Traditional Shifting Agriculture: Tracking Forest Carbon Stock And

Home , Shifting cultivation

Accepted Article © 2012© Blackwell Publishing Ltd 10.1111/j.1365 differences between this version and the Version of Record. Pleasecite this articleas doi: through the copyediting, typesetting, pagination and proofreading proc This articlehas been accepted for publication and undergonefull peer review buthas not been [email protected] * ‡ ville, Montréal, Québec,Canada 3P8 H3C † University, 1205 avenuedu Docteur * Joha in western Panama. Title: Traditional shifting agriculture: tracking forestcarbon stock and biodiversity through time Articletype : Primary Research Articles Accepted Date 15: Revised Date : 25 Received Date: 23

Department of Biology and Environmental Global and ClimateChange Centre, McGill Corresponding Author: JohannePelletier; Phone number: + 1 Smithsonian Tropical Research Institute,Apartado 2072, Balboa, Panama eatet f egah, Universit Geography, of Department nnePelletier - 2486.2012.02788.x

* - - , - May Jan Jun

‡

ClaudeCodjia, - - 2012 - 2012 2012

- † Penfield, Montréal, Québec, Canada H3A 1B1 Catherine Potvin d Qée à otél CP 88, ucrae Centre succursale 8888, C.P. Montréal, à Québec du é

* , ‡

- 514 - 398 ess, which may lead to - 6726; Email:

- Accepted Article © 2012© Blackwell PublishingLtd emissions. Finally, community participation yielded important additional benefit study area. This is dueto therapid recovery of forest carbon stocks, which results in limited net indicatethat, over time, be landscapes. remotesensing variables explaining 64.2% theof varia addition of different interventions observed over timeshown is to begood a predictor, with since were ableto separatefour forestclasses expressing different carbon stockinventory in Palo Seco ForestReserve, western Panama. remotesensing information, from taken a 12 the effect of the theor net carbon balance. Inpresent the work, wepropose and test a methodology to monitor monitor this land in several developing countries, thereis still very limited information available on how to transparent,robust, and as accuratepossible. as Whileshifting cultivationa is dominantpractice countries to quantify green Reducing Emissions from Deforestation and forestDegradation (REDD+) requires developing ABSTRACT Type of Paper: Primary Research Article Forest intervention, Remotesen Keywords: Reducing Emissions from Deforestation and forest Degradation, Shifting cultivation, Running title: Tracking shifting cultivation through time adequate for tracking land - intervention which demonstrate expected trends inabove

Multi shifting - - use practiuse temporal and multi

shifting cultivation hastransitory a effect on forestcarbon stocks inthe cultivation aboveon - ce force REDD+as littleknown is about house gashouse emissions and removal - use dynamicsuse of the agriculture sing analysis, Land

- spectral medium resolution satelliteimagery is

- - year period, with an in ground carbon stocks - use dynamics,use Cultivated landscapes. tion inforesttion carbon stocks incultivated

s from forests ina manner that is - forest fallowcycle. The results also - ground carbon stocks. the . Wecombine multi - - us

depth community forest areas shiftingof cultivation With remote sensing, we e intensity and time s to measuring

shown to - The year

Accepted Article © 2012© Blackwell PublishingLtd fallow and, thus, thenet carbon balance of this dynamic over timeis not clear. emission levels. A the agriculture al. important source of uncertainty. Using theRep Forest ResourceAssessment data set, Grainger(2008) has identified forest regrowthan as from forests inarobust, t requested to establish anational forest monitoring system to quantify emissions and removals emission reductions that areeventually achieved by REDD+. Participating nationsfurther are constructto a reference emissions level/reference l Werf degradation account for 12%to 17%of global greenhousegas emissions (IPCC, 2007; van der countries (REDD+) as relevantmitigation actions (UNFCCC 2010). Defores sustainable management of forests,and enhancementof forest carbon stocksin developing Emissions from Deforestation and Forest Degradation (REDD Convention Climateon Change (UNFCCC), countrieshave agreed to consider the Reductio As anew post INTRODUCTION REDD+of safeguards. intotaken consideration when national forest moni monitoring carbon stocks,

(2011)further showed thata poor understanding of land

etal. In hisanalysis globalof forest trends using the Food and AgricultureOrganization’s , 2009). Developing countries that wish to participate in REDD+have been requested . Our study providesimportant inputsregarding shifting cultivation, which should be - Kyoto climateregime is being negotiated underthe United Nations Framework - including fallow cycle shifting or cultivation, may induceup to 20% error in reference

n importantfraction Panama’sof territory oscillates between agricultureand

transparency and the valorization of local knowledgefor ransparent, and accurate manner.

ublic of Panama as a model country, Pelletier toring systems are created, given thecontext evel, whicha is benchmark for estimating -

+ use dynamics,use whichrelated are to ), and the role conservation,of tation and forest

biodiversity n of et Accepted Article © 2012© Blackwell PublishingLtd countries tomeasure clearing. course, theconsistency the Of and approach thedefinitions that of are used by akin degradation to than of deforestationthe because rather temporary nature offallow havethe fo cleared been within theentirewhen is monitoring emissions fully captureagriculture ofshifting thedynamics carbon density infallow considering themastypecroplands another since ofunderestimateemissions, might deforestation could over years. ajust few Therefore, under tropicsrapidly, wheregrowfallow effectively as land forest could trees be classified after land, unless corresponds toclassification it criteria defining morepermanent non composition, reduce carbon storagecapacity, and act precursoras a to the establishment of Successiveinterventions or repeated agriculture 2007), depending on forest type andlength the of fallow (Fearnside, 2000; Watson stocks would remain someat intermediate level associated with regrowth (Ramankutty becausethe fallow period allows someregrowth (Watson Forest clearing for shifting cultivation releases less carbon than doespermanent forestclearing cultivat ion or other temporaryclearing may notproduce net emissions theover long According to DeFries et al.et - (2007)have indicated thatland the forestland cover (Peres IPCC (2003) and monitor forestsmonitor activities,and ofREDD+together theimpacts

