DOI: 10.5061/dryad.n8pk0p34b
title: Evolution of invasion syndrome in invasive goldenrod is not constrained by genetic trade-offs

description: A suite of plant traits is thought to make weed populations highly
 invasive, including vigorous growth and reproduction, superior competitive
 ability, and high dispersal ability. Using a breeding design and a common
 garden experiment, we tested whether such an “invasion syndrome” has
 evolved in an invasive range of Solidago altissima, and whether the
 evolution is likely to be genetically constrained. We found an overall
 shift in invasive phenotypes between native North American and invasive
 Japanese populations. The invasive populations were taller and produced
 more leaves, suggesting a superior ability to exploit limited resources.
 The populations also produced more allelopathic compounds that can
 suppress competitor growth. Finally, invasive populations produced more
 seeds, which are smaller and are released from a greater height,
 indicating a potential for superior dispersal ability than the native
 populations. Quantitative genetics analyses found a large amount of
 additive genetic variation in most focal traits across native and invasive
 populations, with no systematic differences in its magnitude between the
 ranges. Genetic covariances among three traits representing invasion
 strategies (leaf mass, polyacetylene concentration and seed size) were
 small. The R metric, which measures the effect of genetic covariances on
 the rate of adaptation, indicated that the covariance neither constrains
 nor accelerates concerted evolution of these traits. The results suggest
 that the invasion syndrome in S. altissima has evolved in the novel range
 due to ample additive genetic variation, and relatively free from genetic
 trade-offs.
description: <strong>Study system</strong> The
 tall goldenrod, <em>Solidago altissima</em> L. (Asteraceae),
 is a perennial forb native to eastern North America (Werner et al., 1980)
 and is a dominant species of old fields and disturbed habitats.
 <em>Solidago altissima</em> was first introduced into Japan in
 the late 1890s as an ornamental plant but only became widespread across
 the country since the 1980s (Fukuda, 1982). Current molecular data
 suggests that invasive Japanese <em>S. altissima</em>
 populations are likely to be introduced primarily from south-eastern North
 America (Sakata et al., 2015), which may overlap with the distribution of
 a putative <em>S. altissima</em> variety,
  <em>pluricephala</em> that has also invaded other parts of
 Asia (Semple et al., 2015). The Japanese populations seem to be founded by
 multiple introduction events, resulting in similar levels of genetic
 diversity within a population at neutral markers as in the native
 populations (Sakata et al., 2015; Uesugi et al., 2020). Moreover,
 broad-sense genetic variation for herbivore resistance traits did not
 differ between the US and Japanese populations (Sakata et al., 2020),
 suggesting that evolution in introduced Japanese populations may not be
 genetically constrained. However, additive genetic variation and
 covariation among traits associated with competitive and dispersal
 abilities has not been examined previously. In its
 native range, a diverse group of specialist and generalist herbivores
 keeps the <em>S. altissima</em> populations in check (Root,
 1996), whereas in the invaded Japanese range, the plant generally escapes
 herbivory, except from <em>Uroleucon</em>
 <em>nigrotuberculatum </em>(a Solidago specialist aphid
 introduced in 1990’s, Cappuccino, 1987; Sugimoto & Matsumoto,
 2000) and <em>Corythucha marmorata</em> (an Asteraceae
 specialist lacebug introduced to Japan in 2000, Kato & Ohbayashi,
 2009; Sakata et al., 2014). Upon establishment in a field patch,
 <em>S. altissima</em> can rapidly grow tall and shade out
 neighbouring plants (Root, 1996), while suppressing germination and growth
 of competitor plants through allelopathy (Kobayashi et al., 2008; Johnson
 et al., 2010; Uesugi et al., 2019). It produces several compounds of
 polyacetylenes in roots, which are released into the soil at a
 concentration known to inhibit the growth of several other plant species
 (Kobayashi et al., 2008; Johnson et al., 2010; Uesugi & Kessler,
 2013; Uesugi et al., 2019). Within a patch, <em>S.
