The intent is to assist local communities derive high end ...



Title: Forest carbon in Papua New Guinea

Authors: Julian C. Fox 1*, Cossey K. Yosi 1,2 and Rodney J. Keenan 1

Affiliations: 1. Department of Forest and Ecosystem Science, The University of Melbourne Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia. *Correspondence: Phone: +61 3 9250 6862, Fax: +61 3 9250 6886 email: jcfox@unimelb.edu.au. 2. Papua New Guinea Forest Research Institute, PO Box 314, Lae 411, Morobe Province, PNG.

Key words: Carbon, Biomass, Sequestration, Forest dynamics, Flux, Degradation, Selective-logging, REDD, ENSO, Deforestation, Secondary.

Abstract

Quantifying forest Carbon (C) in primary and secondary tropical forest is one of the challenges of climate change mitigation initiatives such as reduced emissions from deforestation and degradation (REDD). Papua New Guinea (PNG) has become the focus of the REDD initiative, but defensible estimates of forest C are lacking. Here we present a methodology for estimating forest C from a large Permanent Sample Plot (PSP) network, and report the first defensible estimates of forest C in undisturbed and selectively-logged (degraded, secondary) forest in Papua New Guinea. This paper represents the first published account of this large and important census of PNG’s diverse tropical forests.

Average above ground live biomass in trees greater than 10cm diameter (AGLB>10cm) in 135 selectively-logged 1 hectare plots was 66 Mg C ha-1 with a standard deviation (SD) of 19, while for 20 undisturbed plots the average was 110 Mg C ha-1 (SD 28). By estimating unmeasured above ground C components, total above ground biomass (AGB) was estimated as 92 Mg C ha-1 and 154 Mg C ha-1 for selectively-logged and undisturbed forest respectively. Our estimate for undisturbed forest is lower than biome averages for tropical equatorial forest; 180 Mg C ha-1 IPCC (2006), 202 Mg C ha-1 Lewis (2009). Our estimate for degraded secondary forest is higher than previous estimates, suggesting that the selective-logging practiced in PNG (targeting high-value species above a 50cm diameter limit) has a lesser impact on forest C than other anthropogenic disturbances. Secondary forests in PNG have previously been assumed to hold little value for either timber or carbon, but these higher estimates suggest that they should be valued, and perhaps actively managed for the carbon they contain. Provincial averages for AGLB>10cm in selectively-logged forest varied from Central and Oro Provinces with averages 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 New Guinea, and represent a critical step toward REDD implementation.

Introduction

Papua New Guinea (PNG) along with other rainforest nations have recently become the focus of climate change mitigation efforts with the ‘reducing emissions from deforestation and degradation’ (REDD) initiative of the United Nations Framework Convention on Climate Change (UNFCCC). Developing tropical countries such as PNG face many challenges in reporting for the REDD initiative. Estimating forest C pools in different forest stratum such as primary and secondary forest is an important precursor to REDD implementation (Gibbs et al. 2007). Here we contribute to REDD implementation for PNG by quantifying above ground carbon (C) stock in undisturbed (primary) and selectively-logged (secondary, degraded) forest across a PSP network initiated and maintained by Papua New Guinea Forest Research Institute (PNGFRI). A majority (112) of the PSPs were established in selectively-logged forests, with undisturbed forest being relatively poorly represented (13 plots). Based on PSPs in secondary forest we can determine defensible provincial averages for above ground biomass (AGB) for the secondary forest stratum.

Secondary forest can be defined as forest which has been disturbed and is at some stage of regeneration, and have been estimated to comprise 40% of all tropical forest (Brown and Lugo, 1990). Considering that 40% of terrestrial biomass is stored in tropical forests (Phillips et al. 1998), secondary tropical forest thus represents a very significant global C pool (c. 20%) that has considerable (but unverified) potential for C flux into the future (Brown et al. 1996, Fehse et al. 2002). Secondary tropical forest remains a poorly understood resource relative to primary forest (Sierra et al. 2007b). Many studies fail to adequately distinguish between primary and secondary forest (Houghton et al. 2001), and merge estimates of forest C over the two stratum. Consistent with this, secondary forest in PNG is a large and poorly understood resource considered to hold little value for either timber or carbon (PNGFA pers. comm.). Using statistics from 2002, PNG Forest Authority estimated that undisturbed forest covers an area of c. 29.7 million hectares whilst secondary forest covers c. 3.3 million hectares (PNGFA pers. comm.). However, recent studies indicate that the area of secondary forest may be rapidly expanding (see Shearman et al. 2009). Assessment of this large and expanding forest resource is a priority and could potentially facilitate it’s inclusion in climate change mitigation efforts.

