Wood density (WD) is believed to be a key trait in driving growth strategies of tropical forest species, and as it entails the amount of mass per volume of wood, it also tends to correlate with forest carbon stocks. Yet there is relatively little infor- mation on how interspecific variation in WD correlates with biomass dynamics at the species and population level. We determined changes in biomass in permanent plots in a logged forest in Vietnam from 2004 to 2012, a period representing the last 8 years of a 30 years logging cycle. We measured diameter at breast height (DBH) and estimated aboveground bio- mass (AGB) growth, mortality, and net AGB increment (the difference between AGB gains and losses through growth and mortality) per species at the individual and population (i.e. corrected for species abundance) level, and correlated these with −1 −1 WD. At the population level, mean net AGB increment rates were 6.47 Mg ha year resulting from a mean AGB growth −1 −1 −1 −1 −1 −1 of 8.30 Mg ha year , AGB recruitment of 0.67 Mg ha year and AGB losses through mortality of 2.50 Mg ha year . Across species there was a negative relationship between WD and mortality rate, WD and DBH growth rate, and a positive relationship between WD and tree standing biomass. Standing biomass in turn was positively related to AGB growth, and net AGB increment both at the individual and population level. Our findings support the view that high wood density spe- cies contribute more to total biomass and indirectly to biomass increment than low wood density species in tropical forests. Maintaining high wood density species thus has potential to increase biomass recovery and carbon sequestration after logging. Keywords Biomass · Carbon dynamics · Demographic rates · Tropical forest · Vietnam Introduction forest. In tropical forest ecosystems, carbon is mainly stored in living biomass in standing trees and soil, while a smaller Tropical forests play a prominent role in driving the global amount is stored in litter and dead wood (Malhi et al. 2009; carbon cycle (Malhi et al. 2011), yet the mechanisms driving Ngo et al. 2013; Sierra et al. 2007). Most studies focus on carbon dynamics in tropical forests are still poorly under- aboveground biomass (AGB) (Chave et al. 2008a, b; Lewis stood. One of the major challenges in studying forest car- et al. 2013; Malhi et al. 2004; Vieira et al. 2004) where bon dynamics involves the quantification of biomass and living tree biomass is typically estimated using allometric its relation to the structure and species composition of the models (Chave et al. 2005). Biomass dynamics in forests are driven by the amount of standing biomass on the one hand and the individual rates of Electronic supplementary material The online version of this growth, recruitment and mortality on the other (Chave et al. article (https ://doi.org/10.1007/s1026 5-018-1042-9) contains 2008a; Keeling et al. 2008). Amounts of standing biomass in supplementary material, which is available to authorized users. mature tropical forests may differ considerably both within * Vu Thanh Nam and between regions (Slik et al. 2010). Similarly, biomass Nam@vnforest.gov.vn dynamics through growth, recruitment and mortality also differ widely between different tropical forests (e.g. Chave Department of Biology, Utrecht University, Padualaan 8, et al. 2008a, b; Djomo et al. 2011; Malhi et al. 2004, 2009). 3584 CH Utrecht, The Netherlands One of the factors that may affect the variation in bio- Present Address: Vietnam Administration of Forestry, No 2, mass and associated biomass dynamics of forests is the Ngoc Ha, Ba Dinh, Hanoi, Vietnam species composition and associated distribution of func- Centre for Crop Systems Analysis, Wageningen tional traits (Iida et al. 2012; Poorter et al. 2008, 2010; University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands Vol.:(0123456789) 1 3 612 Journal of Plant Research (2018) 131:611–621 Vieira et al. 2004). In this respect, wood density (WD) is First, how much biomass and carbon is stored in the for- often used as one of the key traits to indicate functional est and how is it distributed among different components? groups (Muller-Landau 2004). Low WD species are associ- Second, to what extent are demographic rates and biomass ated with rapid growth and a resource acquisitive growth dynamics associated with wood density, across species? strategy, while high WD species are associated with slow These questions are addressed for a forest in the Central growth and stress resistance (Chave et al. 2005, 2009). As Highland of Vietnam, which was selectively logged 30 years such, variation in demographic rates between species can prior to our study. be linked to WD (Hietz et al. 2013). Furthermore, as WD represents the amount of mass and carbon per unit volume, it is directly linked to forest carbon stocks. This raises the Materials and methods question to what extent variation in WD drives variation in tree demographic rates at the individual level and how this Study site and plot layout affects carbon dynamics at the population level. A number of studies found a fairly consistent negative The study was conducted in an evergreen forest correlation between WD and mortality rates, suggesting (108°17′75″E and 14°35′35″N) in K’Bang district, Gia Lai