study site and data
The study was conducted in the moist lowland tropical forest on Barro Colorado Island (BCI), a research reserve of the Smithsonian Tropical Research Institute. Rainfall averaged 2637 ± 462 mm year−1 for the period 1929–2001, with a 4-month dry season between January and April (Condit et al. 2001). Mean humidity is 77.9%, and daily maximum and minimum temperatures are 30.81 °C and 23.42 °C (1971–2001 average). Further meteorological information about BCI is available at http://www.stri.org/tesp. The forest is partly deciduous, with about 10% of canopy leaves dropped at the peak of the dry season (Condit et al. 2001). The 50-ha plot was established in 1981 (Hubbell & Foster 1983, 1986; Condit 1995, 1998; Condit et al. 1995; Leigh 1999). All stems ≥ 1 cm in trunk diameter were mapped, measured and identified to species between 1981 and 1983 (taxonomy follows Croat 1978; D’Arcy 1987, or Condit et al. 1996, unless specified otherwise). The census was repeated in 1985 and every 5 years thereafter.
More than 200 000 individual trees of over 300 species have been recorded in each census. From 1985 forward, trunk diameter (henceforth referred to as diameter) was measured to the nearest millimetre at 1.30 m above ground or above buttresses. If a trunk was swollen at 1.30 m, the measurement was taken 20 mm lower (Condit 1995, p. 50). Points of measure (POM) were painted on buttressed stems during 1990–2000. In 1982, measurements were not always taken above buttresses, so we excluded the 1982 census from our analysis. Multi-stemmed, broken and re-sprouting trees were recorded and handled separately in growth analyses. Measurement problems were handled during each census by checking field sheets for consistency and by re-measuring problematic trees. A subsample of 1715 trees was re-measured to assess independently the quality of diameter data. About 96% of the data had a relative error of less than 10%. For a more detailed description of the field methodology, the reader is referred to Condit (1998).
estimate of above-ground biomass stock
We used four allometric regression equations to estimate AGB as a function of stem diameter, tree height and wood specific gravity, and different regressions for saplings and lianas.
We have measured heights of 1414 trees of 83 species in the BCI plot using a Laser rangefinder (see O’Brien et al. 1995 on part of this data set). Height was modelled as a function of diameter using an asymptotic allometric regression (Thomas 1996):
H = c(1 − exp(−aDb))(eqn 1)
where c, a and b are species-specific parameters (see Table A1 in Supplementary Material). This model allows a height asymptote c; standard log-log regressions without the asymptote can considerably overestimate the size of large trees (Thomas 1996). For some species, however, the value of c does not correspond to a realistic height asymptote (e.g. 394 m for Alchornea costaricensis, see Table A1). In this situation, the asymptote in height is never reached, and equation 1 is equivalent to H = caDb. For species lacking their own regression, an equation based on combined data from all species was used. Only one common species, Gustavia superba (Lecythidaceae), lacked its own regression.
Wood specific gravity (oven-dry weight divided by green volume) is known for 123 species occurring in the BCI plot, mostly from the literature (Ovington & Olson 1970; van der Slooten et al. 1971; Chudnoff 1984; Wieman & Williamson 1989; Chichignoud et al. 1990; Lorenzi 1992; Malavassi 1992; Brown 1997; Fearnside 1997), but also from field work at BCI (H.C. Muller-Landau, unpublished results). For some species, only wood density at 12% moisture content was available; these were converted to wood specific gravity by multiplying by 0.8 (Brown 1997). All estimates of wood density are reported in Table A1. For the remaining species, we used the average of the mean density of these 123 species (0.54 g cm−3). This average is lower than the mean reported by Brown (1997) for tropical America (0.60 g cm−3, averaged over 470 species).
