Quantifying and understanding carbon storage and sequestration within the Eastern Arc Mountains of Tanzania, a tropical ...

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Title:
Quantifying and understanding carbon storage and sequestration within the Eastern Arc Mountains of Tanzania, a tropical biodiversity hotspot
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English
Creator:
Wilcock, Simon
Phillips, Oliver L.
Platts, Philip J.
Balmford, Andrew
Burgess, Neil D.
Lovett, Jon C.
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Springer (Carbon Balance and Management)
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Abstract:
Background: The carbon stored in vegetation varies across tropical landscapes due to a complex mix of climatic and edaphic variables, as well as direct human interventions such as deforestation and forest degradation. Mapping and monitoring this variation is essential if policy developments such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation) are to be known to have succeeded or failed. Results: We produce a map of carbon storage across the watershed of the Tanzanian Eastern Arc Mountains (33.9 million ha) using 1,611 forest inventory plots, and correlations with associated climate, soil and disturbance data. As expected, tropical forest stores more carbon per hectare (182 Mg C ha-1) than woody savanna (51 Mg C ha-1). However, woody savanna is the largest aggregate carbon store, with 0.49 Pg C over 9.6 million ha. We estimate the whole landscape stores 1.3 Pg C, significantly higher than most previous estimates for the region. The 95% Confidence Interval for this method (0.9 to 3.2 Pg C) is larger than simpler look-up table methods (1.5 to 1.6 Pg C), suggesting simpler methods may underestimate uncertainty. Using a small number of inventory plots with two censuses (n = 43) to assess changes in carbon storage, and applying the same mapping procedures, we found that carbon storage in the tree-dominated ecosystems has decreased, though not significantly, at a mean rate of 1.47 Mg C ha-1 yr-1 (c. 2% of the stocks of carbon per year). Conclusions: The most influential variables on carbon storage in the region are anthropogenic, particularly historical logging, as noted by the largest coefficient of explanatory variable on the response variable. Of the non-anthropogenic factors, a negative correlation with air temperature and a positive correlation with water availability dominate, having smaller p-values than historical logging but also smaller influence. High carbon storage is typically found far from the commercial capital, in locations with a low monthly temperature range, without a strong dry season, and in areas that have not suffered from historical logging. The results imply that policy interventions could retain carbon stored in vegetation and likely successfully slow or reverse carbon emissions. Keywords: Eastern Arc Mountains; Tanzania; IPCC Tier 3; REDD+; Forest; Disturbance; Degradation; Ecosystem service
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Willcock et al. Carbon Balance and Management 2014, 9:2 http://www.cbmjournal.com/content/9/1/2; Pages 1-17
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doi:10.1186/1750-0680-9-2 Cite this article as: Willcock et al.: Quantifying and understanding carbon storage and sequestration within the Eastern Arc Mountains of Tanzania, a tropical biodiversity hotspot. Carbon Balance and Management 2014 9:2.

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© 2014 Willcock et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
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RESEARCHOpenAccessQuantifyingandunderstandingcarbonstorage andsequestrationwithintheEasternArc MountainsofTanzania,atropicalbiodiversity hotspotSimonWillcock1,2*,OliverLPhillips1,PhilipJPlatts3,AndrewBalmford4,NeilDBurgess5,6,JonCLovett1, AntjeAhrends7,JulianBayliss4,NikeDoggart8,KathrynDoody9,EibleisFanning10,JonathanMHGreen11, JaclynHall12,KimLHowell13,RobMarchant3,AndrewRMarshall3,14,BonifaceMbilinyi15,PantaleonKTMunishi15, NishaOwen10,16,RuthDSwetnam17,ElmerJTopp-Jorgensen18andSimonLLewis1,19AbstractBackground: Thecarbonstoredinvegetationvariesacrosstropicallandscapesduetoacomplexmixofclimatic andedaphicvariables,aswellasdirecthumaninterventionssuchasdeforestationandforestdegradation.Mapping andmonitoringthisvariationisessentialifpolicydevelopmentssuchasREDD+(ReducingEmissionsfrom DeforestationandForestDegradation)aretobeknowntohavesucceededorfailed. Results: WeproduceamapofcarbonstorageacrossthewatershedoftheTanzanianEasternArcMountains(33.9 millionha)using1,611forestinventoryplots,andcorrelationswithassociatedclimate,soilanddisturbancedata.As expected,tropicalforeststoresmorecarbonperhectare(182MgCha-1)thanwoodysavanna(51MgCha-1).However, woodysavannaisthelargestaggregatecarbonstore,with0.49PgCover9.6millionha.Weestimatethewhole landscapestores1.3PgC,significantlyhigherthanmostpreviousestimatesfortheregion.The95%Confidence Intervalforthismethod(0.9to3.2PgC)islargerthansimplerlook-uptablemethods(1.5to1.6PgC),suggesting simplermethodsmayunderestimateuncertainty.Usingasmallnumberofinventoryplotswithtwocensuses( n =43) toassesschangesincarbonstorage,andapplyingthesamemappingprocedures,wefoundthatcarbonstorageinthe tree-dominatedecosystemshasdecreased,thoughnotsignificantly,atameanrateof1.47MgCha-1yr-1(c.2%ofthe stocksofcarbonperyear). Conclusions: Themostinfluentialvariablesoncarbonstorageintheregionareanthropogenic,particularlyhistorical logging,asnotedbythelargestcoefficientofexplanatoryvariableontheresponsevariable.Ofthenon-anthropogenic factors,anegativecorrelationwithairtemperatureandapositivecorrelationwithwateravailabilitydominate,having smallerp-valuesthanhistoricalloggingbutalsosmallerinfluence.Highcarbonstorageistypicallyfoundfarfromthe commercialcapital,inlocationswithalowmonthlytemperaturerange,withoutastrongdryseason,andinareas thathavenotsufferedfromhistoricallogging.Theresultsimplythatpolicyinterventionscouldretaincarbon storedinvegetationandlikelysuccessf ullysloworreversecarbonemissions. Keywords: EasternArcMountains;Tanzania;IPCCTier3;REDD+;Forest;Disturbance;Degradation;Ecosystemservice *Correspondence: S.P.Willcock@soton.ac.uk1SchoolofGeography,UniversityofLeeds,LeedsLS29JT,UK2SchoolofBiologicalSciences,UniversityofSouthampton,Southampton SO171BJ,UK Fulllistofauthorinformationisavailableattheendofthearticle 2014Willcocketal.;licenseeSpringer.ThisisanOpenAccessarticledistributedunderthetermsoftheCreativeCommons AttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,andreproduction inanymedium,providedtheoriginalworkisproperlycredited.Willcock etal.CarbonBalanceandManagement 2014, 9 :2 http://www.cbmjournal.com/content/9/1/2