that high WD species tend to live longer and could thus province, in the Central Highland of Vietnam. The topogra- grow to greater size (King et al. 2006; Muller-Landau 2004; phy of the area is mostly flat with an altitude of 500–600 m. Wright et al. 2010). But results regarding growth are less Annual precipitation is approximately 2,300 mm with a clear. Low WD species were shown to exhibit faster rates of 3–4 months dry season. Mean annual air humidity is 82% diameter growth than high WD ones (Muller-Landau 2004; and mean annual temperature is 23 °C (GSO 2013). The Wright et al. 2010), as was predicted by theoretical models soils in the area are classified as Ferralsols (Lung et al. (Anten and Schieving 2010). But, as low WD species entails 2011). A map of the location of the study site is provided in less mass per unit volume, it is unclear whether this faster the supporting information (see Fig. 1, S1). diameter growth also translates into faster biomass incre- The forest at the study site was selectively logged for the ment. Unfortunately, few studies have considered relation- first time between 1980 and 1982 with a harvesting inten- ships between WD and AGB increment rates. Contrary to sity of about 30–35% of the standing volume and focusing the notion that high WD species are associated with slow solely on species producing timber suitable for construction. growth, Keeling et al. (2008) found positive correlations A total of six permanent plots (100 m × 100 m) were estab- between WD and AGB increment rates, at least on fertile lished and inventoried in the study site in 2004 by the High- sites in a mature tropical Amazonian forest. land Tropical Forest Research Centre (hereafter Highland In order to scale from species individual plant differences FRC). The forest was never logged again, therefore the plots in demographic rates to population level biomass dynamics, had a 30–32 years recovery period in 2012. We collected differences in abundance between species should be taken additional data in the same six permanent plots in 2012. into account. The relative abundance of species with differ - ent WD may vary depending on environmental conditions Measurements on aboveground components and disturbance history (Ketterings et al. 2001; Wiemann and Williamson 2002). To our knowledge however there is At each inventory (in 2004 and 2012) in each permanent plot, no study that has determined the relationship between vari- all trees with a diameter at breast height (DBH) larger than or ation in WD and biomass dynamics associated with growth, equal to 10 cm were identified at the species level based on recruitment and mortality, both at the individual and popula- Vietnamese guidelines (MARD 2000) and numbered. Across tion level. Such an analysis would strongly contribute to our these six plots, a total of 105 species were found. Of each understanding of the processes that play a role in forest car- individual, DBH (using a diameter tape accuracy of ± 1 mm) bon dynamics (Keeling et al. 2008). It may also contribute and height (H) (using a Blumleiss altimeter) were measured. to forest management especially in tropical forests, where To determine the amount of biomass in saplings selective logging regimes are often implemented. Selective (1 cm ≤ DBH < 10 cm) at the time of measurement in 2012, logging tends to focus on harvesting commercial species we established a subplot (25 m × 25 m) in the centre of each with often relatively high WD (Sist et al. 2014), and it is permanent plot. In each subplot, all saplings were identified therefore important to determine how the associated changes at the species level and numbered. Across these six subplots, in species composition could impact biomass dynamics of a total of 67 sapling species were found, of which 61 species the forest (Bunker et al. 2005). were also found as adults. Of each individual, DBH and H This study focuses on the biomass dynamics after logging (using a Blumleiss altimeter and pole altimeter for tree’s in tropical forest stands, with particular emphasis on the role height < 5 m accuracy of ± 0.1 m) was measured. of interspecific variation in WD. We address two questions: 1 3 Journal of Plant Research (2018) 131:611–621 613 −3 To determine WD (g cm ) and carbon content for each Of standing dead trees (stumps included) we measured species, two wood core samples with a length of around DBH and H in the same way as for the living trees. Trees 15 cm and a diameter of 0.5 cm were taken from opposite were determined to be dead based on their shape and bark positions on the stem at DBH of one (with a DBH close to structure (in addition to lack of leaves) by us. the mean DBH of that species in the plots) or more trees (see Biomass and carbon content of woody debris (including Nam et al. 2016). WD of each sample was determined as dry stems, branches and snags) and litter on the forest floor were mass divided by its fresh volume (Chave 2005): determined within the same four square frames (2 m × 2 m each) using the same procedure in which shrubs were sampled. WD = (1) × d × L Measurements on belowground components where L was the total length (cm), d the mean diameter of Soil carbon content was determined in two square soil pro- the sample (cm) and M the dry mass (g) of the sample (after files (50 cm × 50 