forest-carbon-in-png-julian-fox-et-al-biotropica

To be submitted to Biotropica – Do not circulate 1Title: Forest carbon in Papua New Guinea 3Authors: Julian C Fox 1*, Cossey K Yosi 1,2 and Rodney J Keenan 5Affiliations: Department of Forest and Ecosystem Science, The University of 6Melbourne Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia 7*Correspondence: Phone: +61 9250 6862, Fax: +61 9250 6886 email: 8jcfox@unimelb.edu.au Papua New Guinea Forest Research Institute, PO Box 314, Lae 9411, Morobe Province, PNG 10 11Key words: Carbon, Biomass, Sequestration, Forest dynamics, Flux, Degradation, 12Selective-logging, REDD, ENSO, Deforestation, Secondary To be submitted to Biotropica – Do not circulate 1Abstract Quantifying forest Carbon (C) in primary and secondary tropical forest is one of 3the challenges of climate change mitigation initiatives such as reduced emissions from 4deforestation and degradation (REDD) Papua New Guinea (PNG) has become the focus 5of the REDD initiative, but defensible estimates of forest C are lacking Here we present 6a methodology for estimating forest C from a large Permanent Sample Plot (PSP) 7network, and report the first defensible estimates of forest C in undisturbed and 8selectively-logged (degraded, secondary) forest in Papua New Guinea This paper 9represents the first published account of this large and important census of PNG’s diverse 10tropical forests 11 Average above ground live biomass in trees greater than 10cm diameter 12(AGLB>10cm) in 135 selectively-logged hectare plots was 66 Mg C -1 with a standard 13deviation (SD) of 19, while for 20 undisturbed plots the average was 110 Mg C -1 (SD 1428) By estimating unmeasured above ground C components, total above ground biomass 15(AGB) was estimated as 92 Mg C -1 and 154 Mg C ha-1 for selectively-logged and 16undisturbed forest respectively Our estimate for undisturbed forest is lower than biome 17averages for tropical equatorial forest; 180 Mg C -1 IPCC (2006), 202 Mg C ha-1 Lewis 18(2009) Our estimate for degraded secondary forest is higher than previous estimates, 19suggesting that the selective-logging practiced in PNG (targeting high-value species 20above a 50cm diameter limit) has a lesser impact on forest C than other anthropogenic 21disturbances Secondary forests in PNG have previously been assumed to hold little value 22for either timber or carbon, but these higher estimates suggest that they should be valued, 23and perhaps actively managed for the carbon they contain Provincial averages for 2 To be submitted to Biotropica – Do not circulate 1AGLB>10cm in selectively-logged forest varied from Central and Oro Provinces with 2averages of c 50 Mg C ha-1 to Western Province with c 80 Mg C ha-1 Observed forest C reported here are the first defensible measurements for Papua 4New Guinea, and represent a critical step toward REDD implementation 6Introduction Papua New Guinea (PNG) along with other rainforest nations have recently 8become the focus of climate change mitigation efforts with the ‘reducing emissions from 9deforestation and degradation’ (REDD) initiative of the United Nations Framework 10Convention on Climate Change (UNFCCC) Developing tropical countries such as PNG 11face many challenges in reporting for the REDD initiative Estimating forest C pools in 12different forest stratum such as primary and secondary forest is an important precursor to 13REDD implementation (Gibbs et al 2007) Here we contribute to REDD implementation 14for PNG by quantifying above ground carbon (C) stock in undisturbed (primary) and 15selectively-logged (secondary, degraded) forest