Environmental and land-use variables determining the abundance of Ambrosia artemisiifolia in ...

17 Pages·2011·0.56 MB·English

Preslia83:219–235,2011 219
Environmental and land-use variables determining the abundance
of Ambrosia artemisiifolia in arable fields in Hungary
Vliv faktorů prostředí na abundanciAmbrosia artemisiifoliana zemědělské půdě v Maďarsku
Gyula Pinke1,Péter Karácsony1,Bálint Czúcz2&Zoltán Botta-Dukát2
1Faculty of Agricultural and Food Sciences, University of West Hungary, H-9200
Mosonmagyaróvár, Hungary, e-mail: [email protected]; 2Institute of Ecology and
Botany, Hungarian Academy of Sciences, H-2163 Vácrátót, Hungary
PinkeG.,KarácsonyP.,CzúczB.&Botta-DukátZ.(2011):Environmentalandland-usevariables
determiningtheabundanceofAmbrosiaartemisiifoliainarablefieldsinHungary.–Preslia83:
219–235.
AmbrosiaartemisiifoliaisthemostnoxiousinvasivespeciesofweedinHungary.Theaimofthis
studywastoquantifytheenvironmentalandland-usefactorsthatexplainthevarianceinitsabun-
danceinarablefields.Asurveyof243arablefieldswascarriedoutacrossHungary,and19environ-
mentaland12land-usefactorsweremeasured.Thesewereusedasexplanatoryvariablesinclassifi-
cationandregressiontreemodels.TheabundanceofA.artemisiifoliawassignificantlyhigheratthe
edgesthanatthecentresoffields.Themostimportantland-usevariablesexplainingthevariancein
abundanceofA.artemisiifoliawerecroptypeandcropcover,withthehighestabundancerecorded
insunflowerfieldsandfieldswithlowcropcover.Thefollowingexplanatoryenvironmentalvari-
ableswereassociatedwithsignificantlyhigherA.artemisiifoliaabundance:sandyoracidicsoils,
meanAprilprecipitation>39mm,meanannualprecipitation>592mmandmeanMaytemperature
<15.5°C.Ambrosiaartemisiifoliawassignificantlylessabundantinfieldswithsoilscontaining
highconcentrationsofNa,KandMn.Bothfarmersandnatureconservationistsshouldbemade
awareoftheconditionsandpracticesthatfavourragweedsothattheycandevelopeffectiveand
selective ragweed control practices, particularly in arable habitats with a high diversity of weeds.
Keywords:agriculture,arablefields,decisiontrees,invasion,invasiveplants,ragweed,weed
distribution, weed ecology
Introduction
Commonragweed(AmbrosiaartemisiifoliaL.),anannualalienplantofNorthAmerican
origin, isone of the most noxious invasive speciesin Europe, particular because itpro-
duceslargequantitiesofallergenicpollen,whichcausesseverehealthproblems(Kazinczi
etal.2008b).TheinvasivenessofA.artemisiifoliaisattributedtoseveralfeaturesofthe
plant.Firstly,ithasawideecologicaltoleranceandcancolonizealargerangeofdisturbed
habitats(Fumanaletal.2008b).Itsinvasionisalsofacilitatedbyitslargepersistentseed
bank(Fumanaletal.2008a),hugeplasticityinseedmass(Fumanaletal.2007),resistance
to certainherbicides (Kazinczi etal. 2008c), allelopathiceffect(Kazinczi etal. 2008b),
arbuscular mycorrhizal fungal symbiotic interactions (Fumanal et al. 2006), the lack of
naturalenemies(MacKay&Kotanen2008),thehighgeneticvariabilityofinvasivepopu-
lations(Gentonetal.2005,Chunetal.2010)andthehighincidenceofout-crossingincol-
onizing populations (Friedman & Barrett 2008).
Ambrosiaartemisiifoliaisdispersedpredominantlybyhumanactivities,viaagricultural
machines,soilorseedtransport,butwaterdispersalalongrivercorridorsanddispersalby
220 Preslia83:219–235,2011
birds are also documented (Bassett & Crompton 1975, Bohren et al. 2006, Lavoie et al.