cm × 100 cm depth, each) at two opposite oven-drying at 90 °C to constant mass). The WD of the spe- corners within each permanent plot in 2012. These soil pro- cies was calculated as the average of WD of the two wood files were located at least 2 m away from the nearest stand- core samples. In total, WDs of 97 species were determined ing trees to avoid hitting their roots. First, soil core samples at the Wood Science Laboratory of the Forestry University (diameter of 4.5 cm and length of 20 cm) were taken at of Vietnam. The number of trees sampled for WD depended two depths (0–50 and 50–100 cm) and weighed to deter- on their abundance: three trees for the five most abundance mine fresh mass, after which soil subsamples were dried to species, two for the subsequent 16 species and one for the −3 determine soil bulk density (g cm ) at the Highland FRC. rest. For the eight other species of which we could not deter- Second, the carbon content of these samples was determined mine WD (i.e. broken samples), we used the average WD as described for wood. of the species of the same genus if present, and otherwise −3 The amount of fine roots (diameter ≤ 2 mm) was deter- the mean WD of species of the six plots which is 0.6 g cm mined in the abovementioned soil profiles, but we did not (Hertel et al. 2009). The two latter groups of species were determine fine root content separately at each depth of the excluded from the demographic analyses and were only soil profiles. First, roots in soil were collected carefully by included in estimates of forest mass. Carbon content in hand and cleaned under running water, and their FW was stem tissue was determined on subsamples that were finely measured with an electronic balance (accuracy of ± 1 g). ground at the Laboratory of the Ecology and Biodiversity Second, ~ 50 g subsamples were collected and oven dried Group of Utrecht University, The Netherlands (hereafter, at 75 °C until constant mass and the DW/FW ratio was cal- Utrecht lab) using a CHN-Elemental Analyzer (CE instru- culated. The carbon content in fine roots was determined as ment, inter-science BV, Breda, The Netherlands). In total, we described for wood. determined the wood carbon content for 90 species (for all of which we had determined WD). In the case of the remaining 15 species, measurements failed due to defective samples. Calculations The species for which we were unable to determine WD and C content, were generally rare with only one or a few small Biomass and organic matter components: Above ground bio- stems in our plots. −1 mass (AGB, kg tree ) and coarse root (diameter > 2 mm) Biomass in shrubs (including seedlings with stem −1 biomass (RB, kg tr ee ) of each standing woody living tree DBH < 1 cm), was measured in 2012, by laying out four (DBH ≥ 10 cm) in each measurement year were estimated square frames (2 m × 2 m) in the four corners of each per- using allometric equations that were developed for our study manent plot. We then harvested all aboveground parts per- site (Nam et al. 2016): taining to shrubs and determined their fresh weight (FW). The dry weight (DW) and fresh weight (FW) ratio (DW/FW AGB = exp(−3.051 + 0.966 ln(DBH H)+ 0.305 ln(WD)) hereafter) was determined on subsamples of about 50 g FW, (2) which were oven-dried at the Highland FRC at 75 °C until RB = exp(−1.651 + 1.934 ln(DBH)+ 1.06 ln(WD)) (3) constant mass, and the total DW of shrubs was then calcu- with DBH and H the diameter at breast height (cm) and lated by multiplying the FW of the whole frame by the DW/ height (m) of each tree in each plot, AGB and RB are in FW ratio determined on the subsample. The carbon content kg dry biomass per tree. Equations were calibrated using in shrubs was determined on subsamples at the Utrecht Lab destructive samples of 300 for AGB and 40 trees for RB (see with the same methods as described above, however, without −3 Nam et al. 2016). For WD (g cm ), we used the average distinguishing between species. value per species determined as described above. 1 3 614 Journal of Plant Research (2018) 131:611–621 We also used Eqs. (2) and (3) to determine the AGB and of small trees had been planted in gaps after logging), thus RB of saplings (1 cm ≤ DBH < 10 cm) and dead standing its estimated demographic rates do not reflect their natural trees. To correct for the fact that part of the mass in dead values. Information about the study species can be found in trees is already decomposed, we used a correction factor the supporting information (see Table 1, S1). −1 based on visual assessment of decomposition state: 1 for The mortality rate per year (m, % year ) between two dead trees with no signs of decomposition, 0.75 for moder- measurements for each species was estimated as follows: ately decomposed trees and 0.5 for highly decomposed trees Ns1 − Ns2 −1 (Latte et al. 2013). m = 100 × × t (5) Ns1 The carbon content of each tree was estimated by multiply- ing the species specific C content by the AGB and RB values where Ns is the number of living trees at measurement 1, of each tree. As carbon content was determined on stem sam- Ns (N ≥ N ) is the number of surviving trees at measure- 2 S1 S2 ples only, this assumes that C content of leaves and roots were ment 2 and t is the time (in years) between the two measure- similar to those of stems. For the 