Biomass estimation in the Tapajos National Forest, Brazil

Forest Ecology and Management 154 (2001) 371±382

Biomass estimation in the Tapajos National Forest, Brazil Examination of sampling and allometric uncertainties Michael Kellera,b,*, Michael Palaceb, George Hurttb a

b

USDA Forest Service, International Institute of Tropical Forestry, Rio Piedras, PR 00928-5000, USA University of New Hampshire, Complex Systems Research Center, Morse Hall, Durham, NH 03824, USA

Abstract Changes in the biomass of Amazon region forests represent an important component of the global carbon cycle but the biomass of these forests remains poorly quanti®ed. Minimizing the error in forest biomass estimates is necessary in order to reduce the uncertainty in future Amazon carbon budgets. We examined forest survey data for trees with a diameter at breast height (DBH) greater than 35 cm from four plots with a total area of 392 ha in the Tapajos National Forest near Santarem, Para, Brazil (38040 S, 548950 W). The average frequency of trees greater than 35 cm DBH was approximately 55 ha 1. Based on tree diameters, allometric relations, and published relations for biomass in other compartments besides trees of DBH > 35 cm, we estimated a total biomass density of 372 Mg ha 1. We produced a highly conservative error estimate of about 50% of this value. Trees with diameters greater than 35 cm DBH accounted for about half of the total biomass. This estimate includes all live and dead plant material above- and below-ground with the exception of soil organic matter. We propagated errors in sampling and those associated with allometric relations and other ratios used to estimate biomass of roots, lianas and epiphytes, and necromass. The major sources of uncertainty in our estimate were found in the allometric relations for trees with DBH greater than 35 cm, in the estimates of biomass of trees with DBH less than 35 cm, and in root biomass. Simulated sampling based on our full survey, suggests that we could have estimated mean biomass per hectare for trees …DBH  35 cm† to within 20% (sampling error only) with 95% con®dence by sampling 21 randomly selected 0.25 ha plots in our study area. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Biomass; Error estimation; Allometry; Amazon; Brazil

1. Introduction Changes in land use and land cover account for a net ¯ux from the land to the atmosphere of approximately 1.6 Pg C yr 1. This value is uncertain to at least 1.0 Pg C yr 1 (Schimel, 1995). The Amazon region of South America, the largest tropical forest area on earth, is an active area of land use and land cover change (Nepstad et al., 1997). According to recent estimates, deforestation in the Amazon region of *

Corresponding author.

Brazil accounted for approximately 0.2 Pg C yr 1 released to the atmosphere during the period 1989± 1998 (Houghton et al., 2000). The largest source of uncertainty (60%) in this estimate was the biomass of the Amazon forest. Biomass estimates for undisturbed forests are critical to determine the carbon loss associated with a wide range of land use and land cover change processes. Deforestation is the most obvious of these changes. Other land uses that also modify the original biomass of a forest include logging and wild®re (Nepstad et al., 1999), and the degradation of forests

0378-1127/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 1 ) 0 0 5 0 9 - 6

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at the edge of existing clearings (Laurance et al., 1997). In contrast to the biomass content of the forest, the land area deforested in Brazil is relatively well quanti®ed. Each year, the Brazilian National Institute for Space Research (INPE) coordinates a survey of deforestation for the Brazilian portion of the Amazon region. This survey based on manually processed LANDSAT products coordinated by the INPE measured an average rate of deforestation of about 22 500 km2 yr 1 from 1978 to 1988. The deforestation rate was somewhat slower, approximately 17 000 km2 yr 1, from 1988 to 1998 data (Houghton et al., 2000). Deforestation area estimates based on automatically processed LANDSAT data were about 25% lower (Houghton et al., 2000; Skole and Tucker, 1993). 2. Biomass estimates for the amazon region The estimation of total biomass for the Amazon region forests has been a subject of polemical debates (e.g. Brown and Lugo, 1992; Fearnside, 1993). Prior to 1995, estimates of the average biomass density for forests in the Brazilian Legal Amazon ranged greatly from 155 to 352 Mg ha 1 (Brown et al., 1995). Recently the range of biomass estimates has converged somewhat and has shifted upwards as more biomass compartments are included. Fearnside (1997a) estimated 464 Mg ha 1 for total pre-logging forest biomass density. Houghton et al. (2000) estimated a range of 290±464 Mg ha 1 for total current biomass based on two allometric approaches. Recent estimates (Houghton et al., 2000; Fearnside, 1997a) and earlier estimates (e.g. Brown and Lugo, 1992) depend on the same underlying database of surveys conducted by the FAO and the RADAMBRASIL projects from the 1950s through the early 1980s. Regional estimates of biomass depend upon local plot scale measurements that are expanded geographically (Brown, 1997). At the local scale, the biomass can be measured directly by destructive sampling or it may be estimated using allometric equations. For the Amazon region, as for other forested areas, direct measurements of biomass are rare (e.g. Fittkau and Klinge, 1973). Allometric equations allow plot scale biomass estimates to be made on the basis of existing

