Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense Against Herbivory

vol. 193, no. 1 the american naturalist january 2019

Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory

Philip G. Hahn,1,* Anurag A. Agrawal,2 Kira I. Sussman,1 and John L. Maron1

1. Division of Biological Sciences, University of Montana, Missoula, Montana 59812; 2. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York 14853 Submitted February 5, 2018; Accepted September 7, 2018; Electronically published November 21, 2018 Online enhancements: appendixes. Dryad data: http://dx.doi.org/10.5061/dryad.62dk17g. — abstract: such fundamental trade-off between plant growth and de- A central tenet of plant defense theory is that adaptation — to the abiotic environment sets the template for defense strategies, im- fense has long underpinned plant defense theory. Perhaps posing a trade-off between plant growth and defense. Yet this trade- the most prominent plant defense theory, the resource avail- off, commonly found among species occupying divergent resource en- ability hypothesis (RAH; Coley et al. 1985), posits that high- vironments, may not occur across populations of single species. We resource environments select for fast-growing species that in- hypothesized that more favorable climates and higher levels of herbiv- vest in replacing biomass over producing defenses due to the ory would lead to increases in growth and defense across plant popu- relatively higher cost of producing defenses in these environ- lations. We evaluated whether plant growth and defense traits co- varied across 18 populations of showy milkweed (Asclepias speciosa) ments. In contrast, species adapted to low-resource/stressful inhabiting an east-west climate gradient spanning 257 of longitude. environments are hypothesized to evolve inherently slow A suite of traits impacting defense (e.g., latex, cardenolides), growth growth rates but high levels of defense due to the high costs (e.g., size), or both (e.g., specific leaf area [SLA], trichomes) were mea- of replacing tissue lost to herbivores in these environments sured in natural populations and in a common garden, allowing us to (Coley et al. 1985). The fundamental insight of the RAH is evaluate plastic and genetically based variation in these traits. In nat- that underlying variation in resource conditions dictates ural populations, herbivore pressure increased toward warmer sites the evolution of optimal growth rates of plant species, which with longer growing seasons. Growth and defense traits showed fl strong clinal patterns and were positively correlated. In a common in turn in uences the adaptive value of devoting fewer or garden, clines with climatic origin were recapitulated only for defense more resources to defense. traits. Correlations between growth and defense traits were also On the basis of its intuitive appeal and widespread em- weaker and more negative in the common garden than in the natural pirical support (Endara and Coley 2011; Massad et al. populations. Thus, our data suggest that climatically favorable sites 2011), the RAH has also been invoked to explain intraspe- likely facilitate the evolution of greater defense at minimal costs to cific patterns in plant defense allocation. Although some growth, likely because of increased resource acquisition. studies support the RAH (e.g., Woods et al. 2012; Burghardt Keywords: Asclepias, common garden, intraspecific trait variation, 2016; Stevens et al. 2016), there is generally poor support local adaptation, plant defense, plant-insect interactions. for it within species (Hahn and Maron 2016). The RAH has also been extended to describe intraspecific variation in growth and defense at both the physiological level and Introduction the evolutionary level (Herms and Mattson 1992). For ex- Life-history trade-offs are a cornerstone of evolutionary ecol- ample, the growth-differentiation balance hypothesis pre- ogy. Particular traits are constrained because gains in their ex- dicts that resource availability underlies a trade-off between pression come at the expense of other important traits. One growth and defense, with plants in high-resource conditions allocating to rapid growth over allocation to defense (Loomis 1953; Herms and Mattson 1992). Currently, no well-established theory makes predictions * Corresponding author; email: [email protected]. about how evolutionary patterns of plant growth and defense ORCIDs: Hahn, http://orcid.org/0000-0001-9717-9489; Agrawal, http:// orcid.org/0000-0003-0095-1220. may vary among populations within species that occur Am. Nat. 2019. Vol. 193, pp. 20–34. q 2018 by The University of Chicago. across resource gradients. Although not fully appreciated 0003-0147/2019/19301-58243$15.00. All rights reserved. in the plant defense literature, there are several reasons DOI: 10.1086/700838 why patterns of plant growth and defense may differ among

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Population Variation in Plant Defense 21 populations compared with among species. First, the RAH weed (Asclepias speciosa; Apocynaceae) distributed across a assumes that habitat specialization underpins different gradient of climate favorability and herbivore pressure. We growth-defense strategies among species (Fine et al. 2004; coupled measurements of plant traits in situ within natural Van Zandt 2007; Endara and Coley 2011; Pearse and Hipp populations with the measurement of traits of plants originat- 2012). For example, a classic test of the RAH compared spe- ing from the same populations grown in a common garden. cies adapted to stressful white sand soils to those that grow This approach has several benefits with regard to understand- on much more benign red clay soils (Fine et al. 2004). Species ing how resource gradients influence herbivore pressure and with fast growth evolved in nutrient-rich red clay soils, affects broad-scale patterns in plant defense. First, measuring whereas species with high defense evolved in white sand traits in situ among populations distributed across a climate soils, where nutrients where not abundant enough to sup- gradient can inform how climate influences natural pheno- port growth rates that could outpace herbivory (Fine et al. typic variation in plant defense traits, including both environ- 2004, 2006). Yet single species experience less extreme vari- mentally induced (i.e., plasticity) and genetically based con- ation in resources across their range than do species occupy- tributions (Castillo et al. 2013; Abdala-Roberts et al. 2016a, ing divergent habitats, and this may result in subtle evolu- 2016b). Second, because geographically separated popula- tionary changes rather than the evolution of alternative tions have experienced repeated bouts of selection imposed growth-defense strategies. Second, because the main predic- by abiotic and biotic factors under their unique environmen- tion is that resources set growth rate and, in turn, defense tal conditions (Agrawal 2011), common-garden experiments levels, the RAH assumes equal herbivore pressure in high- can reveal evolved differences in defense traits among popu- and low-resource environments (Coley et al. 1985). This as- lations as set by the environment (i.e., genetic differentiation; sumption has some empirical support for different habitats Anstett et al. 2015; Stevens et al. 2016; Kooyers et al. 2017; in close spatial proximity (Fine et al. 2004, 2006). In contrast, Tomiolo et al. 2017). As such, among-population studies that single species that occur across resource (and correspond- combine field observations with common gardens have the ing productivity) gradients may be exposed to predictable in- potential to evaluate how variation in resource availability creases in herbivore pressure (Miller et al. 2009; Pennings and herbivore pressure might underpin patterns of defense, et al. 2009; Rasmann et al. 2014b), which may drive greater both plastically and genetically, within species (Pennings et al. defense in high-resource environments (Hochberg and Baalen 2009; Woods et al. 2012; Rasmann et al. 2014a;Baskettand 1998; Koffel et al. 2018). Third, if herbivore abundance co- Schemske 2018). varies with increased resource abundance, plants might evolve We used this approach to test the following predictions strategies that include higher growth and defense. However, regarding how climate and herbivore pressure might influ- resource availability may increase overall plant “vigor” (i.e., ence the evolution of growth and defense among populations plants have more resources to allocate to multiple traits), (as outlined by Hahn and Maron 2016). First, in natural pop- which could mask trade-offs among other traits (Agrawal ulations and in a common garden, growth and defense traits 2011). In summary, while trade-offs may be expected among among populations should increase along a gradient of cli- species, neutral or positive growth-defense relationships may mate favorability (i.e., there should be clines in these traits). be more likely among populations due to adaptation to dif- Because of similar responses across a gradient of climate fa- ferential herbivory and resource availability (Hahn and Maron vorability, growth and defense traits should be positively 2016). correlated, unlike what is predicted by the RAH. Positive Empirically, most among-population studies have focused correlations between growth and defense traits should be pos- on plant defense and have not typically measured concomi- sible because populations in more favorable climates have tant variation in plant growth. As a result, most existing stud- greater resource acquisition than populations in less favor- ies do not inform understanding of how growth and defense able climates (van Noordwijk and de Jong 1986; Hochberg covary among populations that occur across resource gra- and Baalen 1998), whereas negative correlations would suggest dients. The few that have examined among-population pat- genetic, physiological, or allocation costs. As a non-mutually- terns often do not find growth-defense trade-offs (Hahn and exclusive alternative to these predictions, intraspecific strate- Maron 2016). We hypothesize that more favorable resource gies of growth and defense may be phenotypically plastic. For environments should facilitate greater levels of growth, sup- instance, if clines occur in the natural populations but not in port greater herbivore numbers, and therefore favor greater a common garden, this would suggest an important role for plant defense as well. Thus, we have predicted that within phenotypic plasticity, where traits respond to climatic condi- species, there should be positive growth-defense relation- tions but did not evolve differences. If, however, selection is ships among populations that occur across resource gra- strong enough to overcome gene flow, then we expect clines dients (Hahn and Maron 2016). to have a genetic basis and to be evident in the common gar- Here, we evaluate how multiple plant growth and defense den. Furthermore, differences in resource acquisition or pat- traits vary and covary among 18 populations of showy milk- terns of allocation to growth might mask underlying trade-

