Simultaneous Pulsed Flowering in a Temperate

Journal of Ecology 2015, 103, 316–327 doi: 10.1111/1365-2745.12362 Simultaneous pulsed ﬂowering in a temperate legume: causes and consequences of multimodality in the shape of ﬂoral display schedules

Susana M. Wadgymar*, Emily J. Austen, Matthew N. Cumming and Arthur E. Weis Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks, St. Toronto, Ontario M5S 3B2, Canada

Summary

1. In plants, the temporal pattern of floral displays, or display schedules, delimits an individual’s mating opportunities. Thus, variation in the shape of display schedules can affect the degree of population synchrony and the strength of phenological assortative mating by flowering onset date. A good understanding of the mechanisms regulating the timing of flowering onset has been developed, but we know less about factors influencing subsequent patterns of floral display. 2. We observed unusual multimodal display schedules in temperate populations of the annual legume Chamae- crista fasciculata. Here, we ask whether ‘flowering pulses’ are simultaneous among individuals and populations and explore potential underlying mechanisms and consequences of pulsed flowering. 3. We monitored daily flower production for individual plants from genetically divergent populations during a series of field experiments that manipulated three potential influencers of display schedule shape: average daily temperature, pollinator availability and watering schedules. We measured floral longevity to isolate the contributions of flower retention and flower deployment to display schedules. We assessed relationships between flowering and environmental variables and compared estimates of population synchrony, individual synchrony and the strength of assortative mating with those of 29 unimodally flowering species from the area. 4. We observed simultaneous flowering pulses in all experiments, with peaks aligned among individuals and populations despite variation in flowering onset and/or duration. Pulses were not the result of increases in average temperature, pollinator availability or variation in watering schedules. Seasonal fluctuations in temperature correlated with floral longevity and flower deployment, suggesting that the shape of display schedules may be plastic in response to fluctuations in temperature. Average population and individual synchrony differed only slightly from those of the species with unimodal schedules, while the average strength of assortative mating for flowering onset date was strongly reduced (0.21 in C. fasciculata vs. 0.35 for the 29 other species). 5. Synthesis. Researchers should take caution in assuming that components of display schedules are genetically or developmentally correlated with flowering onset. Variation in the shape of display schedules can influence patterns of gene flow within or between populations, with potential effects on the strength of phenological assortative mating and subsequent responses to selection. Key-words: Chamaecrista fasciculata, floral longevity, flower deployment, individual synchrony, phenological assortative mating, population synchrony, reproductive ecology

variation in patterns of flowering determines the potential for Introduction non-random mating among plants with distinct flowering The opportunities for pollen exchange among plants are onset dates (phenological assortative mating), potentially dependent on temporal patterns of flower production, or flow- influencing the efficacy of selection on flowering onset and ering phenology. Coupled with other factors, including the correlated traits (Weis & Kossler 2004; Weis 2005). Despite composition of the pollinator community, the spatial layout of these far-reaching effects, we have a limited understanding of members of the population or the mating system of a species, the factors affecting the schedule of flowers on display across synchrony in flower production among individuals can affect the season, or how variation in the shape of these display outcrossing rates within populations (Loveless & Hamrick schedules influences the temporal structure of a plant’s mating 1984; Young 1988; Ims 1990; Ison et al. 2014). Individual pool. Species in temperate regions tend to flower in a unimodal *Correspondence author: E-mail: [email protected] fashion over the span of several weeks, with flower production

© 2014 The Authors. Journal of Ecology © 2014 British Ecological Society Simultaneous pulsed flowering in a temperate legume 317 increasing at a rapid rate to a maximum, or peak, display size, (Primack 1978; Lloyd 1980), where a decrease in flower num- followed by a steady decline in flower number as all internal ber in the deployment schedule reflects a temporary resource resources are diverted away from flower production and shift to fruit maturation after a period of effective pollination towards maturing fruit (Rabinowitz et al. 1981; Herrera 1986; (Stephenson 1981). This phenomenon has been observed in Weis, Nardone & Fox 2014). Display schedules of this shape the tropics, where a lack of seasonality permits flowering have been described by algebraic functions that estimate peak year-around and display schedules are often multimodal (New- flowering dates and the dispersion of flower production over strom, Frankie & Baker 1994). The dependence of flower the season (Malo 2002; Clark & Thompson 2011). However, deployment on internal resource availability can be detected in perennials, the shape of individual- or population-level dis- when monitoring flower number where pollinator services are play schedules can be variable across years (Pico & Retana limited or absent and few to no fruit are being matured. 2001). This suggests that plasticity in the symmetry of display Fluctuations in display schedules influence the probability schedules (skew), the magnitude of peak flowering (kurtosis), of pollen exchange between any two individuals: plants that the number of days where flowers are produced (duration) or fluctuate in synchrony will share more mating opportunities the number of flowering peaks (modality) may be more com- than those fluctuating independently. The shape of display mon than currently appreciated. In fact, a close examination of schedules dictates the degree of phenological synchrony cases where individual display schedules have been tallied typ- within and among individuals, which in turn can influence ically reveals short-term fluctuations in flower number about a rates of outcrossing and selfing via pollinator movements, or smoother, underlying seasonal pattern (Malo 2002; Clark & geitonogamy. Synchrony in display schedules is an essential Thompson 2011; Austen, Jackson & Weis 2014; Weis, Nar- requirement for random mating in plants. Thus, variation in done & Fox 2014). This phenological ‘noise’ may be the result schedule shape may also alter the strength of phenological of immediate developmental responses to factors influencing assortative mating by flowering onset date in a population flower deployment (the opening of new flowers) or floral lon- (Fox 2003), although this has yet to be formally tested. gevity (the retention of previously open flowers), which In this study, we examine mechanisms contributing to, and together comprise the flowers on display each day. Fluctua- consequences of, multimodal display schedules in temperate tions in display and deployment schedules may occur simulta- populations of the annual legume Chamaecrista fasciculata neously across individuals, indicating a shared plastic response (Michx.). The occurrence of multiple flowering peaks, or to the same external stimuli. But what causes these day-to-day pulsed flowering, is unusual at these latitudes and offers an fluctuations? opportunity to examine the factors that shape display sched- The display schedule can be sensitive to many factors, ules and how variation in shape ultimately influences syn- including environmental conditions or resource availability chrony and phenological assortative mating. We aim to (1) (Bustamante & Burquez 2008); resource partitioning among verify pulsed flowering in Chamaecrista fasciculata, (2) vegetative, defensive and reproductive functions (Bazzaz determine whether flowering pulses occur simultaneously et al. 1987); or meristem availability and allocation (Bonser across individuals and genetically differentiated populations, & Aarssen 1996). The contribution of these factors can be (3) assess whether flowering pulses, and the intervals between teased apart experimentally (Diggle 1995); however, under- them, are the result of intermittent shifts in internal resource standing the proximal mechanisms underlying schedule shape allocation away from flower production and towards fruit presents several challenges. Subtle variation in schedule shape maturation, (4) establish whether flowering pulses correlate may only be detected with fine-scale monitoring of flowering with fluctuations in abiotic variables and (5) evaluate effects at the individual level (Miller-Rushing, Inouye & Primack of flowering pulses on synchrony and assortative mating for 2008; Morellato et al. 2010). Display schedules may be influ- flowering onset date. To accomplish this, we account for vari- enced by environmental factors that present both seasonal ation in floral longevity to distinguish between display sched- trends and daily fluctuation (e.g. precipitation, temperature). ules, which include flowers of any age available for These time-series data are often messy and autocorrelated, pollination, and deployment schedules, which involve the requiring special methods for analysing relationships between opening of new flowers each day. variables (Hudson 2010; Brown et al. 2011). Additionally, the daily environment can influence both flower deployment and floral longevity (Augspurger 1983; Primack 1985), requir- Materials and methods ing fine-scale data to distinguish between the contributions of each to the display schedule. STUDY SPECIES Temporal shifts in internal resource allocation can also Chamaecrista fasciculata (Fabaceae, subfamily Caesalpinioideae), or affect the shape of display and deployment schedules the partridge pea, is a self-compatible annual legume that prefers (Stephenson 1981; Stanton, Bereczky & Hasbrouck 1987). sandy soils in prairie and disturbed habitats (Foote & Jackobs 1966). Plants face a trade-off between current reproduction (resource Its distribution spans the eastern half of the United States and Mex- investment in seed maturation) and future reproduction ico, with the northern range limit running along the Canadian border (resource investment in the production of new flowers). The from Minnesota to New York (Irwin & Barneby 1982). potential number of flowers deployed in a given day can be Plants consist of a central stalk with several branches that each inversely related to the number of fruit being matured develop multiple compound racemes (Garrish & Lee 1989). The

