SEARCH

SEARCH BY CITATION

Keywords:

  • Amazon tropical rain forest;
  • partitioning soil respiration;
  • anthropogenic dark earth;
  • Terra Preta do Indio

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] This study explored biotic and abiotic causes for spatio-temporal variation in soil respiration from surface litter, roots, and soil organic matter over one year at four rain forest sites with different vegetation structures and soil types in the eastern Amazon, Brazil. Estimated mean annual soil respiration varied between 13–17 t C ha−1 yr−1, which was partitioned into 0–2 t C ha−1 yr−1 from litter, 6–9 t C ha−1 yr−1 from roots, and 5–6 t C ha−1 yr−1 from soil organic matter. Litter contribution showed no clear seasonal change, though experimental precipitation exclusion over a one-hectare area was associated with a ten-fold reduction in litter respiration relative to unmodified sites. The estimated mean contribution of soil organic matter respiration fell from 49% during the wet season to 32% in the dry season, while root respiration contribution increased from 42% in the wet season to 61% during the dry season. Spatial variation in respiration from soil, litter, roots, and soil organic matter was not explained by volumetric soil moisture or temperature. Instead, spatial heterogeneity in litter and root mass accounted for 44% of observed spatial variation in soil respiration (p < 0.001). In particular, variation in litter respiration per unit mass and root mass accounted for much of the observed variation in respiration from litter and roots, respectively, and hence total soil respiration. This information about patterns of, and underlying controls on, respiration from different soil components should assist attempts to accurately model soil carbon dioxide fluxes over space and time.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] Soil respiration (Rs) releases 75–80 billion tons of C each year [Schlesinger, 1977; Raich and Potter, 1995; Raich et al., 2002]. This efflux is more than 11 times the recent rate of C produced from human combustion of fossil fuels [Marland and Boden, 1993]. So even a slight proportional change in global Rs could significantly alter atmospheric CO2 levels, and hence climate. Rs usually accounts for a large proportion of terrestrial ecosystem respiration [Lavigne et al., 1997; Janssens et al., 2001] and variation in Rs may determine whether an ecosystem is a net source or sink of CO2 [Valentini et al., 2000; Davidson et al., 2006]. Yet despite its clear importance for global C cycling and climate change, understanding of the processes controlling spatial and temporal variation in Rs is limited. This is largely because soil is a complex and spatially heterogeneous mixture of different compounds (e.g., ground surface organic litter, live roots, and soil organic matter pools). Understanding the individual responses of these compounds to environmental change and the net effect upon Rs remains a key objective for research into ecosystem C cycling and biosphere-atmosphere interactions.

[3] Rs is derived from autotrophic respiration by roots (Rr) and heterotrophic respiration by microorganisms that decompose ground surface organic litter (Rl) and soil organic matter or SOM (Rsom). In this study, Rsom also includes CO2 derived from microbial decomposition of root tissue and exudates, and contributions from mycorrhizal fungi. These different sources of soil CO2 may respond to environmental change in different ways, whilst estimates of the autotrophic component of Rs range between 12–93% depending upon the ecosystem studied and the method used to estimate Rr [Hanson et al., 2000]. Rl and Rsom are directly driven by microbial activity, which, in turn, is strongly affected by temperature [Davidson et al., 1998; Fang and Moncrieff, 2001] and available moisture [Davidson et al., 1998; Sotta et al., 2004]. This explains frequent observations, particularly in temperate and boreal regions where diurnal and seasonal fluctuations in temperature are greatest, that Rs rises as soil becomes warmer and wetter [e.g., Savage and Davidson, 2001]. However, both Rl and Rsom are also partly decoupled from local soil conditions because they are affected by the supply and quality of substrate from above-ground in the form of organic litter and root exudates [Melillo et al., 1982; Högberg et al., 2001]. Rr is also partly a product of the level of metabolic activity within root tissue, affected by factors such as soil temperature [see Atkin et al., 2000, and references therein], water availability [Bouma et al., 1997; Burton et al., 1998], N supply [Ryan et al., 1996; Zogg et al., 1996], and the supply of photosynthate from above-ground [Högberg et al., 2001; Nordgren et al., 2003], influenced by ecosystem GPP and plant allocation strategy. Thus, Rs and its component fluxes may display substantial spatial and temporal variability which is not readily attributable to changes in soil temperature and moisture. This variation reflects changes in both the total amount of respiring tissue (e.g., root mass) or available substrate, (e.g., surface litter mass) and the rate of respiration per unit mass of tissue or substrate (specific root respiration: SRR, specific litter respiration: SLR). Understanding the extent and causes of this variability represents an important step towards accurately modelling ecosystem C cycling, and up-scaling localized measurements across larger spatial scales for comparison with top-down measurement systems (e.g., satellites, flux towers). The overall objectives of this study, therefore, were to (1) partition Rs into Rl, Rr and Rsom over one full seasonal cycle at four rain forest sites with contrasting vegetation and soil types in the eastern Amazon; (2) investigate potential biotic (roots, ground surface litter) and abiotic (soil moisture, soil temperature) causes for observed differences in respiration within and between sites and seasons; and (3) quantify the contributions of component mass and respiration per unit mass to total Rr and Rl.