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). 22 The American Naturalist offs (van Noordwijk and de Jong 1986; Koricheva 1999). toxic to most animals but is attacked by several species of Therefore, statistically controlling for climate or size may re- highly specialized insect herbivores. Three well-studied resis- veal underlying trade-offs that are masked by differences in tance traits in milkweed are cardenolides, trichomes, and la- resource acquisition, either plastically (i.e., in the field) or tex (Zalucki et al. 2001; Agrawal and Fishbein 2006). Carde- evolved (i.e., in the common garden). nolides, or cardiac glycosides, are toxic steroids that inhibit the Na1/K1-ATPase in animals (Agrawal et al. 2012). Latex is a Material and Methods sticky substance excluded from damaged plant tissue that functionsasaphysicaldefenseagainst insect herbivores (Ag- Study Species and Source Populations rawal and Konno 2009). Similarly, trichomes function as a Asclepias speciosa (showy milkweed) is a long-lived herba- physical deterrent to insect herbivores, although they can also ceous perennial plant that occurs in a variety of grassland function in drought tolerance (Agrawal and Fishbein 2006; habitats (i.e., riparian to upland) over much of western North Agrawal et al. 2009). Cardenolides and latex are both inducible America. Reproduction occurs clonally through the produc- by damage from insect herbivores (Malcolm and Zalucki tion of underground rhizomes or sexually through pollina- 1996; Zandt and Agrawal 2004; Bingham and Agrawal 2010). tion. Asclepias species are self-incompatible and typically pol- We identified 18 study populations of A. speciosa across linated by large-bodied hymenoptera (Wyatt and Broyles an east-west transect in the northern part of its range (fig. 1; 1994). Pollen is transferred in packages (i.e., pollinia), which app. A; apps. A–E are available online). Populations ranged results in all of the seeds within a fruit sharing a single father from western Minnesota to central Washington and span ap- (i.e., seeds are full siblings within a fruit). Asclepias speciosa is proximately a threefold gradient in mean annual precipita-

Figure 1: A, Map of study locations across a gradient of temperature seasonality (i.e., standard deviation of 30-year monthly average temperatures). White dots show populations surveyed only in the field; black dots indicate populations that were surveyed in the field and also included in the common garden. The arrow points to the location of the common-garden site. Lower panels show the first principal component axis (Bioclim PC) constructed from all 20 Bioclim variables plotted against mean annual temperature (B; 7C) and mean annual precipitation (C; mm). Color of points (B, C) indicate the position of the population along the Bioclim PC, with warmer colors indicating more favorable climates (i.e., warmer, longer growing seasons) for our study species. Climate data are 30-year averages from the Bioclim database.

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Population Variation in Plant Defense 23 tion, a 67C gradient in mean annual temperature, and strong or potential for resource acquisition, as temperature (when variation in seasonality (fig. 1). not extreme) and growing season length can significantly increase a plant’s growth and energy budget (Korner 1991; Stamp 2004; Pearse and Hipp 2012). This interpretation is Field Surveys and Climate Variables consistent with our observations in the field that plants Within each study population, we measured stem density, tended to be larger and more clonal on the higher end of this insect abundance, and plant traits. All populations were gradient (P.G.H., personal observation). Asclepias speciosa is visited twice in 2016 (June and July) and once in 2017 (late fairly drought tolerant and occurs in roadside ditches, slight July or early August). At the first visit in June 2016, we sur- depressions, and dry upland habitats (Young-Mathews and veyed 4–12 individual ramets (mean, 10.2 plants per popula- Eldredge 2012). As such, temperature and growing season tion) spaced 12 m apart within each population, except at length are likely more important than precipitation in dictat- one site where all four surveyed ramets were ∼1 m apart. ing performance. The second PC axis explained 21.4% of the In a 1-m2 quadrat surrounding each focal ramet, we counted total variation in the Bioclim variables among study popula- the number of A. speciosa ramets and the total number of tions. This second PC axis was largely composed of variables milkweed-specialist insects. We focus on the most abundant related to temperature and precipitation extremes. For ex- insects, the obligate milkweed-specialist beetle species Tetra- ample, mean temperature in the warmest quarter and max- opes femoratus (Cerambycidae) and Chrysochus cobaltinus imum temperature of the warmest month loaded strongly (Chrysomelidae). Tetraopes tetrophthalmus were observed positively, whereas precipitation in the driest quarter (i.e., on A. speciosa plants in the Minnesota populations and were winter snowfall for most of our sites) and precipitation of included in the insect counts because they have been re- the driest month (i.e. snowfall in February for most of our ported to feed on A. speciosa (Price and Wilson 1979). We sites) loaded strongly negatively (app. B). Since the biological observed other insect species in low abundance, and these interpretation of this axis is less clear, we focus on the first PC species (e.g., Danaus plexippus, Aphis asclepiadis,andLygaeus axis for subsequent analyses. kalmii) were not included in the insect counts. We estimated We also obtained soil property data from the USDA herbivore damage as the percentage of leaf tissue removed by Web Soil Survey website for our 18 populations (https:// chewing herbivores on four to five leaves per plant at regular websoilsurvey.sc.egov.usda.gov) and used PCA to create intervals along the length of the stem. We collected one of the a soil fertility variable (app. C). fully expanded topmost leaves to measure trait data on each of the surveyed plants (see below). Common-Garden Experiment To quantify changes in climate relevant to insect abundance and plant performance across populations, we extracted Within 13 of the study populations (fig. 1), we collected the19Bioclimvariablesplusaltitude from the WorldClim da- three to seven follicles (i.e., fruits) from different ramets tabase (Hijmans et al. 2005) for each of our 18 study popula- as seed sources for plants to be grown in a common garden. tions. The Bioclim variables are a set of climatic variables that We attempted to collect follicles from ramets separated by capture information related to annual climate conditions (e.g., 12 m; however, in some of the smaller populations we col- mean annual temperature, annual precipitation, and annual lected follicles from ramets within ∼1 m of each other. The range in temperature) as well as seasonal variation in climate clonal growth form of A. speciosia via underground rhi- metrics (e.g., precipitation in the warmest or coldest quarters) zomes made it impossible to distinguish among individuals that best relate to species physiology (O’Donnell and Ignizio in the field, but our goal was to sample from at least two 2012) and are frequently used in biogeographic studies (e.g., clonal individuals per population. We aimed for maximiz- Pearse and Hipp 2012; Borer et al. 2014; Abdala-Roberts ing replication at the population level (rather than maxi- et al. 2016a). We performed a principal component analysis mizing within-population samples) because our hypotheses (PCA) to reduce the axes of variation. The first PC axis ex- explicitly relate to how growth and defense traits vary plained 58.3% of the total variation in Bioclim climate vari- among rather than within populations. ables among study populations. The first axis was largely In June 2016, we planted seeds and rootstocks (grown from composed of variables related to temperature (mean annual field-collected seed) of individuals from each of the 13 popu- temperature, minimum temperature) loading at one end of lations into a field common garden at the MPG Ranch near the axis and temperature seasonality, temperature annual Missoula, Montana (lat. 46.67903, long. 2114.03429; fig. 1). range, and mean annual precipitation loading at the opposite All populations included individuals planted from seed, end (see app. B for full loading values). We interpret the first whereas seven populations were supplemented by individuals PC axis, characterized by higher temperature, longer growing first grown in a greenhouse for ∼3 months and allowed to seasons, and more benign climates (hereafter referred to as senesce prior to outplanting. We planted one to four full the “Bioclim PC”), as representing higher resource availability siblings from each of two to seven follicles (i.e., genetic fam-

