J. For. Res. (2015) 26(1):47–55 DOI 10.1007/s11676-015-0029-9
ORIGINAL PAPER
Forest aboveground biomass estimates in a tropical rainforest in Madagascar: new insights from the use of wood specific gravity data Tahiana Ramananantoandro • Herimanitra P. Rafidimanantsoa Miora F. Ramanakoto
•
Received: 7 April 2014 / Accepted: 27 April 2014 / Published online: 23 January 2015 Ó Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2015
Abstract To generate carbon credits under the Reducing Emissions from Deforestation and forest Degradation program (REDD?), accurate estimates of forest carbon stocks are needed. Carbon accounting efforts have focused on carbon stocks in aboveground biomass (AGB). Although wood specific gravity (WSG) is known to be an important variable in AGB estimates, there is currently a lack of data on WSG for Malagasy tree species. This study aimed to determine whether estimates of carbon stocks calculated from literature-based WSG values differed from those based on WSG values measured on wood core samples. Carbon stocks in forest biomass were assessed using two WSG data sets: (i) values measured from 303 wood core samples extracted in the study area, (ii) values derived from international databases. Results suggested that there is difference between the field and literaturebased WSG at the 0.05 level. The latter data set was on average 16 % higher than the former. However, carbon stocks calculated from the two data sets did not differ significantly at the 0.05 level. Such findings could be attributed to the form of the allometric equation used which gives more weight to tree diameter and tree height than to The online version is available at http://www.springerlink.com Corresponding editor: Zhu Hong T. Ramananantoandro H. P. Rafidimanantsoa M. F. Ramanakoto De´partement des Eaux et Foreˆts, Ecole Supe´rieure des Sciences Agronomiques, Universite´ d’Antananarivo, BP 175, 101 Antananarivo, Madagascar T. Ramananantoandro (&) Groupe Ecole Supe´rieure du Bois, Atlanpoˆle Rue Christian Pauc, BP 10605, 44306 Nantes Cedex 3, France e-mail:
[email protected]
WSG. The choice of dataset should depend on the level of accuracy (Tier II or III) desired by REDD?. As higher levels of accuracy are rewarded by higher prices, speciesspecific WSG data would be highly desirable. Keywords Biomass estimates Carbon stocks Data quality Madagascar REDD? Wood specific gravity
Introduction Forests, including tropical forests, play an important role in reducing greenhouse gas concentrations in the atmosphere because of the amount of carbon they contain per unit area (Canadell et al. 2008; Luyssaert et al. 2008). Deforestation and forest degradation have an impact on the future potential of the forests to remove additional carbon dioxide (CO2) from the atmosphere (Chave et al. 2008; Saatchi et al. 2011). The relative contribution of deforestation and degradation of tropical forests to the total emissions of CO2 was 20 % in the 1990’s, but was revised in 2008 to 12 ± 6 % by van der Werf et al. (2009). To reduce CO2 emissions from forests, a new conservation approach, ‘‘Reducing Emissions from Deforestation and forest Degradation’’ (REDD?), was set up in 2007. REDD? is characterized by its direct link between financial incentives for conservation and the amount of carbon stored in the forest (Ebeling and Yasue 2008; Kindermann et al. 2008). To generate benefits from REDD?, governments must develop an effective system of measurement, reporting and verification (MRV) of carbon stocks (Herold and Skutsch 2011; Plugge et al. 2011). Quantification of carbon emissions—or avoided emissions—requires information on the rate of deforestation and the carbon stocks at a given time (Gibbs et al. 2007; Moutinho and Schwartzman 2005). In
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REDD?, estimating the carbon in aboveground biomass (AGB) is the most pragmatic approach (Plugge et al. 2011). AGB is commonly quantified using allometric equations. Most often, variables involved in such equations are tree diameter, height, and wood specific gravity (WSG). For Tier III, high resolution methods and accurate allometric equations are required. In principle, potential benefits from carbon credits should result in higher value since methodologies for monitoring carbon stocks and emission reductions are more accurate. The development of sitespecific allometric models adapted to Malagasy forests constituted a major step towards this goal (Vieilledent et al. 2012). They estimate above ground biomass better than generic models of Brown (1997), Brown et al. (1989) and Chave et al. (2005), which are often used as references. WSG is considered as an important variable for estimating AGB (Brown 1997; Chave et al. 2005, 2009; Henry et al. 2010; Nogueira et al. 2005; Vieilledent et al. 2012; Williamson and Wiemann 2010a). However, data on the specific gravity of Malagasy wood are almost non-existent. There are more than 4,000 tree species in Madagascar (MEF 2009) and yet the only local data available are those of Vieilledent et al. (2012) for 256 species or genera. Rakotovao et al. (2012) studied the properties of 187 Malagasy commercial wood species but the species considered were already included in Vieilledent et al. (2012). Some authors (Blanc et al. 2009; Bryan et al. 2010) use international databases (e.g. Chave et al. 2005, 2009; Reyes et al. 1992; Zanne et al. 2009) to obtain WSG values for their species of interest. The research question is whether specific local data are needed to estimate AGB given that 96 % of Malagasy trees and shrubs species are endemic (Schatz 2000). More specifically, this paper addresses the following two questions: (1) Do carbon stocks estimated using (i) WSG data collected in the field and (ii) data from international databases, differ significantly? (2) Given the difficulty of sampling wood in the field, is the WSG of the most abundant species sufficient to represent the WSG of congeneric species?