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BackgroundTropicalforestsaregloballysignificantecosystems;accountingfor~50%ofglobalforestarea[1],storing 45%ofallcarboninterrestrialvegetation[2-4],maintaininghighbiodiversity[5],andprovidingecosystem services,suchastimber,non-timberforestproducts[6], andclimatechangemitigation[7,8].However,withinthe lastfewdecades,vastareasoftropicalforestshavebeen convertedtootherland-usesordegraded.Forexample, between1990and1997,4.4-7.2millionhectaresof humidtropicalforestwereconvertedeachyearandan additional1.6-3.0millionhectaresofforestwerevisibly degraded[9].Thisprocessincreasedintheearly2000s, withanestimated5.1-5.7millionhectaresofhumid tropicalforest(and3.5-4.7millionhectaresofdrytropicalforest)deforestedperyearbetween2000and2005 [10].Thegradualandsustainedreductioninforestqualityandquantityhasresultedinsubstantialemissionsof CO2[11].Globally,deforestationandforestdegradation accountedfor6-20%ofanthropogenicGHGemissions inthe1990sandearly2000s[12-14].Tropicalregions makeasubstantialcontributiontothis,emitting0.7-1.5 PgCyr-1between1990and1999[9,15-17]and0.71.5 PgCyr-1between2000and2007[13,16-18].Theseprocessesalsoimpactthefuturepotentialofforeststoremovecarbonfromtheatmosphere[7,19,20]. Recently,attemptstomitigateincreasinganthropogenicCO2emissionsthroughreducingemissionsfrom degradationanddeforestation(REDD+)havebeeninstigated[21].TheREDD+programmeisaimedatcontributingtoareductioningreenhouseemissionswhilst providingeconomicincentivesforbettermanagement andprotectionofforests.Thispolicyhasbeenwidely welcomedandmayprovideafinancialincentivetosignificantlyreducecarbonemissions[22,23],althoughthe equityandjusticeissuessurroundingtheimpactonlocal livelihoodsareactivelydebated[24,25].KeytechnicalissuesforthesuccessfulimplementationofREDD+include(butarenotlimitedto)theaccuracyofmonitoring systems,preventingleakageandestablishingaccurate historicalbaselines.Thus,thesuccessofREDD+,inpart, restsonrobustscientificinformationonthemagnitude andextentofcarbonstorageintropicalregionsandhow itchangesovertime[26]. TheIntergovernmentalPanelonClimateChange(IPCC) provideathree “ Tier ” systemthroughwhichcarbonstocks andemissionscanbereported,eachwithadifferentlevel ofmethodologicalcomplexityandaccuracy.Tier1isthe simplestmethod,usingglobaldefaultvaluesobtainedfrom theIPCCliterature[27,28].TheintermediateTier2level improvesonTier1byusingcountryspecificdata.Tier 3isthemostrigorousapproach,usinglocalforestinventorydata,focusingonthedirectmeasurementof trees,repeatedoveratimeseries[27-29].Herewe developaTier3methodologyfortheEasternArc Mountains(EAM)watershedarea. Theestimatesbecomeprogressivelymorerobustfrom Tier1to3duetochangesintwomainsystematicerrors [29].Thefirst,completeness,referstothenumberof IPCCcarbonpoolsthatareincluded,withstudiesincludingallfivepools(abovegroundlive,litter,coarse wooddebris[CWD],belowgroundandsoilcarbon)consideredcomplete.Thesecond,representativeness,derivesfromthesubstantialnaturalvariabilityinthe carbonstoredacrosslandscapes,evenwithinabiomeor country[30].Theabovegroundbiomassofaforest withinalandscapemaydifferconsiderablyfromglobal default(Tier1)valuesorevenfromcountry-specific (Tier2)values.Forexample,inthePeruvianAmazon, datafromtheLosAmigosConservationConcession[31] wereshownnottoberepresentativeofforestsnationally.Nearbyforestssituatedtothenorthandsouthof thislocalstudyareestimatedtocontain20-35%lesscarbonperunitarea[32],suggestingthatLosAmigosConservationConcessionisanareaoflocallyhighbiomass. SinceTier3methodsaccountforvariationobserved withinbiomesandcountries,therepresentativenessof thecarbonestimatesishigherthanthoseassociatedwith Tier1and2methodologies[32,33]. However,Tier3methodsaremoreexpensive[34,35] andsomenationsmaylackthecapacitytoadoptsuch methods[36].Whilst,insomecases,thecapabilityto applyTier3guidelinesisbeingrapidlydeveloped,multitemporalinventorydataanddataonhistoricalcarbon stockchangescantakeseveraldecadestoaccrue[37,38]. ItisexpectedthatREDD+requirementswillallowdata provisionsfromseveraltiersinasinglereport.Highly variableand/orsubstantialcarbonpoolsshouldbeestimatedusingTier3methodology(e.g.forestaboveground livecarbon[ALC]),whilstTier1orTier2methodology maybesufficientforsmallercarbonpools(e.g.CWD)or carbonpoorlandcovercategories(e.g.bareground). InTier3methods,inordertoextrapolatefromplot data,itisnecessarytodevelopcorrelationswithremotelysenseddatatoscaletothestudyareaorcountrywideestimates.Generally,carbonstorageiseitherestimatedviastatisticalcorrelationwithelectromagnetic properties,ground-truthedbyvolumetricmeasurements, suchasdiameteratbreastheight(DBH),whichareconvertedtobiomassestimatesusingallometricequations. Avarietyofremotelysenseddatasourceshavebeen employedforcarbonmappingandthesecanbeaggregatedintofourgroups:photographicimagery,RADAR, LiDAR,andancillarygeographicinformationsystems (GIS)data(seeAdditionalfile1:SI1foranevaluationof eachmethod).Here,weuseancillaryGISdataassuch datahavethreemainadvantages:1)wideavailability,often freeofcharge;2)asuitableresolution(e.g.90m[39]);andWillcock etal.CarbonBalanceandManagement 2014, 9 :2Page2of17 http://www.cbmjournal.com/content/9/1/2

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3)correlationswiththeseancillaryGISdatamayindicate whichvariablesdirectlyaffectcarbonstorage.Developing anunderstandingofhowthesevariablesinfluencecarbon storageisvitalforaccuratescenariosoffutureemissions. Here,wecorrelatecarbonstorageestimatesfromtree inventoryplots( n =1,611,mediansize=0.1ha)with dataonclimatic(e.g.temperature,precipitation,and solarradiation),edaphic(e.g.soilwaterholdingcapacity andsoilfertility)andproxyvariablesfordirecthuman interventions(e.g.governancetype,distancefromthe maineconomicdemandcentres,populationpressure, andhistoricallogging),andvariablesthatderivefrom climate-humaninteractions(e.g.burntareaindex)forthe TanzanianwatershedoftheEasternArcMountains(hereafter,EAM[40]),whichcovers33.9millionha(Figure1; seeSwetnametal(2011)[41]forfurtherdetails).WedevelopTier3typecorrelationequationstoestimatethe totalALCstoredacrosstheforestedandwoodedland covercategories,anadvancementonpreviousTier2estimatesfortheregionpresentedinWillcocketal(2012) [42].Additionally,weinvestigatethemostinfluentialcorrelatesofspatialdifferencesincarbonstorageandhow theseresultfromchangesineitherspeciescompositionaffectingwooddensity(specificgravity)orthenumberof largetreespresent.Lastly,asmallernumberofinventory plots(n=43,mediansize0.1ha)havetwocensuses,and byapplyingthesamemappingprocedures,weassess changesincarbonstorageovertime,providingafirstorderestimateofsequestrationacrosstheregion.ResultsCarbonstocksUtilising1,611plotsandscalingtothe33.9millionha studyareaweestimatethat1.32(95%confidence Figure1 TheEasternArcMountainsofTanzaniaandKenya[40]. ThestudyareaistheEasternArcwatershedinTanzania[41]. Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page3of17 http://www.cbmjournal.com/content/9/1/2

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interval[CI]rangesfrom0.89to3.16)PgCwasstored intheabovegroundlivevegetationintheyear2000 (Figure2;Table1).Woodlandandbushlandcontributedmosttotheamountofstoredabovegroundlive carbon(ALC)inthestudyregion,withopenwoodland storingthemostALC(0.49[0.47to1.60]PgCover9.6 millionha);followedbybushland(0.29[0.15to0.51] PgCover5.0millionha)andclosedwoodland(0.18 [0.13to0.61]PgCover1.8millionha). Bestestimatevaluesfromourmethodology,perunit area,ineachlandcoverclass,aregiveninTable2.Forest containedthegreatestALCperunitarea,withhighest valuesinsub-montaneforest(189[95to588]Mgha-1), followedbylowland(182[152-to360]Mgha-1),upper montane(166[69to533]Mgha-1),montane(130[62 to702]Mgha-1),andforestmosaic(121[55to485] Mgha-1).WoodlandsheldlessALCthanforests,with closedwoodlandstoring100(70to331)Mgha-1and openwoodlandstoring51(38to165)Mgha-1(Table2), butmorethanthelandscapeaverageof39(26to93) Mgha-1. Oursequestrationmodelsuggeststhatthelandscape maybelosing0.05(-0.07to0.26)PgCyr-1(meannetflux toatmosphereof1.47[-2.13to7.75]MgCha-1yr-1).Of the12.3millionhaoftree-dominatedlandinourstudy area,only1.4%(0.17millionha)showsacarbondecrease overtheentire95%CIrangeandonly0.8%(0.10million ha)adefinitecarbonincrease(Figure3).Thelocations showingnetcarbonuptakeareintheUdzungwamountains,whilethelocationswithnetreductionsincarbon storagearemainlyinthePareandUsambaramountains.LinksbetweencarbonstockandinfluentialvariablesThevariablesthatinfluencecarbonstorageandsequestrationmaybeinferredfromrelationshipswithinthecorrelationmodels.Forwardselectionresultsarepresentedin thefollowingparagraphsasthesebestindicatecausalrelationships[43-45].Ingeneral,backwardmodelswerein Figure2 Abovegroundlivecarbonstorageinthestudyarea(a),withupper(b)andlower(c)pixelbased95%CI. Seetextfordetails onMethods. Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page4of17 http://www.cbmjournal.com/content/9/1/2