[4] We focused upon sites in the Amazon because the region plays an important role in global biogeochemical cycles [Houghton et al., 2001; IPCC, 2001], and displays a high degree of spatial heterogeneity in terms of many ecosystem properties [Williams et al., 2002], but may experience an increase in drought conditions over this century due to a possible increase in El Niño-Southern Oscillation events [Trenberth and Hoar, 1997; Schöngart et al., 2004] driven by global climate change, and reductions in rainfall caused by regional deforestation [Shukla et al., 1990] and fire [Andreae et al., 2004].

2. Materials and Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

2.1. Site and Experimental Design

[5] The experimental site is located in the Caxiuanã National Forest, Pará State, north-eastern Brazil (1°43′3.5″S, 51°27′36″W). The forest is a lowland terra firme rain forest with a high annual rainfall (∼2500 mm) and a pronounced dry season [Fisher et al., 2006]. Across the entire year, mean soil surface temperature is ∼25°C (±5°C), whilst diurnal variation is typically 1–2°C. The most widespread soil type is a highly weathered yellow Oxisol (US Department of Agriculture soil taxonomy). There are also patches of relatively fertile soil, called anthropogenic dark earths (ADE) or Terra Preta do Indio, which were modified by indigenous populations of pre-Columban inhabitants [Da Costa and Kern, 1999; Lehmann et al., 2003]. To represent regional variation in soil type, one-hectare measurement sites (see Table 1 for additional site details) were located on a well drained sandy Oxisol (Sand site), a clay-rich Oxisol (Clay site), and an ADE (Fertile site). In January 2002, a fourth one-hectare measurement site, also on sandy Oxisol soil, was modified by the installation of plastic panels at two meters height to exclude a proportion of incident rainfall (Dry site). The perimeter of the Dry site was trenched to a mean depth of one meter to minimize lateral flow of water into the site. Data from the fourth year of the rainfall exclusion on the Dry site were used to examine Rs under drier conditions than currently exists naturally. A detailed site inter-comparison, before the imposition of the drought treatment on the Dry site, indicates that soil and vegetation characteristics on the Sand and Dry sites were similar (P. Meir et al., manuscript in preparation, 2007).

Table 1. Key Vegetation and Soil Features for Each Site Surveyeda
Site CharacteristicsSandDryClayFertile
  • a

    Values indicate mean and, where possible, 5th and 95th percentiles around the mean (in parentheses).

  • b

    All individuals over 0.1 m diameter at breast height, measured in January 2005.

  • c

    Means of 25 replicate measurements taken each month at each site in 2005 (25 × 12 = 300 replicates), no data are available for the Fertile site.

  • d

    Calculated from data in Ruivo and Cunha [2003] and Sotta [2006], percentiles could not be calculated because neither data source presents error estimates.

  • e

    Roots less than 5 mm diameter.

  • f

    Calculated from root depth profile data presented by Fisher et al. [2007]. Profiles were available only for the Sand and Dry sites. Therefore, the root profile for the Sand site was applied to estimate stocks in the Clay and Fertile sites.

Vegetation
   Tree density (stems ha−1)b434421419544
   Stem basal area (m2 ha−1)b24242537
   Leaf area index (m2 m−2)c5 (4, 7)5 (3, 6)6 (4, 7)
Soild
   Clay content (%)18134220
   C content 0–0.05 m depth (g kg−1)9122749
Carbon stocks
   Total 0–1 m depth (t ha−1)100 (94, 111)103 (98, 108)109 (103, 117)206 (201, 216)
   Surface litter (t ha−1)2 (1, 4)2 (1, 3)2 (0, 3)3 (1, 6)
   Roots 0–0.3 m depth (t ha−1)e6 (2, 13)5 (2, 8)7 (4, 12)5 (2, 10)
   Roots 0–1 m depth (t ha−1)e,f8 (3, 17)6 (2, 10)9 (5, 16)6 (3, 13)
   Soil 0–0.3 m depth (t ha−1)d354445111
   Soil 0–1 m depth (t ha−1)d909598197

2.2. Measurements

[6] Monthly measurements of Rs were made at 25 replicate points, at 20 meter intervals along a regularly spaced grid, within each site using an Infra-Red Gas Analyzer or IRGA (EGM-4 and SRC-1 chamber, PP Systems, Hitchin, UK.). Two months prior to the initiation of the measurement program plastic collars were inserted into the soil at each measurement location, to a depth of approximately 2 cm, to ensure a good seal between the IRGA chamber and soil. Rs (kg CO2 m−2 s−1) was calculated as:

  • equation image

where ΔCT represents the change in CO2 within the chamber (ppm) per unit time (seconds), P is atmospheric pressure (Pa), t is the temperature of the air within the chamber (°C), Vch is the total internal volume of the chamber (m3) and A is the ground area covered by the chamber (m2). All measurements showed a positive linear relationship between C and T, indicating a constant rate of CO2 release from the soil into the atmosphere.

[7] An additional 18 locations (9 each in November 2004 and June 2005, corresponding to the peaks of the dry and wet seasons respectively) were selected at 30 meter intervals along a regularly spaced grid, in each site to: (1) estimate the percentage contribution of surface litter, roots, and soil organic matter (SOM) to total Rs, and (2) examine factors controlling spatial and temporal variation of Rs in greater detail. At these points, Rs was measured twice with the IRGA: once with surface organic litter and once without. We defined surface litter as identifiable plant material on the ground surface which did not pass through a 0.5 mm mesh diameter sieve. Collected litter was carefully cleaned of inorganic detritus, roots, and mycorrhizae. The area of soil measured by the IRGA was then extracted as a soil core (diameter = 0.14 m, depth = 0.3 m) using opposable semi-circular cutting blades, and the roots were carefully removed by hand and cleaned of detritus. Fresh roots from each core were then placed into a cuvette which was connected to an IRGA that measured the rate of CO2 accumulation within the cuvette. Root and litter samples were then dried at 70°C to constant mass and weighed. Two mass measurements were made for root samples: 1) roots less than five mm diameter, and 2) total. At all Rs measurement locations, instantaneous measurements of volumetric soil moisture (CS616 probe, Campbell Scientific, Loughborough, U.K.) and soil temperature (Testo 926 probe, Testo Ltd., Hampshire, U.K.) were taken at a soil depth of 0.3 m.

