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Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers
RM. Deadwood biomass: an underestimated carbon stock in degraded
tropical forests? Environmental Research Letters 2015, 10(4), 044019.
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Environ. Res. Lett. 10 (2015) 044019 doi:10.1088/1748-9326/10/4/044019
LETTER
Deadwood biomass: an underestimated carbon stock in degradedtropical forests?
MarionPfeifer1,6, Veronique Lefebvre1, Edgar Turner2, JeremyCusack3,MinShengKhoo1, VunKChey4,Maria Peni5 andRobertMEwers1
1 Department of Life Sciences, Imperial College London, Silwood ParkCampus, Buckhurst Road, Ascot, Berkshire SL5 7PY,UK2 Insect EcologyGroup,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK3 Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road,Oxford,OX1 3PS,UK4 Forest ResearchCentre, Sabah ForestryDepartment, POBox 1407, 90715 Sandakan, Sabah,Malaysia5 The Royal Society SEARRP,DanumValley Field Centre, POBox 60282, 91112 LahadDatu, Sabah,Malaysia6 Author towhomany correspondence should be addressed.
E-mail:[email protected], [email protected], [email protected], [email protected], [email protected], [email protected] and [email protected]
Keywords: woody debris, tropical humid forests, selective logging, Malaysia, Southeast Asia, Stability of Altered Forest Ecosystems Project,above-ground carbon
Supplementarymaterial for this article is available online
AbstractDespite a large increase in the area of selectively logged tropical forest worldwide, the carbon stored indeadwood across a tropical forest degradation gradient at the landscape scale remains poorlydocumented.Many carbon stock studies have either focused exclusively on live standing biomass orhave been carried out in primary forests that are unaffected by logging, despite the fact that coarsewoody debris (deadwoodwith⩾10 cmdiameter) can contain significant portions of a forest’s carbonstock.Weused afield-based assessment to quantify how the relative contribution of deadwood to totalabove-ground carbon stock changes across a disturbance gradient, fromunlogged old-growth forestto severely degraded twice-logged forest, to oil palmplantation.Wemeasured in 193 vegetation plots(25 × 25m), equating to a survey area of >12 ha of tropical humid forest locatedwithin the Stability ofAltered Forest Ecosystems Project area, in Sabah,Malaysia. Our results indicate that significantamounts of carbon are stored in deadwood across forest stands. Live tree carbon storage decreasedexponentially with increasing forest degradation 7–10 years after loggingwhile deadwood accountedfor >50%of above-ground carbon stocks in salvage-logged forest stands,more than twice theproportion commonly assumed in the literature. This carbonwill be released as decompositionproceeds. Given the high rates of deforestation and degradation presently occurring in Southeast Asia,ourfindings have important implications for the calculation of current carbon stocks and sources as aresult of human-modification of tropical forests. Assuming similar patterns are prevalent throughoutthe tropics, our datamay indicate a significant global challenge to calculating global carbon fluxes, asselectively-logged forests now representmore than one third of all standing tropical humid forestsworldwide.
1. Introduction
Coarse woody debris (CWD), comprising standingdead trees and fallen trunks and branches, is importantfor various ecological functions in tropical forestecosystems. Dead wood provides a habitat for wildlifesuch as wood-feeding termites (Eggleton et al 1995),
cavity-nesting birds (Gibbs et al 1993), saproxylicbeetles (Grove 2002) and bats (Giles 2012). It facil-itates tree regeneration by providing ‘nurse logs’(Fukasawa 2012), interacts with disturbance regimessuch as fire (Pyle et al 2009), and acts as a significantcarbon and nutrient reservoir (Chambers et al 2000).CWD may account for roughly 10% of total carbon
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storage across tropical forests (Pregitzer andEuskirchen 2004), and may store as much as 33% oftropical forests’ above-ground biomass (Clarket al 2002, Rice et al 2004). Yet, the relative contribu-tion of CWD to carbon stocks in logged tropical forestsremains poorly documented (Clark et al 2002, Bakeret al 2007).
Carbon stored in the dead wood of a forest stand isone of the five carbon pools identified by the Inter-governmental Panel onClimate Change that should bemeasured and monitored for carbon book-keeping(Watson et al 2000). Accurate accounting of thesepools is essential formitigation, e.g. via REDD+ (redu-cing emissions from deforestation and degradation +enhancing forest carbon stocks) (Mertz et al 2012).Decomposition of dead wood contributes to carbonemissions originating from forests, and themagnitudeof this contribution will depend on the variation inCWD stocks due to forest type and age (Pregitzer andEuskirchen 2004, Kissing and Powers 2010), mortalitypulses (Rice et al 2004), topography affecting treemortality (Gale 2000), chemical composition of thedebris and hence the forest’s tree species composition(Baker et al 2007), and the land use history and man-agement of an area (Eaton and Lawrence 2006, Kauff-man et al 2009).
