SEARCH

SEARCH BY CITATION

Keywords:

  • soil organic carbon;
  • global carbon cycle;
  • carbon inventory;
  • climate;
  • texture

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[1] We present data on soil organic carbon (SOC) inventory for 7050 soil cores collected from a wide range of environmental conditions throughout Australia. The data set is stratified over the spatial distribution of trees and grass to account for variability of SOC inventory with vegetation distribution. We model controls on SOC inventory using an index of water availability and mean annual temperature to represent the climatic control on the rate of C input into the SOC pool and decomposition of SOC, in addition to the fraction of soil particles <63 μm in diameter as a measure of textural control on SOC stabilization. SOC inventories in the top 30 cm of soil increase from 35 mg/cm2 in the driest regions to a modeled plateau with respect to a threshold of water availability at 335 mg/cm2, excluding variables controlling SOC decomposition. Above this threshold, decomposition factors begin to control SOC inventory, which we attribute to energetic control on microbial decomposition rates, and relatively weak stabilization of SOC in association with fine particles. When combined, these relationships provide an overall prediction of SOC inventory that accounts for 89–90% of the variance observed in the measured data set. Deviations from this relationship are most likely due to additional factors that also control decomposition rate such as hydrochemical and soil drainage conditions not accounted for by soil texture. Outliers within this data set are explained with respect to these conditions.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[2] The soil organic carbon (SOC) pool plays a crucial role in the global carbon cycle because it dominates total terrestrial organic carbon storage, (one half to two thirds of the total terrestrial pool) and exchanges carbon with the atmosphere on a range of relatively short timescales (100–102 years [Davidson et al., 2000; Fung et al., 1997; Schlesinger, 1991; Townsend et al., 1995; Trumbore, 1993, 1997; Trumbore et al., 1996]). Despite this significance, there remains a need for robust estimates of the spatial and temporal variability of the magnitude of the SOC pool and the rates of carbon exchange with living biomass and the atmosphere [Knorr et al., 2005]. Although past estimates of the global pool of SOC have ranged from 700 Pg to 2946 Pg, most recent estimates generally converge on a value of about 1500 Pg [Aitjay et al., 1979; Batjes, 1996; Bird et al., 2001; Bohn, 1982; Bolin, 1970; Eswaran et al., 1993; Schlesinger, 1977], although these estimates vary somewhat with the depth of the SOC pool considered, a factor considered in detail by Jobbágy and Jackson [2000].

[3] In order to provide reliable predictions, successful global carbon cycle models must accurately represent fundamental controlling processes and conditions in robust model structures that can be validated by comprehensive data sets collected over a wide range of controlling environmental conditions. However, global observational data sets of SOC are currently incomplete and large-scale validating data sets are needed in global carbon cycle models [Bird et al., 2001]. Toward this end, we present a uniformly collected and analyzed data set of SOC at two depth intervals at the scale of the Australian continent. We then examine the relationship between spatial variability of SOC inventory and the fundamental environmental controls on SOC at the continental scale.

[4] The magnitude of SOC stored in a given soil depends most fundamentally on the rate of input (mostly from biomass detritus), and the rate of loss through decomposition and transport from the soil [Amundson, 2001]. Despite this simplicity, measurement of the global pool of SOC is problematic because these rates are not easily measured on the timescales of tens to thousands of years over which they commonly occur. In addition, the total SOC pool consists of a spectrum of smaller pools with different input and turnover rates, and there are complex interactions between individual pools. Variation of the SOC inventory has been described at a variety of spatial scales ranging from global [Eswaran et al., 1993; Post et al., 1982] to regional [Janssens et al., 2005; Moraes et al., 1995; Schwartz and Namri, 2002; Spain et al., 1983; Wu et al., 2003], to local [Powers and Schlesinger, 2002; Schimel et al., 1985]. Modeling efforts are focused on synthesizing the variation in SOC over controlling environmental variables such as climate, soil texture and biotic factors, providing insight into interactions between fundamental controls [Parton et al., 1994; Schimel et al., 1994]. A similarly wide range of studies have measured SOC inventory variations with soil forming factors, focusing specifically on climatic [Alvarez and Lavado, 1998; Post et al., 1982], edaphic [Arrouays et al., 1995; Oades, 1988; Percival et al., 2000; Raghubanashi, 1992] and biotic [Finzi, 1998] factors. These factors controlling SOC inventory can be stratified according to their spatial significance. At the global to regional scale, climatic controls dominate. SOC inventory tends to be high in cold and/or wet regions, owing to a combination of biological factors related to either high biomass productivity or slow decomposition, or both. Such first-order, climatic controls on SOC inventory have been approximated by readily measured climatic variables such as mean annual precipitation and temperature (MAP, MAT [Amundson, 2001; Bird et al., 2001; Trumbore, 1993]). At a more local scale, within narrow ranges of climatic variation, SOC inventory varies with respect to second-order edaphic factors, primarily soil drainage, which controls by more elemental factors of biomass productivity and decomposition rates. These edaphic factors can similarly be attributed to simple, easily quantified variables such as soil texture [Arrouays et al., 1995; Percival et al., 2000; Telles et al., 2003]. Biotic controls on carbon inventory are dominated by plant and litter chemistry, which, in turn affect decomposition rates. Variation in litter chemistry is a third-order determinant that can be attributed to vegetation distribution and plant growth form, which is most variable in regions of mixed tree-grass vegetation (mostly tropical savanna and desert biomes [Bird et al., 2001]). With these constraints in mind, we model SOC inventory in terms of simple climatic and textural variables, using a sampling strategy that is spatially stratified across the range of vegetation distribution.

