O R I G I NA L A RT I C L E doi:10.1111/evo.12639

Quantifying hummingbird preference for floral trait combinations: The role of selection on trait interactions in the evolution of pollination syndromes Charles B. Fenster,1,2,3 Richard J. Reynolds,1,2,4 Christopher W. Williams,2,5,6 Robert Makowsky,7 and Michele R. Dudash1,2 1

Department of Biology, University of Maryland, College Park, College Park, Maryland 20742

2

Mountain Lake Biological Station, 240 Salt Pond Road, Pembroke, Virginia 24136 3

E-mail: [email protected]

4

Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham

5

Frostburg State University, Frostburg, Maryland 21502

6

National Institutes of Health, NIDDK, Bethesda, Maryland 20892

7

Phoenix, Arizona 85086

Received March 27, 2014 Accepted February 18, 2015 Darwin recognized the flower’s importance for the study of adaptation and emphasized that the flower’s functionality reflects the coordinated action of multiple traits. Here we use a multitrait manipulative approach to quantify the potential role of selection acting on floral trait combinations underlying the divergence and maintenance of three related North American species of Silene (Caryophyllaceae). We artificially generated 48 plant phenotypes corresponding to all combinations of key attractive traits differing among the three Silene species (color, height, inflorescence architecture, flower orientation, and corolla-tube width). We quantified main and interaction effects of trait manipulation on hummingbird visitation preference using experimental arrays. The main effects of floral display height and floral orientation strongly influenced hummingbird visitation, with hummingbirds preferring flowers held high above the ground and vertically to the sky. Hummingbirds also prefer traits in a nonadditive manner as multiple two-way and higher order interaction effects were important predictors of hummingbird visitation. Contemporary trait combinations found in hummingbird pollinated S. virginica are mostly preferred. Our study demonstrates the likelihood of pollination syndromes evolving due to selection on trait combinations and highlights the importance of trait interactions in understanding the evolution of complex adaptations. KEY WORDS:

Correlational selection, floral evolution, pollination syndromes, trait interaction.

Darwin’s (1859) confidence that natural selection could explain the evolution of “organs of extreme perfection and complication” reflects a multivariate perspective and is demonstrated in his discussion of the orchid flower as having “various and sundry contrivances” to ensure reproduction (Darwin 1862). The importance of trait complexity in the neo Darwinian synthesis is exemplified by Simpson’s (1944) extension of Wright’s (1931) concept of the  C

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adaptive landscape to include trait combinations, where peaks in the landscape represent advantageous trait combinations. However, in the context of selection dynamics of complex adaptations it is an open question as to whether selection acts on the main effects of traits and or on the interaction effects among these traits. Demonstration that flowers are complex adaptations has a long history in evolutionary studies. Stebbins (1950, 1951)

C 2015 The Society for the Study of Evolution. 2015 The Author(s). Evolution  Evolution 69-5: 1113–1127

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demonstrated a nonrandom distribution of floral traits across the angiosperms, with far fewer trait combinations observed at the family level than one would expect by chance alone. Contemporary interpretation suggests two nonexclusionary explanations. First, certain floral traits and trait combinations may enhance reproductive fitness. Second, particular floral traits and trait combinations may result in greater diversification rates, for example, bilateral floral symmetry is associated with higher species diversification (Sargent 2004). Either case relies on selection underlying the assembly of trait combinations, yet we have limited evidence of trait combinations as targets of selection. Demonstrating selection acts on trait interactions can be informative at several scales. Focusing solely on main effects may result in missing selection acting on traits if selection is trait context dependent and selection on trait interactions may maintain polymorphisms if alternative trait states can be favored (e.g., Brodie 1992). Trait interaction also suggests the importance of trait evolution order. If the evolution of trait combinations underlies differences in clade diversification, then the evolution of adaptive trait interactions may be a rate-limiting step in the contemporary patterns of diversity. Phenotypic selection methods can be used to quantify correlational selection in the wild, which implies that selection acts on the covariance among traits, that is, trait interactions (Lande and Arnold 1983; Schluter and Nychka 1994; Blows and Brooks 2003; Reynolds et al. 2010a). We view correlational selection as selection acting on trait combinations or their interactions (e.g., Schluter and Nychka, 1994; Reynolds et al. 2010b). Attempts to quantify correlational selection often utilize phenotypic variation found in contemporary natural populations. An alternative approach uses experimental manipulations to quantify the contribution of main and interactive effects in terms of fitness or fitness proxies (reviewed for flowers in Fenster et al. 2004; Campbell 2009). An advantage to trait manipulation is the minimization of trait correlation that can result in a nondeterminant matrix preventing the estimation of selection gradients (Lande and Arnold 1983). There is a long history of testing the adaptive hypotheses of traits in plant evolutionary studies through experimental manipulation of the flower (Clements and Long 1923) and these approaches are still in use (Cresswell and Galen 1991; Campbell et al. 1996; Fulton and Hodges 1999; Schemske and Bradshaw 1999; Temeles and Rankin 2000; Gomez et al. 2006; Sletvold and Agren 2011; Campbell et al. 2014). Simple trait manipulations have also been used successfully in animal studies (e.g., Ketterson et al. 1996; Sinervo and Svensson 2002; McGlothlin et al. 2005; de Heij et al. 2011; Balmford et al. 2013) to test adaptive hypotheses of traits. However, very few tests of adaptive trait combinations have gone beyond single- or two-trait manipulations and often only focus on main effects. Thus they