15 species (DBH ≥ 10 cm) ment (Sheil et al. 2000; Wright et al. 2010). In this case rates and 6 species (1 cm ≤ DBH < 10 cm) for which we could not were calculated primarily over the whole 2004–2012 period determine C content, we used the carbon content value of a and thus t = 8 years. −1 species of the same genus if present, or otherwise the mean Tree recruitment rate per year (r, % year ) between two value (46.2%) of the carbon content of the 90 species for measurements were estimated as: which we were able to determine carbon content. R12 −1 The AGB and RB of each tree, total tree biomass (AGB r = 100 × × t (6) Ns1 −1 and RB) and their respective carbon stocks (Mg ha ) were then summed to determine total biomass and total carbon where R is the number of new living trees at measurement stock for standing living trees in each plot. 2, which recruited to DBH ≥ 10 cm. Amounts of carbon per ha in shrub, litter, woody debris The average growth rate (G ) in terms of both DBH, ind and fine roots were estimated by multiplying the carbon con- AGB and TB (total biomass, AGB and RB) per tree per spe- tent in each component by its dry mass. The carbon in soil cies were calculated following King et al. (2006) as: −3 organic matter (SOC, kg m ) in each soil layer (0–50 cm and Ns2 50–100 cm) was estimated by multiplying the percentage of X − G = (X − X )∕t (7) −3 ind 2,i 1,i carbon in the soil (Ps, %), the soil bulk density (Sd, kg m ) Ns2 i=1 and the volume of each soil layer (V, m ) (Djomo et al. 2011; Ngo et al. 2013; Usuga et al. 2010). SOC in each soil profile where X refers to the entity (DBH, AGB or TB), suffixes −3 (kg m ) was the sum of the SOC in these two soil layers: 1 and 2 again refer to the two subsequent inventories and i denotes tree i (i = 1 → N ). To make our calculations S2 SOC = Sd × Ps × V (4) comparable with data from the literature, and because our Total carbon stock in each plot was the sum of the values estimate root mass was less accurate than those of AGB, of each component: standing living trees (DBH ≥ 10 cm), biomass dynamics per tree are expressed in terms of AGB saplings (1 cm ≤ DBH < 10 cm), shrub, litter, woody debris, (AGB-G ). Average relative growth rates (i.e. AGB growth ind fine roots and soil. per unit AGB or DBH growth per unit DBH, AGB-RGR Demographic rates: Demographic rates were calculated and DBH-RGR, hereafter) were calculated by replacing X primarily for each species (or at the individual level) using by ln(X) in Eq. (7). trees with DBH ≥ 10 cm based on the measurements in 2004 Subsequently we calculated how different species con- and 2012. The rate expressed the demographic rates (growth, tributed to biomass dynamics at the population level. Here recruitment and mortality) for each species on a per capita population is defined as biomass dynamics of all individu- basic. For all calculations of demographic rates and popu- als of a species with DBH ≥ 10 cm in a given area (i.e. lation level biomass dynamics we only considered the 42 hectare land area) which excludes smaller individuals most abundant species which had more than 20 individuals (they constitute less than 5% of AGB). To this end, we per species in the six permanent plots and their DBH was calculated the net AGB increment per species per hectare ≥ 10 cm. WD of all these species had been measured by us. (AGB-I , hereafter) as: pop These species accounted for more than 89% of total bio- AGB − I = AGB − G + AGB − R − AGB − M mass and 87% of individuals of the total in the forest plots. pop pop pop pop In this analysis, the species Dipterocarpus alatus (total 56 (8) individuals) was excluded due to a silvicultural treatment where AGB-G , AGB-R and AGB-M are the changes pop pop pop for this species after the first logging event (around 20 years in biomass per species per hectare associated with growth, ago) in one of the late-recovery forest plots (i.e. a number recruitment and mortality (i.e. thus expressed at population 1 3 Journal of Plant Research (2018) 131:611–621 615 level), respectively. AGB-G was calculated as the total biomass (AGB and RB) of living trees (DBH ≥ 10 cm) and pop cumulated AGB growth of all surviving trees of a given soil carbon contributed approximately equally: 48.5 and species at the measurement 2 (essentially the same as mul- 47.1%, respectively. tiplying G for AGB from Eq. (7) by N ). AGB-R was The average AGB of the large trees (DBH ≥ 10 cm) and ind S2 pop calculated as the total biomass of all newly recruited trees saplings (DBH < 10 cm) accounted for 82.2% of the total into the size class DBH ≥ 10 cm and AGB-M as the total forest biomass, while the coarse and fine root biomasses pop mass of trees present at the first census and dead at the sec- accounted for 13.5%. The other components, such as stand- ond one. All these values were divided by plot area to nor- ing dead trees, woody debris, shrub and litter contributed malize to per ha rates (Astrup et al. 2008). very little to the total (4.3%). In terms of carbon stocks, the −1 aboveground components (162.3 ± 9.0 Mg C ha ) contrib- Statistical analysis uted less than the belowground components (193.1 ± 3.2 Mg −1 C ha ). Carbon in soil organic matter accounted for approx- The data in the six plots were pooled. The differences in imately 87% of the total amount of carbon in