T. Ramananantoandro et al.
Madagascar (47°540 –47°560 E and 18°530 –18°550 S). It is located 67 km east of the capital, Antananarivo, and can be reached via National Highway # 2. This site is an educational and experimental site for the Department of Forestry of the Agronomy School, University of Antananarivo. No previous studies of WSG had been undertaken at this site. Annual precipitation averages 2,300 mm varying from 40 to 310 mm on a monthly basis. The temperature ranges from 13.7 to 20.2 °C, with an annual average of 17.5 °C. The relatively high elevation (800–1,300 m) confers a permanent relative humidity on the region with an average value of 82 %. The climate is tropical and humid. The terrain is rugged, characterized by overall slopes of 50 %, reaching 90 % in some places (Rajaonera 2008). The natural vegetation is a montane eastern wet forest type, characterized by evergreen foliage, high tree density, reduced height and pluristratified structure. More than half of trees in Mandraka forest have diameter at breast height (DBH) between 5 and 15 cm. Trees of DBH greater than 40 cm are almost non-existent (Rajaonera 2008). Field sampling Species abundance and tree diameter and height were drawn from the work of Rajaonera (2008) who established that the primary forest of Mandraka covered a total area of 9.91 ha fragmented into four forests (Fig. 1). In each fragment, a floristic inventory was conducted on a transect of 125 9 20 m, i.e. an area of 0.25 ha, divided into three compartments according to predefined tree diameter ranges (Fig. 2). The overall forest contained 73 species of 52 families and 42 genera (Rajaonera 2008).
Materials and methods Our general approach was to assign a value of WSG per species. Once a WSG value had been assigned to each species, these values were used to calculate the AGB and forest carbon stock using an allometric equation. Finally, carbon stocks were compared statistically. Study site This study was carried out in the natural forest of Mandraka, District of Manjakandriana, Region of Analamanga,
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Fig. 1 The four forest fragments in Mandraka forest, Madagascar: F1 3.40 ha, altitude 1,277–1,328 m; F2 4.47 ha, altitude 1,278–1,313 m; F3 1.61 ha, altitude 1,281–1,312 m; (4) F4 0.42 ha, altitude 1,339–1,366 m (Rajaonera 2008). Forest inventories were taken in each forest fragment
AGB estimates in a tropical rainforest in Madagascar
49
were drawn from existing databases without collecting samples. Each approach is detailed below.
Fig. 2 Forest inventory plot used by Rajaonera (2008). Compartment A: area 125 9 5 m for the inventory of trees with diameters between 5 and 15 cm; Compartment B: area 125 9 10 m for trees with diameters between 15 and 40 cm; compartment C: area 125 9 20 m for trees with diameters greater than 40 cm
Allometric equation used The choice of a model is a crucial step because the largest source of error in estimating biomass is associated with it (Chave et al. 2004). Site-specific models are preferred to international standard models (Vieilledent et al. 2012) because allometric relationships differ from one region to another depending on environmental factors (such as soil and climate) and functional traits of species (such as wood density and crown architecture). However, there are no allometric equations available for Mandraka natural forest. Thus, based on the climatic conditions of the study site and Vieilledent et al. (2012) findings, AGB was calculated following Chave et al. (2005), using the formula for moist/ wet forests and taking tree height into account. A ¼ exp 2:977 þ ln qD2 H ¼ 0:0509 qD2 H ð1Þ where, A is estimated aboveground biomass (kg), D is mean diameter at breast height (cm), H is mean tree height (m) and q is the WSG (dimensionless). The model including tree height was chosen since Chave et al. (2005), Feldpausch et al. (2012), Marshall et al. (2012) and Scaranello et al. (2012) pointed out that neglecting tree height in the estimation of biomass leads to significant errors. This model was developed from various tropical forests based on the compilation of data since the 1950s from 27 study sites in America, Asia and Oceania. The samples were collected from 2,410 trees of DBH ranging from 5 to 156 cm. Assigning a value of wood specific gravity to species Three approaches were tested for assigning values of WSG to species. The first approach consisted of measuring the WSG of each species. However, due to the amount of work involved in this first approach, a second approach was considered. In Approach 2, the species of the same genus were assigned the WSG value of the most abundant species. In these two approaches, the WSG values used were determined through laboratory measurements on wood samples taken from trees in the forest under study. Finally, in Approach 3, the WSG values assigned to each species
