Ecological Applications, 14(4) Supplement, 2004, pp. S72±S88 ᭧ 2004 by the Ecological Society of America

RESPIRATION FROM A TROPICAL : PARTITIONING OF SOURCES AND LOW CARBON USE EFFICIENCY

JEFFREY Q. CHAMBERS,1,2,4 EDGARD S. TRIBUZY,2 LIGIA C. TOLEDO,2 BIANCA F. C RISPIM,2 NIRO HIGUCHI,2 JOAQUIM DOS SANTOS,2 ALESSANDRO C. ARAUÂ JO,2 BART KRUIJT,3 ANTONIO D. NOBRE,2 AND SUSAN E. TRUMBORE1 1University of California, Earth System Sciences, Irvine, California 92697 USA 2Instituto Nacional de Pesquisas da AmazoÃnia, C.P. 478, Manaus, Amazonas, Brazil, 69011-970 3Alterra, University of Wageningen, Alterra, 6700 AA Wageningen, The Netherlands

Abstract. Understanding how tropical forest carbon balance will respond to global change requires knowledge of individual heterotrophic and autotrophic respiratory sources, together with factors that control respiratory variability. We measured leaf, live wood, and soil respiration, along with additional environmental factors over a 1-yr period in a Central Amazon terra ®rme forest. Scaling these ¯uxes to the ecosystem, and combining our data

with results from other studies, we estimated an average total ecosystem respiration (Reco) of 7.8 ␮mol´mϪ2´sϪ1. Average estimates (per unit ground area) for leaf, wood, soil, total heterotrophic, and total autotrophic respiration were 2.6, 1.1, 3.2, 5.6, and 2.2 ␮mol´mϪ2´sϪ1, respectively. Comparing autotrophic respiration with net (NPP) esti- mates indicated that only ϳ30% of carbon assimilated in was used to con- struct new tissues, with the remaining 70% being respired back to the atmosphere as autotrophic respiration. This low ecosystem carbon use ef®ciency (CUE) differs consid-

erably from the relatively constant CUE of ϳ0.5 found for temperate . Our Reco estimate was comparable to the above-canopy ¯ux (Fac) from eddy covariance during de®ned sustained high turbulence conditions (when presumably Fac ϭ Reco) of 8.4 (95% CI ϭ 7.5± 9.4). Multiple regression analysis demonstrated that ϳ50% of the nighttime variability in

Fac was accounted for by friction velocity (u*, a measure of turbulence) variables. After accounting for u* variability, mean Fac varied signi®cantly with seasonal and daily changes in precipitation. A seasonal increase in precipitation resulted in a decrease in Fac, similar to our soil respiration response to moisture. The effect of daily changes in precipitation

was complex: precipitation after a dry period resulted in a large increase in Fac, whereas additional precipitation after a rainy period had little effect. This response was similar to that of surface litter (coarse and ®ne), where respiration is greatly reduced when moisture is limiting, but increases markedly and quickly saturates with an increase in moisture. Key words: ecophysiology; ecosystem model; forest dynamics; global ; net ecosystem exchange; NPP/GPP ratio; rainforest; respiration; respiration photosynthesis ratio.

INTRODUCTION Because NEP is the difference of two large ¯uxes of Studies of net ecosystem production (NEP) are be- gross primary production (GPP) and ecosystem res- coming an increasingly important area of applied eco- piration (Reco), quantifying NEP accurately is dif®cult. logical research because NEP quanti®es the carbon Nevertheless, a relatively small NEP ¯ux of 0.5 Mg Ϫ1 Ϫ1 source or sink strength of an ecosystem. This net ex- C´ha ´yr , if scaled over the entire Amazon basin, change of carbon between and the atmo- results in a globally signi®cant accumulative sink of Ϫ12 sphere can play a key role in regulating the human- about 0.2 ϫ 10 g C/yr. Small NEP ¯uxes over large areas that are dif®cult to quantify accurately and pre- induced increase in CO2 and concomitant global chang- es predicted to result from this increase (Intergovern- cisely are of global importance. mental Panel on Climate Change [IPCC] 2001). Although respiration is of equal importance to pho- Amazon forests play a particularly important role the tosynthesis in determining NEP, few studies (Goulden atmospheric exchange of carbon, and the carbon bal- 1996, Lavigne and Ryan 1997, Law et al. 1999) have ance of these ecosystems is a topic of active research. attempted to estimate total ecosystem respiration (Reco). While photosynthesis is a distinct process, Reco inte- Manuscript received 2 October 2001; revised 7 July 2002; grates a number of disparate processes, including au- accepted 22 July 2002; ®nal version received 16 September 2002. totrophic (Ra; leaf, stem, root) and heterotrophic (Rh; Corresponding Editor: A. R. Townsend. For reprints of this Spe- from litter and soil organic matter decomposition) cial Issue, see footnote 1, p. S1. sources. Adding to this complexity, R comprises mi- 4 Present address: Tulane University, and Evo- a lutionary Biology, 310 Dinwiddie Hall, New Orleans, Lou- tochondrial, alternative-pathway, and futile cycles, and isiana 70118 USA. E-mail: [email protected] photorespiration, along with the numerous physiolog- S72 August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S73 ical processes which demand respiratory support in- of EC and chamber data, it is important to determine cluding growth, protein turnover, ion uptake, phloem under what conditions nighttime Feco from EC data pro- loading, nitrate reduction, and maintenance of ion gra- vides a reliable estimate of Reco. dients (Amthor 2000, Cannell and Thornley 2000, Accurately quantifying the relative importance of re- Thornley and Cannell 2000). Measures of heterotrophic spiratory sources and pathways is an important ®rst respiration integrate the activity of a wide variety of step to advance our understanding of Reco. In addition, substrates and decomposers. Fungi, for example, are to move beyond simply quantifying ¯uxes, studies of probably more important for decomposing surface lit- factors controlling respiratory variability are needed, ter, whereas bacteria are more important for decom- and are little understood for tropical forests. The over- posing mineral associated soil organic matter (SOM). all objective of this study was to develop a better un-

Thus, while respiration is often measured as the inte- derstanding of the components of Reco for a tropical grated sum of many processes, to develop a more pro- forest in the central Amazon. We accomplished this by cess level understanding, relating individual respira- (1) quantifying the contributions of leaf (Rl), live wood tory sources to factors that control variability is im- (Rw), soil (Rs), autotrophic (Ra), and heterotrophic (Rh) portant. respiration to Reco, (2) developing a methodology for

Integrative studies of Reco are quite limited for trop- estimating ecosystem-scale ¯uxes from limited cham- ical forest ecosystems. Odum (1970) provided a com- ber measurements, (3) quantifying how a number of prehensive summary of extensive and detailed forest environmental and ecological factors control variabil- metabolism studies at El Verde, Puerto Rico (Odum et ity in Reco components, (4) comparing our scaled cham- al. 1970, Odum and Jordan 1970), and many Reco com- ber data with nighttime EC data, and (5) evaluating Ra ponents were quanti®ed. Additional studies have quan- and net primary productivity (NPP) estimates to cal- ti®ed Reco or Ra for tropical forests (MuÈller and Nielsen culate a forest carbon use ef®ciency (CUE). 1965, Kira 1975, Yoda et al. 1983), although errors MATERIALS AND METHODS associated with harvested tissues, static (e.g., alkali trap) CO2 ¯ux chambers, and other methodological is- Site characteristics sues make an accuracy assessment of these pioneering This study was carried out in the Central Amazon studies dif®cult. Investigations of individual respira- near the city of Manaus, Brazil, in collaboration with tory sources in closed-canopy tropical forests using the National Institute of Amazon Research (Instituto more accurate dynamic chambers (Nay et al. 1994) are Nacional de Pesquisas da AmazoÃnia, INPA). Mean an- limited and include soil (Trumbore et al. 1995, Sotta nual temperature is 26.7ЊC and rainfall varies through- 1998), live wood (Ryan et al. 1994, Meir and Grace out the year with distinct dry season during July, Au- 2002, Nepstad et al. 2002), leaf (Reich et al. 1991, gust, and September when there is usually Ͻ100 mm/ Meir et al. 2001), and coarse litter (Chambers et al. mo (Fig. 1a). It is dif®cult to quantify average annual 2001c) studies. precipitation, with one record for Manaus showing a The eddy covariance (EC) method may provide a statistically signi®cant increase from 1900 mm in 1910 means for measuring Reco at night. However, because to 2300 mm in 1980 (Fig. 1b). Precipitation measured nighttime atmospheric turbulence in the Amazon basin at the ZF2 site base camp was 3550 mm in 2000 and is often quite low (e.g., Trumbore et al. 1990), a sig- 2380 mm in 2001. Vegetation is old-growth closed- ni®cant amount of respired carbon can be trapped with- canopy terra ®rme (non¯ooded) forest. A total of 1176 in the canopy leading to an accumulation of CO2 below tree species were identi®ed in the nearby Ducke reserve the EC measurement height (Grace et al. 1995, Goulden (Ribeiro et al. 1999), and some trees can live for more et al. 1996). This is often corrected by measuring the than 1000 years (Chambers et al. 1998). rate of CO2 accumulation below the canopy (Fbc; the Investigations were carried out at an INPA reserve storage ¯ux), and the total ecosystem ¯ux (Feco) is es- located along the ZF2 road on two 20 ϫ 2500 m per- timated by summing Fac and Fbc (Fan et al. 1990). How- manent forest inventory plots referred to as the Jaca- ever, there is some debate over what conditions allow randa plots (2Њ36Ј S, 60Њ12Ј W). These long and narrow the sum of Fac and Fbc to be an unbiased estimate of plots capture ecosystem variation associated with the