2.3. Data Analysis

[8] For each core, Rl (g m−2 hr−1) was estimated as the difference between the first (with litter) and second (without litter) IRGA measurements. SLR (g g−1 hr−1) was calculated by dividing Rl by sample dry litter mass. SRR (g g−1 hr−1) was calculated by dividing the respiration rate of fresh root samples placed in the cuvette by sample dry mass of roots less than five mm diameter. Rr (g m−2 hr−1) was then estimated by multiplying SRR by 1/A. Estimates of Rr, following this method, integrated both root growth and maintenance respiration, and are likely to be conservative because they consider only the contribution from roots in the 0–0.3 meter soil layer and ignore the contributions of mycorrhizae and microbes dependent upon root exudates [Nguyen, 2003; Jones et al., 2004]. Instead, in this analysis, these sources of CO2 were ascribed to Rsom. No consistent change in SRR over time since root excision was found (data not shown), so we propose that our estimates of SRR are not likely to be strongly biased by root excision [Amthor, 1994; Burton et al., 1998]. Rsom (g m−2 hr−1) was estimated as the difference between measured Rs and the sum of estimated Rl and Rr, for each measurement point.

[9] Monthly measurements of Rs were used to estimate total monthly and annual Rs, while detailed core measurements (in November 2004 and June 2005) were used to partition Rs into Rl, Rr and Rsom, for each site. To do this, we made several assumptions. Estimates of the proportional contribution of individual soil components derived from the June 2005 measurements were applied to monthly Rs measurements during June, April and May. Estimates of contributions taken in November 2004 were applied to monthly Rs measurements during November, October and December. The intervening two three-month Rs measurement periods were assigned values of the proportional contribution of soil components intermediate to the June and November measurement periods. This approach clearly simplifies reality but provides approximate estimates of seasonal and annual Rl, Rr and Rsom. All measurements were made during the day. However, no significant difference between overall day (07:00–19:00) and night time (19:00–07:00) respiration values was found (P = 0.48, n = 9), and diurnal temperature variation at the site was minimal (1–2°C).

[10] Linear regression was used to assess whether spatial heterogeneity in soil moisture, soil temperature, litter mass and root mass could explain observed variation in Rs and its component fluxes. It was assumed that CO2 flux from any individual component of Rs (e.g., roots, surface litter) was adequately described by:

  • equation image

where Rc is component respiration (g m−2 hr), Cm is component mass (g), Crr is component respiration rate per unit mass (g g−1 hr−1), and E is measurement error. In this study, Rr was not directly measured, but was calculated as solely the product of root mass and SRR. In addition, SLR was estimated as the residual variation in Rl, once variation in litter mass was accounted for. Therefore, our estimates of Rr and SLR are likely to include some component of measurement error. A stepwise regression was performed which quantified the individual and combined contributions of estimated Cm and Crr to Rc of roots and litter. Statistical analysis was carried out using SPSS 13.0 for Windows (SPSS Inc., Chicago, U.S.A). Data were subject to a natural logarithmic transformation, where necessary, to conform to the assumptions of parametric analysis.

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

3.1. Spatial and Temporal Variation in Respiration From Soil and Its Components

[11] There was substantial variation between sites in the respiration variables recorded (Table 2 and Figure 1). Estimated mean annual site Rs varied between 13–17 t C ha−1 yr−1, which was partitioned into 0–2 t C ha−1 yr−1 from litter, 6–9 t C ha−1 yr−1 from roots, and 5–6 t C ha−1 yr−1 from soil organic matter (Table 2). On average, 51% of the total range in Rs values recorded across all sites and measurement periods was also observed within each site and period. A large proportion of the recorded variation in Rs was, therefore, caused by within-site spatial heterogeneity, rather than systematic changes between sites and measurement periods. Site mean fluxes ranged between 5–13%, 40–75%, and 14–54% of total Rs for litter, roots, and SOM, respectively (Table 2). Mean Rsom contribution declined from 49% during the wet season to 32% in the dry season (Figure 1f), while Rr contribution displayed the opposite trend: increasing from 42% in the wet season to 61% during the dry season (Figure 1e). In contrast, Rl contribution showed no clear seasonality, though experimental precipitation exclusion on the Dry site was associated with an apparent reduction in Rl of approximately 90% relative to the unmodified sites (Table 2 and Figure 1d).

image

Figure 1. Temporal trends in (a) rainfall, (b) volumetric soil moisture, respiration from (c) soil, (d) litter, (e) roots, and (f) soil organic matter on all sites. Black symbols, directly measured data; grey symbols, estimated data from combination of directly measured (1) Rs (Figure 1c) and (2) contribution of Rl, Rr and Rsom to Rs recorded in November 2004 and June 2005 (Table 2). Sites: circles, Sand; crosses, Dry; squares, Clay; triangles, Fertile. Error bars indicate SE of the mean, n is 25.

