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Impact of Fragmentation on Carbon Uptake in Subtropical Forest Landscapes in Zhejiang Province, China

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Jiejie Jiao
Jiejie Jiao
Jiejie Jiao
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Yan Cheng
Yan Cheng
Yan Cheng
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Pinghua Hong
Pinghua Hong
Pinghua Hong
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Jun Ma
Jun Ma
Jun Ma
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Abstract and Figures

Global changes cause widespread forest fragmentation, which, in turn, has given rise to many ecological problems; this is especially true if the forest carbon stock is profoundly impacted by fragmentation levels. However, the way in which forest carbon uptake changes with different fragmentation levels and the main pathway through which fragmentation affects forest carbon uptake are still unclear. Remote sensing data, vegetation photosynthesis models, and fragmentation models were employed to generate a time series GPP (gross primary productivity) dataset, as well as forest fragmentation levels for forest landscapes in Zhejiang province, China. We analyzed GPP variation with forest fragmentation levels and identified the relative importance of the phenology (carbon uptake period—CUP) and physiology (maximum daily GPP—GPPmax) control pathways of GPP under different fragmentation levels. The results showed that the normalized mean annual GPP data of highly fragmented forests during the period from 2000 to 2018 were significantly higher than those of other fragmentation levels, while there was almost no significant difference in the annual GPP trend of forest landscapes with all fragmentation levels. Moreover, the percentage area of the control variable, GPPmax, gradually increased with fragmentation levels; the mean GPPmax between 2000 and 2018 of high-level fragmentation was higher than that of other fragmentation levels. Our results demonstrate that the carbon uptake capacity per unit area was enhanced in highly fragmented forest areas, and the maximum photosynthetic capacity (physiology-based process) played an important role in controlling carbon uptake, especially in highly fragmented forest landscapes. Our study calls for a better and deeper understanding of the potential of forest carbon uptake, and it is necessary to explore the mechanism by which forest fragmentation changes the vegetation photosynthetic process.
Location, forest type distribution, and classified forest fragmentation levels of forest landscapes in Zhejiang, China. (a) The location of Zhejiang, China. (b) Distribution of main forest types in Zhejiang. (c) Forest fragmentation levels in Zhejiang. UNC: unclassified forest, PAT: patch forest, TRA: transitional forest, PER: perforated forest, UND: undetermined forest, EDG: edge forest, INT: interior forest. The forest type data were derived from the MODIS MCD12Q1 land cover product (from 2010), while the map of fragmentation levels was calculated using the forest fragmentation model (see Section 2.2) in this study.
Location, forest type distribution, and classified forest fragmentation levels of forest landscapes in Zhejiang, China. (a) The location of Zhejiang, China. (b) Distribution of main forest types in Zhejiang. (c) Forest fragmentation levels in Zhejiang. UNC: unclassified forest, PAT: patch forest, TRA: transitional forest, PER: perforated forest, UND: undetermined forest, EDG: edge forest, INT: interior forest. The forest type data were derived from the MODIS MCD12Q1 land cover product (from 2010), while the map of fragmentation levels was calculated using the forest fragmentation model (see Section 2.2) in this study.
… 
(a) Annual GPP dynamics of different fragmentation levels and (b) linear trend distribution of annual GPP of forest landscapes in Zhejiang between 2000 and 2018, and the comparison of the annual GPP trend values among different fragmentation levels. The red asterisk indicates the annual GPP trend of PAT is significantly (p <0.05) than that of other fragmentation categories. PAT: patch forest, TRA: transitional forest, PER: perforated forest, EDG: edge forest, and INT: interior forest.
(a) Annual GPP dynamics of different fragmentation levels and (b) linear trend distribution of annual GPP of forest landscapes in Zhejiang between 2000 and 2018, and the comparison of the annual GPP trend values among different fragmentation levels. The red asterisk indicates the annual GPP trend of PAT is significantly (p <0.05) than that of other fragmentation categories. PAT: patch forest, TRA: transitional forest, PER: perforated forest, EDG: edge forest, and INT: interior forest.
… 
Distribution of different control variables and their relative importance. (a) Spatial distribution of different control variables, (b) spatial distribution of their relative importance on annual GPP during 2000-2018, (c) comparison of relative importance among three control variables, and (d) pixel number percentages of three control variables for different fragmentation levels. PAT: patch forest, TRA: transitional forest, PER: perforated forest, EDG: edge forest, and INT: interior forest.
Distribution of different control variables and their relative importance. (a) Spatial distribution of different control variables, (b) spatial distribution of their relative importance on annual GPP during 2000-2018, (c) comparison of relative importance among three control variables, and (d) pixel number percentages of three control variables for different fragmentation levels. PAT: patch forest, TRA: transitional forest, PER: perforated forest, EDG: edge forest, and INT: interior forest.
… 
Spatial distribution of the mean values of carbon uptake period (CUP) and maximum daily GPP (GPPmax) for subtropical forest landscapes in Zhejiang Province during 2000-2018. (a) The spatial map of the mean CUP during 2000-2018. (b) The spatial map of the mean GPPmax during 2000-2018.
Spatial distribution of the mean values of carbon uptake period (CUP) and maximum daily GPP (GPPmax) for subtropical forest landscapes in Zhejiang Province during 2000-2018. (a) The spatial map of the mean CUP during 2000-2018. (b) The spatial map of the mean GPPmax during 2000-2018.
… 
Classification criteria, area, and area percentage of each forest fragmentation level of the forest fragmentation model in forest landscapes in Zhejiang, China. PAT: patch forest, TRA: transi- tional forest, PER: perforated forest, UND: undetermined, EDG: edge forest, and INT: interior forest.
+1
Classification criteria, area, and area percentage of each forest fragmentation level of the forest fragmentation model in forest landscapes in Zhejiang, China. PAT: patch forest, TRA: transi- tional forest, PER: perforated forest, UND: undetermined, EDG: edge forest, and INT: interior forest.