forest inventories. Most biomass and forestry plots are small (circa 1 ha, cf. Phillips et al., 1998). In contrast, our work here focuses on a 392 ha survey of trees. We quanti®ed biomass density for the survey area and we also identi®ed the major sources of error in our estimates. The large area of our survey allows us to make more robust estimations of sampling errors than that are possible from studies of few small plots. 3. Field measurements and data analysis We de®ned biomass to include all live and dead plant materials including the forest ¯oor and woody debris. We compare all measurements on an oven-dry basis (generally 608C). Other organisms in the ecosystem contribute a trivial amount of biomass compared to plants (Fittkau and Klinge, 1973). We excluded soil organic matter, a large pool in Amazon region soils (Moraes et al., 1995). Following deforestation in the Amazon region, both gains and losses of soil organic matter have been documented at particular sites and the regional effect of deforestation on soil organic matter remains highly uncertain (Houghton, 1997). A ®eld inventory of trees was conducted during March 1997 in four blocks of approximately 100 ha each in a 3200 ha logging concession at the Tapajos National Forest south of Santarem, Para in the Brazilian Amazon region. The selection of the blocks corresponded to the anticipated timing of future logging operations and thus was not random. We do not necessarily consider these blocks representative of the logging concession or of the Tapajos National Forest. The entrance to the study site (38040 S, 548950 W) is located at 83 km south of Santarem on the BR-163 (Santarem±Cuiaba) Highway. Foresters and technicians of the FundacËaÄo Floresta Tropical (FFT) surveyed the full area of all blocks with parallel trails at 50 m intervals using ®berglass measuring tapes and staff compasses. Distances along the trails were marked at 50 m intervals. All trees with DBH greater than 35 cm were tagged and diameters at 1.3 m height were measured using a diameter tape. Diameters for buttressed trees were measured immediately above the buttresses; this is consistent with procedures used in development of the allometric equations discussed below. The ground positions of the trees were recorded

M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

to the nearest meter using an orthogonal coordinate system dependent upon the survey trails. Field data including an identi®cation number, ground position and diameter were recorded in the ®eld and transferred to a database. For convenience, we grouped trees in 10 cm diameter classes (1±10) where class 1 is represented by trees of 35 cm  DBH < 45 cm and class 10 includes all trees with DBH  125 cm. We also estimated the density of trees in two smaller classes (size classes 1 and 0) as discussed below. We estimated above-ground live biomass using four alternative allometric equations that were calculated for moist tropical forest trees (Brown, 1997) or speci®cally for Amazon region trees (Araujo et al., 1999; Carvalho et al., 1998) (Table 1). Araujo et al. (1999) presented several allometric relations. They found that their equation (1) was the best ®t to their data and we followed their recommendation to use this equation. The allometric equations all related DBH to the ovendry biomass of a tree (Table 1). These allometric equations also have been used for recent estimations of Amazon biomass (Houghton et al., 2000; Fearnside, 1997a). Fig. 1 displays the four selected curves compared to biomass determined for individual trees. Oven-dry above-ground biomass per plot for DBH  35 cm (AGB35 ) was calculated as X f …DBHi † (1) AGB35 ˆ where f(DBHi) is the allometric function relating DBH of tree i to dry biomass. In order to expand our biomass estimates to trees below 35 cm, we used the model proposed by Gillespie et al. (1992) to estimate the number of stems in smaller diameter classes. According to this model, in a mature forest stand the de Liocourt quotient (q), the ratio of the number of stems between adjacent diameter classes, is nearly constant

373

(Meyer, 1952). The number of stems N for diameter class j can be estimated by Nj ˆ qNj‡1

(2a)

where qˆ

Nj‡1 Nj‡2

(2b)

As recommended by Gillespie, we calculated q from the two lowest size classes for each of four survey plots (class 1, 35  DBH < 45 cm and class 2, 45  DBH < 55 cm diameter) and modeled stem frequency according to Eqs. (2a) and (2b) for two additional classes, 1 and 0 (15  DBH < 25 cm and 25  DBH < 35 cm). In order to estimate biomass for the modeled classes, we applied the allometric relations to the average diameter of integer sized trees within the classes (i.e. 19 and 29 cm for the two modeled classes). In classes 1±9, where all stems were measured, substitution of this average diameter for the true diameter into the allometric equations resulted in a mean difference of less than 0.2% compared to the original biomass estimate. Corrections for additional biomass components were estimated using approximations found in the literature discussed below. We attempted to account for both sampling errors and for other sources of uncertainty to produce an extremely conservative error estimate. Sampling errors related to the spatial variations in biomass density were estimated from the variation of the four sampled blocks. Additive errors were summed directly when they were correlated and summed in quadrature when they were independent. The direct sum represents an upper bound on the error because it assumes that the errors are perfectly positively correlated (Taylor, 1997). We assumed that the other sources