2007). This species was introduced into Europe in various ways in the late 19th century,
mainlyasacontaminantofcrop(Chauveletal.2006)andbirdseed(Brandes&Nitzsche
2007).Itcolonizesmostlydisturbedhabitatssuchasroadmargins,riverbanks,wasteland
andcultivatedfields(Fumanaletal.2008b).CurrentlyA.artemisiifoliaseverelyadversely
affectsthehealthofpeopleandreducescropyieldsinmanyEuropeancountries,including
France,Italy,Hungary,Croatia,UkraineandRomania(Song&Prots1998,Fumanaletal.
2008b,Hodisan2008,Kazinczietal.2008b).Globalwarmingmightenhanceitsnaturaliza-
tioninmorenortherlycountries(Dullingeretal.2009,Essletal.2009).
The invasion of the Carpathian-basin began in the 1920s, after the First World War,
when cerealseedcontaminatedwithseedof A.artemisiifoliawasimported through the
seaportsoftheformerAustro-HungaryMonarchy(Szigetvári&Benkő2008,Csontoset
al.2010).Thiswasfollowedbyaslownorthwardspreadaccompaniedbysporadiclong-
distancedispersalevents,whichresultedinitbecomingnaturalizedinmostagricultural
areasintheCarpathianBasin.According toHungarian nationalweedsurveys,ragweed
occupiedthe21stpositiononthelistofthemostabundantarableweedsin1950,then8th
in1970,4thin1988andfirstin1997.Thespreadofthisspecieswasprobablyfavouredby
boththesocialist-typelarge-scaleagriculture(~1960–1990)andthelessintensivefarming
practices in the post-communist period, after 1990 (Kiss & Béres 2006).
AccordingtothelatestHungariannationalweedsurvey,A.artemisiifoliastillretainsits
primacy among species of agricultural weeds and is still increasing in abundance and
spreadingintothenorth-westernpartsofthecountry(Nováketal.2009).Hungaryisnot
homogeneouslyinfestedwithragweed;someregionsarehighly(meancoverabove10%),
whileothersareless(meancoverunder0.5%)infested,suggestingthatcertainenviron-
mentalandland-usefactorsmightinfluencetheabundanceofA.artemisiifoliainarable
fields.Thereareseveralrecentpapersonthedistribution,habitatpreferencesandmethods
used to control A. artemisiifolia (e.g. Balogh et al. 2008, Kazinczi et al. 2008a, b, c,
Szigetvári&Benkő2008),buttherearenocomplexbroad-scale(e.g.country-level)stud-
iesonthelinksbetweendifferentfactorsandragweedabundance.Theaimofthisstudy
wastoidentifythoseenvironmentalandland-usevariablesthatdetermineragweedcoloni-
zation of arable fieldsand the appropriate statisticalmethodology. The identificationof
the factors positively or negatively associated with the abundance of A. artemisiifolia
mightprovidenewinformationabouttheecologyofthisspecies,whichcouldbeusedto
optimise the methods used to control this weed.
Materials and methods
Data collection
TheterritoryofHungarywasdividedinto56gridcellsbetween45°30'–49°00'Nlatitude
and16°00'–23°00'Wlongitude(Fig.1).Ineachcell,farmerswereidentifiedwhopermit-
tedaccesstotheirfieldsandwerewillingtobeinterviewedaboutland-usefactors.Ineach
cell,fivearablefieldsbelongingtooneortwofarmerswererandomlychosen.Ingridcells
crossingthecountryborders,wherethegreaterpartofthecellisinaneighbouringstate,
usuallyonlythreefieldswereselected.Thisprocedureresultedinasetof243arablefields
evenly distributed across Hungary (Fig. 1). The weeds growing in these fields were
Pinkeetal.:Ambrosiaartemisiifoliainarablefields 221
Fig.1.–Thelocations,inthegeographicgridcellsusedforstratification,ofthe243fieldssurveyedacrossHun-
gary. At this scale individual points may represent a number of fields.