Many previous studies of tropical forest C and C flux have been plagued by methodological problems that limit the veracity of the estimates (Clark et al. 2001b, Phillips et al. 2002). Several important problems have been identified; small plot sizes of less than 1 ha (0.25 ha is common) limit the representativeness of measured forest and is likely to result in overestimates generated by larger trees being over-represented (Brown and Lugo 1992); There is lack of replication in both time and space which will again limit the representativeness of measured forest and will result in results skewed toward patches of forest with the highest biomass (Clark et al. 2001a, 2001b, Phillips et al. 2002, 2004). These methodological problems are exaggerated for tropical forests, due to large spatial variations in structure, productivity, and the presence of large trees. There is clearly a need for studies with large plots that sample widely across both time and space (Clark et al. 2001a, 2001b, Phillips et al. 2004). The PSPs used in this study overcome many of these methodological issues, and provide a sound basis for the estimation of forest C and C flux; plots are large (1 ha) and are replicated through both space and time. We will estimate above ground live biomass (AGLB>10cm) for each PSP measurement and examine national and provincial averages.

Another methodological issue is introduced when tree variables measured for timber inventory purposes are used to estimate biomass and C (Lindner and Karjalainen 2007). Two methods are commonly used; the first converts tree volumes to biomass using a biomass expansion factor (Segura 2006); the second uses previously developed allometric equations to estimate biomass per tree as a function of tree parameters such as tree diameter, tree height and wood density (Brown et al. 1989, Clark et al. 2001, Chave et al. 2003, Baker et al. 2004). These allometric equations will have been derived from biomass harvesting studies (Brown et al. 1989, Chambers et al. 2001, Chave et al. 2001), and their application is dependent on the availability of equations for a similar forest, and also for similarly sized trees (Chave et al. 2005). Many allometric equations use only diameter to predict tree biomass, however, including wood density and height can improve the accuracy of tree level predictions (Chave et al. 2004, Chave et al. 2005), particularly considering the variation in tree architecture and wood density in tropical forests. Despite possible errors in estimating AGLB, Baker et al. (2004) found that estimated AGLB flux was unaffected by to the type of allometric equation used. Given the absence of allometrics for PNG we are forced to convert measured tree parameters to tree biomass using allometrics derived from other equatorial tropical forest. In doing this, it is important to include drivers of tree architecture and the physiological characteristics that determine C composition such as diameter, height, and wood density (Chave et al. 2005).

The only previous estimate of forest C in PNG known to the authors is Edwards and Grubb (1977); in a study of lower montane rainforest (at 2500m) they estimated total biomass of 175 Mg C ha-1 consisting of 151 Mg C ha-1 in tree AGLB. In a major compilation of biomass measurements in primary tropical forests, Clark et al. (2001a) revealed that above ground biomass (AGB) varied from as low as 40 to as high as 250 Mg C ha-1. Despite many assessments of primary tropical forest biomass, comparatively few studies differentiate between primary and secondary forest (Houghton et al. 2001). However, this is changing with the realisation of the increasing importance of secondary forests in the composition of tropical landscapes. For example, Sierra et al. (2007a) explicitly compare C stock in primary and secondary Colombian rainforest (AGB 111 Mg C ha-1 and 21 Mg C ha-1 respectively). They found that the AGB was most sensitive to anthropogenic disturbance, with significant differences in estimates for primary and secondary forest.

The objectives of this study are to quantify above ground C pools in secondary and primary forest in PNG. This required a sound methodology with considered error correction techniques and the development of appropriate tree allometrics. Results will elucidate the role of degraded secondary forests as a C pool. Assessment of the C contained in these forests may facilitate their potential inclusion in REDD negotiations. We also intend to provide provincial averages of secondary forest C for specific application within PNG.

Materials and Methods

We estimate AGLB>10cm for each PSP measurement. Consistent with previous studies, AGLB>10cm will be reported in megagrams of carbon per hectare (Mg C ha-1). The C content of biomass will be reported assuming that dry biomass is 50% C (Clark et al. 2001a, Houghton et al. 2001, Malhi et al. 2004). This is an acceptable approximation; however, the wood C fraction does exhibit some small variation across species and tree ages (Elias and Potvin 2003).

PNGFRI’s PSP database

Over the last 20 years PNGFRI has established and remeasured over 135 PSPs across PNG covering all major forest types. A map of PNG showing provincial boundaries and PSP locations is shown in Figure 1. Each PSP plot is one hectare in size and is divided into 25 sub-plots of 20 m x 20 m. The spatial location, diameter, height, and crown characteristics are recorded for all trees over 10cm. The PSP database represents a strong basis for the estimation of biomass and C in these forests. For further details of the PSP data refer to Yosi et al. (2009).