2. Field Sampling Methodology

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[5] The locations of the 48 regions sampled from Australia in this study are shown in Figure 1. They included 31 regions with predominantly sandy soil (<20% particles finer than 63 μm) and 17 regions where soil textures varied within a group of narrow climatic limitations. The regions were sampled using the stratified methodology described by Bird et al. [2001]. Carbon inventories were estimated across the distribution of trees and grass using a set of samples taken near trees and away from trees (“tree” -T and “grass” -G samples of Figure 2). At each of the 31 regions of sandy soil, a total of 200 soil cores were collected according to the following protocol (summarized in Figures 2 and 3). Within each region, five transects were established. Each transect was separated by a minimum distance of 1 km from neighboring transects. Five sampling locations were selected, with minimum separation of 100 m, along each transect. At each of these locations, eight soil cores were taken, four near trees (being three cores at 0–5 cm depth, 0–5T; and one core at 0–30 cm depth, 0–30T) and four away from trees (three cores 0–5G; one core 0–30G; see Figure 2). The soil corer was pounded into the soil to the specified depth (5 or 30 cm) after any surface litter horizon had been removed. We sampled to a maximum of 30 cm depth, because this is the generally assumed value of maximum penetration of agricultural plowing, and hence represents the pool of SOC most vulnerable to anthropogenically mediated loss. The 5-cm-depth interval was used primarily for isotopic studies to follow but reinforces trends of SOC inventory observed in the 0- to 30-cm depth interval.

image

Figure 1. Map of Australia showing sampling regions.

Download figure to PowerPoint

image

Figure 2. Placement of “grass” and “tree” cores at each location using the stratified sampling methodology described here.

Download figure to PowerPoint

image

Figure 3. Summary of field sampling locations and transects per soil region according to the stratified sampling methodology.

Download figure to PowerPoint

[6] The three 0- to 5-cm cores for each vegetation type were subsequently bulked for analysis. This sampling regime produced one 0–5T, 0–30T, 0–5G and 0–30G sample at each of the five locations along each of the five transects (i.e., a total of 100 samples) in each of the 31 sandy soil regions. The same protocol was used to locate the sampling sites in the 17 additional regions of variable soil textures. However, at each location a total of six 0–5 and four 0–30 cm samples were taken. Thus at each of these locations, 10 soil cores were taken, five near trees (being three cores at 0–5 cm depth, 0–5T; and two cores at 0–30 cm depth, 0–30T) and five away from trees (three cores 0–5G; two cores 0–30G). This process resulted in a total of 50 cores (5 transects × 10 cores) from each of these regions. Where the tree canopy was closed, the same protocol was followed, but the G samples, still designated G, were taken midway between surrounding tree trunks, and the T sample taken halfway between the G sample and the nearest trunk. The regional average fractional spatial cover of woody vegetation, ft, measured at each sampling location, was used to arithmetically apportion the representation of tree and grass samples in the regional estimates of SOC inventory. Where ft was greater than 0.5, the ft used to apportion the sample types was 0.5.

3. Sample Preparation and Analytical Methodology

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[7] Within each region, individual samples were subsampled, and these subsamples were then bulked at (1) the transect level, and (2) the region level. This yielded 20 transect level “single-bulk” samples per region (one each of 0–5T, 0–30T, 0–5G and 0–30G for each of the five transects), and four region level “bulk-bulk” samples (one each of 0–5T, 0–30T, 0–5G and 0–30G; see Figure 3). Each of the single-bulk samples contains one fifth of an initial sample from each of the five locations along the transect. The single-bulk sample types are referred to as 5T-B, 5G-B, 30T-B and 30G-B. Each bulk-bulk sample contains one twenty-fifth of an initial sample from each of the 25 locations of that type within the region. The bulk-bulk sample types are referred to as 5T-BB, 5G-BB, 30T-BB and 30G-BB. Measurements of any soil property on the -BB samples are considered to represent the mean value for the region, while an estimate of the spatial variability of the property can be determined from the variability evident in the five -B samples.

[8] Mass concentration of solids (or “dry bulk density”) for each of the four -BB samples, and 20 -B samples from each region was determined based on the average of all individual samples of that type. Organic carbon concentration of the -BB samples was measured manometrically after combustion to CO2 and cryogenic purification. Other measurements were made during the course of this data collection, which will be discussed in subsequent work. The stable carbon isotope ratio of CO2 produced by combustion was measured by dual-inlet mass spectrometry at The Australian National University (ANU). Carbon concentrations and stable carbon isotope ratios of -B samples were measured using elemental analysis-continuous flow mass spectrometry at the ANU. Nitrogen concentration and isotope ratios of the 0- to 5-cm -BB samples were measured using elemental analysis-continuous flow mass spectrometry at CSIRO Land and Water, and at the FEEA stable isotope lab at the University of St. Andrews. For each of the 31 sandy soil regions, -BB samples of tree and grass samples from the 0- to 30-cm-depth interval were physically mixed in proportion to ft (a “bulk-bulk-bulk” -BBB sample) and analyzed for 14C activity by conventional methods in the Radiocarbon Dating Laboratory at RSES, ANU. Organic matter from a selected set of sandy soil regions was allowed to respire in closed vessels at constant moisture content for 30–45 days and the CO2 produced by respiration collected for AMS 14C analysis at the ANSTO Lucas Heights AMS facility. These samples were selected from the regions falling along two climatic gradients of mean annual temperature and precipitation (MAT, MAP). Fourier transform infrared (FTIR) spectroscopy using the middle infrared (MIR) spectrum was used at CSIRO Land and Water, Adelaide, to make predictions of soil properties such as the fraction of SOC attributed to charcoal and particulate organic carbon (POC; >53 μm).

[9] Climatic data for the Australian regions were interpolated from Australian Bureau of Meteorology records by John Carter (methods described by Jeffrey et al. [2001]). Spatial estimates of global solar radiation and precipitation were prepared by Sandra Berry (methods described by Berry and Roderick [2002]) using ESOCLIM software available from the Centre for Resource and Environmental Studies at the Australian National University. Climate data for regions outside Australia were estimated from nearby meteorological stations, while radiation data were estimated from daily measurements over the period 1983–1993 made available through NASA's Langley Atmospheric Sciences Data Center Distributed Active Archive Center (DAAC). Least squares regression was performed using statistical curve fitting software (MacCurveFit) and mathematical modeling software (Mathematica). Data were weighted according to the inverse of errors measurements between transects as described above. While the data set covers the range of climates represented in Australia, it is biased toward wetter locations. Mean MAP for the 48 sites in the data set is 876 mm while the continental spatially average MAP is 430 mm.