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closeagreementwithforwardmodels(Tables3and4; Additionalfile1:TablesS1-S3). Carbonstorage(adjustedR-squared[AdjR-sq]=0.18) iscorrelatedpositivelywiththenaturallogarithmof thepopulationpressurewithdecayconstantof12.5km (p-value<0.001)andincreasedby1Mgha-1forevery 8700kmfromaroad(p-value<0.010),andevery30,000 unitsinthecostdistancetoDaresSalaam(p-value< 0.010).Carbonstoragedecreasedby1Mgha-1forevery 1Cincreaseinmeanannualmonthlytemperaturerange (p-value<0.001),every2.7%riseinthetotalavailable watercapacityofthesoil(p-value<0.001),andevery Table1Abovegroundlivecarbonstoredwithinthestudyareafortheyear2000,estimatedbythisandprevious studiesStudyAboveground livecarbon,Pg (95%CIrange) MethodologyResolution(m2)Disturbanceincluded?Tanzanian on-thegrounddata? Presentstudy* – Tier31.32(0.89-3.16)Correlationequationsderived usingremotelysensed influentialvariables. 100Anthropogenicvariablesrepresent humandisturbance.Natural disturbancevariablesalso included. Yes Willcocketal(2012)* – OriginalTier2[ 42 ] 1.58(1.56-1.60)Landcoverbasedlook-uptable.100Onlywherelandcovercategories areidentifiedasdisturbed (e.g.croplandmosaics). Yes Willcocketal(2012) – HarmonisedTier2[ 42 ] 1.64(1.52-1.76)Landcoverbasedlook-uptable.100Onlywherelandcovercategories areidentifiedasdisturbed (e.g.croplandmosaics). Yes Baccinietal(2012) – Tier1[ 3 ] 2.03DerivedfromMODISandGLAS LiDARdata. 500Partiallyincludesdisturbance throughimpactsoncanopy heights. Yes Saatchietal(2011) – Tier1[ 4 ] 0.83DerivedfromMODIS,SRTM, QSCATandGLASLiDAR. 1000Partiallyincludesdisturbance throughimpactsoncanopy heights. No Hurttetal(2006) HYDE-SAGE – Tier1[ 46 ] 0.63ModelledfromtheMiamiLU ecosystemmodelwithcropland datafromtheCentrefor SustainabilityandtheGlobal Environment. ~110,000Containssimplesubmodelsof naturalplantmortality,disturbance fromfire,andorganicmatter decomposition,aswellaswood harvesting. No Hurttetal(2006)HYDE – Tier1[ 46 ] 0.41ModelledfromtheMiamiLU ecosystemmodel. ~110,000Containssimplesubmodelsof naturalplantmortality,disturbance fromfire,andorganicmatter decomposition,aswellaswood harvesting. No Baccinietal(2008) – Tier1[ 47 ] 0.34DerivedfromMODISandGLAS LiDARdata. 1000Partiallyincludesdisturbance throughimpactsoncanopy heights. No*ThisstudyandWillcocketal(2012)arenotindependentastheyarederivedfromthesameunderlyingdataandutilisethesamelook-uptablevalues. Table2Themean(and95%CI)estimatesofforestcharacteristicsinvestigatedinthisstudy(carbonstorage,carbon sequestration,WSG,theinterceptfromthepowerlawrelationshipandthegradientfromthepowerlawrelationship) separatedbylandcovercategoryLandcovercategory[ 41 ]Carbonstorage (Mgha-1) Carbonsequestration (Mgha-1yr-1) WSG(gcm-3)Theinterceptfromthe powerlawrelationship Thegradientfromthe powerlawrelationship LowlandForest(<1000m) 182(152to360)-0.91(-7.08to4.29)0.60(0.59to0.60)6.01(2.94to5.17)-0.93(-1.04to-0.82) Sub-montaneforest (1000-1500m) 189(95to588)-2.02(-11.06to1.29)0.58(0.57to0.58)5.95(3.68to8.23)-1.31(-1.48to-1.14) MontaneForest (1500-2000m) 130(62to702)-2.03(-11.85to1.07)0.60(0.59to0.60)6.95(3.51to10.39)-1.57(-1.82to-1.32) Upper-montaneforest (>2000m) 166(69to533)-2.08(-10.49to1.23)0.60(0.58to0.60)7.03(4.60to9.45)-1.61(-1.93to-1.26) Forestmosaic 121(55to485)-1.18(-6.69to2.92)0.56(0.56to0.56)9.22(6.98to11.46)-1.90(-1.99to-1.81) ClosedWoodland 100(70to331)-1.24(-7.91to2.63)0.64(06.2to0.65)6.67(4.95to8.60)-1.55(-1.85to-1.30) OpenWoodland 51(38to165)-1.49(-7.53to2.05)0.61(0.59to0.62)6.38(4.88to7.82)-1.45(-1.70to-1.19) Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page5of17 http://www.cbmjournal.com/content/9/1/2

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4.4monthincreaseinthemeannumberofdry monthsannually(p-value<0.050).Carbonstoragewas 2.1Mgha-1lowerinareaswherehistoricallogging waspresent(p-value<0.010),and4.2Mgha-1higherin areasunderthecontroloflocalcommunities/governments (p-value<0.010).Thus,carbonstorageishighinareas farfromthecommercialcapital,withalowmonthly temperaturerange,withoutadryseason,thathavenot sufferedfromhistoricalloggingandareunderlocal community/governmentcontrol(Figure4;Table3). Therateofcarbonsequestrationcorrelatedwith threeprincipalcomponent(PC)axes(presentedin orderofinfluence;AdjR-sq=0.41).Carbonsequestrationwasnegativelycorrelatedwiththesoilfertilityaxis (PC5;p-value<0.050),warmertemperaturesandlonger dryseasons(PC3;p-value<0.050),andwithincreasedanthropogenicdisturbance(PC1;p-value<0.010).Thus,carbonsequestrationwashighestinlessfertileareaswith littleornodroughtandlittleanthropogenicdisturbance (Table4). Woodspecificgravity(WSG;AdjR-sq=0.28;see Additionalfile1:SI2)wasmoststronglyaffectedbythe annualmeanburnedareaprobability(increasingby 1gcm-3forevery0.04increase;p-value<0.001)and thetotalavailablewatercapacityofthesoil(decreasing by1gcm-3forevery82.0%increase;p-value<0.001). Thus,WSGishigherinburntareaswithlittleavailable water(Additionalfile2:FigureS1;Additionalfile3: FigureS2;Additionalfile1:TableS1). Theinterceptofthepowerlawrelationship(anindicationofpotentialstemdensity[seeAdditionalfile1:SI3]; AdjR-sq=0.30)wasmostaffectedbythenaturallogarithmofthepopulationpressurewithdecayconstantof 12.5km(positivecorrelation;p-value<0.001)andthe meanannualmonthlytemperaturerange(increasingby 1.0forevery1.2Cincrease;p-value<0.001).Thus,the Figure3 Abovegroundlivecarbonsequestrationintree-dominatedlandcovercategorieswithinthestudyarea(a),withupper(b)and lower(c)pixelbased95%CI. SeetextfordetailsonMethods. Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page6of17 http://www.cbmjournal.com/content/9/1/2