- is higheris than estimate related(DeFriesemissions rest. Furthermore,werest. arguecultivation is thatshifting

shifting cultivation, consideringasfallows new cultivation, shifting temporaryfallow

in most croplands (Tschakert croplands in most

forest areaforest ratherthan patches individual that

et al. - fallo , 2006; Eaton & Lawrence, 2009). , themanagement, to unit w cycles,w however, can affectspecies should beconsideredasshould cultivated - use dynamics resulting from shifting etal. , 2000). On ave

forested land. In the forested humid land. et al ., 2007), while ., 2007),

et al. rage,carbon

, 2007). T , be considered

et al. -

term. et al.et , 2000). o , Accepted Article © 2012© Blackwell PublishingLtd Community participatory methods areused alongsideremote sensing and forestcarbon information gathered over a 12 shifting cultivation new approach to monitor consequently, ofselective theestimation contribution logging carbon2002; Asner stocks (Gerwing, havehistories, understanding thatsuchactivities beentheimpacts haveonforest to key inventories, fielduse together information onland with quantified (Asner logging has of made use MatricardiBrandt& 2006; Townsend, 2006; Matricardi small include Such clearingassessmentsand increase regrowth. thepossi been land as meanscomplex suggestedanadequate oftracking permanent Multi farming. spectral fallowsand offields, thef signature various lengths, shifting cultivation systemsare tocapture particularly difficult becausethecomplex of the absencearise is guidance. clear from cultivation shifting of measurementand reporting practices. with the importance areaccounting allfluxes, of of significant atgood for the root - scale (StoneLefebvre, & intervention Asner 1998; In building advancements on made in studies selectiveof logging, o In onetomonitoring ofREDD+, commoncontext challengeemissio the

et al.

which couldused be underREDD+, et al. , 2007), and by using, 2007), sub , 2004b; Souza , degradation and forestcarbon stock enhancementin thecont methodologies that allow the extent ofmethodologies theextent these activities that tobe allow - yearassessmentsa series using time imagery ofsatellite have - year period with an in

et al.

et al. et al. , 2004a; Souza Souza , 2004a; , 2005a; Asner , 2010). Compellin , 2010). - pixel information (Souza pixel - by c depth forest carbon stock inventory. ombining multi practices and intervention

et al.

et al. et al. s toCO , 2005;Broadbent

g - Mertz (2009) showedMertz that , 2005b) and, requent inclusion ofrequent inclusion cover dynamics , 2006). Forest carbon, 2006).

research on sel research on bility of detectingbility of - year remote sensing 2 ur study provid

emissions. et al. , 2003;

ective ns that that

et al.

ext of es ,

a Accepted Article © 2012© Blackwell PublishingLtd live within the protected area; half of which overlaps with the indigenous territory (ANAM/CBMAP, 2006). annual precipitation> is andCentral the Cordillera. Average dailytemperature inregion the is 26 and ºC Atlanticside of western Panama Protector de Palo Seco This study was conducted ineastern the part theof Palo Seco ForestReserve (BPPS, Study Area MATERIALS AND forestcarbon stocks incultivated landscapes. interventions that wereobserved over time through remote se collection firewoodof and timber for domesticuse. their livelihood, and predominantlywas the result shiftingof cultivation butalso included the the multiple use of the studyby area local inhabitants, who depend naturalon resource practices and biodiversity. Forest intervention, as it was considered in this research, reflected indicesand fraction im dynamics; the 2) ability to capture forest carbon density with the timeseries of vegetation study. inventories to obtain comprehensiveland

According to ANAM/CBMAP (2006) about focusedWe on three aspectsof forest intervention in western Panama: forest 1) area

METHODS

ages; and 3)the relationships between forestcarbon density, land in Spanish),which a is pro

2500 mm, which is evenly distributed throughoutthe year

(Fig. 1)

at the junction between the Talamanca Mountain Range - use h use

istory and information theon territory under tected area

Weexpected that the combined effects of , 000 people

covering nsing would enable usto predict , mostly

167,409 ha

indigenous, the located theon Comarca mean Bosque

presently s for - use Accepted Article © 2012© Blackwell PublishingLtd surfaceThe area that was covered by the remote sensing analysis is 60 x 60 km. Remote Sensing Analysis declinedeven in the area, according to local residents (Pelletier, 2012). to the creation of pasture for cattle ranching, which is a dominant not land ha195.5 of young fallow(< (J. 5 y) Pelletier, households that were ha 5.4 landof infallowfor morethan five years (Pelletier, 2012). Aggregate land ofuse 45 according to the crops that SD (Smith, 2005). According to household interviewsin thearea, period the of cultivation (mean + system for new farms or from fallowland is usually notburned butleft is to decayin the fields ages. Because theof absence distinct a of dry season, the vegetation that is clea cultivated within a shifting cultivation system, resulting 549 inhabitants hadwho migrated from theCricamola Rive 1983), the provincesother annual rate deforestationof ( Mosaquites, and Ng ) was) 1.7 äbe construction timberfor domesticuse permitted, is together with subsistence agriculture (J. - Bu Previously described aspristine forest ( glé area wherethe field study was conducted ± 1.5 y, w . personal communication Although the BPPSis .

(ANAM/ITTO, 2003). M ultiple varieties bananas, of hile that fallowingof was 3 interviewed consisted of 90 ha crops,of 163 ha of old fallow(> 5 y), and

are - 2.3%) inthecountry between 1990 and 2000

planted. More than half of the respondents had at least 3.6 ha±

a multi

), the Comarca), Ngäbe - r Delta. useprotected area where unpublished data peach palms Gaceta Oficial dePanamá .8

At the time of study,our the population was ± 2.6 Fallowy. length

was notwas colonized until 1975 by the Ngäbe,

inmosaic a of fallowplots different of - , Buglé itsel and various tuber crops ). The use). The fireof has been limited the collection firewood of f experiencedhighest the

vary , 28septiembre de de -

use compared to the

considerably practice, and has

red in this are Accepted Article © 2012© Blackwell PublishingLtd performed in Geomatica (version 9.1, PCI Geomatics). were created for theyears 1999, 2000, 2004, 2007, and 2011. These procedures were used to create a forest/non water, forest, agriculture) ( usingnearest the neighbors re with a Orthorectification of each image correction using REFLECT detection changeof over time. imagesThe wereradiometrically, atmospherically, and geome Preprocessing of the images sensing analysis. the used bothof ASTERand TM5 Landsat imagery. Figure presents2 schema a ofremote the cloud time over I n Table 1). - depth field information thatwas collected in thisstudy - 1500 ha. The effect of forestintervention foreston carbon density was studied using a series of fivesatellite images taken between 1999 and 2011. Thelimited availability of - free imagesdetermined the Garmin A supervise

Legend HCx

d classification separated each image into coverfive classes (cloud, shade,

using m GPS GPS

- software that was

forest binary map and to mask out non - sampling method, Each image wassubmitted to atmospheric and radio device was perfowas aximum likelihood classification. Cloud and shademasks were study to lessthan a 4

(Garmin International, USA; WAAS system rmed based on code 6S routines us images ing ground control points (GCP)

- year temporal resolution and required

werebrought to a 15m pixel concentrated specific a on area of trically corrected to facilitate - forest areas. For

(Bouroubi - enabled)and,

metric collected et al. est maps

resolution , 2010).