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provide little evidence supporting or refuting the hypotheses that the evolution of complex phenotypes reflects natural selection acting on combinations that include three or more traits. Previously we demonstrated three related native North American hermaphroditic species of Silene, S. virginica, S. stellata, and S. caroliniana exhibit contrasting pollination systems, hummingbird, nocturnal moth pollination, and large bee combined with diurnal moth pollination, respectively, with floral traits corresponding to the respective pollination syndromes (Reynolds and Fenster 2008; Reynolds et al. 2009). Long-term phenotypic selection studies of S. virginica demonstrate the dominant signature of contemporary phenotypic selection to be quadratic selection on floral trait combinations (Reynolds et al. 2010b) and artificial manipulation of floral traits, either individually (Dudash et al. 2011) or in two-trait combinations (Fenster et al. 2006) significantly influences hummingbird visitation rates to S. virginica, our proxy for potential reproductive success. We again use experimental manipulations to more thoroughly assess the contribution of the main and interactive effects of trait combinations that contribute to the potential evolution of floral design among the Silene species. We limit ourselves to five floral traits that contribute to pollinator attraction, ignoring those traits that contribute to reward and efficient pollen transfer. To our knowledge, manipulation of this many traits in all combinations has never been conducted. We test whether the flower is best understood as an adaptive combination of traits, by quantifying whether hummingbird attraction can be better explained by the inclusion of trait interaction or by main effects alone. This approach allows us to infer the selective mechanisms underlying both the evolution of the attractive features of S. virginica flowers and the maintenance of trait differentiation between hummingbird pollinated S. virginca and its congeners. In essence, does the evolution of different pollination syndromes manifested within Silene reflect a history of trait context dependent selection? Because we test the effect of trait combinations across species, we are not testing the role of selection directly on the evolution of these trait combinations, as a specific trait combination may never have been present in any ancestral population. Rather we examine the role of selection on trait combinations on the maintenance of trait divergence and whether the combination of traits that we commonly observe across the angiosperms as initially recognized by Stebbins (1951) is adaptive in more complex ways than can be predicted by main effects alone. If we had restricted trait manipulations only to character states found in present-day populations, then we would not have been able to ascertain the role of selection beyond the phenotypic limits typical of species differences, that is, determine the contribution of selection to the evolution of character states from ancestral to derived conditions.

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Methods STUDY SYSTEM

We experimentally manipulated five floral attractive traits representative of S. virginca, its sister species S. caroliniana, and closely related S. stellata (Burleigh and Holtsford 2003, Popp and Oxelman 2007) through the use of artificial flowers that differed in flower color (red, pink, white), flower presentation (vertical, facing upwards or horizontal, parallel to the ground), corolla tube diameter (narrow or wide), inflorescence architecture (compact or diffuse), and height of the flower above the ground (short or tall). Specific trait combinations are: S. virgnica—red, horizontally facing flowers with narrow corolla tubes in diffuse inflorescences held high off the ground; S. caroliniana—white to pink, vertically facing flowers with narrow corolla tubes in compact inflorescences held close to the ground; S. stellata—white, horizontally facing flowers with wide corolla tubes in compact inflorescences held high off the ground. An additional 45 combinations represent all hybrid combinations of these attractive floral traits. Thus, this experimental approach resulted in 48 unique artificial plant phenotypes that were each composed of three floral stems. We asked how ruby throated hummingbird (Archilochus colubris) visitation preferences, the one species of hummingbird on the east coast of North America, responded to the 48 unique trait combinations. We focus on hummingbirds because hummingbirds, unlike the pollinators of the other Silene species, can readily be trained to visit an area, and will make many visits throughout the day. Hummingbird visitation rate corresponds closely to the amount of fluorescent dye movement in S. virginica (Dudash et al. 2011), which itself is a good pollen analog (Fenster et al. 1996). Consequently, visitation rate to a particular floral combination is a reasonable proxy of both male and female reproductive success. STUDY SITE

Experiments were conducted at the University of Virginia’s Mountain Lake Biological Station, Giles County, Virginia, USA (1330 m, Salt Pond Mountain, 37o 22’32’’N, 80o 31’20’’W). The arrays were conducted on station property in a 15 × 20 m deer-protected enclosure wooded on three sides. ARTIFICIAL FLOWER AND PLANT CONSTRUCTION

(Please see Appendix S1 in Supporting Information: Fig. S1). ARRAY SETUP

(Please see Appendix S1 in Supporting Information: Fig. S2). EXPERIMENTAL ARRAY PROCEDURES

We conducted a total of 28 replicate arrays (See Appendix S1 in Supporting Information for further detail). Immediately prior to

each array all 48 artificial plants were placed into new randomized positions to avoid systematic spatial bias by hummingbirds among the experimental units across the replicate arrays. Hummingbirds forage by minimizing flight distance (Wolf and Hainsworth 1990), thus successive visits within a bout are likely not independent of each other but any systematic bias introduced by hummingbirds’ tendency to move to nearby plants was minimized by the randomization procedure. However, the effect introduced by hummingbird movement patterns associated with minimizing energy expenditure during an array could not be accounted for owing to the size of the model needed to capture the spatial component. Thus we did not account for hummingbird movement in our model. However, any effect associated with subsequent movements to nearby artificial plants contributed to unaccounted error and would only weaken any signal of hummingbird preference to particular traits and trait combinations exhibited by the artificial plants and not result in systematic bias. After randomization of the 48 artificial plants for each new array, the floral tubes were filled with 200 μl of 25% sucrose solution (10–15 × the normal volume of nectar; Reynolds et al. 2009). Such high amounts of reward were used because we did not want flowers to be quickly emptied and subsequently avoided by hummingbirds. Thus we minimized choices based on the amount of reward present during each experimental array run rather than on the attraction traits exhibited by the 48 artificial plants. Having ample rewards likely increased visitation rates and the number of hummingbirds attracted to the arrays, in turn likely changing the context of trait choice by hummingbirds. However, given that our results are consistent with previous phenotypic selection experiments and trait divergence among the three Silene species (see next), we are confident that our experiment was not unduly affected by the increased nectar rewards in the artificial flowers. Artificial plants were stratified randomly such that each unique plant phenotype was equally represented seven times on each side of the array throughout the course of the experiment (Fig. S2). One observer was assigned to each side to quantify hummingbird visitation. Arrays on average had 12–15 hummingbirds present and lasted 20–45 min and ceased when the hummingbirds demonstrated behaviors consistent with depleted floral resources in any one of the four sides. We recorded the number of first visits to each side of the array, the number of plant visits, and the total number of floral visits for each plant phenotype. All metrics are measures of hummingbird preference to a particular phenotype. The first artificial plant a hummingbird visited in each observer’s side was credited with the first visit and is thus a measure of most attractive phenotype based on visitation order for each side within each array. About 10 first visits were recorded for each of the four sides in each of the 28 array replicates.