Determination wood specific gravity by sampling (Approach 1) Due to time and budget constraints, the study was limited to 44 tree species out of the 73 species existing in Mandraka forest. These 44 species belonged to 35 genera and 33 families (Table 2 Appendix 1). The species identified by Rajaonera (2008) as most abundant, were all part of the species studied (indicated by the symbol * in Table 2 Appendix 1). The 44 studied species accounted for 78 % of total tree abundance in the natural forest of Mandraka. Wood samples were taken from healthy trees, i.e. trees that were neither hollow nor suffering from decay inside the trunk. These trees were selected randomly. Species vernacular names were provided by a local guide, and confirmed by herbarium specimens for the most difficult species to identify. Wood properties vary radially from the pith to the bark (Baille`res et al. 2005). These variations may be strong or weak, depending on the shade tolerance of the species, tree age and diameter, soil fertility or other factors (Wiemann and Williamson 1989a, 1989b, 2010b; Woodcock and Shier 2003). To avoid harvesting trees, WSG of each standing tree was estimated by a non-destructive method. 15 mm-diameter wood cores were sampled at 1.30 m above ground, with an electric drill powered by a generator. Due to the possible existence of tension wood caused by the sloping ground, wood cores were taken in the downstream part of the slopes. Each core was stored in a sealed plastic bag to minimize moisture loss prior to arrival at the laboratory. In order to take account of the radial variation of WSG, each core was cut into 1 cm segments, starting 0.5 cm from the pith. Segments containing bark and pith were excluded. In sum, 303 core samples were collected. The number of cores obtained per species varied from 2 to 13 with an average of 6–7. In total 3,250 segments were cut from these cores. WSG of a core segment is defined as its oven-dry weight divided by its saturated volume, relative to the density of water (Williamson and Wiemann 2010b). The saturated state was obtained by immersing the segments in a container filled with water for 5–7 days. The anhydrous state was obtained by oven-drying the cores to constant weight at 103 °C. Weight was measured using a precision balance with 0.01 g resolution. The saturated volume was measured using the Archimedes water displacement method (Nogueira et al. 2005). Two cases were considered to define the WSG of a tree: the pith was situated at the centre of the trunk or the pith was eccentric due to the presence of tension wood. If the
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pith was located in the centre, and assuming a circular trunk shape, the area of the ring of each core segment was determined. The area-weighted mean of the core segments was used to determine the average WSG for an individual tree (Eq. 2), as demonstrated by Muller-Landau (2004) and (Williamson and Wiemann 2010b). X 2 2 Mn 2 WSG ¼ L ð2Þ L =Lmax n n1 n¼1 to max Vn where, WSG is the wood specific gravity of a tree, n represents any segment, Mn is the anhydrous weight of the segment n (g), Vn is the saturated volume of the segment n (cm3), Ln is the distance between the pith and the distal end of segment n (cm), Lmax is the radius of the tree (cm). If the pith was eccentric, Eq. 3 was used instead and WSG calculations are similar to the calculation of a circular segment area. X .X WSG ¼ A q A ð3Þ B B n n n n¼1 to max n¼1 to max where, WSG is the specific gravity of a tree, qn is the specific gravity of a segment Bn, ABn is the surface of the ring represented by the segment Bn. ABn ¼ An An1 where R2
An ¼ 2n ðhn sin hn Þ with n: Distance of the segment Bn relative to the center of the wood core, hn is an angle depending on Rn and calculated as follows hn ¼ 2 arccos Rdn where d is the distance between the center of the wood core and the pith. Each species’ WSG was defined as the average WSG of the sampled trees. Calculations were performed with R software, version i386 2.15.3 (R Development Core Team 2012). Determination of WSG using a genus approach (Approach 2) For Approach 2, the value of the WSG attributed to a species was the WSG of the most abundant species belonging to the same genus. This approach was proposed by Slik (2006) for estimating the WSG of Indonesian trees but the impact on AGB estimates was not investigated. The abundance of each species was calculated in each inventory plot and generalized to the scale of each forest fragment and the whole forest. Determination of WSG using published data (Approach 3)
from 205 sources worldwide. The latter gives information on WSG of 8,412 taxa, 1,683 genera, and 191 families from different regions of the world. To assign WSG values to each species, local databases were preferentially selected. If values were not available, the Global Wood Density Database was used by first applying a selection filter ‘‘Region’’ (Madagascar), and if necessary, the filter ‘‘Continent’’ (tropical Africa) and finally, if needed, any other tropical regions (South East Asia, Papua New Guinea, Australia, Oceania). For each tree, the WSG was taken as the mean species value. If the species was not cited, density was estimated as the genus mean, or family mean if the genus was not represented. Estimate of aboveground biomass and carbon stock Tree AGB was calculated using allometric equations, inventory data and WSG values. AGB within a given inventory plot was the sum of tree AGBs belonging to this plot. Fragment AGB was based on AGB plot and fragment area. Finally, the whole forest AGB was the sum of fragment AGBs. Carbon stocks were estimated at 50 % of dryweight biomass, as per the common practice in the literature (Ziter et al. 2013). Comparison of the three approaches WSG values obtained for each approach were compared. Given that there are four forest fragments in Mandraka, to verify the three approaches statistically, carbon stocks were calculated for each forest fragment. Carbon stock estimated using WSG data according to each approach was then compared. The Shapiro–Wilk test was used to verify whether the observations were normally distributed and Levene’s test checked homogeneity of variances. If the assumptions were met (normality of observations and homogeneity of variance), an analysis of variance (ANOVA) along with Fisher’s LSD test were used to compare the averages and identify identical groups. Otherwise, the non-parametric Kruskal–Wallis test was used for comparison of the averages and then the Mann–Whitney U test for paired comparison.