 altissima</em> primarily spreads through rhizome production
 (Hartnett & Bazzaz, 1983), but colonization of new patch relies on
 wind-born seeds that are produced in abundance (Uesugi et al.,
 2020). <strong>Experimental
 design</strong> <em>Seed sources and
 quantitative genetic breeding design: </em> Seeds
 were collected in 2016 from three native populations in the south-eastern
 United States within the known range of <em>S. altissima</em>
 var. <em>pluricephala</em> where invasive Japanese populations
 are likely to have originated (Durham, NC, Spartanburg, SC, and Murrells
 Inlet, SC; Sakata et al., 2015, Suppl. Table 1). Three invasive
 populations in Japan were sampled across a similar latitudinal range
 (Utsunomiya, Shizuoka, and Otsu). A principal component analysis (PCA) of
 variation in 19 WorldClim climatic variables among sampled populations
 indicated that invasive Japanese populations generally experience wetter
 summers and drier winters than native US populations (PC1, explaining 52.9
 % of variance). Within each range, our study populations varied across a
 temperature gradient (PC2, explaining 35.5% of variance), with both ranges
 spanning similar PC2 coordinates (Suppl. Fig. 1, Suppl. Table 1 &
 2).   Within each population, we collected seeds from
 ~50 maternal plants, which grew at least 10 m apart in the field to
 minimize the probability of sampling the same genetic individual multiple
 times. We germinated ~10 seeds per maternal plant in a common greenhouse
 environment (temperature at 18-30 °C and relative humidity at 60%) at
 Monash University, Victoria, Australia, and grew a single individual per
 maternal family in 12-cm pots with potting mix with Osmocote. Each
 population resulted in 18-35 parental (P<sub>1</sub>)
 individuals (Suppl. Table 1). P<sub>1</sub>
 individuals from each population were split into groups of five
 individuals (4-7 replicated groups per population) and crossed within a
 group using a partial diallel design with reciprocals but no selfing
 (Lynch & Walsh, 1998). Prior to the anthesis, we bagged branches
 of inflorescence (~ 5cm long) with nylon bags to avoid accidental pollen
 transfer. When &gt; 50 % of the flowers on a branch had opened, we
 removed the branch of the sire plant, and rubbed it against an intact
 branch on a dam plant. Each plant sired four other individuals within the
 group and received pollen from the four. We also made a self-pollination
 cross as a negative control (because <em>S. altissima</em> is
 a self-incompatible species, self-crosses did not produce any viable
 seeds). This resulted in total of 760 crosses (20 crosses per group x 38
 groups) across the populations. <em>Common garden
 experiment: </em> In 2017, three viable achenes
 from each cross were selected, and photographed under a dissecting scope
 for later examination of achene size (see “<em>Seed dispersal
 ability</em>” below). Seeds of the F<sub>1</sub>
 generation were germinated as above, and two seedlings per cross were
 transplanted to individual 10-cm diameter pots. A lack of germination from
 some crosses resulted in a total of 1324 experimental individuals. Potted
 plants were placed in evenly spaced trays of 17 pots, across six benches
 (“blocks”) in the greenhouse as above. Trays were rotated weekly to
 mitigate effects of microclimate within the greenhouse until
 flowering. <strong>Trait
 measurements</strong> We measured 10 traits that
 are thought to mediate plant invasiveness, including growth rate, maximum
 stem height, specific leaf area (SLA), leaf mass, rhizome mass,
 inflorescence mass, days to first flower, flowering duration, root
 polyacetylene concentration, and seed size.