Accepted Article © 2012© Blackwell PublishingLtd 1 temporarilyNon as kn areasof that were clearly identified asnon vegetation fraction, and soil fraction. The thresholds werechosen as Forestin the binary map and then, inten Each imagewas classified into Forest, Intervened forest,and Non Temporal changeanalysis soil three end pixels demonstratedbest the fit of the linear spectral mixture model.final The m image of the time series. For each image, the selected end material bands fractions into of reference end mixtureanalysis e al.,et Vegetation Index (NDVI); theModified Soil Adjusted Vegetation Index (MSAVI) (Huete, 1988; Qi appliedWe three vegetation and near processingImage

t al.,t owledgeacquired by JP

(Details inAppendix) sity sity forestof

1994); and theModified Adjusted Soil Vegetation Index 2010) (Details inAppendix thenWe selected end

The term Non The term on the ground. the on - members: green vegetation, non - based classification transforms the pixelreflectancethat is obtained from all

- use practices in terms canopyof cover. Intervened forest was - Forest would be in - forest

To select end .

. Incontext the REDD+,of pixels classified as Intervened forest or

in the image classification classification image the in - mem

bers and performed aspectral mixture analysis. -

- members, whichpure are pixels representing observable identified using an index threshold theon MSAVIaf, green - members, six imagesubsets were extracted from each dicative forestof degradation. The 1999, 2000, 2004, and infrared indices to each image: theNormalized Differen - - intervened forestand based on two years fieldof photosynthetic vegetation (e.g., wood debris), and does not ref not does - members were thosefor which the - aerosol free (MSAVI er to permanent land permanent erto -

Forest by comparing theindex values 1

representing the

firstclassified odel includedodel - af use change. change. use ) (Matricardi) Spectral

Accepted Article © 2012© Blackwell PublishingLtd from landowners for the carbon inventory prior to field A visits. shortsurvey of thelandowners led one by practical measurements training. Afterworking for two weeks with JP, two teams were formed, including individuals witha comprehensive knowledge of the local flora, givenwas three al. adequatelyin forested landscapes (Clark& Clark, 2000; Nascimento & Laurance, 2002; Chave s sampling points were chosen perforestcategory for a total survey area ofha. 13.3 Thearea (Garmin International map stratified random samplingfor points the four categories of the simplified forest changecover Hawth’s Forest Carbon Inventory throughout the timeseries were excluded fro landwas that revegetated, and Recent intervention (< y).6 Pixels classified as Non resultingThe map included categories four (Table 2): Forest,Old intervention (> 6 y), Deforested changecover map according to the intensity of forest resulting in81 classes. To adequately calibratestocks C inthefield, wesimplified this forest (ESRI, U were compared on a per 2007 maps, which includedthree the classes (Forest, Intervened forest, Non ampled for each category fell within the recommendations made to captureC stocks , 2004). Fieldwork tookin place July

(Figure 3) SA) Seven Seven men wereselected by Analysis Tools (version 3.27),which an is extension to

the local coordinator and the byother JP. localThe coordinator obtained permission . resultingThe forestcover . Fort y - seven sampling points were chosen usingGarmin a Legend ,USA

- pixel basis to assess changes through timeusing map algebra inArcGIS ). Eachsampling point covered > 0.25 haand a minimum 11 of

the

- change map revealedcomplex a land August 2010. local m m the field survey. community to inventory forest carbon. The group,

- use practicesuse and time

ArcGIS

were -

- used generateto coverdynamic, - Forest) (Figure 3), since

HCxGPS device - - Forest Forest intervention. - day

Accepted Article © 2012© Blackwell PublishingLtd morphospecies. Local Spanish Ngäberé or names, leaves (flowers and fruits when available) of contents dynamics inshifting cultivation systems are variable, with measured, in part measured in the other 168 plots. Below quadrat (Kirby and andcm 1≥ cm BD. Litter and allvegetation with BD < 1cm harvestedwas ina 50 x 50 cm diameter(BD, 10 cm aboveground level) of allsaplings, shrubs, palms and lianas that were < 5 representing the four f Following IPCC (2003)guidelines, a key category analysis was performed on 20 plots estimated. Downed woody debris ≥ 10 cm were measured following Kirby and Potvin (2007). vegetation 5 detailed by Condit (1998)in each 15 radiusm plot; a 6 radiusm sub (banana tree),and tree ferns≥ 10 cm DBH was measured tonearest the mm following rules (VertexIV Hypsometer coordinates of each plot This transectapproach was chosen to ac location, four 15 m inventoryplots. conductedwas to determineland We identifiedWe 7056 diameterThe at breast height (DBH, 1.3 m) a of Circular ground plots were deployed following Dalle & Potvin (2004). For each sampling are – relatively 10 cm DBH. Theh

because complicationsof involved in taking direct measure - Potvin 2007). As these pools were radius plots were laid out a on orestcategories. Weestablished two3 x 3 quadratsm to measurebasal / unaffected by this practice(Tschakert Transponder , together with individual plants eight standingof trees that had snapped below thecrown was - use historyuse and whatproducts were extrac

360° - its slope, weretaken atcentre its using a Vertexlaser groundstocks C and soil organic(SOC C countfor forestheterogeneity. geographic The

Package ≥ cmto 5 DBH, whi ; 160 Haglöf relatively unimportant, they werenot - m ll trees,

transect somestudies finding thatSOC Sweden

etal.

palms, lianas, herbaceous plants ch corresponded to 167

, 2007; Bruun ) for a total of 188 survey plots. . - plotwas established for

ted from the ments. SOC ) were not etal. , 2009).

Accepted Article © 2012© Blackwell PublishingLtd 188) or aggregatedwe the data pertr tookWe the residuals to control for thetransect effect of the forestcarbon stock variable (n = spatial correlation for forestcarbon density inthe field the at smallest distanceclass (< 200 m). and fractions, as well as (ii) landand use biodiversity. studiedWe cha Statistical Analysis fraction equivalentto 50% theof biomass content was usedfor lia AGB wa perhectare byvalue correcting size plot or transect length the for slope (Van Wagner,1982). ground biomass (AGB) (Table 3). We first estimated AGB at the plotlevel (Mg) and scaled the Allometric models wereused Biomass calculation and carbon estimation the Smithsonian Tropical Research Institute (STRI). Professor Mireya D. Correa A.,Director theof National Herbarium of Panama and botanist with identification. Leaf specimens werepressed, dried the commonmost trees species, photographs leavesof and trunks were collected to support nas (Kirby & Potvin, 2007),and s converted to C usingmean a 47% C value for biomass the content of trees,palms, and A spatial correlogram based Moran's on I coefficient detected a s ngesin forestcarbon stock in

to convert vegetation and debris woody measurements to above assumingsame the percentage for fern and banana trees. AC

ansect (n= 47; 4 plots each) by using the mean C value.

relation to (i) the timeseries of vegetation indices , and identified to genus familyor by

coarse woo lightly significant dy debris .