Table 1 Allometric equations used to calculate oven-dry tree biomass M (kg)a Equation

a 2

(A) M ˆ a ‡ b DBH ‡ c DBH (B) M ˆ exp‰a ‡ b ln…DBH†Š (C) M ˆ a…b DBHc † (D) M ˆ 1000a exp‰b ‡ c ln…DBH=100†Š

42.69 2.134 0.6 0.6

b 12.8 2.53 4.06 3.323

c

Reference

1.242 ± 1.76 2.546

Brown (1997) Brown (1997) Araujo et al. (1999) Carvalho et al. (1998)

a DBH is diameter (cm) at breast height (1.3 m). The values a, b and c are best ®t parameters. In Equations (C) and (D), the parameter a is an adjustment for oven-dry mass/fresh mass (Carvalho et al., 1998).

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Fig. 1. (a) Allometric relations for estimation of tree above-ground biomass from DBH. Four different relations ((A)±(D) from Table 1) are plotted along with individual tree biomass data from Brown (1997) (*) and Araujo et al., 1999 (). (b) Same as (a) with x-axis expanded to show detail for smaller diameters.

M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

of uncertainty would have a multiplicative effect. Therefore, we calculated positive and negative error bounds using the following equation: Propagated error ˆ …estimate  sampling error†  …1  other error†

(3)

In order to assess the effect of sample area on the biomass estimate, we took a Monte Carlo approach to sub-sample the full data set. We wrote a simple program in Microsoft Visual Basic to select random points and then to locate square plots of 0.09, 0.25, 0.49, 1.00, 1.96, and 4.00 ha using periodic boundary conditions to avoid bias at plot edges. AGB35 for each sub-sampled plot was calculated using Eq. (1) where the allometric function used was Equation (A) of Table 1. This procedure was repeated automatically 500 times for each block size. We calculated the number of plots n needed to estimate the mean within an error d at an error rate a by iterative solution of nˆ

2 s2 t‰a;n

1Š

d2

(4)

where t is the Student's t-statistic and s2 the population variance estimate (Zar, 1996, p. 107). 4. Survey results and biomass estimation We estimated total biomass based on the tree survey data complemented by measurements made at the Tapajos National Forest site by Silver et al. (2000) and general relations for Amazon region forests derived from literature. We treated the following biomass compartments separately: (1) above-ground live trees with DBH  35 cm; (2) above-ground live trees, 15 cm  DBH < 35 cm; (3) above-ground live trees and shrubs, DBH < 15 cm; (4) vines and epiphytes; (5) all below-ground biomass (soil organic matter excluded); (6) above-ground ®ne necromass and (7) above-ground coarse necromass. Spatial variation in sampling biomass was estimated using the four blocks. Simulated re-sampling of smaller plots allowed us to examine the effect of plot size and number on tree …DBH  35 cm† biomass estimation. A total of 21 679 trees were measured and the average frequency of trees greater than 35 cm DBH was approximately 55 ha 1 (Table 2). We estimated

375

Table 2 Stand characteristics based on …n ˆ 4† blocks surveyed in the Tapajos National Forest Diameter class

Lower limit (cm)

Upper limit (
Trees (ha 1)

95% confidence interval

1 2 3 4 5 6 7 8 9 10

35 45 55 65 75 85 95 105 115 125

45 55 65 75 85 95 105 115 125 ‡

23.04 13.07 8.10 5.02 2.79 1.51 0.93 0.31 0.24 0.29

4.57 2.67 1.16 0.92 0.34 0.39 0.21 0.12 0.13 0.10

55.30

9.19

Total

stem density of about 168 trees ha 1 from 15 to 35 cm diameter using Eqs. (2a) and (2b). The tree frequency by class showed a clear geometric progression (Fig. 2). The ratio q relating the number of trees in a class of diameter d cm to the class of diameter …d ‡ 10† cm averaged approximately 1.7 for classes 1±6. The ratios (q) used the calculation of above-ground biomass in each of the four survey plots (ratio of class 1, 35  DBH < 45 cm to class 2, 45  DBH < 55 cm diameter) were 1.82, 1.82, 1.73, and 1.69 for blocks 18, 9, 2, and 3, respectively (Fig. 2). We calculated above-ground biomass for trees of DBH  15 cm using four separate allometric relations and combined the results by diameter class (Fig. 3). The estimated biomass ranged from 222 to 270 Mg ha 1 (Table 3). While Equations (A)±(C) (Table 1) predict surprisingly similar total biomass density for trees, the similarity results from compensating errors among size classes. With the exception of Equation (C) (Araujo et al., 1999, Eq. (1)), the Table 3 Biomass density (Mg ha 1) calculated using four separate allometric relations for measured and modeled size classesa A

B

C

D

15 cm < DBH < 35 cm DBH > 35 cm

47 177

39 184

68 155

46 223

Total DBH > 15 cm

224

222

223

270

a

The four allometric equations (A)±(D) are found in Table 1.