flower buds produced on each raceme can be held in stasis for 4 to plots were heated by 1.5 °C during the day and 3 °C at night, in 10 days until blooming, resulting in multiple buds awaiting anthesis accordance with diurnal warming projections (Easterling et al. 1997) at the same time. Flowering and growth continue until first frost, and and local warming predictions (Colombo et al. 2007), while six iden- plants typically generate 100 to 800 flowers over the course of 30 to tical plots were unheated. Heated and ambient plots were otherwise 60 days. Flowers produce no nectar, are exclusively buzz-pollinated exposed to natural conditions. Each plot contained five randomly and are reported to remain open for just 1 day (Thorp & Estes 1975). selected individuals from each of four populations (Table 1). We collected seeds from five populations of C. fasciculata in the In experiment 2, we manipulated pollinator access to plants and fall of 2009. Two were from the U.S. mid-western states of Minne- thus resource allocation to fruit/seed maturation. This study took place sota (MN, 44.8011°N, 92.9647°W) and Missouri (MO, 38.4979°N, in a field that had been undisturbed for two decades. For each popula- 90.5610°W), while three were from the eastern states of Pennsylvania tion, seven individuals were planted within each of six plots that (PA, 40.1790°N, 76.7248°W), Virginia (VA, 37.5061°N, 77.7342°W) either allowed or excluded pollinators. Pollinator-excluded plots were and North Carolina (NC, 35.8900°N, 79.0092°W). Where possible, covered with a tent made of fine-mesh bridal tulle to prevent pollina- three fruit were collected from each of 50–100 plants located at least tion. We hung a sheet of this netting on the south (sun facing) side of 5 m apart along a transect (the approximate genetic neighbourhood plots open to pollinators to account for any shading affects. size for this species, Fenster 1991). In experiment 3, we manipulated watering schedules to determine whether variation in water availability influences the shape of display SUMMARY OF EXPERIMENTS schedules. We randomly assigned 12 plots to 1 of 3 watering regimes, ~91 L of water applied every 2 weeks, ~45.5 L of water applied We collected daily flower counts for individuals from all populations every week or ~13 L of water applied every 2 days. Thus, all plots of Chamaecrista over the course of three common garden experi- received the same total volume of water, but at different schedules. ments conducted over 2 years in a field setting (Table 1). This study We staggered planting dates to obtain concurrently flowering cohorts explores the patterns of flower deployment and floral longevity in from select populations that otherwise have minimal flowering over- these experiments; data addressing other questions will be reported lap (Table 1). We planted seven seedlings from each population- elsewhere. cohort combination (hereafter simply referred to as population) per All experiments took place at the University of Toronto’s field sta- plot, each in their own quadrant to avoid asymmetric competition tion, the Koffler Scientific Reserve at Joker’s Hill (KSR, 44.0300°N, among planting groups. We excluded natural precipitation by cover- 79.5275°W). This site is just north of Chamaecrista’s current distribu- ing plots with 2.7 9 3.7 m roofs made of clear plastic, slanted south- tion limit in eastern North America, but just within the latitudinal ward towards the direction of summer rain events. Rain gutters along limit west of the Great Lakes. Each experiment included treatments the southern edges directed precipitation away from the plots. We that ultimately manipulated aspects of flowering phenology (Table 1). measured the per cent volumetric water content (VWC) within each In each study, seedlings that had been planted on the same day were quadrant of each plot for a portion of the days where flowers were transplanted into the field 20 cm apart in a hexagonal array with a counted (TDR 100 Soil Moisture Metre, Spectrum Technologies, Inc., ring of equally spaced plants around focal individuals to absorb edge Aurora, IL, USA). affects. All other competitors within the plots were cleared. Unless In each experiment, we counted flowers on all individuals every otherwise noted, all experiments began in May. With few exceptions day. In all, we tallied over 148 000 flower observations. Experi- (detailed below), flowers were counted on each individual every day ments 1 and 2 had days of missing data (7 of 93 and 8 of 86 days, until a killing frost occurred. Daily precipitation data were collected respectively). We interpolated expected flower counts from a linear from a rain gauge monitored by Environment Canada at the nearby function running from the day before to the day after the missed Buttonville Airport (43.8608°N, 79.3686°W) while temperature and counts. There were never more than two consecutive days without humidity measurements were recorded by a weather station at KSR data collection, so our interpolations were unlikely to distort the true (HC-S3 probe, Campbell Scientific, Edmonton, Canada). pattern of flowering. Interpolated data were used in all graphs and In experiment 1, we manipulated thermal regimes in order to analyses. extend the growing season to that of a latitude approximately 5° fur- In experiment 3, we measured floral longevity for a subset of con- ther south. Temperature has been shown to strongly advance flower- secutive days. On a given day, every flower on each individual was ing onset dates in many species (Parmesan & Yohe 2003); however, marked with a felt tip marker on the inside of the rigid upper petal. no studies have monitored subsequent patterns of flowering to see On subsequent days, the number of flowers remained open and whether display schedules are similarly affected. We used infrared unwithered with colours from previous days was recorded and new heaters to warm 3-metre-diameter plots by a desired amount above flowers were counted and marked with a different colour. We used ambient (design per Kimball et al. 2008). Temperatures were moni- these data to measure floral longevity and to determine the proportion tored at the plot level with infrared radiation scans (SI-111 infrared of newly deployed flowers contributing to display schedules. In total, radiometer, Campbell Scientific, Edmonton, Canada). Six of these the longevities of 7011 flowers were monitored. Markings did not

Table 1. A summary of several experiments conducted at the Kofﬂer Scientiﬁc Reserve at Joker’s Hill; the treatments applied, the year of study, the populations involved [Minnesota (MN), Pennsylvania (PA), Illinois (IL), Missouri (MO), Virginia (VA) and North Carolina (NC)] and the approximate number of individuals per population and treatment combination