 <em>Growth, morphology, and phenology</em>:<em>
 </em> We estimated the relative growth rate of
 individual plants by measuring height on week 8 and week 10 after
 transplanting, which corresponded with the period of rapid vertical
 growth. Relative growth rate was determined as ([height at week 10 – week
 8]/14 days). In week 15, we collected three fully expanded leaves from
 each plant at the height of 60 cm above the base. Each set of three leaves
 was scanned together, dried for 48 hours at 50 °C and weighed. Leaf area
 was measured using ImageJ (version 1.51), and specific leaf area (SLA) was
 calculated as [fresh leaf area/dry mass]. To estimate
 flower onset and flowering duration, we checked plants daily during the
 flowering period (week 14 through 22) and marked the dates we observed the
 first flower and the last senesced flower. Flowering duration for each
 plant was calculated as days from flowering onset to final day of
 flowering. We harvested plant biomass between weeks 22 and 25 as
 individual plants finished flowering. Aboveground biomass was separately
 harvested for leaves, stems, inflorescence, and ramets. We used
 inflorescence mass as a proxy for seed production (Root, 1996). We were
 unable to directly estimate seed production because <em>S. altissima
 </em>is an insect-pollinated, self-incompatible species, and does
 not naturally set seeds in the greenhouse. Belowground rhizomes were
 harvested by removing roots and washed in water. All harvested samples
 were dried for 48 hours at 50 °C before weighing.
 <em>Polyacetylene analysis</em>:
 Root samples were collected for polyacetylene analysis in week 15
 by removing a subsample of root tissues from each plant. Root samples were
 flash frozen in liquid nitrogen and stored in -80 °C for later analysis.
 Following Uesugi et al. (2019), approximately 200 mg fresh weight of root
 tissue per sample was crushed with mortar and pestle in liquid nitrogen,
 sonicated in extraction buffer (1ml of 90% methanol) for 6 min, and left
 in the dark at room temperature for 24h. Samples were centrifuged, and 0.5
 ml aliquot was filtered with 0.45 mm syringe filter. The samples were
 analysed with high-performance liquid chromatography (HPLC) at Monash
 University using Agilent Infinity 1260 equipped with C18 reserve-phase
 column (ED-C18, 2.7μm, 150×3.0mm). The elution method was: 0–5min, 0–20%
 of acetonitrile; 5–25min, 20–95% of acetonitrile and 25–30min, 95% of
 acetonitrile, with a flow rate of 0.5ml min<sup>–1</sup> and
 injection volume of 2µl. Peaks of polyacetylene compounds were identified
 using UV spectra and quantified at 254 nm (Uesugi et al., 2019).
 <strong>Seed dispersal ability </strong>
 Dispersal ability of wind-dispersed seeds increases with the time
 they spend airborne (i.e., longer time taken to drop to the ground; Cody
 & Overton, 1996; Tabassum & Bonser, 2016). To test if seed
 morphology correlates with drop time, we conducted a separate experiment,
 where we measured the drop time, achene size and pappus length for 60
 randomly selected seed samples. We measured the time taken for a seed to
 fall a 50 cm distance down a glass cylinder (10 cm diameter) by
 videotaping and analysing the footage with BORIS software (Friard
 & Gamba, 2016). Each seed sample was dropped twice, and the
 average was used for subsequent analysis. The seed was then photographed
 under dissecting scope, and the achene area (hereafter referred as ‘seed
 size’ for simplicity) and the maximum pappus length were estimated using
 ImageJ. Seed size and pappus length were positively
 correlated (linear regression: <em>coefficient </em>= 0.56,
 <em>t </em>= 4.7, <em>P</em> &lt; 0.0001). A
 multiple regression analysis with model selection using partial
 <em>F</em>-test showed that seed size alone was the best
 predictor of seed drop time (<em>coefficient</em> = -0.75,
 <em>t</em> =2.8, <em>P</em> = 0.006,
 <em>R<sup>2</sup></em> = 0.14). Thus, we used seed
 size as a proxy for seed dispersal ability (i.e., smaller seed size
 increases seed dispersal ability) in the subsequent analyses.
 Seed size of plants used in the common garden experiment was
 estimated using the photographs taken prior to seed germination (see
 above) using ImageJ. For each cross family, the size of the three largest
 seeds out of five were measured. Because seeds were bulk germinated for
 each maternal family, we could not directly link the seed measurement with
 a specific individual plant sample used in the experiment.
 <strong>Statistical analysis</strong>
 All statistical analyses were undertaken using R in the RStudio
 environment (version 3.4.3) (R Core Team, 2017).