… 
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RemoteSens.2024,16,2393.https://doi.org/10.3390/rs16132393www.mdpi.com/journal/remotesensing
Article
ImpactofFragmentationonCarbonUptakeinSubtropical
ForestLandscapesinZhejiangProvince,China
JiejieJiao
1,2
,YanCheng
3
,PinghuaHong
4
,JunMa
5
,LiangjinYao
2
,BoJiang
2
,XiaXu
1
andChupingWu
2,
*
1
StateKeyLaboratoryofSubtropicalSilviculture,SchoolofEnvironmentalandResourcesScience,Zhejiang
A&FUniversity,Lin’an311300,China;2023202012007@stu.zafu.edu.cn(J.J.);xuxia1982@zafu.edu.cn(X.X.)
2
ZhejiangHangzhouUrbanEcosystemResearchStation,ZhejiangAcademyofForestry,
Hangzhou310023,China;lj890caf@hotmail.com(L.Y.);jiangbo2246@hotmail.com(B.J.)
3
HangzhouImmigrationInspectionStation,Hangzhou311223,China;hxj0201@hotmail.com
4
YuhangEcologicalandEnvironmentalMonitoringStationofHangzhou,Hangzhou311100,China;
hongpinghua87@gmail.com
5
MinistryofEducationKeyLaboratoryforBiodiversityScienceandEcologicalEngineering,
SchoolofLifeSciences,FudanUniversity,Shanghai200433,China;ma_jun@fudan.edu.cn
*Correspondence:wcp1117@hotmail.com
Abstract:Globalchangescausewidespreadforestfragmentation,which,inturn,hasgivenriseto
manyecologicalproblems;thisisespeciallytrueiftheforestcarbonstockisprofoundlyimpacted
byfragmentationlevels.However,thewayinwhichforestcarbonuptakechangeswithdifferent
fragmentationlevelsandthemainpathwaythroughwhichfragmentationaffectsforestcarbonup-
takearestillunclear.Remotesensingdata,vegetationphotosynthesismodels,andfragmentation
modelswereemployedtogenerateatimeseriesGPP(grossprimaryproductivity)dataset,aswell
asforestfragmentationlevelsforforestlandscapesinZhejiangprovince,China.WeanalyzedGPP
variationwithforestfragmentationlevelsandidentifiedtherelativeimportanceofthephenology
(carbonuptakeperiod—CUP)andphysiology(maximumdailyGPP—GPP
max
)controlpathwaysof
GPPunderdifferentfragmentationlevels.Theresultsshowedthatthenormalizedmeanannual
GPPdataofhighlyfragmentedforestsduringtheperiodfrom2000to2018weresignificantlyhigher
thanthoseofotherfragmentationlevels,whiletherewasalmostnosignificantdifferenceinthe
annualGPPtrendofforestlandscapeswithallfragmentationlevels.Moreover,thepercentagearea
ofthecontrolvariable,GPP
max
,graduallyincreasedwithfragmentationlevels;themeanGPP
max
between2000and2018ofhigh-levelfragmentationwashigherthanthatofotherfragmentation
levels.Ourresultsdemonstratethatthecarbonuptakecapacityperunitareawasenhancedin
highlyfragmentedforestareas,andthemaximumphotosyntheticcapacity(physiology-basedpro-
cess)playedanimportantroleincontrollingcarbonuptake,especiallyinhighlyfragmentedforest
landscapes.Ourstudycallsforabeeranddeeperunderstandingofthepotentialofforestcarbon
uptake,anditisnecessarytoexplorethemechanismbywhichforestfragmentationchangesthe
vegetationphotosyntheticprocess.
Keywords:forestcarbonsequestration;vegetationgrowth;landscapepaerns;grossprimary
productivity;phenology
1.Introduction
Forestsareakeycomponentinterrestrialecosystems[1];globally,theyplayanim-
portantroleinprovidingavarietyofecologicalservicesformankind,suchasabsorbing
andstoringcarbon,maintainingsoilandwater,andpurifyingair[2–4].However,the
world’sterrestrialbiomeshavebeensignificantlyaffectedbyhuman-inducedglobal
changes[5].Theglobalforestareahasshrunkbyaboutone-thirdsincethe1850s[6],most
ofwhichwasinducedbynaturaldisturbancesandtheexpansionoftheagriculturaland
Citation:Jiao,J.;Cheng,Y.;Hong,P.;
Ma,J.;Yao , L.;Jiang,B.;Xu,X.;Wu,
C.ImpactofFragmentationon
CarbonUptakeinSubtropicalForest
LandscapesinZhejiangProvince,
China.RemoteSens.2024,16,2393.
hps://doi.org/10.3390/rs16132393
AcademicEditors:ZhixinQi,LeYu,
LeiFang,KasturiDeviKanniahand
BrianAlanJohnson
Received:22May2024
Revised:25June2024
Accepted:28June2024
Published:29June2024
Copyright:©2024bytheauthors.
LicenseeMDPI,Basel,Swierland.
Thisarticleisanopenaccessarticle
distributedunderthetermsand
conditionsoftheCreativeCommons
Aribution(CCBY)license
(hps://creativecommons.org/license
s/by/4.0/).
RemoteSens.2024,16,23932of14
urbanindustries[7].Therapidandvastlossofforestresultsinthespliingofspatially
contiguousdistributedforestlandscapeintosmallpatches,leadingtoworldwideforest
fragmentation[8].Consideringthatsomekeyfunctionsofforestscanbedamagedbyfrag-
mentationthroughthedegradationofvegetationbiomassandbiodiversity[9,10],itisnec-
essarytocomprehensivelyunderstandtheimpactsofforestfragmentationonecosystems.
