice about the potential for the log-transformation to change the

significance of a genotype-by-environment interaction. Similar

concerns about scale transformations providing misleading infer-

ences have also been made in the seemingly unrelated field of

inbreeding depression (Johnston and Schoen 1994). Thus, the ad-

vice we give is likely to have resonance beyond the community of

biologists interested in plant tolerance of herbivory. Regardless of

the particular field of study, as Stanton and Thiede (2005) advice,

it makes little sense to weigh statistical convenience more heavily

than biological insight.

ACKNOWLEDGMENTSWe thank W. G. Abrahamson and C. P. Blair, and two anonymous review-ers for constructive comments on the manuscript. The National ScienceFoundation provided financial support under the grants DEB-0515483 toW. G. Abrahamson and MJW and DEB-0614395 to DEC and M. D. Eu-banks. Additional support was provided by The University of Virginia’sBlandy Experimental Farm through NSF BIR-9512202. Any opinions,findings, and conclusions expressed in this material are those of the au-thors and do not necessarily reflect the views of the National ScienceFoundation.

LITERATURE CITEDAbrahamson, W. G., and A. E. Weis. 1997. Evolutionary ecology across three

trophic levels: goldenrods, gallmakers, and natural enemies. PrincetonUniv. Press, Princeton, NJ.

Agrawal, A. A., S. Y. Strauss, and M. J. Stout. 1999. Costs of induced responsesand tolerance to herbivory in male and female fitness components of wildradish. Evolution 53:1093–1104.

Fineblum, W. L., and M. D. Rausher. 1995. Tradeoff between resistance andtolerance to herbivore damage in a morning glory. Nature 377:517–520.

Fornoni, J., J. Nunez-Farfan, and P. L. Valverde. 2003a. Evolutionary ecologyof tolerance to herbivory: advances and perspectives. Comments Theor.Biol. 8:643–663.

Fornoni, J., P. L. Valverde, and J. Nunez-Farfan. 2003b. Quantitative geneticsof plant tolerance and resistance against natural enemies of two naturalpopulations of Datura stramonium. Evol. Ecol. Res. 5:1049–1065.

Hawkes, C. V., and J. J. Sullivan. 2001. The impact of herbivory on plants indifferent resource conditions: a meta-analysis. Ecology 82:2045–2058.

Hochwender, C. G., R. J. Marquis, and K. A. Stowe. 2000. The potential for

and constraints on the evolution of compensatory ability in Asclepias

syriaca. Oecologia 122:361–370.Johnston, M. O., and D. J. Schoen. 1994. On the measurement of inbreeding

depression. Evolution 48:1735–1741.Mauricio, R., M. D. Rausher, and D. S. Burdick. 1997. Variation in the defense

strategies of plants: are resistance and tolerance mutually exclusive?Ecology 78:1301–1311.

Meyer, G. A. 1993. A comparison of the impacts of leaf- and sap-feedinginsects on growth and allocation of goldenrod. Ecology 74:1101–1116.

Olejnik, S. F., and J. Algina. 1987. Type I error rates and power estimatesof selected parametric and nonparametric tests of scale. J. Edu. Stat.12:45–61.

Paige, K. N. 1999. Regrowth following ungulate herbivory in Ipomopsis aggre-

gata: geographic evidence for overcompensation. Oecologia 118:316–323.

Painter, R. H. 1958. Resistance of plants to insects. Annu. Rev. Entomol.3:267–290.

Rosenthal, J. P., and S. C. Welter. 1995. Tolerance to herbivory by a stemboringcaterpillar in architecturally distinct maizes and wild relatives. Oecologia102:146–155.

Shen, C. S., and C. E. Bach. 1997. Genetic variation in resistance and toleranceto insect herbivory in Salix cordata. Ecol. Entomol. 22:335–342.

Simms, E. L., and J. Triplett. 1994. Costs and benefits of plant responses todisease: resistance and tolerance. Evolution 48:1973–1985.

Stanton, M. L., and D. A. Thiede. 2005. Statistical convenience vs biologicalinsight: consequences of data transformation for the analysis of fitnessvariation in hetereogeneous environments. New Phytol. 166:319–338.

Stinchcombe, J. R., and M. D. Rausher. 2001. Diffuse selection on resistanceto deer herbivory in the ivyleaf morning glory, Ipomoea hederacea.Am.Nat. 158:376–388.

———. 2002. The evolution of tolerance to deer herbivory: modificationscaused by the abundance of insect herbivores. Proc. R. Soc. Lond. B.269:1241–1246.

Stowe, K. A. 1998. Experimental evolution of resistance in Brassica rapa: cor-related response of tolerance in lines selected for glucosinolate content.Evolution 52:703–712.

Stowe, K. A., R. J. Marquis, C. G. Hochwender, and E. L. Simms. 2000.The evolutionary ecology of tolerance to consumer damage. Annu. Rev.Ecol. Syst. 31:565–595.

Strauss, S. Y., and A. A. Agrawal. 1999. The ecology and evolution of planttolerance to herbivory. Trends Ecol. Evol. 14:179–185.

Strauss, S. Y., W. Watson, and M. T. Allen. 2003. Predictors of male andfemale tolerance to insect herbivory in Raphanus raphanistrum. Ecology84:2074–2082.

Tiffin, P., and M. D. Rausher. 1999. Genetic constraints and selection acting ontolerance to herbivory in the common morning glory Ipomoea purpurea.Am. Nat. 154:700–716.

Van Der Meijden, E., M. Wijn, and H. J. Verkaar. 1988. Defence and re-growth, alternative plant strategies in the struggle against herbivores.Oikos 51:355–363.

Welter, S. C., and J. W. Steggall. 1993. Contrasting the tolerance of wild anddomesticated tomatoes to herbivory: agroecological implications. Ecol.Appl. 3:271–278.

Wise, M. J., and W. G. Abrahamson. 2005. Beyond the compensatory con-tinuum: environmental resource levels and plant tolerance of herbivory.Oikos 109:417–428.

———. 2007. Effects of resource availability on tolerance of herbivory: areview and assessment of three opposing models. Am. Nat. 169:443–454.

Associate Editor: T. Juenger

2 4 3 4 EVOLUTION SEPTEMBER 2008