 <em>Divergence in multivariate invasive phenotypes:
 </em> We tested for overall trait differences
 between native and invasive populations using a multivariate linear mixed
 model, fitted with the <em>lmer</em> function of the
 <em>lme4</em> package (Bates et al., 2014). To include seed
 size in the multivariate model, we used the family means for all trait
 values in this analysis. The response variables included standardized
 values (scaled to mean = 0, standard deviation = 1) of 10 focal traits. We
 used trait, range (native or introduced), and their interactions as fixed
 effects, and dam and sire as random effects. In this model, we also
 included source populations’ climatic variables representing variation in
 temperature within each range (PC2 in Supp. Table 1 & 2) as a
 fixed covariate to control for clinal divergence within each range
 (Colautti et al., 2009, Woods & Sultan, 2022). In a separate
 model, we tested variability among populations within each range by
 modelling as above, but populations as nested within a range. PC2 of
 source populations was removed from this model, as the values were
 identical for all individuals within a population. Significance of fixed
 effects was evaluated using Wald
 <em>χ</em><sup>2</sup> tests.
 All interaction terms that included trait effect were significant
 (see Results), indicating that traits were differentially affected by
 plant origin. Thus, we conducted univariate analyses with linear mixed
 models as above with range and PC2 as fixed effects, and dam and sire as
 random effect. We then contrasted populations in univariate models, where
 population was modelled as nested within a range as a fixed effect, and
 conducted post hoc tests using the <em>emmeans</em> function
 (<em>emmeans</em> package). 
 <em>Estimation of univariate genetic variances:
 </em> To explore the amount of genetic variance
 in each of the 10 focal traits within each population, we first conducted
 univariate analyses by fitting a linear mixed model with ASReml-R (Butler
 et al., 2009). Data were scaled to mean = 0, and standard deviation = 1
 prior to analysis (Bolstad et al., 2014). Additive genetic variance in
 each trait was estimated from pedigree information (i.e., parental
 identities in the crosses). We also included family identities (where
 offspring of reciprocal crosses belong to the same family) to estimate
 non-additive genetic variance, which includes variance due to dominance
 and epistasis (Lynch & Walsh, 1998). Block (i.e., greenhouse
 benches) was included as a fixed effect to control for variability in
 greenhouse environment. <em>Correlation between
 genetic variance and the magnitude of trait divergence:
 </em> We tested if the traits that harbour more
 additive genetic variance (V<sub>A</sub>) diverged more
 between native and invasive ranges (Opedal et al., 2023). The magnitude of
 trait divergence was estimated as the standardized range effect for each
 trait from the above analysis (“<em>Divergence in multivariate
 invasive phenotypes”</em>). The amount of V<sub>A</sub>
 for each trait was determined as the median of the mean
 V<sub>A</sub> for the six populations calculated above
 (“<em>Estimation of univariate genetic variances”</em>). We
 explored the relationship between divergence and V<sub>A</sub>
 using a linear regression analysis.  
 <em>Estimation of genetic (co)variances among invasive
 traits: </em> Genetic covariances among traits
 can either constrain or facilitate trait divergence between the ranges.