Download figure to PowerPoint

Table 2. Annual Respiration From Soil and Its Components, Contribution of Surface Litter, Roots, and Soil Organic Matter to Total Soil Respiration, and Specific Respiration of Litter and Roots, for Each Sitea
 SandDryClayFertile
  • a

    Values indicate mean (5th percentile, 95th percentile), n is 18, except estimates of percentage contribution from components where n is 9.

Annual respiration
   Total soil (t C ha−1 yr−1)13 (9, 20)13 (8, 18)13 (10, 18)17 (13, 30)
   Litter (t C ha−1 yr−1)1 (1, 2)0 (0, 1)1 (1, 2)2 (1, 4)
   Roots (t C ha−1 yr−1)6 (4, 9)7 (4, 9)7 (6, 10)9 (8, 17)
   SOM (t C ha−1 yr−1)6 (4, 10)5 (4, 8)5 (4, 6)6 (4, 9)
Litter contribution
   Nov 2004 (%)9 (0, 29)6 (0, 15)9 (0, 23)13 (0, 42)
   Jun 2005 (%)10 (2, 19)5 (0, 14)10 (2, 25)11 (0, 25)
Root contribution
   Nov 2004 (%)48 (19, 81)55 (30, 85)64 (42, 85)75 (50, 91)
   Jun 2005 (%)38 (19, 60)41 (9, 69)48 (32, 66)41 (26, 59)
SOM contribution
   Nov 2004 (%)46 (4, 81)39 (11, 60)28 (4, 54)14 (6, 28)
   Jun 2005 (%)51 (31, 70)54 (31, 81)42 (23, 60)48 (33, 67)
Specific respiration rate
   Litter (g CO2 kg−1 hr−1)0.3 (0, 0.6)0.2 (0, 0.5)0.3 (0, 0.7)0.4 (0, 1.5)
   Roots (g CO2 kg−1 hr−1)0.4 (0.2, 0.8)0.5 (0.2, 0.9)0.3 (0.2, 0.6)0.7 (0.3, 1.2)

3.2. Factors Affecting Total Soil Respiration

[12] Several non-linear models were fitted to the monthly Rs data, but none explained above 0.07% of observed variation in Rs. So the data were log-transformed and analyzed with a linear regression. Soil temperature did not contribute significantly to the model, and so was removed. There was a significant relationship between volumetric soil moisture and monthly Rs (Figure 2, F = 30, d.f. = 1, 763, Ra2 = 0.04, p < 0.001). Given the low Ra2, the significance of the relationship between Rs and soil moisture probably reflects the large sample size, rather than strong evidence of any causal link.

image

Figure 2. Relationship between monthly soil respiration and volumetric soil moisture. Data from all sites and months have been pooled. Data: grey symbols, individual values; black symbols, mean of 15 values. Sites: grey circles, Sand; grey crosses, Dry; grey squares, Clay; grey triangles, Fertile.

Download figure to PowerPoint

[13] A subset of Rs measurements, made in November 2004 and June 2005, were used to examine factors affecting Rs in more detail. Based upon these data, with a smaller sample size, neither soil temperature nor volumetric soil moisture (Figure 3a) could explain observed variation in Rs. Instead, regression analysis revealed that ground surface litter and root mass in the surface 0.3 meter soil layer together were more useful predictors of Rs, accounting for 44% of observed spatial variation in Rs (Figures 3b and 3c, F = 17, d.f. = 1, 68, Ra2 = 0.44, p < 0.001). The majority of this variation (31%) was attributable solely to heterogeneity in soil surface root mass (Figure 3c), while litter mass accounted for the remaining 13% (Figure 3b).

image

Figure 3. Relationship between soil respiration and (a) volumetric soil moisture, (b) root dry mass and (c) surface litter dry mass. Root mass represents the quantity of root material (<5 mm diameter) retrieved from a 0.3 m deep soil core corresponding to the area enclosed by the IRGA chamber (area = 154 cm2). Litter mass represents the quantity of organic material retrieved from the ground surface within the IRGA chamber. Measurement periods: grey symbols, November 2004; black symbols, June 2005. Sites: circles, Sand; crosses, Dry; squares, Clay; triangles, Fertile.

Download figure to PowerPoint

3.3. Factors Affecting Respiration From Litter, Roots, and Soil Organic Matter

[14] Based upon the subset of measurements made in November 2004 and June 2005, there was no significant relationship between volumetric soil moisture and Rl, Rr and Rsom (data not shown). Heterogeneity in ground surface litter mass accounted for 25% of observed variation in Rl (Figure 4b). The majority of variation in Rl was, therefore, attributed to differences in SLR and measurement error (Figure 4a). In contrast, fine root mass explained 73% of variation in Rr, (Figure 5b) while changes in SRR accounted for 16% (Figure 5a).

image

Figure 4. Relationship between surface litter respiration and (a) specific litter respiration and (b) litter dry mass. Litter mass represents the quantity of organic material retrieved from the ground surface within the IRGA chamber (area = 154 cm2). Measurement periods: grey symbols, November 2004; black symbols, June 2005. Sites: circles, Sand; crosses, Dry; squares, Clay; triangles, Fertile.

Download figure to PowerPoint

image

Figure 5. Relationship between root respiration and (a) specific root respiration and (b) root dry mass. Root mass represents the quantity of root material (<5 mm diameter) retrieved from a 0.3 m deep soil core corresponding to the area enclosed by the IRGA chamber (area = 154 cm2). Measurement periods: grey symbols, November 2004; black symbols, June 2005. Sites: circles, Sand; crosses, Dry; squares, Clay; triangles, Fertile.