Selective logging, which can cut deep into forestinteriors, is a major land use change process withshort-term and long-term impacts on emissions ofcarbon dioxide (Feldpausch et al 2005). The Interna-tional Timber Trade Organisation (ITTO) estimatesthat 350 million ha of humid tropical forests con-tributed to timber production in 2005 (ITTO 2006);approximately 31% of the total humid tropical forest
area based on remotely sensed forest cover estimatesfor the same year (Hansen et al 2010). Since then, thepractice of selective logging, either legal or illegal, hasbeen on the rise in many of the ITTO’s 33 membercountries, reaching 403 million ha in 2010 (Blaseret al 2011). Timber harvesting focuses on high-valuespecies and removes large, high-biomass trees from aforest stand. Logging infrastructure and residualcanopy damage can dominate carbon emissions inlogged forests (Pearson et al 2014). Logging eventsadditionally accelerate the formation of CWD, fromwhich carbon is released as decomposition progresses(Feldpausch et al 2005,Houghton 2005).
Themajority of studies investigating causes of spa-tial variation in biomass and carbon stocks of tropicalforests remain focussed on living trees (Malhiet al 2006, Saatchi et al 2007, Houghton et al 2009),with the assumption that these represent thelargest fraction of total above-ground biomass (Nasci-mento and Laurance 2002). Meanwhile, the decreaseof live tree biomass in response to selective loggingcombined with the extent of tropical forest area underlogging may well mean that the importance of CWDstocks is increasing. Here, we analyse, whether (1)CWD stocks and their relative contribution to overallabove-ground carbon increase with increasing forestdisturbance, and whether (2) any variation inCWD stocks can be linked to forest attributes suchas micro-topographic and structural traits. Weaddress these questions by focussing on the forestdegradation landscape of the Stability of Altered ForestEcosystems (SAFE) Project in Sabah, Malaysia (Ewerset al 2011).
0 1.25 2.5 5 Kilometers 4 Kilometers6 Kilometers0 0 211.5 3
0 1.25 2.5 5 kilometers
117º0ºE 117º30ºE
117º0ºE 117º30ºE
4º45º30ºN
4º33ºN
NASTER DEM
[m asI]0 10 20 40 kilometers
> 1500-3,000> 400-600> 600-800
> 800-1,000> 200-400< 200
>1000-1,500 > 3000-6,500
33ºN
30ºN
Figure 1. Location of vegetation plots within the SAFE landscape. The average altitude of plots is 439 mabove sea level (range:238–618 m) based onASTER global digital elevationmodel 2 (a product ofMETI andNASA). Selected close-ups of the samplingdesign in forest stands and plantations are overlaid on 3(R)-2(B)-1(G) SPOT-5HRG-2 images (pan-sharpened at 2.5 m spatialresolution) recorded on 28/04/2009.
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Environ. Res. Lett. 10 (2015) 044019 MPfeifer et al
2.Methods
2.1. The SAFEdegradation landscapeThe SAFE Project (4 38’N to 4 46’N, 116 57’ to 117 42’E; figure 1) features a gradient of forest disturbancefrom unlogged primary forest through to severelydegraded forest and oil palm plantation (Ewerset al 2011). There were 193 vegetation plots(25 × 25 m) established at SAFE in 2010 (Marsh andEwers 2013) and distributed among 17 samplingblocks (table 1): three oil palm plantation blocks (OP1and OP2 planted in 2006 and OP3 planted in 2000),two primary forest blocks (OG1 andOG2), two lightlyor illegally logged forest blocks (OG3 and VJR), fourtwice-logged forest blocks (LFE, LF1–LF3) and sixsalvage-logged forest blocks that are currently beingconverted into a fragmented agricultural landscape(A–F) (Ewers et al 2011). In the latter two categories,sites were selectively logged once during the 1970s andagain in the late 1990s to the early 2000s, removingmedium hardwoods (Drybalanops and Dipterocarpus)and lighter hardwoods (Shorea and Parashorea). Thelogging rotations at lightly-logged and salvage-loggedblocks were implemented under a modified uniformsystem, removing an estimated 113 m3 ha−1 extractionduring the first rotation followed by an estimated37 m3 ha−1 extraction during the second rotation. Inthe salvage-logged forest, timber restrictions werelifted during the second rotation and the forest was re-logged three times with an estimated cumulativeextraction rate of 66 m3 ha−1 (Struebig et al 2013).Selective rounds of logging resulted in heavilydegraded stands, with a high density of roads and skidtrails, a paucity of commercial timber species, fewemergent trees, and the dominance of pioneer andinvasive vegetation.
Vegetation plots were established across the forestdegradation landscape according to a hierarchicalsampling design as an objective procedure to assessregional stores (Ewers et al 2011). The design was cho-sen to ensure unbiased decisions as to where to estab-lish vegetation monitoring plots in the field (Ewerset al 2011). Plots were located at roughly equal altitudeand oriented to minimize potentially confoundingfactors such as slope, latitude, longitude and distanceto forest edges (prior to controlled forest-to-oil palm
conversion currently being carried out at SAFE)(Ewers et al 2011).