Aseriesofecologicalproblems,particularlycarbonlossandtheintensificationofthe
greenhouseeffect,aredirectlyinducedbyforestfragmentation[11–13].Severalstudies
haveindicatedthathumanactivitiesinducemoreforestedgeareas,aggravatingfrag-
mentationanddecreasingcarbonstorage[14,15].However,carbonuptakeisalsoproven
tobeenhancedinhighlyfragmentedforests[16,17].Thecomplexvariationincarbonup-
takeunderdifferentfragmentationlevelsproposesademandforassessingtherelation-
shipandtheinfluencepathwaybetweenforestfragmentationandvegetationcarbonup-
take,whichcanenhanceourinsightsintothefuturecarbonuptakepotentialofforestsin
thecontextofwidespreadforestfragmentation.Inparticular,inthecontextofintenseland
usecausedbyurbanization,forestsshowahighdegreeoffragmentation[8].Inaddition
tothedirectimpactofforestareachangeonvegetationcarbonsequestration,itisunclear
howforestdistributionpaernsaffectforestcarbonsequestration;thiswillgreatlylimit
theeffectivenessofurbanforestmanagementandecologicalconservationmeasures,and
willalsolimitourunderstandingofregionalcarboncycledynamics.
Anumberofmethodshavebeenusedtomeasureforestfragmentationlevels,with
thepatchorclasslevel’sfragmentation-relatedlandscapepaernmetricsbeingadopted
bymoststudiestoquantifythelevelsofforestfragmentation[12,16,18].However,these
approacheshaveneglectedthefactthatforestfragmentationisalandscape-scaleissue
containingcomplexspatialprocesses[19].Althoughtherearesomehierarchicalap-
proaches,whicharebasedonspatialprocesses,tomeasuringthelevelsofforestfragmen-
tation[20–22],theseweremainlyusedtoestimatethestatusofforestfragmentation,and
haverarelybeenusedtostudytheimpactsoffragmentationonecologicalfunctionsdue
tothedifficultyofaccessingfieldobservationdataforrelevantfragmentationlevels.In
reality,theclassificationresultsofthefragmentationlevelsfromthesemethodsmayhave
greatpotentialinstudiesinvestigatingtheecologicaleffectsofforestfragmentation,par-
ticularlythosewiththeassistanceoftheremotesensingapproach,toestimateecological
functionindicatorsatlargespatialscales.
Theprocessesofforestcarbonuptakecanbesimplysummarizedastheabsorption
ofatmosphericcarbondioxidebyplantsthroughphotosynthesis[23];grossprimary
productivity(GPP),adirectmeasurementofthetotalamountofcarbonassimilatedby
vegetation,isgenerallyconsideredasareflectionofvegetationcarbonuptake.Inaddition,
thevegetationcarbonstockishighlyrelatedtotheaccumulationofannualGPPovera
longperiod,whichismainlycontrolledbytwofactors—thecarbonuptakeperiod(CUP)
andthemaximumdailyGPP(GPPmax),whichareassociatedwithvegetationphenology
andphysiology,respectively[24,25].Bothofthesefactorsareaffectedbytheclimateand
soilnutrientchangecausedbyforestfragmentation[26–28];therefore,detectingthevari-
ationinCUPandGPPmaxunderdifferentfragmentationlevelsiscrucialforexplainingthe
pathwayorthemechanismoftheimpactoffragmentationonforestcarbonuptake.
Inthisstudy,forestlandscapesinZhejiangprovince,China,wereselectedasthe
studyareainwhichtoexaminetherelationshipbetweenforestfragmentationandGPP.
Multiplesourcesofremotesensingdatawereappliedtocalculateforestfragmentation
levelsandtosimulatevegetationGPP.WehaveproposedabasicassumptionthattheGPP
anditstwocomponents—CUPandGPPmax—varybetweenforestfragmentationlevels.
BasedontheanalysisofthevariationinGPP,CUP,andGPPmaxwithfragmentationlevels,
wewouldliketoanswerthefollowingtwoscientificquestions:(1)Howdoesforestcarbon
uptakechangewithdifferentfragmentationlevels?(2)Whatisthemainpathwayaffecting
forestcarbonuptakeunderdifferentfragmentationlevels?

RemoteSens.2024,16,23933of14
2.MaterialsandMethods
2.1.StudyArea
Zhejiangprovince,China,whichextendsacross27.03°–31.18°Nand118.02°–
123.17°E,wasselectedasthestudyarea(Figure1).TheforestcoverageofZhejiangprov-
inceisabout59.4%,mostofwhichisdistributedinthemountainsandhills.Thesubtrop-
icalmonsoonclimateisthedominantclimateofZhejiangprovince;ithashot,rainysum-
mersandcold,drywinters,andhasobviouscharacteristicsofseasonalchange.Themean
annualtemperatureofZhejiangisbetween15and18°C,whiletheaverageannualpre-
cipitationrangesbetween980and2000mm.ThegrowingseasonusuallystartsinMarch
andendsinOctober.Theevergreenbroadleafforestsandmixedconiferous–broadleaffor-
estsarethemainvegetationtypesofthestudyarea(Figure1);therearemorethanthirty
treespeciesintheforestlandscapesofZhejiangprovince.
Inthisstudy,thetropicalforestlandscapesofZhejiangwereselectedasthestudy
areaprimarilyforthefollowingreasons:ontheonehand,Zhejianghasadevelopedecon-
omy,wherebyurbanizationandotherhumanactivitiesinZhejianghavecauseddrastic
landusechanges.Inaddition,Zhejianghasalargeforestareaandahighforestcoverage;
thesetwofactorsresultinthedistributionpaernofsubtropicalforestsinZhejiangcon-
tainingalltypesofforestfragmentation,aswellashavingtypicalgradientsofforestfrag-
mentation.Ontheotherhand,Zhejianghasturnedtothestageofecosystemprotection
afteritsearlydevelopment;thechangeinforestcoverafter2000isquitelimitedcompared
tothatofitsneighboringregions,whichmakesitconvenientforthisstudytocomparethe
productivityanditstrendamongstaticfragmentationtypes.