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M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

Fig. 2. Stem frequency (log scale) versus mid-point diameter for twelve 10-cm diameter classes of trees from four blocks of about 100 ha each surveyed in the Tapajos National Forest. Stems in classes of DBH  35 cm were measured. Stem frequencies for two smaller diameter classes were estimated using Eqs. (2a) and (2b).

Fig. 3. Above ground biomass density estimated for trees by diameter class using four different allometric relations (Table 1). Data for all 392 ha surveyed were used to construct this plot.

M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

maximum biomass is found in class 1 …35  DBH < 45†. In contrast, Equation (C) predicts far greater biomass for DBH below about 30 cm than the other three equations. Diameter classes 1±4 …35 cm  DBH < 75 cm† accounted for over 50% of the biomass regardless of the allometric equation selected. Including the modeled classes ( 1 and 0), the six smallest classes greater than 15 cm DBH account for at least two-thirds of the estimated above-ground biomass of trees. We estimated above-ground biomass for trees with DBH less than 15 cm using the average ratio (0.36) of biomass for trees below 15 cm to those in diameter classes 15±55 cm in two 0.5±1 ha plots measured in ``tierra ®rme'' forest at San Carlos de Rio Negro, Venezuela (Jordan and Uhl, 1978). We estimated vine and epiphyte biomass using the ratio of vines plus epiphytes to above-ground live biomass (0.07) measured directly by Fittkau and Klinge (1973) at a site near Manaus, Brazil. Silver et al. (2000) worked in two of the four blocks covered by our tree survey. They measured belowground biomass in 23 pits of 1 m depth and at least 1 m2 area. Pits were nearly evenly distributed on sand

377

and clay soils. Based on soil survey results (Silver et al., 2000), we assumed that the ratio of clay soil area to sand soil area in our blocks was 3 and then we calculated weighted averages and propagated the errors accordingly. The assumption of a ratio of sand to clay soil area was necessary because root biomass density depended on soil type, and adequate maps of soil types were not available for the full study area. Root biomass below 1 m depth is poorly known although deep roots are very important for forest function in the Amazon region (Nepstad et al., 1994). Cairns et al. (1997) reviewed root biomass studies from a global database including 39 tropical sites. We used their Eq. (1) as an estimate for total root biomass (63 Mg ha 1) from our site. Comparison of this to the results from Silver et al. (2000) implies that there should be roughly 22 Mg ha 1 of root biomass below 1 m depth. Silver et al. (2000) also measured the standing stocks of ®ne necromass in our study blocks. We used their average values from sand and clay soils by calculating means and errors similarly to the procedure for roots. There are no measurements of coarse necromass at our site and such measurements are extremely scarce in the Amazon region. We used a

Table 4 Biomass density (Mg ha 1) estimate for the study sitea Mass (Mg/ha)

Sampling 95% CI (Mg/ha)

Other error (%)

Above ground live Trees …DBH  35 cm† Trees …15 cm < DBH < 35 cm† Trees …DBH < 15 cm† Vines and epiphytes

177 47 40 18

24 10 8 2

20 50 50 50

Sub-total above ground

282

44

Above-ground necromass Fine Coarse

8 19

2 3

50 50

‡7 ( 5) ‡14 ( 11)

Below-groundb All below-ground

63

9

50

‡45 ( 36)

372

56

Total biomass a

Propagated 95% CI (Mg/ha) ‡64 ‡39 ‡32 ‡12

( ( ( (

54) 29) 24) 10)

‡147 ( 117)

‡205 ( 164)

Uncertainties are estimated for sampling (95% con®dence interval) and for other sources of error. For the propagated errors of individual components, positive and negative error bounds are calculated by Eq. (3). Errors for all components with the exception of ®ne necromass are correlated to estimated tree biomass …DBH  35 cm† so errors have been added directly. The uncorrelated component (®ne necromass) was added in quadrature. b Based on measurements by Silver et al. (2000), we estimate mean (95% CI) root mass to 1 m depth as 41(15) Mg ha 1.