Experiment Treatment Year MN PA MO VA NC n

1 Heat 2011 X X X X 30 2 Pollination 2011 X X X X X 75 3 Watering schedule, Planting date 2012 X X 84

© 2014 The Authors. Journal of Ecology © 2014 British Ecological Society, Journal of Ecology, 103, 316–327 Simultaneous pulsed flowering in a temperate legume 319 seem to deter pollinators from visiting flowers or produce any adverse will decrease rapidly at larger lags, eventually becoming negative, reactions in the flowers themselves (personal observation). because the flowering peaks of the display schedules being compared would be misaligned if shifted by more than a day or two. In contrast, comparisons of unimodal display schedules would produce cor- COMPARISON OF FLOWERING PHENOLOGIES AMONG relation coefficients that varied in size and direction at larger lags, POPULATIONS depending on the difference in flowering onset and duration between the populations being compared. Lastly, if the display schedules of For data from experiment 2, we used linear mixed models to deter- different populations were completely independent, correlation coeffi- fl mine whether pollination treatment in uenced the total number of cients would be small and less consistent in sign at all lags and in all fl fl owers produced or the owering duration in all populations. We comparisons, regardless of schedule modality. included pollination treatment, population and their interaction as fixed effects and plot as a random effect. In these analyses, and in subsequent models, we account for any variance heterogeneity among RELATIONSHIPS BETWEEN FLOWERING PHENOLOGY groups with error variance covariates as per Zuur et al. (2009) using AND ENVIRONMENTAL VARIABLES the nlme package (Pinheiro et al. 2014) in R (R Development Core Team 2014). We calculated cross-correlation functions lagged up to 5 days to We formally assessed whether flowering pulses occurred simulta- determine whether display schedules correlate with average daily tem- neously across populations by examining the cross-correlation func- perature, total daily precipitation and average daily humidity. Again, fi tions between pairs of populations, which produces correlation we t phenological and environmental times series with ARIMA coefficients between time series that are aligned or shifted (lagged) by models to remove any autocorrelation from the data. In experiment 3, a certain number of days. This analysis is not a complete estimate of where natural precipitation was excluded, we analysed the relationship synchrony among populations; rather, it correlates patterns of flower- between display schedules and the volumetric water content of the ing between populations only for the period of time where both were soil within each plot. Due to data availability for soil moisture read- in flower. All correlation analyses were calculated using the propor- ings, we were only able to do this for the MN population planted tion of total flowers in bloom each day, which standardizes flowering early. fl output across populations with different display sizes. We chose to The relationship between temperature and oral longevity in experi- compare populations that vary in flowering onset date, duration, ment 3 was examined using logistic regressions. For each population, fl genetic origin, treatments within experiments and across experiments the mean proportion of owers open for 2 days was regressed on the fl conducted in the same year in order to capture the extent of pheno- average temperature for the 24 h proceeding ower deployment (calcu- logical concordance between distinct groups. lated here as the average of temperature readings taken every 15 min fl Cross-correlations between time series that are themselves auto- from 8 AM the day of ower deployment to 8 AM on the subsequent correlated can result in inflated variances that produce erroneously day). We used the average slope and intercept from these logistic fl large cross-correlation coefficients (Zuur et al. 2009; Brown et al. regression equations to calculate the number of newly deployed owers ’ 2011). To account for autocorrelation in any of the phenological each day based on that day s average temperature. This allowed us to fl data, we applied auto-regressive integrated moving average (AR- estimate oral deployment schedules for each population in each IMA) models to each time series prior to calculating cross-correla- experiment. To examine effects of environmental variables on patterns fl tion coefficients (Box & Jenkins 1970). The order of auto- of ower deployment, we repeated the cross-correlation analyses regressive and moving average terms were chosen by examining between deployment schedules and abiotic variables. fi the extended sample autocorrelation functions for each time series Where suf cient data were available, we analysed the effects of and minimizing the Akaike information criterion (AIC). Only signif- watering treatment on display schedules (for all but the NC late popu- icant coefficients were included in the final models. All ARIMA lation) and soil moisture content (for the MN early population) in fi models were analysed using the TSA package (Chan & Ripley experiment 3 via generalized least squares tted models, with water- fi 2012) in R. ing treatment, day and their interaction included as xed terms (again fi To test whether flowering pulses were augmenting the phenological using the nlme package in R). A signi cant interaction term would correlations between populations, we compared the cross-correlation indicate that the display schedules or soil moisture levels were vari- functions of our observed data to the cross-correlation functions of able among watering treatments throughout the growing season. We simulated unimodal data. For each population, a simulated, unimodal accounted for temporal autocorrelation in observations among days display schedule was created by rearranging daily flower counts so (nested within plot) by incorporating an auto-regressive error structure that the maximum flowers on display occurred at the mid-point of the of order 1 (display schedule analysis) or an exponential correlation flowering duration, and the remaining flower counts were arrayed in error structure that can account for irregularly spaced observations descending order on either side of the new peak date of flowering. In through time (soil moisture analysis, Zuur et al. 2009). Models with this way, we preserved the total number of flowers produced, the var- the lowest AIC values were selected for these analyses. iation in daily flowering display size (flower counts), the onset date fl and the duration of owering for each population, and can compare SYNCHRONY AND PHENOLOGICAL ASSORTATIVE cross-correlation functions when only the modality of the display MATING schedule had been altered. As before, we converted data to propor- tions and employed ARIMA models to remove autocorrelation prior We estimated population synchrony as per Weis, Nardone & Fox to each analysis. (2014), where synchrony information is extracted from an n 9 n If flowering pulses occur simultaneously across populations, we matrix of pairwise mating opportunities, Φ, among all of the display fi expect to see a strong, positive correlation coef cient at a lag of schedules of studied individuals (n). Each matrix element of Φmf is 0 days despite differences in flowering onset or duration between calculated as the product of a father f’s proportional contribution to populations. With our multimodal data, we predict that correlations the pollen pool in the population each day of the flowering season

© 2014 The Authors. Journal of Ecology © 2014 British Ecological Society, Journal of Ecology, 103, 316–327 320 S. M. Wadgymar et al. and the number of opportunities for pollen receipt presented by that all ﬂowers open on the same day have the same potential to mother, m (each estimated by their number of open ﬂowers, Weis & exchange or receive pollen.

Kossler 2004). In a perfectly asynchronous population (i.e. no plant To place our estimates of Sp,Si and q into a broader context, we flowers at the same time as any other), the diagonal elements of Φ compare them with estimates for 29 other species naturally occurring are 1/n and the non-diagonal elements are 0. In contrast, in a perfectly at KSR. Most of these old-field species exhibited unimodal display synchronous population (i.e. all individuals display the same number schedules that are more typical of temperate regions (Weis, Nardone of flowers each day), all elements equal (1/n)2. The degree of syn- & Fox 2014, see Fig. S8). The data were collected in 2008 from chrony among plants in a population, Sp, is calculated as the ratio of approximately 50 individuals per species. Flowers were counted every the first eigenvalue of the mating matrix, k1, to the sum of all n 3 days, so individual synchrony was estimated with a modified ver- eigenvalues: sion of eqn 2:

