Climate warming drives invasion history of Ambrosia artemisiifolia in central Europe

23 Pages·2017·2.4 MB·English

Preslia90:59–81,2018 59
Climatewarmingdrivesinvasionhistoryof Ambrosiaartemisiifolia
incentralEurope
OteplováníovliňujeinvaziAmbrosiaartemisiifoliavestředníEvropě
Thomas Mang1,2,Franz Essl2,3,4,Dietmar Moser1,2&Stefan Dullinger2
1ViennaInstituteforNatureConservation&Analyses,Giessergasse6/7,1090Vienna,
Austria;2DivisionofConservationBiology,VegetationEcologyandLandscapeEcology,
Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14,
1030Vienna,Austria,e-mail:[email protected];3EnvironmentAgencyAustria,
SpittelauerLände5,1090Vienna,Austria;4CentreofExcellenceforInvasionBiology,
StellenboschUniversity,Stellenbosch,SouthAfrica
MangT.,EsslF.,MoserD.&DullingerS.(2018):Climatewarmingdrivesinvasionhistoryof
AmbrosiaartemisiifoliaincentralEurope.–Preslia90:59–81.
Itisthoughtthatfutureclimatechangeislikelytofosterbiologicalinvasions,buteffectsofcli-
matewarmingoninvasionsinrecentdecadesarelittleexplored.Inthispaper,weanalysethehis-
toryofthespreadofAmbrosiaartemisiifolia(commonragweed)incentralEuropeinorderto
determinetheeffectsofclimatewarming.Inaddition,weinferthelikelyhistoryofthisspecies’
spread and current distribution from incomplete documentation. The area studied in central
Europe includes Austria, the Czech Republic, Germany, Hungary, Liechtenstein, Slovakia,
SloveniaandSwitzerland.Thesecountrieswererepresentedbyalatticeofgridcellsofsize5'×3'
(~6×6km2),andwemodelledthisspecies’increaseinrangeinthisgridfrom1900to2010atan
annualresolution.Thespreadwasmodelledtobedrivenbythespatio-temporalvariationinenvi-
ronmentalsuitabilityofgridcellsasdeterminedbyclimaticconditionsandlanduse,propagule
production and dispersal from invaded cells, and ‘background’ introductions from unknown
sources. A hierarchical Bayesian modelling approach accounted for lagged and incomplete
recordsofoccurrenceandspatio-temporalvariationinsamplingintensity.Wefittedmodelswith
differentrepresentationsofclimatevariationovertime,andfurthercontrastedthehierarchical
modeltoasimplifiedmodel,whichassumedthatrecordsaccuratelyreflectedthespecies’actual
spread.Climaticconditionswerethemostimportantdeterminantsofenvironmentalsuitability
forinvasion,andsuitabilityalsoincreasedwithincreaseintheproportionofurbanareaandlength
ofrailwaysingridcells.Temperaturewas,onaverage,2.7°Chigherintheenvironmentallymore
suitablecellsthaninthelesssuitablecells.Thepatterninthespreadwasdeterminedbylocal
rangeexpansionfrommultiple,spatiallyscatteredpointsofintroduction.Assumingalineartrend
in climate warming over the modelling period provided a better model fit than using annual
weatherconditionsorthelong-termaverageoftheclimate.Themodelestimatedthatby2010
onlyabout60%oftheactuallyinvadedgridcellswererecorded,andrecordslaggedbehindactual
colonizationsbyyearsuptodecades.Inferencesofthehierarchicalandsimplifiedmodelsdif-
feredquantitatively.Weconcludethatbyusingourmodellingframeworkitispossibletoseparate
spatialeffectsofclimateonthespreadofnon-nativespeciesfromtemporaleffects,andthatcli-
matewarminghasalreadypromotedthespreadofAmbrosiaartemisiifoliaincentralEurope.The
recordeddistributionreflectsonlyapartofthisspecies’actualdistribution.Properlyaccounting
forincompleterecordshenceimprovesinferencesaboutthedynamicsofspread.