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We define plant visits as the number of times a hummingbird visited a particular plant phenotype during each array. In order for two visits to one plant to be counted, the hummingbird had to leave the plant that it was visiting and go to another plant and then return. Plant visits measures the overall attraction of a floral trait combination ( plant) across the experiment. Flower visits were recorded when there was any movement from one flower to another within the same plant (composed of three stems), but not when there was repetitive probing to the same flower. This metric is important because it mimics natural foraging behavior of hummingbirds when more than one flower is open on a plant at a given instance, and should correlate with male reproductive success in S. virginica (Dudash et al. 2011), because geitonogamy (transfer of pollen between flowers on a plant) is very low (Dudash and Fenster 2001). STATISTICAL MODELING

To quantify main and interaction effects, we consider the artificial plant phenotype representing a particular five-trait combination as the experimental unit of sampling upon which the frequency of three dependent variables (first plant, total plant, and total flower visits) by hummingbirds was recorded. Thus, the number of degrees of freedom = 48 artificial plants combinations × 28 array replicates = 1344 for all of our analyses. Next we describe our approaches to quantify the contribution of trait main effects and trait interactions to metrics of hummingbird floral preference. Likelihood ratio tests To test the hypothesis that individual trait main effects and their interactions are important explanatory variables of hummingbird visitation, we fit a series of nested log-likelihood (LL) ratio models. We used this approach because the number of interaction effects to test was large (14 two-ways, 16 three-ways, 9 fourways, and 2 five-ways) and because there were no a priori specific interactions predicted to be nonzero, only that we anticipated interaction effects would be an important source of visitation by hummingbirds. If the experiment was analyzed as fully factorial and inference made on every parameter then we would have a multiple testing problem as the probability of finding at least one significant result is 1 – (1 – 0.95)n and at alpha = 0.05 is  0.91. Because of the multiple testing burden and the lack of a priori expectation that specific interactions would be significant, we used likelihood ratio hypothesis tests to assess the evidence that two-, three-, four-, or five-way interactions are important sources of variation for hummingbird visitation. The experimental outcomes were modeled as binomial random variates, that is, a cumulative series of Bernoulli trials where successes were defined as the sum of hummingbird visits to an artificial plant representing a given trait combination in a given array (see Appendix S1 for rationale and

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detailed explanation). The full model contained the grand mean, all five main effects, (corolla tube width (TW = 2 levels), floral height (FLHT = 2 levels), flower color (FC = 3 levels), flower presentation (PRES = 2 levels), and inflorescence architecture (IA = 2 levels)), and all two, three, four and five-way interaction effects thereof. The model equation is as follows with logit link function of the binomial distribution: p (yi = 1) =

exp (ηi ) 1 + exp (ηi )

where p(yi ) is the probability of a successful visit for individual plant i and y is a random variable  Bi(np): ηi = B0 + B X i where the linear predictor, η, is the sum of the intercept, βo, and the product of the vector of parameters β1×48 (some of which may be set to 0 depending on the model) and Xi is a 48×1 vector of the dummy coded effects and interactions for the ith trait combination. The resulting estimated parameters (e.g., β) from the full model were the intercept, six main effects (four binary traits and one with three levels), 14 two-way, 16 three-way, 9 four-way, and 2 five-way interactions for a total of 48 model parameters. We also fit four nested models where the nonzero parameters were, (1) main effects, (2) main effects + two-way interactions, (3) main effects, two and three-way interactions (4) and everything except the five-way terms. For example, comparing model (4) to the full model tests whether five-way interaction effects are important sources of variation for hummingbird visitation. Similarly, model (4) versus (3) tests the four-way interactions and likewise model (3) versus (2) tests the three-way interactions. We tested the statistical significance of the groups of main and interaction effects using ANOVA by comparing twice the difference in LL (χ2 df full-df reduced ) between the full and reduced models The glm function in R 2.14 was used to fit the full and nested models (R Core Development Team 2009). The implementation of binomial regression with GLM function is made by specifying logit link and family = binomial. One limitation of using the nested LL approach is that we can only assess at which level of two-, three-, four-, or five-way interactions is important, without exactly knowing which of the, for example, 16 individual three-way interaction effects is most important. Therefore, we used a model selection approach to determine which particular traits and trait interactions best explain hummingbird preference (see next). Best subsets regression We used best subsets regression (Hastie et al. 2009) to determine the best model’s composition of particular trait main effects and trait interactions as predictors of each dependent variable. The advantage of this approach is that we can identify specific trait main effects and trait combinations that are most important