Download figure to PowerPoint

[15] Volumetric soil moisture had no clear effect upon Rl (F = 2, d.f. = 1, 45, Ra2 = 0.02, p = 0.16) or litter mass (F = 0.1, d.f. = 1, 69, Ra2 = −0.01, p = 0.75). Root mass, in contrast, increased significantly with soil moisture (Figure 6a, F = 17, d.f. = 1, 70, Ra2 = 0.19, p < 0.001), while SRR decreased (Figure 6b, F = 13, d.f. = 1, 69, Ra2 = 0.15, p = 0.001). The net outcome of these two opposing patterns was that Rr was not clearly affected by soil moisture (Figure 6c, F = 0.1, d.f. = 1, 70, Ra2 = −0.01, p = 0.79).

image

Figure 6. Relationship between volumetric soil moisture and (a) root dry mass, (b) specific root respiration and (c) root respiration. Root mass represents the quantity of root material (<5 mm diameter) retrieved from a 0.3 m deep soil core corresponding to the area enclosed by the IRGA chamber (area = 154 cm2). Measurement periods: grey symbols, November 2004; black symbols, May 2005. Sites: circles, Sand; crosses, Dry; squares, Clay; triangles, Fertile.

Download figure to PowerPoint

4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

4.1. Annual Respiration Estimates

[16] Based upon a global review of Rs partitioning across biomes [Subke et al., 2006] we estimated mean Rs in tropical deciduous forests of 14 t C ha−1 yr−1 (ranging from 8–24 t C ha−1 yr−1, from 10 studies reviewed), compared to values from this study of 13–17 t C ha−1 yr−1 (Table 2). Across all sites surveyed in this study, estimated mean annual heterotrophic contribution to total Rs was 40–52%, compared to a mean of 51% (ranging from 27–76%) from other studies in the same ecosystem [Subke et al., 2006]. By comparison, temperate broadleaf and boreal coniferous forests appear to have lower mean Rs of 9 and 7 t C ha−1 yr−1 respectively, and slightly higher mean heterotrophic contribution to Rs (∼57%), compared to tropical forest ecosystems [Subke et al., 2006].

[17] The quantity of C respired from each soil component relative to C stocks in each component provides clues about the rate of soil C cycling on each site. For example, while the total amount of litter on the Dry site was similar to the Sand site (2 t C ha−1, Table 1), Rl on the Dry site was ∼90% lower. On both sites, the measured C input into surface organic litter (4 and 3 t C ha−1 yr−1 on the Sand and Dry sites respectively; D. B. Metcalfe, unpublished data, 2007) was higher than the estimated quantity of C released via Rl. There are several explanations for this apparent imbalance: (1) the system is not in steady state and therefore surface litter stocks should accumulate on both sites, or steady state conditions do exist but (2) C is removed from the surface litter (∼3 t C ha−1 yr−1 on both sites) through mechanisms other than respiration (e.g.: conversion into SOM, leaching), and/or (3) Rl has been underestimated in this study. We propose that additional measurements; including repeated measurement of litter stocks over time, sampling of dissolved organic C in soil, direct measurement of litter decomposition with litter bags [Nepstad et al., 2002; Cleveland et al., 2006], and Rl measurement at sufficient temporal frequency to capture short-lived surges in respiration after rainfall events [Lee et al., 2002; Savage and Davidson, 2002], could distinguish between these different explanations.

[18] Estimated Rsom on the Fertile site was similar to the other sites (Table 2), even though estimated soil C stock in the 0–0.3 meter soil layer was over twice as large. This suggests that a relatively large proportion of the soil C stock at the Fertile site may be recalcitrant, compared to the other sites. This interpretation is consistent with much of the few existing data on this unusual soil type [Da Costa and Kern, 1999; Lehmann et al., 2003]. Given the sensitivity of most Amazonian soils to many current forms of agriculture, there is substantial interest in how these soils have sustained such a high level of fertility after hundreds, sometimes thousands, of years of cultivation, and potentially how to recreate them across the Amazon again [see Mann, 2002]. Within this context, this study provides insights into how, and why, the ADE or Terra Preta do Indio soil on the Fertile site differs from the more widespread highly weathered Oxisol soils on the other sites.

[19] Our estimates of Rr did not include contributions from roots below 0.3 meter soil depth. Other studies in the Amazon estimated that up to 20% of total Rs was produced below 1 meter depth, and attributed this to substantial respiration from live roots and root-derived SOM in deeper soil layers [Davidson and Trumbore, 1995; Trumbore et al., 1995]. However, based upon soil CO2 production profile data recorded at the Sand and Dry sites [Sotta, 2006], we estimate that soil below 1 meter depth accounted for only 12% of total respiration (within the upper 3 meter soil layer) at these sites, while the 0–0.3 meter soil layer sampled in this study produced approximately 75% of total respiration. We suggest, therefore, that the extent of Rr underestimate in this study is likely to be relatively minor. Clearly, though, further work is required to resolve the contribution of deep roots to C cycling in this ecosystem.