[10] We reduce the climatic effects on SOC inventory to a single variable by using a formulation of the annual availability of water, W, which is defined by Berry and Roderick [2002]. This formulation provides an index of water availability to plants, although it does not take into account runoff, surface albedo, and longwave radiation fluxes into and away from the surface,

  • equation image

where MAP is mean annual precipitation rate in mm/yr, Qs is mean annual global solar radiation in J/m2/yr, ρw is the density of liquid water (∼1000 kg/m3 at 25°C), and L is the latent heat of evaporation of water (∼2.5 × 106 J/kg H2O at 25°C). W (in m/yr or mm/yr) is thus the difference between MAP and the mean annual amount of water that would be evaporated if all of the global solar radiation received at the surface was used to evaporate water. W is most often a negative number in Australian environments. To make a convenient regression variable that is always positive we calculate the maximum annual potential evaporation rate of water given the maximum annual global solar radiation at Earth's surface with no precipitation. A convenient offset for W is provided by clear day maximum solar irradiance on a normal plane at Earth's surface of approximately 1 kW/m2. Integrated over the annual period at the equator, this equates to 1 × 1010 J/m2/yr, providing a maximum evaporation rate of water (Wmax) of 4000 mm/yr. We use this value to define the annual water availability index, W* as

  • equation image

and use this as the climatic regression variable in this paper.

4. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[11] A summary table of data collected for each of the Australian soil regions appears in the auxiliary material. Additional data collected following a similar protocol for sandy soils regions outside of Australia, cited in this study, is reported in previous work [Bird et al., 2001, 2002a, 2002b, 2003]. These data are shown here but not used in the regression analysis used to derive equations for SOC inventory, but are rather used to “pin down” relationships of SOCTG to climate outside the range of climates typical of Australia and to validate the applicability of the modeled relationships to other regions.

[12] SOCTG in the maximum depth interval sampled (0–30 cm) of the 31 Australian soil regions vary from about 35 to 960 mg C/cm2, in a range of environments where MAT varies from about 12° to 28°C and MAP varies from 170 to 2160 mm/yr. There is a relatively weak statistical correlation between our measurements of SOCTG and both MAP and MAT (Figure 4), as is observed in a number of SOC inventory studies [cf. [Amundson, 2001]. We explore in greater detail the nature of climatic constraints on SOC inventory using more derived variables in the following discussion.

image

Figure 4. Linear regression of SOCTG to (a) mean annual precipitation rate (MAP) and (b) mean annual temperature (MAT) for the sand soil data set (31 Australian regions). Center point is mean of five transects with error bars representing variance between transects.

Download figure to PowerPoint

[13] Error measurements between five transects are an average of 18% and 24% of the mean SOCTG for the 0- to 5- and 0- to 30-cm intervals for all five transects (25 sampling locations). Measured differences of SOC inventory between tree (SOCT) and grass (SOCG) sampling locations become most significant in arid climates, where the distribution of trees in savanna and desert ecosystems becomes patchy (Figure 5). Where the annual water availability index, W*, is less than about 2000 mm/yr (typical of most of Australia), SOCT and SOCG can differ from the spatially proportioned value (SOCTG) by as much as a factor of 2. Above a threshold of approximately 2000 mm/yr W*, SOCT and SOCG differ by no more than a factor of ±0.2 times SOCTG.

image

Figure 5. Relationship between the annual water availability index, W*, and the fractional difference between “tree” and “grass” samples used in the stratified sampling approach. Here fdiff.T–G = equation image, where SOCT and SOCG are the measured inventories from tree and grass samples, SOCTG is the canopy cover-weighted average of SOCTG used to represent the value over the entire region. The open and solid circles represent the mean of five transects with error bars representing variance between transects. Note that the difference becomes most significant (outside the measured spatial error between transects) in hot, dry climates (where W* is less than ∼2000 mm/yr, and where the spatial distribution of trees and grass is increasingly patchy).

Download figure to PowerPoint

5. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

5.1. Spatial Variability of SOC Inventory and the Utility of Stratified Sampling

[14] Bird et al. [2001] has previously discussed the need for a relatively labor-intensive stratified sampling regime for the purpose of obtaining reliable measurements of SOC inventory at large spatial scales necessary for global estimates and model validation. Previous work following this regime has demonstrated that the range of SOCTG values obtained from single locations within one transect of five sampling locations can vary by nearly an order of magnitude [Bird et al., 2003]. Our measurements of average differences from the mean for five transects in each region shows that this error is reduced by using five transects, but there is still significant variation about the mean at these large regional scales (average of 18 and 24% of SOCTG for 0- to 5- and 0- to 30-cm intervals). Using bulked samples from 25 sampling locations, we have retained the accuracy and precision of the labor-intensive sampling regime, but also reduced the analytical load that would otherwise be required for such large sample sets. We expect that the need for stratified sampling with a large number of sampling locations becomes more significant for the purpose of obtaining reliable measurements of stable and radiogenic isotopes at such large spatial scales (J. G. Wynn et al., A continental-scale measurement of the isotopic composition of soil organic carbon, Australia: Implications for modeling soil carbon dynamics, submitted to Global Biogeochemical Cycles, 2006) (hereinafter referred to as Wynn et al., submitted manuscript, 2006).