¼ k =Rk Sp 1 eqn1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ii SSi Ii Ci In the case of complete synchrony, all elements of Φ are equal; the Si ¼ eqn 4 Ii Ti ½Ii Ci 1 first eigenvalue will be 1/n and the remaining ones will be zero. Thus, = Sp 1 when all plants have identical display schedules. With a com- where Ii represents the sampling interval (e.g. Ii = 3 when counts pletely asynchronous population, all eigenvalues are equal to 1/n, and are made every 3 days) and Ci is the number of days where flowers as per eqn 1, Sp = 1/n. Thus, this measure scales between 1/n and 1. were counted. We analysed differences in mean Sp, q and Si between When n is large, 1/n approaches 0. Chamaecrista populations and the KSR species using two-sample t- We developed a measure of individual synchrony, Si, to quantify tests (if variances were equal) or Welch’s two-sample t-tests (if vari- the opportunity for geitonogamous pollen transfer (see Supporting ances were unequal). Information, SI, for details). This measure incorporates the effects of uniformity of display schedules (scaled to total flower production) and flowering duration, such that Si = CVi /√Di, where CVi is the Results coefficient of variation of the individual i’s schedule, and Di is the schedule duration. This equation can be easily rearranged to: COMPARISON OF FLOWERING PHENOLOGIES AMONG pffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffi POPULATIONS ¼ SSi Di Si eqn2 Population-level flowering pulses were simultaneous across Ti Di 1 populations and were the result of simultaneous pulsing at where schedule synchrony is estimated by the sum of the squared the individual level. Individuals produce one or more flower- deviation of the observed daily flower production (SS ) relative to the i ing pulses in alignment with their neighbours despite differ- total flowers counted (Ti) and corrected for schedule duration. A value ences in flowering onset and flowering duration within and of Si = 1 indicates that all flowers within a plant could exchange pol- among populations or treatments. We first present the len with every other flower on that plant, while Si = 0 when flowers observed display schedules and later present the cross-corre- are distributed evenly across days (SSi = 0), thus minimizing the opportunities for geitonogamy. Synchrony is technically undefined at lation analyses that formally test for simultaneity of flower- the limit where Di = 1, but we assign this maximum possible syn- ing pulses. chrony a value of 1. We make the assumption that any open flower, Consider the example of the MN and MO populations in regardless of age, can receive and contribute pollen equally and so experiment 1 (Fig. 1). Under the ambient temperature regime, conduct calculations on display schedules rather than deployment the former began flowering 17 days after the latter, on aver- schedules. age, yet both share a flowering peak on day 234 and another The strength of phenological assortative mating for a given trait near day 241. This is also true of plants that were artificially can be quantified by the phenotypic correlation between potential warmed in this experiment, where flowering pulses between mates, q (Weis & Kossler 2004). Here, we estimate the potential for heated and ambient treatments overlap despite the advance- assortative mating by flowering onset date; however, variation in any fl component of schedule shape can influence an individual’s mating ment of owering onset in heated plots. Flowering pulses pool (Fox 2003). We can characterize q by extracting the proportion were produced simultaneously across the experiment regard- of all mating opportunities in a population that occur between two less of thermal treatment or genetic origin. Similar patterns individuals from the mating matrix, Φ. For hermaphrodites, like were seen between ambient and heated treatments within the C. fasciculata, q is: PA and NC populations (Fig. S1), although the scant temporal overlap in display schedules precluded simultaneous puls- P P ÂÃ Umf ðzm zÞðzf zÞ ing between populations. q ¼ m f P 2 eqn 3 Overlap among populations in display schedules was Xmðzm zÞ m enhanced in experiment 2, which included a pollinator exclusion treatment. Exclusion led to increased resource investment where z is the date of flowering onset, m and f represent the mother in flower production (Fig. 2), including an extension of flow- and father of a potential mating pair, / is the element of Φ corre- mf fl sponding to the proportion of mating opportunities between mother, ering duration until the end of the season for the early- ower- ing populations (Pollination F = 129.30, P < 0.001; m, and father, f, and Xm is the proportion of flowers in the population (1, 20) produced by m. When q = 0, the population is mating randomly with Population F(4, 20) = 35.00, P < 0.001; Pollination*Population respect to flowering onset date. We make the assumptions that all F(4, 20) = 10.53, P < 0.001), and an increase in the total num- flowers on display by an individual are equally likely to set seed and ber of flowers produced in all populations (Pollination

100 Pollinated MN Heated (a) Unpollinated MN Ambient 0.1 (a) NC Ambient MO Heated MO Ambient 0.0 80 0.1 (b)

0.0 60 0.1 (c)

0.0 Individual 40 0.1 (d)

0.0

20 Proportion produced of flowers 0.1 (e)

0.0 0 212 232 252 272 0.1 (b) Julian date

Fig. 2. The population-level display schedules for populations in experiment 2 from the open pollination (solid) and pollinator- produced excluded (dotted) treatments. Data are shown for the (a) MN, (b) PA, 0 (c) MO, (d) VA and (e) NC populations. In addition, panel (a) Proportion of flowers 212 232 252 272 292 includes the display schedule for the NC population in ambient condi- Julian date tions (dashed) from experiment 1 located ~0.5 km away. Fig. 1. (a) Individual-level display schedules from experiment 1. Individuals from the MN and MO populations from both heated and fi = ambient treatments are staggered along the y-axis in order of flower- coef cient of r0 0.50 (Fig. 3a). As predicted, this positive ing onset date. The size of each circle reflects the proportion of total correlation disappears when time series are misaligned by flowers on display by an individual on a given day. (b) Population- 1 day or more. Almost all pairs are weakly negatively corre- level display curves for each of the same population and treatment lated at a lag of 3 days (r3 = 0.15), suggesting that 6 days combinations. The height of these lines reflects the proportion of total may be the most common length of time between the pulses flowers on display by that group on a given day. We show data from a subset of populations for visual clarity; see Fig. S1 for the remain- of these display schedules. When repeating this analysis using ing data. simulated, unimodal data, all consistent associations among the display schedules of these groups disappeared (r0 = 0.04, Fig. 3b). Together, these results imply that display schedules

F(1, 20) = 14.51, P < 0.01; Population F(4, 20) = 1.51, P > in Chamaecrista are highly synchronized among these geneti-

0.05; Pollination*Population F(4, 20) = 1.40, P > 0.05). Multi- cally differentiated populations grown under varied thermal ple, simultaneous flowering pulses were still produced when and pollination environments and that this can be attributed to resources were not diverted towards fruit maturation. Thus, the simultaneous pulsing of floral displays. flower pulses in display schedules are not a consequence of periodic diversions of internal resources away from flower RELATIONSHIPS BETWEEN FLOWERING PHENOLOGY production and towards fruit maturation. AND ENVIRONMENTAL VARIABLES The artificially extended flowering durations in experiment 2 (Fig. 2) enabled us to observe phenological overlap Flower pulses can be the result of temporary increases in between populations that were otherwise completely or par- either flower deployment or floral longevity. Logistic regres- tially temporally isolated. Flowering pulses in display sched- sions revealed a negative relationship between average daily ules appear to be aligned between pollinated and unpollinated temperature and floral longevity in experiment 3 (Fig. 4, MN groups, both within and between populations during periods early odds ratio = 0.64, Z = 15.98, P < 0.001; NC early of overlap. The most striking example is the simultaneous odds ratio = 0.52, Z = 32.22, P < 0.001; MN late odds pulsing of the early-flowering MN population when unpolli- ratio = 0.62, Z = 14.49, P < 0.001), with floral longevity nated in experiment 2 and the later-flowering NC population increasing sharply from 1 to 2 days in all populations when from experiment 1, which was planted 0.5 km away (shown temperatures declined below 16–19 °C. Monitoring floral lon- in Fig. 2a). gevity allowed us to distinguish newly deployed flowers from Statistical support for simultaneous pulsing in display all that were on display, and with this distinction, we con- schedules is presented in Fig. 3a. Cross-correlation coeffi- structed deployment schedules for each population. Deploy- cients are shown for the 10 population and treatment compari- ment schedules are multimodal, with pulses of deployed sons with sufficient temporal overlap to allow meaningful flowers occurring simultaneously among populations (Fig. 5 tests. The display schedules of the various populations and and see Figs S2 and S3). Furthermore, flowers retained from experimental treatments of Chamaecrista are significantly previous days also appear to occur in pulses. These data sug- positively correlated at lag 0, with a mean correlation; gest that floral longevity is mediated by temperature in this

−0.5 −0.25 0 0.25 0.5 (a) (b)

