Do ploidy level and nuclear genome size and latitude of origin modify the expression of Phragmites

19 Pages·2016·0.97 MB·English

by  Laura A. Meyerson

BiolInvasions(2016)18:2531–2549
DOI10.1007/s10530-016-1200-8
PHRAGMITES INVASION
Do ploidy level and nuclear genome size and latitude
of origin modify the expression of Phragmites australis traits
and interactions with herbivores?
. . .
Laura A. Meyerson James T. Cronin Ganesh P. Bhattarai
Hans Brix . Carla Lambertini . Magdalena Lucˇanova´ .
Shelby Rinehart . Jan Suda . Petr Pysˇek
Received:28December2015/Accepted:19June2016/Publishedonline:29June2016
(cid:2)SpringerInternationalPublishingSwitzerland2016
Abstract We studied the relationship between variationintraitsthatwestudiedforP.australis,with
genomesizeandploidylevelvariationandplanttraits latitudeoforiginbeinggenerallyabetterpredictorof
for the reed grass Phragmites australis. Using a trait values and that ploidy level and its interaction
common garden approach on a global collection of with monoploid genome size and latitude of origin
populationsinAarhus, Denmark,weinvestigated the alsocontributedtotraitvariation.Wealsofoundthat
influenceofmonoploidgenomesizeandploidylevel forfourtraits,tetraploidsandoctoploidshaddifferent
ontheexpressionofP.australisgrowth,nutritionand relationshipswiththemonoploidgenomesize.While
herbivore-defense traits and whether monoploid for tetraploids stem height and leaf water content
genome size and ploidy level play different roles in showed a positive relationship with monoploid
plant traitexpression.Wefoundthatbothmonoploid genome size, octoploids had a negative relationship
genome size and latitude of origin contributed to with monoploid genome size for stem height and no
relationship for leaf water content. As genome size
within octoploids increased, the number of aphids
Guesteditors:LauraA.MeyersonandKristinSaltonstall/ colonizing leaves decreased, whereas for tetraploids
Phragmitesinvasion.
L.A.Meyerson(&)(cid:2)S.Rinehart S.Rinehart
TheUniversityofRhodeIsland,Kingston,RI02881, DepartmentofBiology,SanDiegoStateUniversity,
USA SanDiego,CA,USA
e-mail:[email protected]
S.Rinehart
J.T.Cronin(cid:2)G.P.Bhattarai DepartmentofEvolutionandEcology,Universityof
LouisianaStateUniversity,BatonRouge,LA,USA California,Davis,Davis,CA,USA
G.P.Bhattarai M.Lucˇanova´ (cid:2)J.Suda
IndianRiverResearchandEducationCenter,University DepartmentofBotany,FacultyofScience,Charles
ofFlorida,FortPierce,FL,USA UniversityinPrague,Bena´tska´ 2,12800Prague,Czech
Republic
H.Brix(cid:2)C.Lambertini
DepartmentofBioscience,PlantBiology,Aarhus P.Pysˇek
University,OleWormsAlle1,8000AarhusC,Denmark DepartmentofEcology,FacultyofScience,Charles
UniversityinPrague,Vinicˇna´ 7,12844Prague,Czech
M.Lucˇanova´ (cid:2)J.Suda(cid:2)P.Pysˇek Republic
InstituteofBotany,TheCzechAcademyofSciences,
Za´mek1,25243Pru˚honice,CzechRepublic
123
2532 L.A.Meyersonetal.
there was a quadratic, though non-significant, rela- intraspecific variation in contributing to the ecological
tionship. Generally we found that tetraploids were breadthofaspeciesanditsabilitytoadapttochanging
taller, chemically better defended, had a greater environments (Albert et al. 2010; Sides et al. 2014).
number of stems, higher leaf water content, and Greaterintraspecificfunctionalvariabilityispredictedto
supported more aphids than octoploids. Our results allow plant populations to adjust to a wider range of
suggest trade-offs among plant traits mediated by competitiveandabioticconditionstherebyconferringa
genome size and ploidy with respect to fitness and broader niche (Sides et al. 2014). Some evidence
defense.Wealsofoundthatthelatitudeofplantorigin indicates that within-species genetic variation mirrors
is a significant determinant of trait expression sug- interspecificdiversity(e.g.HughesandStachowicz2004;
gesting local adaptation. Global climate change may Stachowicz et al. 2007; Cardinale et al. 2012) and that
favor some genome size and ploidy variants that can intraspecific variation,includingkaryologicaldiversity,
tolerate stressful environments due to greater pheno- maydrivecommunitystructureandecosystemprocesses
typic plasticity and to fitness traits that vary with (Levin 2002; Reusch and Hughes 2006; Johnson et al.
cytotype which may lead to changes in population 2009;CrawfordandRudgers2013)viaplantfunctional
genome sizes and/or ploidy structure, particularly at traits (Lavorel et al. 2007; Hull-Sanders et al. 2009).
species’rangelimits. Intraspecific functional variation may be important at
small and intermediate scales due to environmental
Keywords Cytotype(cid:2)Globalclimatechange(cid:2) filtering and high interspecific variability at the global
Latitudeoforigin(cid:2)Nucleargenomesize(cid:2)Plant scale (Albert et al. 2010), and because adaptation to
defense(cid:2)Plantinvasion(cid:2)Polyploidy change plays out at the population and subpopulation
scales(e.g.,microevolutionaryscale;Balaoetal.2011).