Reco. Several researchers acknowledge that under stable undulating local topography. Mean elevation is ϳ100 atmospheric conditions, some CO2 may be not be cap- m with Ϯ50 m between plateaus and valleys. Soils tured by the eddy covariance method, leading to un- along a typical toposequence comprise Oxisols on pla- derestimates in Reco (Goulden 1996, Malhi and Grace teaus, Utilsols on slopes, and Spodosols associated with 2000). This potential bias may be corrected by remov- small valley streams (Bravard and Righi 1989). Surface ing (®ltering) ¯ux data when turbulence is low, al- (to 5 cm) clay content decreases (from ϳ75% to 5%), though determining the appropriate threshold is a chal- and sand content increases (from ϳ10% to 85%), when lenging task (Miller et al. 2004), and there is some moving from plateau to valley (Ferraz et al. 1998). debate over whether or not such a correction is needed Although soil characteristics vary continuously with (ArauÂjo et al. 2002). To allow instructive comparisons slope, it is dif®cult to establish clear boundaries, and S74 JEFFREY Q. CHAMBERS ET AL. Ecological Applications Special Issue

Rw was measured using an infrared gas analyzer (IRGA, LI 6252 with an LI-670 ¯ow control unit; Li- Cor, Lincoln, Nebraska, USA) operated as a closed dynamic chamber (CDC) (Norman et al. 1997) with a ¯ow rate of 1.0 L/min. Semicylindrical polyvinyl chlo- ride (PVC) chambers (250±400 mL) were cinched to

the base of the tree (at a height of ϳDb,orϳ1.3 m) using nylon straps. Closed-cell foam coated with sili- con was glued to the rim of the chamber to provide a malleable seal that conformed to irregularities in the trunk. Leakage was checked by breathing around the chamber after each measurement. The measurement in- terval spanned ϳ1±2 min. Flux from the base of each tree was quanti®ed eight times between August 2000 and June 2001 at ϳ4±6 wk intervals. We used multiple

regression and ANOVA to test Db, month, growth class

(1±5), and stem surface temperature (Ts) as predictor

variables. We hypothesized that Rw would vary sea- sonally and be positively correlated with growth rate, but due small temperature variations at 1.3 m height,

would be independent of Ts.

Leaf respiration

Leaf respiration (Rl) measurements were made at am-

bient leaf temperature (Tl) using a LiCor 6400 (Li-Cor, Lincoln, Nebraska, USA) equipped with a blue/red FIG. 1. (a) Annual and (b) monthly variability in precip- LED light source turned off, creating a dark chamber. itation for Manaus, Brazil from 1910 to 1983 (data from Measurements were logged only after the total coef- National Climatic Data Center, Asheville, North Carolina, USA). There was a signi®cant increase in precipitation during ®cient of variation (a LiCor parameter) fell below 1%, the 20th century for the annual record. The monthly record and the IRGAs were ``matched'' prior to each mea- shows that precipitation variability is always reduced during surement (4±8 min). Matching reroutes the out¯ow of the dry season, whereas wet-season months are occasionally the sample IRGA to the reference IRGA allowing cal- dry. ibration to an identical CO2 source. Rl was measured at approximately hourly intervals over a combined the landscape is approximately equally divided among 24-h period for the same leaf from a number of large plateau, slope, and valley forests. trees (Ͼ14 m in height) located near three canopy ac- cess towers along the ZF2 road. Measurements were Stem respiration carried out from April to November 2001. These data Fifty trees were selected from ®ve relative growth were combined to test for hourly variation, and vari- classes using data collected during the ®rst year of a ation with Tl, using regression and ANOVA. We hy- seasonal tree growth study (Silva et al. 2002). Brie¯y, pothesized that because all leaves were located high in

ϳ300 trees from the Jacaranda plots were randomly the canopy, Tl would be an important factor controlling selected from three size classes. Each tree was out®tted diurnal Rl variability. Due to the time consuming nature with a dendrometer band used for measuring small of this work, we did not attempt to quantify leaf res- changes in circumference (i.e., Ͻ0.05 mm). Using these piration for trees Ͻ14 m in height, or to quantify within increments, average woody tissue production (10 mo canopy variation for individual trees. from September 1999 to July 2000) was estimated for Our goal was to measure nonphotorespiratory mi- all trees using an allometric model that predicts tree tochondrial CO2 release (Rl). Although measuring this mass as a function of base diameter (Db; at 1.3 m height ¯ux at night is relatively straightforward, there is grow- or above the buttresses) and expressed as mg growth´kg ing evidence that, all other factors being equal, Rl in Ϫ1 Ϫ1 (dry mass) biomass ´d (Chambers et al. 2001b). the light is lower than Rl in the dark (Graham 1980, Next, trees were classi®ed into four classes each rep- Atkin et al. 1998, 2000a, Peisker and Apel 2001). Also, resenting 25% of total wood production and a ®fth class placing a sunlit leaf into a dark chamber introduces a for trees that experienced no measurable growth during number of artifacts including a postillumination CO2 the 10-mo period. Ten trees were randomly selected burst (PIB) and light enhanced dark respiration from each of these ®ve growth classes (n ϭ 50) and (LEDR), in addition to actual inhibition of Rl by light woody tissue respiration (Rw) was quanti®ed. (Atkin et al. 2000b). Both PIB and LEDR are transient August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S75

phenomena that last for up to ϳ20 and 500 s, respec- and Fbc. Thus, most of Feco is captured by Fbc during tively, after the leaf is placed in a dark chamber (Atkin stable conditions, and most of Feco is captured by Fac et al. 1998). Inhibition of Rl by light is apparently con- during turbulent conditions. Malhi et al. (1998) found tinuous and results in an ϳ40% decrease in Rl (Peisker that the threshold between these two states at night and Apel 2001). Because we waited until the CO2 ¯ux occurred at a friction velocity (u*; a measure of at- from the leaf equilibrated (4±8 min), the effects of PIB mospheric turbulence) of ϳ0.1 m/s. Ideally, the sum and LEDR were minimized. However, to account for of Fac and Fbc (Feco) would be independent of changes light inhibition of Rl, we reduced our daytime ¯ux es- in atmospheric turbulence, and Feco would provide a timates by 40%. continuous estimate of nighttime Reco. However, Malhi