assessedinfour50m2plots,whichwereselectedatrandom.Oneplotwaslocatedatthe
edgeofthefield,butwithinthecultivatedarea,andthreeplotswerelocatedatdifferent
distances(between10and300m)fromtheedgeofthefield.Percentageplantcoversof
A. artemisiifolia, other weeds and crop species in the plots were estimated visually
between27Julyand25August2009,whenA.artemisiifoliawasfullygrown.Intotal,972
plotsweresampled(fourplotsineachof243fields).Thissurveyfocusedonfieldswith
cropsthatareregularlygrownthroughoutHungary.Accordingly,thecropsintheselected
fieldswereeithermaize(ZeamaysL.)orsunflower(HelianthusannuusL.),orthestubble
ofdifferentcereals:wheat(TriticumaestivumL.),triticale(TriticosecalerimpauiWittm.),
barley(HordeumvulgareL.andHordeumdistichonL.),oats(AvenasativaL.)andoilseed
rape(BrassicanapusL.).Soilsamples(~1000cm3fromtheupper10cmlayer)werecol-
lected,afterremovingthesurfacelitter,ineachfieldinthesameareaastheweeddata.The
samplesfromthefourplotsineachfieldweremixedthoroughlyandanalysedinalabora-
tory(belongingtoUISUngarnGmbH)accreditedbyDAP(GermanAccreditationSystem
for Testing).
Information on crop management (land use) was collected directly from the farmers
using a dedicated questionnaire in which the following characteristics of their practice
wererecorded:sowingdate,precedingcrop,organicmanure,thetypeandamountoffer-
tilizerandherbicide,mechanicalweedcontrol,tillagesystem,tillagedepthandfieldsize.
Intotal12groupsofland-usevariableswereincludedintheanalysis(asshowninbrackets
below). The variable(1) ‘crop type’ included maize,sunflower and stubble. In order to
reducethenumberofcategoriesandavoidrarefactors,thestubbleofthedifferentcereals
222 Preslia83:219–235,2011
andoilseedrapewereplacedinonegroup,‘stubble’.(2)Cropcover(%)and(3)sowing
datewerealsoincludedintheanalysis.Thevariable(4)‘precedingcrop’includedallthe
above and some additional crops: alfalfa (Medicago sativa L.), potato (Solanum
tuberosumL.),soybean(GlycinesojaL.)andbean(PhaseolusvulgarisL.).Thedifferent
cerealspecieswereherealsoplacedinonegroup,‘cereal’.(5)Organicmanure(t·ha–1),
(6)thetype(N,P O ,K O,MgO,CaO)andamount(kg·ha–1)offertilizerand(7)thetype
2 5 2
(53activeingredients)andamount(g·ha–1orl·ha–1)ofherbicide,(8)mechanicalweed
control(times),(9)fieldsize(ha),(10)tillagesystem(conventionaltillage,minimumtill-
age,no-tillage)and(11)tillagedepth(cm)werealsoincludedintheanalysis.Inaddition,
(12)thelocationoftheplot(edgeorfieldcentre)wasalsoincluded.Duetothegreatvari-
ety of herbicides used this factor was later excluded from the statistical analysis.
For each field investigated 19 environmental variables were compiled, including (a)
soilproperties,suchas(1)soilpH(KCl),(2)soiltexture(coarsesand,sand,sandyloam,
loam,clayloam,clay),assessedonthebasisofStefanovitsetal.(2005),(3)thecontent
(m/m%)ofsalt,referingtothetotalamountofsaltinthesoilthatcanbedissolvedinwater,
(4)humusand(5)CaCO,(6)thecontent(mg·kg–1)ofP O ,(7)K O,(8)Na,(9)Mg,(10)
3 2 5 2
NO -NO -N,(11)SO ,(12)Cu,(13)Mn,(14)Zn;(b)climaticconditions,representedby
2 3 4
(15)meanannualandmonthly(fromApriltoAugust)temperaturesand(16)meanannual
andmonthly(fromApriltoAugust)rainfall,obtainedfromtheHungarianMeteorological
Service (HMS 2001) and WorldClim Databases (Hijmans et al. 2005); (c) geographic
position(measuredusingaGPSreceiver)intermsof(17)sitelongitude,(18)latitudeand
(19) altitude. Geographic coordinates were included in the analysis as surrogates of
unmeasured environmental variables (e.g. climate). Furthermore, longitude and latitude
are also related to the residence time of A. artemisiifolia, as it is continuing to slowly
spreadfromsouth-westtonorthandeastinHungary.Therangesandcategoriesofenvi-
ronmental and land-use gradients used in this study are given in Tables 1 and 2.