- Accepted Article © 2012© Blackwell PublishingLtd the RDA model abundance speciesof i,and diversity Simpson number (1/ Shannon diversity number (exp(H), whereH = Analysis (RDA). biodiversityThree indices, the richness (number of species perplot), the Standing dominant morphospecies), dueto cloud contamination the of 2011 image) remotesensing variables from either1999 to 2007 ( response variable: sum of standingdown+ C debriswoody C) with backward elimination of 2012), leaving 183 observations for the identified asbeing contaminated by cloud hazeor and wereremoved (Legendre Legendre,& indicesThe werenormalized using B discriminant stock variables that conveyed information about pastinterventions. Forestabove availab and Fractions) Soil that variables (including vegetation indices, and Green vegetation, Non s were used asdependent the variable. le with Hawth's Analysis Tools. The relationThe between Two multiple regression models wereused to predict total ab To evaluate theclassification of the forest four categories, we eachFor image theof time series, we extracted the means ofremote six sensing

C andC

analysis ( . Five spatial variables representing spatial structuresat differentscales

down Woody debris C,response as variables, was examine LD corresponded to each field plotusing polygon the zonal statistics A) theon remote sensing variables for the 188 plots that w land biodiversity measures(biodiversity indices and identity of - use types use

ox Theseremote sensing variables wereused asexplanatory - Cox transformation prior to analysis; five outliers were LD , A.

spatial structure

Σ data series.

p i

ln( n

= 47)= or 1999 to 2011 ( p i ) , and where D,where D =

as explanatory variables - photosyntheticvegetation, ove p performed alinear i Σ

is theproportional ( p - ground(univariate C d by Redundancyd by i - ) ground carbon 2 n ), = 28; missing data

wereincluded to e , revisited.

and and Accepted Article © 2012© Blackwell PublishingLtd above correlations as they had been cultivated sincethe last imagein 2007. Foreffect. the Fore from the field and verified with the landowners. In both cases, wecontrolled for the transect from the remote sensing analysis and inforestcarbon among stocks land use typesobserved examined differences inforest carbon sto with t dummy binary variables (Legendre& Legendre, 2012). Variation partitioning was performed (db MEM). Thecategorical variables (land included three various fractions, including the explanatory dataset was forward selected separately in order to assess the magnitudeof the fractions of the variation in above identity of the dominant (categorical) and spatial one variable (medium scale). resulted ina simplified model consisting ofland the (VIF),in orderto minimize the correlation among variables forward selection was used to obtain a parsimonious RDA model and verify for inflated variance were normalized and standardized whilethe response variables werenormalized. Global 2011) werealso included in thisRDA model. Prior to analysis, t selected he varpart()function theof vegan packagein R - ground C. One usedWe variation partitioning inorder quantifyto the various uniqueand combined with the - (r way ANOVA and subsequent multiple means comparisons (post ) werecalculated between the plantwith greatest the DBH intheplotsand t

biodiversity indice All statistical analysis was performed inR use distanceof stcategory that identifiedwas with remotesensing, five plots wereexcluded combined - s, identity theof dominant, land based Moran’s eigenvector maps (db MEM) (Borcard - ground C explained b

- cksamong forest ones (Borcard use typesuse and Dominant the identity) were recoded as - usetype

(Oksanen y each y explanatory variable. Each

et al.et Also, Pearson product

- (R Development Core Team, 2005) (Borcard use categories that werederived s (categorical), the richness,the , 2011). The e henumeric explanatory variables

etal. - use, anduse, fivespatial variables

etal. , 2011) , 2011) xplanatory datasets .

hocTukey HSD)

- moment . Thisprocedure

et al.et otal ,

Accepted Article © 2012© Blackwell PublishingLtd Intervention, Deforested Revegetated, and Recent Intervention groups were 99.1± 12.0 Mg ha from remote sensing. The respective mean total above third axes showed theseparation between Old and Recentinterventions (Datanot shown). revegetated and Interventions for the first two axes (Figure 6). biThe discriminantanalysis showsgroups’ the separation among the categories Forest,of Deforested strongly d remotesensing variables(86.3% correct classification),while “Recent intervention” leastwas forest N pattern landof use change. Thisland I areas from the beginning of 1999 to the end of 2000 (Figure5). part of the intervened impededwas by cloud coverin 2007 and 2011. Forestdiminished area from 1999 to 2004, and area 1,500over ha duringtime the period covered by the satellite imagery (12 years) (Figure 5) through timefollowing human intervention. Our a A major study objectiveto is developbetter a understanding changesin of forest carbon stocks Tracking changesin forest areas RESULTS ntervened forest p on - forestland illustrated is for 1999 to 2004 - use categories,use the “Forest ANOVA used was to compareforest carbon stocks of the forestfour categories obtained DiscriminantAnalysis ( Spatiallyexplicit tracking of F

ifferentiated from theother ixels re forestarea was reduced, a with corresponding increasein non verted F to LD

A) corr ” category was most efficiently classified the on basis of

- orestpixels show orestthrough time,indicating a cyclical ratherthan linear use dynamicuse among categories ectly classified 80.7% theof observations.Of the four

(Figure 3).

(75.8%) (Table 4). biThe bility to fully understand changesin forest s largethat a fraction theof Non - groundstocks C f

the F

orest , I - plotof the ntervened forest, or theor Forest, Old - plot of the second andsecond - forested - forest or the

- Accepted Article © 2012© Blackwell PublishingLtd isyear evidence that Substantial collinearity between some indices/fractions (NDVI, MSAVI, MSAVI N which explained 64.2% transects (i.e., 188 plots) some transects. Inthis first analysis, remote sensing variables for 2011 were excluded for variables. Cloud coverin 2011 that obscured sampling resultedplots in a lack of information for Total above Explaining forest carbon densitywith the time seriesof remote sensing variables sensing analysis (8 %). above in thisarea a particularat pointin thepast, which could resulted have in t deforested) according to thefield information. landsli attributed consistent with the field information in83% of cases (44 of 53 plots). Four categories ( intervention categories ( categories 1 Tukey HDS; p , 85.1 ± , 85.1 ± 10.6 Mg ha one one of - de(1) ground(50.7 C Mg ha D The the remote sensing variables , . was foundwas

had experienced landslidesinsection a of the plot(3), or were adjacentto a - , while ground(Sta C eforested re

= 0.030). Old intervention did not differ significantly from the other forest one - 1

, 65.0 9.2± Mg ha some (F

of 3, 179 other Tukey HDS; p .