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). 24 The American Naturalist ilies) for each population (mean of 15.1 individuals [range, 5– natural populations and as the number of ramets per individ- 23] from a mean of 5.2 families [range, 2–7] per population). ual plant in the common garden. Foliar traits were measured The common garden has a total of 201 individuals, and all on one of the topmost fully expanded leaves in 2016 on plants plants were watered during the first year to promote estab- in the natural populations and on mature plants in the com- lishment. Plant traits (see “Plant Traits” below) were mea- mon garden following established protocols (Agrawal 2004; sured on mature plants in the second year (fig. A1; figs. A1, Hahn and Maron 2018). SLA was calculated as the leaf area B1 are available online) to ensure that any potential maternal (cm2) per gram of dry tissue. We estimated trichome density effects that might be manifest early in plant life history (due by counting the number of trichomes under a dissecting to, e.g., variations in seed size) were unlikely to affect trait scope in a 33-mm2 area. Latex production was estimated by values (Roach and Wulff 1987). There were no differences cutting the tip (∼1 cm) off of a topmost fully expanded leaf in any traits between the seed and rootstock plants in the sec- (i.e., the leaf pair that was not collected) and collecting the ex- ond year of the garden (data not shown), so seed and root- uding latex on a 1.5-cm diameter piece of filter paper that was stock plants were pooled for all analyses. preweighed in a centrifuge tube. The tubes were reweighed to estimate latex production (mg). We determined cardenolide concentration (mg/g dry tissue) by high-performance liquid Plant Traits chromotography using a Gemini C18 reversed-phase column We measured nine traits on plants in the field and common and an Agilent 1100 series instrument with a diode array de- garden related to growth or reproduction, resource acquisi- tector following Rasmann et al. (2009). Leaf d13C (an integra- tion, stress tolerance, and resistance to herbivores (table 1). tive measure of water use efficiency; Farquhar and Richards Many traits measured have multiple potential functions (ta- 1984) and percent N were analyzed by the Northern Arizona ble 1). For example, high leaf nitrogen and high specificleaf University’s Stable Isotope Laboratory via elemental analysis area (SLA) are primarily related to growth rate and resource isotope ratio mass spectrometry. Additionally, on plants in acquisition, but they are also expected to contribute to greater the field, we recorded the number of follicles during our July palatability to herbivores (Schädler et al. 2003; Agrawal and surveys. Most plants in the common garden had many ramets, Fishbein 2006). In addition to functioning as an herbivore re- but the growth form resulted in them all emerging from the sistance trait, trichomes can also function as a stress tolerance root crown (fig. A1). There was very little natural colonization trait by reflecting sunlight and increasing the boundary layer of specialist insect herbivores, the plants remained mostly un- (Ehleringer et al. 1976). damaged throughout the 2-year common-garden experiment, Stem height (cm) was measured from the soil surface to the and damage did not influence plant traits (data not shown). apical meristem. We estimated ramet density as the number Thus, measurements in the common garden reflect mainly of stems in a 1-m2 area surrounding each focal ramet in the constitutive defenses, whereas in the natural populations we

Table 1: Traits measured, their function (primary and secondary), and time of measurement in the natural populations and common garden Trait or variable Trait codea Trait function Secondary function Natural Garden Beetle (Tetraopes femoratus) abundance NA Herbivore pressure NA 2016, 2017 NA Percent leaf damage NA Herbivore pressure NA 2016, 2017 NA Stem height (cm) Height Growth/size NA 2016, 2017 2017 No. rametsb No_ram Growth/size NA 2016, 2017 2017 Fruit (pod) productionc Pods Reproductive effort NA 2016, 2017 2017 Specific leaf area (cm2/g) SLA Resource acquisition Leaf toughness (herbivore resistance) 2016 2017 d13C dC13 Water use efficiency NA 2016 2017 Foliar nitrogen (%) LeafN Resource acquisition Herbivore preference/performance 2016 2017 Foliar cardenolides (mg/g dry leaf tissue) Cards Resistance trait NA 2016 2017 Latex production (mg) Latex Resistance trait NA 2017 2017 Trichome density (no./33 mm2) Tric Resistance trait Stress tolerance/water retention 2016 2017 Note: NA p not applicable. a Trait codes are used in figure 3 and are abbreviated further in figure 4. b Estimated as the number per 1 m2 in the natural populations and as the number of ramets per plant in the common garden. c Estimated as the number of fruits per plant in the natural populations and as the total dry mass of fruits in the common garden.

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Population Variation in Plant Defense 25 are measuring natural levels of defense, which include consti- We constructed a similar model for data from the com- tutive defenses plus any changes induced by herbivory. At the mon garden except that we used data from each individual end of the growing season, we collected, air dried, and weighed plant instead of population means: all mature fruits on garden plants. Further details on trait mea- p b 1 b 1 b 1 1 yi T X Xpop[i] TX Xpop[i] apop[i] btrait(pop)[i] surement protocols are given in appendix D. 1 cfam(pop:trait)[i], ∼ j2 Statistical Analysis a Gaussian(0, pop), ð2Þ ∼ j2 b Gaussian(0, trait(pop)), To evaluate changes in herbivore abundance and damage ∼ j2 : across the climate gradient, we correlated beetle density c Gaussian(0, fam(pop:trait)) fi and percent leaf damage measured in situ at each eld site The fixed effect variables in this model are identical to with the Bioclim PC. We also correlated beetle density those in equation (1) except that yi now represents individual with percent leaf damage. We used site means averaged plants in the common garden instead of population means. across both survey years for these analyses. The Bioclim PC now represents the climate at the site where fi — To test our rst prediction that growth and defense the populations originated. The a term is a random effect — pop[i] would vary positively along the climate gradient we used that estimates the variation among population-specific inter- separate models for the natural population and common- cepts, and the btrait(pop)[i] term is a random effect that estimates garden data. We used population means from the natural the population-specific intercepts for each trait. Additionally, population data because our questions are at the population we included the cfam(pop:trait)[i] term to account for individuals level and we did not permanently mark individuals in the within full-sibling families nested within population and trait. fi eld; thus, we could not track individuals from year to year. As with the data from the natural populations, we originally Prior to analysis, trait values were transformed as necessary also included the Soils PC and beetle abundance (or percent to meet the assumptions of normality and then standardized herbivore damage) as predictor variables in these models. m p j p by centering ( 0) and scaling ( 1) so that values were However, neither variable was significantly related to any comparable across traits. We constructed the following linear traits, and thus they were not included in the final model mixed model: (app. C). — p b 1 b 1 b 1 To test our corollary prediction that traits that were pos- yi T X Xpop[i] TX Xpop[i] apop[i], — ð1Þ itively correlated with the climate gradient would covary ∼ j2 : a Gaussian(0, pop) we used PCA and correlation matrices. We conducted PCAs on three versions of the trait data set: (1) raw trait values, We use conventional mixed model notation (Gelman (2) climate-corrected residual trait values (i.e., residuals from and Hill 2007). The term yi represents the (centered and the clinal models described in prediction 1), and (3) size- scaled) value for each of the nine traits measured in the corrected residual trait values (i.e., residuals from a linear 18 populations. For each of the nine traits (T), the model model controlling for stem height and number of stems) sep- b estimated an intercept T.TheBioclimPCvariable(Xpop[i]), arately for traits measured in the natural populations and the estimated at the population level, has a mean slope of common-garden plants. For all PCAs, we used population b X across all traits. We are most interested in the inter- means of the nine trait values. We also calculated a correla- b action between trait and the Bioclim PC ( TX), which tion matrix for each of these trait data sets, separately for the estimates unique slopes for each trait–by–Bioclim PC com- natural populations and the common-garden plants. The bination. Thus, this component of the model is similar to correlation matrices allowed us to evaluate the significance conducting nine linear regressions between trait values of the bivariate correlations to supplement the PCAs. and the Bioclim PC. The apop[i] term estimates the variation Traits correlations calculated using the residuals from the among population-specific intercepts to account for the climate models (i.e., controlling for climate) for plants mea- multiple traits measured on each population. This ap- sured in the natural populations allow us to statistically con- proach is similar to a MANOVA followed by univariate trol for potential differences in acquisition of resources across analyses of each trait and yielded nearly identical results the gradient. If climate is not driving covariation among traits (not shown), but it has the advantage of estimating all (through plasticity and/or adaptation), then the correlations coefficients of interest in one model (specifically, bTX in would be similar using the raw and climate-corrected data. eq. [1]). We also originally included the Soils PC and beetle However, if, as predicted, climate drives covariation among abundance (or percent herbivore damage) as predictor traits, then statistically controlling for climate should elimi- variables in these models. However, neither variable was nate correlations among the raw trait values. Since the com- significantly related to any trait, and thus they were not in- mon garden experimentally controls for climate variation, cluded in the finalmodel(app.C). statistically controlling for climate when examining trait cor-