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M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

ratio of coarse necromass to above-ground live biomass (0.07) determined for forest sites outside of Manaus (Chambers et al., 2000). We estimated a total biomass density of 372 Mg ha 1 (Table 4). As shown in Table 3, three of the four allometric relations selected (A±C) provide nearly identical estimates of the total above-ground tree biomass …DBH  35 cm†. We selected Brown's (1997) equation 3.2.3 (Equation (A), Table 1) as the basis of our estimate. Use of Equation (D) (Table 1) (Carvalho et al., 1998) would result in a total tree biomass that is about 20% greater (Table 3). Errors presented in Table 4 relate both to the errors of sampling expressed as 95% con®dence intervals and to other sources of uncertainty discussed below. Sampling error was relatively small where sampling was intense such as for trees and ®ne necromass. For example, based on the four sampled blocks of about 100 ha each, we estimated that the 95% con®dence interval related to sampling for trees above 35 cm DBH was less than 15% of the mean. Our overall estimate of sampling error (at 95% con®dence) based on four 100 ha survey blocks was about 15% of total biomass. Other sources of uncertainty dominate our analysis of error. We estimate these sources of uncertainty very

conservatively and believe that they are reliable with 95% con®dence. The largest absolute uncertainties in the propagated error terms are for trees …DBH  35 cm† and for below-ground biomass (Table 4). The selected allometric functions gave biomass estimates that varied by as much as 20% (Table 3). Therefore, we estimated an uncertainty of 20% in the biomass compartment of trees with DBH  35 cm. For biomass compartments that were estimated by ratios to the above-ground tree biomass (trees of DBH < 35 cm, vines and epiphytes, coarse necromass, and all below-ground biomass), we do not have any clear guide for error estimation and therefore we assigned an error of 50% to these categories. We also assigned a 50% error to the ®ne necromass compartment to account conservatively for seasonal variations that were not re¯ected in the sampling by Silver et al. (2000). We used a Monte Carlo re-sampling from the full data set of trees with DBH  35 cm to simulate results one would obtain from sampling small plots. As expected, the variability in estimated average biomass decreased with plot size (Fig. 4). The coef®cient of variation for 4 ha areas was slightly less than 20%. We calculated the total number of square plots of areas

Fig. 4. Coef®cient of variation (standard deviation/mean) in biomass for trees …DBH  35 cm† for plots simulated by a Monte Carlo approach.

M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382 Table 5 Required number of plots and total sampling area for estimation of mean biomass to within 20% of the mean with 95% con®dence Plot size (ha)

n

Total area (ha)

0.09 0.25 0.49 1.00 1.96 4.00

43 21 15 10 8 6

3.87 5.25 7.35 10.00 15.68 24.00

from 0.09 to 4.00 ha required to estimate mean biomass within 20 with 95% con®dence for our site based on Eq. (4). The number of plots required drops sharply with increasing plot size, but the total area sampled increases as plot size increases (Table 5). Equal errors would be generated by sampling either six plots of 4 ha or 21 plots of 0.25 ha. However, the total sampling area for 4 ha plots would be more than four times as great. 5. Discussion Estimated total biomass for our study site (372 Mg ha 1) fell within the range of published values expected for Amazon region forests. Our survey was quite extensive so errors related to sampling of trees with DBH  35 cm contributed only slightly to the total error. Errors in allometric relations for trees and other biomass compartments, the uncertainties related to the biomass of trees with DBH less than 35 cm, and the lack of information on below-ground biomass dominated the error terms for our estimate. Improvement of the biomass estimate for the study site could be attained with measurement of stem frequency and DBH for trees with DBH less than 35 cm and of root biomass below 1 m depth. Small errors in the measurement of DBH have a minimal effect on the ®nal biomass determinations. Total tree biomass density calculated for classes 1±10 using the average diameter of integer sized trees and Equation (A) (Table 1) yields an estimate of 175 versus 177 Mg ha 1 (Table 3) calculated as the sum of all biomass from all measured trees. Most of this difference is accounted for by variation in the largest size class. It appears that highly accurate measurement of individual trees was relatively unimportant to