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densityofsmallerstemsincreasesinareaswithahigh populationpressureandlargetemperaturefluctuations (Additionalfile3:FigureS2;Additionalfile4:FigureS3; Additionalfile1:TableS2). Correlationsidentifiedfo rthegradientofthepower lawrelationship(anindicationoftheproportionof largerstems;seeAdditionalfile1:SI3)werebroadly theinverseofthoseidentifiedfortheintercept.The gradientofthepowerlawrelationshipwasmostaffectedbythenaturallogarithmofthepopulationpressurewithdecayconstantof20.8km(negativecorrelation; p-value<0.001)andthemeanburnedareaprobabilityin thefourthquarter(decreasingby1.0forevery0.2increase;p-value<0.001).Thus,theproportionoflarge stemswasgreaterinareasexperiencingfewdisturbancesfrompeopleorfire(Additionalfile3:FigureS2; Additionalfile5:FigureS4;Additionalfile1:TableS3). Wheninvestigatingthemostinfluentialcorrelatesof spatialdifferencesincarbonstorageandhowtheseresultfromchangesineitherspeciescompositionaffecting wooddensity(specificgravity)orthenumberoflarge treespresent,wefoundthatthefinalTier3carbonstorageestimateswerepositivelycorrelatedwithbothsizefrequencydistributionestimates(bothinterceptandgradient[p-values<0.001]),andnegativelycorrelatedwith WSGestimates(p-value<0.001)andmaximumheight estimates(p-value<0.001;Additionalfile1:seeSI4).All possibleinteractionswereinvestigatedandweresignificant(AdjR-sq=0.35;p-values<0.001),however,the majorityoftheexplanatorypowerlaywithinthesecond orderinteractions(AdjR-sq=0.33;p-values<0.001; Additionalfile1:TableS5).Broadly,WSGandtheproportionoflargerstemshadlargestinfluenceoverthe carbonstorageestimate.Consideringonlysecondorder Table3Thecoefficientsandassociatedp-valuesofthevariablescorrelatedwithabovegroundcarbonstorageusing bothforwardandbackwardselectionproceduresVariable(whereappropriate,unitsaregiveninbrackets)GroupForwardBackward Coefficientp-valueCoefficientp-value (Intercept) n/a-1.21E+033.14E-03-2.80E+007.55E-01 Naturallogarithmofthepopulationpressurewithdecayconstantof12.5km Anthropogenic1.06E+001.06E-051.42E+002.27E-06 Naturallogarithmofthepopulationpressurewithdecayconstantof16.7km Anthropogenicn/an/a1.42E+002.27E-06 Distancetoroads (km)Anthropogenic1.15E-041.09E-031.78E-041.30E-05 Historicallogging – Partiallylogged (nologging/partiallylogged)Anthropogenic-2.10E+001.09E-03-3.83E+004.97E-07 CostdistancetoDaresSalaam Anthropogenic3.41E-052.00E-032.58E+005.46E-03 Naturallogarithmofthecostdistancetomarkettowns Anthropogenic-6.05E-015.24E-02-9.85E-011.89E-02 Governance – local (national/local/joint/unknown)Anthropogenic4.24E+009.29E-03n/an/a Governance – national (national/local/joint/unknown)Anthropogenic-7.95E-039.78E-01n/an/a Governance – unknown (national/local/joint/unknown)Anthropogenic6.26E-017.10E-01n/an/a Meanannualmonthlytemperaturerange (C)Climatic-9.79E-012.00E-16-1.15E+001.98E-13 Meanannualminimummonthlytemperature (C)Climaticn/an/a1.09E+003.07E-16 Meanannualmaximummonthlytemperature (C)Climaticn/an/a-1.15E+001.98E-13 Meannumberofdrymonthsannually Climatic-2.28E-012.57E-02-3.09E-015.58E-03 Totalavailablewatercapacityofthesoil (vol.%,-33to-1500kPAconformingtoUSDAstandards) Edaphic-3.75E-011.16E-05-8.59E-013.05E-05 Totalnitrogencontentofthesoil (gkg-1)Edaphicn/an/a-4.13E-012.50E-03 Totalcarboncontentofthesoil (gkg-1)Edaphicn/an/a6.18E+001.15E-03 pHofthesoil (pH)Edaphicn/an/a1.73E+002.96E-02 Spatialautocorrelationterm5 Spatial6.45E+013.15E-036.60E+001.18E-01 Spatialautocorrelationterm7 Spatial-8.48E-013.57E-03-1.71E-011.45E-01 Spatialautocorrelationterm4 Spatialn/an/a6.60E+001.18E-01 Spatialautocorrelationterm3 Spatialn/an/a-1.71E-011.45E-01 Table4Thecoefficientsandassociatedp-valuesofthe variablescorrelatedwithabovegroundcarbon sequestrationVariableCoefficientp-value (Intercept) 0.0320.890 PC1 -0.1120.006 PC3 -0.2550.010 PC5 -0.4120.012 Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page7of17 http://www.cbmjournal.com/content/9/1/2

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interactions,inareasoflowpotentialstemdensity,carbonstorageispositivelycorrelatedwithmaximumcanopyheight(Additionalfile6:FigureS5).However,the oppositecorrelationisobservedinareasofhigherstem density.Althoughsimilarinteractionsareobservedbetweenbothsize-frequencydistributionestimates(gradientandintercept),theinteractionbetweenWSGand maximumcanopyheightisinverse,withcarbonstorage onlyshowingpositivecorrelationswithmaximumcanopyheightinareasofhighWSG.Bothsize-frequency distributionestimatesalsointeractedsimilarlywith WSG,withbothshowingpositivecorrelationswithcarbonstorageinareasoflowWSG,butnegativecorrelationsinareasofhighWSG(Additionalfile6:FigureS5). Finallycarbonsequestrationcorrelationvalueswerepositivelycorrelatedwithcarbonstorageestimates(p-value< 0.001),indicatingthatareasstoringthemostcarbonare alsothosethatareincreasinginstockatthefastestrate.DiscussionTier3correlation-basedmethodvs.Tier1and2methodsOurestimatesof1.3PgCstoredacrossthe33.9million hectaresislargerthanmostpreviousTier1estimates [46-48],althoughbelowthemostrecentlyproducedestimate[3](Table1).Underestimationoftheamountof carbonstoredintheEAMregioninglobalanalysescan bearesultoftheirpoorresolutionand/orapplicationof datafromotherregionswhichmaydiffersystematically comparedtoEastAfricanforests,woodlandsandsavannas[42].Whenseparatedbylandcovercategory,our locallyderivedcarbonestimatesarecomparabletothose presentedinotherlocal[49-52]andglobalstudies,the latteroftencontaininglittleornodatafromEastAfrica [3,4,46,47,53].Thissuggestsdifferencesbetweenourestimatesandotherstudieshavearisenbecausemany previousstudiesmappedcarbonstorageatlowerresolution[3,4,46,47,53].Whenconsideringhomogenous landscapes,scaleeffectsareunlikelytocauseadramaticdifferenceincarbonestimates.However,inhighly fragmentedandheterogeneouslandscapes,suchasEast Africa,theeffectsofscalearelikelytobesubstantial.Forestfragments,typicallyofhighcarbonstorage,maybe omittedatlowerresolutions,being ‘ replaced ’ bymore dominant,butlowcarbon,landcovercategories(e.g.open woodland),resultinginunderestimationofcarbonstorage. Itmustbenotedthat,thelandscape-scaleconfidenceintervalssurroundingourTier3estimatesareconsiderably widerthanthosearoundpreviousestimates[3,4,42,47,53]. ThisresultisconsistentwithHilletal(2013),whoalso showedincreasingmethodologicalsophisticationdoesnot (f)Total available water capacity Total available water capacity of the soil (%)(e)Historical logging (d)Governance (c) (b)Temperature range (a)Number of dry months Historical logging Governance Mean annual monthly temperature ran g e (oC)Mean number of dry months annuallyNo logging Figure4 Themodelledeffectofmostinfluential,significantanthropogenic(a,b,andc),climatic(dande)andedaphic(f)variablesof abovegroundlivecarbonstorage. Dashedredlinesindicatethemodelled95%CI.Thedataisindicatedbyblacklinesabovethex-axis. Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page8of17 http://www.cbmjournal.com/content/9/1/2