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). 26 The American Naturalist relations from the common-garden plants does not have a tively correlated with beetle abundance (slope p 2:551:11 p : p : p : fi clear interpretation and did not qualitatively affect the cor- SE, F1, 16 4 8, P 043, R 0 48; g. 2B). relations (table E2; tables A1, B1, C1–C3, E1–E4 are available Prediction 1: Among natural populations and in a com- online). Similarly, the size-corrected PCA and correlations al- mon garden, plant growth and defense traits are positively low us to test for correlations among traits after correcting for correlated with climate favorability. There was an overall sig- differences in allocation to biomass. If corrected correlations nificant positive correlation of the Bioclim PC with the mean differ from raw values measured in the natural populations, trait value, pooled across all traits, measured in the natural b p : 5: p : p : this would indicate that plastic differences in resource acqui- populations ( X 0 19 06, F1, 15:6 21 7, P 0003). sition related to plant size alters the allocation patterns to The interaction between traits and the Bioclim PC was mar- fi p : p : other traits. Similarly, if correlations differ from raw values ginally signi cant (F8, 121:0 1 7, P 070), indicating that measured in the common garden, this would indicate that the slopes between the trait values and the Bioclim PC dif- evolved differences in size alters the allocation patterns to fered slightly. Five of the nine traits showed a significant pos- other traits. Data from this article are deposited in the Dryad itive relationship with the Bioclim PC; the other four traits Digital Repository: http://dx.doi.org/10.5061/dryad.62dk17g showed no significant relationship with the Bioclim PC (Hahn et al. 2018). (fig. 3A–3C). As with the natural populations, the relationship between the Bioclim PC (at the site of origin) and the mean value of Results all traits trended positive for populations grown in the common garden, although this relationship was not significant Variation in herbivore abundance and herbivore damage (b p 0:055:05, F : p 2:4, P p :149). The interaction across the climate gradient. Beetle abundance (natural log 1 X 1, 11 9 between traits and the Bioclim PC was significant (F : p 1 transformed) was positively related to the Bioclim PC 8, 73 9 2:5, P p :017), indicating that the slopes between the trait (slope p 0:0550:01 SE, F p 15:1, P p :001, R p 1, 16 values and the Bioclim PC differed among the nine traits. 0:69; fig. 2A). While percent leaf damage was not related to Three of the nine traits (SLA, cardenolides, and latex) the Bioclim PC directly (F p 0:9, P p :35), it was posi- 1, 16 showed positive relationships with the Bioclim PC; the other six traits showed no significant relationship with the Bioclim PC (fig. 3D–3F). Corollary to prediction 1: Among natural populations and in a common garden, growth and defense traits will be positively correlated. Most traits were positively correlated with each other in the natural populations, although the strength of these correlations were variable, as evident in the principal component ordination (fig. 4A). Height and number of ramets were positively correlated with latex production (height: r p 0:53, P p :02; number of ramets: r p 0:54, P p :02; fig. 4B). Latex and cardenolides were also correlated (r p 0:55 , P p :023; fig. 4D), as were SLA and leaf nitrogen (r p 0:52, P p :04) and latex and d13C(r p 0:58, P p :018). The only negative correlation was (marginally significant) between trichome density and SLA (r p 20:44, P p :087). Full correlation matrices are available in table E1. Statistically correcting for climate eliminated most of the correlations among traits that showed clinal patterns (table E2). For instance, correlations between height and latex (r p 0:08, P p :75), latex and cardenolides (r p 0:24, P p:35), and latex and d13C(r p 0:03, P p :90) were no longer significant. Correlations between height and number of ramets (r p 0:52, P p :028), leaf nitrogen and SLA (r p 0:51, P p :04), and trichome density and SLA (r p 20:49, Figure 2: Relationship between the Bioclim PC and beetle (Tetraopes P p :054) remained significant (or marginally significant). femoratus) density (A;m22) and percent leaf damage imposed by chewing insects (B). Data were measured in situ in the field popula- Several other pairwise correlations became stronger after cor- tions. Points are population means, averaged across the two survey recting for climate (e.g., SLA and trichome density; table E2). years. Colors refer to Bioclim PC values. Statistically correcting for size also strengthened the degree

Figure 3: Coefficients (i.e., slopes) for the relationship between each standardized trait value and the Bioclim PC in natural populations (A)and the common garden (D). Bars show 95% confidence intervals. Traits with significant coefficients (i.e., traits that show clinal variation; P ! :05 ) are shown in red, and nonsignificant coefficients (P 1 :05) are shown in black. Clinal relationships for the number of ramets and cardenolides are shown for the field (B, C) and common garden (E, F). Each point refers to a population mean trait value; color refers to Bioclim PC value. See table 1 for descriptions of trait codes.

of several negative correlations but overall did not qualitatively fig. 4H). The relationship between height or the number of change the results (table E3). ramets and latex was not significant in the common garden In the common garden, correlations on the raw trait val- (fig. 4F). There were also negative correlations between sev- ues were more variable, ranging from positive to negative eral growth and defense traits: height and trichomes (r p (fig. 4E). While the correlation between latex and carde- 20:67, P p :013), number of ramets and cardenolides nolides was not significant (r p 0:32, P p :29), after removal (r p 20:54, P p :058; fig. 4G), and d13C and cardenolides of one extreme outlier (Studentized residual p 3:8, P p (r p 20:65, P p :016). Correcting for climate or size did :003) this relationship was significant (r p 0:68, P p :014; strengthen some correlations but did not qualitatively change

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Figure 4: Principal component (PC) ordination of nine traits measured in the natural populations (A) and common garden (E). Plant trait loading vectors are plotted as arrows. Correlations of standardized trait values in the natural populations (B–D) and common garden (F–H) are shown. Population means (circles) are colored by Bioclim PC values. In H, the relationship between latex and cardenolides in the garden is nonsigniﬁcant, but it is signiﬁcant with the outlier removed (indicated by#; P p :02). Note that the axes for B–D and F–H differ between the natural populations and the common garden. See appendix E for full correlation matrices, and see table 1 for descriptions of trait codes.