Data analysis
The abundance (percent cover) of ragweed at the edges of fields and the average abun-
dance in the three plots in each field were compared using a Wilcoxon paired test.
Next,theland-useandenvironmentalvariableswereusedasexplanatorypredictorsin
a series of statistical models. The response variables in this analysis were either the (i)
plant cover values of Ambrosia artemisiifolia, or (ii) three simplified cover categories,
whichwerenone (0), low(<10%) orhigh (>10%), or(iii)itspresence/absencestatus.
Resultsforfieldedgesandfieldswereanalysedseparately,butdataforthethreeplotsfrom
the same field were averaged prior to analysis. The different simplifications of the
response variables (i.e. cover, cover categories and presence-absence data) may be
affectedbydifferentenvironmentalandland-usefactors,andtheiranalysismayhighlight
differentaspectsofragweed’sdependenceonenvironmentalconditionsandland-use.As
a raw measure of ragweed abundance, estimated cover values seem to convey the most
information about the environmental preferences of this species. Nevertheless, above
acertainthresholdtheexactcovervaluemaybe(i)morerelatedtostochasticeventsthan
thefactorsstudiedand(ii)practicallyirrelevantwithrespecttothenegativeeffectsofthis
species.Presence/absencedata,ontheotherhand,canbeusedtofocusdirectlyonthecon-
ditions associated with the absence of ragweed.
Pinkeetal.:Ambrosiaartemisiifoliainarablefields 223
Table 1.– Ranges and categories of environmental variables used in this study.
Factor Min Max
Geographical position
Altitude (m) 82 415
Latitude (N) 45°52'09'' 48°32'59''
Longitude (EO) 16°49'57'' 22°32'95''
Climatic conditions
Mean annual precipitation (mm) 470 766
Mean April precipitation (mm) 33 63
Mean May precipitation (mm) 51 81
Mean June precipitation (mm) 62 92
Mean July precipitation (mm) 49 97
Mean August precipitation (mm) 46 93
Mean annual temperature (°C) 8.8 11.2
Mean April temperature (°C) 9.2 11.9
Mean May temperature (°C) 13.9 16.6
Mean June temperature (°C) 17.3 19.7
Mean July temperature (°C) 19.0 21.5
Mean August temperature (°C) 18.5 21.1
Soil properties
Soil pH (KCl) 3.48 7.85
Soil texture (coarse sand, sand, sandy loam, loam, clay loam, clay) – –
Salt (m/m%) 0 1.14
Humus (m/m%) 0.5 6.43
CaCO (m/m%) 0.01 37.3
3
PO (mg · kg–1) 44.8 2810
2 5
KO (mg · kg–1) 57.8 1310
2
Na (mg · kg–1) 19.0 650
Mg (mg · kg–1) 19.5 1660
NO-NO-N (mg · kg–1) 0.49 390
2 3
SO (mg · kg–1) 6.25 774
4
Cu (mg · kg–1) 0.31 13.1
Mn (mg · kg–1) 8.39 506
Zn (mg · kg–1) 0.1 22.8
Table 2. – Ranges and categories of land-use variables used in this study.