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necessarilyresultinreduceduncertainty,asisoftenassumed[54].Confidenceintervalsderivedfromlook-up tablevaluesmayshowasystematicbias.Therangesprovidedareanartefactofthestudyarea,thenumberofland covercategoriesandtheresolution,aswhensummed acrossalargenumberofpixels,pixelerrorismostlynegatedasunderestimatesinonepartofthelandscapeare counterbalancedbyoverestimatesinotherparts.The95% CIdevelopedfromcorrelationequationsareeffectively basedonnumerouscontinuousvariables,containingthe uncertaintyrelatingtoanthropogenic,climaticandedaphicvariables,thushavemanythousandsofpossible combinations,severelylimitingtheabilityofthe ‘ lawof averages ’ toact.Hence,the95%CIpresentedinthisinvestigationmaybetterreflectthatoftheactuallandscape, containingmorevariablesthatmake-upthecomplex landscapeheterogeneity(i.e.improvedrepresentativeness), althoughthisisonlytrueforthosepixelsestimatedusing thecorrelationequations(86%oftheEAMbutonly52% ofthestudyarea).Therefore,thelook-uptable95%CI presentedinWillcocketal(2012),andusedinthisstudy, mayunderestimateuncertainty[42].Futurestudiesshould expandtheexistingplotnetwork(Figure1),enablingthe correlationequations(andimproved95%CI)tobeappliedtotheentirestudyarea.Thisprocesshasalready begununderanewWWF-REDD+project(whichfocusses onbettersamplingthedata-deficientlandcovercategories identifiedinthisstudy[55])andtheNationalForestMonitoringandAssessment(NAFORMA)project[56,57].LinksbetweencarbonstockandinfluentialvariablesTheresultspresentedhereindicatethatALCstoragein tree-dominatedecosystemsiscorrelatedwithanthropogenic,climaticandedaphicvariables.However,inallour modelsthereisalargeamountofunexplainedvariation (R-squaredvaluesforourcorrelationmodelsvarybetween0.18and0.41).Thisislikelytobeduetothree mainreasons(Additionalfile1:SI6).Firstly,althoughwe usedthehighestresolutiondatasetsthatarefreelyavailable,severaloftheassociatedvariablesareofrelatively poorresolutionacrosstheEAM(including;wind,light andsoilnutrientvariables[Additionalfile1:TableS6]). ThisisparticularlyimportanthereaslowresolutionGIS dataisunlikelytocorrelatewellwiththeresponsevariablesfromourplotnetworkasmanyplots(withhigh variance[58])mayfallwithinasinglecell[59].Thus, ourstudymaybebiasedagainstretaininglowresolution explanatoryvariablesinourmodels.Secondly,contemporaryforestcharacteristicsaretheresultofgrowth,recruitmentandmortalityovermanyyears.Itisdifficult toobtaindataonhistoricalvariablesandyetthesecould havehadasignificantimpactonpresentdaycarbon storageandotherforestcharacteristics[60].Thirdly, presentdayinformationisalsolacking,forexample datasetsdescribingphysicalsoilpropertiesinthestudy areaareunavailable.Thus,futureworkisneededtodevelopadditionalhighresolutionGISdata,particularly forhistorictimeperiods. Ofthevarianceexplainedinourforwardandbackwardmodels,directanthropogenicfactorsarethemost influentialexplanatoryvariables(asnotedbythelargest coefficientofexplanatoryvariablesontheresponsevariable,incontrasttothose[e.g.temperature]withsmaller p-valuesbutalsosmallerinfluence[Table3])andsoare thefocusofourremainingdiscussion(seeAdditional file1:SI5fordiscussionofclimaticandedaphicvariables). Withinourstudyarea,peopleareclusteredaround highcarbonareas(Figure4).Wesuggestthiscouldbe duetotheseareashavingfavourableclimaticconditions withmoremoistureforplant(andthuscrop)growth. Further,theincidenceofmalariaislowerathighelevations[61],makingtheselocationsmorehabitableforhumanpopulations.Thusthereisapeakinpopulation densitynearthebaseofhigh-carbonmontaneforests [40].Ourinterpretationthatitisthelandscapesuitabilitydrivinghumanpopulationdensityisconsistentwith theobservationthatwhenindividuallocalitiesare followedovertime,degradationatthelocallevelcaused bythepopulationisevident[62,63].Thisemphasises thatourresultsarenotproofofcausationandthatthe driversmaybeacorrelateoftheexplanatoryvariables retainedinourmodels(Additionalfile1:SI6).Ourresultsalsoshowadecreaseincarbonstorageinpreviouslyloggedareasandinareasnearerthecommercial capital,DaresSalaam.Thisconfirmspreviousreports thatareasnearthecapitalhavelowerbiomassdueto thelocaldemandoflowgradetimberbythecity,aswell asinternationaldemandforhighgradetimberviathe city ’ sport[62];emphasisingtheconnectionsbetween theruralandurbanlandscape,andhowthesphereof urbaninfluencedriveschangeinruralecosystems.Futureinvestigationsshouldusesimulationmodellingand directexperimentationtoidentifyiftheinfluentialvariableshighlightedherecanbeconfirmedasdriversof carbonstorageandsequestration,providingadeeperunderstandingoftheprocess-basedrelationships. Thedecreaseincarbonstorageasaresultoflogging (51-77%oftheALCisretained)isofsimilarmagnitude tootherreportedestimates[64].However,thehistorical loggingdataweutilisedwasbasedonexpertopinion (Additionalfile1:TableS6)so,givenitsimportance,furtherworkdevelopingandevaluatinghistoricalvariables isneeded(Additionalfile1:TableS7).Weobservea comparabledecreaseduetodifferinggovernance.Land undernationalcontrolholdsbetween40%and65%of theALCstoredinareasunderdecentralisedgovernance. Thisperhapsindicatesthatdecentralisationofmanagement(e.g.participatoryandcommunityledforestry)isWillcock etal.CarbonBalanceandManagement 2014, 9 :2Page9of17 http://www.cbmjournal.com/content/9/1/2

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successfulinourstudyarea[37,65].However,itisnot possibletoprovecausationwithintheframeworkofthis study.Manylocallymanagedforestsarelocatedinthe south-eastofourstudyareawithinanareaofnaturally highcarbonstorage,whereaslandundernationalcontrolcoversmuchlargerareas,includingthedry,carbonpooreast.Hence,ourfindingthatcarbonstorageis higherinareasunderdecentralisedcontrolmaybean artefactofthedifferingareaswherethistypeofland managementoccurs.Furtherstudiesmonitoringchange incarbonstorageovertimeunderthetwodifferentgovernanceregimeswouldenabletheeffectoflandmanagementtobedetermined. Theoveralleffectsoncarbonstoragearearesultof manychangesinforestcharacteristics.BothWSGand theproportionoflargerstemsdecreasewithincreasing anthropogenicdisturbance,however,stemdensity( = 10cmDBH)increases.Anthropogenicdisturbance,for examplelogging,isoftenacommercialactivityandresultsinthepreferentialremovalofthelargest,most valuablestems[62].Themoreopencanopy,following stemremoval,wouldresultinincreasedrecruitment fromyoungforesttrees[66],leadingtothehighnumbersofsmallstemsobserved.However,theopposite wouldbeexpectedinwoodlandsandsavannas,with moreopencanopiesresultinginmoregrass,highfireintensityandsolessrecruitment[67,68].Ourresultshighlighthowinfluentialthenegativeeffectofpeopleon tropicalforestcarbonstoragecanbe.Thisassertionis supportedbydatafromacrossthetropics[69-71].The significantimpactofanthropogenicactivitiesimplies thatREDD+could,atthelocalscale,havesignificant positiveimpactsoncarbonstorage.However,careful policydesignstolimitleakageofdeforestationandencouragetheinvolvementofthelocalpopulationare neededtoensureREDD+schemesachievetheircarbon storageandsequestrationaims[72]. Likecarbonstorageanditscomponents,carbonsequestrationisalsocorrelatedwithanthropogenic,climaticandedaphicvariables.Weestimatethatsome localities(forexampletheUdzungwaMountainsNationalPark;Figure4)provideacarbonsinkofcomparableper-areamagnitudetomodelledestimatesinEast Africa[73]andtothatobservedoverrecentdecadesin structurallyintactAfricanforest[7].However,many areasofforestandwoodlandwithinthestudyareaexperienceahighlevelofdegradationanddisturbance, andsoarenetsources.Here,wehaveshownthatanthropogenicdisturbanceisakeydeterminantofthe trendincarbonstorageovertimeineasternTanzania. ImportantlocationsofhighcarbonlossesarethePare andUsambaramountains(Table5),whichhistorically haveseenthehighestratesofdegradationanddisturbance [74].ThenationalpopulationofTanzaniaisincreasing [75]andthismayincreasethepressureontree-dominated ecosystemswhichcouldresultinthestudyareabecoming asignificantsourceofcarboninthefuture.Furthermore, theeffectofincreaseinanthropogenicpressurescouldbe compoundedbypotentialdecreaseincarbonstorageasa resultofincreasingtemperatures[76,77]andchangesin soilnutrients(seeAdditionalfile1:SI5).However,these futureeffectscouldbecomplicatedbyincreasinglevelsof atmosphericCO2,varyingeffectivenessoflegallyprotectedareasandshiftingconsumptionpatterns.ConclusionsOurresultsshowthattheamountofcarbonstoredin forestsacross33.9millionhaoftheEasternArcMountainsofTanzaniaisconsiderable:1.32(0.89to3.16)Pg. Ourestimateissignificantlyhigherthanmostprevious estimates.However,ourmoresophisticatedmethodalso hashigheruncertainty,implyingthatothermethods maysubstantiallyunderestimatetheuncertaintyinvolved.Withinthetree-dominatedlandcovercategories, historicalloggingisthemostinfluentialdirectanthropogenicfactor,whilethemeannumberofdrymonthsis themostinfluentialenvironmentalfactor,withanorder ofmagnitudelessimpactoncarbonstorage.Weshow thatWSG,size-frequencydistributionvariablesand heightvariablesareallimportantindeterminingcarbon storage.Ourestimatesindicatethat,between2004and 2008,tree-dominatedcommunitiesacrossthestudy areasshowednosignificantchange,howeversomeareas Table5Carbonstoredandsequesteredacrossthe individualmountainblocksoftheEAMrange(thetotalis denotedinbold)EasternArc Mountain Block[ 40 ] Area,km2Abovegroundlive carbonstorage,Tg Meancarbon sequestration, Mgha-1yr-1Tier3Willcocketal (2012)-Original Tier2[ 42 ] NorthPare5101.932.382.60 SouthPare2,3278.969.592.41 West Usambara 2,94513.5215.963.64 East Usambara 1,1455.917.632.79 Nguu1,5629.3412.711.89 Nguru2,56515.1118.861.79 Ukaguru3,24313.3920.631.42 Uluguru3,05715.9213.911.35 Rubeho7,98436.8440.961.06 Malundwe330.290.291.80 Udzungwa22,788101.73104.051.01 Mahenge2,60623.5812.080.19 Total50,765246.53259.061.19 Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page10of17 http://www.cbmjournal.com/content/9/1/2