Multiple linear regression included seven remote sensing variables, ndingwoody+ C debris C) was regressed againstremote the sensing thevariation inforestcarbon density (R vegetated category

remotesensing variables from the same year could be interchanged = 5.19,= p=0.0019 - 1 ). Only four). Only site was used harvestto fuel and construc

stood out as indubitab = 0.006) - 1 , and 52.2 7.4± Mg ha plots ), specifically), between

that identifiedwas remote by sensing was ,

visited

Wood harvesting may havebeen moreintensive as well as remain

Forest and Deforested Revegetated ly superiorly to theothers. inconsistent - 1 . Significantdifference 2 - adjusted = 0.578; Table 5). Forest and Recent tion wood (butnot hearea exhibiting low

sites relativeto the remote af ) theof same

that

were mi

between

47 s - Accepted Article © 2012© Blackwell PublishingLtd 26.2% Standingof plus C woody debris (Figure8). Together the land use variables and the togetherwith one theof main plants thatcultivated is in croplands,associated is low with levels Standing of C, dominant speciesassociated is with high levels Standingof C.Conversely, the positively related to Woody debris C Not7). surprisingly, Crop andland Fallow standingis species C richness species richness,and sp is predicted by the explanatory includingmatrix landdominant use, species identity, plot demonstrated that 61.4 % theof variation inabove effectThe landof useand biodiversity foreston carbon stocks was explored using RDA, which Explaining forest carbon densitywith theland MSAVI debriswith C aregression model based on the NPV Fraction 1999, Soil Fraction 2011 and explained 47.1%( explains carbon stock density better than simple examination theof results from any single year. the multiple regression models, suggesting thatthe cumulative effectof intervention foreston with only small changesin explanatory power.

af 2007. Variancepartitioning showsdominant that morphospecies identity aloneexplained RDAThe ordination triplot shows that the explanatory variable most closely A second Gu

arumo R multipleregression, 2 - adjusted , ace Penca

from db , = 0.401) of the variationin standing above while the

and

Balso .

MEM ( The presencThe with reduced sampling space , which areabundant in fallow - usesnegatively are related to Standingbut Cslightly R 2 - -

use practicesuse and biodiversity had the highest loadings for woody debris (Figure adjusted Every year of the timeseries was represented in e of - ground standing carbo Sangrillo, Mayo,

= 0.422).= size

including 2011 (n=28) or

lands - ground C and woody Zapatero n stocksand woody C

. banano

trees as related to , which is

Accepted Article © 2012© Blackwell PublishingLtd areas. monitoring forestof degradation a with a variety sideof benefits. findings The thatare presented beloware relevant to the time,over and 3)support for community monitoring to evaluaterelated forestcarbon changes, stockchanges, 2) evidence that shiftin cultivation areasusing affordable medium Our provides study cycle hasbeen shown to affect the accuracy of forest emission estim lackThe of knowledge of theland DISCUSSION 6). Croplands, which inturn differs signific not does differ from 6.0 haMg C 1 land fi total above 2.4%). Last,the Pearson 14.2% theof variation. Spatial components alone playminor a role in explaining variation ( andidentity the theof dominant species. combinationThe theseof three variables explains biodiversity indicesexpl ), intermediate), for Old fallow/Secondaryforest and Fallowland ( eld. Mean total above

- use classesuse (F

ANOVA compared the carbon stocks of four land - - groundis C 1

) and) lowest for Cropland (29.1 ± 6.7 Mg C ha 3,184

1) a new 1) methodological approach to tracking the dynamics shiftingof Old Fallow/

= 24.59, p= <0.0001). Stocks werehighest for Forest(112.5 10.8± haMg C

- positive with ained 7.1% theof variance. Landhas usean effect on both biodiversity ’s ground(and C associated standard errors)differ significantly among

correlation between theDBH the of dominant treein each plot and Secondaryforest but differ does from that Fallowof and - use dynamicsuse that are associated with the agriculture nd C stock enhancement for REDD+in shifting cultivation

r = g cultivation may hav antly fromand Fallow Old fallow/Secondaryforest (Table 0.897 - resolution imagery and to help predict forestcarbon , n=186, p 2.2e < - use classesuse thatwere identified in the - 1 e limitede e ; Figure ; Forest9). above - 16. 78.4 ± 12.2 Mg C ha

ates (Pelletieret ffects foreston C stocks - ground C - al., 2011). 1 ; 54.0 ; ± - fallow i.e., - Accepted Article © 2012© Blackwell PublishingLtd facilitatedetectability its selective logging, visible patterns that areassociated with log decks, roads, and skid marks problems than selectivelogging, because itsof patchy spatial structure. When monitoring of omitting the effect fallowof clearings. adequately, we can avoid possible errors inflatingof deforestation rates(DeFries al., et 2007) or considereddegradation. as deforested. proposeWe that, in the REDD+context, those temporary clearings should be time allowsclearings thatare temporary todifferentiated be from those that remain traceable using only onepoint in time. capture complexland shownhave that approachour using multi moredifficult to detect theseactivities through satellit cultivation may involvea change in carbon stocks withouta change inforest area, making it systemschallenging is and requires insightsinto their temporal dynamics. In effect,shifting context REDD+,of quantifyingemissions C and removals from forestsin these complex delineating theseareas (Messerli cultivation location and intensity of this practice knowledgeregarding shifting cultivation and fallow area(Fearnside, 2000; Houghton, 2010), changethrough time Shifting cultivation landscapesare characterized by a mosaic differentof land Methodology for assessing impactsof shifting cultivation In effect, the monitoring of shifting cultivatio

landscape mosaics havebeen proposed and could be very useful inspatially (Mertz, 2009; Pad - use dynamic of small (Stone & Lefebvre, 1998; Asner