Results WSG data characteristics for each approach
With the third approach, WSG values were collected from the literature. Two databases were used (i) Vieilledent et al. (2012) bringing together the values of WSG of 256 species or genera from Madagascar, and (ii) the Global Wood Density Database (Zanne et al. 2009) compiling information
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WSG measured on the segments varied from 0.18 to 0.89 with an average of 0.49. Table 2 Appendix 1 summarizes the WSG data characteristics for each approach considered. The WSG of the 44 species studied varied from 0.26
AGB estimates in a tropical rainforest in Madagascar
(Trema orientalis) to 0.75 (Brexiela sp.) with an average of 0.506. Sixty percent of the values were in the range 0.4–0.6 for Approach 1, and 66 % for Approach 2. In Approach 3, WSG varied from 0.33 to 0.84, which seems higher than densities encountered in the first two approaches. Sixty percent of the values were within the range 0.5–0.7 and 77 % were from Zanne et al. (2009) and 23 % from Vieilledent et al. (2012). Of 44 species and the two databases combined, 17 were exact matches, 19 were estimated by genus (G) and 8 by family (F). ANOVA followed by Fisher’s LSD test showed that the average values of WSG in Approach 3 were significantly higher, on average by 16 %, than those calculated using the first two approaches (p = 0.001, df = 129, a = 0.05). The most notable differences occurred when WSG was estimated by the family approach. Comparison of carbon stocks calculated based on each approach The average carbon stocks calculated on the basis of WSG values by species in Table 2 Appendix 1 were (36.8 ± 6.5), (36.8 ± 6.5) and (41.7 ± 7.4) Mgha-1 for Approaches 1, 2 and 3, respectively. ANOVA associated with Fisher LSD test concluded that carbon stocks calculated using the three approaches were not significantly different (p = 0.959, a = 0.05, df = 2). Variances were not significantly different between the groups (p = 0.958, a = 0.05, df = 2 for Levene and p = 0.971, a = 0.05, df = 9 for Bartlett). Samples were normally distributed (a = 0.05, p = 0.995 and 0.976 for Shapiro–Wilk and Lilliefors respectively). Such findings could be attributed to the form of the allometric equation used which gives more weight to tree diameter (square) and tree height than to WSG. The amount of biomass and carbon stock per approach based on the four forest fragments is detailed in Table 1. AGB calculated using the WSG values of Approach 1 and Approach 2 were almost the same. Despite the fact that the analysis of variance did not show a significant difference at the 0.05 level, AGB for Approach 3 was systematically higher than that calculated for Approaches 1 and 2. The Table 1 Aboveground biomass density (AGBD) and carbon stock per approach
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amount of carbon was 5 Mgha-1 greater with Approach 3 than with Approaches 1 and 2.
Discussion Since estimating the carbon stock is a basic requirement of REDD?, this study responds to a relevant question on the estimation of AGB and carbon stocks using allometric equations including WSG as a covariate. Although this study found no statistical difference between carbon stocks calculated using the three approaches, the current trend is towards a requirement for better accuracy. Calculations have shown that there is a difference of 5 Mgha-1 between the carbon stocks obtained using data measured in the field and data available in the international literature. In 2011, the carbon price for a REDD? project was $12 per ton of CO2 equivalent (Peters-Stanley and Hamilton 2012) or $44 per ton of carbon. Therefore, the difference between Approach 1 and Approach 3 is $220 per hectare. This difference is of considerable importance for extensive forests, especially given the low purchasing power of the Malagasy population. Site-specific allometric equations are considered to be the most accurate and it is also necessary to include accurate data in order to be able to sell carbon credits at a better price. Tree diameter is easily measured and height measurement has recently undergone marked development thanks to ultrasonic instruments (Feldpausch et al. 2012; Marshall et al. 2012; Vieilledent et al. 2012). For WSG, two aspects must be considered: the method of calculation and the phylogenetic level of interest. This study has shown that for Mandraka forest, the WSG of the most abundant species is sufficiently representative to be taken as the WSG of congeneric species. This result is in accordance with results from Slik (2006) who found that wood density of tree species in Palaeotropical forests of South–East Asia can be estimated based on the average wood density of the genera to which they belong. Information on species abundance is accessible via inventory data. Ultimately, the choice between using local values or data from databases
AGBD per forest fragment
Approach 1
Approach 2
Approach 3
Unit
AGBD fragment F1
62,623
62,416
70,893
kg ha-1
AGBD fragment F2 AGBD fragment F3
76,252 89,972
76,350 90,147
86,718 100,687
kg ha-1 kg ha-1
AGBD fragment F4
72,024
72,035
83,479
kg ha-1
Average AGBD
73.62
73.63
83.42
Mg ha-1
Standard deviation
13.12
13.13
14.91
Mg ha-1
Carbon stock
36.81
36.81
41.71
Mg ha-1
Standard deviation
6.56
6.56
7.45
Mg ha-1
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T. Ramananantoandro et al.