PSPs were measured following a field procedure (PNGFRI 1994). Despite this well developed and uniformly applied field procedures, problems arise in large databases due to measurement and transcription errors (Baker et al. 2004). To identify potential errors the distribution of diameter increments was examined, and those less than -0.2, or greater than 2.6 cm yr-1 were flagged for investigation. This represented approximately 1% on each tail of the increment distribution, and 2% of all increments in total. These values are similar to those used by Chave et al. (2003) and Baker et al. (2004) to flag erroneous measurements. Examination of diameters for flagged trees often revealed transcription errors, such as an extra zero, or a missing zero. These were corrected on a tree by tree basis, with careful adjustment of the erroneous diameter measurement. When it was clear a measurement error had occurred, the erroneous diameter was corrected using a species specific diameter growth model. This methodology was followed to avoid the significant biases introduced if erroneous records are removed (Chave et al. 2003), and to improve on previous error corrections using interpolation (e.g. Chave et al. 2003), stand-level (e.g. Baker et al. 2004) or species-level averages (e.g. Rice et al. 2004). Measurement errors tend to most prevalent in larger buttressed trees, and removal of these records can have a significant effect on biomass estimates.

Species-specific increment model for error correction

First we needed to identify a diameter-diameter increment base model that is most appropriate for observed tree growth. The relationship between diameter and diameter increment is non-linear and sigmoidal; the curve starts at the origin and rises to a maximum diameter increment at an inflection point before falling to an asymptotic diameter increment (Zeide 1993, Huang and Titus 1995). Several base models that characterise this relationship were examined; the Box-Lucas function (1) (Box and Lucas 1959), and a simplified model (2) from Huang and Titus (1995);

[pic] (1)

[pic] (2)

Where Dincr is diameter increment in cm yr-1, D is measured diameter at breast height over bark, and a and b are parameters to be estimated.

Species-specific models were initially fitted using NLIN in SAS, and the Box-Lucas base function was found to provide superior fit for species represented on PSPs. However, model fitting for individual trees within PSPs is affected by a nested dependence structure; Diameter-diameter increment relationships for the same species within a PSP will be more similar than that between each PSP, as trees on the same plot will be subject to the same local environmental conditions, and will be of a similar forest type (Fox et al. 2001). We can explicitly account for this nested dependence using a non-linear mixed model, with a separate random parameter for each PSP. To facilitate this, SAS’s Proc NLMixed was used to fit the non-linear Box-Lucas mixed model (Wolfinger and O’Connell 1993, Davidian and Giltinan 2003). This will ensure correct statistical inference within and between PSP plots, as well as plot localised increment predictions that can replace erroneous measures. Fitted Box-Lucas models for four species are shown in Figure 1. Horsfieldia spp. and Celtis latiflia are both pioneer species, and their curves have a maximum diameter increment at a small diameter of approximately 25cm, and then approach zero increment for the larger diameters. Pometia pinnata and Celtis phillippensis are climax species with higher diameter increments across the full range or diameters. Model (1) parameters for the 50 most common species on PSPs can be found in Table S2 of supplementary material.

Species-specific predictions of diameter increment were applied when measurement errors had occurred; less than -0.2, or greater than 2.6 cm yr-1 and with no obvious transcription errors. Some pioneer species (Macaranga spp., Spondias spp., Hernandia spp., palaquium spp., melanolepis spp., antiarus spp., litsea spp., trichospermum spp., artocarpus spp., sterculia spp., Trema spp., Elaeocarpus spp., Labula spp., Endospermum spp, Octomeles spp) were found to have valid growth rates that exceeded 2.6 cm yr-1. Trema spp. and Macaranga spp. appear capable of extraordinary growth rates of up to 6 cm yr-1. These exceptional growth rates were not altered. From the total of 153900 tree records, 326 (0.2%) were obvious transcription errors that were manually corrected, and 3418 (2%) were erroneous measurements that were corrected using modeled diameter increment.

A large number of PSP plots were affected by the El Niño-Southern Oscillation (ENSO) event of 1997/1998 and these plots were set aside, as the high rates of tree mortality resulted in declines in forest C and a negative flux that skewed analysis of unaffected remeasurements.

Estimating above ground living biomass

The first step in quantifying forest C is to estimate AGLB in standing trees. There has been much recent work on the development of allometric equations for estimating biomass for tropical forests from tree inventory information. Typically, they are models derived from destructively sampled trees and easily measured biometric variables such as diameter and height (Liddell et al. 2007). In an extensive study of allometric models for tropical forests, Chave et al. (2005) found the most important predictors of AGB were diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). They developed a model (3) for wet tropical forests that was used to estimate AGB for trees on PSPs;

[pic] (3)

Where Di is diameter in centimeters, Hi is total height in meters, and qi is wood specific gravity in grams per cubic meter for tree i. The resulting AGLBi estimated from the equation is the total biomass of the stem, crown and leaves for tree i in kilograms. Chave et al. (2005) found that locally, the error on the estimation of a tree’s biomass was in the order of ±5%. Average AGBL (and other tree statistics) for the 50 most common species on PSPs can be found in Table S1 of supplementary material.

Total AGLB>10cm was quantified by summing tree level AGLBi estimates for all j trees on the one hectare plots (4).

[pic] (4)

Plot level AGLB>10cm estimates are in Mg C ha-1.