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for hummingbird visitation by testing each possible effect (i.e., main effects and all possible interactions) without imposing an order of entrance into the model. In brief, the algorithm iterates through all combinations of effects for a subset of size p = 1 to 48 (48 is the total number of all main and interactive effects) for each dependent variable, selecting the set of effects that minimizes the AIC (Akaike Information Criteria) score of a particular model of size p. The scores are compared (see Fig. S3 for an example) and the most parsimonious model is chosen based on a predefined decision algorithm. Thus the approach is different than a typical test because models of different sizes are compared based on AIC scores, accounting for both fit (using the likelihood score) and the number of parameters. Additionally, this approach allows the main effects or trait interactions to enter the model independent of the other parameters in the model (e.g., a model may be composed of two main effects, and a four-way interaction). Due to restrictions of the program, flower color (with three character states) was dummy coded such that FC1 = 1 if white, FC2 = 1 if pink, and both parameters are 0 if red. We used the “bestglm” package version 0.33 (McLeod and Xu 2010) in R for the three dependent variables, assumed normally distributed error terms, and set the Information Criteria to “AIC.” Although it would have been preferable to use a binomial error term, best subsets regression is unable to handle more than 30 parameters or employ a heuristic search if the error terms are anything but normal. Given that normal approximations for such count data can be reasonable, we feel this approximation is justified. To determine if our results were model/error term specific, we also employed a LASSO approach (Hastie et al. 2009) in the “subselect” package (version 0.12–3) and found that not only were higher order interactions selected as part of the best models, but the list of parameters chosen was very similar between the two methods (results not shown). AIC is an Information Theoretic approach that quantifies the information in a model while imposing a penalty term for each parameter (the lower the AIC score the better). AIC allowed us to readily compare the nonnested models that occur during each step of best-subsets regression. To account for the stochastic variability that is inherent in any statistical analysis, we used a confidence approach suggested in Burnham and Anderson (2010). We assumed that the most parsimonious model was the one with the fewest number of parameters with a corresponding AIC score within 3.22 information units of the model with the lowest score. In effect, this assumes models with a corresponding evidence ratio of within 5:1 of the model with the lowest score are equivalent and the least parameterized option is the model that best fits the data for each dependent variable. This conservative approach preferentially selects less-parameterized models and therefore makes the inclusion of higher order interactions more stringent compared to using the models associated with only the lowest AIC score.

Results To quantify the contributions of main and interactive effects of the manipulated plant phenotypes on hummingbird visitation preference, we quantified 1176 first visits, 2072 plant visits, and 3846 floral visits from the 28 experimental arrays (data deposited at 10.5061/dryad.754c6). LL RATIO TESTS

The full model containing all main effects and possible interactions is highly significant for the first visit (p = 2.8E-77), plant visit (p = 1.6E-69), and flower visit (p = 4.3E-170) outcome variables. The full model includes 48 parameters (of which at least one is statistically significant) and 1296 residual degrees of freedom. The overall main effects on hummingbird visitation preference across all 28 arrays are presented in Table 1. Hummingbirds overall preferred red flowers held high off the ground similar to the states found in hummingbird pollinated S. virginca. Hummingbirds also preferred flowers with wide floral tubes, with clumped inflorescences, and flowers facing vertically (upwards); character states found in nonhummingbird pollinated S. caroliniana and S. stellata. However, the role of these main effects in predicting hummingbird preference is very much dependent on trait context as manifested by the importance of interaction effects (Table 2). The likelihood ratio tests demonstrate that the strongest signal from the nonadditive interaction effects comes collectively from the two-way interactions (lowest P-value), and this is consistent for all outcome variables. Furthermore, ANOVA indicates that additional three-, four-, and five-way interaction effects are also important depending on the outcome variable (Table 2). Specifically, for the outcome variable first visits, all levels of interaction effects except the five-way were statistically significant indicating that for first visits we could detect nonadditivity in hummingbird visitation with the most sensitivity. The two-way interaction effects were highly associated with first visits by hummingbirds (p = 3.9E-04), plant visits (p = 5.1E-05), and flower visits (p = 3.3E-10; Table 2). For the outcome variable first visits, the three- and four-way interactions were also statistically significant, p = 0.035 and 0.015, respectively (Table 2). The fiveway interaction was only important with the flower visit outcome variable (p = 0.006, Table 2). BEST SUBSETS REGRESSION

First visits For the visitation metric first visits, the most parsimonious model contained six parameters (not including the intercept). As determined by order of entry and consistent inclusion in the most informative models, flower height was the most important predictor of which plant phenotype was visited first followed by

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Table 1. Mean visitation (one SE in parentheses) using the visitation metrics number of first visits, plant visits, and flower visits to 48 artificial plants each composed of three flowers across 28 arrays. The floral and inflorescence traits manipulated for this experiment

were flower color, flower height, inflorescence architecture, flower presentation, and floral tube width. The trait state of hummingbird pollinated S. virginica is designated in parentheses. Means and one SE of number of visits were computed across the number of plants observed for visitation according to each character state and are given on a per plant basis across all 28 arrays. For binary traits the number is 28 arrays × 24 plants per state) = 672 and for color the number is 28 arrays × 16 plants per state = 448. Thus for the metric first visit, on average a red plant was the first visited once per array and to scale up to the values (total first visits) recorded in Figure 1B one would multiply 1.0 × 448 to get a total of 448 visits to red plants.

Trait

First visit

Flower color: Red (S. virginica) White Pink Flower Height: High (S. virginica) Low Inflorescence architecture: Clumped Diffuse (S. virginica) Flower Presentation: Horizontal (S. virginica) Vertical Tube width Narrow (S. virginica) Wide

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Flower visit

1.0 (0.068) 0.82 (0.057) 0.80 (0.055)

1.7 (0.088) 1.5 (0.082) 1.5 (0.077)

3.7 (0.17) 2.7 (0.16) 2.8 (0.16)

1.3 (0.057) 0.40 (0.031)

2.2 (0.074) 0.91 (0.049)

4.1 (0.15) 1.6 (0.090)

0.94 (0.049) 0.81 (0.050)

1.6 (0.064) 1.5 (0.071)

3.1 (0.13) 2.7 (0.13)

0.78 (0.047) 0.97 (0.051)

1.4 (0.066) 1.7 (0.068)

2.6 (0.13) 3.1 (0.13)

0.84 (0.048) 0.91 (0.050)

1.5 (0.067) 1.6 (0.067)