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wereidentifiedaslargesinks(0.8%ofthestudyarea) andotherslargesources(1.4%ofthestudyarea),showingtheimportanceoftakingalandscapescaleapproach. Thecarbonmapsproducedandstatisticalrelationships documentedcanassistpolicy-makersindesigningpoliciestomaintainandenhancecarbonstorageforclimate mitigationandotherecosystemservices.MethodWecollateddatafrom2,462treeinventoryplotswithin ourstudyarea(seeAdditionalfile1:SI3),thenapplieda qualitycontrolandstandardisationprotocol.Thisconsistsoftwomainsteps:(1)Metadataqualitycontrol;and (2)Measurementbiasdetection. Firstly,allplotslackingarecordedspatiallocationand afixedareawerediscarded(770plots).Plotswhereone ormorediameteratbreastheight(DBH)datawere knowntobemissingwerealsoexcluded(7plots).Furthermore,plotssmallerthan0.025ha(16plots)were deemedtoproduceunreliablecarbonestimatessoalso removedfromthedataset. Secondly,toassesspossiblemeasurementbias,i.e.not measuringoverbuttressesandsooverestimatingbiomass [78],theremainingplotsweregroupedbytheleadfield researcher.Size-frequencydistributions,using10cmsize classes,werecreatedforeachofthesegroups.Forestsizefrequencydistributionsaresuggestedtoconformtothe-2 powerlawbasedonmetabolicscaling[79].Althoughit hasbeenarguedthatthisruleisnotgloballyapplicable [80],manystudiesacceptthisasatheoreticalmaximum valuefortheabundanceoflargestems[81].Thus,researcherswithmanyplotsabovethismaximumvalue likelymeasuredstemsaroundbuttressesandsowereremoved(1researcher,100Plots). Thequalitycontrolandstandardisationprocedureresultedinadatasetof1,611treeinventoryplots(median 0.1ha,mean0.1ha,mode0.1ha[43plotswithmultiplecensuses;median0.1ha,mean0.5ha,mode 1.0ha];Figure1;seeAdditionalfile1:SI3forafurther information)fromwhichwecalculatedplot-levelstand structureindicesandabovegroundcarbonstorageper unitarea(seeAdditionalfile1:SI2forfulldetails).Weobtainedtheexponentandinterceptofthepopulationsizefrequencydistributionusingthepowerlawfitforeach plotusingthelog-logtransformationmethod.Whereby, foreachplot,wecreated10cmbinsize-frequencydistributionsbasedonDBH,andalinearmodelofthelogarithmoffrequencyagainstthelogarithmofthesizeclass wasfitted.Whilstnotasaccurateasthemaximumlikelihoodestimationmethod,oursimplermethodismore stableformanyofourplots,providingboththeintercept andslopeindicatorsofpopulationstructure[82]. WeobtainedWSGdataviathephylogeneticinformationprovidedbyourtreeinventoryplots.Weuseda globalwooddensitydatabasetoextractspeciesaverage WSG[83].Thisprocedurepr ovidedover32,000trees withWSGdata.Whenthiswasnotpossiblewe adoptedahierarchicalappr oach,firstapplyingtheappropriategenusaverageifavailable(~14,000trees)beforeconsideringfamilyaverage(~9,500trees),plot average(~4,500trees)anddatasetaverage(~80trees) inturn[84].IncludingWSGasanadditionalparameter inallometricequationsreducesthebiomassestimation error[49,85,86]. Inaddition,weestimatedplotbiomassusingmoist foresttreeallometry[86]basedonmeasurementsof DBHfromourtreeinventoryplots,WSG(asdescribed above)andheightdata(derivedfromourdatasetusing thebestfitDBH-heightequationform[Equation5.1;see Additionalfile1:SI4],ifnotmeasuredinthetreeinventoryplots).Finally,carbonwasassumedtobe50%of biomass[7]. Forasmallernumberofplots,multiplemeasurements wereavailableovertime(n=43;meanplotsize=0.5ha; meanmeasurementperiod=3.9years).Wecalculated changesincarbonstorageratesbydividingthedifferenceincarbonstorageestimatesbetweencensusesby thenumberofyearsseparatingthem. Forour1,611geo-referencedtreeinventoryplots,we obtainedfurtherinformationonvariablesfallingintofive broadcategories;anthropogenic,climatic,geographic, edaphic,andpyrologic(medianresolution1.0ha,mean resolution22.0ha,moderesolution1.0ha;Additional file1:TableS6).Anthropogenicdata,furtherdivided intosixsubcategories,wereobtained:(1)population pressurevariables(n=14relatedvariables)wereobtainedfromPlatts(2012)[87](seeAdditionalfile1:SI7); (2)DaresSalaamrelatedvariables(n=3;e.g.distanceto DaresSalaam),(3)markettownrelatedvariables(n=3; e.g.distancetomarkettowns),and(4)infrastructurerelatedvariables(n=2;e.g.distancetoroads)werederived fromavailabletopographicmaps;(5)historicallogging (n=1)fromSwetnametal(2011)[88];and(6)governance(n=1)fromtheWorldDatabaseonProtected Areas[89].Climatedataweredividedintothreesubcategories(precipitation[n=2;maximummeancumulative waterdeficitandmeannumberofdrymonthsannually],temperature[n=4;meanannualtemperature, meanannualminimummonthlytemperature,meanannualmonthlymaximumtemp erature,andmeanannual monthlytemperaturerange]andwindspeed[n=1]) andwerederivedfromtheTropicalRainfallMeasuring Mission[90,91],WorldClim[92,93],andUnitedStates NationalAeronauticsandSpaceAdministrationSurface meteorologyandSolarEnergy[94]datasets.Similarly, geographicdatahavetwovariables(aspect[n=1]andincomingsolarradiation[n=1])derivedfromShuttleRadar TopographyMission[93]andNationalRenewableEnergyWillcock etal.CarbonBalanceandManagement 2014, 9 :2Page11of17 http://www.cbmjournal.com/content/9/1/2