Figure1.Location,foresttypedistribution,andclassifiedforestfragmentationlevelsofforestland-
scapesinZhejiang,China.(a)ThelocationofZhejiang,China.(b)Distributionofmainforesttypes
inZhejiang.(c)ForestfragmentationlevelsinZhejiang.UNC:unclassifiedforest,PAT:patchforest,
TRA:transitionalforest,PER:perforatedforest,UND:undeterminedforest,EDG:edgeforest,INT:
interiorforest.TheforesttypedatawerederivedfromtheMODISMCD12Q1landcoverproduct
(from2010),whilethemapoffragmentationlevelswascalculatedusingtheforestfragmentation
model(seeSection2.2)inthisstudy.
RemoteSens.2024,16,23934of14
2.2.ForestMapDataandFragmentationLevelsClassification
Consideringthefactthatforestcoverchangemayaltertheforestfragmentationcat-
egorieswithtime,thisstudyrequiresatemporalstablestatusofforestcover.Inorderto
excludetheinfluencefromforestcoveragechangeontheaccuracyoftheclassificationof
forestfragmentationlevels,ourstudyonlyfocusedontheforestareasinZhejiangprov-
incethathavenotchangedsince2000.Weusedgridsof500minsizetocalculatethe
proportionofforestlossbetween2000and2018(theratiooftotalforestlosspixelstototal
pixelsina500mgrid)basedontheGlobalForestChangedataset[29];thegridswitha
forestlossproportionoflessthan5%wereretainedandwererepresentativeofunchanged
forestlandscapes.Moreover,a50mresolutionforestmapfrom2010,generatedusing
ALOSPAL SAR- 2,MODIS,andLandsat-7/8satellitedata[30],wasselectedasthebasic
forestcoverdatatoconductaclassificationofforestfragmentationlevelsbasedonthe
retainedgrids(unchangedforestlandscapes).
Aforestfragmentationmodel[20,21]thatiswidelyusedtoestimatethestatusof
forestfragmentationbasedonsatellite-derivedforestmapswasemployedinthisstudyto
classifyforestfragmentationlevels.Thisfragmentationmodelwasdevelopedbasedon
theconsiderationofcausingforestlossasaresultofspatialdisturbances;ithasbeen
widelyappliedtoestimateforestfragmentationbyanalyzingforestdistributionmaps
[31,32].Additionally,forestareadensity(Pf)andforestconnectivity(Pff)aretwokeyindi-
catorsinthismodelfordefiningthefragmentationtypeofagiven“window”or“land-
scape”basedonaforest/non-forestbinarymap[20].Thesizeofthe“window”reflects
differentspatialscalesintheestimationofforestfragmentation.PfandPffarecalculated
usingthefollowingequations:
𝑃
 (1)
𝑃
 𝐷

𝐷
(2)
wherePfistheproportionofforestpixelsinagivenwindow,calculatedbydividingthe
numberofforestpixels(Nf)bythetotalnumberofpixels(Nw)inthewindow;Pffisthe
forestconnectivity,calculatedbydividingthepixelpairnumberthatincludesatleastone
forestpixel(Dff)bythepixelpairnumberthatincludestwoforestpixelsincardinaldirec-
tions(Df).
Alook-uptable(Table1)containingthecriteriaofthetwoindicators(DffandDf)was
usedtoconducttheclassificationofforestfragmentationlevels.Sixfragmentationtypes,
includingpatch(PAT),transitional(TRA),perforated(PER),undetermined(UND),edge
(EDG),andinterior(INT),wereobtained.Thefragmentationlevelsofthesesixtypesare
shownasPAT>TRA>PER>UND>EDG>INT.However,UNDwasexcludedfromour
studyduetothefactthatitonlyexistsinextremecases.Inaddition,inordertomatchthe
resolution(~500m)oftheGPPdata,a9×9size“window”wasadoptedinthisstudy.
Tab l e1.Classificationcriteria,area,andareapercentageofeachforestfragmentationlevelofthe
forestfragmentationmodelinforestlandscapesinZhejiang,China.PAT:patchforest,TRA:transi-
tionalforest,PER:perforatedforest,UND:undetermined,EDG:edgeforest,andINT:interiorforest.
Fragmentation
Categories
Classification
Criteria
Area
(104ha)
AreaPercentage
(%)
PATPf<0.4263.033.7
TRA0.4<Pf<0.6129.516.6
PER0.6<=Pf<1andPf>Pff226.128.9
UND0.6<=Pf<1andPf=Pff0.70.1
EDG0.6<=Pf<1andPf<Pff153.019.6
INTPff=19.01.2
RemoteSens.2024,16,23935of14
2.3.TimeSeriesGPPDataset
Asatellite-basedlight-useefficiencymodel—thevegetationphotosynthesismodel
(VPM)—wasusedtosimulatedtimeseriesGPPdatasetfrom2000to2018forthestudy
area.TheVPMwasdrivenusingtheMODISMOD09A1surfacereflectancedataset,the
MCD12Q1landcoverdataset,theMYD11A2landsurfacetemperaturedataset,andNCEP
reanalysisIIclimatedata.Thefinal500mresolutionGPPofeach8-dayperiodduring
2000–2018wasobtained.ThedetailsofthesimulationprocessesusingtheVPMhavebeen
reportedinpreviousstudies[33,34].
Basedonthe500m8-dayperiodGPPdataset,theannualGPPforeachyearbetween
2000and2018wasobtainedbyaggregatingallthedatainarelevantyear.Inaddition,the
GPPmaxforeachyearbetween2000and2018wasobtainedbyextractingthepeakvaluefor
eachpixelineachyear.Sincethevegetationinourstudyareausuallyhasobviouschar-
acteristicsofseasonalchange,thetemporaldynamiccurveofGPPgenerallyshowsauni-
modalshape.Therefore,inthisstudy,thegrowingseasonisdefinedusingarelative
thresholdmethod,assuggestedinpreviousstudies[35,36];thethresholdissetas20%of
theGPPmaxinagivenyear.Thecarbonuptakeperiod(CUP)foreachyearbetween2000
and2018wasobtainedbycountingthenumberofdaysinwhichtheGPPwashigherthan
thevalueof20%oftheGPPmaxineachyear.