2.2.Quantifying forest attributes within vegetationplotsWe measured all 193 permanent vegetation plots forabove-ground biomass and deadwood in 2010 and2011, equating to a total survey area of >12 ha. We setout each 25× 25m plot with North–South orientation(using a slope correction factor to account fortopography) and tagged each tree that had more than50% of its visible roots located inside the plot(Marthews et al 2012). Each tree was measured forDBH (in cm) using a diameter tape and a subset oftrees was fitted with a dendrometer to allow re-measurements in the coming years. We measuredheight (in m) of trees ‘by eye’. For validation of ourheight measurements, we compared height estimatesin the field to height estimated based on region-environment-structure models describing height-DBH allometric relationships in Asian forests:Hmod = exp (0.2797 + 0.5736*ln(DBH) + 0.0120*A+0.0034*PV+−0.0449*SD+ 0.0191*TA) (Feldpauschet al 2011). For subsequent analyses, to account forpotential errors in height estimates, we binned esti-mated tree heights into equal classes of 5 m length each(e.g. 0–5, 5–10, 10–15 m). Climatic parameters for theSAFE landscape (mean annual temperatureTA= 24.8 °C, dry season length SD= zero monthswith rainfall <100 mm, annual precipitation coeffi-cient of variation PV= 10.27%) for inclusion in theallometric model were extracted from the WORD-CLIM datasets (Hijmans et al 2005). Basal area A(m2 ha−1) was derived from tree DBH measurementsfor each plot and averagedwithin forest stands.
Above-ground biomass (AGBlive) was derived foreach tree from tree size, using an improved pan-tropi-cal algorithm (Chave et al 2014) and, additionally,using two global and three regional algorithms (seetable S1 in the electronic supplementary material).Here, we restrict ourselves to reporting results basedon Chave et al’s (2014) model (developed in 2005 andimproved in 2014) as a recent study showed that itprovided better biomass estimates than regional algo-rithms when applied to destructive samples from EastKalimantan (Rutishauser et al 2013).We used our pan-tropical model with a mean oven-dry wood specific
Table 1.Density of CWDwasmeasured acrossfive decay classes following protocolsmodified fromChao et al (2008) andKeller et al (2004).In 2014, we randomly sampled deadwood pieces, extracted samples as drill plugs, cubes or powder bags depending on decay status, andanalysed those samples in the field lab.We averaged across samples to obtainwood density per piece and then averaged across all pieces (n:number of pieces) to obtainmeanwood density (wd, g cm−3) for each decay class (dc).
dc Description fromHarmon et al (1995) wd n
1 Little decay, bark cover extensive, leaves and fine twigs present 0.40 ± 0.03 18
2 No leaves andfine twigs, bark starting to fall off, logs relatively undecayed 0.58 ± 0.08 24
3 Nobark and few branch stubs (notmovingwhen pulled), sapwood decaying 0.37 ± 0.03 40
4 Nobranches and bark, outer wood case hardened, inner wood decomposing 0.26 ± 0.02 33
5 Wood often scattered across the soil surface, logs elliptical in cross-section 0.16 ± 0.06 4
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Environ. Res. Lett. 10 (2015) 044019 MPfeifer et al
gravity of 0.64 g cm−3, estimated for species in thenearby Lambir Hills National Park (King et al 2006).Oil palms have a fundamentally different physicalstructure to forest trees, so we estimated AGBlive inoil palm plantations separately. We computed drymass in Mg for each palm tree from its height (in m)
using = +AGBpalm
0.3747 * height * 100 3.6334
1000(Thenkabail
et al 2004).Wood-specific gravity is an important predictor of
above-ground biomass and decreases, averaged at plotlevel, with increasing level of disturbance as the speciescomposition of the forest changes (Slik et al 2008). Toinvestigate the possible scale of the bias between ourconservative approach and an approach using woodspecific gravity values reflecting different disturbancelevel of forest stands, we used Slik et al’s (2008) esti-mates derived for forest plots in Eastern Borneo. Foreach tree, we computed AGBsim as above but usingwood specific gravity estimates drawn randomly(N= 100) from a normal distribution of wood specificgravity values. This distribution was defined by max-imum and standard deviation and depended on thetree’s location: primary forests (0.64 ± 0.18) (Kinget al 2006), and lightly logged forests (0.57 ± 0.02),twice logged forests (0.54 ± 0.03), and salvage loggedforests (0.41 ± 0.05) (Slik et al 2008).
In each plot, we counted woody debris items, dis-tinguishing between standing dead wood, fallen deadwood (including fallen branches) and hanging deadwood. We classified each individual piece of CWDinto one of five decay classes (Baker et al 2007). Weexcluded fine woody debris <10 cm diameter from thedataset. For standing dead wood, diameter was mea-sured at one end (D1) as DBH or above the root but-tress as appropriate, and height was estimated visually.Diameter at the upper end (Dupper =D2) was estimatedusing the taper function (Chambers et al 2000). Majorbranches over 10 cm are recorded in a similar way. Inpractice, we rarely encountered standing dead treeswith big branches as they fall off quite quickly and sobranches were generally measured on the ground. Forfallen woody debris, we measured the length and dia-meter at both ends (D1, D2) using a tape measure andduring later density assessments the Leica Disto D2laser distance measure. We made a note if they were abranch.