Bothgenomesizeandploidylevelhavepreviously
been implicated in invasion success (e.g. Kubesˇova´
Introduction etal. 2010; teBeest etal. 2011; Suda etal. 2015) but
rarely considered simultaneously (but see Balao et al.
Identifyingtraitsthatfacilitatethesuccessofinvasive 2011), even though including both in explanatory
speciesandpredictingoutcomesoftheinteractionsof models may greatly increase their power to predict
such traits with the environment continues to be a invasiveness (Pandit et al. 2014). For example, poly-
priority for invasion ecologists (Ku¨ster et al. 2008; ploidy can facilitate invasion success by ‘‘pre-adapt-
Pysˇeketal.2009,2015).Overthelasttwodecadesit ing’’ species to conditions in the introduced range
has become clear that no single trait can account for relative to diploids, and the associated higher genetic
theinvasionsuccessofaspecies(The´baultetal.2011) diversity in polyploids may enhance invasiveness
andinvasionsuccesscanalsovaryoverbiogeographic (Henery et al. 2010; te Beest et al. 2011) whereas
space further complicating the identification of key smallerholoploid(C-value)andmonoploid(Cx-value)
invasivetraits(Croninetal.2015). genome sizes (sensu Greilhuber et al. 2005) often
Studies searching for determinants of invasiveness contribute to faster growth rates (e.g. Lavergne et al.
rarelyconsiderunderlyinggeneticfactorssuchasgenetic 2010;FridleyandCraddock2015).Holoploidgenome
polymorphism,heterozygosity,andkaryologicalfactors, sizeis the amount ofDNA inthe whole chromosome
such as nuclear genome size, number of somatic complementofthenucleuswithachromosomenumber
chromosomes and ploidy level—all of which may n, irrespective of ploidy number whereas monoploid
differentially modify plant traits and interactwith each genomesizeistheamountofDNAinonechromosome
otherandenvironmentalfactors(e.g.,climate)toaffect setofanorganism(Sudaetal.2015).
traitexpression(teBeestetal.2011;Sudaetal.2015). Currently,littleisalsoknownaboutwhethertrade-
Understandinghowkaryologicalvariation(genomesize offsexist between intraspecific cytotypes withrespect
and ploidy) influences plant traits across a range of toplantfunctionaltraits(butseeThe´baultetal.2011;
environments is of particular interest for researchers Hao et al. 2013), including plant defense against
seeking to improve predictive capacities in species herbivores and pathogens, and most work in this
invasions and range expansions (Suda et al. 2015). direction has examined differences between diploids
Moreover,studieshaveincreasinglyidentifiedtheroleof and higher ploidy levels. Data on enemy impact and
123
Doploidylevelandnucleargenomesizeandlatitudeoforigin 2533
plant defenses in relation to ploidy level and genome (56(cid:3)8103000N; 10(cid:3)8007000E) during the summer of
sizeissparse(JanzandThompson2002;Mu¨nzbergova´ 2012toexaminetherelationshipbetweenplanttraits,
2006;Halversonetal.2008;Hull-Sandersetal.2009; genome size and ploidy (see ‘‘Appendix’’), and
Arvanitis etal. 2010; Hahnand Dornbush2012) even herbivory. P. australis is a cosmopolitan species
thoughherbivoresandpathogensplayanimportantrole within Poaceae and is adapted to wide climatic and
in invasion success (e.g. Keane and Crawley 2002; latitudinalranges(±60(cid:3)),includingextremeenviron-
Mitchelland Power2003;Fagan etal. 2005; Mitchell ments.Itexhibitsahighgeneticdiversity(Saltonstall
etal.2010).OneexampleinvolvesCardaminepraten- 2002), intra- and interspecific hybridization (Meyer-
sis where octoploids exhibited greater tolerance to son et al. 2010, 2012; Lambertini et al. 2012) and
herbivory than tetraploids (Boalt et al. 2010). Impor- variation in ploidy (Clevering and Lissner 1999;
tantly,agall-formingmidgeonlyattackedtheoctoploid Keller2000;Lambertinietal.2006).Thespecieshas
suggestingthatchangesinploidycanleadtohostshifts globally distributed diverse cytotypes (4x - 12x,
in herbivores (Arvanitis et al. 2010). In other species, based on x = 12) and GS variability up to 22 %
differentcytotypescansufferhigherorlowerlevelsof withincytotypes(Sudaetal.2015).