Estimating total above ground biomass (AGB)

It is appropriate and reasonable to estimate total AGB from the measured component (AGLB>10cm) based on previously established relationships and literature reviews (Gibbs et al. 2007). AGB can be estimated as the sum of AGLB>10cm and AGLB in trees less than 10cm (AGLB10cm (Brown and Lugo 1982). CWD is potentially a very large C pool, particularly in disturbed forest, and may constitute 10-40% of above ground biomass (Uhl and Kauffman 1990). Based on field observation we estimate CWD to be 15% of the AGLB>10cm. Therefore, we estimate total NLB as 20% of AGLB>10cm. This is at the upper end of previous estimates [10-20% (Houghton et al. 2001, Achard et al. 2002)], but this appropriate given the large quantity of CWD in secondary forest.

Following the estimated contribution of AGLB10cm and AGB for each PSP remeasurement, the following methodology was followed to generate overall and provincial averages. Initially, remeasurements that were affected by ENSO induced fires in 1997/1998 were separated from the main analysis. PSPs in selectively-logged and undisturbed forest were also analysed seperately. Averages for AGLB>10cm and AGB were estimated across all remeasurements of PSPs. This assumes that PSP remeasurements are a representative sample of the growth stages of selectively logged forest. The sample size of selectively-logged and undisturbed PSPs, and the number that were affected by ENSO fires is shown in Table 1.

Results

Above ground live biomass

Average AGLB>10cm for 341 non fire-affected measurements in selectively-logged forest between 1992 and 2008 is 66 Mg C ha-1 (SD 19). Average AGLB>10cm for 20 non fire-affected measurements in undisturbed forest is 110 Mg C ha-1 (SD 28). From equation (4) we can estimate average AGB for selectively-logged forest as 92 Mg C ha-1, and undisturbed forest as 154 Mg C ha-1.

Figure 3 and Table 2 show the average AGLB>10cm in selectively-logged measurements for each province of Papua New Guinea. Averages vary from Oro and Central Provinces with c. 50 Mg C ha-1 to West Sepik, Western and East New Britain Provinces with 75-80 Mg C ha-1. These differences reflect differences in forest characteristics and productivity across the provinces. Central and Oro Provinces have drier, less productive forests, while Western Sepik, Western, and East New Britain Province have highly productive wet forests. Table 3 indicates that the number of remeasurements in each province varies from a minimum of 9 in Manus to 67 in Morobe Province. More confidence should be placed in estimates for provinces with many measurements such as Morobe, East New Britain, West New Britain, and Madang. Table 2 also details comparisons of provincial AGLB>10cm to undisturbed AGLB>10cm. The average change (ΔAGLB>10cm) is also included as a percentage.

Discussion

Forest timber inventory data has been widely used to estimate forest C and has the advantage of being an extensive and representative sample of the forest, often at a national level (Brown and Gaston 1995, Phillips et al. 1998, Baker et al. 2004, Lindner and Karjalainen 2007). This is the case with PNGFRI’s PSP network. However, timber inventory data does have important shortcomings for estimating C; the sample may be biased toward productive forest that contains current or future merchantable timber, measured tree parameters may be insufficient for estimating tree biomass or C accurately, non-merchantable trees such as palms are not sampled, trees smaller than 10cm DBH are not sampled, and other important C sinks (understorey, underground, and necromass) are not sampled (Chave 2003). Data used in this study has these shortcomings, but we contend that the application of appropriate allometrics has resulted in sound estimates of AGLB. Despite this, the development of localised allometrics for the forests of PNG may be warranted. The models of Chave et al. (2005) incorporated the biomass harvesting data of Edwards and Grubb (1977) from the montane forests (2400m) of PNG. Given the current interest in forest C, perhaps it is timely to supplement this with a biomass harvesting study in lowland forests. The destructive sampling of 2-3 large trees may be all that is required to check and validate estimates from existing allometric equations (Gibbs et al. 2007). Beyond these shortcomings, timber inventory data has been extensively used for studying C and C flux (Baker et al. 2004), and is a practical and accurate method for estimating the C balance of tropical forests (Chave 2003). The seminal work of Phillips et al. (1994, 1998) and Lewis et al. (2009) that detected increased turnover rates, and increasing biomass in primary Amazonian forest (although contentions exist; Clark 2002, Wright 2005) was also based on forest timber inventory data.

PSP plots are often located in proximity to roads or villages which has implications for anthropogenic disturbances from gardening and cultural burning through the census period. This may have implications for our undisturbed PSPs, as it is possible that they may have been subject to some degree of previous disturbance. This may explain why undisturbed AGB (154 Mg C ha-1) is less than biome averages for tropical equatorial forest compiled by Gibbs and Brown (2007b) (164 Mg C ha-1) the IPCC (2006) (180 Mg C ha-1), and most recently to the compilation of pan-tropical plots in Lewis et al. (2009) (202 Mg C ha-1). However, they fall in the middle of the range identified by Clark et al. (2001a); 40 – 250 Mg C ha-1. The veracity of our AGB estimate for undisturbed is limited by our small sample size. This sample in undisturbed forest needs to be increased, and future work will look at the feasibility of this. Recently we established 2 PSPs in undisturbed high altitude (3000m) Nothofagus forest in Simbu Province. An increased sample of undisturbed forest could facilitate valid within province averages for this stratum, at present we are forced to average across all available plots.

