Plant and Soil 265: 31–46, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands.
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Estimating fine-root biomass and production of boreal and cool temperate forests using aboveground measurements: A new approach Wenjun Chen1,4 , Quanfa Zhang1 , Josef Cihlar1 , Jürgen Bauhus2 & David T. Price3 1 Applications
Division, Canada Centre for Remote Sensing, 588 Booth Street, Ottawa, ON, K1A 0Y7, Canada. of Resources, Environment and Society, Australian National University, Canberra, ACT 0200, Australia. 3 Northern Forestry Centre, Canadian Forestry Service, 5320 - 122 Street, Edmonton, AB, T6H 3S5, Canada. 4 Corresponding author∗ 2 School
Received 23 October 2002. Accepted in revised form 26 January 2004
Key words: boreal, cool temperature, fine-root biomass, fine-root production, forest, new approach, regional estimation, turnover rate
Abstract Information of fine-root biomass and production is critical for quantifying the productivity and carbon cycle of forest ecosystems, and yet our ability to obtain this information especially at a large spatial scale (e.g., regional to global) is extremely limited. Several studies attempted to relate fine-root biomass and production with various aboveground variables that can be measured more easily so that fine-root biomass and production could be estimated at a large spatial scale, but found the correlations were generally weak or non-existed at the stand level. In this study, we tested a new approach: instead of using the conventional way of analysing fine-root biomass at the stand level, we analysed fine-root data at the tree level. Fine-root biomass of overstory trees in stand was first separated from that of understory and standardized to a common fine-root definition of < 2 mm or < 5 mm diameter. Afterwards, we calculated fine-root biomass per tree for a ‘representative’ tree size of mean basal area for each stand. Statistically significant correlations between the fine-root biomass per tree and the diameter at the ground surface were found for all four boreal and cool temperate spruce, pine, fir and broadleaf forest types, and so allometric equations were developed for each group using a total of n = 212 measurements. The stand-level fineroot biomass of trees estimated using the allometric equations agrees well with the measurements, with r 2 values of 0.64 and 0.57 (n = 171), respectively, for fine-roots < 2 mm and < 5 mm diameter. This study further estimated fine-root production as the product of fine-root turnover rate and fine-root biomass, and determined the turnover rate as a function of fine-root biomass, stand age, and mean annual temperature. The estimates of tree fine-root production agree well with reported values, with r 2 value of 0.53 for < 2 mm and 0.54 for < 5 mm diameter (n = 162) at the stand level.
Introduction Knowledge of the spatial distribution and temporal dynamics of forest productivity is essential for sustainable forest management, and may play a role in the management of forest carbon stores for mitigating global climate change (Hall, 2001; Dixon et al., 1994; Houghton et al., 1996, 2001; Chen et al., 2000a, b, c). To some extent, the productivity and carbon cycle of ∗ FAX No: (613) 947-1383.
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forest ecosystems are regulated by fine-root biomass and production (McClaugherty et al., 1982; Pregitzer et al., 1995; Dixon et al., 1994; Chen et al., 2002). Fine-roots control the uptake of water and nutrients from the soil, and thus control photosynthesis rate because many of the world’s forest ecosystems, particularly at high latitudes, are nutrient-limited (van Cleve et al., 1991). The amount of fine-root biomass also strongly influences the rate of autotrophic respiration, which may consume up to 80% of gross photosynthesis (Ryan et al., 1997). Furthermore, fine-root
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Figure 1. Relationships between fine-root biomass, Bf r , and (a) total root biomass for a database compiled by Vogt et al. (1996) with sample number n = 88 and r 2 = 0.10, and (b) basal area at the breast height for a dataset compiled in this study with n = 124 and r 2 = 0.21. Data sources are listed in Table 3.