et al. (1998) also found that Feco varied with changes Soil respiration in meteorological conditions, suggesting dif®culties when using F to estimate R . Thus, although F To explore how Rs varies with changes in topography eco eco eco and soil texture, 54 PVC collars were equally distrib- may not provide a constant measure of nighttime Reco, uted among plateau, slope, and valley soils along a under conditions of sustained high turbulence (SHT), portion of one Jacaranda plot, with each collar sepa- Feco may provide a reliable estimate of Reco (Goulden rated by 15 m. Chamber design and installation was 1996). In addition, because Fbc approaches zero during analogous to Trumbore et al. (1995). The IRGA system SHT conditions, Fac alone should provide an estimate and methodology was the same as used for stem res- of Reco. piration with the exception that the larger chamber We selected a subset of the EC data for analyses. (4.5±6.0 L) had a small pressure equilibration vent. First, time of night was limited to 1900±0500 hours to Flux from each chamber was quanti®ed eight times avoid photosynthetic ¯ux interference. In addition, at during the day (0800±1600 hours) between July 2000 very low u*, ¯ux variability was greatly reduced, and and June 2001 at ϳ4±6-wk intervals. Collars were in- the error model was fundamentally different than for stalled about six months before the ®rst measurement, higher u* values, so we only considered data with u* and were kept in place between sampling dates. In con- above 0.01 m/s. To ensure constant variance of the junction with soil surface ¯ux measurements, soil tem- residuals, extreme outlier observations (n ϭ 63) were perature (Ts) and soil volumetric water content (VWC) also removed. Of a total of 4631 nighttime observations (TDR probes, HydroSense; Campbell Scienti®c, Lo- (half-hour averages), 4287 observations were consid- gan, Utah, USA) were also measured. Relationships ered. Due to negative ¯uxes (the CO2 concentration were explored using multiple regression and ANOVA. decreased from one interval to the next), Fac and Feco

We hypothesized that the Rs vs. VWC relationship were offset by the appropriate constants so that neg- would be curvilinear, where respiratory activity from ative values could be included in the error model and very dry and very wet soils would be reduced, and allow transformation analysis of the response variable. respiratory activity would peak at intermediate mois- Our ®rst goal was to determine the conditions under ture contents. We also hypothesized that Rs would be which Feco and Fac were independent of changes in u*, independent of Ts due to small seasonal and daily and then estimate Reco from the tower data under these changes in Ts. conditions. To explore how Fac and Feco varied with changes in u*, we used multiple regression analysis. Eddy covariance data Evaluated predictor variables were u* (averaged every We used eddy covariance (EC) data collected from half hour) and the half-hourly change in u*(⌬u*). Also, the ZF2-K34 tower from 31 August 1999 to 16 Sep- to explore how the long-term average u* explained tember 2000 (ArauÂjo et al., in press). This tower is variation in Fac and Feco, a set of u* averages varying located within 2 km of the locations where chamber from four (u4*) to eight (u8*) hours were calculated. measurements took place. Although the chamber and SHT conditions under which Fac and Feco were no longer EC data did not occur over the same interval of time, dependent on changes in u* were then determined. A they both occurred during periods with relatively high best estimate of Reco was calculated as the average Fac precipitation. Annual precipitation measured at the ZF2 and Feco under SHT conditions. Finally, we compared base camp beginning June 2000 was 2740 mm, whereas Reco estimates from EC and scaled-chamber data to de- beginning August 1999 annual precipitation was 3190 termine if they differ signi®cantly. mm (cf. Fig. 1). Next, after accounting for variability in Feco and Fac We were interested in conditions where the EC ¯ux due to turbulence, the effects of daily and seasonal was a reliable measure of Reco, although these condi- variability in precipitation was explored. Evaluated tions are dif®cult to determine. The above-canopy EC precipitation variables were the 20-d average precipi-

¯ux (Fac) is often corrected for the below canopy stor- tation (Pr20) and daily precipitation (Prday) preceding age ¯ux (Fbc) because CO2 accumulates within the can- each EC observation. We employed the 20-d average opy when turbulence is low, and the total ecosystem because Toledo (2002) demonstrated that respiratory

(or biotic) ¯ux (Feco) is calculated as the sum of Fac activity from ®ne surface litter was most strongly cor- S76 JEFFREY Q. CHAMBERS ET AL. Ecological Applications Special Issue related with the 20-d average, as well as daily changes in precipitation. The respiratory ¯ux from surface litter is susceptible to large variation with changes in mois- ture content (Chambers et al. 2001c, Toledo 2002), and is probably responsible for a considerable amount of daily and seasonal variability in Reco.

Statistical analysis All statistical analyses were performed using SAS (version 8.2) software. For ANOVA, data were trans- formed to ensure normality before testing for signi®- cant differences among means using Bonferroni's method. Transformations were tested using maximum likelihood analysis, and in some cases optimum power transformation was employed to explore transforma- FIG. 2. A simulated data set demonstrates that extrapo- tion likelihood as a function of powers of the response lating predictions from a regression equation with a trans- formed response variable can introduce bias when there is a variable. For multiple classi®cation variables, inter- large amount of unexplained variability (i.e., low r2). The actions among variables were tested. When group var- simulated data comprised soil respiratory ¯ux from a 1-ha iances were not equal (Brown-Forsythe test), variance grid divided into 10 000 1-m2 cells where the ¯ux varied with weighted ANOVA results were used. changes in soil texture. Each cell was assigned a ¯ux based on a random normal deviate (RND) drawn from a distribution For regression analysis, transformation of the re- with a mean of log(10 ␮mol´mϪ2´sϪ1) (1.0 in the transformed sponse variable was carried out in the same manner as units), and a SD that varied from 0.25 to 1.0 in log units, for ANOVA to ensure residuals were equally distrib- along with a linear increase in ¯ux with soil clay content. uted about the regression line with constant variance (homoscedasticity). Outlier analysis was performed us- where m is the mean and SD the standard deviation of ing Studentized residual analysis. Potential outlier ob- the transformed data (Burington and May 1970). In servations (P Ͻ 0.05) were removed when it was clear regression analysis with transformed data, additional that the data points were in error, or acted as leverage problems arise. For example, if the y-axis is log trans- points. The amount by which the response variable formed, a data point 1 SD above the regression line has changes when the continuous variable changes may be more weight (in the original units) than a point 1 SD different within various groups de®ned by classi®ca- below the regression line, so the error model is not tion factors, or with levels of continuous variables. To balanced in the original units. If the regression equation account for these effects, all one-way interactions is used to calculate a summed measure (e.g., an ac- among predictor variables were tested for signi®cance. cumulative ¯ux), bias is introduced. As shown in Fig. In cases where only higher order terms were statisti- 2 using simulated data, this bias can be particularly cally signi®cant, all lower order terms contained in large when the correlation coef®cient is small. One way these higher-order terms were also included (Aiken and to account for this bias is to use Monte Carlo random- West 1991). In some cases, data was constrained to a ization methods (Kalos and Whitlock 1986, Manly limited portion of the total range to avoid problems 1991) to account for error distributed about the re- associated with nonconstant variance, and leveraging gression line by creating bootstrap estimates (Manly of the regression curve. In developing multiple re- 1991) using gression and ANOVA models, we opted for ®rst es- tablishing the form of the relationship using single pre- yЈϭRND(y intЈ , SD res) ϩ bx (2) dictor (x) variables, then adding predictors and testing where each prediction yЈ is estimated by drawing a for interactions one at a time. random normal deviate (RND) from a distribution with

a meany intЈ and SD of the residuals (SDres) (in trans- Scaling ¯uxes formed units), and bx can be any number of regression When extrapolating results from a limited population variables and coef®cients. Although Eq. 1 could be towards developing an accumulative measure (e.g., used to estimate an unbiased average in the case of a from leaves to canopy), relationships that are not nor- logarithmic transformation, Eq. 2 is more general and mally distributed can introduce bias. In some cases, the can be used when other transformations are employed, or when a number of explanatory variables vary con- bias can be solved analytically. For example, the mean tinuously. The important point is that although residual (y ) of a variable that is (natural) log-normally dis- ave error may be equally weighted about the regression line tributed is given by in the transformed units, when predictions from re- (m ϩ SD/2) yave ϭ exp (1) gression equations are scaled over a large number of August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S77

individuals, summed errors in the original units are not and where mb, ␳, dmin, and dmax are the dry mass of the equally weighted about the regression line. branches, the wood density (kg dry mass/m3 fresh vol- ume), and the maximum and minimum diameters of Scaling stem respiration branches, respectively. Eq. 4 has been veri®ed for trees in Pasoh, Malaysia (Yoda 1983), and Yamakura et al. Scaling respiration measurements made at the base (1987) demonstrated that the pipe model theory ex- of the tree toward estimating whole-tree ¯ux presents plained crown shape differences in Sebulu, East Ka- some dif®culties. Some studies estimate the respiratory limantan. Total surface area for branches derived from ¯ux per unit live tissue volume, and then scale chamber Eq. 4 is given by Yoneda (1993) as values to whole trees by estimating the total amount of live tissue (Ryan and Waring 1992, Ryan et al. 1997, Abϭ␲K ln(d max/d min) (6)