Factors Min Max
Crop type (maize, sunflower, stubble) – –
Crop species (8 species) – –
Crop cover 0 100
Date of sowing (3 January 2008–30 November 2009) – –
Preceding crop type (maize, sunflower, cereal, others) – –
Preceding crop species (12 species) – –
Organic manure (t · ha–1) 0 60
Amount of fertilizer (kg · ha–1)
N 0 261
PO 0 104
2 5
KO 0 150
2
MgO 0 24.5
CaO 0 43
Mechanical weed control (number of treatments) 0 2
Field size (ha) 0.24 524
Tillage system (conventional tillage, minimum tillage, no tillage) – –
Tillage depth (cm) 0 60
Plot location (field edge, field core) – –
224 Preslia83:219–235,2011
Decisiontreemodelswereusedtoestablishrelationshipsbetweenthepredictorsand
theresponsevariables.Decisiontrees(alsoknownasclassificationandregressiontrees)
arenon-parametricstatisticalmethodsthatestimatetheresponsevariableasapiecewise
constantfunctionofthepredictorsbysplittingthesampleusingthemostinfluentialpre-
dictorvariableandassigningconstantvaluestoeachresultingsub-domaininarecursive
process(Breimannetal.1984).Accordingly,suchmodelscanhandlenonlinearrelation-
ships,verylargesetsofmixedtype(i.e.bothcategoricalandcontinuous)predictors,and
theresultsare easy to interpret and indicatethevariablethatsignificantlydiscriminates
betweenclasses(Crawley2007).Furthermore,astheyhandlethepredictorsonebyone,
theyareessentiallyfreefromproblemscausedbymulticollinearity.Decisiontreemodels
arefrequentlyusedtostudythedistributionofinvasiveorganisms(Usioetal.2006,Hejda
etal.2009,Pyšeketal.2010)andanalysethefactorsaffectingarableecosystems(Ferraro
et al. 2009).
Decisiontreemodelswerefittedusingthepartyadd-onpackage(Hothornetal.2006)
intheR2.9.2environment(RDevelopmentCoreTeam2009).Thefunctionctreegener-
atesabinarydecisiontreethroughrecursivepartitioningandappliessimplepermutation
testsbased on the conditional inference framework of Strasser & Weber (1999) at each
recursive step. Accordingly, the ctree framework is free of the variable selection bias,
commontomostotherclassificationorregressiontreealgorithms.Furthermore,permuta-
tiontestsprovideastatisticallysoundinherentstoppingruleforthemodelbuildingpro-
cess, which eliminates problems of under- and over-fitting.
Results
Ambrosia artemisiifolia was not recorded in 29 fields and in 48 it occurred only at the
edgesofthefields.Itsmaximumcoverwas70%attheedgesand60%atthecentreofthe
fields.Itsmeancoverwas11.9%attheedgesand5%atthecentresofthefields,respec-
tively.TheabundanceofA.artemisiifoliawassignificantlyhigherattheedgesthanatthe
centresoffieldswhenallfieldswereanalysedtogether.Thesameresultswereobtained
when the different crop types were analysed separately (Table 3).
Table3.–ComparisonofAmbrosiaartemisiifoliacoverattheedgesandcentresoffieldsusingWilcoxonpairedtests.
Studied subset of data Test statistic Number of fields Type I error rate (P value)
All fields 19094.5 243 < 2.2 ×10–16
Maize fields only 3487 102 1.588 ×10–14
Sunflower fields only 2016 71 3.405 ×10–11
Stubble only 1150.5 70 0.02272
Classification and regression treesfor edges offields
Usingcovervalues,croptypewasthepredictorvariablethatdeterminedthefirstsplitin
theregressiontree(Fig.2).Sunflowerfieldswereseparatedfrommaizeandstubblefields.
ForsunflowersubsequentsplitsweredeterminedbysoilpHandmeanMaytemperature;
sunflowerfieldswithasoilpH<5.03orwithsoilpH>5.03andameanMaytemperature
<15.5°CwereassociatedwiththehighestcovervaluesofA.artemisiifolia.Usingcover
Pinkeetal.:Ambrosiaartemisiifoliainarablefields 225
Fig.2.–RegressiontreemodelofthecovervaluesofAmbrosiaartemisiifoliarecordedattheedgesoffields.