Keywords:alienspecies,Ambrosiaartemisiifolia,biologicalinvasion,centralEurope,cli-
matechange,climatewarming,commonragweed,hierarchicalBayesianmodelling,imperfect
detection,propagulepressure,rangeexpansion,spread
doi:10.23855/preslia.2018.059
60 Preslia90:59–81,2018
Introduction
Therateatwhichhumansmovespeciesaroundtheglobehasgreatlyincreasedoverthe
courseofthe20thcentury.Asaconsequence,morethan1.5species,onaverage,arecur-
rentlyestablishingperdaysomewhereoutsidetheirnativerange(Seebensetal.2017).
Some of these species become invasive, i.e. they rapidly spread over large areas with
potentiallysevereeffectsonnativeecosystemsorhumansocieties(Vitouseketal.1996,
Pyšek&Richardson2010,Vilŕetal.2011,Blackburnetal.2014).Thesebiologicalinva-
sionsaredrivenbyaplethoraofinteractingfactors,whichessentiallystructurearound
biological traits of alien species and recipient communities, propagule pressure,
biogeographicalhistoryandtheenvironmentoftheinvadedterritory(Catfordetal.2009,
Pyšeketal.2017).
Climate is an ecologically important component of the environment. A changing cli-
mate,therefore,islikelytohaveramificationsforbiologicalinvasions.Inparticular,cli-
matewarmingisexpectedtoincreasethelikelihoodandfrequencyofinvasions(Dukes&
Mooney1999,Ward&Masters2007).Themechanismsinvolvedmayincludeincreased
disturbancesanddestabilizationofresidentcommunities(Gilmanetal.2010,Diezetal.
2012),openingupnewintroductionpathways(Hellmannetal.2008)orallowthesurvival
andreproductionofpotentialinvadersinareasthatwerepreviouslyclimaticallyunsuitable
forsuccessfulcolonization(Ward&Masters2007,Waltheretal.2009,Mainka&Howard
2010).Whileapronouncedclimatewarmingisexpectedinfuturedecades,acleartrendof
increasingtemperatureswasalreadyrecordedduringthelastdecadesofthe20thcentury
andearly21stcentury(Stockeretal.2013).However,whilerangeshiftsofnativespecies
inresponsetorecentclimatewarmingarewelldocumented(Rootetal.2003,Parmesan
2006,Chenetal.2011)andsimilareffectsonalienspeciesrangesareexpected(Bradleyet
al.2010,Dullingeretal.2017),theactualimprintofthisrecentclimatewarmingonthe
spreadofbiologicalinvadersremainslittleexplored(Waltheretal.2007).Alikelyreason
forthisisthedifficultyofdetecting‘fingerprintsofclimatewarming’intheprogressofan
inherently dynamic invasion. In the course of biological invasions, continued introduc-
tions, local population build-up, the spread from established invasion foci and lag times
(Sakaietal.2001,Allendorf&Lundquist2003)typicallyresultincontinuouschangesin
speciesdistributionsandlocalabundancesforalongtime,irrespectiveofchangesinthe
environment.Asmanyalienspeciesfrequentlycolonizedisturbedhabitats(Chytrýetal.
2008),thedynamicsofspreadmayalsobedrivenbychangesintheenvironmentotherthan
climaticconditions,forexamplebychangesinlanduseanddisturbanceregimes.More-
over,disentanglingclimateeffectsfromthoseofotherdeterminantsofbiologicalinvasions
ishamperedbyacommonlyunsystematicandoftenbiasedrecordingofthedevelopment
ofspread(Delisleetal.2003,Meinesz2007).Theactualspreadanditsdocumentationmay
beseparatedbyvariablespatio-temporaltimelags(Aikioetal.2010).Asaresult,available
recordsareoftenofaspreadinwhichthecolonizationdynamicsandclimatictrendsare
decoupledbecauseofanerraticdetectionhistory.