the below- −1 AGB (Mg ha ) for each DBH size class between two meas- ground component, while roots (coarse and fine roots of both urements (2004 and 2012) of the six plots were determined standing living and dead trees) accounted for 13%. by paired-test. Regression analyses were used to analyse the relationship between mean values per species for growth, AGB dynamics (2004–2012) recruitment, mortality and AGB-I (all taken as dependent pop variables) and WD (as independent). The total AGB of trees (DBH ≥ 10 cm) in each of the diam- We also conducted a multiple regression with AGB-G eter size classes (classes: 10.0–29.9 and 30.0–49.9 cm) was ind as dependent and WD and trees biomass (AGB size, average higher in 2012 than in 2004 (P < 0.05), but for the larger AGB per tree per species at the second census) as independ- class (DBH ≥ 50.0 cm) the difference was not significant ents. Similarly, we did a multiple regression of population- (P > 0.05). In both censuses, the largest amount of AGB was level AGB growth (AGB-G ) and net AGB increment rates found in trees of DBH class of 40 cm (range from 30 to pop (AGB-I ) against WD and species abundance (number of 50 cm) (Fig. 1a). The proportion of the AGB (the percent- pop individuals per species present in 2004, Ns1). age between AGB of this size class and the total AGB) for All calculations were performed by IBM SPSS statistics this DBH class (30–50 cm) did not change significantly over 21.0. 8 years: it constitutes slightly over 30% of the total AGB. The number of trees within the DBH class of 10–30 cm, accounted for almost 75% of all trees, while their AGB was 20% of the total at both censuses (Fig. 1b). Results Among species there were some changes in their rank- ing in total AGB (i.e., AGB of all individuals of a species) Biomass and carbon stock in the forest between 2004 and 2012, but the ten species with the highest AGB remained the same and accounted for 52% of total In the final census in 2012, the average total carbon stock AGB in 2012. At both censuses the three dominant families, −1 in the forest was 355.4 ± 9.8 Mg C ha (Table 1) ranging Mangnoliaceae, Caesalpinioidae and Myrtaceae, contrib- −1 from 324 to 393 Mg C ha across the six plots, to which uted about 40% to the total AGB. Table 1 Total carbon and biomass stocks (mean ± standard error of mean) in different components of the six plots in 2012 Component Total Standing woody trees Fine roots Shrub Standing Woody Litter Soil dead trees debris (0–100 cm) DBH DBH (< 10) (≥ 10 cm) AGB (Mg 327.2 ± 19.7 7.1 ± 0.6 6.36 ± 1.0 −1 ha ) RB (Mg 43.4 ± 2.7 2.7 ± 0.2 1.06 ± 0.1 −1 ha ) Total mass 406.6 ± 22.6 370.6 ± 22.5 9.9 ± 0.8 9.14 ± 0.5 1.8 ± 0.1 7.42 ± 1.1 5.5 ± 0.9 1.9 ± 0.1 −1 (Mg ha ) (91.2%) (2.4%) (2.3%) (0.4%) (1.8%) (1.4%) (0.5%) Total carbon 355.4 ± 9.8 172.2 ± 10.4 4.5 ± 0.3 4.0 ± 0.2 0.7 ± 0.1 3.5 ± 0.5 2.4 ± 0.4 0.7 ± 0.1 167.2 ± 3.5 (Mg C (48.5%) (1.3%) (1.1%) (0.2%) (1.0%) (0.6%) (0.2%) (47.1%) −1 ha ) 1 3 616 Journal of Plant Research (2018) 131:611–621 (P > 0.05, Fig. 2d). Contrary to the results for DBH, there was a positive relationship between AGB size (P < 0.05), AGB-G (P < 0.05), TB-G (P < 0.05), and WD (Fig. 2e, ind ind f, h). We did not find a relationship between AGB-RGR and WD (Fig. 2g). In the multiple regression of AGB-G versus ind AGB size and WD, we found a strong positive relationship between AGB-G and AGB size but the WD effect was no ind longer significant (Table 3). In short, species with high WD had relatively low relative diameter growth rates but were also relatively large and had comparatively high absolute biomass growth rates. These higher growth rates were not directly due to WD but rather resulted from the fact that high WD species were larger, and thus grew faster in absolute terms. The relationship between wood density and AGB growth and net‑AGB increment at population level Among the 42 most abundant species we found a positive relationship between WD and both its population level AGB-G (P < 0.05), and AGB-I (P < 0.05) (Fig. 3a, pop pop b). Multiple regression showed that abundance (Ns1) was Fig. 1 Dynamics of aboveground biomass (a) and tree density (b) in significantly related with AGB-G and AGB-I , but the pop pop 2004 and 2012 relation with WD was only significant for AGB-I and pop insignificant to AGB-G (Table 3). Nevertheless, the most pop We calculated biomass and tree dynamics over a period abundant species (Paramichelia braianensis), with a rela- −3 of 8 years (Table 2). The average net AGB increment in the tively low WD (0.52 g cm ), had the largest contribution to −1 −1 −1 −1 six plots was 6.47 Mg ha year , while AGB loss rate due these measures with AGB-G of 0.98 Mg ha year and pop −1 −1 −1 −1 to trees mortality was 2.50 Mg ha year . AGB increase AGB-I of 0.75 Mg ha year (Fig. 3). pop due to tree recruitment into the DBH ≥ 10 cm was very small (25% of AGB mortality rate). We found the average mortal- ity rate to be lower than the recruitment rate across the 6 Discussion plots (P < 0.01). Biomass and carbon stock in the forest Individual level demographic rates and relation with wood density To characterize our forest relative to others we first briefly discuss standing biomass and carbon stocks. The total car- −1 When comparing between species there was a positive cor- bon stock at our site