 Our limited sample sizes for each population precluded us from examining
 the multivariate genetic (co)variance structure for all 10 traits, thus we
 used a subset of traits to examine genetic variances in a multivariate
 sense. We selected three traits representing invasive strategies—leaf mass
 representing exploitative competition, polyacetylene concentration for
 interference competition, and seed size for dispersal ability—to test
 whether genetic covariances among these traits would constrain evolution
 of invasion syndrome. These traits showed significant or marginal levels
 of additive genetic variation in all six populations with univariate
 analyses (Fig. 2), allowing us to estimate genetic covariances among
 them. We generated genetic (co)variance matrices
 (<strong><em>G</em></strong>-matrices) for each of
 the six populations in a Bayesian framework using an animal model with the
 <em>MCMCglmm</em> package (Hadfield, 2010). As in the
 univariate analysis with ASReml-R, random effects included pedigree
 information and family identities, and block effect was modelled as a
 fixed effect. The model in matrix form was: where y was
 the vector of observations on multivariate traits, 
 <strong> </strong>was the design matrix for the
 fixed effect () of blocks within the greenhouse,
  and  were design matrices for the random effects
 estimating additive genetic variance (; from the pedigree
 information) and non-additive genetic variance (; modelled as
 ‘family identity’ as above), respectively. The residual () was
 an unstructured covariance matrix containing the variances of (and
 covariances between) leaf mass and polyacetylenes, but because we were
 unable to assign seed size to individual plants (seeds were germinated in
 bulk for each maternal family), we fixed the covariance between seed size
 and all other traits to zero.  We used half-Cauchy
 distributions with weakly informative priors based on phenotypic variances
 of the three traits. Posterior distributions were sampled from 3,150,000
 Markov chain Monte Carlo (MCMC) iterations sampled every 3000 iterations
 after an initial burn-in of 150,000 iterations. We checked convergence
 from plots of traces and posterior distributions, and calculated
 autocorrelations between samples (all were below the recommended level of
 0.1, yielding an effective sample size close to 1,000 for each parameter).
 We used 1,000 MCMC samples to estimate the mean and 90% HPD intervals for
 additive genetic covariances of the three traits for each
 population.    <em>Does genetic covariances
 constrain evolution in an invasive range?</em>
 The theory of resource allocation trade-offs predicts that
 evolution of invasion syndrome may be constrained by negative genetic
 correlations among invasive traits in the introduced range. In contrast,
 their positive genetic correlations would facilitate concurrent evolution
 of invasive phenotypes. We tested how the genetic covariance structure
 might affect population’s “evolvability”—an evolutionary trajectory of a
 population relative to the direction favoured by selection (Hansen
 & Houle, 2008) in the invasive range of <em>S.
 altissima</em> (hereafter, “novel selection”). A relative size of
 evolvability estimated with an observed full
 <strong><em>G</em> </strong>matrix, over
 evolvability estimated with a partial
 <strong><em>G</em></strong> matrix without genetic
 covariances (<em>R</em>-score), would indicate the
 contribution of covariances on multivariate trait evolution (Agrawal
 & Stinchcombe 2009). Following Hansen and Houle
 (2008), evolvability  in the direction of selection gradients
 can be calculated as: where  is
 the angle between the vector representing a selection gradient,
 , and the evolutionary response, i.e.,  Based on the
 observed pattern of phenotypic divergence in the focal traits between
 native and invasive <em>S. altissima</em> populations, we
 assumed that novel selection would favour increased values of leaf mass
 and polyacetylene concentration, and decreased values of seed size that
 enhance dispersal ability when smaller. Because the novel selection
 gradients, , are unknown in this system, we simulated them by
 randomly generating a selection gradient for each trait, while setting
 their signs corresponding to the above expectations (Hangartner et al.,
 2020). To estimate the uncertainty in evolvability estimates, we used
 1,000 MCMC samples of <strong><em>G</em></strong>
 in each population and <strong> </strong>generated
 for each of the <strong><em>G</em></strong>
 samples. The metric <em>R </em>(Agrawal & Stinchcombe,
 2009) was calculated as: [(evolvability calculated with full
 <strong><em>G</em></strong> matrix) /
 (evolvability with all off-diagonal elements in
 <strong><em>G</em> </strong>set to zero)].
 <em>R</em> &lt; 1 implies that covariances constrain the
 evolution of traits in the direction of selection, whereas
 <em>R</em> &gt; 1 implies that covariances facilitate
 evolution. We calculated 1,000 samples of <em>R</em> values
 and tested against 1 using 90% HPD intervals.
 <em>Estimation of genetic covariances among reproductive
 traits: </em> Examining the relationship between
 V<sub>A</sub> and trait divergence revealed that rhizome mass
 and seed size each had ample V<sub>A</sub> within each
 population but showed relatively low magnitude of trait divergence between
 the ranges (see Results, Fig. 3). As a post hoc test, we explored the
 hypothesis that evolution of rhizome mass and seed size may be constrained
 by genetic trade-offs between other reproductive traits, such as days to
 first flower. We selected flowering phenology as a trait representing
 sexual reproduction, as later flowering plants tend to achieve larger
 aboveground biomass and produce more inflorescence (and are therefore
 assumed to produce more seeds) than early flowering plants (see Results).