2.4.DataAnalysis
2.4.1.ComparisonofGPPTrendamongDifferentFragmentationLevels
Inordertoexploretheimpactofforestfragmentationonvegetationproductivity,we
comparedthemeanannualGPPandthenormalizedmeanannualGPPamongdifferent
fragmentationlevelsfrom2000to2018.ThenormalizedmeanannualGPPthroughout
2000–2018wascalculatedbydividingthemeanannualGPPfrom2000to2018bythePf
foreachpixelinordertoexcludetheinfluenceofforestareasonGPPvaluesforapixel.A
one-wayANOVAwasadoptedtotestthesignificanceofthedifferencebetweenmeanan-
nualandmeannormalizedannualGPPdatabetween2000and2018amongforestfrag-
mentationlevels.Meanwhile,theLSDmultiplecomparisonmethodwasusedtodetect
thedifferencebetweenmeanannualandnormalizedmeanannualGPPdatabetween2000
and2018undervariousfragmentationlevels.
ThelineartrendoftheannualGPPduring2000–2018foreachpixelandtheentire
Zhejiangprovincewascalculated,andtheslopevalueofthelinearregressionlinewas
representedasthemagnitudeofthetrend.TheannualGPPtrendofdifferentfragmenta-
tionlevelsthroughout2000–2018wasalsocomparedusingaone-wayANOVA.
2.4.2.DetectingtheRelativeImportanceoftheControlPathwaysofGPP
InordertoexplorethecontrolpathwaysofGPPinourstudyarea,weconducted
multiplelinearregressionbetweenGPPandeitherCUP,GPPmax,oracombinationofCUP
andGPPmaxforeachpixeloftheperiodbetween2000and2018.Ametricassessingmeth-
odinthe“relaimpo”packageintheRsoftware[37]wasemployedtoquantifytherelative
importanceofeachindividualvariableinlinearmodelsforeachpixelinthisstudy;the
variablewiththehighestrelativeimportancevaluewasregardedasthecontrolvariable
ofGPP.Therelativeimportanceofeachindividualcontrolvariablewascalculatedtore-
flectitsdegreeofcontrol,andthevalueswerenormalizedfrom0to100%.Inaddition,the
pixelnumberpercentagesofthethreecontrolvariableswerealsocalculatedandwere
statisticallycomparedforeachforestfragmentationlevel.

RemoteSens.2024,16,23936of14
2.4.3.ComparisonofGPPControlPathwaysamongDifferentFragmentationLevels
Inordertoanswerthequestionofwhichpathwayofforestfragmentationaffectsveg-
etationproductivity,theCUP,GPPmax,andCUP×GPPmaxwerecalculatedandcompared
amongvariousforestfragmentationlevels.Aone-wayANOVAwasadoptedtotestthe
significanceofthedifferencesinthethreecontrolvariables,andtheLSDmultiplecom-
parisonmethodwasusedtodetectthedifferencebetweenthethreecontrolvariablesun-
dervariousfragmentationlevels.
2.4.4.StatisticalAnalysisandDataProcessingMethods
Therasterdataprocessing,mapping,andstatisticalanalysiswereconductedusing
ArcGIS10.3andRsoftware[38];thesignificanceleveloftheone-wayANOVAwassetto
0.05.
3.Results
3.1.GPPDynamicsofDifferentFragmentationLevels
Therewasasignificant(p<0.05)differenceinthemeanannualGPPin2000and2018
forforestlandscapeswithvariousfragmentationlevelsinZhejiang(Table2).Generally,
forestlandscapeswithfragmentationlevelsofPATandINThadthelowestandhighest
meanannualGPPvalues,respectively,whilenosignificantdifferenceinthemeanannual
GPPwasidentifiedbetweenTRA,PER,andEDG.Althoughasignificant(p<0.05)differ-
enceinthenormalizedmeanannualGPPfor2000and2018inZhejiangwasfoundamong
allforestfragmentationlevels(Table2),thehighestandlowestnormalizedmeanannual
GPPvaluesinforestlandscapeswererepresentedbythePATandINTfragmentationlev-
els,respectively.Similarly,therewasnosignificantdifferenceinthenormalizedmeanan-
nualGPPbetweenPERandEDG.
Tab l e2.ComparisonofmeanannualGPPandmeannormalizedannualGPPamongdifferentforest
fragmentationcategoriesfor2000and2018.ThemeannormalizedannualGPPwascalculatedasthe
ratioofthemeanannualGPPtotheproportionofforestpixelsinaforestlandscape.Thecharacters
indicatetheresultsofmultiplecomparisonsbetweenanytwocategories.PAT:patchforest,TRA:
transitionalforest,PER:perforatedforest,EDG:edgeforest,andINT:interiorforest.
Fragmentation
Categories
MeanAnnualGPP(gCm−2)MeanNormalizedAnnualGPP(gCm−2)
2000201820002018
PAT1040.4±319.1a1101.6±449.4a11,611.3±17,514.1d11,337.4±16,718.4d
TRA1268.7±256.4b1516.8±351.1b2979.0±4071.6c3505.2±4629.4c
PER1366.5±193.1bc1643.4±231.5bc1870.6±1276.2b2246.4±1487.0b
EDG1349.8±202.4bc1635.2±246.3bc2199.1±2681.0b2656.5±3158.8b
INT1421.4±197.6c1693.9±213.9c1657.0±1222.9a1972.6±1478.9a
Significant(p<0.05)increasingtrendswerefoundinallfragmentationlevelsandall
typesofforestlandscapesinZhejiang,exceptforthePATlandscape(Figure2a).Thein-
creaseintherateofannualGPPwithregardtovariousfragmentationlevelsfluctuated
from10.6gCm−2year−1inTRAto13.1gCm−2year−1inPER.Spatially,theannualGPP
trendinZhejianggenerallyshowedpositivevaluesformostpixels(Figure2b).Theannual
GPPtrendinPATwassignificantlylowerthanthoseinforestlandscapeswithotherfrag-
mentationlevels,buttherewasnosignificantdifferenceintheannualGPPtrendamong
TRA,PER,EDG,andINT.Thehighervalues(>30gCm−2year−1)oftheannualGPPtrend
weremainlydistributedinthewestandcentralpartofZhejiang,whilethelowervalues
(<−45gCm−2year−1)oftheannualGPPtrendweremainlydistributedinthenorthand
coastalareasofZhejiang.