Apromisingwayofunderstandingtheroleofindividualputativedriversinbiological
invasionsistomodelthedocumentedspreadinspaceandtime(Hastingsetal.2005).Inthe
case of incomplete documentation of spread, hierarchical modelling approaches, which
representactualspreadanddetectionprocessesasseparatemodellayers,areofparticular
interest(Wikle2003,Bledetal.2011,Bromsetal.2016).Here,weapplysuchanapproach
Mangetal.:ClimatewarmingdrivesinvasionhistoryofAmbrosiaartemisiifolia 61
to disentangle a possible signal of climate warming in the invasion history of Ambrosia
artemisiifolia (common ragweed) in central Europe. For this purpose, we analyse how
spatio-temporalvariationinclimate(temperatureandprecipitation)andlanduse(cropland
area,urbanarea,lengthofmotorwayandrailwaynetworks)hasdriventhisspecies’spread
by changing the suitability of local environments for invasion. Moreover, we infer the
imperfectlyrecorded,andhenceunknown,actualdistributionofthisspecies.Wehypothe-
sizethat(i)spatialgradientsinclimateandlandusehadaprofoundeffectonthepatternof
spread,(ii)recentclimatewarminghasacceleratedtheinvasion,and(iii)thedocumented
speciesdistributionrepresentsonlyapartoftheactualdistribution.Inaddition,weassess
theeffectoflaggedandincompleterecordsofoccurrenceontheconclusionsdrawnfrom
the analysis by contrasting the inferences from the hierarchical model with those from
atechnicallysimplifiedmodel,whichassumesperfectdetection.
Materialsandmethods
SpreadofAmbrosiaartemisiifoliaincentralEurope
AmbrosiaartemisiifoliaL.(Asteraceae)isanannualherbaceousplantnativetocentralNorth
America,adaptedtotemperateclimates(Bullocketal.2012,Essletal.2015,andrefer-
encestherein).Individualsarefromseveralcentimetresupto2.5minheight.Thisspe-
ciesiswind-pollinated and monoecious,and strong self-incompatibility resultsin high
levels of outcrossing (Friedman & Barrett 2008). Ambrosia artemisiifolia reproduces
exclusivelybyseed.Seedripeningisterminatedbyautumnfrost,whichkillstheadult
plant,andseeddormancyisbrokenbywintertemperatures(Chapmanetal.2014).The
numberofseedsproducedperplantvarieswithregionandhabitat,buttypicallyranges
from several hundred up to many thousand per plant (Fumanal et al. 2007, Essl et al.
2015).Seeddispersalbywindisrestrictedtotheimmediatevicinityofthemotherplant,
anddispersalbyanimalsandwater(e.g.alongriversorinfloodwater)isofminorimpor-
tance(Bullocketal.2012).Mostlong-distancedispersalismediatedbyhumanagency
andoccursviatradeincontaminatedgoodssuchasgrainandotheragriculturalproducts,
planting seed, bird seed (Chauvel et al. 2006, Chapman et al. 2016), seeds sticking to
agriculturalandmowingmachinery(Karreretal.2011)andtrafficingeneral(Essletal.
2009). Seed persists in thesoilseedbank for severaldecades and asiteoncecolonized
henceremainsoccupiedforalongtime(Fumanaletal.2008).Ambrosiaartemisiifoliais
ofconsiderablepublichealthconcernasthisspecies’wind-blownpollenishighlyaller-
genic(Smithetal.2013).
Ambrosiaartemisiifoliahasbeenintroducedintoareaswithtemperateclimatesglob-
ally(Essletal.2015).InEurope,thefirstrecordsarefromthe19thcentury,butthespe-
ciesdidnotstarttospreadandnaturalizeuntilthefirstdecadesofthe20thcentury(Bull-
ock et al. 2012, Essl et al. 2015, and references therein). Range expansion accelerated
considerablyinthelate20thcentury.IntemperateEurope,thisspeciesoccursmainlyin
warmerlowlandareas.Climaticconditionsareanimportantinvasionfilter,inparticular,
wherethisspecies’potentialdistributionislimitedbylowtemperaturesinnorthernEurope
andmontaneregions,andbymoisturestressinsouthernEurope(Chapmanetal.2014,
Leiblein-Wildetal.2016).Climatewarminginfuturedecadesispredictedtoconsider-
ably enlarge the area in Europe where climatic conditions are suitable for invasion by
62 Preslia90:59–81,2018
Fig.1.–ThechronologyofthefirstrecordsofAmbrosiaartemisiifoliaoccurringineightcountriesincentralEurope
duringtheperiod1900–2010.Gridcellsizeis5'×3'(~6×6km2)andthestudyareaspansover700,000km2.
Mapprojection:Lambertazimuthalequal-area.