Law et al. 1999). This method has advantages because and total stem surface area (As) is given by the sum of it allows a more physiological interpretation of ¯ux Eqs. 3 and 6. variability (Amthor 2000). However, because growth We estimated As for 315 trees harvested in a central respiration is primarily from cambium cells, and main- Amazon forests (Chambers et al. 2001b) using the Eqs. tenance respiration is primarily from sapwood cells, 3 and 6. Values for rb, hb, and mb were measured in- the relationship between sapwood volume and Rw can dependently for each harvested tree. The radius at the vary with the growing season (Sprugel 1990). Edwards top of the bole (rmin) was estimated using a taper equa- and Hanson (1996), for example, found that Rw was tion in Chambers et al. (2000), and dmax was assumed only related to sapwood volume during the dormant, to be equal to rmin. The minimum branch diameter (dmin) not the growing season. Respiratory ¯ux per unit sap- was assumed to be 2 mm based on the average value wood volume can also vary between species or with for broad-leaf trees (cited in Yoneda 1993). We then height within the same individual (Yoda et al. 1983, employed regression analysis to explore the relation-

Sprugel 1990, Ryan et al. 1996a) often related to var- ship between Db and As. Next, we applied this regres- iation in tissue nitrogen content (Maier et al. 1998, sion equation to each tree (Ͼ10 cm Db) in the Jacaranda Stockfors and Linder 1998). Furthermore, based on PFIPs (permanent forest inventory plots; 10 ha, 5823 preliminary investigations, we found that a standard trees), and calculated an average stem area index (SAI), method for estimating the volume of living cells (stain- analogous to leaf area index (LAI), which gives the ing with tetrazolium) is dif®cult in tropical forests be- total stem surface area per unit ground area. cause many woods are very dense, dark or red in color Because the 50 trees sampled for stem respiration (i.e., do not stain well), and the area stained is quite were selected from ®ve unbalanced growth classes, diffuse making volume estimates dif®cult. these data could not be simply randomly applied to the An alternative method for scaling chamber ¯ux mea- entire forest. However, because the ϳ300 trees from surements to whole trees is to estimate the stem surface the dendrometer study were selected randomly (Silva area for the entire tree (Kinerson 1975), and then scale et al. 2002), we assumed that the distribution of the ¯uxes similar to using leaf area index (LAI) for scaling total number of trees in each relative growth (mg Ϫ1 Ϫ1 leaf respiration (e.g., Law et al. 1999). Although this growth´kg biomass´d ) class for the entire forest would be the same as their distribution in the dendro- method provides little insight into differences in Rw among individuals, it may be a more accurate method meter study. Thus each tree from the Jacaranda plots (n ϭ 5283) was randomly assigned a growth class based of scaling Rw to the ecosystem because estimating total surface area is more straightforward than estimating to- on their frequency in the dendrometer study. Respi- ration per unit area was then randomly assigned to each tal sapwood volume, especially in a species-rich tropical tree's surface area using the A vs. D relationship. forest. The main stem surface area can be estimated as s b a truncated cone (Yoneda 1993) and calculated by Scaling leaf respiration 22 Aboleϭ␲(r bϩ r min)͙h bϩ (r bϪ r min) (3) Extrapolating leaf-level respiration measurements toward estimating ecosystem-scale ¯uxes using LAI is where rb and rmin are radii at the base and top of the dif®cult. We adopted a unique approach that employs bole, and hb is the height of the bole. Yoneda (1993) applied the pipe-model theory of Shinozaki et al. estimates of total leaf surface area for individual trees. (1964) to the frequency distribution ␾(d) of diameter Fifty trees were harvested in the central Amazon to (d) of tree branches, and found the relationship can be quantify biomass, moisture, and nutrient contents (N. modeled by Higuchi, unpublished data). For each tree, leaf mass and leaf moisture content was measured. To calculate Ϫ2 ␾(d) ϭ Kd (4) the total leaf surface area (Al) for each of these trees, we needed the speci®c leaf area (SLA, cm2 fresh vol- where K is characteristic of each tree and given by ume/g dry mass) for each tree, but SLA had not been

K ϭ 4mb/[␲␳(d maxϪ d min)] (5) measured. To estimate SLA for these trees, three leaves S78 JEFFREY Q. CHAMBERS ET AL. Ecological Applications Special Issue in different canopy positions were collected from the same 50 trees sampled for stem respiration, and SLA was measured for each leaf. We found a highly sig- ni®cant relationship between log(SLA) and leaf mois- 2 ture content (averaged for each tree, radj ϭ 0.38, P Ͻ 0.0001):

log(SLA) ϭ 1.47 ϩ 0.868(Ml) (7) where Ml is leaf moisture content (%). We then esti- mated SLA for the harvested trees by using leaf mois- ture content (which had been measured) as a predictor variable using Eq. 2 (SDres ϭ 0.0849) to account for variability. Al was then calculated for these 50 trees using our SLA estimate and measured leaf dry mass. Next we developed a regression relationship between FIG. 3. Variation in ecosystem Rs (soil respiration) Al and Db, and used this equation to estimate Al for throughout the year and with topography. Rs increased from each tree in the Jacaranda plots. Al was then summed valley to slope to plateau forest, and monthly variability was for each tree and averaged over the total area (10 ha) probably the result of the interaction of a number of processes. R averaged 3.2 ␮mol´mϪ2´sϪ1 over the year for all topographic to estimate an ecosystem LAI (for trees Ͼ10 cm Db). s classes, and averaged 2.6, 3.2, and 3.8 ␮mol´mϪ2´sϪ1 for val- ley, slope, and plateau forest, respectively. RESULTS Stem respiration tochondrial respiration during the day make interpre- There was considerable variation in woody tissue tations of this response dif®cult. respiration per unit stem surface area (Rw) ranging over two orders of magnitude from 0.027 to 3.64 ␮mol´mϪ2 Soil respiration Ϫ1 (stem)´s (n ϭ 391 measurements). This variation was The respiratory ¯ux from the soil was linearly cor- not simply due to differences among species, as within related with soil temperature and curvilinearly corre- species variation is also quite high (J. Q. Chambers, lated with soil VWC content, given by unpublished data). Rw was highly correlated with Db, 2 log(R ) ϭϪ0.124 ϩ 0.021T ϩ 0.641M Ϫ 1.632M2 month and growth class (radj ϭ 0.35, P Ͻ 0.0001) giv- s en by (9) where M is VWC (n ϭ 422 measurements). The mean log(Rs) ϭϪ0.672 ϩ␣(class) ϩ␤(month) response at varying levels of T and M were highly ϩ 0.0041Db (8) signi®cant (P Ͻ 0.0001), although there was consid- 2 where the coef®cients for ``class'' were 1 ϭ 0.516, 2 erable unexplained variability (radj ϭ 0.15). There were ϭ 0.536, 3 ϭ 0.317, 4 ϭ 0.398, and 5 ϭ 0; and the few data with M less than about 0.20, so this model is coef®cients for ``month'' were 8 ϭϪ0.190, 9 ϭ 0.000, only appropriate for relatively moist soil. 2000 and 10 ϭϪ0.067, 11 ϭ 0.0106, 2 ϭ 0.0346, 3 ϭϪ0.135, 2001 were La NinÄa years, when the central Amazon experienced some of the highest rainfall of the past 5 ϭϪ0.057, 6 ϭϪ0.111; and where 8 is August 2000, and 6 is June 2001. The fastest growing trees were century. When ``month'' and ``topography'' were included as class 1 and those showing no measurable growth were classi®cation variables, M and T were no longer sig- class 5. There was no relationship between R and stem s w ni®cant factors, suggesting that seasonal variability and surface temperature. landscape position were largely controlling variability in and . Multiple ANOVA with only ``month'' and Leaf respiration M Ts ``topography'' as predictors, and log-transformed Rs, Leaf respiration (Rl) was measured on leaves from explained 35% of the variability, suggesting that ad-