across a PSP network initiated and 16maintained by Papua New Guinea Forest Research Institute (PNGFRI) A majority (112) 17of the PSPs were established in selectively-logged forests, with undisturbed forest being 18relatively poorly represented (13 plots) Based on PSPs in secondary forest we can 19determine defensible provincial averages for above ground biomass (AGB) for the 20secondary forest stratum 21 Secondary forest can be defined as forest which has been disturbed and is at some 22stage of regeneration, and have been estimated to comprise 40% of all tropical forest 23(Brown and Lugo, 1990) Considering that 40% of terrestrial biomass is stored in tropical To be submitted to Biotropica – Do not circulate 1forests (Phillips et al 1998), secondary tropical forest thus represents a very significant 2global C pool (c 20%) that has considerable (but unverified) potential for C flux into the 3future (Brown et al 1996, Fehse et al 2002) Secondary tropical forest remains a poorly 4understood resource relative to primary forest (Sierra et al 2007b) Many studies fail to 5adequately distinguish between primary and secondary forest (Houghton et al 2001), and 6merge estimates of forest C over the two stratum Consistent with this, secondary forest 7in PNG is a large and poorly understood resource considered to hold little value for either 8timber or carbon (PNGFA pers comm.) Using statistics from 2002, PNG Forest 9Authority estimated that undisturbed forest covers an area of c 29.7 million hectares 10whilst secondary forest covers c 3.3 million hectares (PNGFA pers comm.) However, 11recent studies indicate that the area of secondary forest may be rapidly expanding (see 12Shearman et al 2009) Assessment of this large and expanding forest resource is a 13priority and could potentially facilitate it’s inclusion in climate change mitigation efforts 14 Many previous studies of tropical forest C and C flux have been plagued by 15methodological problems that limit the veracity of the estimates (Clark et al 2001b, 16Phillips et al 2002) Several important problems have been identified; small plot sizes of 17less than (0.25 is common) limit the representativeness of measured forest and is 18likely to result in overestimates generated by larger trees being over-represented (Brown 19and Lugo 1992); There is lack of replication in both time and space which will again limit 20the representativeness of measured forest and will result in results skewed toward patches 21of forest with the highest biomass (Clark et al 2001a, 2001b, Phillips et al 2002, 2004) 22These methodological problems are exaggerated for tropical forests, due to large spatial 23variations in structure, productivity, and the presence of large trees There is clearly a To be submitted to Biotropica – Do not circulate 1need for studies with large plots that sample widely across both time and space (Clark et 2al 2001a, 2001b, Phillips et al 2004) The PSPs used in this study overcome many of 3these methodological issues, and provide a sound basis for the estimation of forest C and 4C flux; plots are large (1 ha) and are replicated through both space and time We will 5estimate above ground live biomass (AGLB >10cm) for each PSP measurement and examine 6national and provincial averages Another methodological issue is introduced when tree variables measured for 8timber inventory purposes are used to estimate biomass and C (Lindner and Karjalainen 92007) Two methods are commonly used; the first converts tree volumes to biomass using 10a biomass expansion factor (Segura 2006); the second uses previously developed 11allometric equations