A. artemisiifolia. Specifically, higher average temperatures during the growing season
and later onset of autumn frost may cause an expansion in this species’ range towards
northernEurope(Cunzeetal.2013,Chapmanetal.2014,Storkeyetal.2014).Ambrosia
artemisiifolia is a poor competitor (Leskovšek et al. 2012) and mainly thrives in dis-
turbedopenhabitatsinandaroundhumansettlements,agriculturalfields,roadsidesand
alongrailwaytracks(Essletal.2009,Pinkeetal.2011,Skálováetal.2017).Thisspecies
prefersnutrient-richsoilsbutcangrowunderarangeofnutrientandsalinityconditions
(DiTommaso2004,Skálováetal.2015).
Studyareaandperiod
TheareaincentralEuropestudiedincludesAustria,theCzechRepublic,Germany,Hun-
gary, Liechtenstein, Slovakia, Slovenia and Switzerland (Fig. 1; see also Mang et al.
(2018)).Thesecountriesspananareaofover700,000km2andcoverarangeoftemperate
climateswithmoreoceanicconditionsinthenorth,lowlandcontinentalareasintheeast
andsouth-east,andcoolmontanetoalpineclimatesintheAlpssituatedinthesouthand
Mangetal.:ClimatewarmingdrivesinvasionhistoryofAmbrosiaartemisiifolia 63
Fig.2.–Annualtimeseries(dot-dashline)and10-yearmovingaverage(solidline)ofthemeantemperature
recordedduringthegrowingseason(April–October)inthestudyarea.Thestudyareacompriseseightcoun-
triesincentralEurope(seeFig.1).
south-westinthestudyarea.Thisareawasrepresentedbyalatticeofgridcellsofsize
5'×3' (~ 6 × 6 km2) corresponding to the Central European Floristic Mapping Project
(Niklfeld1998),comprisingatotalof22,451cells.WemodelledthespreadofA.artemisii-
folia in the study area from 1900 to 2010 at an annual resolution. In parallel with the
global trend, in the late 20th century the average temperature in the study area also
increased(Fig.2).Themeantemperatureinthegrowingseason(April–October)during
theperiod1980–2010was0.65°Chigherthanduringtheperiod1900–1979.
Speciesdistributiondata
Speciesrecordswerecompiledfrommanydifferentsources(e.g.floristicpublications,
floristicmappingprojects,majorherbaria,unpublishedrecordsoftheauthorsandofcol-
leagues).Therecordsweremappedtooneofthe22,451gridcellsandthedates(years)of
therecordswereextractedfromtheoriginalsource(ElectronicAppendix1).Atotalof
11,800 records indicate that 3,598 cells were invaded by this species by 2010 (Fig. 1,
Table1).Recordswerecondensedtotimeoffirstdetectionineachgridcellbyusingthe
dateoftheearliestrecordforeachcell.
Sampling intensity varied both in timeand acrossthestudy area.Averagesampling
intensitydifferedacrosscountries,andincreasedwiththeonsetoftheCentralEuropean
FloristicMappingProjectin1970(Niklfeld1998).Moreover,oncethisspecieswasrec-
ognized to be a particularly detrimental invasive plant, there was a focus on recording
A. artemisiifolia occurrences in selectregions and years. In our modelling, this spatio-
temporalvariationinsamplingintensityisexplicitlyrepresented.
64 Preslia90:59–81,2018
Table1.–ThenumberofAmbrosiaartemisiifoliarecordsfrom1900until2010fortheeightcountriesstudied
incentralEurope,thenumberofgridcellsforeachofthesecountriesinthemodels’latticesystemandthenum-
berofgridcellswithrecords.
Country No.records No.gridcells No.gridcellswithrecords
Austria 5,408 2,441 549(22.5%)
CzechRepublic 430 2,437 129(5.3%)
Germany 1,076 11,422 618(5.4%)
Hungary 3,406 2,747 1,825(66.4%)
Liechtenstein 0 5 0(0%)
Slovakia 414 1,490 141(9.5%)
Slovenia 423 631 189(30.0%)
Switzerland 643 1,278 147(11.5%)
Studyarea 11,800 22,451 3,598(16.0%)
Modeloverview
The model used in this study builds on a recently developed hierarchical modelling
framework(Mangetal.2017).Inthisframework,aninvasionprocesslayerrepresents
theactualspreadofthespeciesbymodellingthetimeoffirstinvasionineachgridcell,
andaseparateobservationprocesslayerrepresentsthehistoryoftherecordsbymodel-
lingthetimeswhenspeciesoccurrenceswerefirstdetected(andrecorded).Inourmodel,
weassumethatthespeciespersistsinagridcellonceinvaded,andthatdetectionmaylag
behindtheinitialinvasionbyanunlimitedperiodoftime.Consequently,thefirstrecord
for a particular cell is the latest possible time when this cell was actually invaded, and
cellswherethespecieshasnotbeenrecordedsofarmayneverthelessalreadybeinvaded.