Volume of each piece of CWD was calculatedusing the ‘frustrum of a cone’ formula (Bakeret al 2007) and summed within plots. We assume thatthe state of decomposition of CWD is correlated withits density (Keller et al 2004). We measured density ofdeadwood pieces across five decay classes sampledrandomly across the SAFE landscape (table 1). Wesubsequently computed CWD mass as the product ofvolume per decay class and wood density for that class.We note that like Keller et al (2004), we found thatmaterial in decay class 1 was, on average, less densethan material in decay class 2. Our lower sample size
may have introduced a sampling error and the dis-tribution in decay class 1 pieces varied strongly acrossforest disturbance types. There may also be a species-dependent effect; a European study found irregularpatterns of CWD density across decay classes for fir,larch and pine species, where the decline with increas-ing decay was only apparent from decay class 3(Paletto andTosi 2010).
For tree material, we converted CWD mass toCWD carbon stock Cdead at 47.4% (Martin and Tho-mas 2011). We estimated above-ground live carbonmass Clive (Mg) from AGBlive assuming that dry-stembiomass of trees has a carbon content of 47.4% (Mar-tin and Thomas 2011). Similarly, we accounted fordisturbance effects on plot—level averages of wood-specific gravity by computing live carbon mass Csim
(Mg) from AGBsim. We used 41.3% to convert bio-mass in oil palms to carbon content, based on findingsfrom oil palm plantations of Sumatra and East Kali-mantan, Indonesia (Vlek et al 2004). All, Clive andAGBlive as well as Csim and AGBsim were summedacross trees within vegetation plots.
CWD in oil palm plantations often consists of palmfronds, which are commonly cut and discarded in pileswithin the plantation to facilitate nutrient recycling andsoil conservation. We measured the base width andlength of each leaf frond in the plot. To establish themass of these fronds, we collected the top frond fromeach pile in each plot, air-dried them for >1month andthen weighed them.Mass of fronds (Mg) wasmodelledas a function of frond base width and frond length. Weused the resulting relationship to estimate the massof each frond within the vegetation plot (Mass =
+( )220.08 0.32* 1000 000)Width * Length
2
Frondbase Frond
(R2adj = 0.69, P<0.00 01). Mass of dead trunks was esti-mated following the approach described above. Wesummed the mass of dead fronds and trunks withineach plot, and converted oil palm mass to Cdead at41.7% (Vlek et al 2004). Across the plots, we couldtherefore partition carbon into that stored in living trees(Clive or Csim) and that in CWD (Cdead). From thesevalues we calculated the importance of deadwood car-bon as Cdead_imp=Cdead/(Cdead +Clive) orCsim_imp=Cdead/(Cdead +Csim).
For each plot, we also recorded canopy closure(sensu fractional vegetation cover), the presence/absence of significant old logging trails that were stillclearly visible in the field, terrain slope and the pre-sence/absence of riverine gullies within plot bound-aries. Mean slope was derived from 12 slopemeasurements within plots, from the centre towardstheNorth, South, East andWest and along theN–NW,N–NE,W–NW,W–SW, S–SW, S–SE, E–NE and E–SEaxes. Based on these slopes, we also determined whe-ther the plot within the landscape was located on aridge, on a slope, or at the bottom of a riverine gully(‘gulliness’). Fractional vegetation cover (in % perplot) was estimated from 12 to 13 hemispherical
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Environ. Res. Lett. 10 (2015) 044019 MPfeifer et al
upward-facing photographs acquired 1 m aboveground level per plot following in house-algorithms(Pfeifer et al 2014) and using CAN-EYE V6.3.8 (Weissand Baret 2010).
2.3.Quantifying the continuous disturbancegradientWe quantified disturbance as the number of loggedtrees relative to the number of trees that would bestanding in each plot if the forest had been leftundisturbed. We used our data for OG1 and OG2 inMaliau Basin Conservation Area as the undisturbedbaseline data. During vegetation assessments, werecorded the cause of death for woody debris pieceswherever possible, distinguishing between naturalcauses (i.e. tree was killed by a liana, a strangler fig orlightning) and anthropogenic causes (i.e. tree was cutor burnt). We summed the number of standing andlogged tree stumps in each plot as well as the numberof live trees with DBH ⩾10 cm. We subsequentlycalculated the disturbance index for each plot asDISTURBANCE=number of logged stumps/(num-ber logged stumps + number of live trees). Note thatoil palms were not counted as live trees as they areexplicitly individuals that did not survive the loggingand deforestation processes. Thus our disturbanceindex scales from zero in primary forest to amaximumof one in oil palm plantations. We compared thisquantitative measure of disturbance to canopy closureestimates (averaged across plots within stands) and toqualitative assessments of forest quality, scored on a
scale between 1 and 5 (1: ‘no trees and open canopywith ginger/vines or low scrub’, 2: open with occa-sional small trees over ginger/vine layer, 3: small treesfairly abundant/canopy at least partially closed, 4: lotsof trees, some large, canopy closed, 5: ‘no evidence oflogging at all, closed canopywith large trees’).