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Laboratory[95,96]datasetsrespectively.Lastly,weextractededaphicdata(n=6)fromtheInternationalSoil ReferenceandInformationCentredatabase[97,98]and fire-relatedvariables(n=5)derivedfromMODISimages[99]. Wethencorrelatedthesevariableswithcarbonstorage,andfollowingthis,itscomponents:WSG,theinterceptofthepowerlawrelationship,andthegradientof thepowerlawrelationship,ineachcaseusinggeneral linearmodels(seeAdditionalfile1:SI2-5).Notransformationswererequiredtoensureanormaldistribution whencorrelatingeitherWSG,theinterceptofthepower lawrelationshiporthegradientofthepowerlawrelationshipwiththeindividualvariables.However,carbon storageestimatesrequiredasquareroottransformation toensureanormaldistributionwithinthegenerallinear models(normalitywasconfirmedusingtheShapiroWilktest;p-value>0.05).Inallmodels,plotswere weightedbythesquarerootoftheirareaasconfidence inbiomassestimationincreaseswiththeareasurveyed [100,101].Landscapescalespatialautocorrelationwas accountedforbyincludingspatialterms(latitude,longitudeandtheinteractionsbetweenthem)inthemodel (Additionalfile1:TableS6)[102].Thenumerouspossibleinteractionswereexcludedfromthemodels,as thesewerefoundtoaddverylittleexplanatorypowerto themodels,onlyincreasingR-squaredvaluesby~0.001 withtheadditionofeachinteractionterm.Allanalyses wereperformedusingR2.12.1[103]andmappedin ArcGISv9.3.1[104]. Whenassessingcarbonsequestration(n=43)fewer degreesoffreedomwereavailable,thereforeexplanatoryvariablesneedtobegrouped.Therefore,weconductedaprinciplecomponents(PC)analysis,obtaining fivePCwhichexplained>90%ofthecumulativevarianceoftheindividualinfluentialvariables(Additional file1:TableS4).Then,covariationofPCwithcarbon sequestrationwasassessedinsteadoftheindividualinfluentialvariables.Carbonsequestrationestimatesrequiredacube-roottransfor mationtoensureanormal distributionwithinthegenerallinearmodels(confirmedusingtheShapiro-Wilktest;p-value>0.05). Thisenabledtheeffectofmultiplevariablestobeexaminedevenwiththislimiteddataset.PCanalysisof thevariableswasperformedonthescaleddatausing theprcomppackage[105]withinR2.12.1[103].All otheraspectsofthemodel(weightingandspatialautocorrelation)wereperformedidenticallytothemodels forcarbonstorageanditscomponents. Themostappropriatemodelwaschosenusingforwardandbackwardstepwiseselection.Forwardmodels aremoreusefulforinferringcausalrelationships[43] andsowerepreferentiallyusedtoinfertheinfluential variablesofcarbonstorageandsequestration.However, averagingforward – backwardsandbackward – forwards predictionsoutperformsconventionalselectionprocedures[43]andsobothmethodswereusedwhenestimatingthespatialdistributionswithinthestudyarea. Akaikeinformationcriterion(AIC)wasusedtoreduce/ expandthemodels,withvariableselectionoccurring whenthevariablereducedthemeansquarederror (MSE)underten-foldcrossvalidation[106].Unlike modelselectionusingR-squared,whichneglectsthe principlesofparsimony,AICconsidersbothmodelfit andcomplexity,resultinginbetterpredictionsand allowinginferencestobemadefrommultiplemodels [107].Modelselectioncontinueduntiltheaddition/removaloffurthervariablesabletoreducecrossvalidation MSEnolongerincreasedAIC,therebyproducingthe best-fitmodelwiththelowestpredictionerror[43]. Withineachcategory(anthropogenic,climatic,geographic,edaphic,andpyrologic),somevariableswere highlycorrelated(Additionalfile1:TableS7)andthis mayconfoundthestepwiseprocedureaseachvariable doesnotcarryenoughdistinctinformation[108].For example,alltemperaturerelatedvariables(Additional file1:TableS7)werecorrelated(R-squared>0.6).However,itisunclearwhichcorrelatedbestwiththevariablesofinterest,e.g.carbonstorageandsequestration. Manystudiesincludemeanannualtemperatureinbiomassmodels[77,109],buttheorysuggeststhatitmaybe thetemperaturerangedrivingthisrelationshipasphotosynthesiscorrelateswithmaximumtemperatures,but respirationwithminimumtemperatures[76,110,111]. Wefoundthat,ifweremovedcorrelatedvariablesprior tomodelselection,thefinalmodelswereartefactsofthe variableswehadselected.Forexample,ifweincludedmean annualtemperatureinthemo del,butnottemperature range,thenthesignificantco rrelationsbetweenmeanannualtemperatureandALCstoragewerefound.However, thesecorrelationswereinsignificantiftemperaturerange wasaddedtothemodel,withthenewlyaddedvariable showingasignificanteffectinstead.Inshort,theresultant modelswereautomaticallybiasedtowards apriori expectations.Toavoidthisbias,wedevisedaprocedurebywhich theinfluentialvariablesincludedinmodelselectionwere selectedbytheirabilitytoexplainvariationwithinthedata ofinterest(e.g.carbonstorage).Allvariables(describe above)wereincludedinmodelselection.Oncethishadrun tocompletionthemodelwasassessed.Thesubcategory withthemostcorrelatedvar iablesretainedwithinthe modelwasselectedandallbutthemostinfluential,significantvariablewereremoved.Forexample,ifallfour temperature-relatedvariableswereincludedintheinitialmodelandthiswasthelargestgroupofvariables thenthisgroupwouldbeselected.Then,ifmeanannual temperaturewasthemostinfluentialandsignificant temperature-relatedvariable,allothertemperature-relatedWillcock etal.CarbonBalanceandManagement 2014, 9 :2Page12of17 http://www.cbmjournal.com/content/9/1/2

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variableswouldbeexcludedinthenextroundofmodel selection.Thus,stepwisemodelselectionwasthenrepeatedforallremainingvariables.Thisprocesswasrepeateduntilnohighlycorrelatedvariablesremained withinthemodelproduced. Sinceonlylandscape-scalevariationwasaccountedfor bythespatialtermsalreadyincludedinthemodel(latitude,longitudeandtheinteractionsbetweenthem; Table1;Additionalfile1:TableS6),itwasnecessaryto investigatetheeffectoflocal-scale(<10km2)spatial autocorrelation[102].Todothis,theseparateforward andbackwardmodels,containingnohighlycorrelated variables(producedabove),weremapped.Then,the sumofthemodelestimateswithinthemapswereextractedat1,3,5,7and10km2resolutions,andincluded asadditionalvariables(representinglocalspatialautocorrelationterms)intothestepwisemodelselection process,whichwasre-runafinaltime[112].However, inallcases,localspatialautocorrelationtermswere rejectedastheydidnotreducecrossvalidatedMSE. Sinceitwasnotnecessarytoincludelocalspatialautocorrelationtermsinthemodels,thepreliminarymaps producedabovecouldberegardedasfinalspatialrepresentationsofthetenbestfitmodels,two(forwardand backward)foreachofthefivevariablesofinterest(carbonstorage,carbonsequestration,WSG,theintercept ofthepowerlawrelationshipandthegradientofthe powerlawrelationship).Eachpairofmaps(forwardand backward)werethencombinedintoasingle,final weightedmeanestimate.Theratiooftherelevantcross validatedMSEoftheforwardandbackwardmodelswas usedtocreatetheweightedmean,withthemodelshowinglowesterrorreceivingthehighestweighting[43]. Thus,weultimatelyproducedfivemaps(fromtenbest fitmodels);oneeachforcarbonstorage,carbonsequestration,WSG,theinterceptofthepowerlawrelationship,andthegradientofthepowerlawrelationship.As ourcarbonstorageestimateswerederivedfromdata representingtreeswithaDBHgreaterthanorequalto 10cm,regionallyestimatesofratiosfromWillcocketal (2012)wereusedtoestimatetheunmeasuredcomponentofALCstorage[42],thiswassummedwithour modelledcarbonstorageestimate,providinganestimate oftotalALCstorage. Althoughthefivemapsproducedcoveredtheentire studyarea,wewereconcernedthatextrapolatingpredictionsbeyondtherangeofobservedpredictorvariables fromourdatasetcouldresultinlarge,unquantifiableerrors.Thus,welimitedthemodelstolocalitieswhereall theassociatevariableswerewithintherangeofthat showninourdataset,thusonlyinterpolatingwithinour correlationmodelsfortree-dominatedlandcovercategories.Foranypixelsoutsidethedatarange,look-up tablemethodswereusedinpreferencetothecorrelation modelestimates.Thus,foreverylandcoverinourstudy areacontainingtrees(openwoodland;closedwoodland; forestmosaic;lowlandforest;sub-montaneforest;montaneforest;anduppermontaneforest[41])thatfell withinthelimitsofourdataset,theestimateofcarbon storagederivedfromthecorrelationequationswasused. Forallotherlandcovercategories,andforthoselocalitiesforwhichpredictorvariablesfelloutsidetheranges ofvaluesusedinmodelconstruction,landcoverbased look-uptablevaluesfromWillcocketal(2012)were usedtoestimateALCstorage[42].Intotal,look-uptable valueswereappliedto52%ofthelandscape,although thiswaspredominantlytolowcarbonlandcovercategories,with86%oftheEAM(whichholdthemajority oftheregionstropicalforest[113])estimatedusingthe correlationapproachdescribedabove.EstimatesofWSG andpopulationstructurewereonlymadeforwooded landcovercategories,withestimatesforareaswithin ourdatasetrangebeingderivedfromtherelevantcorrelationequationsandestimatesforotherareascoming fromlandcoverbasedlook-uptablevaluesderivedfrom themedianvalueofourWSGandpopulationstructure data(weightedbythesquarerootofplotsizeandderivedviasamplingwithreplacement10,000times)for eachlandcovercategory(Additionalfile1:TableS8). Forcarbonsequestration,again,estimateswereonly madeforwoodedlandcovercategoriesforthoseareas insidetherangeofourdatasetestimatesderivedfrom thecorrelationequationswereused.However,unlike carbonstorage,WSGandpopulationstructure,forareas outsidetherangeofourdataset,alandcoverbased look-uptablewasnotusedasseverallandcovercategorieswerepoorlyrepresentedduetothesmallsamplesize available(n=43).Instead,forpixelsoutsidetherangeof thecorrelation-derivedcarbonsequestrationmodel(16% ofpixelswithwoodedlandcover),themedianvalueof datafromourrecensusedplots(againweightedbythe squarerootofplotsizeandderivedviasamplingwith replacement10,000times)wasutilised. Forevery1hapixelofeachmapderivedfromcorrelationequations,weproduced95%confidenceintervals (CI).Ifthepixelestimatewasderivedfromthegeneral linearmodels,thenthepixel95%CIwascalculatedby addingandsubtractingthesquarerootofthecrossvalidationMSE.Forlook-uptablepixelsthelookuptable 95%CIwereused.Thepixel95%CIdescribes,forevery pixel,therangewewouldexpecteachofourestimates toliewithin.However,aswearealsointerestedinestimatingcarbonstorageandsequestrationonalandscape scale,indicationsofuncertaintyarealsorequiredat landscape-scale.Simplysummingthepixel95%CIto derive95%CIoftheoveralllandscape-scaleestimates wouldincorrectlytreatrandomerrorasaregion-wide systematicbias.Thus,toderive95%CIforlandscape-Willcock etal.CarbonBalanceandManagement 2014, 9 :2Page13of17 http://www.cbmjournal.com/content/9/1/2