Theactualtimesoftheinitialinvasionofthegridcellsremainunknownbutareinferred
bymeansofstatisticalmodelfitting.FormodeldetailsseeElectronicAppendix2.
Invasionprocess
Cellsunoccupiedatagiventimearepotentialrecipientcellsexposedtoaninvasionrisk.
Themagnitudeofthisinvasionriskdependsontheenvironmentalsuitabilityofarecipi-
entcellforthisspecies,thefecundityofpopulationsinsourcecells(i.e.thosecellsthat
arealreadyinvadedatagiventime)andthegeographicaldistancebetweenrecipientcell
andsourcecells.Wefurtheruse‘background’introductionsasaglobalpropagulesource
thatisindependentofinvadedsourcecellsinthestudyareaandrepresents,forexample,
introductions via international trade of planting and bird seed commodities that are
knowntobeimportantinthecaseofA.artemisiifolia(Chapmanetal.2016).
In our model, environmental suitability of grid cells varies spatio-temporally and is
givenusingalog-linearmodelcomponent:
a
S (t)(cid:2)evi,t(cid:3)(cid:2) (cid:3) i , (1)
i a
wherev isthevectorofenvironmentalattributesofcelliattimet,(cid:2)isthevectorofasso-
i,t
ciatedweightingparameters(tobeestimatedbymodelfitting),a isthecell’sterrestrial
i
area,andaisthemeanterrestrialareaacrossallcells.Inthisrepresentation,environmental
Mangetal.:ClimatewarmingdrivesinvasionhistoryofAmbrosiaartemisiifolia 65
suitability is a relatively-scaled measure where S(t) = 1 is the threshold that separates
i
negative from positive effects of cell attributes on suitability. Environmental attributes
varyovertime.Inaccordwiththisspecies’preferencesweusedthefollowingsixvari-
ables to characterize the environment: mean temperature and total precipitation during
thegrowingseason(April–October);theproportionofcroplandareaandofurbanarea;
andthelength(scaledrelativetoarea)ofmotorwayandrailwaynetworks(Dullingeret
al.2009,Jolyetal.2011).Non-climatevariableswerelog-transformedtoimprovesym-
metry and reduce the effect of outlier values. All variables were standardized and the
magnitudesoftheobtainedparameterestimateshencerepresentrelativeeffectsizes.For
detailsondatasourcesanddataprocessingseeElectronicAppendix3.