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Population Variation in Plant Defense 29 the correlations of traits compared with the raw values (ta- vore abundance in the more favorable climates may have bles E1–E4). also contributed to higher levels of defense through induced responses to damage in the natural populations. Al- though we did not detect a significant correlation between Discussion beetle abundance (or herbivore damage) and plant defense Our study, which evaluated patterns of how plant growth and traits, climate may serve as a more stable proxy of herbi- defense traits vary and covary across a climatic gradient in vore abundance (Kaspari and Valone 2002; Rasmann natural populations and in a common garden, illustrates et al. 2014b). Alternatively, rather than climate indirectly how climate can fundamentally shape the evolution of plant affecting defensive traits through its effects on herbivore defense within a species. Across a steep gradient in tempera- abundance, the strong relationship between climate and ture and growing season length, we found strong increases in defensive traits could suggest a direct role for climate if de- many defense traits of Asclepias speciosa. Clinal patterns in fense traits are constrained by environmental conditions defense were a result of genetically based population differen- at more stressful sites (as opposed to responding to re- tiation because we documented these patterns in both the laxed selective pressure by herbivores at these sites). Per- natural populations and the common garden. This is notable haps most likely is that both direct and indirect processes because populations inhabiting more favorable climates had are at play, where climate drives herbivore pressure but greater levels of defense than did those from less favorable also facilitates the evolution of greater defense through in- climates (i.e., plants had greater defense in warmer locales creased resource acquisition at more favorable sites (due with longer growing seasons; fig. 3), in sharp contrast to to a longer growing season, in our case). patterns found among species (Endara and Coley 2011) In contrast to defense traits, growth (i.e., plant height and within species (Herms and Mattson 1992). Higher de- and number of ramets) and physiological traits (i.e., water fense in more climatically favorable locales was likely due to use efficiency) only exhibited clines in the natural popula- increased selection driven by greater levels of herbivory (as tions but not in the common garden. Across the gradient a consequence of increased herbivore abundance) at those of climate favorability, plants became larger (taller and sites. Interestingly, clines in growth and physiological traits produced more ramets) but also more tolerant of the drier found in the natural populations were not manifested in conditions through increased water use efficiency, as tem- our common garden, suggesting plastic responses to climate perature and growing season length increased. The lack of variation. Finally, we found mostly neutral or positive cor- genetically based clines in growth traits in the common relations between growth and defense traits (as well as among garden, which tests for adaptation of traits to the local en- defense traits) in natural populations and, to a lesser degree, vironment, is somewhat surprising given the numerous in our common garden (fig. 4). Here again the pattern of pos- previous studies that have documented clinal patterns in itive relationships between growth and defense is opposite the growth and reproductive traits among populations that growth-defense trade-off that underpins much interspecific occur across other abiotic gradients (Clausen et al. 1941; (Coley et al. 1985) and intraspecfic (Herms and Mattson Endler 1977; Stinchcombe et al. 2004; Stock et al. 2014). 1992) plant defense theory. The exception in our study was SLA, a trait primarily related to photosynthetic activity and growth rate (Shipley 2006), which showed a positive relationship with the cli- Clines in Growth and Defense Traits mate of population origin only in the common garden. We found strong positive relationships between climate Despite not finding evidence for clines in growth/phys- and two important defense traits in A. speciosa,latex, iological traits in the common garden, there was substan- and cardenolides. These traits deter specialist herbivores tial population-level differentiation in most of these traits, on a closely related Asclepias species (Agrawal 2004; Ali consistent with findings from a previous greenhouse ex- and Agrawal 2017). That these clines occurred in both periment using a subset of these populations (Waller et al. natural populations and in a common garden indicates 2018). Moreover, in a companion study we found very low that the abiotic environment may play an important role neutral genetic divergence based on microsatellite markers in influencing how plants adapt to their antagonists, either (K.I.S., unpublished data), suggesting that selection is strong indirect or directly. Climate could be playing an indirect enough to overwhelm genetic drift or gene flow among pop- role in shaping plant defense by influencing beetle abun- ulations in close proximity. Growth traits also did not corre- dance (Tetraopes femoratus), which increased in abun- spond with a preliminary screening of variation in soil var- dance across the climate gradient, with beetles in turn iables (see “Statistical Analysis” and app. C). Thus, it is selecting for higher levels of defenses within populations unclear what attribute of the environment has driven popu- inhabiting more favorable climates (Castillo et al. 2013; lation differentiation in growth/physiology, but it is possi- Pellissier et al. 2014; Anderson et al. 2015). Greater herbi- ble that unmeasured environmental variables are driving

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). 30 The American Naturalist the differences in these traits among populations. The loca- sured in natural populations and for those same populations tion of the garden, which was situated near the more favor- in the common garden, we also found notable deviations in able end of the climate gradient (fig. 1), may have influenced the relationships for other traits measured in the natural pop- the growth patterns we detected. Since we had only one gar- ulations and common gardens. Growth and defense trait den location and did not manipulate resources in our garden, correlations in the common garden tended to be more neg- we could not test for G # E interaction, which could have ative and considerably weaker than trait correlations found revealed differences in plasticity among the populations in the natural populations (table E1). For example, relation- (Kawecki and Ebert 2004). Nevertheless, we did find evi- ships between plant size, reproductive effort, or water use dence suggestive of plasticity of these traits in natural popu- efficiency (measured as the stem height or the number of lations, as population means of traits were positively associ- ramets, fruit production, and d13C, respectively) and carde- ated with variation in climatic conditions (figs. 3, 4). Finally, nolides tended to be positive in the natural populations since many of the climate variables were highly correlated, (fig. 4A,4C; app. E), whereas there were significant negative we were not able to identify which aspect of climate (i.e., tem- correlations in the common garden (fig. 4E,4G; app. E). This perature, growing season length, etc.) was most important in may reflect the cost of producing defensive chemicals, at least influencing traits. at the location of our common garden. Interestingly, we did not detect these trade-offs in the natural populations, suggesting that resource conditions may alleviate costs of de- Insight into the Evolutionary Ecology of Growth fense and modulate trade-offs (Donaldson et al. 2006; Ball- and Defense Correlations among Populations horn et al. 2011; Züst et al. 2015). Correlations among Our prediction that there would be positive covariation defense traits, however, were generally positive and fairly among growth and defense traits was partially supported. consistent between the natural populations and the common We not only found positive correlations between several garden (fig. 4D,4H). The difference between the consistency growth and defense traits (e.g., plant size and latex), but we in correlations among defensive traits but differences in also found positive correlations among defensive traits them- growth and defense correlations between the natural popula- selves (e.g., latex and cardenolides) and generally little evi- tions and common garden may be due to selection produc- dence for growth-defense trade-offs. This suggests that evolu- ing fixed differences in defense traits but plasticity in growth tionary changes in defensive traits that occur across climatic traits. gradients, as discussed above, may not come at a cost to other Overall, a critical insight from our study is that climate processes (i.e., other defense or growth traits). For exam- shapes mostly positive trait correlations among populations, ple, the correlation between latex and cardenolides was posi- which contrasts sharply with theories that predict trade-offs tive in both natural populations and the common garden among species (Coley et al. 1985) or within populations (fig. 4D,4H), likely set by a shared pattern of clinal variation (Herms and Mattson 1992). We believe the explanation for in these traits (fig. 3). Statistically correcting for climate elim- this contrasting finding is that populations (with gene flow) inated the correlation between these defensive traits as well as do not evolve alternative strategies but rather alter their allo- between many of the growth and defense traits that were cor- cation patterns within the same general strategy. These differ- related in the natural populations, further corroborating that ences in allocation patterns is further bolstered in that we these correlations were driven by climate (app. E). Consistent found consistent patterns between correlations in the raw with these general results, a study among genetic families trait values as well as after correcting for variables associated within one population (Agrawal et al. 2014) and among 22 with resource acquisition (i.e., differences in climate and size; populations along a latitudinal gradient (Woods et al. 2012) app. E). of a closely related milkweed, Asclepias syriaca, found no correlation between latex and cardenolide production, sug- Generalizing across Species and Gradients gesting that these traits can evolve independently and are not likely to trade off. However, in addition to showing lim- While positive relationships between growth and defense are ited genetic constraints between growth and defense traits being increasingly documented among populations within (and among defense traits), our data highlight that positive species (Hahn and Maron 2016), this may vary depending trait correlations are likely to emerge among traits that re- on the natural history of the species examined. For example, spond similarly to climate gradients. resource limitation may enforce stronger trade-offs for highly One positive feature of our study is that it enabled us to defended species from stressful habitats compared with spe- evaluate the degree to which trait correlations are driven by cies originating from less stressful habitats (Hahn and Maron plastic responses to climate variation versus evolved, genetic 2016; Robinson and Strauss 2018). Chemically defended spe- differences among populations. Overall, while there were some cies like milkweeds (Woods et al. 2012) and mints (Rokaya consistencies between the correlations of defensive traits mea- et al. 2016) or slow-growing species like boreal trees (Stevens