attack depending on both the plant and herbivore or One hundred and sixty-six clones were planted in
pathogen(Halversonetal.2008;ThompsonandMerg thegardenin2001.Adecadepassedpriortothestart
2008;Collinsetal.2013). ofthisstudy,allowingplentyoftimefortheplantsto
Togaingreaterinsightintotherolesofgenomesize acclimate to the Danish climate and for maternal
and ploidy level variation in an invasive plant, we environmental effects to have been virtually elimi-
studied plant traits for the reed grass Phragmites nated. All pots were maintained under identical
australis—oneofthebest-studiedplantspeciesglob- watering (daily) and fertilization regimes (monthly)
ally (Hulme et al. 2013; Meyerson et al. 2016). This andallrhizomesweredividedduringspring2012and
topicisofparticularinterestbecauseoftheimportant repotted in 60 L pots using commercial potting soil
habitat, including ecosystem services, reed grass and sand mixture. Given the large genetic and
provides across its native range in Europe and Asia, genomic variation represented for P. australis in this
and its status as a highly invasive species in North common garden, the collection made it possible to
America (Chambers et al. 1999; Kiviat 2013; Packer address, at the intraspecific level, fundamental rela-
et al. 2016). P. australis is an ideal model species to tionships among genetics and the environment (i.e.
investigate the interactions of genome size, ploidy geographyandherbivory).
levelandplanttraitsbecauseofitsglobaldistribution,
high genetic and genomic diversity, and habitat Genomesizeestimation
breadth,rangingfrombrackishtofreshwatersystems,
temperate to tropical, coastal to inland to high Holoploidgenomesizes(i.e.,theDNAcontentofthe
elevation (Meyerson et al. 2016). Using a common whole chromosome complement with chromosome
gardenapproachonaglobalcollectionofpopulations, number n irrespective of the degree of generative
we investigated (1) the influence of monoploid polyploidy; C-value) of analyzed plants were esti-
genomesizeandploidylevel onthe expressionofP. mated using DNA flow cytometry following the
australis growth, nutrition herbivore-defense traits, simplifiedtwostepprotocolusingtheOttobuffersas
andpalatabilitytoaphids,(2)andwhethermonoploid detailed in Dolezˇel et al. (2007). Bellis perennis
genome size and ploidy level play different roles in (2C = 3.38 pg;Scho¨nswetteretal.2007)wasusedas
planttraitexpression. an internal reference standard and the nuclei isolated
from intact young leaves of actively growing plants
were stained by the intercalating fluorochrome pro-
Methods pidium iodide. Karyologically confirmed tetraploid
(2n = 4x = 48) and octoploid (2n = 8x = 96)
Plantmaterial Phragmites samples from the living collection of the
InstituteofBotany,TheCzechAcademyofSciences
We used the living collection of P. australis from a in Pru˚honice were used as reference points to infer
common garden at Aarhus University, Denmark DNA ploidy levels of analyzed samples. Monoploid
123
2534 L.A.Meyersonetal.
genomesizes(i.e.,theDNAcontentofthemonoploid overwinter on their primary hosts, various Prunus
chromosome set, with chromosome number x; Cx- species,butswitchtograsses likeP. australis during
value)werecalculatedas2C-value/ploidylevel. the spring and summer (Lozier et al. 2009). Mealy
plum aphids often achieve densities [1000 per P.
Growth,nutritionalanddefensetraits australisstem,andoutbreakscancausethedieoffof
all aboveground vegetation (Cronin et al. 2015). We
We measured 10 plant traits (Table 1) related to conductedtwoexperimentsinvestigatingherbivoryby
growth (stem heightand number, the latter measured the mealy plum aphid Hyalopterus pruni that has P.
as the stem number per pot), nutritional quality (% australisasitssummerhost.