Accountingforenvironmentalsuitabilityandthevariouspropagulesources,forrecip-
ientcellitheinvasionriskasafunctionoftimeisgivenby
(cid:8) (cid:11)
g (t)(cid:2)S (t)(cid:3)(cid:10) (cid:14)R (cid:4)t(cid:6)1(cid:5)(cid:3)(cid:2)(cid:3)f (cid:4)d (cid:5)(cid:7)(cid:3)(cid:7)(cid:3) (cid:3)1(cid:4)t (cid:2)t (cid:5)(cid:15), (2)
i i j 2D i,j b s
(cid:9)j(cid:13)(cid:2)(cid:4)t(cid:6)1(cid:5) (cid:12)
where(cid:2)(t–1)isthesetofcellsalreadyinvadedatthegiventime(i.e.potentialsource
cells;weuseanoffsetofoneyearasA.artemisiifoliaproducesseedattheendofagrow-
ingseasonandgerminationoccursinspring),R(t–1)isasourcecell’sestimatedinva-
j
sion level (see below) at that time, the parameter (cid:2) is the propagule production rate of
source cells per unit invasion level, f (d ) is the kernel function for dispersal from
2D i,j
asourcecelltotherecipientcellintwo-dimensionalspacedependentonthegeographical
distancebetweenthecells,d (theEuclideancentroid-to-centroiddistance,inkm),and
i,j
theparameters(cid:3)and(cid:3) definetherateofbackgroundintroductions.Weusedtheapprox-
b
imation R(t – 1) = S(t – 1) to reflect long-term average differences in invasion levels
j j
amongcells,whichdifferinenvironmentalsuitabilityand,specifically,torepresentdif-
ferencesinsourcestrengthamonginvadedcells.Theparameter(cid:2)scalestherateofspread
fromsourcecellstorecipientcellsatthegridcelllevel.Toderivef (d ),weprojected
2D i,j
theone-dimensionalkernelfunctionf (d )=d –(cid:4),where(cid:4)isashapeparameter,into
1D i,j i,j
two-dimensional space and normalized it (Electronic Appendix 4). The parameter (cid:3)
definesthegenericbackgroundintroductionrate,andtheparameter(cid:3) providesanaddi-
b
tionalboostapplicableonlytothemodelstarttime,t (viatheindicatorfunction 1),to
s
determinethenumberofinitiallyinvadedcells.Innumericalterms,equation(2)defines
a rate, with higher values of g(t) corresponding to earlier expected invasion time (for
i
detailsseeElectronicAppendix2andMangetal.(2017)).
Observationprocess
Theobservationprocessmodelsthedelaybetweenfirstinvasionofagridcellandfirst
detectionofthespecies’occurrenceinthatcell(Mangetal.2017).Inourmodel,detection
efficiencyvariesspatio-temporally.Forinvadedcellidetectabilityasafunctionoftimeis:
h (t)(cid:2)(cid:5)(cid:3)R(t)(cid:6) (cid:3)emi,t(cid:3)(cid:3), (3)
i i
wheretheparameter(cid:5)definesthebasedetectionrate,theparameter(cid:6)relatesmodelled
invasionleveltodetectability,m isthevectorofsamplingintensityattributesofthecell
i,t
attimet,and(cid:3)isthevectorofassociatedweightingparameters(tobeestimatedbymodel
66 Preslia90:59–81,2018
fitting).Weusedthefollowingvariablestodescribespatio-temporaldifferencesinsam-
plingintensityandthustoalsoaccountforspatio-temporalbiasesinthedistributiondata:
(i)humanpopulationdensityinandaroundagivencellandyear(log-transformedand
standardized);(ii)whetherayearwaspriortotheonsetoftheCentralEuropeanFloristic
MappingProjectin1970(abinaryindicatorvariable);(iii)thedegreeofintensifiedsam-
plingforA.artemisiifoliainparticularregionsandyears,measuredcontinuouslyin[0,1]
asacell’sshareoftheareainregionswithintensifiedsampling;andwhether(iv)agrid
cellwasin Germany, or (v) in thegroup Czech Republic, Slovakiaand Slovenia(both
variablesmeasuredcontinuouslyin[0,1]asacell’sshareoftheareainthesecountries).
InAustria,intensifiedsamplingforA.artemisiifoliaoccurredinselectpoliticaldistricts
since1996andinadditionaldistrictssince2005.InHungaryandSwitzerland,intensified
samplingstartedin1995and2005,respectively,butthisinformationwasonlyavailable
atthecountrylevel.Intensifiedsamplingresultedinastrongincreaseinthenumberof
records from these countries. Effectively, the latter three variables make it possible to
estimateseparatedetectabilityfor(a)thesetofcountriescomposedofAustria,Hungary,
SwitzerlandandLiechtensteinundereither(a-1)non-intensifiedsampling(givenimplic-
itlybythebasedetectionrate),or(a-2)intensifiedsampling;andfurther,thelong-term
averagedetectabilityfor(b)Germany,and(c)thesetofcountriescomposedoftheCzech
Republic, Slovakia and Slovenia. For details on data sources and data processing see
ElectronicAppendix3.Innumericalterms,equation(3)definesarate,withhighervalues
of h (t) corresponding to earlier expected detection time (for details see Electronic
i
Appendix2andMangetal.(2017)).