AGB estimation methods
We selected four AGB regression models from the literature (Brown et al. 1989; Chambers et al. 2001; Chave et al. 2001; Table 1). We examined how well each method predicted the results of the other methods using 200 subplots of 0.25 ha, and we selected the equation that had the highest mean correlation with the other three methods. These allometric models were constructed from samples of trees > 10 cm diameter. We therefore estimated the AGB of trees < 10 cm diameter using another model constructed from a sample of 66 trees < 10 cm harvested in the Los Tuxtlas region, Mexico (Hughes et al. 1999; Table 1). To account for the variation in wood specific gravity, we assumed that this equation was valid for species close to the mean wood specific gravity of the plot (0.54). For each tree < 10 cm, we then applied the regression of Hughes et al. (1999), then multiplied the obtained value by the tree's wood specific gravity divided by 0.54.
Table 1. Regression equations used to estimate total above-ground biomass in the BCI forest. The first four were applied only to trees ≥ 10 cm diameter. D is the diameter measured at 1.30 above ground, below irregularities, or above buttresses (in cm). ρ is the oven-dry wood specific gravity (in g cm−3). ρav is the mean wood specific gravity of the plot (0.54 g cm−3). H is total tree height (in m) and AGB is the above ground biomass (in kg tree−1)
|1||AGB = exp[–2.00 + 2.42 ln(D)]||378||10 cm||Pantropical||Chave et al. (2001)|
|2||AGB = exp[–0.37 + 0.333 ln(D) + 0.933 ln(D)2 − 0.122 ln(D)3]||316|| 5 cm||Brazil||Chambers et al. (2001)|
|3||AGB = exp[–3.114 + 0.972 ln(D2H)]||168|| 5 cm||Pantropical||Brown et al. (1989)|
|4||AGB = exp[–2.409 + 0.952 ln(ρD2H)]|| 94||10 cm||Pantropical||Brown et al. (1989)|
|Saplings||AGB = ρ/ρav exp[–1.839 + 2.116 ln(D)]|| 66|| 1 cm||SE Mexico||Hughes et al. (1999)|
Liana AGB was estimated separately. An allometric equation was developed from two data sets to estimate the AGB of lianas: one for 17 individuals in Venezuela (Putz 1983) and one for 19 individuals in Brazil (Gerwing & Farias 2000). The allometric equation was ln(AGB) = 0.0499 + 2.053 ln(D), where AGB is expressed in kg and the diameter in cm (S. J. DeWalt & J. Chave, unpublished data). We combined this information with a liana inventory in the BCI forest (Putz 1984) in which the diameter of all lianas above 1 cm was measured in 10 0.1 ha plots (40 × 25 m) near the 50-ha plot. We converted the liana diameters into AGB using the regression equation, and summed over the lianas to get a stand-level estimate of liana per ha.
To calculate the minimal sampling effort required to estimate the mean AGB, we quantified sampling error as follows. First, we computed sampling distributions by subsampling the data using subplots that ranged in size from 10 × 10 m (0.01 ha) to 100 × 100 m (1 ha). Then we computed the 2.5th percentile of the sampling distribution, which we denote as AGB2.5, and the 97.5th percentile of the sampling distribution, which we denote as AGB97.5. For subplots smaller than 1 ha, we computed the 95% confidence interval using the formula
where N is the number of subplots. For 1 ha subplots, there are just 50 samples, so we were unable to obtain the 95% confidence intervals directly; instead we checked that the AGB distribution across subplots was Gaussian and used the formula to estimate the confidence interval, where σ is the standard error. In addition, we computed the spatial autocorrelation among plots for the various estimated quantities (Legendre & Legendre 1983, p. 349).
Habitat variation in above-ground biomass
We examined variation in AGB across diameter classes and habitats. We used the habitat classification of Harms et al. (2001), who assigned each 20 × 20 m subplot of the 50-ha plot into one of seven possible categories: young forest (48 plots, forest cleared 150 years ago), riparian forest (32 plots, referred to as ‘stream’), swamps (30 plots), forest on slopes (284), on the low plateau (620), and on the high plateau (170).