S MNHeated−MNAmbient S

S MOHeated−MOAmbient S SS

S MNPollinated−MNUnpollinated

S MNUnpollinated−NCAmbient

S S MNUnpollinated−NCPollinated

S MNUnpollinated−PAUnpollinated

S PAUnpollinated−MOUnpollinated SS

S VAPollinated−NCPollinated S

S VAPollinated−NCUnpollinated S S

S S MNLate−NCEarly S Lag1 Lag0 Lag0 Lag2 Lag4 Lag1 Lag4 Lag5 Lag2 Lag3 Lag5 Lag3

Fig. 3. Heatmaps summarizing cross-correlation coefficients between the display schedules of select populations. (a) Cross-correlations for observed, pulsed display schedules. (b) Cross-correlations for unimodal display schedule simulations. Correlation coefficients were calculated between time series lagged up to 5 days. The colour and shade of a specific box indicates the sign and magnitude of the correlation coefficient, respectively. An S signifies that the correlation was significant.

coefﬁcients between average daily temperatures and popula- 1.0 MN early tion-level display schedules, and repeated analyses with popu- NC early

MN late lation-level deployment schedules. When examining all ﬂowers displayed, we observed a negative correlation (higher

temperatures, fewer flowers) at a lag of 1 (r1 = 0.24) and a positive correlation at a lag of 4 (r4 = 0.25) in all populations 0.5 and treatments across experiments (Fig. 6a). When accounting for temperature-mediated floral longevity, we find deployment schedules to be less consistently correlated to temperatures at = = a lag of 1 and 4 (r 0.12, r 0.15, respectively, 1 4 fl fi Fig. 6b). The uctuation in the sign of correlation coef cients as lags increase may reflect the fluctuations in average daily Proportion of flowers open for two days two Proportion open for of flowers 0.0 temperatures found in both years (Fig. 5d and see Fig. S3k 10 12 14 16 18 20 22 24 for temperature data). In many populations, average daily Average daily temperature (°C) humidity negatively correlated to both display and deploy- = Fig. 4. Logistic regressions relating floral longevity to average daily ment schedules at lag 0 (r0 0.14 and 0.12, respectively) = temperatures in experiment 3. Data from all watering treatments are and lag 4 (r4 0.17 and 0.16, respectively), while there combined within a population and planting time because the treat- were no consistent relationships between precipitation and ments did not significantly affect flower production. Each point repre- display or deployment schedules (see Figs S4 and S5). fl sents the proportion of two-day-old owers on a given day, averaged Altering the watering schedule in experiment 3 did not across all individuals in a population, regressed on the average daily temperatures of the 24 h proceeding flower deployment. The adjusted affect the display schedules of any population (Fig. S6, * = > r-squared values for MN early, NC early and MN late are 0.86, 0.98 Day Treatment MN early F(2, 500) 0.67, P 0.05; NC and 0.62, respectively (per Naglekerke 1991). early F(2, 483) = 1.30, P > 0.05; MN late F(2, 369) = 0.73, P > 0.05). However, there was a strong negative correlation species. Thus, pulses in display schedules are the result of in all three treatments between VWC and display or deploy- pulses of deployed flowers, but can be amplified by the reten- ment schedules at a lag of 0 (r0 = 0.40 and 0.24, respec- tion of day-old flowers when temperatures are low. tively), as well as a positive correlation at a lag of 5

To examine the direct inﬂuence of temperature on ﬂower (r5 = 0.36 and 0.25, respectively, Fig. S7). Soil moisture con- deployment and retention, we calculated cross-correlation tent differed slightly among treatments throughout the season

0.10 ment of ﬂowering pulses is randomized, average S in Chamae- (a) Retained p crista signiﬁcantly decreases to 0.52 (t = 8.24, Deployed (36.2) 0.05 P < 0.001). Variation in the onset and end dates of individual

display schedules can also influence Sp in C. fasciculata by 0.00 affecting the frequency with which early-flowering plants pro- 0.08 (b) duce flowering pulses outside of the display schedules of late

0.04 bloomers and vice versa. However, the average standard deviation in onset and end dates for populations of Chamaecrista 0.00 were not signiﬁcantly different than those of the KSR species

0.08 (c) (t(24.3) = 1.47, P > 0.05 and t(48) = 1.21, P > 0.05, respectively). Together, these results suggest that the levels of Sp 0.04 observed in Chamaecrista are equivalent to those from the Proportion produced daily of flowers KSR species because flowering pulses were produced simulta- 0.00 neously across individuals. 25 (d) The average strength of phenological assortative mating by 20 flowering onset date was significantly lower in Chamaecrista 15 than in other species (q = 0.21 vs. 0.35, respectively; 10 t43 = 2.63, P < 0.01; Fig. 7e, f and see Table S1). When Temperature (°C) Temperature 209 219 229 239 249 259 269 flowering peaks are randomized, q becomes indistinguishable Julian date from that of the other species (q = 0.39 vs. 0.35, respectively, = > fl Fig. 5. The display schedules (a) MN planted early, (b) NC planted t(43) 0.91, P 0.05). Simultaneous owering pulses in C. early and (c) MN planted late populations from experiment 3, and (d) fasciculata may offer more mating opportunities between average daily temperatures. Display schedules are shown with deploy- early- and late-flowering individuals than typically seen in the fl ment schedules highlighted in dark grey and retained owers highlighted phenologies of temperate species, reducing the strength of in light grey. Deployment schedules were estimated from average daily assortative mating by flowering onset date. temperatures using the logistic regression models shown in Fig. 4.

Discussion (Watering treatment F(2, 287) = 3.89, P < 0.05; Day F = 4.42, P < 0.05; Treatment*Day F = 2.58, (1, 287) (2, 287) MULTIMODALITY AND DISPLAY SCHEDULE SHAPE P < 0.10), with greater levels of VWC in the two-week treatment than in the one-week or control treatments. Display Display schedules in Chamaecrista fasciculata are multi- schedules would have differed among treatments if VWC modal, with flowering pulses produced simultaneously among directly influenced flower deployment or retention. It is possi- individuals and populations. In temperate regions, simulta- ble that correlations between display or deployment schedules neous pulsing has only been demonstrated in several and VWC are driven by unmeasured factors that correlate wind-pollinated Juncus species (Michalski & Durka 2007). with VWC (e.g. soil porosity). Chamaecrista is of tropical descent, perhaps evolving from a rain forest tree to a savanna shrub prior to its colonization of temperate zones (de Souza Conceicß~ao et al. 2009). Multimo- SYNCHRONY AND PHENOLOGICAL ASSORTATIVE dality in display schedules is more common in the tropics MATING (Newstrom, Frankie & Baker 1994), and the unique pattern of

Average synchrony among populations, Sp,inC. fasciculata flowering found in C. fasciculata may be explained by its was comparable to that of natural populations of species tropical origin. However, in the tropics, simultaneous flower- located at KSR (Sp = 0.63 vs. 0.66, respectively; t43 = 0.77, ing pulses have only been formally documented in several P > 0.05; Fig. 7a, b and see Table S1), while the average Brazilian Myrtaceae species (Proencßa & Gibbs 1994). It is synchrony within individuals, Si, was significantly higher likely that examples of simultaneous flowering pulses, and (Si = 0.17 vs. 0.14, respectively; t42.5 = 3.41, P < 0.01; multimodality in general, are rare simply because few studies Fig. 7c, d and see Table S1), indicating a greater opportunity have monitored phenology at the level of individuals (Aug- for geitonogamy. spurger 1983) with high enough frequency to capture fluctua- Multimodal display schedules have the potential to drasti- tions in display schedules (Miller-Rushing, Inouye & Primack cally reduce Sp if flowering pulses are misaligned, and the high 2008; Morellato et al. 2010). levels of synchrony we observed can only be maintained if indi- Statistical methods have been developed for describing uni- viduals pulse concurrently. To confirm this, we shuffled the modal display schedules through fitting flexible regression flowering onset dates of all individuals in each population in functions (Malo 2002; Clark & Thompson 2011). However, order to randomize the occurrence of flowering pulses among adequate regression models for display schedules of other individuals. We repeated this randomization 1000 times, recal- shapes may prove elusive, particularly if the number of modes culating Sp for each population, and compare the average of is variable and they occur at irregular intervals. Several these estimates to those of the KSR species. When the align- approaches have been taken to identify modality in display