Non-hierarchicalmodel
Toinvestigatewhethertheobservationprocessmightaffecttheconclusionsdrawnfrom
our analysis, we also used a simplified model that ignored observation issues and
assumedthatthedocumentedspreadaccuratelyreflectedtheactualspread.Inthismodel,
recorddateswerethusdirectlyusedasthecells’invasiontimes.Thissimplifiedmodel
usesonlytheinvasionprocesslayerandisthereforenon-hierarchical.Formodeldetails
seeElectronicAppendix2.
Modelversions
Themainpurposeofourstudywastodetectapotentialeffectofrecentclimatechangeon
theinvasionhistoryofA.artemisiifoliainthestudyarea.Toseparatespatialeffectsofcli-
matefromtemporaleffects,wefittedthreemodelswithdifferentrepresentationsofcli-
matevariationovertimetothespecies’regionalspreadbetween1900and2010.Inthe
firstmodel,climaticconditionsintheindividualcellsweretreatedasconstantovertime
andsettotheaveragetemperatureandprecipitationvaluesrecordedoverthemodelling
period. Spatial climate variation was thus retained but temporal climate variation was
eliminated.Inthesecondmodel,climaticconditionswererepresentedbyanannualtime
series of temperature and precipitation values for each cell. This model integrates the
available information at the most detailed level and is, therefore, henceforth called the
‘mainmodel’.Inthethirdmodel,climatewarmingwasrepresentedinasimplifiedway,
namely as a linearly interpolated trend series. For temperature, we used the (per cell)
regression line from regressing annual temperature values against years. For precipita-
Mangetal.:ClimatewarmingdrivesinvasionhistoryofAmbrosiaartemisiifolia 67
tion, to accountfor its negativecorrelation with temperaturein thestudy area, wefirst
assessed (per cell) the expected change in precipitation per change in temperature by
usingalinearregressionofannualprecipitationvaluesagainstannualtemperatureval-
ues. For each year, we then calculated the difference between recorded and linearly
regressedtemperaturevalues,usedthisdifferencetopredicttheexpectedchangeinpre-
cipitation,andoffsettheannualprecipitationvaluesaccordingly.Allothernon-climatic
descriptors of the environment were represented by annual time series of values in all
threemodels.
Modelfitting,comparisonandvalidation
AllmodelswerefittedtothedocumentedspreadofA.artemisiifoliausingaBayesianinfer-
enceapproachwithMarkovchainMonteCarlo(MCMC)(Gelmanetal.2004,Brooksetal.
2011).Inthehierarchicalmodel,wethusinferredparametersoftheinvasionprocessand
observationprocess,andtheactualinvasiontimesofallgridcells.Inthenon-hierarchical
model,weinferredparametersoftheinvasionprocess.Vague(marginal)priordistributions
wereusedforallmodelparameters(ElectronicAppendix5)andforeachmodeltheposte-
rior distribution was therefore nearly all exclusively determined by the data. For each
modelwesampled100,000MCMCiterationsafteraburn-inperiodof10,000iterations.
Forthehierarchicalmodelthisrequiredaboutninedaysofcomputingtime(perindividual
model).ForfurtherdetailsontheMCMCsamplingschemeseeElectronicAppendix5.
Estimates of the invasion process parameters and observation process parameters
weresummarizedbythemedianandthe95%(central)credibleintervaloftherespective
(marginal)posteriordistributions.Theweightingparametersofenvironmentalattributes
and sampling intensity attributes, (cid:2) and (cid:3), respectively, have 0 as the null-hypothesis
value for a neutral effect and Bayesian kind of significance testing therefore assesses
whetherthecredibleintervaloverlapsthisnull-hypothesisvalue.Estimatedactualinva-
siontimesofgridcellsweresummarizedby(i)themedianandthe95%(central)credible
intervalofthenumberofcellsinvadedbyagivenreferenceyear;and(ii)theprobability
thatacellhadalreadybeeninvadedbyagivenreferenceyear,calculatedasthepropor-
tionofMCMCiterationsinwhichtheestimateoftheactualinvasiontimeofaparticular
cellwaslessthanorequaltothisreferenceyear.
The three models with different representations of climate variation over time were
compared for quality of fit using the Bayesian information criterion (BIC) (Hooten &
Hobbs2015).WeusedthemedianBICvalueoftheposteriordistributionasreference.
AlowerBICvaluerepresentsabetterfit.