This content downloaded from 132.236.027.111 on December 19, 2018 03:09:04 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Population Variation in Plant Defense 31 et al. 2016) and sagebrush (Pratt and Mooney 2013) may be standing among-population variation in plant growth and more likely to exhibit growth-defense trade-offs across popu- defense and may help to resolve conflicting results across lat- lations than less defended fast-growing species. In our study, itudinal or elevational gradients. although we found that most pairwise correlations were neu- fi tral or positive, we did nd some evidence for trade-offs be- Acknowledgments tween growth and a chemical defense (i.e., cardenolides) in the common garden. Another study found greater latex pro- We thank Amy Hastings, Kay Hajek, and Ron White for as- duction in smaller-growing plants of A. syriaca from cooler, sistance with cardenolide analysis. Meghan Fitzgerald pro- northern climates than in larger-growing plants from warmer, vided field assistance and coordination in Washington. We southern climates (Woods et al. 2012). Other defensive traits, thank Philip Ramsey and staff at the MPG Ranch (https:// however, did not follow this pattern (Woods et al. 2012). www.mpgranch.com/) for facilitating the common-garden Similarly, among milkweed species, tropical species tend to experiment. The Columbia Basin Wildlife Refuge and Bob be more chemically defended, whereas temperate species and Ester Masselink provided access to field sites. We are tend to be less defended but more clonal (Pellissier et al. grateful to the editors and reviewers for providing insightful 2016). Information on the habitat in which a species evolved, comments on the manuscript. John Bishop, Jennifer Thaler, therefore, seems critical for describing intraspecific growth- and Jesse Miller provided helpful suggestions on early ver- defense strategies (Rokaya et al. 2016; Stevens et al. 2016; sions of the manuscript. MPG Ranch provided postdoctoral Robinson and Strauss 2018) and, importantly, correlations funding and equipment support for P.G.H. K.I.S. was sup- among growth and defense may differ depending on the ported by a National Science Foundation (NSF) Established traits being compared. Program to Stimulate Competitive Research (EPSCoR) un- Studies of herbivore damage and plant defense across dergraduate fellowship. A.A.A. was supported by NSF large-scale environmental gradients, such as those related to DEB-1619885 and a sabbatical leave from Cornell Univer- latitude and elevation, have been receiving increasing atten- sity. J.L.M. was supported by NSF DEB-1553518. tion (Rasmann et al. 2014b; Anstett et al. 2016). These studies Statement of authorship: P.G.H. and J.L.M. conceived and attempt to test classic theory that predicts that herbivore designed the research; P.G.H. collected and analyzed data and pressure and plant defense should be greater at lower lati- wrote the article; J.L.M. contributed to data collection, writ- tudes and elevation than at higher ones (Dobzhansky 1950; ing, and editing; A.A.A. contributed reagents and to the de- Coley and Aide 1991). However, results have been conflicting sign, writing, and editing; and K.I.S. contributed to data col- (Johnson and Rasmann 2011; Anstett et al. 2016; Moreira lection and editing the article. et al. 2018b), and it is increasingly clear that identifying the underlying environmental variation that occurs across these Literature Cited large gradients may help to reconcile different patterns of herbivory and defense that have been found (Anstett et al. 2016; Abdala-Roberts, L., X. Moreira, S. Rasmann, V. Parra-Tabla, and Stevens et al. 2016; Kooyers et al. 2017; Moreira et al. 2018a). K. A. Mooney. 2016a. Test of biotic and abiotic correlates of lat- Future studies could profit from testing the more general pre- itudinal variation in defences in the perennial herb Ruellia nudiflora. Journal of Ecology 104:580–590. diction that variation in herbivore pressure should drive var- Abdala-Roberts, L., S. Rasmann, J. C. Berny-Mier, Y. Terán, F. iation in plant defense. For example, our study and others Covelo, G. Glauser, and X. Moreira. 2016b. Biotic and abiotic (Malcolm 1994; Pratt and Mooney 2013; Tomiolo et al. factors associated with altitudinal variation in plant traits and her- 2017) have documented clinal patterns consistent with the bivory in a dominant oak species. American Journal of Botany prediction that plant defense increases with herbivore pres- 103:2070–2078. sure along resource gradients. However, patterns are less clear Agrawal, A. A. 2004. Resistance and susceptibility of milkweed: competi- – with regard to latitude or elevation. Finally, our data are con- tion, root herbivory, and plant genetic variation. Ecology 85:2118 2133. sistent with the broader notion that among-population pat- ———. 2011. Current trends in the evolutionary ecology of plant terns of trait correlations are not strongly constrained by al- defence. Functional Ecology 25:420–432. location costs or genetic correlations (Kimball et al. 2013; Agrawal, A. A., and M. Fishbein. 2006. Plant defense syndromes. Ecol- Angert et al. 2014; Niinemets 2015; Walter et al. 2018), as is ogy 87:S132–S149. often the case for comparisons among species (Fine 2004; Agrawal, A. A., M. Fishbein, R. Jetter, J. P. Salminen, J. B. Goldstein, Wright et al. 2004; Defossez et al. 2018). Rather, environmen- A. E. Freitag, and J. P. Sparks. 2009. Phylogenetic ecology of leaf tal variation appears to be more important for structuring surface traits in the milkweeds (Asclepias spp.): chemistry, eco- physiology, and insect behavior. New Phytologist 183:848–867. such trait correlations through differential selection among Agrawal, A. A., and K. Konno. 2009. Latex: a model for understanding populations (Wood and Brodie 2015; Peiman and Robinson mechanisms, ecology, and evolution of plant defense against herbiv- 2017) or plasticity. As such, focusing more broadly on re- ory. Annual Review of Ecology, Evolution, and Systematics 40:311– source gradients provides a more general approach to under- 331.

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Tetraopes femoratus on showy milkweed (Asclepias speciosa) near Missoula, Montana. Photo credit: P. G. Hahn.