was 355.4 Mg C ha with 42% of this −1 relation (P < 0.01) between mortality rate and DBH-RGR (151.0 Mg C ha ) in aboveground biomass (AGB) of living (Fig. 2a). In contrast, mortality rate and species mean WD trees. This is within the range of values found for mature −1 showed a weak negative relationship (P < 0.05, Fig. 2b). tropical forests 57–375 Mg C h a across the tropics (Lewis Similarly, we found a negative relationship between the et al. 2013; Niiyama et al. 2010). It should be noted that the DBH-RGR and WD (P < 0.05, Fig. 2c), whereas we did not forest plots analysed by us had been selectively logged about find a significant relationship between DBH-G and WD 30 years before our measurements. As we have no data on ind Table 2 Biomass and tree dynamics (mean ± standard error of mean) of trees (DBH ≥ 10 cm) in the six plots in 8 years (2004–2012) Demography DBH increment AGB growth AGB recruit- AGB mortality Net AGB incre- Mortality rate Recruitment rate −1 −1 −1 −1 −1 rate (cm year ) rate (Mg ha ment rate (Mg rate (Mg ha ment rate (Mg (% year )(% year ) −1 −1 −1 −1 −1 −1 a year ) ha year ) year ) ha year ) Mean 0.35 ± 0.01 8.30 ± 0.32 0.67 ± 0.04 2.50 ± 0.27 6.47 ± 0.37 1.40 ± 0.10 2.54 ± 0.22 Net AGB increment= (AGB growth + AGB of recruited trees − AGB lost by trees mortality)/8 1 3 Journal of Plant Research (2018) 131:611–621 617 5.0 0.030 r = 0.101 0.025 4.0 p = 0.040 0.020 3.0 0.015 2.0 0.010 r = 0.215 1.0 p = 0.002 0.005 0.0 0.000 0.4 0.5 0.6 0.7 0.8 0.9 0.0 1.0 2.0 3.0 4.0 5.0 -3 WD (g cm ) -1 Mortality rate (% year ) 0.030 0.7 r = 0.121 p = 0.024 0.6 0.025 0.5 0.020 0.4 0.015 0.3 0.010 0.2 0.005 0.1 0.000 0 0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 -3 -3 c d WD (g cm ) WD (g cm ) 3000 100 r = 0.138 p = 0.015 r = 0.097 p = 0.044 0 1 0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 -3 -3 e WD (g cm ) f WD (g cm ) 0.08 100 0.07 0.06 0.05 0.04 r = 0.097 p = 0.044 0.03 1 0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 -3 -3 WD (g cm ) h WD (g cm ) Fig. 2 The relationships between species demographic rates and WD ln(TB-G ). In f, h, Y axis has been log-transformed to present. Sym- ind −3 (g cm ). a DBH-RGR and mortality rate, b mortality rate, c DBH- bols indicate mean species values. The line indicates linear regression RGR, d DBH-G , e AGB size, f AGB-G , g AGB-RGR and h and is only shown when significant ind ind 1 3 -1 -1 DBH-RGR (cm cm year ) -1 -1 DBH-RGR (cm cm year ) -1 AGB size (Kg tree ) -1 -1 AGB-RGR (kg kg year ) -1 -1 AGB-G (kg tree year ) DBH-G (cm year ) ind -1 ind TB-G (kg tree year ) Mortality rate (% year ) ind 618 Journal of Plant Research (2018) 131:611–621 −1 Table 3 Results of multiple-regression analysis across species: (i) at 100 cm and found that 2/3 (116.1 Mg C ha ) of C was in −1 individual-level: mean aboveground biomass growth AGB-G and ind the top 0–50 cm and 1/3 (51.2 Mg C ha ) in the deeper layer WD and mean tree aboveground biomass (AGB size); and (ii) at pop- (50–100 cm). This suggests that limiting measurements to ulation-level: mean aboveground biomass growth (AGB-G ) and pop the top 30 cm would have led to a serious underestimation of increment (AGB-I ) versus WD and abundance (Ns1) pop soil C. In addition, a recent study (Ngo et al. 2013) sampled Variables β Sem P soil C down to 300 cm depth and found that 40% of C was (i) Individual-level (AGB-G ) located deeper than 1.0 m. Thus, our work and that of oth- ind WD 3.550 7.688 0.647 ers point to the importance of measuring soil C well beyond AGB size 0.014 0.002 0.0001 1.0 m depth. (ii) Population-level (AGB-G ) pop WD 182.7 162.6 0.268 Biomass dynamics Abundance (Ns1) 2.635 0.561 0.0001 (iii) Population-level (AGB-I ) In this study we assessed forest level carbon and biomass pop WD 304.9 139.6 0.035 dynamics, and biomass dynamics of different species at the Abundance (Ns1) 1.718 0.481 0.001 individual and population level in a forest that was recover- ing for 30 years from a significant logging event. Particularly we addressed the extent to which species biomass dynam- the pre-logging biomass it is difficult to determine whether ics are associated with species WD. At the forest level, net AGB levels had already recovered to the pre-logging state. aboveground biomass increment rates were rather high The only long-term study that we know of on post-logging suggesting that 30 years post-logging the forest is still in biomass dynamics (Gourlet-Fleury et al. 2013) showed that a recovery stage. Among species, we found negative rela- 24 years after logging in African semi-deciduous forest, tionships of mortality rate with diameter growth rates and AGB levels had more than recovered to their pre-logging species wood density. We also found that high WD species state. tended to be larger in terms of standing biomass and thus −1 The carbon stock in the soil was 167 Mg C ha account- exhibited higher rates of biomass growth and net above ing for almost half of the total carbon stock in the forest. ground biomass increment both at the individual and popu- Together with the amount of C in roots, which we estimated lation level. These findings support the view that high WD −1 to be 24 Mg C ha , this means that about 54% of forest species contribute more to biomass and biomass increment carbon was found below ground. Estimates of soil carbon