RemoteSens.2024,16,23937of14
Figure2.(a)AnnualGPPdynamicsofdifferentfragmentationlevelsand(b)lineartrenddistribu-
tionofannualGPPofforestlandscapesinZhejiangbetween2000and2018,andthecomparisonof
theannualGPPtrendvaluesamongdifferentfragmentationlevels.Theredasteriskindicatesthe
annualGPPtrendofPATissignificantly(p<0.05)thanthatofotherfragmentationcategories.PAT:
patchforest,TRA:transitionalforest,PER:perforatedforest,EDG:edgeforest,andINT:interior
forest.
3.2.RelativeImportanceofDifferentGPPControlPathways
CUP×GPP
max
wasidentifiedasbeingthecontrolvariableforabout61.8%oftheen-
tireZhejiangprovince;theareapercentagesofthecontrolvariablesforCUPandGPP
max
were19.7%and18.5%,respectively(Figure3a).ThecontrolvariableforCUPwasmainly
distributedinsomepartsofwesternandcentralZhejiang,whilethecontrolvariablefor
GPP
max
wasmainlydistributedincentralandeasternZhejiang.Therelativeimportanceof
thethreecontrolvariablesrangedfrom0to82%,andmostofthemweredistributedinthe
rangeof5~35%(Figure3b).TheareapercentagesofthecontrolvariablesforCUP×GPP
max
weremaintainedatapproximately60%amongvariousfragmentationlevels(Figure3c).
However,thecontrolvariableforCUP(13.2~27.8%)andGPP
max
(11.1~25.0%)stillac-
countedforacertainproportionofareaamongvariousfragmentationlevels.Therewas
nosignificant(p>0.05)differenceintherelativeimportancebetweenanytwofragmenta-
tionlevels,andthemeanrelativeimportanceofvariousfragmentationlevelsrangedbe-
tween25%and28%(Figure3d).
RemoteSens.2024,16,23938of14
Figure3.Distributionofdifferentcontrolvariablesandtheirrelativeimportance.(a)Spatialdistri-
butionofdifferentcontrolvariables,(b)spatialdistributionoftheirrelativeimportanceonannual
GPPduring2000–2018,(c)comparisonofrelativeimportanceamongthreecontrolvariables,and
(d)pixelnumberpercentagesofthreecontrolvariablesfordifferentfragmentationlevels.PAT:patch
forest,TRA:transitionalforest,PER:perforatedforest,EDG:edgeforest,andINT:interiorforest.
RemoteSens.2024,16,23939of14
3.3.GPPControlPathwaysofDifferentFragmentationLevels
Asignificant(p<0.05)differenceinthemeanCUPofforestlandscapesinZhejiang
during2000–2018wasdetectedamongallfragmentationlevels(Table3).Thehighestand
lowestmeanCUPwererepresentedbytheINTandPATforestlandscapes,respectively,
andtherewasnosignificant(p>0.05)differenceinthemeanCUPbetweenTRA,PER,
EDG,andINT.Moreover,themeanGPP
max
ofINTforestlandscapeswassignificantly(p
<0.05)higherthanthatforthePATandTRAlandscapes;nosignificant(p>0.05)difference
inthemeanGPP
max
existedinforestlandscapesinZhejiangbetweenTRA,PER,andEDG,
orbetweenPER,EDG,andINT.Similarly,asignificant(p<0.05)differenceinthemean
CUP×GPP
max
offorestlandscapesinZhejiangduring2000–2018wasdetectedamongall
forestfragmentationlevels(Table3).ThehighestandlowestmeanCUP×GPP
max
values
existedintheINTandPATlandscapes,respectively,andtherewasnosignificant(p>0.05)
differenceinthemeanCUP×GPP
max
betweenPERandEDG.
Spatially,themeanvaluesofCUPandGPP
max
intheforestlandscapesofZhejiang
during2000–2018bothhavehighheterogeneities.ThehighermeanCUPvalues(>330
days)weregenerallyfoundinthesouthandeastpartsofZhejiang,whilethelowermean
CUPvalues(<210days)weregenerallyfoundinthenorthpartofZhejiang(Figure4a).
Amongtheregionswithahighforestcoverage,therewasliledifferenceinthemean
CUPthroughout2000–2018,butthemeanGPP
max
during2000–2018presentedobvious
spatialvariations.Inparticular,thehighestvalues(>12.5gCday
−1
)ofthemeanGPP
max
during2000–2018wereconcentratedindenseforestlandscapesintheeastandnorthwest
partsofZhejiang(Figure4b);however,thevaluesofmeanGPP
max
during2000–2018were
generallylessthan10gCday
−1
inthesouthdenseforestlandscapesinZhejiang.
Figure4.Spatialdistributionofthemeanvaluesofcarbonuptakeperiod(CUP)andmaximumdaily
GPP(GPP
max
)forsubtropicalforestlandscapesinZhejiangProvinceduring2000–2018.(a)Thespa-
tialmapofthemeanCUPduring2000–2018.(b)ThespatialmapofthemeanGPP
max
during2000–
2018.