carbon, % nitrogen, C:N), herbivore defense (total
phenolics, leaf toughness), ambient aphid abundance
Ambientaphidabundanceperstem
per stem, and palatability to aphids, all in the same
pots.Sincewatercontentofleavescanhaveapositive
Backgrounddensitiesofaphidspersourcepopulation
relationshipwiththepopulationgrowthrateofaphids
likely reflect a combination of plant defense, nutri-
(e.g. Johnson 2009; Bhattarai et al. in review), we
tional quality, and positional effects (i.e., location in
estimatedthistraitastheproportionofwaterperunit
thegarden).Historicaleffects(e.g.,temporalautocor-
freshbiomassofthreenewlyopenedleavescollected
relation in abundance) are unimportant because the
from each pot. For nutrient analysis (% carbon, %
mealyplumaphidoverwintersonotherhostsandthen
nitrogen, C:N ratio), the top three leaves were
returns to the garden. To quantify ambient aphid
collected during July 2012 from a single plant per
abundance,wefirstcountedstemsandtheproportion
pot.Leaveswereoven-driedat70 (cid:3)Candgroundtoa
of stems that were infested with aphids. Randomly
finepowder.Carbonandnitrogenwereanalyzedusing
selecting three aphid-infested stems in each pot, we
a CE Instruments Model NC2100 elementalanalyzer
counted the number of leaves with aphids. One
atBrownUniversityEnvironmentalChemistryFacil-
infested leaf per stem was chosen at random and the
ities (http://www.brown.edu/Research/Evchem/
aphidswerecounted.Meanaphidsperstemwerethen
facilities).
estimated as the proportion of stems infested per
Leaf toughness and total phenolics were our
pot 9 mean number of infested leaves per infested
measures of plant defense against herbivores. In
stem 9 meannumberofaphidsperinfestedleaf.
Poaceae, defenses are likely limited to structural
defenses and phenolics. Using a penetrometer (Itin
Scale Co., Inc., Brooklyn, NY), toughness was mea- Palatabilitytoaphids
sured for the fully open uppermost leaf from a
randomly selected stem (force in kg to push a We performed a caging experiment to access palata-
4.8 mm diameter blunt steel rod through the leaf). bilityofP.australispopulationstomealy-plumaphid
Total phenolics (nM/g of dried leaf tissue) were (Hyalopterus pruni, Aphididae). We collected aphids
estimated using a microplate modified version of the fromasinglesourceinanaturallyoccurringstandof
Folin–Ciocalteu method (Waterman and Mole 1994; P. australis within 10 km of the common garden to
Croninetal.2015). minimize the genetic variation among aphids in the
experiment.WecagedadultaphidsonliveP.australis
Aphidabundanceandpalatabilitytoaphids leaves to assess the palatability of the garden popu-
lations to aphids (see Bhattarai et al. in review for
AwidediversityofherbivoresareknowntofeedonP. details).Aphidcolonieswereinitiatedwithtwoadult
australis(Tewksburyetal.2002).WithinEuropeand aphids caged on the youngest fully open leaf on a
North America, the mealy plum aphid Hyalopterus randomlyselectedstemfromeachpot.Aphidsrepro-
pruni (Homoptera: Aphididae) is the most common duce parthenogenetically and produce a colony in a
herbivore in terms of numbers and biomass (Cronin few days. After 10 days, leaves with aphid colonies
et al. 2015). The mealy plum aphid is a Eurasian were collected, transported on ice to the laboratory,
species that was introduced to North America more andstoredinafreezerat-20 (cid:3)C.Withasuitablehost,
than acenturyago (Lozier et al. 2009).These aphids aphidcoloniescanincreaseinsizeupto100timesin
123
Doploidylevelandnucleargenomesizeandlatitudeoforigin 2535
Table1 ModelselectionresultsbasedonAkaikeInformationCriteriacorrectedforfinitesamplesize(AICc)
Trait Model AICc DAICc AkaikeWt
%Carbon Cx,L,L9L,H 287.4 0.0 0.13
P,Cx,P9Cx,L,L9L,H 287.8 0.4 0.11
L,L9L,H 288.0 0.6 0.10
Cx,L,Cx9L,L9L,H 289.1 1.7 0.06
P,Cx,L,L9L,H 289.2 1.8 0.05
%Nitrogen Cx,L 105.2 0.0 0.20
Cx,L,H 105.6 0.5 0.16
Cx,L,Cx9L 106.5 1.3 0.10
P,Cx,L,H 107.0 1.8 0.08
Cx,L,Cx9L,H 107.1 1.9 0.08
P,Cx,L 107.2 2.0 0.07
C:Nratio Cx,L,H 569.8 0.0 0.35
P,Cx,L,H 571.2 1.4 0.18
Cx,L,Cx9L,H 571.7 1.9 0.14
Totalphenolics P,H 75.6 0.0 0.21
P,L,H 75.9 0.2 0.18
P,L,P9L,H 76.5 0.8 0.14
P,Cx,H 77.5 1.8 0.08
Stemheight P,Cx,L,P9Cx,P9L -38.0 0.0 0.11
Cx,L,H -37.7 0.3 0.10
P,Cx,L,P9Cx -37.4 0.7 0.08
L,H -36.7 1.4 0.06
P,Cx,P9Cx -36.3 1.7 0.05
Cx,H -36.2 1.9 0.05
Stemnumber Cx,L,L9L,H 290.3 0.0 0.27
P,Cx,L,P9Cx,L9L,H 290.7 0.5 0.21
P,Cx,L,P9Cx,P9L,L9L,H 291.9 1.6 0.12