Appendix A from P. G. Hahn et al., “Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory” (Am. Nat., vol. 193, no. 1, p. 20)

Site and Garden Details

Table A1: Study sites Code State Study Latitude Longitude Mas Minnesota Natural 43.90164 296.05658 Plo Minnesota Natural 45.19875 296.25313 Blu Minnesota Natural 46.85771 296.45240 Tap North Dakota Natural and garden 46.88187 299.63670 Mng North Dakota Natural and garden 46.89105 2103.38400 Ter Montana Natural and garden 46.72574 2105.42969 She Wyoming Natural and garden 44.80489 2106.93334 Gar Montana Natural and garden 45.52447 2107.41563 Ebil Montana Natural and garden 46.09067 2107.65906 Big Montana Natural and garden 45.82858 2109.96950 Cor Montana Natural and garden 46.33833 2114.06108 Mis Montana Natural and garden 46.84627 2114.06232 Met Montana Natural 46.54276 2114.08850 Ham Montana Natural and garden 46.22593 2114.09644 Arl Montana Natural and garden 47.22072 2114.13657 Her Washington Natural and garden 46.90152 2119.20535 Ell Washington Natural and garden 47.00132 2120.36391 Yak Washington Natural 46.66375 2120.48355

1 Appendix A from P. G. Hahn et al., Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory

Figure A1: Photo of the common garden taken on July 6, 2017, at MPG Ranch near Missoula, Montana. Photo credit: P. G. Hahn.

Appendix B from P. G. Hahn et al., “Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory” (Am. Nat., vol. 193, no. 1, p. 20)

Results of the Bioclim Principal Component Analysis

Table B1: Principal component analysis (PCA) loadings for the first three axes Code PC1 PC2 PC3 Variable: Mean annual temperature mat1 2.218 .255 2.034 Mean diurnal range (mean of monthly max–min temperature) mdr2 2.063 .002 .611 Isothermality ((mdr2/tar7) # 100) iso3 2.264 2.062 .246 Temperature seasonality (standard deviation # 100) tsd4 .285 .080 2.039 Maximum temperature of warmest month tmx5 2.003 .303 .412 Minimum temperature of coldest month tmn6 2.285 .011 2.086 Temperature annual range (tmx5–tmn6) tar7 .273 .063 .183 Mean temperature of wettest quarter twtq8 .247 2.190 .122 Mean temperature of driest quarter tdrq9 2.255 .175 2.204 Mean temperature of warmest quarter twrq10 .166 .354 2.003 Mean temperature of coldest quarter tcdq11 2.285 .039 .036 Annual precipitation map12 .243 2.185 2.124 Precipitation of wettest month pmx13 .283 2.065 2.021 Precipitation of driest month pmn14 2.049 2.453 .060 Precipitation seasonality (coefficient of variation) psd15 .211 .280 .103 Precipitation of wettest quarter pwtq16 .279 2.058 2.060 Precipitation of driest quarter pdrq17 2.051 2.447 .020 Precipitation of warmest quarter pwrq18 .275 2.122 2.101 Precipitation of coldest quarter pcdq19 2.237 2.125 2.276 Altitude alt20 2.096 2.282 .418 Eigenvalue 3.413 2.068 1.564 Proportion variance explained .583 .214 .122 Cumulative proportion variance explained .583 .796 .919 Note: Loadings on PC1 10.2 are shown in bold. Variables are 30-year climate averages defined in O’Donnell and Ignizio (2012). Note that we inverted the first axis (Bioclim PC) so that higher mean annual temperatures, higher isothermality, lower temperature seasonality and range, and lower mean annual precipitation were represented by positive loading values.

1 Appendix B from P. G. Hahn et al., Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory

−4 −2 0246 PC2 0.0 0.2 0.4 −0.2 −4 −2 0 2 4 6 −0.2 0.0 0.2 0.4 PC1

Figure B1: Biplot from the principal component analysis (PCA) conducted on the 20 Bioclim variables (including altitude). Popula- tions (numbers) are plotted in the bivariate space (see app. A for a list of populations).

Appendix C from P. G. Hahn et al., “Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory” (Am. Nat., vol. 193, no. 1, p. 20)

Soil Variables Are Poor Predictors of Clinal Trait Variation Data were extracted for seven soil variables from the USDA Web Soil Survey (https://websoilsurvey.sc.egov.usda.gov) that are relevant to soil fertility: percent sand, percent silt, percent clay, bulk density, available water supply (an estimate of water available to plants at field capacity), percent organic matter, and cation exchange capacity. We next conducted a principal component analysis (PCA) on these seven variables (table C1). The first PC axis was included in the main analyses to predict plant traits (tables C2, C3). Because the Soils PC was not significant in either model, only climate was included in the main analyses.

Table C1: Principal component analysis (PCA) loadings for soil variables extracted from the USDA Web Soil Survey PC1 PC2 Variable: Percent sand .468 2.075 Percent silt 2.403 .347 Percent clay 2.366 2.472 Bulk density .193 2.603 Available water supply 2.393 .275 Percent organic matter 2.367 2.144 Cation exchange capacity 2.398 2.437 Standard deviation 2.041 1.140 Proportion of variance .595 .186 Cumulative proportion .595 .781 Note: Loadings on PC1 10.2 are shown in bold. The ﬁrst axis explains 59.5% of the variation in the seven soil variables.

Table C2: ANOVA for traits in the natural population including the Soils PC axis and herbivore abundance as predictor variables df Effect Sum of squares Mean squares Numerator Denominator FP Bioclim PC 7.504 7.504 1 14.2 9.7 .0076 Trait 6.759 .845 8 105.9 1.1 .3768 log(beetles 1 1) .045 .045 1 17.5 .1 .8124 Soils PC 1.144 1.144 1 13.6 1.5 .2455 Bioclim:trait 14.955 1.869 8 105.5 2.4 .0198 Trait:log(beetles 1 1) 9.111 1.139 8 106.6 1.5 .1777 Trait:soils 3.634 .454 8 105.0 .6 .7880 Note: Results were similar using percent herbivore damage instead of herbivore abundance.

1 Appendix C from P. G. Hahn et al., Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory

Table C3: ANOVA for traits in the common garden including the Soils PC axis and herbivore abundance as predictor variables df Effect Sum of squares Mean squares Numerator Denominator FP Bioclim PC .28 .28 1 9.2 .4 .5471 Trait 6.20 .77 8 71.9 1.1 .3933 log(beetles 1 1) 1.39 1.39 1 10.2 1.9 .1960 Soils PC .06 .06 1 9.6 .1 .7711 Bioclim:trait 17.23 2.15 8 61.9 3.0 .0069 Trait:log(beetles 1 1) 7.75 .97 8 68.9 1.3 .2394 Trait:soils 5.26 .66 8 64.9 .9 .5154 Note: Results were similar using percent herbivore damage instead of herbivore abundance.