2.4.Modelling variation in above-ground carbonstocksPlot disturbance was aggregated to the level of the 17sampling blocks (A–F, LF1–LF3 and LFE, VJR andOG3, OG1 and OG2, OP1–OP3) by averaging acrossplots within blocks (figure 2). We modelled thecontinuous relationships between disturbance and fiveresponse variables representing above-ground carbonstocks (i.e. Clive and Csim, Cdead, Cdead_imp andCsim_imp) using nonlinear and general linear models(family=‘Gaussian’, link=‘identity’) as implementedin the R ‘stats’ package (R Development Core T 2013).We used one-way ANOVA with posthoc Tukey HSDtests for pairwise comparisons to detect generaldifferences between plots with logging trails or riverscompared to plots without loggings trails or rivers.Weemployed linear mixed effects models to predict dead-wood carbon stocks from plot attributes (i.e. terrainsteepness, presence of rivers and trails, gulliness,canopy closure) and their interactions, with foreststand included in the model as a random effect. Wefitted models using maximum likelihood criterionimplemented with the ‘lmer’ command within the Rpackage ‘lme4’ (Bates, Maechler and Bolker 2012).We
Figure 2. Forest stands ranked according to disturbance intensity (see table 2). Primary forests (OG1,OG2) are located in theMaliauBasin Protection Forest Reserve (IUCNCategory Ia) and are part of a continuous forest area that has never been logged. Adjacent tothe Forest Reserve isOG3, whichwas lightly logged in the 1970s and 1990s. TheVirgin Jungle Reserve (VJR, IUCNCategory Ia), hasbeen logged illegally. Logged forest patches are located in continuous forest (LF1–LF3, LFE); salvage logged forests (A–F) are in areasthat will become fragments in an oil palm landscape.Oil palm standsOP1 andOP2were planted in 2006, andOP3 in 2000.
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Environ. Res. Lett. 10 (2015) 044019 MPfeifer et al
computed single predictor models and multiple pre-dictor models. The final model was chosen using thestep function in lmerTest. We choose the anovafunction implemented in lmerTest to estimateP valuesfor parameters based on Satterthwaite’s approxima-tions (Kuznetsova et al 2014).
3. Results
3.1. Above-ground carbon stocks in deadwood andlive treesAbove-ground carbon stocks in CWD of forest standsranged from Cdead = 0.64 (±0.22) Mg/plot(∼10.22 Mg ha−1, in primary forest stand OG2) to2.31 (±0.60) Mg/plot (∼36.93 Mg ha−1, in salvagelogged forest stand A). Above-ground carbon stocks inlive trees ranged from Csim = 0.98 (±0.22) Mg/plot(∼15.71 Mg ha−1, in salvage logged forest stand E) to14.34 (±2.61) Mg/plot (∼229.37 Mg ha−1, in theprimary forest stand OG2) (table 2). The proportionof carbon stored in deadwood varied from 5.4(±2.2)% in primary forest standOG2 to 63.9 (±6.7)%in salvage logged forest standA (table 2).
Above-ground carbon stored in live trees (withwood gravity adjusted to be a function of disturbance)decreased significantly and exponentially with increas-ing disturbance, a pattern that holds true at both plotand stand level (figure 3). Carbon stored in CWD var-ied considerably among and within forest stands,showing only weak trends with disturbance. As expec-ted, the importance of carbon stored in deadwoodincreased with increasing disturbance following a
logistic model (figure 3). Live tree carbon estimatesusing disturbance adjusted wood gravity, Csim, are sig-nificantly lower compared to live tree carbon estimatesbased on constant wood gravity, especially in moredisturbed plots that are characterized by lower carbonstocks (table S2, figure S2). Hence, estimates of theimportance of deadwood carbon, Csim_imp, are higherusing live tree carbon estimates with disturbanceadjusted wood gravity (figure S2, top panel, right).Responses of both live tree carbon and importance ofdeadwood carbon to disturbance follow similar trendusing conservative AGBlive and Clive estimates. How-ever, our analyses indicate amore rapid decline of Csim
at plot level and a more rapid increase in the impor-tance of deadwood, Csim_imp, with increasing dis-turbance (figure 3).
In young oil palm plantations (OP1 and OP2)much of the CWDmass comes fromdead tree remainswith only marginal contributions from cut palmfronds (mean± SE= 0.11%±0.06). In the mature oilpalm stand, a larger amount of the CWDmass is con-tributed by palm fronds (34.9%± 13.0).We emphasizethatmoisturemay have been left in the oil palm frondsafter drying potentially leading to slight overestimatesin the amount of CWD carbon and in the importanceof CWDcarbon inmature oil palm plots.