For model validation, we also fitted models for the period 1900–2005 using only
recordsupto2005andretainedallsubsequentrecordsasindependentdata.Wethenused
10,000drawsfromtherespectiveposteriordistributions(everytenthMCMCiteration),
including cells’ estimated actualinvasion statesby 2005, to simulateboth theinvasion
process and the observation process further until 2010. For the hierarchical model, we
calculated the probabilities of cells being invaded and detected as such by 2010 as the
proportionofsimulationrunsinwhichinvasionanddetectionoccurredby2010.Forthe
non-hierarchicalmodel,wecalculatedtheprobabilitiesofcellsbeinginvadedby2010as
theproportionofsimulationrunsinwhichinvasionoccurredby2010.Forallcellsthat
werenotdocumentedasalreadyinvadedby2005(19,374cellsintotal),wethencom-
68 Preslia90:59–81,2018
paredtheseprobabilitiesagainsttherecordsfromtheperiod2006–2010usingthearea
underthereceiveroperatingcharacteristic(ROC)curve,AUC.
Results
Invasionprocess
Inthe‘mainmodel’,amongthevariablesusedtocharacterizetheenvironmentalsuitabil-
ity(ES)ofgridcellsforinvasionbyA.artemisiifolia,temperature,precipitation,thepro-
portionofurbanareapercellandthelengthofrailwaynetworkspercellweresignificant
(Table 2). Of these variables, temperature was the most important (largest parameter
magnitude),followedbyprecipitation.Duringtheperiod1980–2010,forcellswithES(cid:16)1
temperature (mean temperature during the growing season) was, on average, 11.9 °C,
whereasforcellswithES>1temperaturewas,onaverage,14.7°C,andfurther15.9°C
forthesubsetofcellswithES>2(Table3).Precipitationwas,onaverage,fairlysimilar
across the gradient of ES values. However, as precipitation was negatively correlated
with temperature in the study area, among cells with equal temperature the cells with
more precipitation were more suitable. The proportion of urban area was, on average,
0.09forcellswithES>1butonly0.02forcellswithES(cid:16)1.Forthe(area-scaled)length
ofrailways,thisdiscrepancywasevenmoreextremewithaveragevaluesof1.44forcells
withES>1and0.17forcellswithES(cid:16)1.Ingeneral,cellswithhigherESvalueswere
characterizedbyhighervaluesofallenvironmentalattributesexceptprecipitation(Table3).
Ofthedifferentclimateregionsinthestudyarea,thelowlandcontinentalareasinand
aroundthePannonianBasininthesouth-eastandthesurroundingsofthewarmRhineval-
ley in the westwere identified as mostsuitable for invasion (Fig. 3A,B). Environmental
suitabilitywaslowerinregionswithanoceanicclimateinthenorthofthestudyareaandin
montane regions. Finally, environmental suitability was minimal in the alpine regions
(Fig.3A,B).Forthemajorpartofthe20thcenturyenvironmentalsuitabilitychangedonly
marginally(Table4A;medianduringtheperiod1900–1949:0.94;andduringtheperiod
1950–1979: 0.97) and without a conspicuous spatial pattern (Fig. 3C). However, pro-
nounced climate warming during recent decades triggered a strong average increase in
environmentalsuitability (Table 4A;median during the period 1980–2010:1.15) and so
throughoutthestudyarea(Fig.3D).Asaresult,thenumberofcellswithsuitableenviron-
mentalconditionsalsoincreasedstronglyduringtheperiod1980–2010(Table4B).
Aninvadedneighbouringcellwasastrongerpropagulesourcethanbackgroundintro-
ductions in general, and particularly so in regions where suitable climate promoted
sourcestrength(Table2).Thespreadpatternwasthereforedrivenbytheinterplayofthe
establishmentofscattered,initialinvasionfociandsubsequentlocalspreadwithexpo-
nential acceleration as progressively more cells were invaded and thus functioned as
additionalpropagulesources.Thedispersalkernelparameterestimateindicatesthatmost
dispersaloccurredwithinadistanceofafewcells(Fig.4,Table2).
Observationprocess
The modelled invasion level within a cell had a moderate effect on the detectability of
speciesoccurrences(Table2).Detectabilitysignificantlyincreasedwiththehumanpop-