Average secondary forest AGB was 92 Mg C ha-1; this is higher than secondary forest resulting from other land uses such as shifting agriculture (Sierra et al. 2007a; AGB 21 Mg C ha-1) and more intensive selective-logging practices in other regions (Pinard and Putz 1996; AGB 68 Mg C ha-1). This may be because selective-logging as practiced in PNG (targeting high-value species above a 50cm diameter limit) has a lesser impact on forest C. Despite intentions of randomly selecting forest for census from within secondary forest, the PSP network is susceptible to plot selection bias; plots may have been positioned in areas that contained future merchantable timber, with heavily degraded areas or areas with no potential for future timber stock being avoided. There is no way to evaluate this plot selection bias, and we need to remain mindful of this potential bias that may have inflated our estimates of secondary forest AGB.

Secondary forests in PNG have previously been assumed to hold little value for either timber or carbon (PNGFA pers. comm.), but the higher estimates reported here suggest that they should be valued, and perhaps actively managed for the C they contain. Secondary forest in PNG is dominated by species that are not highly valued for timber, however, these same species have equal value to the highly prized Kwila (Intsia bijuga) when valued for the C they contain. It has been suggested that accessible primary forest may soon be exhausted of its high value timber species (Shearman et al. 2008). If this is the case, commercial logging operators will turn their attention to secondary forest, and the secondary timber species it contains. If re-entry of secondary forests by commercial logging operators occurs, then these forests could potentially be included in REDD negotiations. The analysis of secondary forest C we have reported here suggests that these forests should be valued for the significant C resource they contain, and perhaps explicitly managed to that end.

Clark (2002) and Phillips et al. (2002) compiled an extensive dossier of the measurement errors that may affect tropical forest census. From this list, and our personal experience, we believe the most significant source of error for the PSPs are the measurement errors associated with buttressing. Buttressing is very common in rainforest trees, and becomes more prevalent as the trees get larger, with stem deformity often reaching several metres up the bole from the base. These deformations make accurate measurement of actual bole dimensions at breast height (1.3m) almost impossible, and bias can be introduced (Clark 2002, Phillips et al. 2002, Baker et al. 2004). For PNGFRI PSPs diameter measurements are made further up the bole at the point of cessation of the buttress, with paint used to mark the new point of measure (point of measurement height is also recorded). When measuring above the buttress is impossible, it is acceptable policy to estimate diameter by holding a measuring tape against the bole at breast height (these measurements are marked as estimates on measurement sheets) (PNGFRI 1994). In the process of estimation it is likely that error will have been introduced, particularly when we consider that it affects the largest trees that will be having the largest influence on biomass estimates (Chave et al. 2003). To avoid such estimates researchers recommend using ladders to access bole above the buttress (Phillips et al. 2002, Baker et al. 2004), but this has never been done in PNG. The prevalence of this measurement error was tested by examining the variance of diameter increments for large trees that were measured above breast height, and large trees that were estimated; we expected a larger variance on increments for the estimated measurements, but instead the variance on estimated diameters increments was smaller. Examination of plot sheets revealed that estimated diameters often stayed the same between censuses, thus eliminating diameter estimation procedures as source of error. Phillips et al. (2002) conclude that even poorly measured forest plots can provide a reasonable estimate of carbon dynamics, as long as the types of measurement error are consistent through time, and do not change systematically. A distinct advantage of the PNG PSPs is that they were measured using consistent methodology (PNGFRI 1994) by a single team of experienced field assistants over the last 15 years.

We have presented a methodology for estimating forest C inclusive of an error correction methodology and required allometrics and have reported defensible estimates of above ground C in recovering selectively-logged and primary forest.

Acknowledgements

Several people from Papua New Guinea Forest Research Institute have been instrumental in establishing and maintaining the PSP network. Forova Oavika, Cossey Yosi, Joe Pokana and Kunsey Lavong have managed PSP establishment and remeasurement over the last 15 years. Janet Sabub has provided secretarial and data entry services. Field assistants were Stanley Maine, Timothy Urahau, Matrus Peter, Amos Basenke, Gabriel Mambo, Silver Masbong, Dingko Sinawi and Steven Mathew.

The PSP program was established in 1992 under the International Tropical Timber Organization (ITTO) research project ‘Intensification of Growth and Yield Studies of previously Logged-over Forests in Papua New Guinea’. From 2001 to 2005, Australian Centre for International Agricultural Research (ACIAR) project FST/1998/118 (Planning methods for sustainable management of timber stocks in Papua New Guinea) provided funds to support the re-measurement of 32 PSPs. ACIAR project FST/2004/061 (Assessment, management and marketing of goods and services from cutover native forests in Papua New Guinea) is providing funding for ongoing maintenance and remeasurement of these plots as well as the management of the PSP database. As of March 2009, ACIAR project FST/2004/061 had funded the remeasurement of 40 PSP plots.