production has been estimated to represent 10–60% of the net primary production (NPP) of forest ecosystems, defined as the difference between the gross photosynthesis rate and the autotrophic respiration rate (Vogt et al., 1996; Gower et al., 1997). All these facts point to the need for spatially explicit estimation of fine-root biomass and production if one wishes to obtain reliable spatial estimates of forest productivity and carbon cycling. Several attempts have been made to investigate the large-scale dynamics of fine-root biomass in relation to easily measurable variables (e.g., Kurz et al., 1996; Cairns et al., 1997; Vanninen and Mäkela, 1999; Santantonio 1989; Vogt et al., 1996; Li et al., 2003). The problem of course is that because roots are generally buried, our ability to accurately estimate fine-root biomass and production over extensive areas is extremely limited. For example, Kurz et al. (1996) and Cairns et al. (1997) found the ratio of fine-root biomass to total root biomass, rather than root biomass itself, was well correlated with total root biomass. For
16 data points compiled by Kurz et al. (1996), the value of square of linear correlation coefficient (r 2 ) for the relationship between the fine-root biomass and the total root biomass was 0.28. Using a larger database (n = 88) compiled by Vogt et al. (1996), we obtained an r 2 value of 0.1 for the same relationship (Figure 1a). Another study by Vanninen and Mäkela (1999) found that basal area was a good predictor of fine-root biomass for Scots pine stands, after the data are stratified according to site quality. To stratify a large data set compiled from many studies according to site quality, however, is impractical because most site quality assessments are (by definition) qualitative and expressed in comparison to other sites in a specific study. In some other studies, site quality is not reported at all. Consequently, a site labelled as ‘good’ in one study can potentially be labelled as ‘poor’ in another. Hence, without stratification, the correlation between fine-root biomass and basal area is likely to be poor. For the datasets compiled in this study (n = 124), the r 2 value was 0.21, indicating a weak though statistically significant relationship (Figure 1b). Santantonio (1989) found a linear relationship between fine-root and foliage biomass for several conifer species, but data from other studies indicated that this relationship was variable (e.g., Vanninen and Mäkela 1999). Vogt et al. (1996) investigated a wide range of other relationships between fine-root biomass and abiotic/biotic factors, including soil texture, annual precipitation, annual air temperature (mean, maximum or minimum), levels of N, P, or K in aboveground litterfall, and the mean residence time of forest floor N. The latter researchers looked at both the entire data set and grouped the data according to climatic forest types (i.e., boreal needleleaf evergreen, cool temperate broadleaf deciduous, cool temperate needleleaf evergreen, etc.). At the climatic forest type scale, which is close to that of the present study, they found that the value of r 2 was less than 0.3 for all factors when n > 10. Thus, extrapolation of fine-root biomass sample data to regional or larger scales needs further investigation. If estimation of fine-root biomass is a challenging problem, estimating fine-root production is even more difficult (Vogt et al., 1996; Nadelhoffer and Raich, 1992). Vogt et al. (1996) analyzed fine-root production against variables similar to those used in their fineroot biomass analysis aforementioned, and found that r 2 < 0.2 with n > 10 for all factors at the climatic forest type scale. While investigating the relationships between fine-root production and aboveground litter-
33 fall at the global scale, Na delhoffer and Raich (1992) found contradictory results depending upon the methods of deriving fine-root production. When estimated using the ecosystem N budget method, fine-root production was positively correlated with litterfall (r 2 = 0.36, n = 16). However, for estimates using the sum of seasonal changes in fine-root biomass (sequential core method), the difference between annual maximum and minimum fine-root biomass (maximumminimum method), or root growth into root-free cores (ingrowth core method) was not correlated with litterfall (Nadelhoffer and Raich, 1992). For all data sets combined, there was no significant correlation between fine-root production and litterfall (r 2 = 0.05, n = 59). Estimation of biomass and production for aboveground components, however, has been much more successful (Ter-Mikaelian and Korzukhin, 1997; Gower et al., 1999; Chen et al., 2002). Is there any lesson we can learn from the aboveground studies in order to improve the estimation of belowground fineroot biomass and production? In this study, we first compared the methodologies available for estimating biomass of fine-roots and aboveground components, and then proposed and tested a new approach to improve fine-root biomass and production estimations.
Material and methods A new approach for estimating fine-root biomass The most important methodological difference between estimates of aboveground and fine-root biomass is that the biomass of aboveground components is usually estimated at the tree level (Ter-Mikaelian and Korzukhin, 1997) whereas fine-root biomass is typically estimated at the stand level (e.g., Kurz et al., 1996; Cairns et al., 1997; Vogt et al., 1996). For aboveground components, researchers typically sample trees over a wide-range of tree sizes and measure the biomass of these components for each tree. Tree-level allometric equations are then developed by relating the biomass of these components to easily measurable variables such as tree diameter at breast height (DBH). Biomass of all these aboveground components can then be estimated at the stand-level using these allometric equations in combination with the tree size distribution (e.g., Baskerville, 1965; Chen, 2002). In contrast, measurements of fine-root biomass generally represent a stand – level average instead of that of a specific
Figure 2. Diagram showing a new approach for estimating fine-root biomass and production, where Bf r is the fine-root biomass per hectare at the stand level, Bf rt is the fine-root biomass per tree, T is annual mean temperature, A is stand age, ρf r is the fine-root turnover rate, and Pf r is the fine-root production. Pf r may be estimated using the measured Bf r (solid line) or estimated Bf r (broken line).