20 canopy trees located near three access towers over ditional factors besides M and Ts were controlling var-

24-h periods (n ϭ 488 measurements) and varied from iability in Rs. Calculating mean Rs for varying levels a few small negative values to 6.1, and averaged 0.54 of ``month'' and ``topography'' using Eq. 1, assuming Ϫ2 Ϫ1 ␮mol´m leaf´s . Rl was higher during the day than equal area for topographic classes, demonstrated that at night even after reducing daytime rates by 40% (see maximum monthly mean ¯ux was about twice as high Materials and methods), although the difference was (4.1 ␮mol´mϪ2´sϪ1) as the minimum monthly mean ¯ux Ϫ2 Ϫ1 not statistically signi®cant. The daytime increase was (2.1 ␮mol´m ´s ), and Rs for valley forest was sig- signi®cantly correlated with changes in leaf tempera- ni®cantly lower than slope or plateau (Fig. 3). Although ture, although problems associated with calculating mi- it appears that the wet season ¯ux was higher than the August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S79

Scaling leaf respiration

There was a strong relationship between log(Al) and

log(Db) using the leaf mass data from the 50 harvested trees given by

2 log(Albb) ϭϪ2.21 ϩ 4.22 log(D ) Ϫ 0.918 log(D ) (11)

2 where radj ϭ 0.65, P Ͻ 0.0001, and SDres ϭ 0.304 (Fig. 5). Thus, Eq. 11 estimates log-transformed average to-

tal leaf area for each tree as a function of Db. Applying this equation to each of 5823 trees in the Jacaranda PFIPs, using Eq. 2 to account for variability, gave a

mean LAI (for trees Ͼ10 cm Db) of 4.7. Use of Eq. 2 was important since use of Eq. 11 without accounting FIG. 4. Relationship between total stem surface area cal- for variability about the regression line gave a mean culated using the Yoneda (1993) model and base diameter LAI of 3.7. Assigning Rl rates to each tree based on (Db) for 315 harvested trees. Trunk and crown surface areas the hourly means and standard deviations (Eq. 2) from were estimated separately and then summed. This relationship was used to calculate a stem area index (SAI) of 1.7 using the LI-6400 chamber data demonstrated little diurnal data from two 5-ha forest inventory plots. variability in Rl per unit ground area (RlЈ ) (Fig. 6). The Ϫ2 Ϫ1 diurnal range ofRlЈ was 1.6 to 3.5 ␮mol´m ´s , av- eraging 2.6 ␮mol´mϪ2´sϪ1, or 0.55 ␮mol´mϪ2 leaf´sϪ1. dry season (Fig. 1) ¯ux, based on average soil VWC, Results from EC data the wettest months were actually May, April, and June, Fac and Feco were only independent of u* variables and the driest months were November, September, and during a very restricted set of conditions that for the January. Averaged over the year assuming equal area purposes of this paper de®ne sustained high turbulence in each topographic class, R was 3.2 ␮mol´mϪ2´sϪ1. s (SHT). Feco was independent of u* when u* was above 0.16 m/s and ⌬u* was between Ϫ0.05 and 0.05 which

Scaling stem respiration occurred for 105 nighttime hours. Fac was independent of u* when u* was above 0.21 m/s and ⌬u* was be- There was a strong functional relationship between tween Ϫ0.05 and 0.05 which occurred for 53.5 night- total stem surface (A ) area as calculated using Eqs. 1 s time hours. Since F and F during SHT conditions and 4, and D (Fig. 4) given by ac eco b were approximately log-normally distributed, the mean 2 log(Asbb) ϭϪ0.105 Ϫ 0.686 log(D ) ϩ 2.208 log(D ) ¯ux during respective SHT conditions was calculated using Eq. 1 and averaged 8.8 (95% CI ϭ 8.1±9.7) 3 Ϫ 0.627 log(Db) (10)

This equation was used to estimate As for each tree from the Jacaranda plots (n ϭ 5283). The 306 trees in the dendrometer study (Silva et al. 2002) were parti- tioned into relative growth classes as class 1, 3.3%; class 2, 6.9%; class 3, 13.7%; class 4, 52.0%; and class 5, 24.1%; with class 1 being the fastest growing trees, and class 5 showing no growth. These frequencies were used to randomly assign each tree in the Jacaranda plots a growth class. Rw was then assigned to each tree in the Jacaranda plots as a function of Db, class, and month (Eq. 8), including random variability about the regres- sion line using Eq. 2 (SDres ϭ 0.271). Summing Rw for each tree and averaging over the 10 ha of ground area provided estimates of Rw per unit ground area (RwЈ ), Ϫ2 Ϫ1 which varied from a high of 1.3 ␮mol´m ground´s FIG. 5. Leaf mass and moisture content from 49 harvested Ϫ2 Ϫ1 in December to a low of 1.0 ␮mol´m ´s in June, with trees were used to predict total leaf surface area (Al), and Al an overall mean of 1.1 ␮mol´mϪ2´sϪ1. In addition, ap- was correlated with tree base diameter (Db). Extrapolating this model using Db data from two 5-ha PFIPs (permanent plying Eq. 10 to all trees from the Jacaranda PFIPs forest inventory plots), and accounting for variability using gave a stem area index (SAI) of 1.7, and an average Eq. 2, gave a leaf area index (LAI) of 4.7 for trees Ͼ10 cm Ϫ2 Ϫ1 Rw of 0.6 ␮mol´m stem´s . in base diameter. S80 JEFFREY Q. CHAMBERS ET AL. Ecological Applications Special Issue

icant explanatory variable. Using averaging times rang- ing from four to eight hours demonstrated that the

eight-hour average (u 8* ) was the most signi®cant term (Table 1, no. 3). This ``®nal'' u* model accounted for

physical variability in Fac associated with u*, the change in u* (⌬u*), and the long-term average u* pre-

ceding the present observation (u 8* ). When precipitation variables were added to the ®nal u* model, both seasonal and daily changes in precip- itation were important (Table 1, no. 4). Although there was only a small increase in the amount of unexplained

variability, this was expected because variability in Fac due to turbulence is much greater than variability due to physiological changes in respiratory processes. The low P value for the precipitation variables is more per- FIG. 6. Hourly changes in ecosystem leaf respiration es- timated from scaled chamber data using an allometric leaf tinent than the correlation coef®cient, and demonstrates area model (Fig. 5). The daytime ¯uxes were reduced by 40% that mean Fac response to changes in precipitation was to account for light inhibition of mitochondrial respiration. signi®cantly different than zero. Predictions from this There were no hour 7 measurements. model indicate how Fac varied with precipitation when using SHT conditions for u* variables (presumably Ϫ2 Ϫ1 Ϫ2 Ϫ1 where F ϭ R ) (Fig. 7). First, F decreased as the ␮mol´m ´s for Feco, and 8.4 (7.5±9.4) ␮mol´m ´s ac eco ac preceding 20-d average precipitation (Pr ) increased. for Fac. Because Fac and Feco under SHT conditions were 20 highly variable (large SD) and log-normally distributed, This is similar to the ®nding of ArauÂjo et al. (2002) use of Eq. 1 was important. The simple arithmetic using the same EC data that Feco was depressed in the wet season and elevated in the dry season. The effect means of Fac and Feco under SHT conditions were 7.4 and 7.7 ␮mol´mϪ2´sϪ1 (respectively), and a difference of Prday on Fac depended on the value of Pr20, as dem- of ϳ1.0 ␮mol´mϪ2´sϪ1 is quite large when calculating onstrated by the signi®cant interaction term (Table 1, carbon balance. Fac was not statistically different from no. 4). When a rainy day occurred after a number of

Feco during SHT conditions because Fbc approaches dry days, Fac was elevated, whereas when a rainy day zero. occurred after a number of rainy days, Fac was slightly