379

estimate biomass in our survey. Accurate measurements of individual trees are required for growth analyses. Allometric relations for the prediction of aboveground biomass from DBH may affect our total biomass estimates by about 20%. Brown et al. (1995) found that biomass for a site in Rondonia, Brazil varied by less than 10% for a comparison of a general allometric relation similar to Equation (A) (Table 1) to a speci®c allometric relation developed for their site. Chambers et al. (in press) have analyzed existing allometric relations and proposed a new equation based on data from 315 trees. Their allometric relation produced similar biomass estimates (not shown) to Equation (A) (Table 1) except in the largest size classes. Brown et al. (1995) found that 15 trees of DBH  55 cm accounted for over half of the biomass in a 1 ha plot in Rondonia, Brazil. They emphasized the need for more and better allometric data for large trees. We caution against a misinterpretation of this recommendation. Although the largest trees are impressively massive, they are also quite rare. Improvement of the allometric relations for very large trees will result in only minimal improvements in total biomass estimates. Assuming that the estimates for the largest size class …DBH  125 cm† are uncertain to a factor of two introduces a difference of less than 3% to the total biomass ®gure. As shown in Fig. 3, most of the biomass reside in the middle size classes (0±4). Inspection of Figs. 1 and 3 suggests that it may be more cost-effective to resolve allometric uncertainties for the middle size classes …35 cm  DBH < 75 cm†. Our biomass estimate could have been improved considerably by inclusion of more information on trees in smaller size classes. We conservatively considered the estimation of tree frequency in using Eqs. (2a) and (2b) highly uncertain (50%) although Gillespie et al. (1992) found that for estimation of two 10 cm diameter classes below the minimum size sampled, biomass estimates varied by at most 30% in nine forest types in Surinam. We note however that the Surinam data covered large forest areas and therefore may be expected to deviate less from the model than from our study area of 392 ha. For vegetation below 15 cm DBH, we used data from near San Carlos de Rio Negro where survey was unusually complete (Jordan and Uhl, 1978) to calculate a ratio (0.36) of above-ground live biomass below 15 cm DBH to

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M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

above-ground live biomass from 15 to 55 cm DBH. The wet forest at San Carlos may have a different distribution of trees than the moist Tapajos Forest. Use of the San Carlos data may cause an overestimate for biomass for live plants with DBH less than 15 cm. While SalamaÄo et al. (1998) found that the ratio of tree biomass in the range of 5±15 cm DBH to tree biomass from 15 to 55 cm DBH was only 0.19 for a moist forest site in Eastern ParaÂ, this is within the limits of error that we established for this compartment (50%). As noted by Kauffmann et al. (1998), biomass for small trees surveyed in forest plots in Rondonia varied inversely with the biomass of large trees and therefore a single ratio for biomass for small versus large trees may result in incorrect estimation of total biomass. Below-ground biomass contributed about 20% of our total estimated uncertainty. Silver et al. (2000) demonstrated that the sampling error for roots can be reduced to reasonable levels. Their sample penetrated to 1 m depth which probably represents most of the root biomass. In general, the area immediately below tree stems has not been sampled. Working in Eastern ParaÂ, Brazil, SalamaÄo et al. (1998) estimated that the below-ground extension of tree trunks accounted for 5% of above-ground live biomass. These biases were presumably accounted for by Cairns et al. (1997) however their global estimates for tropical moist forests are based on only 39 sites, less than half of which are lowland moist forests sites. Errors in the estimation of necromass and vine and epiphyte biomass represent secondary sources of uncertainty because of the relatively small mass in these compartments. We did not consider a number of potential biases and potential systematic errors in our tree survey and biomass estimate. First, we did not account for the effect of hollow trees; Brown et al. (1995) considered that this is probably unimportant. Second, we did not account speci®cally for the biomass of tree buttresses. However buttressed trees were included in the original data used to establish allometric relations. The allometric relations used do not account for the variations in wood density either among species or among size classes of trees (Fearnside, 1997b). Because of the large number of species involved (over 350 species in the 392 ha), it is unlikely that the effect of species and wood density variability on biomass estimates using allometric relations will be resolved without a

monumental effort. Where a limited number of species are particularly abundant such as in young secondary forests, efforts to improve allometric relation by reference to particular species result in greater accuracy for biomass estimates (e.g. Saldariaga et al., 1988; Uhl et al., 1988). We expected that expansion from a limited number of small plots would introduce considerable uncertainty to biomass estimates (Fig. 4). However, we found that for estimation of biomass, sampling variability for DBH classes >35 cm contributes an error of <20% with 95% con®dence when sampling 21 plots of 0.25 ha. Design of future surveys of biomass in the Amazon may bene®t from sampling relatively few randomly selected small plots for appropriate sampling strata. Existing databases for the Brazilian Amazon region include a substantial number of plots. For example, Fearnside (1997a) used 2954 plots from the RADAMBRASIL and FAO surveys conducted from the 1950s through the early 1980s in his estimate of biomass for the legal Amazon region. Fearnside (1997a) strati®ed these plots into 78 ``ecoregions.'' Fearnside (1997a) reasonably de®ned an ecoregion as a forest class in one of Brazil's nine states in the legal Amazon region. Unfortunately, not all ecoregions were covered by the existing surveys. Were all ecoregions covered, on average, there would had been about 38 plots per ecoregion. Assuming these plots were all about 1 ha in size, randomly located in relatively homogeneous areas, and had biomass distributed normally and similarly to our 400 ha survey, then the sampling error in tree biomass would only be 10% (Eq. (4)). Unfortunately, these conditions are very unlikely to hold. First, forest survey plots are not necessarily selected at random. Houghton (1997) suggested that RADAMBRASIL plots were biased by their proximity to clearings. Finally, we cannot say that tree biomass in other areas of the Amazon would be distributed similarly to the 400 ha area that we surveyed at the Tapajos National Forest. Recent biomass estimates for the Brazilian Amazon region such as those by Fearnside (1997a) and Houghton et al. (2000) and earlier estimates such as those by Brown and Lugo (1992) depend mainly on the same database of forest plots sampled over three decades. The locations of these sampling plots were not all randomly selected and were not rigorously