RemoteSens.2024,16,239310of14
Tab l e3.ComparisonsofmeanCUP,meanGPPmax,andmeanCUP×GPPmaxofforestlandscapesin
Zhejiangamongdifferentfragmentationcategoriesduring2000–2018.Thecharactersindicatethe
resultsofmultiplecomparisonsbetweenanytwocategories.PAT:patchforest,TRA:transitional
forest,PER:perforatedforest,EDG:edgeforest,andINT:interiorforest.
Fragmentation
Categories
CUP(days)GPPmax(gCm−2day−1)CUP×GPPmax
PAT256.1±73.4a8.7±2.2a2339.4±949.7a
TRA310.7±49.7b9.6±1.6b3041.2±737.1b
PER329.8±28.5b10.2±1.3bc3369.1±548.5c
EDG327.8±30.3b10.1±1.3bc3320.6±551.2bc
INT332.9±28.1b10.7±1.5c3547.7±524.7d
4.Discussion
4.1.ImpactsofForestFragmentationonCarbonUptake
ThemeanannualGPPthroughout2000–2018generallydeclinedfromINTtoPAT
forestlandscapes(Table2);thisisinlinewithpreviousstudies,whichsuggestthatcarbon
lossinforestsisaggravatedbyfragmentationlevel[39–41].Thecarbonlossisdirectly
aributedtotheforestlossunderforestfragmentation,whichmaybemainlycausedby
naturalandhumandisturbances,suchaslogging,fire,andagricultureandurbanexpan-
sion[5,27,42].However,ourresultsalsoshowedthatthenormalizedmeanannualGPPof
PATforestlandscapesduring2000–2018wassignificantlyhigherthanthatofotherfrag-
mentationlevels(Table2),whichimpliesthatthecarbonuptakeofhigherfragmented
forestareaswasenhancedwhenexcludingtheinfluenceofforestareadensity.Asimilar
resultwasfoundinatemperatebroadleafforestofnorthAmerica[16],wheretheforest
growthratedecreasedwiththedistancefromforestedge.Thevariationsinlightavaila-
bility[43]andthesensitivitytoclimate[44,45]oftheforestswithdifferentfragmentation
levelsarelikelytoberesponsiblefortheirdifferencesincarbonuptake.
Inthisstudy,exceptforthePATforestlandscapes,therewasnosignificantdifference
intheannualGPPtrendthroughout2000–2018amongfragmentationlevels(Figure3);
thiscouldbeaributedtotwopossiblereasons.Ontheonehand,forestlandscapeswith
ahighfragmentationlevelaremorelikelytobedistributednearurbanareas,andthe
changedenvironmentwithahighertemperatureandCO2concentrationandbeerhu-
manmanagementandprotection(e.g.,artificialirrigation)hadapositiveinfluenceonfor-
estcarbonuptake.Ontheotherhand,forestlandscapeswithdifferentforestfragmenta-
tionlevels(exceptPAT)wereevenlydistributedinZhejiang(Figure1),whichledtopar-
allelforestagestructuresexistingindifferentfragmentationlevels;thisresultsuggests
thattheimpactofforestfragmentationonthechangerateofvegetationproductivityis
verylimited.
4.2.ImpactsofForestFragmentationonControlPathwaysofCarbonUptake
Sincevegetationcarbonuptakeprocessescanbedividedintophenologicalandphys-
iologicalfactors,thecontrolpathwayofGPPiseitherphenological,physiological,orboth.
OurresultsshowedthatCUP×GPPmaxwasthemostwidespreadcontrolvariableofGPP,
atmostlymorethan60%,during2000–2018forallfragmentationlevels(Figure3),which
isconsistentwithapreviousstudythatreportedthatGPPwasjointlycontrolledbyphe-
nologyandphysiologyprocesses[24].However,theindividualCUPorGPPmaxstillcon-
trolledtheGPPinsomeforestlandscapesinZhejiang.Forexample,forestcarbonuptake
wasmainlycontrolledbytheCUPinmountainareas,whilethecontrollingroleofGPPmax
onGPPusuallyoccurredinthecentralandcoastalareaswithaflatterrain(Figure3).Our
studyindicatesthatthedominantpathwaysofforestcarbonuptakearecloselyrelatedto
topographicconditions.
Moreover,ourresultsshowedthatthepercentageareaoftheGPPmaxcontrolvariable
graduallyincreaseswithfragmentationlevel;themeanGPPmaxofPATduring2000–2018
RemoteSens.2024,16,239311of14
wasfoundtobehigherthanthatofotherfragmentationlevels(Table3).Thismeansthat
themaximumphotosyntheticcapacityofvegetation,mainlyreflectedbyGPPmax,isim-
portantincontrollingcarbonuptakeinhighlyfragmentedforestlandscapes.Thefactors
oflessinter-individualcompetition[46,47]andmorecarbondioxidefertilization[48,49]
arelikelypossibledriversoftheobservedimportanceofGPPmaxathigherfragmentation
levels.
Inaddition,thecontrolvariablesoftheCUPandCUP×GPPmaxofINTforestland-
scapeswereobviouslyhigherthanthoseofotherfragmentationlevels.Thissuggeststhat
phenology-basedprocessesbenefitfromlessfragmentedforestlandscapesandaremost
likelytobeaributedtothewarmingconditionscausedbytheedgeeffect[50]inhighly
fragmentedforests.