Both Fac and Feco were dependent on turbulent con- depressed (Fig. 7). ditions during most nighttime hours. However, Feco is a composite variable (the sum of Fac and Fbc) with a complex error structure, and after accounting for var- TABLE 1. Summary of regression equations used to estimate iability associated with u* terms, no biologically mean- the above-canopy ¯ux (Fac) as a function of u* and pre- cipitation variables (Prday,Pr20). ingful correlations were found. The Fac data had a sim- pler error structure that allowed a more sensitive ex- 2 No. Regression equation radj ploration for ecologically important information. Fac 2 was offset by a constant (4) so that negative ¯uxes 1 Fac ϭ (0.342 ϩ 0.365 log[u*] ϩ 0.654 log[u*] 0.474 ϩ 0.230 log[u*]3 )Ϫ2 Ϫ 4 could be included in the analysis, and Fac was inverse 2 F ϭ (0.418 ϩ 0.263 log[u*]32ϩ 0.798 log[u*] 0.489 square-root transformed (1/͙Fac ϩ 4 ). There was a ac Ϫ 0.477⌬u* Ϫ 0.504 log[u*]⌬u* highly signi®cant relationship between transformed Fac Ϫ2 and log(u*) (above 0.01 m/s) (Table 1, no. 1). The third- ϩ 0.553 log[u*]) Ϫ 4 32 3 Fac ϭ (0.510 ϩ 0.245 log[u*] ϩ 0.692 log[u*] 0.517 order term was important because Fac at low and high values of u* were relatively stable, and there was a ϩ 0.254 log[u*]8 Ϫ 0.440 log[u*]⌬u* ϩ 0.110 log[u*]log[u*]8 Ϫ 0.350⌬u* steep increase in Fac at intermediate values of u*. The 0.465 log[u*] 0.0410 log[u*]2 )Ϫ2 4 change in u* from one (half hourly) observation to next ϩ ϩ 8 Ϫ 32 (⌬u*) was also an important variable (Table 1, no. 2), 4 Fac ϭ (0.511 ϩ 0.245 log[u*] ϩ 0.691 log[u*] 0.523 ϩ 0.250 log[u*]8 Ϫ 0.431 log[u*]⌬u* demonstrating that variation in Fac was not only de- pendent on the value of u*, but also on whether or not ϩ 0.113 log[u*]log[u*]8 ϩ 0.468 log[u*] 0.347 u* 0.030Pr 0.0408Pr Pr u* had changed from the previous observation. For Ϫ ⌬ Ϫ dayϩ 20 day 2 Ϫ2 ϩ 0.0367 log[u*]820ϩ 0.0155Pr ) Ϫ 4 example, the predicted Fac for a u* of 0.4 with ⌬u*of 0.3 (i.e., large increase in u* from the previous hour) Note: The variables are listed in order of signi®cance as ranked by the F statistic. Both Pr and Pr are in units of was about twice that predicted for a u* of 0.4 with u* day 20 ⌬ mm/h so that regression coef®cients would fall within the of 0.0 (i.e., no change in u* from the previous obser- same order of magnitude. Prday and the interaction term vation). In addition, the average u* for a number of PrdayPr20 were signi®cant at P Ͻ 0.0001, and Pr20 was signif- hours preceding the present observation was a signif- icant at P ϭ 0.02. August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S81

FIG. 7. Predicted variability in the above- canopy eddy covariance ¯ux (Fac) using regres- sion equation no. 4 from Table 1 and variability in seasonal (Pr20) and daily (Prday) precipitation. The predictions are based on SHT (sustained high turbulence) conditions (atmospheric tur- bulence u* ϭ 0.20 m/s, u 8* ϭ 0.20 m/s at 8 h, and ⌬u* ϭ 0.0) where Fac is presumably a re- liable measure of Reco. The accuracy of these mean predictions is indicated by the 95% CI for high and low Pr20, although the predictions also change with the exact values used for SHT con- ditions.

DISCUSSION about 1.0 g H2O/g dry litter. Fungi are the primary Processes contributing to ecosystem respiration decomposers of surface litter, and are sensitive to mois- ture stress (Dix and Webster 1995). The respiratory ¯ux Both Rs and Rw demonstrated signi®cant seasonal from ®ne and coarse surface litter is greatly reduced variability, with the variation most pronounced in Rs. when moisture is limiting, and rapidly attains maxi- The soil ¯ux consists of both heterotrophic and auto- mum rates soon after a rainfall event. trophic respiratory sources, and heterotrophic respi- Contrary to our expectations, the highest Rs corre- ration is probably more sensitive to moisture stress. sponded to lowest soil moisture content (VWC), al- For example, Chambers et al. (2001c) demonstrated though VWC was rarely less than about 0.20 (Fig. 8). that respiration from coarse surface litter (dead trunks The observed decline in mean Rs with an increase VWC and branches Ͼ10 cm diameter, Rcsl), which amounted may have been caused by inhibition of respiratory ac- Ϫ2 Ϫ1 to an ecosystem ¯ux of ϳ0.5 ␮mol´m ´s , declined tivity due to inadequate oxygen supply in saturated sharply once moisture content fell below 0.5 g H2O/g soils (Davidson 1993, Shuur 2003). There may also be dry wood. Also, Toledo (2002) found that respiration seasonal variability in Ra (including soil Ra) associated from ®ne surface litter (Rfsl; leaves, small twigs, etc.) with changes in phenology. A large increase in litterfall dropped appreciably when moisture content fell below is associated with the onset of the dry season (Fig. 1) in the Central Amazon (LuizaÄo and Schubart 1987, LuizaÄo 1995), which probably reduces LAI, ecosystem photosynthetic capacity (Malhi et al. 1998), and root

respiration (Rr). Wood production is also reduced throughout the dry season in the central Amazon (Silva et al. 2002), which may also be driven by seasonal changes in LAI, and there is presumably a similar be- lowground growth response. Thus the main contribu-

tors to Rs (Rr, Rfsl, Rcsl, RSOM) may often be responding in different ways to changing environmental condi- tions. For example, while respiration from surface litter may be declining due to moisture stress, respiration from soil organic matter may be increasing because soil dries more slowly. Comparing our estimates with an earlier study, Yoda

(1983) measured total autotrophic respiration (Ra) for FIG. 8. Relationship between soil respiration (Rs) and soil volumetric water content (VWC) measured using a TDR a lowland tropical forest in Pasoh, Malaysia using a probe. The relationship was curvilinear with Rs declining rap- static chamber method (KOH titration) on harvested idly once VWC increased above about 0.30. Some very high tissues, and aboveground Ra for trees Ͼ9.5 cm Db was VWC values were associated with saturated soils in the val- Ϫ2 Ϫ1 leys, where dense roots helped to create soil gaps ®lled with 7.3 ␮mol´m ´s . In comparison, aboveground Ra (Rl Ϫ2 Ϫ1 water (i.e., a portion of the area integrated by the TDR probe ϩ Rw) from this study was 3.7 ␮mol´m ´s . Nay et had a VWC of 100%). al. (1994) found that static chamber methods tend to S82 JEFFREY Q. CHAMBERS ET AL. Ecological Applications Special Issue