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scaleestimates,werandomlyallocatedeachpixelanestimatewithintherangedictatedbyits95%pixelCI,and summedthesevaluesacrosstheentirelandscape.This processwasperformed10,000timesandthemedian valueand95%CI(the250thand9,750thrankedvalues, whichmaynotbeequallydistributedaroundthemedian)forabovegroundcarbonstorageandsequestration inthestudyareawereobtained. Forthefinalmodelofcarbonstorageestimates,weinvestigatedhowthecomponentsofcarbonstorage(population structure,WSGandtreeheight)interactedtoultimately producetheecosystemserviceofcarbonstorage.Weobtainedestimatesofmaximumcanopyheightfromthebest fitDBH-heightequation[Equation5.1;seeAdditionalfile1: SI4andAdditionalfile7:FigureS6],andcombinedthis spatiallywithourcorrelatio nmodelderivedestimatesof WSG,theinterceptofthepowerlawrelationshipandgradientofthepowerlawrelationship.Wethencorrelatedthese againstourestimatesofcarbons torage,allowingallpossible interactions,andselectedthebest-fitmodel(viaAIC)using bothforwardsandbackward sstepwiseregression. Ethicalapprovalfortheabovestudywasobtained fromtheFacultyofEnvironmentResearchEthicsCommittee,inaccordancewiththeUniversityofLeedsresearchethicspolicy.AdditionalfilesAdditionalfile1: Supportingtext(includingS1-7andTablesS1-12). Additionalfile2:FigureS1. ThespatialvariationofWSGintreedominatedlandcovercategorieswithinthestudyarea(a),withupper (b)andlower(c)pixelbased95%CI.Seetextfordetailsonmethods. Additionalfile3:FigureS2. Themostinfluential,significantinfluential variablesonWSG(aandb),theinterceptofthepowerlawrelationship (candd),andthegradientofthepowerlawrelationship(eandf). Dashedredlinesindicate95%CI. Additionalfile4:FigureS3. Thespatialvariationintheinterceptof thepowerlawrelationship(aproxymeasureforpotentialstemdensity)in treedominatedlandcovercategorieswithinthestudyarea(a),withupper (b)andlower(c)pixelbased95%CI.Seetextfordetailsonmethods. Additionalfile5:FigureS4. Thespatialvariationinthegradientofthe powerlawrelationship(aproxymeasurefortheproportionoflarger stems)intree-dominatedlandcovercategorieswithinthestudyarea(a), withupper(b)andlower(c)pixelbased95%CI.Seetextfordetailson methods. Additionalfile6:FigureS5. The2ndorderinteractionsrelatingmy carbonstoragederivatives(woodspecificgravity,maximumcanopy height,theinterceptofthepowerlawrelationship,andthegradientof thepowerlawrelationship[shownhereasWSG,height,intercept,and gradientrespectively])toabovegroundlivecarbonstorage.Dashedred linesindicate95%CI. Additionalfile7:FigureS6. TheeffectofMATontreeheightfora rangeofDBH.Thedata(points)correspondtoDBHrangeswhereasthe Gompertzmodelfits(solidlines)illustratetherelationshipformid-point ofthisrangeonly.Dottedlinesrepresentthe95CIofthemodelfits. Abbreviations AIC: Akaikeinformationcriteria;ALC:Abovegroundlivecarbon; CI:Confidenceinterval;CV:Crossvalidation;CWD:Coarsewoodydebris; DBH:Diameteratbreastheight;EAM:EasternArcMountains;eCEC:Effective cationexchangecapacity;GIS:Geographicinformationsystems;HYDE:History DatabaseoftheGlobalEnvironment;IPCC:IntergovernmentalPanelonClimate Change;IUCN:InternationalUnionforConservationofNature;KITE:York InstituteforTropicalEcosystems;MAT:Meanannualtemperature;MSE:Mean squarederror;PC:Principalcomponents;REDD+:ReducingEmissionsfrom DeforestationandForestdegradation;SAGE:CentreforSustainabilityand GlobalEnvironment;WSG:Woodspecificgravity. Competinginterests Theauthorsdeclarethattheyhavenocompetinginterests. Authors ’ contributions Themajorityofthisjointly-authoredpublicationwasledbySW.Contributions tothecollaborativedatasetcamefromPJP,AA,ND,KD,EF,JG,JH,KH,ARM, BM,PKTM,NO,EJTJ,AM,SWandRDS.TheanalysiswasperformedbySW, supervisedbyOLPandSLL.ThemanuscriptwaspreparedbySW,with assistancefromOLP,SLL,AB,PP,NDDandRM.Allauthorsreadandapproved thefinalmanuscript. Acknowledgements ThisstudyispartoftheValuingtheArcresearchprogramme(http:// valuingthearc.org/)fundedbytheLeverhulmeTrust(http://www.leverhulme. ac.uk/).Manuscriptpreparationandlateranalysestookplaceunderthe ‘ WhichEcosystemServiceModelsBestCapturetheNeedsoftheRuralPoor? ’ project(WISER;NE/L001322/1),fundedwithsupportfromtheUnited Kingdom ’ sEcosystemServicesforPovertyAlleviationprogram(ESPA;www. espa.ac.uk).ESPAreceivesitsfundingfromtheDepartmentforInternational Development(DFID),theEconomicandSocialResearchCouncil(ESRC)and theNaturalEnvironmentResearchCouncil(NERC).SLLwasfundedbya RoyalSocietyUniversityResearchFellowship;SWadditionallybythe StokenchurchCharity;OLPwassupportedbyanAdvancedGrantfromthe EuropeanResearchCouncilandisaRoyalSociety-WolfsonResearchAward holder.Thefundershadnoroleinstudydesign,datacollectionandanalysis, decisiontopublish,orpreparationofthemanuscript.WethanktheTanzanian CommissionforScienceandTechnology(COSTECH),theTanzanianWildlife Institute(TAWIRI)andtheSokoineUniversityofAgriculturefortheirsupportof thiswork,aswellasallthefieldassistantsinvolved.Furthermore,wewouldlike tothankthetwoanonymousreviewers,whosecommentsandinsightvastly improvedthemanuscript. Authordetails1SchoolofGeography,UniversityofLeeds,LeedsLS29JT,UK.2Schoolof BiologicalSciences,UniversityofSouthampton,SouthamptonSO171BJ,UK.3EnvironmentDepartment,UniversityofYork,YorkYO105DD,UK.4DepartmentofZoology,UniversityofCambridge,CambridgeCB23EJ,UK.5WWFUS,Washington,USA.6UNEPWorldConservationMonitoringCentre, CambridgeCB30DL,UK.7GeneticsandConservation,RoyalBotanticGarden Edinburgh,Edinburgh,UK.8TanzanianForestConservationGroup,Dares Salaam,Tanzania.9FrankfurtZoologicalSociety,FrankfurtD-60316,Germany.10TheSocietyforEnvironmentalExploration,LondonEC2A3QP,UK.11STEP Program,PrincetonUniversity,Princeton08544,USA.12Departmentof Geography,UniversityofFlorida,POBox117315,Gainesville,Florida,FL 32611,USA.13TheUniversityofDaresSalaam,DaresSalaam,Tanzania.14CentrefortheIntegrationofResearch,ConservationandLearning, FlamingoLandLtd,MaltonYO176UX,UK.15SokoineUniversityof Agriculture,POBox3001,Morogoro,Tanzania.16EDGEofExistence, ConservationProgrammes,ZoologicalSocietyofLondon,London,UK.17DepartmentofGeography,StaffordshireUniversity,Stoke-on-TrentST42DF, UK.18DepartmentofBioscience,AarhusUniversity,AarhusCDK-8000, Denmark.19DepartmentofGeography,UniversityCollegeLondon,London WC1E6BT,UK. 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