to estimate biomass per tree as a function of tree parameters such as 12tree diameter, tree height and wood density (Brown et al 1989, Clark et al 2001, Chave 13et al 2003, Baker et al 2004) These allometric equations will have been derived from 14biomass harvesting studies (Brown et al 1989, Chambers et al 2001, Chave et al 2001), 15and their application is dependent on the availability of equations for a similar forest, and 16also for similarly sized trees (Chave et al 2005) Many allometric equations use only 17diameter to predict tree biomass, however, including wood density and height can 18improve the accuracy of tree level predictions (Chave et al 2004, Chave et al 2005), 19particularly considering the variation in tree architecture and wood density in tropical 20forests Despite possible errors in estimating AGLB, Baker et al (2004) found that 21estimated AGLB flux was unaffected by to the type of allometric equation used Given 22the absence of allometrics for PNG we are forced to convert measured tree parameters to 23tree biomass using allometrics derived from other equatorial tropical forest In doing this, To be submitted to Biotropica – Do not circulate 1it is important to include drivers of tree architecture and the physiological characteristics 2that determine C composition such as diameter, height, and wood density (Chave et al 32005) The only previous estimate of forest C in PNG known to the authors is Edwards 5and Grubb (1977); in a study of lower montane rainforest (at 2500m) they estimated total 6biomass of 175 Mg C ha-1 consisting of 151 Mg C ha-1 in tree AGLB In a major 7compilation of biomass measurements in primary tropical forests, Clark et al (2001a) 8revealed that above ground biomass (AGB) varied from as low as 40 to as high as 250 9Mg C ha-1 Despite many assessments of primary tropical forest biomass, comparatively 10few studies differentiate between primary and secondary forest (Houghton et al 2001) 11However, this is changing with the realisation of the increasing importance of secondary 12forests in the composition of tropical landscapes For example, Sierra et al (2007a) 13explicitly compare C stock in primary and secondary Colombian rainforest (AGB 111 Mg 14C ha-1 and 21 Mg C ha-1 respectively) They found that the AGB was most sensitive to 15anthropogenic disturbance, with significant differences in estimates for primary and 16secondary forest 17 The objectives of this study are to quantify above ground C pools in secondary 18and primary forest in PNG This required a sound methodology with considered error 19correction techniques and the development of appropriate tree allometrics Results will 20elucidate the role of degraded secondary forests as a C pool Assessment of the C 21contained in these forests may facilitate their potential inclusion in REDD negotiations 22We also intend to provide provincial averages of secondary forest C for specific 23application within PNG To be submitted to Biotropica – Do not circulate 1 2Materials and Methods We estimate AGLB>10cm for each PSP measurement Consistent with previous 4studies, AGLB>10cm will be reported in megagrams of carbon per hectare (Mg C -1) The 5C content of biomass will be reported assuming that dry biomass is 50% C (Clark et al 62001a, Houghton et al 2001, Malhi et al 2004) This is an acceptable approximation; 7however, the wood C fraction does exhibit some small variation across species and tree 8ages (Elias and Potvin 2003) 10PNGFRI’s PSP database 11 Over the last 20 years PNGFRI has established and remeasured over 135 PSPs 12across PNG covering all major forest types A map