Ϫ2 Ϫ1 overestimate low rates, and underestimate high rates, timated a mean Rw of 0.6 ␮mol´m stem´s for a forest at least for soil respiration. This methodological bias near Santarem, Brazil; Ryan et al. (1994) estimated may explain part of the difference between these es- means of 1.2 and 0.8 ␮mol´mϪ2 stem´sϪ1 for two tree timates, although damage to tissues harvested by Yoda species at La Selva in Costa Rica; and Meir and Grace et al. (1983) may have also increased respiratory ac- (2002) estimated an average of ϳ0.6 ␮mol´mϪ2 stem´sϪ1 tivity. Nevertheless, Yoda (1983) quanti®ed Ra for trees for 23 species in RondoÃnia, Brazil. Reich et al. (1991) Ϫ2 Ϫ1 Ͻ9.5 cm Db, understory and undergrowth (Ru), which found an average large tree Rl of 0.8 ␮mol´m leaf´s contributed an additional 1.4 ␮mol´mϪ2´sϪ1, or 19% of in a closed-canopy forest near San Carlos del Rio Ne- the total Ra ¯ux. Although 19% may be an overestimate gro, Venezuela. Estimates of LAI for Amazon forests because static chambers tend to overestimate low ¯ux range from 4.4 to 7.5 (Williams et al. 1972, Saldarriaga rates (presumably found in the understory), our Reco 1985, McWilliam et al. 1993, Roberts et al. 1996) in- estimate is entirely missing this source. If we assume cluding 5.7 for a forest near Manaus (McWilliam et al. that the difference between Yoda (1983) and this study 1993). are entirely methodological, our estimate of Reco should be increased by at least 0.4 ␮mol´mϪ2´sϪ1 to account EC data for vegetation Ͻ10 cm Db. There were only a restricted set of conditions (SHT) Our R estimate is probably low due to a number eco where Feco and Fac were independent of turbulence as of factors. First, a closed dynamic chamber (CDC) such measured by u* variables. This suggests that Reco can as employed here for Rw and Rs tends to create a positive only be directly estimated by the EC method during pressure with respect to the local atmosphere, which nighttime conditions that are infrequent. However, may inhibit gas ¯ux (Lund et al. 1999). Although few there is some question over whether or not high con- studies have evaluated this effect directly, Nay et al. centration soil CO2 can be entrained in the eddy ¯ux (1994) found that a CDC consistently underestimated during SHT conditions, leading to overestimates of a known ¯ux by ϳ15%. We employed an equilibration Reco. If this was the case, we would expect Feco and Fac vent on our soil chambers to help equalize pressure to always be dependent on u* because CO2 trapped in (Trumbore et al. 1995), although our stem chambers the soil would probably rarely if ever be fully ¯ushed. had no vent. Second, Yoda (1983) found that Rw for a If changes in the concentration of soil CO2 driven by given stem diameter increased with canopy height, and turbulence are minimal, Fac during SHT conditions if this phenomenon is general, our branch respiration should provide a reliable estimate of Reco. estimate (a portion of Rw) is probably low. It is worth Despite the paucity of SHT conditions at night, Fac noting that Levy et al. (1999) found a small portion of under SHT conditions was predicted with a reasonably the stem CO2 ef¯ux may have ultimately come from small 95% CI. It appears that nighttime EC data under the soil, although this potential bias would not have turbulent conditions are valuable, and additional years signi®cantly affected our Rw estimate. Third, we only will allow more accurate estimates of nighttime Reco. measured Rl during the dry season months, and it is Moreover, additional data should also allow more pre- likely that Rl is higher in wet season months, similar cise analyses of ecological factors that control vari- to our Rw results, and also supported by seasonal var- ability in respiratory processes, which are much more iability in tree growth (Silva et al. 2002) and maximum subtle than changes in Fac driven by turbulence. Our carbon assimilation rate (Malhi et al. 1998). Finally, analyses have demonstrated that despite turbulence surface roots in the valley forests tend to aggregate on (i.e., u*) controlling a large amount of Fac variability, the surface in a root mat where respiration chambers there was still important ecological information con- could not be installed, so Rs for valley forest may be tained in the EC data as demonstrated by the signi®- underestimated. There are few signi®cant factors we cance of seasonal and daily changes in precipitation. can identify that would result in large systematic over- Comparison of chamber and EC data estimation of Reco components in this study, although more work on Rl during the day is needed to better Given the many potential sources of error, our scaled- Ϫ2 Ϫ1 quantify light inhibition of Rl. chamber Reco estimate of 7.8 ␮mol´m ´s is probably

Comparisons with other studies support the conclu- not statistically different from our nighttime Fac esti- Ϫ2 Ϫ1 sion that our Reco estimate is conservative. Sotta (1998) mate under SHT conditions of 8.4 ␮mol´m ´s (7.5± estimated Rs for a plateau forest ϳ10 km from our study 9.4 ϭ 95% CI). We did not attempt the dif®cult task of Ϫ2 Ϫ1 site of 6.9 ␮mol´m ´s , whereas Wofsy et al. (1988) calculating a 95% CI for our scaled-chamber Reco es- Ϫ2 Ϫ1 estimated Rs of 4.1 ␮mol´m ´s for plateau forest at timate, although most identi®ed factors would tend to

Ducke reserve ϳ50 km from our site. Similarly, Trum- underestimate of the actual ¯ux. If Fac under SHT con- bore et al. (1995) working in a drier forest near Par- ditions is a reliable measure of Reco, our Reco estimated Ϫ2 Ϫ1 agominas, Brazil, estimated a Rs of 6.2 ␮mol´m ´s , from Fac is probably more accurate than the scaled- and Meir et al. (1996) measured a mean Rs of 5.5 for chamber data because Fac integrates all respiratory ac- a forest in RondoÃnia, Brazil. Nepstad et al. (2002) es- tivity, and may be a reasonably direct measure of Reco. August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S83

Conversely, chamber measurements allow exploration allocated aboveground is used to construct new tissues, of factors that control variability in individual sources, the remainder being Ra. and provide valuable information for developing a pro- Raich and Nadelhoffer (1989) offered a means for cess level understanding of respiratory variability. estimating belowground productivity by constraining Interestingly, both the scaled chamber and EC data ¯uxes which assumes that net changes in carbon pools showed a similar response to changes in precipitation. are much smaller in magnitude than gross inputs and

First, Rs declined with increasing soil moisture, with outputs, and Amazon soil studies support this quasi-

Rs being depressed during wet months and elevated steady-state assumption (Telles et al. 2003). However, during dry months, although dry months were rela- an important parameter in the Raich and Nadelhoffer tively wet during this study. The EC data also dem- (1989) method is the partitioning of belowground res- onstrated a decrease in Fac with increasing 20-d average piration which is dif®cult to measure directly. Thus, precipitation (Fig. 7), which may have been largely based on temperate forest (Waring et al. 1998) and driven by a reduction in Rs under high VWC when low modeling (Dewar et al. 1999) studies, belowground oxygen concentration can limit respiratory activity. CUE is often assumed to be a constant (0.50). However, Second, Chambers et al. (2001c) and Toledo (2002) since our estimate varied signi®cantly from this con- demonstrated that respiration is very low from dry litter stant, we used our measured aboveground CUE to ap- (coarse and ®ne), increases sharply with increasing proximate belowground CUE. Using this approach, we moisture, and quickly saturates at maximum rates. Sim- estimate a belowground NPP of 2.6 Mg C´haϪ1´yrϪ1, and combined with aboveground NPP, an ecosystem ilarly, Fac increased appreciably after a rainfall event when the 20-d average was low (i.e., wet-up of dry NPP of 9.0 Mg C´haϪ1´yrϪ1; (but see Clark et al. 2001a, litter), whereas additional rainfall when the 20-d av- b, and Chambers et al. 2001b for potential missing NPP components). Partitioning belowground respiration erage was high resulted in a slight decrease in Fac (Fig. 7). provides ecosystem estimates for Ra and Rh of 21.0 and Ϫ1 Ϫ1 More nighttime EC data under SHT conditions 8.5 Mg C´ha ´yr , respectively (Fig. 9, Table 2). Rs should provide additional information on the cumula- was about equally divided between Ra (roots) and Rh (RSOM ϩ Rfsl) similar to results from Hogberg et al. tive response of Reco to changing environmental con- ditions. However, it is important to point out that an (2001) that a large fraction of soil respiration was from apparently insigni®cant difference of 0.26 recently assimilated carbon. If we assume that Reco ap- ␮mol´mϪ2´sϪ1 corresponds to a annual ¯ux of 1.0 Mg proximates GPP, gross photosynthetic assimilation (Ag) C´haϪ1´yrϪ1, which if simply scaled over the entire Am- of carbon by this Central Amazon forest is ϳ30 Mg Ϫ1 Ϫ1 azon basin (5 ϫ 1012 km2) results in a globally signif- C´ha ´yr . icant 0.5 Pg yrϪ1 carbon sink or source. Thus, devel- Amthor (2000) compiled data from other tropical oping whole ecosystem carbon balance with an accu- forest studies (MuÈller and Nielsen 1965, Odum 1970, racy of greater than Ϯ1.0 Mg C´haϪ1´yrϪ1 may not be and Kira 1975) to calculate a related autotrophic res- possible using ¯ux measurements. Nevertheless, mech- piration to gross photosynthesis (Ra/Ag ϭ 1 Ϫ CUE) anistic studies into factors controlling carbon cycling ratio, and including our estimate here, Ra/Ag averages variability at the cellular to ecosystem scale will aid 0.26 for tropical forests. In contrast, Ra/Ag ratios for considerably in the development of predictive models temperate forests compiled from ϳ20 studies (Amthor that can simulate ecosystem response to disturbance 2000) ranged from 0.32 to 0.72 and averaged 0.54. and atmospheric changes. Waring et al. (1998) found a conservative Ra/Ag ratio of 0.53 Ϯ 0.04 (Ϯ 1 SD) for 12 temperate forest sites NPP, GPP, and CUE which used comparable methods. Thus, it appears that many tropical forests are an exception to the presum-