−0.5 −0.25 0 0.25 0.5 (a) (b)

S MN Heated MN Ambient PA Heated S PA Ambient S S MO Heated S MO Ambient S S S NC Heated S S S NC Ambient S S MN Pollinated S S S MN Unpollinated S S PA Pollinated S S PA Unpollinated SS S MO Pollinated S SS S MO Unpollinated S S S VA Pollinated SS SSVA Unpollinated S S S S NC Pollinated S S S NC Unpollinated S S MN Early S NC Early MN Late Lag0 Lag1 Lag0 Lag2 Lag2 Lag4 Lag1 Lag4 Lag3 Lag3 Lag5 Lag5

Fig. 6. Heatmaps summarizing the cross-correlation coefficients between population-level (a) display schedules or (b) deployment schedules and average daily temperatures. As in Figure 3, the colour and shade of a box reflect the sign and magnitude of the correlation coefficient, respectively, and those with an S indicate significant correlations. For each population, the number of new flowers each day was estimated from that day’s average temperature using the average coefficients from the three logistic regression models in Fig. 4. schedules, including the use of cumulative flowering density temperature dependent (Corbet et al. 1993) and flowers are curves to examine bimodality (Aldridge et al. 2011), left unpollinated (Blair & Wolfe 2007; Elzinga et al. 2007; coefficients of variation to quantify temporal variability in Castro, Silveira & Navarro 2008). Additional work may dis- flower production (Pico & Retana 2001; Michalski & Durka tinguish between mechanisms contributing to variation in flo- 2007) and principal coordinate analyses to distinguish ral longevity (Yasaka, Nishiwaki & Konno 1998) and may between unimodal and bimodal phenologies (Austen, Jackson reveal whether newly deployed and retained flowers have the & Weis 2014). Each method has its own merits and limita- potential to contribute equally to fruit production (e.g. compa- tions, and like the time-series analyses used here, many rable stigma receptivity, pollinator attraction). of these approaches cannot yield concrete estimates for the Temperature may also influence display schedules by trig- number of modes or the dates at which they occur (but see gering the opening of flowers (Fig. 6b). Correlations between Aldridge et al. 2011). The development of methodology display schedules and temperature have been found in other capable of describing variation in modality, and detecting temperate species where the display schedules of individuals significant departures from unimodality, may be necessary for and populations can be multimodal (Pico & Retana 2001; Mi- characterizing many fine- and broad-scale phenological chalski & Durka 2007). However, this is the first attempt to patterns. identify associations between environmental variables and flower deployment independent of display schedules. In these studies, and in ours, interannual variation in temperature pro- CAUSES OF VARIATION IN DISPLAY AND DEPLOYMENT files through the growing season may partially explain varia- SCHEDULES tion in modality within and across years. We observed In C. fasciculata, display schedule shape is likely dictated by temperatures at KSR to fluctuate while generally decreasing environmental conditions. Temperature affected the shape of throughout the growing season (Fig. 5d and see Fig. S3k), display schedules by influencing the life span of individual and our results suggest that the degree of modality in display flowers (Fig. 4), as is seen in other species (Vesprini & Pacini schedules of C. fasciculata may be a plastic response to tem- 2005). This may occur because cooler temperatures preserve perature or a correlated variable at the time of flower bud floral tissues (Primack 1985) or because bee activity is also maturation and flower opening.

7 (a) 11 (c) 7 (e) Frequency

0 0 0

6 (b) 13 (d) 5 (f)

3 Frequency

0 0 0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

Sp Si ρ

Fig. 7. Histograms showing the distribution of estimates of population synchrony (Sp), individual synchrony (Si) and the strength of phenological assortative mating by flowering onset date (q) for all populations and treatments of C. fasciculata from experiments 1–3 (panels a, c and e, respectively) and for the 29 species located at the Koffler Scientific Reserve (panels b, d and f, respectively). Calculations were not made for treatments where pollinators were excluded. Vertical, dotted lines represent the average estimate in each panel.

Flowering pulses in C. fasciculata are not caused by the ing or the occurrence of geitonogamy, this result suggests that intermittent diversion of internal resources away from flower multimodal display schedules at population and individual lev- production and towards fruit maturation, although we found els may not alter the potential for geitonogamy from that of that the total number of flowers and flowering duration were unimodal schedules. However, we might expect large display resource-limited (Fig. 2). Patterns of flower and fruit produc- sizes (‘pulses’) at the individual level to increase opportunities tion are inherently linked because both are constrained by a for geitonogamous selfing if pollinators move less frequently shared resource pool, and adjustments to resource allocation among individuals (Harder & Barrett 1995). On the other hand, can be made via changes in flower production or through pulsed flowering across neighbouring plants may promote out- seed and fruit abortion (Stephenson 1981). Flowering and fru- crossing if pollinators forage among individuals with large dis- iting schedules can also be correlated if fruit development play sizes. In C. fasciculata, levels of individual and times are constant and flowering peaks produce subsequent population synchrony may interact with several aspects of floral pulses of maturing fruit (Rojas-Sandoval & Melendez-Acker- morphology shown to reduce geitonogamy, including herkoga- man 2011). Such tight correlation does not seem to occur in my (Webb & Lloyd 1986), enantiostyly (Fenster 1995; Jesson C. fasciculata, which can hold initiated fruit in stasis for & Barrett 2002) and the presence of a stiff hooded petal that weeks until an unknown stimulus prompts the selective matu- acts as a flight guide (Wolfe & Estes 1992). The net effect of ration of some fruit and abortion of others (Lee & Bazzaz these floral traits and of the shape of population-level display 1982a,b). While patterns of flower and fruit production may schedules may contribute to the high outcrossing rates observed be independent in C. fasciculata, variation in display schedule in several populations of C. fasciculata (~80%, Fenster 1991). shape may influence the rate and timing of fruit and seed dis- The display schedules observed here may also partially persal or predation in other species (Mahoro 2002). explain reports of population structure in Chamaecrista.In this species, pollen movement is localized and populations are subdivided into small patches of related individuals (Fenster POPULATION AND INDIVIDUAL SYNCHRONY 1991). Flowering pulses in neighbouring plants produce large Population synchrony was, on average, indistinguishable from floral displays that may encourage pollinators to forage pri- that of the 29 species studied at KSR (Fig. 7a,b), while esti- marily within small groups of individuals. Coupled with short mates of individual synchrony for C. fasciculata were only seed dispersal distances (Fenster 1991), the effects of popula- slightly higher than the KSR species (Fig. 7c,d). If display tion-level flowering pulses on pollinator movements may gen- schedule shape were the only determinant of rates of outcross- erate this fine-scale population structure.