3.2. LinkingCWDstocks to plot environmentsIn forest stands, CWD volume, CWD carbon and theproportion of carbon stored in deadwood, Csim_imp,increased significantly with disturbance (P< 0.01) insingle predictor linear mixed effects models. Csim_imp
Table 2.Distribution of above-ground carbon stocks (mean and standard error) in live biomass (Csim,Mg/plot) and coarsewoody debrismass (Cdead,Mg/plot) across SAFE’s forest stands. Estimates of Csim and importance of deadwood carbon versus live tree carbon(Csim_imp) accounted for changes inwood specific gravity with increasing level of disturbance when computing live tree abovegroundbiomass (for details see text). Standswere ranked from lowdisturbance to high disturbance (i.e. number of logged trees relative to thenumber of trees that would be standing in each plot if the forest had been left undisturbed) (see alsofigure 2). Forest quality (quality) wasalso estimated fromqualitative criteria (scored from 1: very disturbed to 5: not disturbed) for each plot (n: number of plots) and thenaveragedwithin forest stands. The relative contribution of coarse woody debris carbon to above-ground carbon stocks (Csim_imp) increa-ses with disturbance of the forests. Fractional vegetation cover (FCover) does not show consistent trendswith disturbance.
Stand n Csim Cdead Csim_imp (%) FCover (%) Quality
mean se mean se mean se mean se mean ± se
OG2 9 14.33 2.61 0.64 0.22 5.44 2.19 80.1 3.2 5.0 ± 0.0
OG1 9 11.89 2.45 1.69 0.51 14.65 4.63 77.8 3.0 4.7 ± 0.2
VJR 8 3.80 0.54 1.14 0.39 21.89 5.96 76.8 2.5 3.4 ± 0.2
OG3 9 12.84 1.71 1.24 0.33 9.86 2.62 71.1 1.0 4.2 ± 0.2
LF1 9 2.30 0.34 2.10 1.28 32.28 8.43 85.2 2.3 3.0 ± 0.2
D 16 1.13 0.21 1.03 0.39 37.81 8.72 70.9 3.6 2.2 ± 0.1
F 16 2.31 0.32 1.73 0.62 36.64 6.64 72.7 2.3 2.9 ± 0.2
E 16 0.98 0.22 0.80 0.17 50.02 6.47 69.9 2.9 2.1 ± 0.3
LF2 9 4.33 0.34 1.39 0.39 22.10 4.21 79.7 3.0 3.1 ± 0.1
LFE 8 4.07 0.97 0.93 0.30 23.75 7.81 82.5 2.5 3.3 ± 0.3
LF3 9 3.98 0.33 1.13 0.15 23.03 3.75 80.2 2.1 2.9 ± 0.1
C 16 1.28 0.30 1.07 0.28 47.60 5.77 72.6 3.6 2.2 ± 0.2
A 16 1.36 0.33 2.31 0.60 63.91 6.70 71.0 4.4 2.4 ± 0.3
B 16 1.64 0.29 1.75 0.49 48.58 5.02 75.5 3.1 2.7 ± 0.2
OP3 9 0.28 0.12 0.16 0.04 50.85 12.15 60.2 6.5 —
OP2 9 0.37 0.20 2.18 1.15 70.76 9.80 36.6 6.2 —
OP1 8 0.11 0.03 0.42 0.07 76.53 4.73 44.1 7.3 —
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Environ. Res. Lett. 10 (2015) 044019 MPfeifer et al
also decreased with increasing terrain steepness(P= 0.057). However, that effect disappeared afteraccounting for disturbance impacts. The proportionof carbon stored in deadwood was significantly higherin forest plots with skid trails (P< 0.001), which alsohave significantly lower live tree carbon stocks(P< 0.05). The proportion of carbon stored in dead-wood was significantly higher in plots located onridges as compared to plots in gullies (P< 0.05; TukeyHSD), but showed no other patterns in response toplot attributes.We also found significantly higher Csim
stocks on plots without rivers (P< 0.05), but this doesnot propagate into effects onCsim_imp.
Final models predicting Csim_imp retained forestblocks as significant random effect (P< 0.01) and dis-turbance (P< 0.001) with an interaction effectbetween presence of skid trails and gulliness (P= 0.05)as significant fixed effects. Final models predictingCWD volume or CWD carbon only retained land useas significant fixed effect each (P< 0.01).
3.3. Validating tree heights and disturbancemetricCanopy closure did not show a trend with thedisturbance index at plot or stand level (excludingheavily managed oil palm stands). The forest qualityscore decreased significantly with disturbance acrossplots and forest stands (linear model, plot level:R2adj = 0.18, P< 0.001; stand level:R2
adj = 0.55, P< 0.01)(figure 4). Modelled average tree heights were higherthan measured average tree heights but not signifi-cantly so (see figure S1 in electronic supplementarymaterial in supporting information), thus validatingthe visual estimates of heightmade in thefield.