depends on the level of accuracy desired (Marshall et al. 2012) and the price difference between such levels. This study focused on 44 species that represent 78 % of the forest as a whole. If complete data for all 73 existing species is considered on the basis of WSG data reported in international databases, the carbon stock of the whole Mandraka forest would be (45.48 ± 3.47) Mgha-1. For comparison, carbon stocks in other rainforests of Madagascar: Fandriana Marolambo, Betaolana and Fort Dauphin, are 97.78, (155 ± 44) and (193 ± 56) Mgha-1 respectively (Vieilledent et al. 2012). The difference may be due to the very small proportion of trees with diameter greater than 20 cm in the Mandraka forest. This study provided WSG values for 14 species not yet listed in either of the databases consulted. These data will therefore be added to existing databases. The calculation of the WSG from core samples performed in this study followed the method of Williamson and Wiemann (2010b). These authors pointed out the various errors that could occur in the determination of tree WSG, such as failure to consider the radial variation of WSG. Taking account of this radial variability required us (i) to cut wood cores into 1 cm segments and (ii) to calculate the WSG of the tree as the weighted average of the WSGs of the individual segments. In the calculation, weights are the surface of the ring represented by the segment. To date, few studies using WSG have considered this method of calculation. In addition, we propose a formula to calculate WSG for trees with eccentric pith, a novel feature of this work. WSG values listed in the databases were not determined by this same segmenting method, but on rectangular samples and using a simple arithmetic mean. The values of WSG obtained in this study are not significantly different from those obtained by Vieilledent et al. (2012) on 8 Malagasy species common to both studies: Anthocleista madagascariensis, Syzygium emirnense, Chrysophyllum boivinianum, Oncotsemum sp., Tambourissa madagascariensis, Homalium sp., Macaranga sp., Schefflera sp. (ANOVA, p = 0.237, df = 16, a = 0.05). Vieilledent et al. (2012) measured WSG values on
rectangular samples 10–15 cm long 9 2 cm wide 9 2 cm thickness taken from harvested trees at both ends and in the middle of the tree bole, and on a branch. The method of determining WSG from wood cores used in this study therefore leads to similar results obtained from a destructive method.
Conclusions A first conclusion of this study is that, for Mandraka natural forest, the WSG values obtained from wood core samples extracted in the study area are significantly different from WSGs reported in the existing international databases. However, carbon stocks calculated from the two data sets do not differ significantly at the 0.05 level. Therefore, the estimation of AGB and carbon stock in forests can rely on the available WSG data. In addition, this study also suggests that the most abundant species of a given genus may be sufficient to represent other species of the same genus. This result is important as it could alleviate the tedious work of wood sampling in the field. Ultimately, the choice between using local values or data from available databases depends on the level of accuracy desired within REDD? and price differences between accuracy levels. These results are valid for Mandraka forest in Madagascar but they should be validated for other types of forests that differ in forest biomass, climate and species richness. Acknowledgments The equipment used for this study was supported by TWAS (The World Academy of Sciences) and CIRAD (Centre de Coope´ration Internationale en Recherche Agronomique pour le De´veloppement). The authors thank Rado Razafimahatratra for his assistance with the R software, Susan Becker and Mark Irle for proofreading the manuscript.
Appendix See Table 2.
Table 2 Wood specific gravity per species and per approach Scientific name
Family
Approach 1 WSG
Std
n
Approach 2
Approach 3
WSG
WSG
Ref.
Ft.
Agauria sp. Agauria (DC.) Benth. & Hook
Ericaceae
0.500
0.042
4
0.500
0.491
[2]
F
Albizia gummifera (J.F. Gmel.) C.A. Smith*
Fabaceae
0.410
0.062
9
0.410
0.500
[2]
s
Anthocleista madagascariensis Baker*
Gentianaceae
0.670
0.056
6
0.670
0.790
[1]
s
Bosqueia danguyana Leandri
Moraceae
0.596
0.032
8
0.596
0.499
[2]
s
Brexiela sp.
Celastraceae
0.746
0.059
6
0.746
0.737
[2]
F
Calophyllum sp.