%Watercontent P,Cx,L,P9Cx,P9L,Cx9L -321.4 0.0 0.18
P,Cx,L,P9L,Cx9L,Cx9Cx -321.2 0.2 0.16
P,Cx,L,P9L,Cx9L -320.4 1.0 0.11
P,Cx,L,P9L,Cx9Cx -319.7 1.6 0.08
P,Cx,L,P9L,Cx9L,Cx9Cx,H -319.7 1.6 0.08
Leaftoughness Cx,H 330.2 0.0 0.27
Aphidsperstem P,Cx,L,P9Cx,P9L,Cx9Cx 5378.0 0.0 0.60
Palatabilitytoaphids P,Cx,L,P9L,Cx9Cx,L9L,H 1721.9 0.0 0.42
(colonymassat10days)
CandidatemodelswithaD value(=AICc -AICc )ofB2andtheAICcweights(w)arereported
i i min i
P,ploidy(4xor8x);Cx,monoploidgenomesize;L,latitudeoforigin;H,longitudeoforigin
10 days but still do not compromise colony growth Statisticalanalysis
becauseofintraspecificcompetitionorleafdeteriora-
tion (Bhattarai et al. in review). Aphids per colony Weusedgeneralizedlinearmodelstotestwhetherthe
were counted, dried at 40 (cid:3)C and weighed. We used expressionofP.australisgrowth,nutritional,defense
colonymassat10 daysasanindexofpalatability. andpalatabilitytoherbivoretraitswereinfluencedby
123
2536 L.A.Meyersonetal.
ploidy, monoploid genome size and the latitude and value,latitudeoforigin,longitudeoforigin)shouldbe
longitude of origin of the populations. Only the two included in the candidate models for each trait. The
most common ploidy levels of P. australis were AICcscoreforthebasemodelwiththemainpredictor
considered in this study, 4x (n = 126 source popula- variables (ploidy, Cx-value, ploidy*Cx-value) and
tions) and 8x (n = 40). Because holoploid genome covariate in question was compared with the AICc
size (C-value)is closely correlated with ploidy level, scoreforthebasemodelplusthequadratictermforthe
we used monoploid genome size (Cx-value) as an covariateinquestion.Ifthelattermodeldidnotreduce
explanatory variable. Ploidy was treated as a fixed the AICc score by C2 relative to the base model, the
effect in the model while Cx-value, latitude and quadratic term was excluded from the list of terms
longitudeoforiginweretreatedascovariates.Latitude usedforconstructingcandidatemodels(seeBurnham
oforiginwasincludedbecausetheexpressionofplant andAnderson2010).
traits, including those related to interactions with Candidate models were constructed using all pos-
herbivores,oftencovarywithlatitude(e.g.Schemske siblecombinationsofpredictorvariables.Restrictions
etal.2009)andthisisparticularlytrueforP.australis tothepossiblecombinationsofvariablesincludedthe
(Cronin et al. 2015). Longitude is closely correlated requirement that interaction terms could only be
with previously described P. australis phylogeo- present in the model if their main effects were also
graphic groups (see Lambertini et al. 2006). We also present in the model. Candidate models were ranked
included a quadratic term for each covariate (e.g., byAICcfromlowesttohighestvalueandAICswitha
latitude2) to assess nonlinearity in the relationship D value(= AICc - AICc )ofB2weredeemedto
i i min
betweenatraitandthecovariate.Wewereparticularly have substantial support (Burnham and Anderson
interested in testing for a ploidy 9 Cx-value (or 2010). We also report the AICc weights (w) which
i
ploidy 9 Cx-value2) interaction that would indicate indicate the weight of evidence (as a proportion) in
that the relationship between a trait and monoploid favorofmodelibeingthebestmodelgiventhesetof
genome size differs fundamentally between ploidy candidate models. As the Proc Glimmix procedure
levels. Finally, all first-order interactions among does not report goodness-of-fit for the models, we
predictor variables were considered. Data were ana- emphasizeeffectsizesofthefactorsinthemodel(i.e.,
lyzed using SAS 9.3 Proc Glimmix with normally proportional differences in least-squares means or
distributed errors and an identity link function (SAS slopesinrelationships).
InstituteInc.,Cary,NorthCarolina). In order to visualize the relationship between a
Analysis of ambient aphid abundance per stem response variable (i.e., any of the plant traits) and a
required a slightly different analytical approach. particularpredictorvariable(e.g.,monoploidgenome
Aphid counts per stem was Poisson distributed. size), we used the following procedure. We repeated
Therefore,forthisplantmetric,weusedageneralized the generalized linear model analysis for the AICc-
linearmodelwithLaplaceestimationmethod,Poisson best model, with the exclusion of the predictor
distributionoferrorsandaloglinkfunction(SASProc variable in question, and then obtained the residuals.