Appendix D from P. G. Hahn et al., “Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory” (Am. Nat., vol. 193, no. 1, p. 20)

Details on Plant Trait Measurements Stem height (cm) was measured from the soil surface to the apical meristem. Ramet density was estimated as the number of stems in a 1-m2 area surrounding each focal ramet in the natural populations and as the number of ramets per individual plant in the common garden. One of the topmost fully expanded leaves was collected to measure foliar traits of plants in natural populations (in June 2016) and in the common garden (in July 2017 and August 2017). We measured the following traits on leaves collected in the field in June 2016 and in the common garden in July 2017. Specific leaf area (SLA) was calculated as the leaf area (cm2) per gram of dry tissue. Collected leaves were dried for 148 h at 607C. Trichome density was estimated by counting the number of trichomes under a dissecting scope in a 33-mm2 area. Latex production was estimated by cutting the tip (∼1 cm) off of a topmost fully expanded leaf (i.e., the leaf pair that was not collected) and collecting the exuding latex on a piece of filter paper (1.5 cm diameter) that was preweighed in a centrifuge tube. The tubes were reweighed to estimate latex production (mg). Leaf d13C, percent N, percent C, and C∶N were analyzed at the Northern Arizona University’s Stable Isotope Laboratory via elemental analysis isotope ratio mass spectrometry. In August 2017, we collected leaves to determine cardenolide concentration. Leaves were dried for 148 h at 507C. Cardenolide concentration was measured using high-performance liquid chromotography (HPLC) following Rasmann et al. (2009). In brief, 100 mg of dry leaf tissue was ground to a fine powered using a Wiley mill in 2-mL tubes containing steel balls. A mixture of 1,200 mL of methanol (MeOH) plus 20 mg of Digitoxin (internal standard) dissolved in 20 mL of MeOH was added to each sample. Samples were then shaken in a Retsch grinder and centrifuged at 47C for 12 min at 14,000 rpm. The supernatant was then pipetted into a 96-well plate, dried using a SpeedVac, resuspended in 500 mL of MeOH, and shaken, and 500 mL of resuspended sample plus 500 mL of MeOH was filtered through a filter tube containing Sephadex and 500 mL of MeOH in 1.2-mL tubes. Samples were dried using a SpeedVac at 357C, resuspended in 200 mL of MeOH, and shaken; 15 mL of each sample was then injected into an Agilent 1100 series HPLC, and compounds were separated on a Gemini C18 reversed-phase column (3 um, 150#4:6 mm; Phenomenex). Cardenolides were eluated on a constant flow of 0.7 mL/min with acetonnitrile20.25% phosphoric acid in a water gradient as follows: 0–5 min, 20% acetonitrile; 20 min, 70% acetonitrile; 20–25 min, 70% acetonitrile; 30 min, 95% acetonitrile; and 30–35 min, 95% acetonitrile. UVabsorbance spectra were recorded between 200 and 400 nm by means of a diode array detector. Peaks with absorption maxima between 214 and 222 nm (centered at 218 nm) were recorded as cardenolides and quantified at 218 nm. Concentrations were calculated and standardized by peak areas of the known digitoxin concentration. We calculated total cardenolides (mg/g dry leaf tissue mass).

Appendix E from P. G. Hahn et al., “Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory” (Am. Nat., vol. 193, no. 1, p. 20)

Trait Correlation Matrices

Table E1: Correlation matrix conducted on raw trait values of population means for the nine traits measured in the natural populations (n p 18 populations; above 1∶1 diagonal) and the common garden (n p 13 populations; below 1∶1 diagonal) Height No_ram Pods SLA dC13 Leaf N Tric Cards Latex Height 1 .75 2.06 2.06 .13 .13 2.25 .29 .53 No_ram .18 1 .28 .09 .33 .44 2.14 .23 .54 Pods .48 .41 1 2.06 .19 .18 .26 .21 .14 SLA .55 .04 .45 1 .17 .52 2.44 2.13 2.12 dC13 2.03 .43 .14 2.36 1 .46 .06 .27 .58 LeafN 2.20 .32 2.44 2.21 .19 1 2.25 .05 .26 Tric 2.67 2.46 2.42 2.25 2.14 2.33 1 .32 .28 Cards 2.18 2.54 2.50 .27 2.65 .13 .29 1 .55 Latex 2.06 2.22 2.36 2.08 .05 2.10 .23 .32 1 Note: Significant correlation coefficients (P ! :05) are in bold; marginally significant values are in italics (:05 ! P ! :1). The correlation between latex and cardenolides is significant in the common garden (underlined; r p 0:68, P p :014) after removal of an outlier (Studentized residual p 3:8, P p :003). See table 1 for descriptions of trait codes.

Table E2: Correlation matrix conducted on climate-corrected residual trait values of populations means for the nine traits measured in the natural populations (n p 18 populations; above 1∶1 diagonal) and the common garden (n p 13 populations; below 1∶1 diagonal) Height No_ram Pods SLA dC13 Leaf N Tric Cards Latex Height 1 .52 2.17 2.14 2.54 2.20 2.46 2.08 .08 No_ram .26 1 .30 .03 2.28 .22 2.38 2.24 .02 Pods .52 .39 1 2.16 .12 .07 .20 .16 .08 SLA .54 .25 .61 1 .17 .51 2.49 2.23 2.35 dC13 .05 .35 .10 2.20 1 .29 2.17 2.15 .03 LeafN 2.18 .29 2.47 2.17 .15 1 2.43 2.25 2.15 Tric 2.75 2.42 2.41 2.45 2.04 2.31 1 .28 .28 Cards 2.43 2.46 2.56 2.11 2.56 .31 .17 1 .24 Latex 2.22 2.04 2.35 2.54 .40 2.02 .11 2.15 1 Note: Significant correlation coefficients (P ! :05) are in bold; marginally significant values are in italics (:05 ! P ! :1). See table 1 for descriptions of trait codes.

Table E3: Correlation matrix conducted on size-corrected residual trait values (i.e., corrected for height and number of ramets) of populations means for the nine traits measured in the natural populations (n p 18 populations; above 1∶1 diagonal) and the common garden (n p 13 populations; below 1∶1 diagonal) Pods SLA dC13 Leaf N Tric Cards Latex Pods 1 2.26 2.22 2.28 .09 .05 2.20 SLA .30 1 2.04 .46 2.59 2.33 2.46 dC13 .01 2.42 1 .16 2.20 2.03 .42 LeafN 2.64 2.10 .03 1 2.54 2.29 2.26 Tric .03 .18 2.01 2.55 1 .26 .45 Cards 2.36 .49 2.57 .37 2.03 1 .28 Latex 2.33 2.07 .17 2.04 .18 .24 1 Note: Significant correlation coefficients (P ! :05) are in bold; marginally significant values are in italics (:05 ! P ! :1). See table 1 for descriptions of trait codes.

1 Appendix E from P. G. Hahn et al., Population Variation, Environmental Gradients, and the Evolutionary Ecology of Plant Defense against Herbivory

Table E4: Loadings from the principal component analysis (PCA) on traits measured in the common-garden and natural populations Raw Climate corrected Size corrected Loading PC1 PC2 PC1 PC2 PC1 PC2 Natural populations: Height .394 .060 .046 2.374 ...... No_ram .477 2.078 2.086 2.548 ...... Pods .234 .023 .074 2.646 2.112 .540 SLA .024 2.596 2.594 .147 .516 2.014 dC13 .344 2.167 2.186 .185 .015 2.618 LeafN .265 2.500 2.509 2.099 .463 2.232 Tric .100 .517 .483 .234 2.500 .060 Cards .331 .261 .290 2.006 2.327 2.048 Latex .504 .147 .144 2.146 2.387 2.515 Standard deviation 1.800 1.405 1.371 1.060 1.609 1.274 Proportion of variance .360 .219 .311 .186 .370 .231 Cumulative proportion .360 .579 .311 .498 .370 .602 Common garden: Height .372 2.357 .489 2.028 ...... No_ram .404 .263 .313 .432 ...... Pods .459 2.208 .492 2.210 .449 2.364 SLA .177 2.544 .370 2.225 2.162 2.545 dC13 .230 .486 .073 .476 .330 .542 LeafN 2.016 .339 2.104 .585 2.521 .328 Tric 2.427 .056 2.425 2.247 .128 2.137 Cards 2.401 2.330 2.245 2.123 2.571 2.259 Latex 2.245 2.016 2.163 .269 2.220 .287 Standard deviation 1.744 1.469 1.689 1.262 1.255 1.184 Proportion of variance .338 .240 .379 .212 .311 .277 Cumulative proportion .338 .578 .379 .590 .311 .588 Note: Three PCAs were conducted on (1) raw (centered and scaled) trait values, (2) climate-corrected residual trait values, and (3) size-corrected residual values. See table 1 for descriptions of trait codes.