2.8 (0.13) 3.0 (0.13)

flower presentation (Fig. S3A). Hummingbirds preferentially visited tall flowers over short flowers and flowers facing vertically over flowers oriented horizontally. Several two-way interactions were important predictors in the final model. For the interaction of corolla tube width ×floral height (Fig. 1A), there was little difference in preference between narrow and wide corollas when the flowers were presented closer to the ground. However, hummingbirds preferred the noncontemporary floral character state of wide corollas over narrow corollas when the flowers were presented at a greater height. For the second interaction of flower color × floral presentation (Fig. 1B), hummingbirds preferred to visit red flowers over pink or white flowers whether the flower was presented horizontally or vertically. However, the preference for red was not as great when the flowers were presented vertically. The three-way (Fig. 2, floral height×flower color × inflorescence architecture) and four-way interaction terms (Fig. 3, floral height × flower color × floral presentation × inflorescence architecture) were also important predictors in the final model. These three- and four-way interactions demonstrated that diffuse inflorescence architecture and horizontal presentation, contemporary S. virginica character states, can be favored when expressed in the appropriate character context. Specifically, the three-way interaction between height×flower color×inflorescence architecture resulted in hummingbirds demonstrating the greatest preference

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Plant visit

for tall, red, and diffuse inflorescence plants, whereas the fourway interaction indicates that hummingbirds preferred tall, red, diffuse inflorescence plants with horizontal flower presentation. Plant visits Using the response variable plant visits, the most parsimonious and explanatory model contained eight parameters not including the intercept. As for first visits, the first variable to enter the model was flower height (Fig. S3B). At a size of k = 4 (including the intercept) the first interaction that entered the model was the five-way trait interaction (Fig. S3B). This five-way interaction remained in the model, demonstrating the relative importance of its effect. The main effects with the most explanatory power were flower height and flower presentation (Fig. S3B). There were three lower order interactions that contributed to the best model. The first, flower height×flower color (Fig. 1C), reflects hummingbirds preference for red flowers over white and pink, but the preference was greater when flowers were presented higher off the ground. Hummingbirds also preferred white over pink flowers only when the flowers were held higher off the ground. The second two-way interaction of inflorescence architecture×flower color (Fig. 1D) reflects hummingbird preference for red flowers but the preference is strongest when the flowers are organized within a diffuse inflorescence versus a clumped inflorescence. Preference for pink flowers over white

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Model deviance statistics for the three visitation metrics testing the level(s) at which interactions effects are associated with hummingbird visitation to artificial plants that represent floral trait combinations found in three closely related North American Silene

Table 2.

species. ANOVAs tested whether a given level was statistically significant by comparing the change in deviance from the full and reduced models. If main effects are statistically significant then selection may act on traits independently. If two-way or higher order interactions are statistically significant then there is evidence that selection acts on trait combinations.

Y = First visits ‡Model 1(all main effects-ME) ∗Model 2 ME + two-way ∗Model 3 ME + two and three-way ∗Model 4 ME + two, three and four-way Model 5 ME + all interactions Y = Plant visits ‡Model 1(all main effects-ME) †Model 2 ME + two-way Model 3 ME + two and three-way Model 4 ME + two, three and four-way Model 5 ME + all interactions Y = Flower visits ‡Model 1(all main effects-ME) †Model 2 ME + two-way interactions Model 3 ME + two and three-way interactions Model 4 ME + two, three and four-way interactions ∗Model 5 ME + all interactions

Null deviance (df)

Residual deviance (df)

2115.4 (1343)

1705.7 (1337) 1666.9 (1323) 1639.3 (1307) 1618.7 (1298) 1614.2 (1296)

2535.1 (1343)

2144.8 (1337) 2100.4 (1323) 2092 (1307)

2077.3 (1298) 2073.3 (1296) 5056.4 (1343)

4215.7 (1337) 4146.9 (1323) 4122.5 (1307) 4108.9 (1298) 4098.7 (1296)

‡P < 1× 10–80 , †P < 1× 10–04 , ∗P < 5× 10–02 . Statistically significant effects are in bold.

flowers was only manifested when the flowers were in clumped inflorescences. For the two-way interaction, floral height×floral presentation (Fig. 1E), hummingbirds only demonstrated a preference for vertically presented flowers when the flowers were close to the ground. In addition, the four-way interaction flower height×flower color×floral presentation×inflorescence architecture (not shown since similar to Fig. 3) and the five-way interaction floral presentation×inflorescence architecture×flower height×corolla tube width×flower color (Fig. 4) were also important predictors in the final model. The four-way interaction, similar to the four-way interaction for first visits, demonstrated that interaction effects could reconstruct contemporary S. virginica character states; tall, red, diffuse inflorescence with horizontal flowers attracted the most hummingbird visits. However, we see in the five-way trait interaction (Fig. 4) a previous pattern manifested in the two-way interactions that preference for some character states found in nonhummingbird pollinated species are either neutral or less preferred than those character states found in contemporary hummingbird pollinated S. virginica. For instance there is little difference between horizontally versus vertically presented flowers or red versus white flowers in hummingbird preference when flowers were held high off the ground.

Flower visit Unlike the other response metrics, the most parsimonious model for flower visits mainly consisted of main effects and contained four parameters not including the intercept. Similar to the response variables first and plant visits, plant height was the most important predictor of flower visits, followed by flower presentation and inflorescence architecture, with hummingbirds consistently preferring to visit tall, vertically facing flowers in diffuse inflorescences (Fig. S3C). Finally, the two-way interaction of corolla tube width×flower height entered the model (Fig. 1F, S3C). As with plant visits, hummingbirds prefer wide corolla tubes over narrow corolla tubes only when the flowers are presented high off the ground.

Discussion The overwhelming focus on the evolutionary significance of floral evolution has been on single traits (Fenster et al. 2004; van der Niet and Johnson 2012). However, both statistical approaches used here demonstrate the importance of main effects and interactions of the manipulated floral traits, especially the two-trait interactions, as predictors of hummingbird preference. The ANOVA approach addresses the question, for example, does the two-way

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Figure 1.