Calophyllaceae
0.552
0.032
9
0.552
0.670
[2]
s
Cassinopsis madagascariensis Baillon
Icacinaceaea
0.475
0.027
3
0.475
0.600
[2]
F
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AGB estimates in a tropical rainforest in Madagascar
53
Table 2 continued Scientific name
Family
Approach 1 WSG
Std
n
Approach 2
Approach 3
WSG
WSG
Ref.
Ft.
Chrysophyllum boivinianum (Pierre) Baehni
Sapotaceae
0.498
0.022
10
0.498
0.540
[1]
s
Dilobeia thouarsii Roem. & Schult. Dombeya lucida Are`nes*
Proteaceae
0.751
0.037
9
0.751
0.844
[2]
s
Malvaceae
0.496
0.073
10
0.496
0.470
[2]
s
Dombeya laurifolia (Bojer) Baillon
Malvaceae
0.519
0.040
6
0.496
0.470
[2]
s
Erythroxylum corymbosum Boivin ex Baillon
Erythroxylaceae
0.543
0.046
7
0.543
0.547
[2]
G
Harungana madagascariensis Lamarck ex. Poiret
Hypericaceae
0.342
0.046
13
0.342
0.470
[1]
s
Helichrysum sp.
Asteraceae
0.443
0.020
4
0.443
0.761
[2]
F
Homalium sp.
Salicaceae
0.488
0.054
7
0.488
0.770
[1]
s
Ilex mitis (L.) Radlkofer*
Aquifoliaceae
0.438
0.032
11
0.438
0.480
[2]
s
Macaranga sp. Thouars
Euphorbiaceae
0.352
0.046
9
0.352
0.410
[1]
G
Macaranga sp. haerophylla Baker
Euphorbiaceae
0.345
0.010
5
0.352
0.410
[1]
G
Mapouria sp.
Rubiaceae
0.409
0.029
3
0.409
0.542
[2]
G
Micronychia madagascariensis Oliver
Anacardiaceae
0.418
0.034
10
0.418
0.729
[2]
F
Mussaenda sp. Linnaeus Nesogordonia sp. Baillon
Rubiaceae Malvaceae
0.370 0.383
0.030 0.032
6 5
0.370 0.383
0.759 0.655
[2] [2]
F G
Nuxia capitata Baker
Stilbaceae
0.665
0.025
10
0.665
0.661
[2]
G
Ochrocarpus parvifolius Elliot
Clusiaceae
0.563
0.031
5
0.563
0.700
[2]
F
Ocotea sp. 1
Lauraceae
0.478
0.017
4
0.476
0.522
[2]
G
Ocotea sp. 2
Lauraceae
0.476
0.022
6
0.476
0.522
[2]
G
Ocotea sp. 3
Lauraceae
0.455
0.040
3
0.476
0.522
[2]
G
Ocotea sp. 4
Lauraceae
0.548
0.068
9
0.476
0.522
[2]
G
Oncostemum sp. A. Juss.
Primulaceae
0.588
0.020
4
0.588
0.510
[1]
s
Pittosporum verticillatum Bojer
Pittosporaceae
0.512
0.026
2
0.512
0.547
[2]
G
Protorhus ditimena H. Perrier*
Anacardiaceae
0.501
0.043
10
0.501
0.678
[2]
s
Ravensara acuminata (Willd. ex Meisn.) Baillon
Lauraceae
0.600
0.046
10
0.548
0.655
[2]
G
Ravensara crassifolia (Baker) Danguy
Lauraceae
0.548
0.045
11
0.548
0.655
[2]
G
Ravensara sp.
Lauraceae
0.545
0.061
4
0.548
0.655
[2]
G
Schefflera sp. J.R. Forst. & G. Forst
Araliaceae
0.394
0.072
9
0.413
0.510
[1]
s
Schefflera voantsilana (Baker) Bernardi Syzygium emirnense (Baker) Labat & G.E. Schatz
Araliaceae Myrtaceae
0.413 0.617
0.057 0.044
10 7
0.413 0.672
0.510 0.560
[1] [1]
G s
Syzygium cumini (L.) Skeels*
Myrtaceae
0.672
0.051
6
0.672
0.722
[2]
G
Tambourissa madagascariensis Cavaco
Monimiaceae
0.482
0.030
6
0.482
0.510
[1]
G
Tina sp.