tree because linking fine-roots to a specific tree is very difficult – if not impossible (Vogt et al., 1998). Direct tracing of fine-roots to individual trees is only possible under some particular situations such as a sub-arctic lichen woodland where fine-roots tend to occur directly below the lichen mat and penetrate the soil humus layer only slightly if at all (Rencz and Aclair, 1980). The second methodological difference is that aboveground estimation based on allometric equations generally deals with a clearly definable overstory, while fine-root biomass estimation at a large scale generally does not distinguish overstory from understory layers (e.g., Kurz et al., 1996; Cairns et al., 1997; Vogt et al., 1996). Thus, it is very difficult to quantify the relationship between fine-root biomass and other more easily measurable variables (commonly aboveground) such as DBH and basal area. For instance, total root biomass and basal area would be low for a young stand, yet the fine-root biomass of the understory herbs and shrubs could be extremely high. The third methodological difference is that there are many definitions for fine-roots, ranging from < 0.5 mm to < 10 mm (Vogt et al., 1996), while there is potentially little confusion in defining the aboveground components. These comparisons suggest that we may be able to improve fine-root biomass estimation by (1) standard-
34 izing fine root definitions by convert fine root biomass measurements to the commonly used < 2 mm or < 5 mm; (2) partitioning fine-roots into overstory and understory vegetation layers; and (3) analysing fineroot biomass at the tree-level instead of at the more conventional stand-level. A new, tree-level approach for estimating fine-root biomass is thus proposed (Figure 2). In this new approach, we first separate the fine-roots of overstory and understory, and we then standardize fine-root definition in order to conduct cross-site analyses. Because trees within a stand typically have a range of sizes, it is essential to know the tree size distribution so that an appropriate mean tree size representative of the stand can be determined (Chen, 2002). Once the representative tree size is identified, the corresponding fine-root biomass per tree can be estimated by dividing total fine-root biomass (kg ha−1 ) by stand density (stem ha−1 ). Allometric equations can then be used to estimate fine-root biomass per tree for the ‘representative’ tree size. Method for estimating fine-root production As discussed in the introduction, previous studies have shown that directly correlating fine-root production to other factors has given unsatisfactory results (Vogt et al., 1996; Nadelhoffer and Raich, 1992). Vogt et al. (1996) also showed that factors correlated with fineroot biomass usually did not correlate with fine-root production, and vice verse. This indicates that the dynamics of fine-root biomass production and turnover rate are different, and should be investigated separately. Given the fact that fine-root biomass can be estimated independently, we only need to determine fine-root turnover rate in order to quantify fine-root production. At a measurement site, fine-root turnover rate (ρf r ) is given by Pf r /Bf r , where Pf r is the fineroot production and Bf r the fine-root biomass. The fine-root turnover rate is then correlated with other variables (Figure 2). There are two commonly used definitions of ρf r in the literature. Aber et al. (1985) and Aerts et al. (1992) used annual mean Bf r in the equation. An alternative definition by Gill and Jackson (2000) calculated ρf r as Pf r divided by the annual maximum of Bf r . Nevertheless, the annual mean and maximum of Bf r ’s were found to be well correlated (Gill and Jackson, 2000), so ρf r can likely be estimated from either. In this study, we use the mean value because most fine-root biomass data were averaged over a year.
Table 1. Number of data points, n, for which the ratio of overstory tree fine-root biomass to total fine-root biomass (both < 2 mm and < 5 mm) was measured in boreal and cool temperate shade-tolerant, shade-intolerant needleleaf, and shade-intolerant broadleaf forest stands
Shade-tolerant Shade-intolerant needleleaf Shade-intolerant broadleaf
n
Reference∗
16 38 10
1–6 4, 7–11 3–5, 12
∗ (1) Smith et al., 2000; (2) Liu and Tyree, 1997; (3) Bauhus and Messier, 1999; (4) Steele et al., 1997; (5) Finer et al., 1997; (6) Grier et al., 1981; (7) Vogt et al., 1987; (8) Gholz et al., 1986; (9) Haland and Brekke, 1989; (10) Makkonen and Helmisaari, 1998; (11) Finer and Laine, 1998; (12) Ruark and Bockheim, 1988.
A number of studies have examined the relationships between ρf r and various biophysical factors (e.g., Gill and Jackson, 2000; Persson, 1992; Haynes and Gower, 1995; Nadelhoffer, 2000; Joslin et al., 2000). At global scale, Gill and Jackson (2000) found strong exponential relationships between mean annual temperature and ρf r in shrublands (r 2 = 0.55) and grasslands (r 2 = 0.48), but only a weak relationship in forests (r 2 = 0.17). Other studies have demonstrated that age, tree species, carbon economy, nutrient availability, water status, soil toxicity, and allelopathy may all affect fine-root turnover rate (e.g., Persson, 1992; Haynes and Gower, 1995; Nadelhoffer, 2000; Joslin et al., 2000). For instance, ρf r tends to be higher in younger stands, and at sites where nutrient and water regimes are less favourable (Persson, 1992). Because of the difficulty in quantitatively defining site quality based on information provided in literature for each sites, fine-root biomass density (kg m−2 ) is used as a proxy for site quality (Vogt at al., 1996). Clearly, many factors may affect ρf r and a multiple linear regression seems necessary to adequately represent ρf r using these factors. In this study, we focused on the examination of the relationships between ρf r and annual temperature, age, and fine-root biomass. Data sources Data were collected from the literature for a number of studies that measured fine-root biomass of both overstory tree species and understory herb and shrub vegetation (Table 1). We limited our data collection to studies conducted in the boreal and cool temperate regions since our primary research interest is Canada’s forest ecosystems (Liu et al., 1999; Bauhus and Messier,
35 Table 2. Number of data points, n, for which fine-root biomass of different sizes was measured in boreal and cool temperate forests (dominated, respectively, by spruce, pine, fir, and broadleaf species). Factors used to convert fine-root biomass between different size classes (e.g., f1−2 = Bf r,