 The flowering trait also had high V<sub>A</sub> within each
 population (except for Utsunomiya), allowing us to estimate co-variances
 with rhizome mass and seed size. The same analyses of genetic covariances
 and <em>R</em>-scores for this set of traits were conducted as
 above.
description: # Evolution of “invasion syndrome” in invasive goldenrod is not
 constrained by genetic trade-offs
 [https://doi.org/10.5061/dryad.n8pk0p34b](https://doi.org/10.5061/dryad.n8pk0p34b) This dataset contains multivariate phenotypic traits of *Solidago altissima* from its native and invasive ranges measured in a common garden experiment. Plants were sourced from three populations per range, crossed with a partial diallel design, and phenotyped in a common garden. We found evidence of evolutionary increase in multiple traits associated with invasiveness, whose evolution did not seem to be constrained by genetic tradeoffs among traits.  ## Description of the data and file structure EvolApp_2023_Gmatrix_data.csv contains trait data collected for each of the 1324 individual samples of S. altissima plants. Rows are separated into Type = "Plant" or "seed". "Plant" rows show measurements of all traits for each individual plant, except for "seed.area". Because seeds were bulk germinated for each maternal family, we do not know the size of seeds for particular individual plant that was grown in the greenhouse. "NA" in the seed.area column indicates the lack of seed data in "Plant" row. "Seed" rows present only seed.area, measured for 3 individuals per maternal family. All other trait values are shown as "NA".   EvolApp_2023_goldenrod_ped_indiv.csv contains pedigree information for individual plants used in the study. Column abbreviation for EvolApp_2023_Gmatrix_data.csv: | population | source populations where seeds of S. altissima was originally collected.  | | :-------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | origin | Native or Introduced | | animal | individual plant ID  | | cross\_block | block in which F1's were crossed. There were 5 individuals within a block, all fully crossed without self-fertilisation. | | sire | identity of pollen donor | | dam | identity of maternal plant | | rep | replicates (1 or 2) per maternal family. For each cross, we planted 2 individuals. | | fam | identity of cross family, where offspring of reciprocal crosses belong to the same family | | recipro | identity of maternal family.  | | tray | Tray number where F2's were grown in the common garden. 17 pots were placed in one tray. | | tray.pos | Position of plants within each tray, either E = edge, or C = centre. | | bay | Bay (or block) number within a greenhouse. | | growth.rate | Relative growth rate was determined as (\[height at week 10 – week 8]/14 days). "NA" indicates data not available due to early death of the individual plant or missing data. | | height | final height at harvest in cm. "NA" indicates data not available due to early death of the individual plant or missing data. | | sla | Specific leaf area: \[fresh leaf area/dry mass] of 3 leaves per plant. "NA" indicates data not available due to early death of the individual plant or missing data. | | leaf.mass | Leaf dry biomass at harvest, in g. "NA" indicates data not available due to early death of the individual plant or missing data. | | rhizome.mass | Rhizome dry biomass at harvest, in g. "NA" indicates data not available due to early death of the individual plant or missing data. | | flower.mass | inflorescence dry biomass at harvest, in g. "NA" indicates data not available due to early death of the individual plant or missing data. | | flower.day | Number of days from transplant to first flowering. "NA" indicates data not available due to early death of the individual plant or missing data. | | flower.duration | Number of days between the date of first flower opening and the last flower senescing. "NA" indicates data not available due to early death of the individual plant or missing data. | | polyacetylene | Concentration (in UV absorbance) of polyacetylenes in root tissue, measured using HPLC, per mg of fresh weight. "NA" indicates data not available due to early death of the individual plant or missing data. | | seed.area | Seed size, measured as area of the seed in photographs. "NA" indicates data not available for "Plant" type data. | | Type | Type of data. Plant = traits measured at individual plant level, and Seed = seed area measured for three seeds per cross family. |