This study was conducted while the primary author (JC Fox) was an ACIAR Research Fellow for project FST/2004/061. C. Yosi was supported by an ACIAR John Allwright Fellowship whilst undertaking PhD studies at The University of Melbourne.

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Tables

Table 1. Sample size of PNGFRIs PSP database

| |Plots |Measurements |Non fire-affected measurements |Fire-affected measurements |

|Selectively-logged |122 |410 |362 |48 |

|Undisturbed |13 |23 |19 |4 |

Table 2. Results for average AGLB>10cm by Province

|Province |Average AGLB>10cm |SD |Sample |

|Central Province |51.40 |12.35 |11 |

|East New Britain |74.59 |24.23 |34 |

|East Sepik |62.74 |13.21 |15 |

|Gulf Province |63.23 |17.33 |19 |

|Madang Province |64.94 |17.67 |40 |

|Manus Province |52.97 |21.83 |9 |

|Milne Bay Province |67.30 |12.50 |13 |

|Morobe Province |67.91 |16.61 |67 |

|New Ireland |63.83 |19.85 |31 |

|Oro Province |47.50 |9.70 |20 |

|Southern Highlands |61.30 |14.85 |12 |

|West New Britain |70.85 |17.98 |44 |

|West Sepik |75.08 |15.20 |14 |

|Western Province |81.98 |9.79 |12 |

|Overall |66.16 |18.65 |341 |

Figure legends

Figure 1. Fitted hyperbolic height-diameter model for example species

Figure 2. Fitted Box-Lucas diameter-diameter increment model for example species

Figure 3. Results for average AGLB>10cm by province

Figures

[pic]Figure 1

[pic]Figure 2

[pic]Figure 3

Supplementary material

Table S1. Average statistics for the 50 most common species on PSPs.

|Species |Family |Sample |Average Diam (cm) |Average Ht (m)|Average Diam Incr (cm |Basic wood density (g |Average Carbon (kg) |