Above-ground biomass change, denoted ΔAGB, is due to growth G, plus recruitment R, minus loss M. More precisely, G can be defined as the annual increment of AGB due to the growth of trees that were alive during two successive censuses, M as the annual loss of AGB due to the mortality of trees that died by the second interval, and R as the annual ingrowth of AGB due to recruitment into the minimal diameter class between the first and second censuses. Clark et al. (2001a) refer to AGB increment as the sum of AGB growth plus recruitment.
Denoting the above ground biomass of tree i at time t by AGBi(t) and defining its increment between census time t and census time t + Δt as
ΔAGBi(t) = AGBi(t + Δt) − AGBi(t)(eqn 3)
The total biomass is , and the
total increment is, in Mg ha−1 y−1
where the sum is taken over all the surviving trees in the census plot. The precise interval of measurement between two censuses, Δt, may vary among trees.
By convention, AGB loss is defined by
where EM is the subset of trees alive at time ti and dead at the next census, and T is the mean census period (5 years). Clark et al. (2001a) define AGB recruitment R as given by equations 3 and 4, where AGB at time t is the AGB of a minimum-sized tree, and AGB at time t + Δt is that of the recruited tree. The alternative hypothesis is to assume that the AGB of the tree before it recruited was zero (Y. Malhi et al., unpublished results). When the minimal tree diameter is 1 cm, we tested that both assumptions yield almost undistinguishable results.
Using discrete time steps leads to a slight underestimate of AGB increment and loss because trees that die between two censuses grow some biomass before they die (Delaney et al. 1998). Trees that die between censuses survive, on average, half of the census period Δt. During this period, they grow a fraction (G + R)/AGB of biomass above ground, thus the additional growth term is roughly (G + R)/AGB × M × (T/2). If G+ R= M = 5 Mg ha−1 year−1, T= 5 year, and AGB = 300 Mg ha−1 this additional term would be about 0.2 Mg−1 ha−1 year. However, AGB losses should also be inflated by a term of the same magnitude. Thus, the estimates of AGB change are unaffected.
To reduce measurement error in calculating fluxes, problematic records were checked individually. There were 788 trees (0.3% of the total sample, 2.7 m2 ha−1) with anomalous diameter increases (> 35 mm year−1) or decreases (< 5 mm year−1) between two censuses. The diameter measurement in each of four censuses, 1985–2000, were checked in all 788 cases, and most had one obviously outlying diameter record. We used the three other measurements to replace the egregious one with an interpolated value. After these changes, we were left with five anomalous measurements, large changes where we could not easily detect the error. We excluded these five from estimates of AGB growth and loss (this is equivalent to setting the growth of these trees to zero). In addition, for 5071 cases (out of 591 099) where the point-of-measure of stem diameter changed between censuses, we assumed a zero change: these trees (c. 10% of the stand basal area) were excluded from AGB flux estimates. We also tested the importance of these excluded trees in the estimate of AGB change by assuming that these trees had had an average growth in diameter.
Major trunk or crown loss above the POM was noted in the field in 1985 for all trees, and in 1990–2000 for trees ≥ 10 cm diameter. In 2002, we checked a random sample of 191 of the trees marked as damaged in 2000. For each, we estimated the height at which the trunk broke and the amount of crown lost, compared the broken height to the predicted height from regression, and assumed that trunk biomass was reduced by the fraction of height lost. We further assumed that crown biomass was reduced by the amount of crown that we estimated to be missing. If crown AGB is 25% of total AGB (Malhi et al. 1999; J. Chave, unpublished results), we can convert these figures to a crude estimate of the percentage AGB lost to the break. The mean percentage lost per tree was multiplied by the number of trees recorded with a new break above the POM in each census period. For trees < 10 cm diameter, we had to assume that the percentage AGB loss per tree was the same as it was in the sample of trees > 10 cm diameter we checked in 2002, as no damaged smaller trees were checked.
All trees recorded as broken below the POM, but which survived and sprouted a new stem, were noted in each census (Condit 1998). Here, we treat these cases as mortality and then recruitment in the AGB flux estimates.