[15] Figure 5 shows the utility of stratifying the sample set across the distribution of vegetation physiognomy. The measured difference between SOC inventory at tree and grass locations is generally due to a higher SOC concentration in samples from tree locations, and occurs despite the fact that the mass concentration of solids (dry bulk density) is generally lower near trees. In cases where SOCG exceeds SOCT, this is generally due to a higher bulk density of the SOCG samples rather than a higher concentration of SOC, and most likely reflects slightly better drained conditions at tree sites. A similar increase in variability between tree and grass locations with aridity is evident from the stable carbon isotopic composition (δ13C) of SOC from this data set (Wynn et al., submitted manuscript, 2006). Veldkamp and Weitz [1994] similarly found δ13C variance in tropical savanna systems to be greater by an order of magnitude compared with wetter and more homogenous ecosystems. Hence, by using a stratified sampling regime and ft data to calculate regional SOC inventory weighted for the distribution of vegetation types (SOCTG), we have removed the effects of biotic control by small-scale vegetation distribution and can examine the relationship of SOCTG to climatic and edaphic controls over broad geographical regions. This methodology has provided more accurate representation of SOC inventory in arid climates, typical of most of Australia.

5.2. Climatic Controls SOC Inventory of Australian Sandy Soils

[16] Climatic controls on SOC inventory are commonly thought of in terms of mean annual precipitation and temperature because of their familiarity, data availability and ease of measurement [Amundson, 2001; Jobbágy and Jackson, 2000; Schimel et al., 1994]. However, we find that there is a much stronger correlation between our Australian SOCTG data and W*, our index of water availability that is derived from annual measures of precipitation rate and global solar irradiance. This is because W* is a more accurate measure of the water potentially available to plants for photosynthesis. Climatic controls on SOC inventory are most fundamentally determined by the rate of input from biomass, and the rate of decomposition by heterotrophic microorganisms. The average annual rate of carbon fixation by vegetation, gross primary productivity (GPP), is very well correlated with the amount of water available to plants [Berry and Roderick, 2002, 2004; Roderick et al., 2001]. The availability of water in an environment (i.e., the soil store) is dependent on the rates of input (precipitation) and output (evaporation and net runoff). As solar irradiance increases, potential evaporation (Ep) increases, a factor that needs to be taken into account when considering the availability of water for plant growth, which is accomplished by use of W*. Operationally, a linear equation fit to W* explains much of the variance in the magnitude of SOCTG (R2 = 0.73 and 0.82 for a linear fit). However, on the basis of theoretical constraints discussed below, we prefer to use a sigmoidal function,

  • equation image

to describe the relationship of SOCTG to W*. The sigmoidal equation is a solution to the ordinary differential equation,

  • equation image

ESOC is a term describing the efficiency of SOC storage per unit of water availability, and thus is related to water use efficiency of overlying plants (i.e., the water use efficiency of vegetation for assimilating organic carbon in soil). Λ is a term describing the density limitation to carbon assimilation as SOC (i.e., Λ is the slope of a linear equation between SOC decomposition rate to SOCTG). This model is analogous to density limited growth of populations in time (logistic functions), and implies a density limitation of SOC inventory with respect to water availability. Using this parameter and equation, SOCTG approaches a maximum SOC capacity of equation image at some finite water availability. This sigmoidal equation provides a better fit to the data and is more appropriate to extension beyond the boundaries of the measured Australian data set because predicted SOCTG can never be negative and reaches a maximum asymptote in soils with high W* (high precipitation, low solar radiation, or both). Also, as MAP approaches zero, GPP also approaches zero, as should the regional SOC inventory, SOCTG. This parameter and equation provides more accurate estimates than other empirically based regressions based on global SOC data and MAP and MAT [e.g., Amundson, 2001], which under conditions near the boundaries of an observational data set can predict negative SOC inventory (for example at MAT = 25°C and MAP = 150 mm/yr), or fail to predict a value for SOC inventory (at MAT < 0). Least squares surface regressions for our relationship are shown in Figure 6, and are slightly better than other empirically based predictive equations. R2 values for least squares regression to the weighted data are relatively high: 0.83 and 0.86 for the 0- to 5- and 0- to 30-cm depth intervals.

image

Figure 6. Regression of SOCTG to annual water availability index, W* for the sand soil data set (31 Australian regions), shown with additional data from 12 regions outside Australia (southern Africa, Siberia, Canada, Alaska (United States), Czech Republic, and Malaysia. (a) Linear scale. (b) Linear-log scale. Dashed line shows simple linear fit, and solid line follows sigmoidal curve described here. Center point is mean of five transects with error bars representing standard deviation of data from five individual transects.

Download figure to PowerPoint

[17] Using the relationship to water availability alone, SOCTG of well-drained sandy soils reaches a maximum of ∼110 mg/cm2 for the 0- to 5-cm-depth interval and 335 mg/cm2 for the 0- to 30-cm-depth interval, corresponding to maximum carbon densities of 0.022 and 0.011 g/cm3, respectively. Both predictive equations reach a maximum decrease in the rate of change of change of slope (SOC‴TG(W*) = 0, the “folding-value”) at 1835 mm/yr W*, and exhibit a maximum increase in the rate of change of change of slope at approximately 730 and 800 mm/yr W* for the 0- to 5- and 0- to 30-cm intervals, respectively. The latter value is significant because it represents the limit to SOC inventory (below this point there is essentially zero SOCTG). The threshold of 1835 mm/yr W* is most significant because it is the point above which the availability of water is no longer the dominant controlling variable of SOCTG. A similar limitation by water availability on the mean fraction of photosynthetically active radiation (PAR) intercepted by the canopy over the annual period (equation imagev) was observed by Berry and Roderick [2002] at W* of about 3000 mm/yr. equation imagev is a primary determinant of mean annual GPP (i.e., the rate of photosynthesis [Berry and Roderick, 2004; Roderick et al., 2001]). The threshold of water availability on SOCTG is somewhat lower than 3000 mm/yr most likely because of differences in the seasonal cycles of GPP and transfer of carbon from vegetation to soil, which are approximated by mean annual values here. Similar observations of precipitation limits to GPP and decomposition have been observed in the Great Plains of the United States by Sala et al. [1988] and Gholz et al. [2000], in which GPP increases more rapidly than decomposition between 300 and 800 mm/yr MAP.