4.Discussion
4.1. Above-ground carbon changesOur estimates of CWD stocks for primary forest sitesat SAFE (mass: 21.6 and 57.1 Mg ha−1) lie within thespectrum recorded for lowland tropical forests (Baker
et al 2004) and are similar to estimates of ∼26 (Saneret al 2012) and 41 (Gale 2000) Mg ha−1 previouslyfound for lowland forests in North Borneo. Gale(2000) estimated CWD mass for pieces with ⩾20 cmdiameter to be ∼45 mg ha−1 (Belalong), ∼41Mg ha−1
(Danum) and ∼69Mg ha−1 (Andalau). Deadwoodmass in undisturbed moist tropical forests has beenestimated to be less than 10% (Delaney et al 1998,Saner et al 2012), 15% (Uhl et al 1988) and 19%(Saldarriaga et al 1988) of total above-ground biomassstocks. This is in line with our findings for theproportion of deadwood carbon in primary andlightly-logged forest stands. However, our analysesclearly show that the importance of deadwood carbonstocks increase with forest disturbance to around 20 or30% for twice-logged forest stands and to more than30 or 40%, and as high as 64% in salvage-logged foreststands. This indicates that carbon storage and emis-sions in logged forests may be markedly different tothose in primary forests.
Figure 3. Spatial trends in carbon stocks in response to SAFE’s disturbance gradient. (a) Live tree carbon stocks, Csim, decreasedexponentially (a*exp(b*x)with increasing disturbance fromprimary forest to oil palm. At plot level: a=6.97***, b=−9.81***,pseudo−R2 = 0.27; at stand level: a= 11.68***, b=−14.38**, pseudo−R2= 0.73. (b) Coarsewoody debris carbon stocks, Cdead,varied strongly among andwithin forest stands showing no trendwith disturbance. (c) The importance of coarsewoody debriscarbon, Csim_imp, increased logistically with disturbance (A= asymptote, I= curve inflection point, S= scaling factor). At plot level:A= 0.67***, I= 0.08***, S= 0.08***, pseudo−R2 = 0.43; at stand level:A= 0.67***, I=0.12**, S= 0.09**, pseudo−R2 = 0.74. Fordetails on attributes see also table 1. *** Significant atP< 0.0001. **: significant atP< 0.01.
Figure 4. Inter-comparison of forest quality estimated frommultiple qualitative criteria with our continuous disturbancemetric. Quality decreased significantly with disturbanceacross plots and forest stands (linearmodel, plot level:R2adj = 0.18,P< 0.001; stand level:R2
adj = 0.55,P< 0.01).
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The increase in the importance of deadwood car-bon with disturbance was caused, not by increases inCWDmass, but primarily by a significant reduction inthe above-ground biomass and carbon of living trees(figure 3). Borneo’s lowland rainforests are living car-bon density hotspots (Ruesch and Gibbs 2008). AtSAFE, carbon storage in living trees of unlogged for-ests is around 229 and 190Mg C ha−1. This is similarto earlier findings for lowland rainforests in Borneo(∼229Mg C ha−1) and considerably higher comparedto the Amazonian average of ∼144Mg C ha−1 (Sliket al 2010) or to conservatively-estimated stocks inunlogged forests at nearby Danum Valley of91.9 Mg C ha−1 (Saner et al 2012). Logging removedlarge trees at SAFE and caused residual loggingdamage, thereby significantly reducing above-groundcarbon stocks in live trees to 20–30Mg C ha−1 in someof the logged forest stands (7–10 years post-logging).Our findings imply that logging impacts on live treecarbon stocks may be stronger than the 28.6% reduc-tion 22 years post logging previously reported forSabah’s lowland forests (Saner et al 2012). To verifythat assumption, we are currently re-measuring bio-mass annually in the 193 vegetation plots to obtainestimates of biomass recovery over time.
There was only a weak significant relationshipbetween absolute CWD stocks and disturbance.CWDs stocks at SAFE, as elsewhere, are likely to becaused by a balance between input rates and rates ofdecomposition. Assuming that input rates in unmodi-fied forests are a function of living biomass and do notvary strongly from year to year (Baker et al 2007), thelack of a relationship between AGBlive and CWDmassin our study suggests that current CWD stocks mayreflect pre-logging stocks, which are now slowlydecomposing. However, mean residence time of dead-wood carbon is assumed to be around six to nine years(Chambers et al 2001, Rice et al 2004) and the last log-ging events in the SAFE area took place in the early tomid-2000s, suggesting that most CWD should havealready decomposed. An alternative hypothesis is thathuman activities during selective logging have gener-ated CWD that offsets naturally low rates of CWDproduction. This may help explain the high relativedeadwood carbon stocks observed in some of the for-est stands characterized by a very low amounts of liv-ing biomass. Furthermore, some species decay muchslower than others, and species composition may havechanged post-logging. Either way, our finding that dis-turbed forests increasingly store carbon as dead woodare likely to impact on carbon flux estimates becauseof subsequent decomposition (Blanc et al 2009),which may temporarily offset carbon sequestrationfrom the growth of new and surviving trees.