We argueWe that, by monitoring shifting these cultivation areas

et al.et

(Hett

, 2009; Hett Spatially explicit information pixel on transitions over och & Pinedo

temporal analysis satelliteof imagescan effectively etal. - scale land , 2011b). New ways to look at these

et al. - n brings quitewith it different technical Vasquez, 2010). - e imagerye (Houghton, 2005). Here, we use processesuse that would be not , 2011b; Hett

et al.et

, 2005; Laporte

et al. There , 2011a). In the is a general

- et al.et use typesthat , 2007). For shifting

land

lack of - use Accepted Article © 2012© Blackwell PublishingLtd predictor of forest C stock changes. Effectively, inshifting cultivation areas,detecting this shifting mosaic,we show that cumulating (multiple)interventions over time cana good be dynamics over time can help quantify forest carbon stocks inahuman beovercome with the use longerof time series. which would explain itwhy contains lower carbon stocksthan intact category mighthave been subjectto intervention prior to res the Forest class that identifiedwas in thefield survey (Figure This9). difference maybe the density plotsof that were classifiedForest as by remotesensing is 13.4 haMg C significantdifferences from for intact Amazonia, where moreintense intervention categories(logged and burned forests) present significantdifference. (<6yrs) as well asForestand Deforested devegetate (>6yrs)→Deforested revegetated→Recent intervention Forest (<6yrs). and Recentintervention consequences foreston C stock with following the trend: Forest→Old intervention between dif plots; burned areas being more detected.easily anduse the fireof in shifting cultivation systems may influencethe detectability plantedof likely toidentified be but may require still ground verification. typeThe cropsof sma ult undetectedof forest by remoteuse sensing. Itis possible, for example, thatForest the ll - scaleshifting cultivation, Furthermore,results the of multiple regression indicatethat the tracking land of basisOn the ofremote the sensing analysis performed, we wereable to discriminate ferent intensitiesof forest Theseresults are consistent withresearch that hasbeen conducted in interventions near villagesand rivers roador networks are more

estin terms biomassof (Souza - useand time

d abovegroundmean C presented a - since - our our timeseries, intervention, both of which have

et al.

forest. Thislimitation might - intervened landscape , 2005b). The Cmean i.e., prior to 1999, - 1 that areplanted

lowerthan for - use . In Accepted Article © 2012© Blackwell PublishingLtd impact greenhouseon gas One of the objectives o Impactsof shifting cultivation on carbon stocks images are required to perform this analysis. enhancementfor REDD+. In effect, interested in monitoring shifting cultivation areasi resolutionimportant. is sho three years. In order to detect possible agricultural intensification that wouldindicated be by the length theof time series should be 12 years with a frequency of observation atleast every beforeyear abandonment and may be halfcycle a (Legendre & Legendre,2012). Clearings for shifting cultivation that areused for one series must havea minimum length of two cycles, and its minimum frequency mustbeat least analysis notions. Statistical theory states that the observational w understandingperiodic a of process that isassociated shifting with cultivation requires periodic conclusions with respect to increases inforest areas. sufficientlylong periods of time should be usedfor forest monitoring to avoid errone forest monitoring system for tracking carbon stock changes. intervention over timecould as act anadequate indicator, which rtened fallowlength, which produce would more C emissions, having adequate temporal T InGOFC a his research givesa positiveresult by providinglow a - GOLD (2010)report, it was suggested that images that wereseparated by f this

emissions. Theresults obtained for the land use classesidentified as study is

only affordable only and largely available medium to understand

recultivated after years5 (1 cycle= 6 years); therefore,

n terms forestof degradation andstock C

the role shiftingof cultivation interms o

In contrast, proposewe that

- cost option for countriesare that couldintegrated be into a indowfor periodic eventsin a - resolution ous ous f its Accepted Article © 2012© Blackwell PublishingLtd Dirzo, 1997). Shifting cultivation hasbeen singled a fundamental activity intheeconomy localof communitiesas a multi viewforest that and land usescan maintain important ecosystem s C stock maylimited be (Laurance untouched, ashas it been observed on old pasture in the study area, carbon stockdensity, the latterhaving larger a within there living vegetation, which affects the succession process (Turner al., et 1998), may explain why contributed to rapidstock C recovery (Chazdon, 2003; Robiglio Sinclair, & 2011). residualThe interventions, andforest the the small scale agriculturalof plots,proximity the to matur natureof interventions the i whetherdeforested the areais increasing or not. cloud coverlimited abilityour to determine the netbalance in forest areas; dowe notknow emissions produced by vegetation cle long consistent with the idea that thisshifting mosaic of temporary cleared areas would limited have that substantial above Moreover, differences in mean above as observeda by increase C from Cultivation→Fallow→Old fallow/Secondary forest→Forest. in the - term netemissions, as vegetation regrowth duringfallow the period balancesthe is difference no between the Forest and Old fallow/Secondaryforest classes interms of field suggesta continuum indicative forestof regrowth with carbon stock The rapidThe recovery forestof carbon stocks following shifting cultivation supports the shortThe time frameand capacity for forestto r - groundcanstocks C held be within nlandscape the where we worked - d ominated landscape matrix are characteristicsthat may have

et al.

aring (DeFries et al.,2007). Itimportant is to reiteratethat - gr , 2000; Feldpausch oundamong C classes diminish

- out out as anenvironmentally destructive and - group variance. Also, ifbig treesleft are estoreC stocks can explained be the by

et al.et fallow vegetation. Theseresults are e foreste (seeds . Theprevalence of agroforestry, , 2005). ervices, while alsothey fulfill

the intervention effect on - use systemuse (Noble &

with time.also It signals ), short replenishment, - lived Accepted Article © 2012© Blackwell PublishingLtd other area (Harve structural and floristic complexityfound as inour study area, help may maintain biodiversity configurations that connectforest patches, maintain a diversearray habitats,of and retain high reducecarbon storage inthe dominant treespecies (including proxy measure of forest C stocks. On the hand,other landpractices use that negatively affect Potvin, 2007; Ruiz groundstocks. C species Con alone stands outas the mostimportant factor in explaining variation in above al. shortening theof fallow period, may changethese conditions (Eaton & Lawrence, 2009; Dalle Padoch & Pinedo landscapes (Ickowitz, 2006; Fischer to the maintenance ecosystemof services, such as carbon reservoirs in human that shifting cultivation can have a transitory impact on forestcar al. can be being challengednumerous by studies, which tropics (Geist& Lambin, 2001; Mertz, 2009; Padoch & Pinedo primitivepractice and perceived until , 2011; Robiglio Sinclair,& 2011). , 2003; Ickowitz, 2006; Nielsen y a rational economic and environmental choice for poor farmersin the tropics (Toledo

T While land use hasadirect effect forest on C stocks, theidentity of et al. he

where se , 2008; Chazdon

findings cannotnecessarily be generalized to theoverall protected area O - Vasquez, 2010). Of course, the - different n hand, one thisinformation is consistent with other observations (Kirby & Jaen & Potvin, 2010)and perhaps, coul

land

ecosystem (Kirby & Potvin, 2007). However, the landscape et al. - use practicesuse prevail. may

Zapatero et al. , 2009).

etal.

recently as one theof main drivers deforestationof inthe , 2006; Harvey , 2008; Harvey ,