PHENOLOGICAL ASSORTATIVE MATING, NATURAL Augspurger, C.K. (1983) Phenology, flowering synchrony, and fruit set of six SELECTION AND SCHEDULE SHAPE neotropical shrubs. Biotropica, 15, 257–267. Austen, E.J., Jackson, D.A. & Weis, A.E. (2014) Describing flowering schedule Patterns of selection and assortative mating guide the evolu- shape through multivariate ordination. International Journal of Plant Sci- ences, 175,70–79. tion of phenological traits (Fox 2003). If plant mating is Bazzaz, F.A., Chiariello, N.R., Coley, P.D. & Pitelka, L.F. (1987) Allocating assortative by flowering onset, the genetic variance for flower- resources to reproduction and defense. BioScience, 37,58–67. ing onset (and correlated traits) will be inflated, accelerating Blair, A.C. & Wolfe, L.M. (2007) The association between floral longevity and pollen removal, pollen receipt, and fruit production in the flame azalea (Rho- responses to natural selection. The shape of display schedules dodendron calendulaceum). Canadian Journal of Botany, 85, 414–419. dictates the potential for genetic exchanges among individu- Bonser, S.P. & Aarssen, L.W. (1996) Meristem allocation: a new classification als, and plasticity in schedule shape can influence the degree theory for adaptive strategies in herbaceous plants. Oikos, 77, 347–352. Box, G. & Jenkins, G. (1970) Time Series Analysis: Forecasting and Control. of phenological assortative mating within population (Weis Holden-Day, San Francisco. 2005). This, in turn, can alter the effectiveness of selection on Brown, C.J., Schoeman, D.S., Sydeman, W.J., Brander, K., Buckley, L.B., Bur- phenological (or correlated) traits (Fox 2003). rows, M., Duarte, C.M., Moore, P.J., Pandolfi, J.M., Poloczanska, E., Ven- ables, W. & Richardson, A.J. (2011) Quantitative approaches in climate We found the average strength of phenological assortative change ecology. Global Change Biology, 17, 3697–3713. mating by flowering onset date to be lower in Chamaecrista than Bustamante, E. & Burquez, A. (2008) Effects of plant size and weather on the in other species found at KSR. Simultaneous flowering pulses flowering phenology of the organ pipe cactus (Stenocereus thurberi). Annals of Botany, 102, 1019–1030. offer greater mating opportunities among individuals with dis- Castro, S., Silveira, P. & Navarro, L. (2008) Effect of pollination on floral lon- tinct flowering onset dates, that is mating is closer to random. If gevity and costs of delayed fertilization in the out-crossing Polygala vayre- display schedule shape is partially plastic, as our results suggest, dae Costa (Polygalaceae). Annals of Botany, 102, 1043–1048. Chan, K. & Ripley, B. (2012) Time Series Analysis (R Package, Version 1.01). and mating opportunities are environmentally dictated, the Available at: www.cran.r-project.org/web/packages/TSA/index.html potential evolutionary responses of flowering onset date to selec- Clark, R.M. & Thompson, R. (2011) Estimation and comparison of flowering tion may be reduced and may vary among years or among popu- curves. Plant Ecology & Diversity, 4, 189–200. Colombo, S.J., McKenney, D.W., Lawrence, K.M. & Gray, P.A. (2007) Cli- lations experiencing contrasting environmental conditions. mate Change Projections for Ontario: Practical Information for Policymakers While the genetic and environmental influences on flowering and Planners. Climate Change Research Report CCRR-05, Ontario Ministry onset are well known in several model systems (Mouradov, Cre- of Natural Resources, Queen’s Printer for Ontario, Peterborough, Ontario, Canada. mer & Coupland 2002; Putterill, Laurie & Macknight 2004), it Corbet, S.A., Fussell, M., Ake, R., Fraser, A., Gunson, C., Savage, A. & is often assumed that components of display schedules are genet- Smith, K. (1993) Temperature and the pollinating activity of social bees. ically or developmentally correlated with flowering onset. Here, Ecological Entomology, 18,17–30. fl Diggle, P.K. (1995) Architectural effects and the interpretation of patterns of we have shown evidence that ower display and deployment fruit and seed development. Annual Review of Ecology and Systematics, 26, may be plastic in response to temperature or a correlated variable 531–552. in Chamaecrista, producing distinct, multimodal display sched- Easterling, D.R., Horton, B., Jones, P.D., Peterson, T.C., Karl, T.R., Parker, D.E., Salinger, M.J., Razuvayev, V., Plummer, N., Jamason, P. & Folland, ules in alignment with the thermal regimes typically experienced C.K. (1997) Maximum and minimum temperature trends for the globe. Sci- at KSR. Detailed phenological data from other systems may ence, 277, 364–367. reveal that responses to daily fluctuations in the environment are Elzinga, J.A., Atlan, A., Biere, A., Gigord, L., Weis, A.E. & Bernasconi, G. (2007) Time after time: flowering phenology and biotic interactions. more widespread than currently appreciated. Chamaecrista fas- TRENDS in Ecology and Evolution, 22, 432–439. ciculata may prove to be an excellent candidate for understand- Fenster, C.B. (1991) Gene flow in Chamaecrista fasciculata (Leguminosae) I. ing the ecological and evolutionary causes and consequences of Gene dispersal. Evolution, 45, 398–409. Fenster, C.B. (1995) Mirror image flowers and their effect on outcrossing rate variation in display schedule shape. in Chamaecrista fasciculata (Leguminosae). American Journal of Botany, 82,46–50. Foote, L.E. & Jackobs, J.A. (1966) Soil factors and the occurrence of partridge Acknowledgements pea (Cassia fasciculata Michx.) in Illinois. Ecology, 47, 968–975. Fox, G.A. (2003) Assortative mating and plant phenology; evolutionary and We thank Johnny Randall (North Carolina Botanical Gardens, University of practical consequences. Evolutionary Ecology Research, 5,1–18. North Carolina-Chapel Hill, USA) and John Stanton-Geddes (University of Garrish, R.S. & Lee, T.D. (1989) Physiological integration in Cassia fascicula- Minnesota, USA) for seed collection and John Jensen, Yehong She, Caitlin ta Michx.: inflorescence removal and defoliation experiments. Oecologia, 81, fi Patterson, Tommy Oliver and Rick Berry for valuable eld assistance. We also 279–284. thank David Inouye, Kenneth Whitney, Mark Rees and one anonymous refer- Harder, L.D. & Barrett, S.C.H. (1995) Mating costs of large floral displays in ence for helpful comments on previous versions of this manuscript. This work hermaphrodite plants. Nature, 373, 512–515. was supported by Leaders Opportunity Fund grants from the Canada Founda- Herrera, J. (1986) Flowering and fruiting phenology in the coastal shrublands tion for Innovation and the Ontario Research Fund, and a Discovery Grant of Donana,~ south Spain. Vegetatio, 68,91–98. from the National Science and Engineering Research Council. Hudson, I.L. (2010) Interdisciplinary approaches: towards new statistical methods for phenological studies. Climate Change, 100, 143–171. Ims, R.A. (1990) The ecology and evolution of reproductive synchrony. Trends Data accessibility in Ecology & Evolution, 5, 135–139. Irwin, H.S. & Barneby, R.C. (1982) The American Cassiinae: A Synoptical Data deposited in the Dryad repository: (Wadgymar et al. 2014). Revision of Leguminosae Tribe Cassieae Subtribe Cassiinae in the New World. Memoirs of the New York Botanical Garden, 35, The New York Botanical Garden, New York. References Ison, J.L., Wagenius, S., Reitz, D. & Ashley, M.V. (2014) Mating between Echinacea angustifolia (Asteraceae) individuals increases with their flowering Aldridge, G., Inouye, D.W., Forrest, J.R.K., Barr, W.A. & Miller-Rushing, A.J. synchrony and spatial proximity. American Journal of Botany, 101, 180–189. fl (2011) Emergence of a mid-season period of low oral resources in a mon- Jesson, L.K. & Barrett, S.C.H. (2002) Solving the puzzle of mirror-image flow- tane meadow ecosystem associated with climate change. Journal of Ecology, ers. Nature, 417, 707. 99, 905–913.