Two-way trait interaction effects on average hummingbird visitation rate metrics in 28 artificial plant arrays representing floral

and inflorescence trait combinations of three closely related eastern North American Silene species. Interactions between (A) corolla tube width and flower height, (B) flower color and flower presentation, (C) flower color and flower height, (D) flower color and inflorescence architecture, (E) flower presentation and flower height, and (F) corolla tube width and flower height. Response metric is described by the y-axis. Importance of interactions is based on order of entry in a best-subsets regression. Only the six significant two-way interactions of the fourteen tested are presented.

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Figure 2. Three-way trait interaction of flower color × inflorescence architecture × flower height on the number of total first

visits by hummingbirds to 28 artificial plant arrays representing plant floral and inflorescence trait combinations of three closely related eastern North American Silene species. Importance of the three-way interaction is based on order of entry in a best-subsets regression.

trait interactions plus main effects model of traits fit the data significantly worse for the pattern of hummingbird preference compared to a model also containing three-way interactions and so forth. These hypothesis tests are broad and have limited use because, for example, all 16 three-way interaction effects are not expected to be nonzero. The best subsets regression approach is targeted and accommodates the notion that a large proportion of high magnitude individual effects are not expected. It allows terms to enter the model in any order (higher order interactions can enter before even the main effects), and picks the terms, one at a time, to maximize the reduction in AIC. Next, we discuss the importance of trait interaction in general terms. Then, using the results from the best subsets regression we present our findings within a trait-based context for the Silene model system. We note that the predominance of interaction effects makes interpretation of any given parameter difficult, especially lower level effects relative to higher level interactions (main, two- vs. three-, vs. four-, vs. five-way interactions). We emphasize that trait context matters and potentially plays a large role in floral evolution. ROLE OF TRAIT INTERACTIONS

Main effects and two-way interactions from the ANOVA (Table 2) are important predictors for all three outcome variable metrics of hummingbird visitation. Main effects and higher order interactions are important for first visits (three- and four-way) and flower visits (five-way). These findings suggest that selection on trait interactions is more prevalent than commonly thought, and

may contribute to the patterns of trait covariation we see across the angiosperms (Stebbins 1951). Studies using patterns of phenotypic variation have demonstrated well-integrated multivariate trait combinations to underlay adaptations (e.g., Olson and Miller 1958; Berg 1959, 1960). Through the use of principal component analyses, the decomposition of trait variances and covariances into eigenvectors, and the magnitude of associated eigenvalues can be used to test hypotheses of trait integration (Cheverud 1982, 1984; Wagner 1984; Pavlivcev et al. 2009) and has been used to demonstrate the flower to consist of one or more functionally integrated units (Ordano et al. 2008; Harder 2009; Diggle 2014). The flower as a functional, integrated unit has proved in at least one case to be associated with pollinator type (Perez et al. 2007) and with the degree of pollination specialization (Rosas-Guerrero et al. 2011). In addition, several studies of inter- and intraspecific variation demonstrate that certain combinations of floral traits, that is, the amount of reward, flower size, and placement of the sexual organs relative to the reward, confer higher fitness than alternative combinations (Armbruster 1990; Cresswell and Galen 1991). Our study emphasizes that quantifying the flower one trait at a time may underestimate the role of selection underlying trait transitions and the role of trait context dependency in observed patterns of floral diversification.

BIOLOGICAL CONTEXT OF TRAIT MAIN EFFECTS

Across all three metrics used to assess hummingbird preference, the best subset regression repeatedly identified flower height and flower presentation (vertically vs. horizontally oriented) as having independent (main) important effects. Inflorescence architecture and flower color were less important explanatory variables relative to flower height and presentation based on later entry into the models, which was also conditional on the hummingbird preference metric. Corolla tube width never appeared in our best models as a main effect. The main effects sometimes confirmed that hummingbirds are attracted to traits found in contemporary hummingbird S. virginica (tall and red flowers), but also indicated the contrary, with hummingbirds preferring to visit plants with flowers arranged in dense inflorescences and facing upwards. Flower presentation, inflorescence architecture, and flower color were also influential on hummingbird visitation through interaction effects. Their effects either resulted in hummingbirds preferring trait states found in hummingbird pollinated S. virginica under certain trait contexts, or showing no preference for either state. The importance of inflorescence architecture (floral height and density) to hummingbird attraction demonstrates the necessity of considering the contribution of inflorescence characteristics to our understanding of pollination syndrome evolution (Harder and Prusinkiewicz 2013). However, quantifying the main effects of trait manipulation only provides a partial description of the role

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Figure 3. Four-way trait interaction of inflorescence architecture × flower presentation × flower color × flower height on the number of total first visits by hummingbirds to 28 artificial plant arrays representing plant floral and inflorescence trait combinations of three closely related eastern North American Silene species. Importance of the four-way interaction is based on order of entry in a best-subsets

regression.

of trait change on hummingbird preference based on the other metrics of hummingbird preference. TWO-WAY TRAIT INTERACTIONS

The best subsets regression identified six two-way interactions across the three visitation metrics (several of the two-way interactions collapse to one interaction given that we needed to use dummy variables for flower color; Fig. 1). Flower height was involved in four of the six two-way interactions. Narrow floral tubes, the contemporary character states found in S. virginica, is equally attractive as wide corolla tubes when the flower is short, but hummingbirds prefer the unnatural state of a wide tube when the flowers are held high. This pattern is consistent with hummingbirds associating wide corolla-tubes of S. virginica with greater nectar resources (Fenster et al. 2006). Hummingbirds may only be able to make this choice when flowers are presented high enough off the ground, allowing the birds to perceive differences in the dimensions of the corolla tube from their hovering height. In addition, the projected area of the petals is larger with wider corolla tubes, even though petal length was constant; thus flowers with wider corolla tubes may have been more apparent to the hummingbirds in our arrays. Flowers presented horizontally have about the same visitation as flowers presented vertically when

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combined with tall flowers, although horizontally oriented flowers are discriminated against when combined with short flowers. However, the interpretation of the two-way interactions needs to be tempered by the outcomes of the three-, four-, and five-way interactions. HIGHER ORDER INTERACTIONS

As with the two-way interactions, plant height played an important role, appearing in all higher order interactions. Using the visitation metric, first visits, hummingbird preference reconstructed S. virginica contemporary character states of red flowers held high off the ground in diffuse inflorescences, as inferred by the three-way interaction. The four-way interaction demonstrated that hummingbird preference reconstructed the three-way interaction while also preferring flowers that are horizontally oriented, held high off of the ground and red, again reflecting contemporary character states of S. virginica. The four- and five-way interactions for plant visits similarly indicated that sometimes hummingbirds favored the contemporary trait combinations found in S. virginica or a subset of these traits. The four-way interaction is parallel to the four-way interaction for first visits, in that contemporary S. virginica character states are preferred. However, the five-way interaction indicates

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Figure 4.