Sangrillo

showthat shifting cultivation in many situations

intensification land of

and

et al.et

et al. Mayo, dexplored be further foruse its a as , 2008). Our results supportthe view Shifting cultivation, as performed by , 2008; DeClerck - Vasquez, 2010). This perception is which aretimber species) may bon stocksand may contribute - use practices, including the dominant

et al. - modified , 2010; or to -

et Accepted Article © 2012© Blackwell PublishingLtd al.(2011)et concludethat, when examining thereliability and comparing morecost capacity whom had primary education, werequick to learn the techniquesand how to usethe tools after selected sampling points because of their knowledge theof territory. Theworkers, most of approach. Working with local people provides further evidence to supportnot the only feasibility, butalso the advantage of this biodiversity monitoring whichwell is a winning approach for assessments of carbon stocks and biod people to make detailed measurements of carbon stocks.Yet, community measurementscan be peoplelocal to assessforest biodiversity disturbance,or only few a projects have trained local activities. Skutsch shouldpromoted be and supported when undertaking REDD+, including for monitoring participation of surround REDD+ activities(UNFCCC, 2010). A The value ofcommunity monitoring Lugo,& 1990). foresttype, such the as tropical forest dry for instance, wouldexpected be to be slower (Brow Latino colonists Ng nnex äbepeople,

I of the Cancun Agreement adopted some of the guidelinesand safeguardsshould that - buil - effectivestudy thanhad ifit been done by professional foresters. In effect, Danielsen ding and field practiceas observed by Skutsch (2005) and also probablyresulted in

is part ofsocial a

relevantstakeholders, in particular, indigenous peoples and local communities, slash et al. - and (2009) signal - burn practices.Also, C recovery from shifting cultivation inanother - ecological system thatwill differ substantially if compared to

s that, while several studies havelooked the at capa - has been particularlyefficient for locating therandomly aligned with the safeguards (CIGA These safeguards indicatethat the full and effective iversity, fulfilling important - REDD, 2011). Our study the cost community of city of n Accepted Article © 2012© Blackwell PublishingLtd interest.of IDRC providedwho useful comments the on manuscript. Funding w Pierre Legendre, Liz Brandt, AndréParent, Gilberto Bonilla, and Francis Murchison for their contributions to thefield surveys. We thank Palacio, Rocendo Martinez, Cornelio Jaen, Arcelio Miranda, Nathaly Guerrero, Mire thankWe Joselin Mosaquites,Andres Martinez, Aquilino Martinez, Maximo Serrano, Venancio ACKNOWLEDGEMENTS bydone outsiders. transparency ofprocess, the which would have c community member participation incarbon stock measurement contributed to the ispractices greatof valuein explaining carbon stock variation in the landscape. Finally, complementary information providedlocal by experts biodiversity is facilitated throughapplication the of traditional knowledge. Moreover, the carbonof stocksalone. An additional advantage is incurred when the measurement of local quality comparableto thosecollected by trained scientists,at half thecost. monitoring with forester

to JP, and anNSERC Discovery grantto CP. authorsThe declare that they haveno conflicts Working with local people has brought moreadded

- led measurements, local people can collectforest condition data of

Bill

F.J. ertainlygenerated distrustmuch if ithad been Parsons, and landowners on land and three anonymous reviewers, - valuethan t as provided by FQRNT he strict measurement

- use historyuse and ya Correa,ya

and the Accepted Article © 2012© Blackwell PublishingLtd with clouds, resulting inmissing data. asimage the rest were covered with clouds. Still, the Landsatimage for 2011contaminated is fieldworkwas performed. However, wehad c b a Table1. Timeseries of satelliteimages

For theFor last image theof time series (2011), we ordered imagery tocollected be whilethe SWIR (Short WaveInfrared) VNIR ( Landsat 5 TM 5 Landsat Landsat 5 TM 5 Landsat TM 5 Landsat TERRA ASTER TERRA ASTER Satellite and Satellite and sensor Visible Near Infrared)

03/01/2011 14/03/2007 02/02/2004 22/12/2000 18/01/1999 acquisition Date of Date of

Cover Cloud Cloud 35% 11% 0% 0% 3%

to forwait sixover months to obtainuseable a Bands used Bands SWIR VNIR SWIR 1 1 1 VNIR - - - 5,7 5,7 5,7 a b

Ortho Ortho 0.56 0.28 0.26 0.23 0.28 0.56 0.28 rms

Grid cell 15 m 30 m 30 m 30 m 30 m 15 m 30 m size

Number Number of GCPs 19 19 19 17 20 20 19

Accepted Article

© 2012© Blackwell PublishingLtd place taking activity the of moredescriptive it found aswe revegetated Deforested category simplifica for However, temporarily unstocked. but land forest asremaining b areas. forested Non Table2. Simplified forestcover change categories a In order to be consistent with IPCC terminology, this category should be described as forest land land describedasforest be should category this IPCC terminology, with consistent be to order In

Recent intervention Recent For the purpose of the field survey performed in 2010, we decided that to avoid the risk of arriving in riskof in arriving the avoid to that decided we 2010, in performed survey field the of purpose the For Older intervention Older - Forest areas by not visiting plots class plots visiting not by Forestareas Cloud/Shade revegetated Deforested Non F (< 6y) (> 6y) orest - forest

Excluded from the forest carbon inventory. carbon forest fromthe Excluded cove Cloud/shade sampling. fromthe Excluded during earlieryears. potentially 2007and in appeareddeforested that Land 2007 in asForest classified and 2004images and/or Forest 2000 and 1999 in asforest werethat classified but image 2007 satellite classified asNon (not Forestintervention classi and images 2000 satellite classified asNon (not Forestintervention series. time the of period the over process intervention observable forest any undergone havenot that Forests - land that h that land

ified asNon ified r during 2007, the time period before the forest inventory, forestinventory, the before timethe period 2007, during r as been classified asNon classified as been -

Forest in 2007, as the focus of this survey was on on was survey this of focus as the 2007, in Forest fied as Forest in 2004 and 2007 and 2004 in asForest fied Description - - Forest) observed on the 2004 and/or 2004and/or the on observed Forest) 1999and/or the on observed Forest) -

Forest on the 1999 and/or 2000 and/or 1999 the on Forest

tion purposes, we this called purposes, tion a .