M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

strati®ed according to any overall spatial or vegetation classi®cation scheme. Biases in site selection for these sites are dif®cult to determine, therefore the recent convergence of biomass estimates for the Brazilian Legal Amazon should not comfort us. Based on a carefully designed survey of biomass in the North American boreal forest, Botkin and Simpson (1990) concluded that previous studies had over-estimated biomass by as much as a factor of 4. Compared to a government inventory of all Canadian forests, the estimate of Botkin and Simpson (1990) was lower by about 50%. In this study we produced a biomass estimate for a forest site in Amazonia and we carefully described sources of error in that estimate. Biomass estimates are directly useful in carbon cycle studies. Clearly understanding errors in those estimates is also important for us in developing strategies for reducing the uncertainty in biomass estimates in the future. Our study indicates that an accurate biomass estimate for a given forest site requires measurement of a relatively small number of randomly selected plots. For better regional estimates, improvement in the quality of allometric relations for the above-ground biomass of trees may not be as cost effective at this stage compared to an increase in the number of plots surveyed and an improvement of our knowledge of below-ground biomass. A strategy for regional biomass estimation should supplement previous surveys with new data both to ®ll gaps in earlier sampling and to overlap with previously sampled areas in order to account for unrecognized biases as well as potentially important biomass changes in the last three decades (Phillips et al., 1998). Existing vegetation maps and data from remote sensors may be used as a guide to stratify sampling. Acknowledgements We thank Johan Zweede, Rodrigo Pereira Junior and the foresters and technicians of FFT for their high quality work. We are grateful to Sandra Brown, Jeff Chambers, Fred Scatena, Whendee Silver, and Daniel Zarin for thoughtful advice. Two anonymous reviewers provided detailed and valuable comments that greatly improved the quality of the paper. The USDA Forest Service International Institute of Tropical

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Forestry works in cooperation with the University of Puerto Rico. Support for this research was provided by the USDA Forest Service and the NASA Terrestrial Ecology Program and Land Use and Cover Change Program (NCC5-225 and NCC5-338) and NASA Interdisciplinary Science Grant (NAG5-6137).

References Araujo, T.M., Higuchi, N., Carvalho Jr., J.A., 1999. Comparison of formulae for biomass content determination in a tropical rain forest site in the state of Para, Brazil. For. Ecol. Manage. 117, 43±52. Botkin, D.B., Simpson, L.G., 1990. Biomass of the North American Boreal forest. Biogeochemistry 9, 161±174. Brown, S., 1997. Estimating biomass and biomass change of tropical forests: a primer. FAO Forestry Paper 134. United Nations Food and Agriculture Organization, Rome. Brown, S., Lugo, A.E., 1992. Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia 17, 8±18. Brown, I.F., Martinelli, L.A., Thomas, W.W., Moreira, M.Z., Ferreira, C.A.C., Victoria, R.A., 1995. Uncertainty in the biomass of Amazonian forests: an example from Rondonia, Brazil. For. Ecol. Manage. 75, 175±189. Cairns, M., Brown, S., Helmer, E.H., Baumgardner, G.A., 1997. Root biomass allocation in the world's upland forests. Oecologia 111, 1±11. Carvalho Jr., J.A., Higuchi, N., Araujo, T.M., Santos, J.C., 1998. Combustion completeness in a rainforest clearing experiment in Manaus, Brazil. J. Geophys. Res. 103 (D11), 13199±13915. Chambers, J.Q., Higuchi, N., Schimel, J.P., Ferreira, L.V., Melack, J.M., 2000. Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon. Oecologia 122, 380±388. Chambers, J.Q., dos Santos, J., Ribeiro, R.J., Higuchi, N., in press. Tree damage, allometric relationships, and above-ground net primary production in Central Amazon Forest. For. Ecol. Manage. Fearnside, P.M., 1993. Biomass of Brazil's Amazonian forests: reply to Brown and Lugo revisited. Interciencia 18, 5±7. Fearnside, P.M., 1997a. Greenhouse gases from deforestation in Brazilian Amazonia: net committed emissions. Clim. Change 35, 321±360. Fearnside, P.M., 1997b. Wood density for estimating forest biomass in Brazilian Amazonia. For. Ecol. Manage. 90, 59±87. Fittkau, E.J., Klinge, H., 1973. On biomass and tropic structure of the Central Amazonian rain forest ecosystem. Biotropica 5, 2±14. Gillespie, A.J.R., Brown, S., Lugo, A.E., 1992. Tropical forest biomass estimation from truncated stand tables. For. Ecol. Manage. 48, 68±87. Houghton, R.A., 1997. Terrestrial carbon storage: global lessons for Amazonian research. Ciencia Cultura, J. Braz. Assoc. Adv. Sci. 49 (1±2), 58±72.