| | | | | |yr-1) |cm-1) | |

|Myristica spp. |Myristicaceae |6593 |17 |17 |0.32 |0.385 |65 |

|Pometia pinnata |Sapindaceae |6278 |33 |25 |0.68 |0.58 |488 |

|Syzygium spp. |Myrtaceae |6150 |22 |19 |0.37 |0.61 |214 |

|Canarium spp. |Burseraceae |5165 |22 |20 |0.42 |0.495 |170 |

|Cryptocarya spp. |Lauraceae |4701 |21 |19 |0.43 |0.465 |145 |

|Macaranga inermis |Euphorbiaceae |4063 |16 |16 |0.96 |0.3 |40 |

|Dysoxylum spp. |Meliaceae |3804 |23 |19 |0.38 |0.62 |226 |

|Pimeleodendron amboinicum |Euphorbiaceae |3802 |24 |20 |0.41 |0.48 |182 |

|Ficus spp. |Moraceae |3796 |25 |19 |0.54 |0.345 |206 |

|Planchonella spp. |Sapotaceae |3560 |23 |22 |0.45 |0.46 |183 |

|Horsfieldia spp. |Myristicaceae |3482 |20 |19 |0.35 |0.36 |92 |

|Litsea spp. |Lauraceae |2368 |22 |20 |0.50 |0.4 |148 |

|Calophyllum spp. |Clusiaceae |2344 |25 |22 |0.52 |0.495 |272 |

|Celtis spp. |Cannabaceae |2296 |28 |25 |0.51 |0.5 |355 |

|Garcinia spp. |Clusiaceae |2223 |20 |18 |0.38 |0.645 |160 |

|Microcos spp. |Tiliaceae |2130 |18 |17 |0.45 |0.477 |82 |

|Aglaia spp. |Meliaceae |2060 |19 |18 |0.40 |0.735 |171 |

|Chisocheton spp. |Meliaceae |1852 |21 |18 |0.41 |0.45 |137 |

|Terminalia spp. |Combretaceae |1729 |26 |22 |0.67 |0.515 |308 |

|Homalium foetidum |Salicaceae |1598 |27 |26 |0.52 |0.68 |538 |

|Diospyros hebecarpa |Ebenaceae |1573 |18 |16 |0.25 |0.58 |117 |

|Maniltoa spp. |Fabaceae |1537 |20 |18 |0.28 |0.62 |179 |

|Gnetum gnemon |Gnetaceae |1393 |15 |15 |0.37 |0.477 |47 |

|Sterculia spp. |Malvaceae |1293 |23 |20 |0.54 |0.28 |112 |

|Elaeocarpus spp. |Elaeocarpaceae |1254 |22 |19 |0.68 |0.375 |119 |

|Euodia |Rutaceae |1108 |26 |20 |0.62 |0.36 |225 |

|Barringtonia spp. |Lecythidaceae |1000 |19 |16 |0.33 |0.477 |101 |

|Pterocarpus indicus |Fabaceae |989 |36 |23 |0.63 |0.5 |597 |

|Timonius spp. |Rubiaceae |968 |16 |15 |0.31 |0.477 |57 |

|Medusanthera spp. |Icacinaceae |920 |17 |15 |0.36 |0.477 |73 |

|Gymnacranthera paniculata |Myristicaceae |902 |16 |16 |0.32 |0.477 |62 |

|Prunus spp. |Rosaceae |871 |18 |19 |0.47 |0.477 |95 |

|Endospermum spp. |Euphorbiaceae |833 |23 |22 |0.67 |0.385 |162 |

|Mastixiodendron spp. |Rubiaceae |823 |23 |21 |0.44 |0.615 |277 |

|Anisoptera thurifera |Dipterocarpaceae |795 |28 |25 |0.67 |0.52 |345 |

|Dillenia spp. |Dilleniaceae |762 |28 |21 |0.61 |0.48 |271 |

|Polyalthia oblongifolia |Annonaceae |755 |18 |18 |0.37 |0.48 |100 |

|Teijsmanniodendron bogoriense |Lamiaceae |727 |27 |21 |0.38 |0.477 |261 |

|Vitex spp. |Lamiaceae |691 |46 |28 |0.77 |0.61 |1110 |

|Platea latifolia |Icacinaceae |687 |25 |20 |0.50 |0.477 |186 |

|Blumeodendron spp. |Euphorbiaceae |685 |21 |19 |0.68 |0.477 |142 |

|Semecarpus spp. |Anacardiaceae |675 |17 |16 |0.30 |0.477 |73 |

|Artocarpus spp. |Moraceae |636 |28 |21 |0.69 |0.35 |223 |

|Parastemon versteeghii |Chrysobalanaceae |628 |26 |24 |0.47 |0.477 |250 |

|Palaquium spp. |Sapotaceae |624 |25 |24 |0.49 |0.525 |275 |

|Alstonia spp. |Apocynaceae |622 |28 |22 |0.59 |0.31 |285 |

|Anthocephalus chinensis |Rubiaceae |598 |22 |20 |1.22 |0.365 |154 |

|Garcinia latissima |Clusiaceae |591 |21 |20 |0.29 |0.645 |173 |

|Dendrocnide spp. |Urticaceae |589 |18 |13 |0.30 |0.477 |92 |

|Trichospermum spp. |Tiliaceae |566 |17 |16 |1.38 |0.477 |69 |

|Sloanea spp. |Elaeocarpaceae |520 |28 |22 |0.54 |0.485 |302 |

|Gonystylus macrophyllus |Thymelaeaceae |516 |21 |19 |0.34 |0.477 |166 |

|Nothofagus spp. |Nothofagaceae |510 |33 |23 |0.69 |0.64 |584 |

|Madhuca leucodermis |Sapotaceae |506 |22 |22 |0.34 |0.477 |157 |

|Myristica subalulata |Myristicaceae |502 |18 |17 |0.42 |0.385 |76 |

|Cerbera floribunda |Apocynaceae |499 |25 |20 |0.59 |0.395 |146 |

|Intsia bijuga |Fabaceae |494 |29 |21 |0.57 |0.645 |480 |

|Hopea iriana |Dipterocarpaceae |474 |24 |23 |0.49 |0.785 |353 |

Table S2. height-diameter (HD) and diameter-diameter increment (DDI) model parameters for the 50 most common species on PSPs.