Eachsplitisdescribedwiththepredictorusedatthesplit,theBonferroni-correctedsignificance(P-value)ofthe
splitandthevaluesatwhichthesplitoccurs.Ateachterminalnodethenumberofobservations(n)isgivenalong
withthevaluesoftheresponsevariables(verticalaxesoftheboxplotsshowingmedian,upperandlowerquartile,
minimum and maximum) and their geographic locations (black dots on the small maps).
categoriesatfieldedges,soilKcontentwasthemostimportantexplanatoryfactor,indicat-
inghigherinfestationofA.artemisiifoliainfieldswhereK Owas<429mg(Fig.3).The
2
subsequentsplittingvariablesweregeographicallongitudeandfieldsize,suggestingthe
lowest infestation are recorded at longitudes > 20°13' and in fields > 7 ha. At the pres-
ence/absence level soil Na content was the only significant predictor, showing that
A. artemisiifolia was less frequently present at the edges of fields with a Na content
> 66.9 mg (Fig. 4).
226 Preslia83:219–235,2011
Fig.3.–ClassificationtreemodelofthecovercategoriesofAmbrosiaartemisiifoliarecordedattheedgesof
fields:none(0),low(<10%)high(>10%).Eachsplitisdescribedwiththepredictorusedatthesplit,the
Bonferroni-correctedsignificance(P-value)ofthesplitandthevaluesatwhichthesplitoccurs.Ateachterminal
nodethenumberofobservations(n)isgivenalongwiththevaluesoftheresponsevariables(barchartsshowing
theproportionsofobservationsofthedifferentcovercategories)andtheirgeographiclocations(blackdotsonthe
small maps).
Classification and regression treesfor the centres of fields
UsingtherawcovervaluesforA.artemisiifolia,cropcoverwastheprimarysplittingvari-
ableforthecentresoffields(Fig.5).Fieldswithacropcover≤31.7%werethensegre-
gated on the basis of soil texture, indicating a higher abundance of A. artemisiifolia on
sandysoils.Fieldswithcropcover>31.7%weresubsequentlyseparatedwithrespectto
altitude, indicating higher infestation above 220 m a.s.l. Soil texture was also the most
Pinkeetal.:Ambrosiaartemisiifoliainarablefields 227
Fig.4.–Classificationtreemodelforthepresence/absencedataofAmbrosiaartemisiifoliaattheedgesoffields
(see Fig. 3 for details).
importantfactorwhenthedatawereanalysedincovercategories,withhigherinfestations
recordedinfieldswithcoarsesandandsandysoils(Fig.6).Onothertypesofsoil(sandy
loam,loam,clayloamandclay),meanAprilrainfallwasthesecondmostimportantfactor
associated with high infestations if the mean April rainfall was > 39 mm. For the pres-
ence/absencedata,theoccurrenceofA.artemisiifoliawasprimarilyassociatedwithsoil
Kcontentatthecentresoffields(Fig.7).FieldswithasoilKcontent<279mgwerethen
separatedwithrespecttosoilMncontent,indicatingalowoccurrenceofA.artemisiifolia
infieldswherethesoilMncontentwas>214mg.FieldswithasoilKcontent>279mg
were then segregated on the basis of mean annual rainfall, with a high occurrence of
A. artemisiifoliaassociated with a mean annual rainfall > 592 mm.
228 Preslia83:219–235,2011
Fig.5.–RegressiontreemodelforthecovervaluesofAmbrosiaartemisiifoliaatthecentresoffields(seeFig.2
for details).
Discussion
Field edgeeffect
ThisstudyindicatesthattheabundanceofA.artemisiifoliaissignificantlyhigheratthe
edgesthanthecentresoffields.Herbicidesandfertilizersareoftenlessefficientlyapplied
near the edges of fields and the light conditions there are also usually more favourable
(Wilson&Aebischer1995).Accordingly,thishabitatisarefugiumforseveralrarespe-
ciesofarableland(Friedetal.2009),butcanalsobecomeheavilyinfestedwithnoxious
weeds, such as A. artemisiifolia, as shown by the current study. The cover values of
A.artemisiifoliaweresignificantlyhigherattheedgesthaninthecentresoffieldsforall
thecropsstudied(sunflower,maizeandstubblefields).Onepossibleexplanationforthis
isthatA.artemisiifoliaislesscompetitiveindensecrop standsbecauseitrequireshigh