[18] This model implies that in water-limited environments (<1835 mm/yr W*), both production and decomposition rates of SOC increase linearly with respect to water availability, following the relationship described in equation (3). Above this threshold, environmental controls on photosynthesis are energy limited, and factors other than W* limit photosynthetic carbon fixation, such as availability of other nutrients, CO2, inhibited decomposition and/or differences in radiative flux of PAR. Hence control of SOCTG above this threshold is dominated by changes in nonhydrological factors which limit the rate of photosynthesis (GPP, such as nutrient status), or other factors driving the rate of SOC loss through decomposition or transport, such as energy control on SOC decomposition rate.

[19] The effect of temperature on the rate of decomposition is a known to be a primary climatic control on SOC inventory. It can be taken into account in equation (4) using the parameter Λ, which describes the density limitation on SOC loss through decomposition. In an analysis of the effect of temperature on soil respiration rate, Lloyd and Taylor [1994] describe an Arrhenius type equation in which respiration rate depends on temperature and SOC concentration,

  • equation image

where R is respiration rate (mol C/m2/s), kR is the temperature-dependent rate constant (s−1), E is activation energy (J/mol), ℜ the universal gas constant (8.314 J/mol/K), T is absolute temperature (°K), and d is an empirical constant. We make the assumption that the density limitation to SOC decomposition is a similar function of temperature (due to the response of microbial activity) which can be described over the annual cycle as a function of MAT,

  • equation image

where d1 is another empirical constant. Using a constant value of 53,000 J/mol, a value which provides a reasonable fit to soil respiration data (Lloyd and Taylor [1994], and noting that E may vary somewhat with T), substitution into equation (3) yields

  • equation image

R2 values for least squares regression to this data increase slightly over the relationship to W* alone to 0.87 and 0.85 for the 0- to 5- and 0- to 30-cm-depth intervals, although this relationship significantly reduces a trend in residuals above the threshold of 1835 mm/yr W* at which water availability is not the primary control on SOC inventory (Figure 7).

image

Figure 7. Surface regression of SOCTG to annual water availability index, W* and MAT for the sand soil data set (31 Australian regions): (a) 0–5 cm depth and (b) 0–30 cm depth. Miniature inset shows additional data from 12 regions outside Australia (southern Africa, Siberia, Canada, Alaska (United States), Czech Republic, and Malaysia, and extension of the derived relationship to climates of these regions. Center point is mean of 5 transects with error bars representing 95% confidence interval of statistical fit.

Download figure to PowerPoint

[20] Although we cannot directly compare the MAP- and MAT-based model of 1-m-depth SOC inventory [Amundson, 2001] to our 5- and 30-cm-depth intervals, a regression following the form of the equation of Amundson [2001] shows a somewhat worse fit to our data set (R2 = 0.80 for both the 0- to 5- and 0- to 30-cm-depth intervals) than does our model based on water availability and temperature control of decomposition kinetics (W* and MAT with physically based model parameters and constants, R2 = 0.87 and 0.85). The predictions of our physically based regression model are reasonably comparable with the empirically based prediction of the 1-m-depth SOC inventory from global data separated by ecosystems (SOC30cm(W*, MAT) = 0.415[SOC1m(MAP, MAT)], R2 = 0.71 [Amundson, 2001]). However, the simple MAT-MAP approach consistently underpredicts SOCTG in our measured data for hot dry regions of Australia as compared to cool, wet regions. This may be due to the effect of preservation of a stable pool of SOC near trees in arid environments, a factor which is only accounted for by the stratified sampling regime used here. Our data show that in such arid climates, SOC is preferentially preserved at sampling locations near trees (SOCT) as compared to locations away from trees (SOCG, Figure 5).

[21] The regression equations presented here are good predictors of the magnitude of SOCTG in well-drained sandy soils because variation in nutrient availability and non-climatic factors controlling decomposition and transport losses are very limited in such soils. However, in the global range of soil types, decomposition and transport losses are additionally controlled by edaphic factors, which are addressed in this study in terms of soil texture in the following section. Other decomposition rate limiting factors include the hindrance of gas exchange (oxygen supply and CO2 release) by poor drainage in low-permeability soils and chemical factors inhibiting decomposition rate. Factors controlling SOC loss through transport include loss of dissolved organic carbon to surface and groundwater discharge.

5.3. Extension to Soils of Other Textures

[22] Although edaphic controls on SOCTG can be primarily attributed to a number of factors which control SOC decomposition and transport rates, we use a measure of soil texture (fraction of fine particles) to approximate the edaphic effects soil on SOC preservation. Fine textured soils can increase physical and hydrological protection of SOC by inhibiting decomposition, and physically protecting SOC, which increases both residence time and SOC content [Schimel et al., 1994]. We assume that the percent of soil that passes a 63-μm-diameter sieve is the fraction active in retaining or stabilizing biomass decomposition products (soil in this study being defined as material that passes a 2-mm sieve). This fraction is readily measurable and has been shown to contain the mineral fraction active in stabilizing the 13C-enriched solid products of SOC decomposition [Šantrùčková et al., 2000; Wynn et al., 2005].

[23] We modify the relationships of SOC inventory to W* and MAT in order to separate the variance of measured SOCTG that can be attributed to the role of soil texture, by calculating the degree to which a region is underpredicted (or “overmeasured”) by equation (7),

  • equation image

and attribute the magnitude of fSOC−underp.(W*, MAT) to variation in the fraction of fine soil material, f<63μm (Figure 8). Regions of variable soil with clustered W* and MAT values were sampled to examine variability in the nature of this relationship in several different climatic regions. Linear regression for specific climatic regions shows that much of the underpredicted values can be attributed to soil texture in some of the climatic regions sampled, but not in others. Table 1 shows regression values for an empirical linear fit to an equation,

  • equation image

for each of five climatic regions sampled in Australia for variation with soil texture (deserts, semi-arid savannas, tropical rain forests, temperate rain forests). Although the correlation is mostly positive, R2 values vary from 0.03 to 0.62, and slopes vary from negative to 2.25. Some portion of this variability is likely in part due to the small number of regions used for each individual regression (n = 5–6), and variation of climate within the relatively large regions used (σW* = 23–408 mm/yr). However, from this data it appears that the role of SOC stabilization by fine mineral particles is diminished in cool, temperate environments (Tasmania), where other factors may play a more significant role in inhibiting SOC decomposition, particularly temperature control on respiration rates as discussed above.

image

Figure 8. Regression of the fraction of underpredicted SOCTG to soil texture (fraction of soil less than 63 μm diameter, f<63μm) for four broad climatic regions (deserts, tropical savannas, tropical forests, and temperate forests): (a) 0–5 cm interval and (b) 0–30 cm interval. Center point is mean of five transects with error bars representing standard deviation between transects. Regression lines follow linear equations.