Glimmix). A least-squares regression analysis or plot of the
To help normalize data distributions and homoge- residuals against the predictor variable would reveal
nizevariancesbetweenploidylevels,totalphenolics, the effect of the predictor variable on the plant trait
stemheights,numberofstemsperpot,andaphidmass thatisindependentoftheothermodelfactors onthat
were ln transformed. Quantile–quantile plots and trait. For convenience, the R2 and P values from the
studentized residuals were used to identify potential regression are provided for each case as a means to
outliersinthedistributionoftraitestimates.However, gaugethemodelfit.
in no case did the removal of these data points To determine whether there were any significant
qualitativelychangetheconclusionsofthemodel. relationships between climate and the latitudes and
For each dependent variable, we used Akaike’s longitudes of origin of the P. australis populations
Information Criteria corrected for finite sample size includedinourstudy,wealsoconductedacorrelation
(AICc) to select the most informative model (Burn- analysis using the data base (http://www.worldclim.
ham and Anderson 2010). We began by assessing org/bioclim)at30Arc-secondsresolutionforthefol-
whetherquadratic terms forthe three covariates(Cx- lowing variables: annual mean temperature,
123
Doploidylevelandnucleargenomesizeandlatitudeoforigin 2537
isothermality(meandiurnalrange/temperatureannual significantlyrelatedtoCx-valuewasleaftoughness—
range),temperatureseasonality(standarddeviationof plantswithlargermonoploidgenomestendedtohave
the temperature 9 100), maximum temperature tougherleaves(Fig. 1).
warmestmonth,minimumtemperaturecoldestmonth, For stem height, leaf water content, and ambient
temperature annual range, annual precipitation, sea- aphidabundance,theAICc-bestmodelsuggestedthat
sonal precipitation, precipitation wettest quarter and therelationshipbetweenmonoploidgenomesizeand
precipitation driest quarter, where a quar- each of these traits differed between tetra- and
ter = 3 months. Climate values were derived octoploids (i.e., a ploidy 9 Cx-value interaction;
accordingtothemethodsofHijmansetal.(2005). Table 1). Stem height increased significantly with
increasing Cx-value for tetraploids (R2 = 0.067,
P = 0.004) but decreased with increasing Cx-value
Results for octoploids (R2 = 0.054, P = 0.160) (Table 2;
Fig. 2a). Leaf water content (Table 2; Fig. 2b) was
Themonoploid genomesizefor 4x P.australis plants significantforincreasingCx-valuesintetraploidsbut
included in the experiment was 0.500 ± 0.002 pg not octoploids. Finally, ambient aphid number per
(mean ± SE; n = 126) and ranged from 0.470 pg to stem decreased significantly with Cx-value for octo-
0.573 pg. For 8x plants, Cx-value was 0.501 ± ploids whereas modelselectionfavored a non-signif-
0.002 pg (n = 40) and ranged from 0.485 pg to icant quadratic relationship between aphid number
0.521 pg. The difference in Cx-values between tetra- andCx-valueforthetetraploids(Table 2;Fig. 2c).
and octoploids was not significant (t = 0.25, Latitude of origin was generally a better predictor
164
P = 0.98) suggesting that no genome downsizing of P. australis traits than genome size. Mean R2 was
occurredintheoctoploidsthatwesampled. 0.12 ± 0.03forthe8traitsinwhichlatitudeoforigin
Forthe10P.australistraits,theAICcbestmodels wasintheAICc-bestmodel(seeTables 1,2);almost
are reported in Table 1. In general, the most likely threetimeshigherthanthemeanR2forCx-value(see
models explaining variation in trait expression above). Regardless of ploidy level, % nitrogen
included Cx-value and latitude. Monoploid genome decreasedlinearlywithlatitudeoforigin(R2 = 0.19,
sizewaspresentinatleastoneofthecandidatemodels P\0.001) and the C:N ratio increased linearly with
withsubstantialsupport(DAICc B 2)forall10traits. latitude of origin (R2 = 0.20, P\0.001). Interest-
Infact,itwasinthemodelwiththehighestlikelihood ingly, % carbon and stem number per pot peaked at
(i.e., lowest AICc value) in 9 of 10 cases (the intermediatelatitudesoforigin(theAICc-bestmodel
exception being total phenolics; Table 1). Among all includedaquadraticfunction;Table 2;Fig. 3).