of PNG showing provincial 13boundaries and PSP locations is shown in Figure Each PSP plot is one hectare in size 14and is divided into 25 sub-plots of 20 m x 20 m The spatial location, diameter, height, 15and crown characteristics are recorded for all trees over 10cm The PSP database 16represents a strong basis for the estimation of biomass and C in these forests For further 17details of the PSP data refer to Yosi et al (2009) 18 PSPs were measured following a field procedure (PNGFRI 1994) Despite this 19well developed and uniformly applied field procedures, problems arise in large databases 20due to measurement and transcription errors (Baker et al 2004) To identify potential 21errors the distribution of diameter increments was examined, and those less than -0.2, or 22greater than 2.6 cm yr-1 were flagged for investigation This represented approximately 231% on each tail of the increment distribution, and 2% of all increments in total These To be submitted to Biotropica – Do not circulate 1values are similar to those used by Chave et al (2003) and Baker et al (2004) to flag 2erroneous measurements Examination of diameters for flagged trees often revealed 3transcription errors, such as an extra zero, or a missing zero These were corrected on a 4tree by tree basis, with careful adjustment of the erroneous diameter measurement When 5it was clear a measurement error had occurred, the erroneous diameter was corrected 6using a species specific diameter growth model This methodology was followed to avoid 7the significant biases introduced if erroneous records are removed (Chave et al 2003), 8and to improve on previous error corrections using interpolation (e.g Chave et al 2003), 9stand-level (e.g Baker et al 2004) or species-level averages (e.g Rice et al 2004) 10Measurement errors tend to most prevalent in larger buttressed trees, and removal of 11these records can have a significant effect on biomass estimates 12 13Species-specific increment model for error correction 14 First we needed to identify a diameter-diameter increment base model that is most 15appropriate for observed tree growth The relationship between diameter and diameter 16increment is non-linear and sigmoidal; the curve starts at the origin and rises to a 17maximum diameter increment at an inflection point before falling to an asymptotic 18diameter increment (Zeide 1993, Huang and Titus 1995) Several base models that 19characterise this relationship were examined; the Box-Lucas function (1) (Box and Lucas 201959), and a simplified model (2) from Huang and Titus (1995); 21 Dincr = ( a e −bD − e −aD a −b 22 Dincr = aDe −bD ) (1) (2) To be submitted to Biotropica – Do not circulate 1Where Dincr is diameter increment in cm yr -1, D is measured diameter at breast height 2over bark, and a and b are parameters to be estimated Species-specific models were initially fitted using NLIN in SAS, and the Box- 4Lucas base function was found to provide superior fit for species represented on PSPs 5However, model fitting for individual trees within PSPs is affected by a nested 6dependence structure; Diameter-diameter increment relationships for the same species 7within a PSP will be more similar than that between each PSP, as trees on the same plot 8will be subject to the same local environmental conditions, and will be of a similar forest 9type (Fox et al 2001) We can explicitly account for this nested dependence using a non10linear mixed model, with a separate random parameter for each PSP To facilitate this, 11SAS’s Proc NLMixed was used to fit the non-linear Box-Lucas mixed model (Wolfinger 12and O’Connell 1993, Davidian