|Species |Family |Sample |DDI-a |DDI-b |HD-a |HD-b |

|Myristica spp. |Myristicaceae |6593 |0.035 |0.036 |50.8 |32.9 |

|Pometia pinnata |Sapindaceae |6278 |0.060 |0.010 |53.0 |32.3 |

|Syzygium spp. |Myrtaceae |6150 |0.032 |0.022 |56.1 |37.7 |

|Canarium spp. |Burseraceae |5165 |0.040 |0.023 |55.3 |33.6 |

|Cryptocarya spp. |Lauraceae |4701 |0.042 |0.022 |51.3 |31.6 |

|Macaranga inermis |Euphorbiaceae |4063 |0.164 |0.012 |51.8 |35.5 |

|Dysoxylum spp. |Meliaceae |3804 |0.027 |0.014 |55.1 |38.8 |

|Pimeleodendron amboinicum |Euphorbiaceae |3802 |0.072 |0.046 |53.3 |35.6 |

|Ficus spp. |Moraceae |3796 |0.041 |0.010 |61.8 |49.9 |

|Planchonella spp. |Sapotaceae |3560 |0.040 |0.016 |56.3 |33.2 |

|Horsfieldia spp. |Myristicaceae |3482 |0.047 |0.045 |65.8 |46.9 |

|Litsea spp. |Lauraceae |2368 |0.045 |0.016 |58.9 |39.7 |

|Calophyllum spp. |Clusiaceae |2344 |0.053 |0.015 |62.8 |40.7 |

|Celtis spp. |Cannabaceae |2296 |0.048 |0.016 |71.5 |49.0 |

|Garcinia spp. |Clusiaceae |2223 |0.039 |0.033 |57.6 |39.5 |

|Microcos spp. |Tiliaceae |2130 |0.071 |0.050 |45.7 |28.6 |

|Aglaia spp. |Meliaceae |2060 |0.030 |0.009 |58.1 |38.8 |

|Chisocheton spp. |Meliaceae |1852 |0.038 |0.024 |57.8 |43.5 |

|Terminalia spp. |Combretaceae |1729 |0.064 |0.010 |60.8 |39.6 |

|Homalium foetidum |Salicaceae |1598 |0.040 |0.016 |114.8 |85.1 |

|Diospyros hebecarpa |Ebenaceae |1573 |0.015 |0.000 |59.9 |46.9 |

|Maniltoa spp. |Fabaceae |1537 |0.024 |0.020 |65.8 |48.3 |

|Gnetum gnemon |Gnetaceae |1393 |0.035 |0.022 |31.8 |15.9 |

|Sterculia spp. |Malvaceae |1293 |0.058 |0.024 |65.1 |50.3 |

|Elaeocarpus spp. |Elaeocarpaceae |1254 |0.070 |0.014 |46.5 |29.2 |

|Euodia |Rutaceae |1108 |0.044 |0.000 |51.0 |33.8 |

|Barringtonia spp. |Lecythidaceae |1000 |0.036 |0.035 |67.9 |58.4 |

|Pterocarpus indicus |Fabaceae |989 |0.048 |0.008 |52.3 |37.6 |

|Timonius spp. |Rubiaceae |968 |0.040 |0.076 |30.6 |15.4 |

|Medusanthera spp. |Icacinaceae |920 |0.049 |0.042 |66.8 |57.8 |

|Gymnacranthera paniculata |Myristicaceae |902 |0.037 |0.035 |42.6 |24.0 |

|Prunus spp. |Rosaceae |871 |0.045 |0.009 |52.1 |30.2 |

|Endospermum spp. |Euphorbiaceae |833 |0.483 |0.030 |67.5 |45.8 |

|Mastixiodendron spp. |Rubiaceae |823 |0.029 |0.010 |66.4 |46.4 |

|Anisoptera thurifera |Dipterocarpaceae |795 |0.055 |0.005 |68.7 |44.6 |

|Dillenia spp. |Dilleniaceae |762 |0.076 |0.015 |52.5 |38.4 |

|Polyalthia oblongifolia |Annonaceae |755 |0.032 |0.014 |67.5 |48.4 |

|Teijsmanniodendron bogoriense |Lamiaceae |727 |0.034 |0.029 |66.1 |54.0 |

|Vitex spp. |Lamiaceae |691 |0.075 |0.010 |59.3 |45.7 |

|Platea latifolia |Icacinaceae |687 |0.058 |0.022 |52.5 |36.3 |

|Blumeodendron spp. |Euphorbiaceae |685 |0.171 |0.032 |75.9 |57.7 |

|Semecarpus spp. |Anacardiaceae |675 |0.041 |0.053 |61.5 |44.7 |

|Artocarpus spp. |Moraceae |636 |0.070 |0.010 |68.9 |57.1 |

|Parastemon versteeghii |Chrysobalanaceae |628 |0.042 |0.018 |57.2 |31.4 |

|Palaquium spp. |Sapotaceae |624 |0.032 |0.008 |70.8 |46.0 |

|Alstonia spp. |Apocynaceae |622 |0.044 |0.006 |74.8 |60.6 |

|Anthocephalus chinensis |Rubiaceae |598 |0.168 |0.006 |68.0 |49.6 |

|Garcinia latissima |Clusiaceae |591 |0.031 |0.041 |57.3 |38.1 |

|Dendrocnide spp. |Urticaceae |589 |0.020 |0.010 |71.7 |83.3 |

|Trichospermum spp. |Tiliaceae |566 |1.876 |0.000 |49.8 |34.4 |

|Sloanea spp. |Elaeocarpaceae |520 |0.042 |0.011 |50.0 |31.1 |

|Gonystylus macrophyllus |Thymelaeaceae |516 |0.019 |0.005 |69.3 |51.4 |

|Nothofagus spp. |Nothofagaceae |510 |0.060 |0.006 |49.5 |30.2 |

|Madhuca leucodermis |Sapotaceae |506 |0.044 |0.047 |56.0 |32.5 |

|Myristica subalulata |Myristicaceae |502 |0.037 |0.016 |52.7 |35.4 |

|Cerbera floribunda |Apocynaceae |499 |0.132 |0.028 |43.0 |27.2 |

|Intsia bijuga |Fabaceae |494 |0.056 |0.013 |65.8 |53.8 |

|Hopea iriana |Dipterocarpaceae |474 |0.055 |0.024 |69.8 |46.4 |

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