supportedmodelsforthe10traits(n = 35),Cx-value For four traits, stem height, leaf water content,
was a factor in 86 % of them. Latitude was in the ambient aphid abundance and aphid palatability
AICc-bestmodelfor8of10traitsandwasafactorin (colony mass at 10 days), the relationship between
86 %ofthemodels(Table 1).Indescendingorderof latitude of origin and trait expression differed
importance, the percentage of models with longitude between tetraploids and octoploids (Table 2). For
of origin, ploidy, ploidy 9 latitude of origin interac- example, stem number increased (R2 = 0.11,
tion, andploidy 9 Cx-valueinteractionwas 69 %(9 P = 0.041) and leaf water content decreased
of10traits),60 %(9of10traits),29 %(6of10traits), (R2 = 0.32, P\0.001) with increasing latitude of
and23 %(5of10traits),respectively(Table 1). origin for 8x plants (Fig. 4a) but no relationship
Althoughgenomesizewasconsistentlyincludedas was observed for 4x plants (Fig. 4b). A quadratic
a predictor variable of plant traits, the proportion of function best described the relationship between
plant variation that was explained by Cx-value was palatability to aphids and latitude of origin but
generallylow.Table 2showstheleast-squaresregres- 4x plants exhibited a trough while 8x plants
sionmodelfortherelationshipbetweenCx-valueand exhibited a peak at intermediate latitudes of origin
each plant trait after factoring out the effects of all (Fig. 4c). The relationship between latitude of
other predictor variables from the AICc-best model. origin and ambient aphid abundance per stem was
Thecoefficientofdetermination(R2)wasB0.10inall not statistically significant regardless of the ploidy
cases and averaged 0.045 ± 0.01. One trait that was level (Table 2).
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2538 L.A.Meyersonetal.
Table2 Effectofthemonoploidgenomesize(Cx-value)andlatitudeoforiginoneachPhragmitesaustralistraitstudied
Factor Trait Model R2 P
Monoploidgenomesize %C -0.43(Cx)?0.23 0.001 0.884
%N -1.71(Cx)?0.85 0.098 0.263
CNratio 7.22(Cx)-3.59 0.005 0.395
lnStemheight
4x 2.51(Cx)-1.26 0.067 0.004
8x -5.08(Cx)?2.54 0.054 0.160
lnStemnumber -3.68(Cx)?1.86 0.015 0.116
%water
4x 0.84(Cx)-0.42 0.067 0.009
8x -1.18(Cx)?0.59 0.063 0.187
Leaftoughness 5.87(Cx)-2.95 0.030 0.029
Aphidnumber
4x 167.44(Cx)-163.46(Cx2)-42.79 0.006 0.699
8x -24.59(Cx)?12.00 0.107 0.048
lnAphidcolonymass -627.0(Cx)?614.7(Cx2)?159.6 0.035 0.057
Latitudeoforigin %C 0.11(L)-0.001(L2)-2.62 0.061 0.018
%N -0.02(L)?0.80 0.193 <0.001
CNratio 0.11(L)-4.68 0.203 <0.001
lnStemheight
4x 0.002(L)-0.085 0.007 0.362
8x 0.011(L)-0.424 0.111 0.041
lnStemnumber 0.190(L)-0.002(L2)-4.72 0.293 <0.001
%water
4x -0.0004(L)?0.022 0.005 0.492
8x -0.005(L)?0.194 0.324 0.001
Aphidnumber
4x 0.017(L)-0.949 0.026 0.082
8x -0.004(L)-0.220 0.001 0.817
lnAphidcolonymass
4x -0.550(L)?0.006(L2)?12.05 0.064 0.019
8x 0.504(L)-0.005(L2)-12.08 0.173 0.030
IftheAICc-bestmodelcontainedaploidy9Cx-valueinteraction,separatemodelswerereportedforeachploidylevel.Pvaluesin
boldaresignificant(PB0.05).Valuesforeachtraitwereobtainedastheresidualsfromseparategeneralizedlinearmodelanalyses
thatincludedallvariablesfromtheAICc-bestmodelexceptthetraitinquestion.Least-squaresregressionswereperformedonthe
residualstoobtainthemodelandassociatedstatistics
TestsoftherelationshipbetweenCx-valueandplant-traitvaluewereperformedusingresidualsfromtheAICc-bestmodel,minus
Cx-value and all interactions involving Cx-value. The same approach was used for assessing the relationship between latitude of
originandeachplanttrait
In addition to ploidy-level effects on how trait 30 % higher total phenolics, 14 % fewer stems per
expressionvarieswithlatitudeoforigin,ploidyperse pot, and 7 % fewer aphids per stem than octoploids.
wasamodestlyimportantpredictorforonehalfofthe There were other significant differences between
plant traits considered in this study (Table 3). The ploidy levels, but those differences were associated
mostpronounceddifferenceswerethattetraploidshad with very small effect sizes—tetraploids had 3 %
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Doploidylevelandnucleargenomesizeandlatitudeoforigin 2539
Fig.1 Therelationshipbetweenmonoploidgenomesize(Cx-
value) and P. australis leaf toughness (kg). Leaf toughness
valueswereobtainedastheresidualsfromageneralizedlinear
modelanalysis thatincludedallvariables fromtheAICc-best
model,excludingmonoploidgenomesize.Lineisfitbyleast-
squaresregression
taller stems and 2 % less % water content than
octoploids. All other traits were indistinguishable
betweenploidylevels(Table 3).