4.3.InsightsonLocalForestManagementunderForestFragmentation
Historically,forestsinZhejiang,China,haveexperiencedsevereloggingdisturb-
ances,causingagrimstatusofforestfragmentation.However,theresultsofthisstudy
maystillprovideusefulinsightsintolocalforestmanagement.Firstly,ahighercarbon
uptakecapacityperunitareaexistsinhighlyfragmentedforestlandscapes,remindingus
toexplorethepotentialofforestcarbonuptakeinthecontextofthewidespreadexistence
offorestfragmentation.Inparticular,asoneofthemostdevelopedregionsinChina,
Zhejiangissubjecttointensehumanactivities,suchasrapidurbanizationandeconomic
development,whichhavesignificantlyaffectedthedistributionpaernofnaturalecosys-
tems,suchasforests.Thepositiverelationshipbetweenforestfragmentationandcarbon
sequestrationprovidesanopportunityforZhejiangtobalancedevelopmentandecologi-
calprotection.Secondly,althoughtheforestcarbonuptakemaybeenhancedunderfrag-
mentation,thereisstillagreatneedtoprotecttheforestsduetothefacthatanylossof
forestcoveragewillleadtoafurtherlossofcarbonstock.Finally,thisrelationshipisalso
basedontheimplicationthattheincreaseinthemaximumphotosyntheticcapacitymay
inducetheenhancementofcarbonuptakeinhighlyfragmentedforestlandscapes.There-
fore,itisnecessarytoexplorethemechanismbywhichforestfragmentationchangesthe
vegetationphotosyntheticprocess,whichwillplayanimportantroleinformulatingforest
managementmeasures.Inaddition,althoughsubtropicalforestsinZhejiangProvince
wereselectedasbeingrepresentativeinthisstudy,consideringtheuniversalityofre-
searchapproachesandthewidespreadavailabilityofresearchobjects,ourresultshave
greatpotentialvaluesforunderstandinghowthecarbonuptakeofsubtropicalforestsre-
spondstoforestfragmentationinothersubtropicalregions.
4.4.ResearchUncertaintiesandProspects
Inthisstudy,weexploredhowforestcarbonuptakechangeswithdifferentfragmen-
tationlevelsandwhatthemainpathwayisinwhichfragmentationaffectedforestcarbon
uptakethroughout2000–2018inforestlandscapesinZhejiang,China.Someuncertainties
mayexistinourstudy.Firstly,althoughforestprotectionandsuspendedloggingdisturb-
anceshavebeenimplementedsince2000inthestudyarea[51,52],weassumedthatthe
forestcoveragewaskeptstablewhentheforestlossproportionwaslessthan5%inthe
periodbetween2000and2018;thismaystillinducesomeuncertainties,sincethesucces-
sionofforeststhemselvesmayhavecausedsomechangesinforestcover[53].Secondly,
thedatasetsofforestcoverandGPPwereproducedonalargescale(thewholeofChina)
andhaveahighaccuracy.However,weonlyusedsomepartsofthedatasets,inducing
uncertaintiesintheaccuracyofthedatasets.Lastly,althoughtheforestfragmentation
levelclassificationandGPPestimationwereobtainedbasedoncertifiedmethods
[20,29,54],theaccuracyofthesedatamayalsohavesomeimpactsonthestudyresults.
RemoteSens.2024,16,239312of14
5.Conclusions
Comparedtopreviousstudies,thisstudyhasrevealedthevariationincarbonuptake
withforestfragmentationlevelsatbothlandscapeandregionalscales,andtheforestland-
scapepaernwasfoundtohavecertainimpactsonforestproductivity.Inaddition,the
controlpathwaysofcarbonuptakeunderforestfragmentationwerealsoidentified.These
findingshaveenhancedtheunderstandingofvegetationcarbonuptakeprocessesinthe
contextofwidespreadforestfragmentation.
Ourresultsdemonstratethatcarbonuptakecapacityperunitareawasenhancedin
highlyfragmentedforestlandscapes.However,therewasalmostnosignificantdifference
intheannualGPPtrendthroughout2000–2018amongforestlandscapeswithdifferent
fragmentationlevels.Additionally,althoughforestcarbonuptakewasjointlycontrolled
byphenology-andphysiology-basedprocesses,themaximumphotosyntheticcapacityof
vegetation—thephysiology-basedprocess—playedanimportantroleincontrollingcar-
bonuptake,particularlyinhighlyfragmentedforestlandscapes.Hence,theresultsand
findingsfromthisstudycallforabeeranddeeperunderstandingofthepotentialof
forestcarbonuptakeinconnectiontofragmentation.Thereisanemergingneedtopay
seriousaentiontosustainableforestconservationandprotectioninthecontextofthe
widespreadexistenceofforestfragmentation.Moreover,inordertoformulateappropri-
ateforestmanagementandplanningactivities,itisnecessarytoexplorethemechanism
inwhichforestfragmentationchangesthevegetationphotosyntheticprocessinconnec-
tiontocarbonuptakeandrelease.
Aut horContributions:Conceptualization,J.J.,J.M.andC.W.;methodology,J.J.,P.H.,andJ.M.;for-
malanalysis,J.J.,Y.C.,P.H.,B.J.andC.W.;datacuration,J.J.,Y.C.,P.H.,B.J.andC.W.;writing—
originaldraftpreparation,J.J.;writing—reviewandediting,Y.C.,P.H.,J.M.,L.Y.,B.J.,X.X.andC.W.;
visualization,J.J.andY.C.;fundingacquisition,C.W.andJ.M.Allauthorshavereadandagreedto
thepublishedversionofthemanuscript.
Funding:.ThisresearchwassupportedbytheresearchinstitutesupportprojectofZhejiangProv-
ince(2024F1065-1),“Pioneer”and“LeadingGoose”R&DProgramofZhejiang(2024C03227),Nat-
uralScienceFoundationofChina(32271659),andscientificresearchprogramofShanghaiscience
andtechnologycommission(23DZ1204504).
DataAvailabilityStatement:.ThetimeseriesGPPdatasetwasdownloadedfromhps://doi.pan-
gaea.de/10.1594/PANGAEA.879558?format=html#download.Theforestcovermapisavailableon
requestfromDr.YuanweiQinfromtheUniversityofOklahoma.TheCUP,GPPmax,andforestfrag-
mentationcategoriesmapareavailableonrequestfromthecorrespondingauthors.
Acknowledgments:WethanksYuanweiQinfromtheUniversityofOklahomaforhisshareofthe
forestcovermapofChina.WealsothankJijiaLiufromFudanUniversityforhisparticipateinthe
discussionofthisstudy.
ConflictsofInterest:Theauthorsdeclarenoconflictsofinterest.
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Article
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