than low wood density species in tropical forest. vary widely between studies, and belowground fractions of In our study the net increment of aboveground biomass −1 −1 33–66% have been reported (Djomo et al. 2011; Gibbs et al. at forest level was high (6.47 Mg ha year ) compared 2007; Malhi et al. 2009). to values generally reported for mature tropical forest −1 −1 −1 Part of the variation in the estimates of soil C is associ- (from .0 to 4.8 Mg ha year ) (e.g. Chave et al. 2008a; ated with the variation in sampling depth. Various studies Gourlet-Fleury et al. 2013). Our census spanned the period estimate soil carbon to a depth of 30 cm (e.g. Djomo et al. between 23 and 31 years after logging. Gourlet-Fleury 2011; Wei et al. 2014). Here we measured soil C down to et al. (2013) analysed forests that had been selectively 1000 1000 0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 WD (g cm ) b WD (g cm ) −1 −1 −1 Fig. 3 The relationship between a AGB-G (kg ha year ) and WD (Y axis has been log-transformed to present), b AGB-I (kg ha pop pop −1 year ) and WD of the 42 most abundant species 1 3 AGB-G (kg ha year ) pop AGB-I (kg ha year ) pop Journal of Plant Research (2018) 131:611–621 619 logged between 1984 and 1987 and found that the mean This result is consistent with other findings (Iida et al. 2012; net AGB increment over the subsequent 24 years ranged King et al. 2006; Poorter et al. 2010) and with theoretical −1 −1 between 4.8 and 8.0 Mg ha year , the range encom- models showing that for vertical growth it is mechanically passes the value reported by us, but was 2–4 times higher more efficient to produce thicker stems with low WD than than the net AGB increment value they found in nearby thinner stems with high WD (Anten and Schieving 2010). plots of undisturbed forests. Both our results and those The general notion is that low WD is also associated with of Gourlet-Fleury et al. (2013) suggest that forests may rapid growth in terms of biomass. The arguments are that exhibit accelerated rates of biomass increment for sev- less dense woody tissue permits higher hydraulic conductiv- eral decades following a selective logging event. However ity, therefore greater photosynthetic capacity of trees than another study (Rutishauser et al. 2016) reported 2–13% higher WD (Chave et al. 2009; Keeling et al. 2008). Fur- reduction in tree growth over 25 years after a selective log- thermore, a low WD might be part of a suite of traits (high ging event, indicating that these effects can vary between SLA, high leaf photosynthetic capacity, etc.) associated with sites. rapid growth and a high WD with an opposite set of traits Net AGB increment is roughly the difference between (Chave et al. 2009). Few studies however have considered AGB growth (the combined growth of all surviving trees the relationship between WD and AGB increment in a natu- in a stand) and AGB losses through mortality (i.e., bio- ral forest and none that we know of considered the relation- mass gains through recruitment were very small). Thus, ship between TB growth and WD. Interestingly and contrary high biomass increment rates could be the result of accel- to the general view, we found a positive relation between eration of tree growth, suppression of mortality, or both. average annual AGB and TB growth, and WD at individual Both processes may have played a role at our site. AGB level. Our results for AGB are consistent with the findings −1 −1 growth rate was found to be 8.3 Mg ha year which is of Keeling et al. (2008). within the range of values reported for mature tropical To determine WD, we sampled 1–3 individuals per spe- Amazonian forests (Malhi et al. 2011), but higher than cies with a DBH close to the species mean in our plots. WD most reported values (e.g. Djomo et al. 2011; Hertel et al. can differ between individuals of the same species among 2009). Losses in biomass as a result of tree mortality were other things as a function of DBH (e.g. Nock et al. 2009; indeed lower than the range of values found in Amazonian Woodcock and Shier 2002). Differences in WD observed in forests in the review of Malhi et al. (2009). our study could in theory really have reflected differences in How could relative fast growth and low mortality sev- DBH. In our study however differences in DBH were small eral decades after logging be explained? At our site the and not significantly correlated with WD, and we are there- disturbance by logging was most likely severe; 30–35% fore confident that this issue did not significantly bias the of the standing volume was probably directly harvested results of our study. with a further 10–15% of trees being killed during, or Keeling et al. (2008) proposed several factors that could shortly after, logging (Con et al. 2007; MARD 2005; Sam explain a positive WD growth relationship. These include et al. 2007). Such disturbances open up the forest at least high WD tending to have: (1) high leaf and branch longev- initially, and increase light availability, which stimulates ity (e.g. Kitajima and Poorter 2010) resulting in larger and growth particularly that of light-demanding species such deeper crowns (Keeling et al. 2008) and thus higher whole- as long-lived pioneers (Gourlet-Fleury et al. 2013; Pena- plant photosynthesis (Aiba and Kohyama 1997; Lusk 2002), Claros