382

M. Keller et al. / Forest Ecology and Management 154 (2001) 371±382

Houghton, R.A., Skole, D.L., Nobre, C.A., Hackler, J.L., Lawrence, K.T., Chomentowski, W.H., 2000. Annual ¯uxes of carbon from the deforestation and regrowth in the Brazilian Amazon. Nature 403 (6767), 301±304. Jordan, C.F., Uhl, C., 1978. Biomass of a ``tierra ®rme'' forest of the Amazon basin. Oecol. Plantarum 13, 387±400. Kauffmann, J.B., Cummings, D.L., Hughes, R.F., 1998. Comment on biomass decline in forest fragments. Science 282, 1161a. Laurance, W.F., Laurance, S.G., Ferreira, L.V., Rankin-de Merona, J.M., Gascon, C., Lovejoy, T.E., 1997. Biomass collapse in Amazonian forest fragments. Science 278, 1117±1118. Meyer, H.A., 1952. Structure, growth, and drain in balanced unevenaged forests. J. For. 50, 85±92. Moraes, J.L., Cerri, C.C., Melillo, J.M., Kicklighter, D., Neill, C., Skole, D.L., Steudler, P.A., 1995. Soil carbon stocks of the Brazilian Amazon basin. Soil. Sci. Soc. Am. J. 59, 244± 247. Nepstad, D.C., Carvalho, C.R., Davidson, E.A., Jipp, P.H., Lefebvre, P.A., Negreiros, G.H., Silva, E.D., Stone, T.A., Trumbore, S.E., Vieira, S., 1994. The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures. Nature 372, 666±669. Nepstad, D.C., Klink, C.A., Uhl, C., Vieira, I.C., Lefebvre, P., Pedlowski, M., Matricardi, E., Negreiros, G., Brown, I.F., Amaral, E., Homma, A., Walker, R., 1997. Land-use in Amazonia and the cerrado of Brazil. Ciencia Cultura, J. Braz. Assoc. Adv. Sci. 49, 73±85. Nepstad, D.C., Verissimo, A., Alencar, A., Nobre, C., Lima, E., Lefebvre, P., Schlesinger, P., Potter, C., Moutinho, P., Mendoza, E., Cochrane, M., et al., 1999. Large-scale impover-

ishment of Amazonian forests by logging and ®re. Nature 398, 505±508. Phillips, O.L., Malhi, Y., Higuchi, N., Laurance, W.F., Nunez, P.V., Vasquez, R.M., Laurance, S.G., Ferreira, L.V., Stern, M., Brown, S., Grace, J., 1998. Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282, 439±442. SalamaÄo, R. de P., Nepstad, D.C., Vieira, I.C., 1998. Biomassa e estoque de carbono de ¯orestas tropicais primaÂrias e secundaÂrias. In: Gascon, C., Moutinho, P. (Eds.), Floresta AmazoÃnica: DinaÃmica RegeneracËaÄo e Manejo. Instituto Nacional de Pesquisas de Amazonia, Manaus, Brazil, pp. 99±119. Saldariaga, J.G., West, D.C., Tharp, M.L., Uhl, C., 1988. Longterm chronosequence of forest succession in the upper Rio Negro of Colombia and Venezuela. J. Ecol. 76, 938±958. Schimel, D.S., 1995. Terrestrial ecosystems and the carbon cycle. Global Change Biol. 1, 77±91. Silver, W., Neff, J.C., McGroddy, M., Veldkamp, E., Keller, M., Oliveira Jr., R.C., 2000. Effects of soil texture on belowground carbon and nutrient storage in a lowland Amazonian forest ecosystem. Ecosystems 3, 193±209. Skole, D., Tucker, C., 1993. Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988. Science 260, 1905±1909. Taylor, J.R., 1997. An Introduction to Error Analysis, 2nd Edition. University Science Books, Sausalito, CA. Uhl, C., Buschbacher, R., SerraÄo, E.A.S., 1988. Abandoned pastures in Eastern Amazonia. I. Patterns of plant succession. J. Ecol. 76, 663±681. Zar, J.H., 1996. Biostatistical Analysis, 3rd Edition. Prentice-Hall, Upper Saddle River, NJ.