4.2. Factors Affecting Respiration From Soil, Litter, Roots, and Soil Organic Matter

[20] In this study, an asymptotic response pattern of Rs to moisture was recorded (Figure 2) which is consistent with results from other studies [e.g., Davidson et al., 2000; Schwendenmann et al., 2003; Sotta et al., 2006], but the observed trend was weak (Figure 2). Neither was there a strong relationship between volumetric soil moisture and Rl, Rr and Rsom estimated from the subset of measurements recorded in November 2004 and June 2005 (data not shown), despite the fact that both Rr and Rsom contributions to total Rs changed substantially between the wet and dry seasons and Rl was consistently lower on the Dry site (Table 2 and Figure 1). Though, the respiration time series presented in this study (Figure 1) should be interpreted with caution, given the assumptions inherent in the analysis (see the methods section). Surface soil temperature was relatively invariant at the study site and thus could not account for the observed level of spatio-temporal variation in Rs and its component fluxes.

[21] Several studies in the region have reported a relationship between Rs and soil temperature and/or soil moisture [Meir et al., 1996; Davidson et al., 2000; Sotta et al., 2004, 2006]. For example, Meir et al. [1996] found that soil temperature at five cm soil depth accounted for 76–88% of variation in Rs, at a rain forest site in the south-western Amazon. Sotta et al. [2004] reported a significant effect of both soil temperature and moisture on Rs from a forest in the central Amazon. However, these results were based upon short-term temporal trends (days-weeks) in Rs, whereby repeated measurements were taken from the same locations. Sotta et al. [2004] concluded that “temperature and soil water content….can mostly only explain temporal variation (in Rs), especially in relatively uniform ecosystems.” We suggest that, in addition to spatial patterns in Rs, longer-term temporal (seasonal and inter-annual) trends in Rs, may be confounded by changes in root and litter mass or respiration rate of these components. This is potentially important because seasonal changes in temperature and moisture often coincide with major shifts in leaf litter and root activity [Gosz et al., 1972; Vose and Ryan, 2002]. Spatial and temporal models of soil and ecosystem C cycling could, therefore, be significantly improved through incorporation of litter and root dynamics.

[22] In this study, spatial heterogeneity in litter and root mass (in the surface 0.3 meter soil layer) were more useful predictors of spatial variation in Rs (Figure 3): together accounting for 44% of observed spatial variation in Rs. In particular, variation in SLR and root mass accounted for much of the variation in Rl and Rr respectively (Figures 4 and 5), and hence Rs. The two determinants (mass and respiration rate per unit mass) of component respiration represent different C flux pathways which may each respond to environmental variation in different ways (see Figure 6). For example, increased drought-like conditions in the Amazon may cause increased leaf litter fall [Neilson and Drapek, 1998; Nepstad et al., 2002] and thus surface litter mass, but an associated drop in litter moisture [Couteaux et al., 1995] or litter quality [Melillo et al., 1982] could drive a decline in SLR. Results from this study (Figure 4) suggest that if this happened, a drought-induced decline in SLR would have a much greater impact on the contribution of litter to Rs. These preliminary findings could be improved through the simultaneous application of alternative methodologies for partitioning Rs (e.g.: trenching [Silver et al., 2005]; tree girdling [Högberg et al., 2001]; isotopic tracers [De Camargo et al., 1999]) to compare the resultant estimates of Rl, Rr and Rsom.

5. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[23] This study examined spatial and temporal variation in respiration from soil and its components. There was substantial variation in respiration within and between sites and seasons. Neither volumetric soil moisture nor soil temperature could explain this heterogeneity even though both Rr and Rsom contributions to Rs changed between the wet and dry seasons, and Rl was consistently lower on the Dry site. Instead surface litter and root mass accounted for much of the observed spatial variability in Rs. Specifically, variation in SLR and root mass accounted for much of variation in Rl and Rr respectively, and hence Rs. This information about the underlying controls upon respiration from different soil components has important implications for modeling soil CO2 fluxes over space and time.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[24] This research contributes to the Brazil-led Large Scale Biosphere-Atmosphere Experiment in Amazonia. Work was supported by a Natural Environment Research Council (U.K.) Ph.D. studentship, a Standard Research Grant (NER/A/S/2003/1609), a Royal Society (U.K.) Dudley Stamp Memorial Fund award, and an Elizabeth Sinclair Fund award (School of Geosciences, University of Edinburgh, U.K.). The authors would like to thank Leonardo Sá and Ima Vieira for their scientific support and collaboration, the Museu Paraense Emilio Goeldi for the use of its field station and laboratory facilities, and Bene and Joca for committed field work assistance.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
  • Amthor, J. S. (1994), Plant respiratory responses to the environment and their effects on the carbon balance, in Plant-Environment Interactions, edited by R. E. Wilkinson, pp. 501554, Marcel Dekker, New York.,
  • Andreae, M. O., D. Rosenfeld, P. Artaxo, A. A. Costa, G. P. Frank, K. M. Longo, and M. A. F. Silva-Dias (2004), Smoking rain clouds over the Amazon, Science, 303, 13371342.
  • Atkin, O. K., E. J. Edwards, and B. R. Loveys (2000), Response of root respiration to changes in temperature and its relevance to global warming, New Phytol., 147, 141154.
  • Bouma, T. J., K. L. Nielsen, D. M. Eissenstat, and J. P. Lynch (1997), Estimating respiration of roots in soil: Interactions with soil CO2, soil temperature and soil water content, Plant Soil, 195, 221232.
  • Burton, A. J., K. S. Pregitzer, G. P. Zogg, and D. R. Zak (1998), Drought reduces root respiration in sugar maple forests, Ecol. Appl., 8, 771778.
  • Cleveland, C. C., S. C. Reed, and A. R. Townsend (2006), Nutrient regulation of organic matter decomposition in a tropical rain forest, Ecology, 87, 492503.
  • Couteaux, M., P. Bottner, and B. Berg (1995), Litter decomposition, climate and litter quality, Trends Ecol. Evol., 10, 6366.
  • Da Costa, M. L., and D. C. Kern (1999), Geochemical signatures of tropical soils with archeological black earth in the Amazon, Brazil, J. Geochem. Explor., 66, 369385.
  • Davidson, E. A., and S. E. Trumbore (1995), Gas diffusivity and production of CO2 in deep soils of the eastern Amazon, Tellus, Ser. B, 47, 550565.
  • Davidson, E. A., E. Belk, and R. D. Boone (1998), Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest, Global Change Biol., 4, 217227.
  • Davidson, E. A., L. V. Verchot, J. H. Cattanio, I. L. Ackerman, and J. E. M. Carvalho (2000), Effects of soil water content on soil respiration in forest and cattle pastures of eastern Amazon, Biogeochemistry, 48, 5369.
  • Davidson, E. A., A. D. Richardson, K. E. Savage, and D. Y. Hollinger (2006), A distinct seasonal pattern of the ratio of soil respiration to total ecosystem respiration in a spruce-dominated forest, Global Change Biol., 12, 230239.
  • De Camargo, P. B., S. E. Trumbore, L. A. Martinelli, E. A. Davidson, D. C. Nepstad, and R. L. Victoria (1999), Soil carbon dynamics in regrowing forest of eastern Amazonia, Global Change. Biol., 5, 693702.
  • Fang, C., and J. B. Moncrieff (2001), The dependence of soil CO2 flux on temperature, Soil Biol. Biochem., 33, 155165.
  • Fisher, R. A., M. Williams, R. Lobo do Vale, A. L. da Costa, and P. Meir (2006), Evidence from Amazonian forests is consistent with isohydric control of leaf water potential, Plant Cell Environ., 29, 151165.
  • Fisher, R. A., M. Williams, A. Lola da Costa, M. Malhi, R. F. da Costa, S. Almeida, and P. W. Meir (2007), The response of an eastern Amazonian rain forest to drought stress: results and modelling analyses from a through-fall exclusion experiment, Global Change Biol., in press.
  • Gosz, J. R., G. E. Likens, and F. H. Bormann (1972), Nutrient content of litter fall on the Hubbard Brook experimental forest, New Hampshire, Ecology, 53, 769784.
  • Hanson, P. J., N. T. Edwards, C. T. Garten, and J. A. Andrews (2000), Separating root and soil microbial contributions to soil respiration: a review of methods and observations, Biogeochemistry, 48, 115146.
  • Högberg, P., A. Nordgren, N. Buchmann, A. F. S. Taylor, A. Ekblad, M. N. Högberg, G. Nyberg, M. Ottosson-Löfvenius, and D. J. Read (2001), Large-scale forest girdling shows that current photosynthesis drives soil respiration, Nature, 411, 789792.
  • Houghton, R. A., K. T. Lawrence, J. L. Hackler, and S. Brown (2001), The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates, Global Change Biol., 7, 731746.
  • Intergovernmental Panel on Climate Change (IPCC) (2001), Climate Change 2001: The Scientific Basis, Cambridge Univ. Press, Cambridge, U.K.,
  • Janssens, I. A., et al. (2001), Productivity overshadows temperature in determining soil and ecosystem respiration across European forests, Global Change Biol., 7, 269278.
  • Jones, D. L., A. Hodge, and Y. Kuzyakov (2004), Plant and mycorrhizal regulation of rhizodeposition, New Phytol., 163, 459480.
  • Lavigne, M. B., M. G. Ryan, and D. E. Anderson (1997), Comparing nocturnal eddy covariance measurements to estimates of ecosystem respiration made by scaling chamber measurements at six coniferous boreal sites, J. Geophys. Res., 102, 28,97728,986.