Five-way trait interactions on total number of plant visits across 28 artificial plant arrays. The artificial plants represent

the 48 floral trait and inflorescence combinations representative of all possible combinations of traits found in three closely related North American Silene species. Traits manipulated to generate floral phenotypes were corolla tube width, inflorescence architecture, flower presentation, flower color and flower height. The ∗ represents traits associated with S. caroliniana, S. virginica’s closest relative. Importance of the five-way interaction is based on order of entry in a best-subsets regression.

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that the most preferred character combinations were tall flowers with wide corolla tubes and diffuse inflorescences. Flower color, whether white or red, and floral presentation, whether horizontal or vertical, were equally preferred by the hummingbirds. CONTRARY HUMMINGBIRD PREFERENCE

Hummingbirds favored wide corolla tube width and vertical presentation of the flowers, neither of which is found in S. virginica, but this preference was trait context dependent. Association of contrary characters states with greater pollinator visitation has also been documented in at least one other study utilizing two trait manipulations (Campbell et al. 2014). The question is: why would present-day S. virginica exhibit traits that do not consistently improve attraction? We note however, that these character states, especially floral presentation (whether the flower is presented horizontally or vertically), show no difference when associated with flowers presented high off the ground, that is, there is no cost to attraction when exhibiting these character states. The contemporary expression of a narrow corolla tube and horizontal presentation in S. virginica flowers may have less to do with attraction and more to do with increased pollination precision. Elsewhere we have shown that horizontally oriented flowers impose consistent directionality on hummingbird movement into the flower (hummingbirds enter the flower upright and on the same plane as the flower opening) whereas flowers oriented upwards receive visits from hummingbirds from all compass directions (Fenster et al. 2009). Consequently, horizontally oriented flowers are more likely to be associated with greater pollination precision than upright flowers. Hummingbird preference for wider corolla tubes is consistent with S. virginica having wider corolla tubes than its nearest relative, bee pollinated S. caroliniana (3.6 vs. 1.9 mm, respectively; Reynolds et al. 2009). Although hummingbirds may prefer even wider corolla tubes, narrow corolla tubes may be associated with precise pollination because they restrict the positioning of the pollinator relative to the reproductive organs of the flower (Fenster et al. 2004). Thus, although the selective agent remains the same, when consideration of the fitness component changes from pollinator attraction to pollen transfer precision, we suggest that contemporary S. virginica character states are adaptive. TRAIT MANIPULATION AND PATTERNS OF PHENOTYPIC SELECTION

Our five-trait experimental approach is consistent with previous floral manipulative experiments conducted with S. virginica (Fenster et al. 2006, Fenster et al. 2009, Dudash et al. 2011) and with phenotypic selection analysis on a naturally occurring S. virginica population (Reynolds et al. 2010b). All of these studies recover both main and interactive effects on hummingbird preference. For example, hummingbird visitation rates were greater for

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flowers manipulated to be high off the ground (Dudash et al. 2011) and consistent with the positive directional selection exerted on flower height in a natural population across eight years (Reynolds et al. 2010b). We have also shown that hummingbirds prefer to visit red flowers over white and pink when controlling for flower height (Dudash et al. 2011), but our interpretation changes when considering the manipulation of other traits. Preference for wider corolla tubes is only seen as an interaction with flowers exhibiting larger petals (Fenster et al. 2006) and with floral display height (Reynolds et al. 2010b), similar to our findings here. Furthermore, while we could not detect phenotypic selection on petal length and petal width, we did consistently quantify stabilizing selection on these traits through their higher order interactions as detected through canonical analysis of the gamma matrix (Reynolds et al. 2010b). Phenotypic selection studies, manipulative experiments, and comparative approaches demonstrate the universality of pollinator-mediated selection (reviewed in Fenster et al. 2004; van de Niet and Johnson 2012; Rosas-Guerrero et al. 2014). Pollinators as mediators of selection through female reproductive success is demonstrated when artificially adding pollen reduces or eliminates phenotypic selection (Sandring and Agren 2009; Sletvold et al. 2012; Bartkowski and Johnston 2012). The microevolutionary equivalent of our experiment (here we manipulated traits across macroevolutionary scales) is quantifying the role of correlational selection to phenotypic selection, that is, the detection of selection to increase trait covariance. In the just cited references and other phenotypic selection studies, there is little evidence of correlational selection acting on the flower (Sletvold et al. 2012; Kulbaba and Worley 2012), and when correlational selection is detected, it is often limited to a subset of two-trait interactions (Conner et al. 1996a,b; Valdivia and Niemeyer 2006; reviewed in Campbell 2009; Nattero et al. 2010; Vanhoenacker et al. 2010; Sletvold and Agren 2010, 2010; Bartkowski and Johnston 2012; Campbell et al. 2014). However, when dimension reduction techniques are used, for example canonical analysis (Reynolds et al. 2010a, b), selection acting on trait interactions is often observed. Several factors may have contributed to our ability to quantify hummingbird attraction based on a prevalent signal of trait interaction. First, our study manipulated five plant traits whereas all other studies have confined the exploration of the effect of trait interactions on adaptive features to only two trait interactions. The addition of each trait in our experiment increased the number of treatments by a factor of two (three for the three color treatment), but allowed us to more fully explore the adaptive space relative to studies confined to two-trait interactions. Second, we explored the adaptive landscape at a larger scale by manipulating traits that correspond to trait states found in related and sometimes sympatric species (e.g., color and presentation of the flower), as opposed