We detected some environmental factors thatinfluenced the relative amount of carbon stored inCWD; terrain steepness, land use and the presence ofold logging trails were linked to higher deadwood car-bon ratios. However, environmental factors other
than disturbance measured at SAFE do not appear todrive absolute CWD carbon stocks, but rather affectabove-ground live tree carbon, highlighting the com-plex patterns of topography and disturbance thatunderlie spatial variation in above-ground carbon atlandscape scale (Gale 2000, Vieira et al 2011). Giventhe potential impact of species composition on CWDstocks, including biological drivers into models ofCWD stock variation will be the subject of futurestudies.
4.2. Implications for carbon budgetsForest degradation through selective timber harvest-ing is increasing in frequency and extent (Curranet al 2004, Asner et al 2005) and has now probablybecome a more extensive cause of land use changethan outright deforestation (Asner et al 2009). Satellitemonitoring has revealed the progression of loggingthrough Central Africa (Laporte et al 2007), theAmazon (Asner et al 2005), Borneo (Gaveauet al 2014) and other humid tropical forest regions.
The impacts of widespread logging have recentlyregained the attention of conservation ecologists, asmeta-analyses suggest that selectively-logged forestsretain substantial biodiversity, carbon and timberstocks, with once-logged forest stands retaining 76%of their above-ground carbon stocks one year afterlogging (Putz et al 2012). However, large, landscape-scale assessments of above-ground carbon changesalong a disturbance gradient in tropical forests are rare(Berenguer et al 2014). Carbon change studies typi-cally focus on assessments of either live tree or dead-wood carbon (Keller et al 2004,Malhi et al 2006, Blancet al 2009, Lewis et al 2009). The few studies that quan-tify both living and dead carbon stocks have often beencarried out in primary forest stands alone (Nasci-mento and Laurance 2002, Pereira et al 2002, Riceet al 2004, Malhi et al 2009), or along comparativelysmall transects. For example, Saner et al (2012) sam-pled along linear transects covering just 1 ha in bothselectively logged forests and primary forests andignored fallen dead wood, which represented themajority of deadwood carbon in our plots (>65% in allforest stands except in LF1, where it was 54.1%).
Our analyses show that deadwood carbon stockscan represent a large proportion (>50%) of above-ground carbon stocks in human-modified forests. Weacknowledge that our estimates contain uncertainties.For example, we use a global algorithm to estimateabove-ground biomass in live trees (although trendsremain stable using different biomass algorithms).However, we do account for possible changes in meanstand-level tree wood density over time with dis-turbance that might occur as the high-density com-mercial timber species are removed and more light-wooded successional species become more dominant.And we show that those factors are not changing our
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clear results, which highlight the need for carbon stockstudies to includeCWDestimates in their calculations.
Forest degradation can alter the balance betweencarbon emissions and sequestration in tropical forests.A global assessment of carbon sinks in the world’s for-ests estimate a ‘near-sink’ in tropical forests as carbonemissions from tropical deforestation (∼3 and2.8 Pg C yr−1) are counterbalanced by carbon seques-tration in intact forests combined with forests re-growing after disturbance (∼2.9 and 2.7 Pg C yr−1)(Pan et al 2011). This assessment was criticized foroverestimating carbon sequestration, as the authorsoverlooked tree re-census data and mismatchedbetween sources used for estimates of forest area(Wright 2013). The authors based their analyses onbiomass data taken in old-growth forests and up-scaled to regions. In addition, for the tropics they hadonly sparse ground-based data on deadwood carbonstocks and none from disturbed and re-growing for-ests. Our results imply that for logged forests, at leastin Southeast Asia, they may have underestimated car-bon emissions from dead wood and overestimatedcarbon storage in live trees.
The patterns found at the SAFE landscape mayultimately be representative of tropical forests inSoutheast Asia and human-modified tropical forestselsewhere. Detrimental effects of disturbance may beless severe in selectively logged tropical forests com-pared to outright deforestation and agroforestry (Gib-son et al 2011, Putz et al 2012). However, lower carbonstorage in live biomass and higher temporary carbonstorage in deadwood across degraded forest standsmay have important implications for global assess-ments of carbon sources and sinks such as those byPan et al (2011), as logged forests now represent morethan 30% of all standing tropical humid forests,implying a strong, time-delayed human footprint.
Acknowledgments
We thank the Sime Darby Foundation, the SabahFoundation, Benta Wawasan and the Sabah ForestryDepartment for their generous support of the SAFEProject.We thank Ferry Slik for contributing his woodgravity data, the Royal Society’s South East AsiaRainforest Research Programme for logistical support,and Yayasan Sabah, Maliau Basin Management Com-mittee, the State Secretary, Sabah Chief Minister’sDepartments, the Sabah Biodiversity Council and theEconomic Planning Unit, Putrajaya for permission toconduct research in Sabah. MP, VL and RME aresupported by European Research Council Projectnumber 281986.
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