© 2012© Blackwell PublishingLtd Table3. Cs: Cs: Slope correction factor sqrt(1+(%of slope/100)^2) orrprime=r/(cos(alpha))^2 ρi = species specific wood density value (g cm BA: Basal Area (m2) or equationBApi(DBH)^2/40000 = BD: Basal Diameter(diameter above at 10 cm ground level; cm) DBH: Diameter at Breast Height (diameter at above 1.3m ground level; cm) AGB: Above Ground Biomass Coarse Woody Debris Saplings < 5 DBH,cm >=1cm BD Tree ferns 5cm >= DBH Banana trees DBH>=5cm Lianas >= 5cm DBH Dead trees>=5 cm DBH Tree snags >=5 cm DBH Trees and palms >= 5cm DBH ρdrc: Decay Class Reduction Factor; dependin Accepted Article Allometric models used to convert measures of vegetation and woody debris to AGB

g ifg sound ρdrc=0.453 cm g - Van Wagner (1982); Waddell (2002) Kirby and Potvin (2007) Standley et(2010) al. van Noordwijk et al.( 2003) DeWalt and Chave (2004) Delaney et (1998) al. Nascimento and Laurance (2002) Chave et al. (2005) Source 3) of tree (i), orwood 0.54when density species of or species unknown

- 3) or rotten ρdrc=0.319 cm g

*(π^2/8L)Σ(d^2)+ρdrc*Cs Exp[3.965 1135.3DBH W=0.030DBH^2.13 AGB= Exp[0.298+1.027Ln(BA)] 90% of total AGB live of trees AGB= ρi*BA*(Height)*0.78+ AGB= Exp[ Model

- 3 (Clark et al., 2002); corrected for slope, not correctedfortilt ofindividual pieces s

- + 2.383 ln(BD)] 1.239+1.98*Log(DBH)+0.207*(Log(DBH))^2 - 4814.5

- 0.0281*(Log(DBH))^3+*ρi

Units

Mg Mg Mg Kg Kg Kg g g

Accepted Article © 2012© Blackwell PublishingLtd collinearity (n = to vegetation indices/fractional components after Table5. Parsimonious multiple regression model totalof aboveground carbon stocks inrelation Table4. Residual standard error: 1.592 on 39 degrees of freedom degrees 39of on 1.592 error: standard Residual FracSoil_2004 FracSoil_1999 NDVI_2004 FracNPV_1999 FracGV_2007 MSAVIaf_2000 NDVI_2000 (Intercept) Variables Recent Intervention Old Forest Revegetated Deforested Classification Forest

Intervention Classification table obtainedfrom thelinear discriminantanalysis classification function

Total

47). 47).

1.45E+00 1.41E+00 1.61E+00 - - - 2.40E+00 1.36E Coefficient

1.34E+00 1.13E+00 1.82E+00 - Revegetated 14 Deforested Deforested

53 42

2 4 7 Ob

3.13E 5.54E 5.09E 2.32E Error Std. 3.92E 3.86E 4.24E 2.92E jects assigned by the Classification function function Classification the by jects assigned

------01 01 01 01 01 01 01 01 Forest

65 56 2 2 6 3.697 3.659 3.788 - - - 4.726 0 valuet

4.61 3.601 3.286

backward elimination Intervention

0.000669 0.000748 0.000513 4.25E 0.000883 0.002157 2.96E 1 Pr(>|t|) Old Old 31 24

4 2 4

- - 05 05

Intervention Recent 33 25 1 0 1 and reduction of

correct in correct in Total Total 80.7 75.8 77.4 86.2 79.2 %

Accepted Article © 2012© Blackwell PublishingLtd the non Figure 3. Map of forest coverchange through time inthestudy area for the period 1999 (with series. Figure Schema2. remoteof sensing analysis performed on each theof five images of the time part of the Palo Seco ForestReserve falls with the Ngäbe Figure Regional1. map presentingarea the covered the by remote sensing analysis. Notethat identified in bold. Table6. F R Multiple Fallow Fallow Crop Fallow Crop fallow/Secondar Old Land - statistic: 9.995, on 7 and 39 DF, p DF, 39 and 7 on 9.995, statistic: - - - Old fallow/Secondary Forest use classes use -

- -

Crop Forest - Old fallow/Secondary forest fallow/Secondary Old Post classified and classified image), 2000, and 2004. - square: 0.6421, square:

- hoc multiple comparison tests with Tukey HSD. Significantdifferences are

y forest y

- forest Forest Adjusted R Adjusted

value: 4.399e value:

0.403766 ------0.69685 0.58079 0.98456 0.28771 0.29309 square: 0.5779 square: diff

- 07 0.067535 - - - - 1.12896 0.82184 1.31776 0.6

- 0.6507 lwr 5887

- Buglé indigenous territory.

0.739997 0.075287 0.072698 - - - 0.26475 0.33975 0.65136 upr

0.011428 0.172004 0.164379 0.00026 p adj p

0 0

Accepted Article © 2012© Blackwell PublishingLtd 22.8% theof total variation explained. explained 77.2% theof variance, sampled. observed atthe bottom of the figureis explained by theabsence of woody debris intheplots explanatory variables. The red arrowsrepresent the respon (Spatial variable), scaling type The1. pointed arrows representbiplot the scores theof the dominant tree species(58 factors, main ones are displayed),and the db MEM variable debris explained C) byspecies the richness,land ground trees, palms, lianas, fern trees (Standing C); and Above Figure7 canonical discriminant space. Figure Ordination6. diagram theof sites, which are identified by their color group inthe higherfraction covered by clouds. Fi showsforest the classes identified by remote sensing analysis. and intheComarca Ngäbe Figure Forest4. carbon inventory arealocated inthe Palo Seco Forest Reserve(blue contour) gure Forest5. area changeover timefrom 1999 to 2011. The 2007 and images2011 had a . RDA ordination triplot theof above Both canonical axesare significantat p<0.001. The firstaxis (related to standing C) - Buglé (darkgrey). closeupThe of the forestcarbon inventory area

whilethe second axis (related to woody debris) explained

- ground standing carbon stock density (Above - use (4 factors, k se variables. The linear pattern -

ground woody debris (Woody - 1 are1 displayed ),identity the of Accepted Article © 2012© Blackwell PublishingLtd Asner,G.P., Broadbent, E.N., Oliveira, P.J.C., Keller, M., Knapp, D.E. Silva,& J.N.M. (2006) ANAM/ITTO (2003)Informe final deresultados la de cobertura boscosa usoy del desuelo la ANAM/CBMAP (2006) Plan deManejo Actualizado: BosqueProtector de Palo Seco. Autoridad Adams, J.B., Sabol, Kapos,D.E., V.,Almeida Filho, R., Roberts, Smith, D.A., M.O. & Gillespie, A.R. REFERENCES groundon survey (Right panel). theof forest categories based r on Figure 9. totalMean above above (Land Use, DominantIdentity, Richnessand Spatial variable Figure Venn8. diagram theof variation partitioning following therda model using four variables Condition and logged fate of forestsin theBrazilian Amazon. Panama. 107 pp. Republica de Panama: 1992 Panama, Republica de Panama. Nacional del Ambiente Corredory Biol 52 Application to land (1995)Classification of multispectral imagesbased Academyof Sciences USA - ground standingand C debris woody C.Therectangles represent the spatial variable. , 137 - 154.

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. International Human Dimensions Programme Globon Environment

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