Sapindaceae
0.570
0.049
5
0.570
0.708
[2]
F
Trema orientalis (L.) Blume
Cannabaceae
0.260
0.022
5
0.260
0.330
[2]
s
Uapaca densifolia Baker*
Phyllanthaceae
0.607
0.073
7
0.607
0.642
[2]
s
Weinmannia minutiflora Baker
Cunoniaceae
0.628
0.053
4
0.628
0.571
[2]
s
Zanthoxyllum tsihanihimposa H. Perrier
Rutaceae
0.383
0.026
5
0.383
0.510
[2]
s
* Indicates the seven most abundant species according to Rajaonera (2008) WSG wood specific gravity; n number of cores per species; Std standard deviation; Ref. is database used with [1] Vieilledent et al. (2012), [2] Global Wood density database (Zanne et al. 2009); Ft. is Filter used for the allocation of WSG value from database, with s species, G genus, F family; Approach 1 in case the WSG was measured on each species, Approach 2 in case species of the same genus were assigned the WSG value of the most abundant species, Approach 3 in case the WSG values assigned to each species came from existing databases
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54
References Baille`res H, Vitrac O, Ramananantoandro T (2005) Assessment of continuous distribution of wood properties from a low number of samples: application to the variability of modulus of elasticity between trees and within a tree. Holzforschung 59:524–530 Blanc L, Echard M, Herault B, Bonal D, Marcon E, Chave J, Baraloto C (2009) Dynamics of aboveground carbon stocks in a selectively logged tropical forest. Ecol Appl 19:1397–1404 Bryan J, Shearman PL, Ash J, Kirkpatrick JB (2010) Estimating rainforest biomass stocks and carbon loss from deforestation and degradation in Papua New Guinea 1972–2002: best estimates, uncertainties and research needs. J Environ Manag 91:995–1001 Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer. Food and Agriculture Organisation, Rome, pp 1–55 Brown S, Gillespie AJR, Lugo AE (1989) Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci 35:881–902 Canadell JG, Raupach M (2008) Managing forests for climate change mitigation. Science 320:1456–1457 Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R (2004) Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc B 359:409–420 Chave H, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fo¨lster H, Fromard F, Higuchi N, Kira T, Lescure JP, Nelson BW, Ogawa H, Puig H, Rie´ra B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99 Chave J, Condit R, Muller-Landau HC, Thomas SC, Ashton PS, Bunyavejchewin S, Co LL, Dattaraja HS, Davies SJ, Esufali S, Ewango CEN, Feeley KJ, Foster RB, Gunatilleke N, Gunatilleke S, Hall P, Hart TB, Hernandez C, Hubbell S.P, Itoh A, Kiratiprayoon S, LaFrankie JV, Loo de Lao S, Makana J, Noor MNS, Kassim AR, Samper C, Sukumar R, Suresh HS, Tan S, Thompson J, Tongco MDC, Valencia R, Vallejo M, Villa G, Yamakura T, Zimmermann JK, Losos EC (2008) Assessing evidence for a pervasive alteration in tropical tree communities. PLoS Biol 6 (3): e45 http://www.plosbiology.org/article/ fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pbio. 0060045&representation=PDF. Accessed April 2013 Chave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366 Ebeling J, Yasue M (2008) Generating carbon finance through avoided deforestation and its potential to create climatic, conservation and human development benefits. Philos Trans R Soc B 363:1917–1924 Feldpausch TR, Lloyd J, Lewis SL, Brienen RJW, Gloor M, Monteagudo Mendoza A, Lopez-Gonzalez G, Banin L, Salim KA, Affum-Baffoe K, Alexiades M, Almeida S, Amaral I, Andrade A, Araga˜o LEOC, Araujo Murakami A, Arets EJMM, Arroyo L, Aymard GA, Baker TR, Banki OS, Berry NJ, Cardozo N, Chave J, Comiskey JA, Alvarez E, de Oliveira A, Di Fiore A, Djagbletey G, Domingues TF, Erwin TL, Fearnside PM, Franc¸a MB, Freitas MA, Higuchi N, Honorio E, Iida Y, Jime´nez E, Kassim AR, Killeen TJ, Laurance WF, Lovett JC, Malhi Y, Marimon BS, Marimon-Junior BH, Lenza E, Marshall AR, Mendoza C, Metcalfe DJ, Mitchard ETA, Neill DA, Nelson BW, Nilus R, Nogueira EM, Parada A, Peh KSH, Pena Cruz A, Pen˜uela MC, Pitman NCA, Prieto A, Quesada CA, Ramirez F, Ramirez-Angulo H, Reitsma JM, Rudas A, Saiz G, Saloma˜o RP, Schwarz M, Silva N, Silva-Espejo JE, Silveira M, Sonke´ B, Stropp J, Taedoumg HE, Tan S, ter Steege H, Terborgh J, Torello-Raventos M, van der Heidjen GMF, Vasquez R,
123