As indicated by the frequent occurrence of longi-
tudeoforiginintheAICc-bestmodels(seeTable 1),
there are differences among continents/phylogeo-
graphic groups. Interestingly, there were no interac-
tions between longitude of origin and ploidy number
or genome size. As this study is about karyological
diversity and its effects on plant trait expression, no
furtherdiscussionoflongitudeoforiginiswarranted.
Finally, using the variables listed above from the
Bioclim database (see ‘‘Statistical analysis’’ section),
wefoundastrongnegativerelationship(r = -0.795)
between latitude of origin and annual mean temper-
ature and moderately strong relationships between
latitude of origin and minimum temperature in the
Fig.2 Therelationshipbetweenmonoploidgenomesize(Cx-
coldest month (r = -0.595) and isothermality
value)andP.australis.alnstemheight(cm),(b)percentwater
(r = -0.508). The correlation coefficient was weak content, and c ambient aphid abundance per stem.Values for
(r B 3) for all other variables except temperature stemheight,percentwaterandaphidabundancewereobtained
seasonality, which was also weak at 0.306. All astheresidualsfromseparategeneralizedlinearmodelanalyses
thatincludedallvariablesfromtheAICc-bestmodel,excluding
correlationcoefficientsrelatinglongitudeoforiginto
monoploid genome size. Because the AICc-best model
the 10 variables were weak (r =\3) except precip- (Table1) included a ploidy*Cx-value interaction, separate
itation, which showed a moderate negative relation- least-squares regression lines were fit to tetraploids and
shipwithlongitudeoforigin(r = -0.435)andannual octoploids (see Table2). Quadratic regression was used for
tetraploidsin(c).Note:onlyasingle,pooledregressionlinesis
precipitation, which showed a weak negative linear
provided in (b) because separate lines almost completely
relationship(r = -0.361). overlapped
123
2540 L.A.Meyersonetal.
Fig.3 Therelationshipbetweenlatitudeoforiginandnumber
ofP.australisstemsperpot.Numberofstemsaretheresiduals
from a generalized linear model analysis that included all
variables from the AICc-best model, excluding latitude of
origin.Lineisfitbyleast-squaresregression,bandrepresentsthe
95% confidence limits and dashed lines present the 95%
predictionlimits
Discussion
Recent studies indicated a relationship between
genomesizeandplanttraitsassociatedwithinvasive-
ness such as growth rate and phenology (e.g.
Kubesˇova´ et al. 2010), while others showed differ-
encesbetweenplantdefensetraitsacrossploidylevels
(e.g. te Beest et al. 2011); however, the effects of
genome size and ploidy level have rarely been
addressed simultaneously (Pandit et al. 2014). In our
common garden study we examined 126 populations
of tetraploid P. australis spanning from 14.6(cid:3)N to
61.8(cid:3)N and 40 octoploid populations spanning from
39.5(cid:3)S to 51.2(cid:3)N. We have documented significant
intraspecific variation ingenomesize across globally
distributed populations of P. australis. Furthermore,
weexaminedtherolesmonoploidgenomesize,ploidy Fig.4 The relationship between latitude of origin and P.
australis. a stem number, b percent water content, and
and latitude of origin (and their interactions) play in
cpalatabilitytoaphids(colonymassat10days).Aswiththe
trait expression in a common garden setting that previousfigures,valuesforeachvariablearetheresidualsfrom
allowed us to explore the relative contributions of thegeneralizedlinearmodelanalysesfromtheAICc-bestmodel
thosefactorsonparticularplanttraits. excludinglatitudeoforigin
We found that both monoploid genome size and
latitudeoforigincontributedtovariationinnineoutof This suggests that while genome size and ploidy are
the 10 traits that we studied for P. australis, with eachimportantfactorsthathelpdetermineplanttraits,
latitudeoforiginbeinggenerallyabetterpredictorof eachisonlyacontributingfactorthatinteractswiththe
traitvalues.Moreover,wefoundthatploidyleveland other, as well as the environment, and reinforces the
its interaction with monoploid genome size and idea that no single trait can account for the invasion
latitude of origin also contributed to trait variation. successofaspecies(The´baultetal.2011).Therewasa
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