Kimball, B.A., Conley, M.M., Wang, S., Xingwu, L., Luo, C., Morgan, J. & Webb, C.J. & Lloyd, D.G. (1986) The avoidance of interference between the Smith, D. (2008) Infrared heater arrays for warming ecosystem field plots. presentation of pollen and stigmas in angiosperms. II. Herkogamy. New Zea- Global Change Biology, 14, 309–320. land Journal of Botany, 24, 163–178. Lee, T.D. & Bazzaz, F.A. (1982a) Regulation of fruit and seed production in Weis, A.E. (2005) Direct and indirect assortative mating: a multivariate approach an annual legume, Cassia fasciculata. Ecology, 63, 1363–1373. to plant flowering schedules. Journal of Evolutionary Biology, 18, 536–546. Lee, T.D. & Bazzaz, F.A. (1982b) Regulation of fruit maturation pattern in an Weis, A.E. & Kossler, T.M. (2004) Genetic variation in flowering time induces annual legume, Cassia fasciculata. Ecology, 63, 1374–1388. phenological assortative mating: quantitative genetic methods applied to Lloyd, D.G. (1980) Sexual strategies in plants I. An hypothesis of serial adjust- Brassica rapa. American Journal of Botany, 91, 825–836. ment of maternal investment during one reproductive session. New Phytolo- Weis, A.E., Nardone, E. & Fox, G.A. (2014) The strength of assortative mating gist, 86,69–79. for population date and its basis in individual variation in flowering schedule. Loveless, M.D. & Hamrick, J.L. (1984) Ecological determinants of genetic struc- Journal of Evolutionary Biology, 27, 2138–2151. ture in plant populations. Annual Review of Ecology and Systematics, 15,65–95. Wolfe, A.D. & Estes, J.R. (1992) Pollination and the function of floral parts Mahoro, S. (2002) Individual flowering schedule, fruit set, and flower and seed in Chamaecrista fasciculata (Fabaceae). American Journal of Botany, 79, predation in Vaccinium hirtum Thunb. (Ericaceae). Canadian Journal of Bot- 314–317. any, 80,82–92. Yasaka, M., Nishiwaki, Y. & Konno, Y. (1998) Plasticity in flower longevity Malo, J.E. (2002) Modelling unimodal flowering phenology with exponential in Corydalis Ambigua. Ecological Research, 13, 211–216. sine equations. Functional Ecology, 16, 413–418. Young, H.J. (1988) Neighborhood size in a beetle pollinated tropical aroid: Michalski, S.G. & Durka, W. (2007) Synchronous pulsed flowering: analysis of effects of low density and asynchronous flowering. Oecologia, 76, 461–466. the flowering phenology in Juncus (Juncaceae). Annals of Botany, 100, Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A. & Smith, G.M. (2009) 1271–1285. Mixed Effects Models and Extensions in Ecology With R. Springer, New Miller-Rushing, A.J., Inouye, D.W. & Primack, R.B. (2008) How well do York, NY. fi fl rst owering dates measure plant responses to climate change? The Received 29 July 2014; accepted 15 December 2014 effects of population size and sampling frequency. Journal of Ecology, 96, Handling Editor: Kenneth Whitney 1289–1296. Morellato, L.P.C., Camargo, M.G.G., Neves, F.F.D., Luize, B.G., Mantovani, A. & Hudson, I.L. (2010) The Influence of Sampling Method, Sample Size, and Frequency of Observations on Plant Phenological Patterns and Interpre- Supporting Information tation in Tropical Forest Trees. Phenological Research, Springer, New York. Additional Supporting Information may be found in the online ver- Mouradov, A., Cremer, F. & Coupland, G. (2002) Control of flowering time: inter- acting pathways as a basis for diversity. The Plant Cell, 14, S111–S130. sion of this article: Newstrom, L.E., Frankie, G.W. & Baker, H.G. (1994) A new classification for plant phenology based on flowering patterns in lowland tropical rain forest Figure S1. Individual- and population-level display schedules from – trees at La Selva, Costa Rica. Biotropica, 26, 141 159. experiment 1 not included in the main text. Parmesan, C. & Yohe, G. (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421,37–42. Pico, F.X. & Retana, J. (2001) The flowering pattern of the perennial herb Lob- Figure S2. Display and deployment schedules for populations and ularia maritima: an unusual case in the Mediterranean basin. Acta Oecolog- treatments in experiment 1. ica, 22, 209–217. Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. (2014) Nlme: Linear and Non- Figure S3. Display and deployment schedules for populations and linear Mixed Effects Models. (R package, version 3.1-117). Available at www.cran.r-project.org/web/packages/nlme/index.html treatments in experiment 2. Primack, R.B. (1978) Regulation of seed yield in Plantago. Journal of Ecology, 66, 835–847. Figure S4. Heatmaps summarizing cross-correlation coefficients fl Primack, R.B. (1985) Longevity of individual owers. Annual Review of Ecol- between display or deployment schedules and humidity. ogy and Systematics, 16,15–37. Proencßa, C.E.B. & Gibbs, P.E. (1994) Reproductive biology of eight sympatric Myrtaceae from central Brazil. New Phytologist, 126, 343–354. Figure S5. Heatmaps summarizing cross-correlation coefficients Putterill, J., Laurie, R. & Macknight, R. (2004) It’s time to flower: the genetic between display or deployment schedules and precipitation. control of flowering time. BioEssays, 26, 363–373. R Development Core Team (2014) R: A Language and Environment for Sta- Figure S6. Display schedules for the watering treatments applied in tistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, Avaliable at http://www.R-project.org. experiment 3. Rabinowitz, D., Rapp, J.K., Sork, V.L., Rathcke, B.J., Reese, G. & Weaver, J.C. (1981) Phenological properties of wind- and insect-pollinated prairie Figure S7. Heatmaps summarizing cross-correlation coefficients – plants. Ecology, 62,49 56. between display or deployment schedules and volumetric water con- Rojas-Sandoval, J. & Melendez-Ackerman, E. (2011) Reproductive phenology of the Caribbean cactus Harrisia portoricensis: rainfall and temperature asso- tent in experiment 3. ciations. Botany-Botanique, 89, 861–871. de Souza Conceicß~ao, A., de Queiroz, L.P., Lewis, G.P., de Andrade, M.J.G., de Figure S8. Display schedules for the 29 species located at KSR. Almeida, P.R.M., Schnadelbach, A.S. & van den Berg, C. (2009) Phylogeny of Chamaecrista Moench (Leguminosae-Caesalpinioideae) based on nuclear Figure S9. Estimates of S as the duration of flowering increases for and chloroplast DNA regions. Taxon, 58, 1168–1180. i Stanton, M.L., Bereczky, J.K. & Hasbrouck, H.D. (1987) Pollination thorough- four example display schedules. ness and meternal yield regulation in wild radish, Raphanus raphanistrum (Brassicaceae). Oecologia, 74,68–76. Figure S10. Example display schedules with variable shape, duration, Stephenson, A.G. (1981) Flower and fruit abortion: proximate causes and ulti- and total number of flowers, and their corresponding estimates of Si. mate functions. Annual Review of Ecology and Systematics, 12, 253–279. Thorp, R.W. & Estes, J.R. (1975) Intrafloral behavior of bees and flowers of Cas- sia fasciculata. Journal of the Kansas Entomological Society, 48, 175–184. Figure S11. Estimates of Si for four example display schedules as the Vesprini, J.L. & Pacini, E. (2005) Temperature-dependent floral longevity in interval between flower counts varies from 1 day to 6 days. two Helleborus species. Plant Systematics and Evolution, 252,63–70. Wadgymar, S.M., Austen, E.J., Cumming, M.N. & Weis, A.E. (2014) Data Table S1. Estimates of population synchrony, individual synchrony, from: Simultaneous pulsed flowering in a temperate legume: causes and consequences of multimodality in the shape of floral display schedules. Dryad and the strength of phenological assortative mating for all populations Digital Repository, doi: 10.5061/dryad.vt55c. of Chamaecrista fasciculata and for the 29 species located at KSR.