We de®ne carbon use ef®ciency (CUE) as the ratio ably invariant Ra/Ag ratio found in temperate studies. of production (i.e., NPP) to total gross carbon ®xation Tropical forests often have a high capacity for capturing

(Ra ϩ NPP), and CUE is an important parameter for atmospheric carbon, but only a small fraction of that comparing carbon cycle variability among ecosystems carbon becomes incorporated into new tissues. (Ryan et al. 1996a, 1997, Amthor 2000). At the eco- One possible reason for this difference is that re- system scale, CUE is analogous to the NPP/GPP ratio, spiratory demands per unit photosynthate are simply and provides a measure of what fraction of total carbon greater in tropical forests. Some studies suggest that assimilation becomes incorporated into new tissues. the Ra/Ag ratio should increase with mean annual tem- Using Rw and Rl presented here, and production esti- perature (Woodwell 1983, Ryan 1996b), whereas others mates for wood and leaves (Chambers et al. 2000, suggest this ratio should be relatively constant with 2001b) we calculate a woody tissue CUE of 0.43 and temperature (Gifford 1995, Dewar 1999), and in re- a leaf CUE of 0.25. Weighting for the relative produc- sponse to CO2 fertilization (Cheng et al. 2000). Another tion of each component, total aboveground CUE was possibility is that in nutrient-de®cient forests such as 0.32. Thus we estimate that only about 30% of carbon central Amazon terra ®rme, more carbon is ®xed via S84 JEFFREY Q. CHAMBERS ET AL. Ecological Applications Special Issue

FIG. 9. Summary of estimates provided by this study and literature data used to calculate total ecosystem respiration

(Reco) and carbon use ef®ciency (CUE). The sizes of the ¯ow lines in the ®gure are proportional to the ¯ux, and the letters correspond to the identi®cation in Table 2. Fluxes are given in Mg C´haϪ1´yrϪ1 (1 Mg C´haϪ1´yrϪ1 ϭ 0.264 ␮mol´mϪ2´sϪ1).

photosynthesis than can be utilized by growth and func- tenance (Rm), and wastage (Rwa) respiration components tional respiration. In this scenario, a signi®cant amount were estimated independently, the (Rg ϩ Rm)/Ag ratio of carbon may be respired via the alternative pathway, would be conservative across all ecosystems. It will be and other futile cycles, as wastage respiration (Rwa) informative to investigate whether or not enhanced Rwa

(Lambers 1982, 1997). Perhaps if growth (Rg), main- in tropical forests accounts for different Ra/Ag ratios.

TABLE 2. Descriptions, estimates, and sources of components in Fig. 9.

Identi®cation Symbol Description Estimate Source

a RЈl leaf respiration 9.8 this study b RЈw wood respiration 4.2 this study c Rs soil respiration 12.1 this study d Ru understory respiration 1.5 Yoda (1983) e Rcsl respiration from coarse surface litter 1.9 Chambers et al. (2001a) f Rfsl respiration from ®ne surface litter 2.6 Toledo (2002) g Rr root respiration (soil autotrophic) 5.5 (c Ϫ r) ϫ (1 Ϫ w) h RSOM respiration from soil organic matter 4.0 c Ϫ g Ϫ f i Ra ecosystem autotrophic respiration 21.0 a ϩ b ϩ d ϩ g j Rh ecosystem heterotrophic respiration 8.5 e ϩ f ϩ h k Reco ecosystem respiration 29.5 i ϩ j n NPPl leaf production 3.3 Chambers et al. (2001b) o NPPw wood production 3.2 Chambers et al. (2001b) p NPPs belowground production 2.6 (c Ϫ r) ϫ w q NPPeco total production 9.0 n ϩ o ϩ p r Cl total ®ne litterfall 4.0 Chambers et al. (2001b: references) s LAI leaf area index 4.7 this study t SAI stem area index 1.7 this study u CUEl leaf carbon-use ef®ciency 0.25 n/(n ϩ a) v CUEw wood carbon-use ef®ciency 0.43 o/(o ϩ b) w CUEag aboveground carbon-use ef®ciency 0.32 (n ϩ o)/(n ϩ o ϩ a ϩ b) August 2004 TROPICAL FOREST ECOSYSTEM RESPIRATION S85

As opposed to ecosystems where most respiration is spheric CO2. However, interactions of above- and be- functional (i.e., Rgϩ Rm; Lloyd and Farquhar 1996), an lowground carbon and nutrient cycles are complex ecosystem with a large fraction of respiration as Rwa (e.g., Vitousek 1984, Herbert and Fownes 1995, Lloyd may be relatively insensitive to increases in atmo- et al. 2001), and much additional research is needed to spheric CO2. better understand tropical forests response to elevated

atmospheric CO2.

Forest carbon balance We show here that aboveground Ra for trees in a There is some controversy over whether or not old- Central Amazon forest is considerably greater than growth Amazon forests are acting as carbon sinks. A aboveground NPP, similar to the ®ndings of earlier recent summary of results from eddy covariance (EC) studies that employed less-accurate static-chamber and and ®eld studies suggests that old-growth Amazon for- destructive-harvest methods (Odum 1970, Kira 1975, ests are accumulating ϳ1.4 Mg C´haϪ1´yrϪ1 (Grace and MuÈller and Nielsen 1965, Yoda 1983). Does this ¯ux Malhi 2002, Malhi et al. 1999). However the magnitude represent true respiratory costs, or are many Amazon and even the sign of NEP estimates from EC data are trees sink limited, respiring a substantial fraction of dependent on how the data are analyzed. Carbon bal- carbon via the alternative oxidase, or other futile cy- ance, or even a small carbon source, cannot be ruled cles? If carbon is already being supplied in excess of out from EC data (Miller et al. 2004), although ArauÂjo demand, it seems unlikely that plant production would et al. (2002) suggest that simply removing data during increase in response to elevated atmospheric CO2. low turbulence conditions leads to overestimates of However, if the CO2 ¯ux largely represents true respi- ratory costs, an increase in photosynthesis from ele- nocturnal Reco. Analyses of Amazon forest permanent plot data indicate that trees are accumulating ϳ0.7 Mg vated atmospheric CO2 may result in a larger than av- C´haϪ1´yrϪ1 in aboveground biomass (Phillips et al. erage increase in growth (Lloyd and Farquhar 2000). 1998, 2002). However, Clark (2002) ®nds that meth- Quantifying the ¯ow of carbon through the alternative odological artifacts inherent in the same permanent plot respiratory pathway should help shed light on whether or not tropical forests are carbon limited. data lead to a 95% CI that includes carbon balance. Conversely, Phillips et al. (2002) ®nd that systematic ACKNOWLEDGMENTS errors in permanent plot data are small and quanti®able, We would like to thank Jeff Amthor, Mike Goulden, Mike and that strong evidence supports biomass accumula- Ryan, and an anonymous reviewer for comments on this man- uscript. INPAs Tropical Forestry department (CPST) provid- tion. Separately, empirical modeling of Central Ama- ed invaluable support during all phases of this study. This zon forest biomass dynamics demonstrated that al- work was made possible by support from NASAs LBA-ECO though Amazon forests have a long-term capacity to project, the Piculus project (Pilot Programs of the G-7 Na- sequester carbon in response to an increase in produc- tions), the Jacaranda project (Japanese International Coop- tivity, the annual aboveground sink potential was only eration Agency), and EC data was provided by the LBA- Eustach project with funding from European Commission DG Ϫ1 Ϫ1 ϳ0.5 Mg C´ha ´yr (Chambers et al. 2001a). Thus, Research's 5th Framework Program. the carbon balance of old-growth Amazon forests re- LITERATURE CITED mains an open question, although even under a strong Aiken, L. S., and S. G. West. 1991. Multiple regression: CO2 fertilization response, a sink greater than ϳ1Mg testing and interpreting interactions. Sage Publications, C´haϪ1´yrϪ1 appears implausible. Thousand Oaks, California, USA. 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