and Giltinan 2003) This will ensure correct statistical 13inference within and between PSP plots, as well as plot localised increment predictions 14that can replace erroneous measures Fitted Box-Lucas models for four species are shown 15in Figure Horsfieldia spp and Celtis latiflia are both pioneer species, and their curves 16have a maximum diameter increment at a small diameter of approximately 25cm, and 17then approach zero increment for the larger diameters Pometia pinnata and Celtis 18phillippensis are climax species with higher diameter increments across the full range or 19diameters Model (1) parameters for the 50 most common species on PSPs can be found 20in Table S2 of supplementary material 21 Species-specific predictions of diameter increment were applied when 22measurement errors had occurred; less than -0.2, or greater than 2.6 cm yr -1 and with no 23obvious transcription errors Some pioneer species (Macaranga spp., Spondias spp., To be submitted to Biotropica – Do not circulate 1Hernandia spp., palaquium spp., melanolepis spp., antiarus spp., litsea spp., 2trichospermum spp., artocarpus spp., sterculia spp., Trema spp., Elaeocarpus spp., 3Labula spp., Endospermum spp, Octomeles spp) were found to have valid growth rates 4that exceeded 2.6 cm yr-1 Trema spp and Macaranga spp appear capable of 5extraordinary growth rates of up to cm yr-1 These exceptional growth rates were not 6altered From the total of 153900 tree records, 326 (0.2%) were obvious transcription 7errors that were manually corrected, and 3418 (2%) were erroneous measurements that 8were corrected using modeled diameter increment A large number of PSP plots were affected by the El Niño-Southern Oscillation 10(ENSO) event of 1997/1998 and these plots were set aside, as the high rates of tree 11mortality resulted in declines in forest C and a negative flux that skewed analysis of 12unaffected remeasurements 13 14Estimating above ground living biomass 15 The first step in quantifying forest C is to estimate AGLB in standing trees There 16has been much recent work on the development of allometric equations for estimating 17biomass for tropical forests from tree inventory information Typically, they are models 18derived from destructively sampled trees and easily measured biometric variables such as 19diameter and height (Liddell et al 2007) In an extensive study of allometric models for 20tropical forests, Chave et al (2005) found the most important predictors of AGB were 21diameter, wood specific gravity, total height, and forest type (dry, moist, or wet) They 22developed a model (3) for wet tropical forests that was used to estimate AGB for trees on 23PSPs; 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Submitted to Biotropica 37 38Zeide B (1993) Analysis of Growth Equations Forest Science, 39, 594-616 28 To be submitted to Biotropica – Do not circulate 1Tables 2Table Sample size of PNGFRIs PSP database Non fire-affected Fire-affected Plots Measurements measurements measurements 122 13 410 23 362 19 48 Selectivelylogged Undisturbed 4Table Results for average AGLB>10cm by Province Average Province Central Province East New Britain East Sepik Gulf Province Madang Province Manus Province Milne Bay Province Morobe Province New Ireland Oro Province Southern Highlands West New Britain West Sepik Western Province Overall AGLB>10cm SD Sample 51.40 74.59 62.74 63.23 64.94 52.97 67.30 67.91 63.83 47.50 61.30 70.85 75.08 81.98 66.16 12.35 24.23 13.21 17.33 17.67 21.83 12.50 16.61 19.85 9.70 14.85 17.98 15.20 9.79 18.65 11 34 15 19 40 13 67 31 20 12 44 14 12 341 29 To be submitted to Biotropica – Do not circulate 1Figure legends 2Figure Fitted hyperbolic height-diameter model for example species 3Figure Fitted Box-Lucas diameter-diameter increment model for example species 4Figure Results for average AGLB>10cm by province 30 To be submitted to Biotropica – Do not circulate 1Figures 3Figure 31 To be submitted to Biotropica – Do not circulate 2Figure 5Figure 32 To be submitted to Biotropica – Do not circulate 33 To be submitted to Biotropica – Do not circulate 1Supplementary material 2Table S1 Average statistics for the 50 most common species on PSPs Species Myristica spp Pometia pinnata Syzygium spp Canarium spp Cryptocarya spp Macaranga inermis Dysoxylum spp Pimeleodendron amboinicum Ficus spp Planchonella spp Horsfieldia spp Litsea spp Calophyllum spp Celtis spp Garcinia spp Microcos spp Aglaia spp Chisocheton spp Terminalia spp Homalium foetidum Diospyros hebecarpa Maniltoa spp Gnetum gnemon Sterculia spp Elaeocarpus spp Euodia Barringtonia spp Pterocarpus indicus Timonius spp Family Myristicaceae Sapindaceae Myrtaceae Burseraceae Lauraceae Euphorbiaceae Meliaceae Euphorbiaceae Moraceae Sapotaceae Myristicaceae Lauraceae Clusiaceae Cannabaceae Clusiaceae Tiliaceae Meliaceae Meliaceae Combretaceae Salicaceae Ebenaceae Fabaceae Gnetaceae Malvaceae Elaeocarpaceae Rutaceae Lecythidaceae Fabaceae Rubiaceae Sample 6593 6278 6150 5165 4701 4063 3804 3802 3796 3560 3482 2368 2344 2296 2223 2130 2060 1852 1729 1598 1573 1537 1393 1293 1254 1108 1000 989 968 Average Diam (cm) 17 33 22 22 21 16 23 24 25 23 20 22 25 28 20 18 19 21 26 27 18 20 15 23 22 26 19 36 16 34 Average Ht (m) 17 25 19 20 19 16 19 20 19 22 19 20 22 25 18 17 18 18 22 26 16 18 15 20 19 20 16 23 15 Average Diam Incr (cm yr-1) 0.32 0.68 0.37 0.42 0.43 0.96 0.38 0.41 0.54 0.45 0.35 0.50 0.52 0.51 0.38 0.45 0.40 0.41 0.67 0.52 0.25 0.28 0.37 0.54 0.68 0.62 0.33 0.63 0.31 Basic wood density (g cm-1) 0.385 0.58 0.61 0.495 0.465 0.3 0.62 0.48 0.345 0.46 0.36 0.4 0.495 0.5 0.645 0.477 0.735 0.45 0.515 0.68 0.58 0.62 0.477 0.28 0.375 0.36 0.477 0.5 0.477 Average Carbon (kg) 65 488 214 170 145 40 226 182 206 183 92 148 272 355 160 82 171 137 308 538 117 179 47 112 119 225 101 597 57 To be submitted to Biotropica – Do not circulate Medusanthera spp Gymnacranthera paniculata Prunus spp Endospermum spp Mastixiodendron spp Anisoptera thurifera Dillenia spp Polyalthia oblongifolia Teijsmanniodendron bogoriense Vitex spp Platea latifolia Blumeodendron spp Semecarpus spp Artocarpus spp Parastemon versteeghii Palaquium spp Alstonia spp Anthocephalus chinensis Garcinia latissima Dendrocnide spp Trichospermum spp Sloanea spp Gonystylus macrophyllus Nothofagus spp Madhuca leucodermis Myristica subalulata Cerbera floribunda Intsia bijuga Hopea iriana Icacinaceae Myristicaceae Rosaceae Euphorbiaceae Rubiaceae Dipterocarpaceae Dilleniaceae Annonaceae 920 902 871 833 823 795 762 755 17 16 18 23 23 28 28 18 15 16 19 22 21 25 21 18 0.36 0.32 0.47 0.67 0.44 0.67 0.61 0.37 0.477 0.477 0.477 0.385 0.615 0.52 0.48 0.48 73 62 95 162 277 345 271 100 Lamiaceae Lamiaceae Icacinaceae Euphorbiaceae Anacardiaceae Moraceae Chrysobalanaceae Sapotaceae Apocynaceae Rubiaceae Clusiaceae Urticaceae Tiliaceae Elaeocarpaceae Thymelaeaceae Nothofagaceae Sapotaceae Myristicaceae Apocynaceae Fabaceae Dipterocarpaceae 727 691 687 685 675 636 628 624 622 598 591 589 566 520 516 510 506 502 499 494 474 27 46 25 21 17 28 26 25 28 22 21 18 17 28 21 33 22 18 25 29 24 21 28 20 19 16 21 24 24 22 20 20 13 16 22 19 23 22 17 20 21 23 0.38 0.77 0.50 0.68 0.30 0.69 0.47 0.49 0.59 1.22 0.29 0.30 1.38 0.54 0.34 0.69 0.34 0.42 0.59 0.57 0.49 0.477 0.61 0.477 