Download figure to PowerPoint

Table 1. Regression Values for the Effect of Soil Texture on the Fraction of Underpredicted SOCa
 Climatic Region0–5 cm0–30 cm
W*, mm/yrσW*MAT, °CσMATABR2ABR2
  • a

    Fractional residual of the regression to climate.

Deserts (BIR, INN, COR, ARR, NAP, DIA)8335913.20.62.15−0.4130.61.33−0.2190.62
Semi-arid savannas (ANA, NEH, LAR, HAP, GRO, CRK)10192315.60.40.516−0.2390.031.10−0.1600.42
Tropical rain forests (MUS, COE, BLE, KIR, KEN, HIN)233414317.21.32.25−0.2640.570.993−0.4390.49
Temperate rain forests (STR, BRR, SCA, LOD, SCB)41204084.91.8−0.817−0.1750.35−0.59−1.570.15

[24] For the entire data set of both 0- to 5- and 0- to 30-cm samples, correlation of the soil textural effect is generally very weak. The magnitude of the effect depends partially on temperature, and can be described by

  • equation image

The interrelationships between soil textural control and other potential factors that can inhibit decomposition are in need of further study, and here we use a simple linear empirical correlation. The effect of soil texture in the regions sampled can primarily be observed in climates above ∼10°C MAT, when the fine fraction exceeds ∼0.2, above which SOCTG increases by a factor of about 50% of the predicted SOCTG with respect to climatic variables alone.

[25] Using this equation and the previously described equations for the regression to W* and MAT, we find that SOCTG variability with respect to two variables of climate and texture for the entire Australian soil data set can be defined by the function

  • equation image

In this analysis, adding soil texture to the regression analysis does not explain much more of the variance of SOCTG than does the simple relationship to climate, although SOCTG is very sensitive to f<63 μm, especially in warmer regions. R2 values for least squares regression to this data increase slightly over the relationship to W* and MAT to 0.90 and 0.89 for the 0- to 5- and 0- to 30-cm-depth intervals. In part, the poor fit is because the relative magnitude of the “noise” has been amplified by prior removal of the primary relationship to climate. However, poor correlation of SOCTG with texture also stems from the oversimplification of using particle size to describe a variety of conditions that inhibit decomposition including interrelationships between climate and texture, variability of SOCTG with clay mineralogy, specific surface area of clay minerals, and other soil textural factors not considered using the simple variable f<63μm. Other factors which potentially attenuate decomposition rates such as soil nutrient chemistry [Cleveland et al., 2002; Neff et al., 2002] and hydrochemical factors [Freeman et al., 2001], need to be further considered and parameterized.

[26] Finally, to analyze the source of the remaining variance, we examine the degree to which a soil region is misfit by f(W*, MAT, f<63μm), by using the residual from this modeled equation,

  • equation image

The residual variance can be attributed to factors attenuating SOC decomposition rates not taken into account by using soil texture as a variable, such as direct input of a stable pool of SOC from charcoal, or hydrochemical factors inhibiting decomposition which result in minimally decomposed particulate organic carbon (POC). Figure 9 shows that the regions with underpredicted, “excess” SOCTG (SOCTG > SOCTG, image commonly have either low 14C activity (pM; Figure 9b), or high proportions of SOC attributable to charcoal (Figure 9a), or both. These factors indicate that some of the missed prediction is due to a relatively long-term stability of an “excess” pool of recalcitrant carbon, in the case of only a few outliers. Soil regions with high proportions of SOC attributable to charcoal include many of the finer textured soils of the savanna region of Queensland used in the textural analysis (CRK, GRO, LAR, not measured for 14C activity), and other sandy soils in similar climatic regions (DAR, FIT). The accumulation of POC does not appear to be a factor causing any outliers to prediction of SOCTG (Figure 9c), likely because most factors relating to slowed decomposition have been taken into account by climate and vegetation variables (MAT and stratified sampling). Overpredicted sites (where SOCTG < image are less significant and most commonly have 14C activity (pM) similar to that of the modern atmosphere at the time of sampling, indicating a dominantly labile pool of recently assimilated SOC.

image

Figure 9. Fractional underprediction of SOCTG from the regression to W*, MAT and f<63μm with respect to (a) FTIR spectroscopic prediction of the fraction of SOC which can be attributed to charcoal, (b) radiocarbon activity of bulk SOCTG (0- to 30-cm samples from sand sites only), and (c) FTIR spectroscopic prediction of the fraction of SOCTG which can be attributed to POC (particulate organic carbon).

Download figure to PowerPoint

6. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[27] The soil data set presented here provide a uniformly collected and measured observation of SOC inventory within the scale of climatic variability of the Australian continent. Although a more extensive suite of data is presented, this study focuses on the inventory of SOC and interprets relationships describing SOC inventory with respect to climatic, textural and biotic controls. Local controls of vegetation distribution are taken into account by stratifying sampling locations near trees and away from trees. The difference between such “tree” and “grass” locations becomes significant with decreasing availability of water (W*) in hot and dry tropical savanna and desert ecosystems, a factor which may be overlooked in SOC inventory studies based on soil taxonomic or ecosystem-based measurements without stratified sampling.