T. Ramananantoandro et al. Vilanova E, Vos VA, White L, Willcock S, Woell H, Phillips OL (2012) Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9:3381–3403 Gibbs HK, Brown S, Niles JO, Foley AJ (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2:1–13 Henry M, Besnard A, Asante WA, Eshun J, Adu-Bredu S, Valentini R, Bernoux M, Saint-Andre´ L (2010) Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For Ecol Manage 260:1375–1388 Herold M, Skutsch M (2011) Monitoring, reporting and verification for national REDD? programmes: two proposals. Environ Res Lett 6:1–10 Kindermann G, Obersteiner M, Sohngen B, Sathaye J, Andrasko K, Rametsteiner E, Schlamadinger B, Wunder S, Beach R (2008) Global cost estimates of reducing carbon emissions through avoided deforestation. Proc Natl Acad Sci 105:10302–10307 Luyssaert S, Schulze ED, Bo¨rner A, Knohl A, Hessenmo¨ller D, Law BE, Ciais P, Grace J (2008) Old-growth forests as global carbon sinks. Nature 455:213–215 Marshall AR, Willcock S, Platts PJ, Lovett JC, Balmford A, Burgess ND, Latham JE, Munishi PKT, Salter R, Shirima DD, Lewis SL (2012) Measuring and modelling above-ground carbon and tree allometry along a tropical elevation gradient. Biol Conserv 154:20–33 MEF-Ministe`re de l’Environnement et de Foreˆts (2009) Quatrie`me rapport national de la convention sur la diversite´ biologique. MEF/UNEP, Madagascar, pp 1–120 Moutinho P, Schwartzman S (2005) Tropical deforestation and climate change. Para´ : Instituto de Pesquisa Ambiental da Amazoˆnia Bele´m, Environmental Defense, Washington DC, p 1–131 Muller-Landau HC (2004) Interspecific and inter-site variation in wood specific gravity of tropical trees. Biotropica 36:20–32 Nogueira EM, Nelson BW, Fearnside PM (2005) Wood density in dense forest in central Amazonia, Brazil. For Ecol Manage 208:261–286 Peters-Stanley M, Hamilton K (2012) Developing dimension: state of the voluntary carbon markets 2012. Washington DC. Ecosystem Marketplace. Bloomberg New Energy Finance, New York, pp 17–26 Plugge D, Thomas B, Kohl M (2011) Reduced Emissions from Deforestation and Forest Degradation (REDD): why a robust and transparent monitoring, reporting and verification (MRV) System is mandatory? In: Blanco J, Kheradmand H (eds) Climate change—research and technology for adaptation and mitigation Croatia. In Tech, Europe, pp 1–488 R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed April 2013 Rajaonera ML (2008) Mise en place d’un e´tat de re´fe´rence et d’un plan de suivi e´cologique permanent des vestiges de foreˆt primaire de la station forestie`re de Mandraka. Ecole Supe´rieure des Sciences Agronomiques, Antananarivo, pp 1–84 Rakotovao G, Rabevohitra AR, Collas de Chaptelperron P, Guibal D, Ge´rard J (2012) Atlas des bois de Madagascar. Edition Quae, France, pp 1–13 Reyes G, Brown S, Chapman J, Lugo AE (1992) Wood densities of tropical tree species. United States Department of Agriculture, Louisiana, pp 1–15 Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ETA, Salas W, Zutta BR, Buermann W, Lewis SL, Hagen S, Petrova S, White L, Silman M, Morel A (2011) Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci 108:9899–9905
AGB estimates in a tropical rainforest in Madagascar Scaranello MA, Alves LF, Vieira SA, de Camargo PB, Joly CA, Martinelli LA (2012) Height-diameter relationships of tropical Atlantic moist forest trees in southeastern Brazil. Scientia Agricola 69(1):26–37 Schatz GE (2000) Endemism in the Malagasy tree flora. In: Lourenc¸o WR, Goodman SM (eds) Diversity and endemism in Madagascar. Me´moires de la Socie´te´ de Bioge´ographie, Paris, pp 1–9 Slik JWF (2006) Estimating species-specific wood density from the genus average in Indonesian trees. J Trop Ecol 22:481–482 van der Werf GR, Morton DC, DeFries RS, Olivier JGJ, Kasibhatla PS, Jackson RB, Collatz GJ, Randerson JT (2009) CO2 emissions from forest loss. Nat Geosci 2:737–738 Vieilledent G, Vaudry R, Andriamanohisoa SFD, Rakotonarivo OS, Randrianasolo HZ, Razafindrabe HN, Bidaud Rakotoarivony C, Ebeling J, Rasamoelina M (2012) A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecol Appl 22(2):572–583 Wiemann MC, Williamson GB (1989a) Radial gradients in the specific gravity of wood in some tropical and temperate trees. For Sci 35:197–210
55 Wiemann MC, Williamson GB (1989b) Wood specific gravity gradients in tropical dry and montane rain forest trees. Am J Bot 76:924–928 Williamson GB, Wiemann MC (2010a) Age-dependent radial increases in wood specific gravity of tropical pioneers in Costa Rica. Biotropica 42(5):590–597 Williamson GB, Wiemann MC (2010b) Measuring wood specific gravity correctly. Am J Bot 97(3):519–524 Woodcock DW, Shier AD (2003) Does canopy position affect wood specific gravity in temperate forest trees? Ann Bot 91:529–537 Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC, Chave J (2009) Data from: towards a worldwide wood economics spectrum. Dryad Digit Repos. doi:10.5061/dryad.234 Ziter C, Bennett EM, Gonzalez A (2013) Functional diversity and management mediate aboveground carbon stocks in small forest fragments. Ecosphere 4(7):85
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