0.477 0.477 0.35 0.477 0.525 0.31 0.365 0.645 0.477 0.477 0.485 0.477 0.64 0.477 0.385 0.395 0.645 0.785 261 1110 186 142 73 223 250 275 285 154 173 92 69 302 166 584 157 76 146 480 353 2 35 To be submitted to Biotropica – Do not circulate 1Table S2 height-diameter (HD) and diameter-diameter increment (DDI) model parameters for the 50 most common species on PSPs Species Myristica spp Pometia pinnata Syzygium spp Canarium spp Cryptocarya spp Macaranga inermis Dysoxylum spp Pimeleodendron amboinicum Ficus spp Planchonella spp Horsfieldia spp Litsea spp Calophyllum spp Celtis spp Garcinia spp Microcos spp Aglaia spp Chisocheton spp Terminalia spp Homalium foetidum Diospyros hebecarpa Maniltoa spp Gnetum gnemon Sterculia spp Elaeocarpus spp Euodia Barringtonia spp Pterocarpus indicus Timonius spp Medusanthera spp Gymnacranthera paniculata Family Myristicaceae Sapindaceae Myrtaceae Burseraceae Lauraceae Euphorbiaceae Meliaceae Euphorbiaceae Moraceae Sapotaceae Myristicaceae Lauraceae Clusiaceae Cannabaceae Clusiaceae Tiliaceae Meliaceae Meliaceae Combretaceae Salicaceae Ebenaceae Fabaceae Gnetaceae Malvaceae Elaeocarpaceae Rutaceae Lecythidaceae Fabaceae Rubiaceae Icacinaceae Myristicaceae Sample 6593 6278 6150 5165 4701 4063 3804 3802 3796 3560 3482 2368 2344 2296 2223 2130 2060 1852 1729 1598 1573 1537 1393 1293 1254 1108 1000 989 968 920 902 36 DDI-a 0.035 0.060 0.032 0.040 0.042 0.164 0.027 0.072 0.041 0.040 0.047 0.045 0.053 0.048 0.039 0.071 0.030 0.038 0.064 0.040 0.015 0.024 0.035 0.058 0.070 0.044 0.036 0.048 0.040 0.049 0.037 DDI-b 0.036 0.010 0.022 0.023 0.022 0.012 0.014 0.046 0.010 0.016 0.045 0.016 0.015 0.016 0.033 0.050 0.009 0.024 0.010 0.016 0.000 0.020 0.022 0.024 0.014 0.000 0.035 0.008 0.076 0.042 0.035 HD-a 50.8 53.0 56.1 55.3 51.3 51.8 55.1 53.3 61.8 56.3 65.8 58.9 62.8 71.5 57.6 45.7 58.1 57.8 60.8 114.8 59.9 65.8 31.8 65.1 46.5 51.0 67.9 52.3 30.6 66.8 42.6 HD-b 32.9 32.3 37.7 33.6 31.6 35.5 38.8 35.6 49.9 33.2 46.9 39.7 40.7 49.0 39.5 28.6 38.8 43.5 39.6 85.1 46.9 48.3 15.9 50.3 29.2 33.8 58.4 37.6 15.4 57.8 24.0 To be submitted to Biotropica – Do not circulate Prunus spp Endospermum spp Mastixiodendron spp Anisoptera thurifera Dillenia spp Polyalthia oblongifolia Teijsmanniodendron bogoriense Vitex spp Platea latifolia Blumeodendron spp Semecarpus spp Artocarpus spp Parastemon versteeghii Palaquium spp Alstonia spp Anthocephalus chinensis Garcinia latissima Dendrocnide spp Trichospermum spp Sloanea spp Gonystylus macrophyllus Nothofagus spp Madhuca leucodermis Myristica subalulata Cerbera floribunda Intsia bijuga Hopea iriana Rosaceae Euphorbiaceae Rubiaceae Dipterocarpaceae Dilleniaceae Annonaceae 871 833 823 795 762 755 0.045 0.483 0.029 0.055 0.076 0.032 0.009 0.030 0.010 0.005 0.015 0.014 52.1 67.5 66.4 68.7 52.5 67.5 30.2 45.8 46.4 44.6 38.4 48.4 Lamiaceae Lamiaceae Icacinaceae Euphorbiaceae Anacardiaceae Moraceae Chrysobalanaceae Sapotaceae Apocynaceae Rubiaceae Clusiaceae Urticaceae Tiliaceae Elaeocarpaceae Thymelaeaceae Nothofagaceae Sapotaceae Myristicaceae Apocynaceae Fabaceae Dipterocarpaceae 727 691 687 685 675 636 628 624 622 598 591 589 566 520 516 510 506 502 499 494 474 0.034 0.075 0.058 0.171 0.041 0.070 0.042 0.032 0.044 0.168 0.031 0.020 1.876 0.042 0.019 0.060 0.044 0.037 0.132 0.056 0.055 0.029 0.010 0.022 0.032 0.053 0.010 0.018 0.008 0.006 0.006 0.041 0.010 0.000 0.011 0.005 0.006 0.047 0.016 0.028 0.013 0.024 66.1 59.3 52.5 75.9 61.5 68.9 57.2 70.8 74.8 68.0 57.3 71.7 49.8 50.0 69.3 49.5 56.0 52.7 43.0 65.8 69.8 54.0 45.7 36.3 57.7 44.7 57.1 31.4 46.0 60.6 49.6 38.1 83.3 34.4 31.1 51.4 30.2 32.5 35.4 27.2 53.8 46.4 37