[28] Regionally averaged SOC inventory in well-drained sandy soils from water limited environments can be attributed primarily to climatic factors controlling the input of carbon from photosynthetic production of biomass, which under relatively homogenous global CO2 concentration is correlated with the index of water availability, W* (a function of mean annual precipitation and global solar radiation). Above the threshold of W*, factors other than water availability becomes limiting variables to photosynthesis, and SOC inventory is controlled by factors controlling decomposition and transport rates rather than factors controlling input to SOC from biomass. Rates of SOC decomposition can be attributed to energetic control on soil respiration rates, which follow an Arrhenius-type equation with respect to temperature. This can be taken into account using a density limited model of SOC decomposition. Further, soil texture exhibits a secondary control on SOC inventory, here modeled in terms of the residual to the relationship of SOC inventory to W* and MAT. The derived overall equation for SOC inventory explains 88–89% of the variance observed in regionally averaged samples from two depth intervals of 48 regions in Australia represented by bulked samples from approximately 7050 soil cores. These relationships can be used in global models of the magnitude of the SOC pool, and in understanding processes controlling deviation from the relationships described here. Isotopic measurements of this data set will be used in a forthcoming paper to understand dynamics of the SOC pool (Wynn et al., submitted manuscript, 2006).

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[29] We thank the Australian Cooperative Research Centre for Greenhouse Accounting for funding this research. Analytical work was facilitated by the stable isotope and radiocarbon dating laboratories of the Earth Environment research group of the Research School of Earth Sciences, and Department of Nuclear Physics, the Australian National University, with the technical help of Joan Cowley, Joe Cali, Damian Kelleher and Abaz Alimanovok. Jan Skjemsted, (CSIRO, Adelaide) provided nitrogen isotope measurements and FTIR predictions. AMS radiocarbon activity measurements were supported by a grant from the Australian Institute for Nuclear Science and Engineering (AINGRA03130). Jon Lloyd, Jennifer Harden, and two anonymous reviewers provided helpful discussion and review of this paper.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information
  • Aitjay, G. L., L. P. Ketner, and P. Duvingneaud (1979), Terrestrial primary production and phytomass, in The Global Carbon Cycle-Scope, edited by B. Bolin, et al., pp. 129181, John Wiley, Hoboken, N. J.
  • Alvarez, R., and R. S. Lavado (1998), Climate, organic matter and clay content relationships in the Pampa and Chaco soils, Argentina, Geoderma, 83, 127141.
  • Amundson, R. (2001), The carbon budget in soils, Annu. Rev. Earth Planet. Sci., 29, 535562.
  • Arrouays, D., I. Vion, and J. L. Kicin (1995), Spatial analysis and modeling of topsoil carbon storage in temperate forest humic loamy soils of France, Soil Sci., 159, 191198.
  • Batjes, N. H. (1996), Total carbon and nitrogen inventories in soils of the world, Eur. J. Soil Sci., 47, 151163.
  • Berry, S. L., and M. L. Roderick (2002), Estimating mixtures of leaf functional types using continental-scale satellite and climatic data, Global Ecol. Biogeogr., 11, 2339.
  • Berry, S. L., and M. L. Roderick (2004), Gross primary productivity and transpiration flux of the Australian vegetation from 1788 to 1988 AD: Effects of CO2 and land use change, Global Change Biol., 10, 18841898.
  • Bird, M., H. Šantrùčková, J. Lloyd, and E. Veenendaal (2001), Global soil organic carbon pool, in Global Biogeochemical Cycles in the Climate System, edited by E.-D. Schulze et al., pp. 185199, Elsevier, New York.
  • Bird, M. I., H. Šantrùčková, A. Arneth, J. J. Lloyd, G. Gleixner, and E.-D. Schulze (2002a), Inventories of carbon and isotopes on a latitude gradient through central Siberia, Tellus, Ser. B, 54, 631641.
  • Bird, M. I., H. Šantrùčková, J. J. Lloyd, and E. Lawson (2002b), The isotopic composition of soil organic carbon on a latitude transect in western interior Canada, Eur. J. Soil Sci., 53, 393403.
  • Bird, M. I., E. M. Veenendaal, and J. J. Lloyd (2003), Soil carbon inventories and δ13C along a moisture gradient in Botswana, Global Change Biol., 9, 18.
  • Bohn, H. L. (1982), Estimate of organic carbon in world soils, Soil Sci. Soc. Am. J., 4, 11181119.
  • Bolin, B. (1970), The carbon cycle, Sci. Am., 223, 124130.
  • Cleveland, C. C., A. R. Townsend, and S. K. Schmidt (2002), Phosphorus limitation of microbial processes in moist tropical forests, Ecosystems, 5, 680691.
  • Davidson, E. A., S. E. Trumbore, and R. Amundson (2000), Biogeochemistry—Soil warming and organic carbon content, Nature, 408, 789790.
  • Eswaran, H., E. Van den berg, and P. Reich (1993), Organic carbon in soils of the world, Soil Sci. Soc. Am. J., 57, 192194.
  • Finzi, A. C. (1998), Canopy tree soil interactions within temperate forests: Tree species effects on carbon and nitrogen, Ecol. Appl., 8, 440446.
  • Freeman, C., N. Ostle, and H. Kang (2001), An enzymic latch on a global carbon store, Nature, 409, 149150.
  • Fung, I., et al. (1997), Carbon 13 exchanges between the atmosphere and biosphere, Global Biogeochem. Cycles, 11, 507533.
  • Gholz, H. L., D. A. Wedin, S. M. Smitherman, M. E. Harmon, and W. J. Parton (2000), Long-term dynamics of the pine and hardwood litter in contrasting environments: Toward a global model of decomposition, Global Change Biol., 6, 751765.
  • Janssens, I. A., et al. (2005), The carbon budget of terrestrial ecosystems at country scale—A European case study, Biogeosciences, 2, 1526.