  • Lee, M. S., K. Nakane, T. Nakatsubo, W. H. Mo, and H. Koizumi (2002), Effects of rainfall events on soil CO2 flux in a cool temperate deciduous broad-leaved forest, Ecol. Res., 17, 401409.
  • Lehmann, J., D. C. Kern, B. Glaser, and W. I. Woods (Eds.) (2003), Amazonian Dark Earths: Origin, Properties, Management, Kluwer Acad., Dordrecht, Netherlands.
  • Mann, C. C. (2002), The real dirt on rainforest fertility, Science, 267, 920.
  • Marland, G., and T. Boden (1993), The magnitude and distribution of fossil fuel related carbon releases, in The Global Carbon Cycle, edited by M. Heimann, Springer, New York.,
  • Meir, P., J. Grace, J. Lloyd, and A. Miranda (1996), Soil respiration in a rainforest in Amazonia and in Cerrado in central Brazil, in Amazonian Deforestation and Climate, edited by J. H. C. Gash, C. A. Nobre, J. M. Roberts, and R. L. Victoria, pp. 319329, John Wiley, West Sussex, U.K.,
  • Melillo, J. M., J. D. Aber, and J. M. Muratore (1982), Nitrogen and lignin control of hardwood leaf litter decomposition dynamics, Ecology, 63, 621626.
  • Neilson, R. P., and R. J. Drapek (1998), Potentially complex biosphere responses to transient global warming, Global Change Biol., 4, 505521.
  • Nepstad, D. C., et al. (2002), The effects of partial throughfall exclusion on canopy processes, above-ground production, and biogeochemistry of an Amazon forest, J. Geophys. Res., 107(D20), 8085, doi:10.1029/2001JD000360.
  • Nguyen, C. (2003), Rhizodeposition of organic C by plants: mechanisms and controls, Agronomy, 23, 375396.
  • Nordgren, A., M. Ottosson Löfvenius, and M. N. Högberg (2003), Tree root and soil heterotrophic respiration as revealed by girdling of boreal Scots Pine forest: extending observations beyond the first year, Plant Cell Environ., 26, 12871296.
  • Raich, J. W., and P. S. Potter (1995), Global patterns of carbon dioxide emissions from soils, Global Biogeochem. Cycles, 9, 2326.
  • Raich, J. W., C. S. Potter, and D. Bhagawati (2002), Interannual variability in global soil respiration, 1980-94, Global Change Biol., 8, 800812.
  • Ruivo, M. L. P., and E. S. Cunha (2003), Mineral and organic components in archaeological black earth and yellow latosol in Caxiuanã, Amazon, Brazil, in Ecosystems and Sustainable Development, edited by E. Tiezzi, C. A. Brebbia, and J. L. Uso, pp. 319329, WIT Press, Southampton, U.K.,
  • Ryan, M. G., R. M. Hubbard, S. Pongracic, R. J. Raison, and R. E. McMurtrie (1996), Foliage, fine root, woody tissue, and stand respiration in Pinus radiata in relation to nitrogen status, Tree Physiol., 16, 333343.
  • Savage, K. E., and E. A. Davidson (2001), Interannual variation of soil respiration in two New England forests, Global Biogeochem. Cycles, 15, 337350.
  • Savage, K. E., and E. A. Davidson (2002), A comparison of manual and automated systems for soil CO2 flux measurements: trade-offs between spatial and temporal resolution, J. Exp. Bot., 54, 881899.
  • Schlesinger, W. H. (1977), Carbon balance in terrestrial detritus, Ann. Rev. Ecol. Syst., 8, 5181.
  • Schöngart, J., W. J. Junk, M. T. F. Piedade, J. M. Ayres, A. Hüttermann, and M. Worbes (2004), Teleconnection between tree growth in the Amazonian floodplains and the Nino-Southern Oscillation effect, Global Change Biol., 10, 683692.
  • Schwendenmann, L., E. Veldkamp, T. Brenes, T. O'Brien, and J. Mackensen (2003), Spatial and temporal variation in soil CO2 efflux in an old-growth neotropical rain forest, La Selva, Costa Rica, Biogeochemistry, 64, 111128.
  • Shukla, J., C. Nobre, and P. Sellers (1990), Amazon deforestation and climate change, Science, 247, 13221325.
  • Silver, W. L., A. W. Thompson, M. E. McGroddy, R. K. Varner, J. D. Dias, H. Silva, P. M. Crill, and M. Keller (2005), Fine root dynamics and trace gas fluxes in two lowland tropical forest soils, Global Change Biol., 11, 290306.
  • Sotta, E. D. (2006), Soil carbon dioxide dynamics and nitrogen cycling in an eastern Amazonian rainforest, Caxiuana, Brazil, Ph.D. thesis, Georg-August-Univ. of Göttingen, Germany.
  • Sotta, E. D., P. Meir, Y. Malhi, A. D. Nobre, M. Hodnett, and J. Grace (2004), Soil CO2 efflux in a tropical forest in the central Amazon, Global Change Biol., 10, 601617.
  • Sotta, E., E. Veldkamp, B. R. Guimarães, R. K. Paixão, M. L. P. Ruivo, and S. S. Almeida (2006), Landscape and climatic controls on spatial and temporal variation in soil CO2 efflux in an eastern Amazonian rainforest, Caxiuanã, Brazil, For. Ecol. Manage., 237, 5764.
  • Subke, J. A., I. Inglima, and M. F. Cotrufo (2006), Trends and methodological impacts in soil CO2 efflux partitioning: a metaanalytical review, Global Change Biol., 12, 123.
  • Trenberth, K. E., and T. J. Hoar (1997), El Niño and climate change, Geophys. Res. Lett., 24, 30573060.
  • Trumbore, S. E., E. A. Davidson, P. B. De Camargo, D. C. Nepstad, and L. A. Martinelli (1995), Belowground cycling of carbon in forests and pastures of eastern Amazonia, Global Biogeochem. Cycles, 9, 515528.
  • Valentini, R., et al. (2000), Respiration as the main determinant of carbon balance in European forests, Nature, 404, 861865.
  • Vose, J. M., and M. G. Ryan (2002), Seasonal respiration of foliage, fine roots, and woody tissues in relation to growth, tissue N, and photosynthesis, Global Change Biol., 8, 182193.
  • Williams, M., Y. E. Shimabukuro, D. A. Herbert, S. Pardi Lacruz, C. Renno, and E. B. Rastetter (2002), Heterogeneity of soils in an eastern Amazonian rain forest: implications for scaling up biomass and production, Ecosystems, 5, 692704.
  • Zogg, G. P., D. R. Zak, A. J. Burton, and K. S. Pregitzer (1996), Fine root respiration in northern hardwood forests in relation to temperature and nitrogen availability, Tree Physiol., 16, 719725.

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
jgrg249-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrg249-sup-0002-t02.txtplain text document1KTab-delimited Table 2.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.