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to only manipulating traits strictly within the bounds of variation found within a population (Campbell 2009). For example, in more than 20 years of fieldwork with the three Silene species, we have only observed one S. virginica plant with nonred flowers (pink), and one S. virginica plant with a short inflorescence corresponding to our short inflorescence treatment. The only other exception is that S. caroliniana exhibits consistent polymorphism for flower color, ranging from white to pink. Thus, our results may provide a clearer understanding of the role of hummingbird preference in the maintenance of trait differences between S. virginica with its sister taxa within eastern North America, rather than providing inference on the divergence process itself. Consequently the model of evolution that we tested was whether trait context matters and how the background phenotype affects the maintenance of any particular trait through evolution. We note however, that our experimental manipulation of the parental traits is analogous to QTL analyses using crosses between extreme genotypes in that the genetic architecture of trait differences in the recombinant generations are used as proxies to assess the process of within population trait evolution (Erickson et al. 2004). It may also be that methods such as canonical analysis are analogous to our manipulation of many traits, in that both approaches are likely to pick up trait interaction effects on fitness components because trait interactions may typically involve more than two traits. THE EVOLUTION OF S. VIRGINICA TRAIT COMBINATIONS

One way to further examine the importance of trait interaction, especially those involving flower height, is to consider the combination of traits found in our artificial plants corresponding to hummingbird pollinated S. virginica’s sister species, bee and diurnally hawk moth pollinated S. caroliniana. In our experiment the trait combination of pink flowers, close to the ground, oriented upwards, with narrow corolla tube and a dense inflorescence corresponds to S. caroliniana (star in Fig. 4). Since hummingbird pollination is often derived from bee pollination, (Grant and Grant 1965, Beardsley et al. 2003), then those traits found in S. caroliniana may be considered as ancestral to those found in contemporary S. virginica. A character state transition from S. caroliniana toward S. virginica involving any single trait other than flower height resulted in either no change or a decrease in hummingbird visitation that is, a decline in attractiveness to hummingbirds (Fig. 4). Thus shifts from S. caroliniana toward S. virginica character states for inflorescence architecture (diffuse), flower color (red vs. not), or flower presentation (horizontal) only increased hummingbird visitation (for the response variable plant visits) if these trait shifts occurred with increased flower height. Consequently, order of trait evolution may be important, as traits must evolve simultaneously with or subsequent to the evolution

of flower height. Perhaps when flowers are low to the ground, hummingbirds do not have the opportunity to consider the traits and visit indiscriminately, whereas when flowers are held high off the ground, corresponding to their natural cruising or hovering height, the birds have time to weigh the character combinations before making a visit.

Conclusions We provide evidence that trait interactions contribute to the attractiveness of a hummingbird pollinated flower. Our evidence is consistent with Faegri and van der Pijl’s (1979, p. 23) notion that “each part of each [flower] functions in its own way, but functions of various parts within a [flower] are correlated.” We also provide support for Stebbins’ (1951, 1970) idea that angiosperm trait combinations reflect selection upon suites of traits to provide attraction, rewards, and gamete transmission to conspecifics and supports a role for pollinator-mediated selection in the assembly of trait combinations, specifically pollination syndromes (Rosas-Guerrero et al. 2014). Our results also suggest that when investigating the adaptive significance of traits, interpretations should consider the role of context dependency. This caveat is analogous to cautions of interpreting patterns of phenotypic selection that may change if more traits are measured (Mitchell-Olds and Shaw 1987). We use our proxies of fitness, three metrics of hummingbird visitation preference, to demonstrate that hummingbirds can sometimes reconstruct the contemporary phenotype through a myriad of trait interactions. Furthermore, with some traits, hummingbirds exhibit little preference depending on the character state at other traits (e.g., context dependency). In these cases, trait evolution toward contemporary S. virginica character states may reflect selection acting through different fitness components, for example, through pollen transfer efficiency. Consequently we conclude, as many before us, that natural selection assembles only the fittest phenotypes from the scale of the cell (Weber 1992) to the “arrival of the fittest” combinations (DeVries 1904), which may in turn have a profound influence on the pattern of flower diversification across the angiosperm (Stebbins 1951).

ACKNOWLEDGMENTS We thank H. Wilbur and E. Nagy for logistical assistance, M. Benton, K. Fenster, and T. Fenster for field assistance, F. Brewer for photographic assistance, J. Conner, K. Fenster, J. Recknor, and J. McGlothlin for comments on the manuscript, S. Arnold and B. Brodie for thoughtful discussion, and two anonymous reviewers and the AE, Dr. M. Streisfield, for their constructive criticism. Research was sponsored by the Mountain Lake Biological Station (University of Virginia) to R. Reynolds, M. Dudash, and C. Fenster and NSF REU-Sites award DBI-0453380 to C. Williams and NSF DEB-0108285 to C. Fenster and M. Dudash. Data Archival Location: 10.5061/dryad.754c6

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Associate Editor: M. Streisfeld Handling Editor: T. Lenormand

Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher’s website: Appendix S1. Methodology and Results. Table S1. An illustration of how we modeled visitation as a series of Bernoulli trials. Figure S1. Artificial floral trait plant phenotypes. Figure S2. Experimental design in which combinations representing 48 different floral trait 89 phenotypes (artificial plants) representing combinations of floral traits found in three related 90 North American Silene species were randomly placed prior to each experimental array. Figure S3. Best subsets regression to determine the predictors for the most parsimonious models 98 explaining variation in the outcome visitation variables (a) first visits, (b) plant visits, and (c) 99 flower visits to floral and inflorescence trait combinations  artificial plants by hummingbirds (n 100 = 28 arrays) and their order of entry into the model.

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