et al. 2008; Sist and Nguyen-Thé 2002). While (2) lower respiration rates (Reich et al. 2006; Wright et al. direct effects of opening the forest most probably faded 2004), and a larger resistance to pathogens or hydrological out in our case, the higher light intensity may have ena- stress preventing (periods of) inhibited growth. bled fast-growing individuals to reach the canopy, which Our results however suggest a different explanation. could have increased their survival chance in subsequent There was a negative correlation between mortality rates and years. Evidently these effects may vary and likely depend WD (R = 0.101) consistent with other studies (Chave et al. on environmental factors such as soil fertility and spe- 2009; King et al. 2006) and a positive relationship between cies composition (i.e., the presence of light demanding WD and AGB size. In multiple regressions of growth versus species). AGB size and WD, only AGB size was significant. Together these results indicate that individuals of high WD species Relationship between wood density and growth have lower mortality chances and can on average grow to larger size, and larger trees in turn have higher absolute AGB In the second part of this paper, we analysed the extent to growth rates. which interspecific variation in demographic and associated Similar to the mean rates per tree we also found strong biomass dynamics were associated with species WD. We positive relationships between WD and both total AGB found a negative correlation between DBH-RGR and WD. growth and total net AGB increment at the tree population 1 3 620 Journal of Plant Research (2018) 131:611–621 S, Ewango CEN, Feeley KJ, Foster RB, Gunatilleke N, Guna- level. Our findings support the results of Chave et al. tilleke S, Hall P, Hart TB, Hernández C, Hubbell SP, Itoh A, (2008a), who found high WD species gained more biomass Kiratiprayoon S, James SLdL, LaFrankie V, Makana J-Rm, than low WD species in different forest sites in three conti- Noor MNS, Kassim AR, Samper CN, Sukumar R, Suresh HS, nents (Africa, America and Asia). Tan S, Thompson J, Tongco MDC, Valencia R, Vallejo M, Villa G, Yamakura T, Zimmerman JK, Losos EC (2008a) Assessing Our results indicate that on average a given species with evidence for a pervasive alteration in tropical tree communities. a high WD contributes more to the net biomass increment PLoS Biol 6:0001–0008 in a forest than low WD species, albeit indirectly through Chave J, Olivier J, Bongers F, Chatelet P, Forget P-M, van der Meer its effect on size. Given that high WD probably also entails P, Norden N, Riéra B, Charles-Dominique P (2008b) Above- ground biomass and productivity in a rain forest of eastern slower decomposition, this pattern might be strengthened if South America. Trop Ecol 24:355–366 considered at the ecosystem level. This result should also Chave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE be considered in relation to forest management. Selective (2009) Towards a worldwide wood economics spectrum. Ecol logging is commonly used for commercial timber produc- Lett 12:351–366 Con TV, Duc DT, Hung TT, Hang BT, Van NB, Soa HD, Cam NV, tion, so if loggers tend to focus on species with relatively Dong TL, Khiem CC, Huynh NH, Vien NV, Thinh NV, Son high WD they consequently also focus on those that have NT, Dam NT (2007) Study and application of advance science on average a relatively large contribution to biomass incre- techniques and solution in order to establish sustainable forest ment. Thus selective logging may disproportionally affect management models in the Central Highland, Vietnam (in Viet- namese). Vietnam Academy of Forest Science, Hanoi high WD species. Djomo AN, Knohl A, Gravenhorst G (2011) Estimations of total ecosystem carbon pools distribution and carbon biomass cur- Acknowledgements We would like to thank the Highland Tropical rent annual increment of a moist tropical forest. For Ecol Manag Forest Research Centre for sharing data and allowing us to collect 261:1448–1469 more data. We are grateful to the researchers and technical staff of the Gibbs HK, Brown S, Niles JO, Foley JA (2007) Monitoring and Centre, Mr. Ngo Van Cam, Mr Tran Van Binh, Mr Dinh Chi Giang estimating tropical forest carbon stocks: making REDD a real- and others, for their assistance in the field and laboratory work, and ity. Environ Res Lett 2:045023. https ://doi.org/10.1088/1748- we are also grateful to Associate Professor Le Xuan Phuong and the 9326/1082/1084/04502 3 researchers of the Wood Science Laboratory at the Vietnam Forestry Gourlet-Fleury S, Mortier F, Fayolle A, Baya FL, Ouédraogo D, University for wood density measurements. We would like to thank Bénédet F, Picard N (2013) Tropical forest recovery from log- Tropenbos International for funding the work. ging: a 24 year silvicultural experiment from Central Africa. Philos Trans R Soc B 368:1–11 Open Access This article is distributed under the terms of the Crea- GSO (2013) Administration, Land and Meteorological database of tive Commons Attribution 4.0 International License (http://creat iveco Vietnam period 2006–2012 (in Vietnamese). 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Journal of Plant Research – Springer Journals
Published: May 30, 2018
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