  • Jeffrey, S. J., J. O. Carter, K. B. Moodie, and A. R. Beswick (2001), Using spatial interpolation to construct a comprehensive archive of Australian climate data, Environ. Modell. Software, 16, 309330.
  • Jobbágy, E. G., and R. B. Jackson (2000), The vertical distribution of soil organic carbon and its relationship to climate and vegetation, Ecol. Appl., 10, 423436.
  • Knorr, W., I. C. Prentice, J. I. House, and E. A. Holland (2005), Long-term sensitivity of soil carbon turnover to warming, Nature, 433, 298301.
  • Lloyd, J., and J. A. Taylor (1994), On the temperature dependence of soil respiration, Funct. Ecol., 8, 315322.
  • Moraes, J. L., C. C. Cerri, J. M. Melillo, D. Kicklighter, C. Neill, D. L. Skole, and P. A. Steudler (1995), Soil carbon stocks of the Brazilian Amazon basin, Soil Sci. Soc. Am. J., 59, 244247.
  • Neff, J. C., A. R. Townsend, G. Gleixner, S. J. Lehman, J. Turnbull, and W. D. Bowman (2002), Variable effects of nitrogen additions on the stability and turnover of organic carbon, Nature, 419, 915917.
  • Oades, J. M. (1988), The retention of organic matter in soils, Biogeochemistry, 5, 3570.
  • Parton, W. J., D. S. Schimel, D. S. Ojima, and C. V. Cole (1994), A general model for soil organic matter dynamics: Sensitivity to litter chemistry, texture and management, Soil Sci. Soc. Am. J., 39, 147167.
  • Percival, H. J., R. L. Parfitt, and N. A. Scott (2000), Factors controlling soil carbon levels in New Zealand grasslands: Is clay content important? Soil Sci. Soc. Am. J., 64, 16231630.
  • Post, W. M., W. R. Emanuel, P. J. Zinke, and A. G. Stangenberger (1982), Soil carbon pools and world life zones, Nature, 298, 156159.
  • Powers, J. S., and W. H. Schlesinger (2002), Relationships among soil carbon distributions and biophysical factors at nested spatial scales in rain forests of northeastern Costa Rica, Geoderma, 109, 165190.
  • Raghubanashi, A. S. (1992), Effect of topography on selected soil properties and nitrogen mineralization in a dry tropical forest, Soil Biol. Biochem., 24, 145150.
  • Roderick, M. L., G. D. Farquhar, S. L. Berry, and I. R. Noble (2001), On the direct effect of clouds and atmospheric particles on the productivity and structure of the vegetation, Oecologia, 129, 2131.
  • Sala, O. E., W. J. Parton, L. A. Joyce, and W. K. Lauenroth (1988), Primary production of the central grassland region of the United States, Ecology, 69, 4045.
  • Šantrùčková, H., M. I. Bird, and J. Lloyd (2000), Microbial processes and carbon-isotope fractionation in tropical and temperate grassland soils, Funct. Ecol., 14, 108114.
  • Schimel, D. S., M. A. Stillwel, and R. G. Woodmanesee (1985), Biogeochemistry of C, N, and P in a soil catena of the shortgrass steppe, Ecology, 66, 276282.
  • Schimel, D. S., B. H. Braswell, E. A. Holland, R. McKeown, D. S. Ojima, T. H. Painter, W. J. Parton, and A. R. Townsend (1994), Climatic, edaphic and biotic controls over storage and turnover of carbon in soils, Global Biogeochem. Cycles, 8, 279293.
  • Schlesinger, W. H. (1977), Carbon balance in terrestrial detritus, Annu. Rev. Ecol. Syst., 8, 5181.
  • Schlesinger, W. H. (1991), Biogeochemistry: An Analysis of Global Change, Elsevier, New York.
  • Schwartz, D., and M. Namri (2002), Mapping the total organic carbon in the soils of the Congo, Global Planet. Change, 33, 7793.
  • Spain, A. V., R. F. Isbell, and M. E. Probert (1983), Soil organic matter, in Soils: An Australian Viewpoint, Div. of Soils, Commonw. Sci. and Ind. Res. Org., Melbourne, Victoria, Australia.
  • Telles, E. C. C., P. B. Camargo, L. A. Martinelli, S. E. Trumbore, E. S. Costa, J. Santos, N. Higuchi, and R. C. Olivera Jr. (2003), Influence of soil texture on carbon dynamics and storage potential in tropical forest soils of Amazonia, Global Biogeochem. Cycles, 17(2), 1040, doi:10.1029/2002GB001953.
  • Townsend, A. R., P. M. Vitousek, and S. E. Trumbore (1995), Soil organic matter dynamics along gradients in temperature and land-use on the island of Hawaii, Ecology, 76, 721733.
  • Trumbore, S. E. (1993), Comparison of carbon dynamics in tropical and temperate soils using radiocarbon measurements, Global Biogeochem. Cycles, 7, 275290.
  • Trumbore, S. E. (1997), Potential responses of soil carbon to global change, Proc. Natl. Acad. Sci. U. S. A., 94(8), 284291.
  • Trumbore, S. E., O. A. Chadwick, and R. Amundsen (1996), Rapid exchange between soil carbon and atmospheric carbon dioxide driven by temperature change, Nature, 272, 393396.
  • Veldkamp, E., and A. M. Weitz (1994), Uncertainty analysis of δ13C method in soil organic matter studies, Soil Biol. Biochem., 26, 153160.
  • Wu, H., Z. Guo, and C. Peng (2003), Distribution of storage of soil organic carbon in China, Global Biogeochem. Cycles, 17(2), 1048, doi:10.1029/2001GB001844.
  • Wynn, J. G., M. I. Bird, and V. N. L. Wong (2005), Rayleigh distillation and the depth profile of 13C/12C ratios in soil organic carbon from two soils in Iron Range National Park, Far North Queensland, Australia, Geochim. Cosmochim. Acta, 69, 19611973.

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Sampling Methodology
  5. 3. Sample Preparation and Analytical Methodology
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

Auxiliary material for this article contains all relevant measurements of the soil organic carbon data presented in this paper.

FilenameFormatSizeDescription
gbc1246-sup-0001-README.txtplain text document15KREADME.txt
gbc1246-sup-0002-ts01.txtplain text document42KTable S1. All relevant measurements of the soil organic carbon data presented in this paper.
gbc1246-sup-0003-t01.txtplain text document1KTab-delimited Table 1.

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.