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 temperateforests using aboveground measurements: A new approach

Wenjun Chen1,4, Quanfa Zhang1, Josef Cihlar1, Jürgen Bauhus2 & David T. Price3

1Applications Division, Canada Centre for Remote Sensing, 588 Booth Street, Ottawa, ON, K1A 0Y7, Canada.2School of Resources, Environment and Society, Australian National University, Canberra, ACT 0200, Australia.3Northern Forestry Centre, Canadian Forestry Service, 5320 - 122 Street, Edmonton, AB, T6H 3S5, Canada.4Corresponding author∗

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, regionalestimation, turnover rate

Abstract

Information of fine-root biomass and production is critical for quantifying the productivity and carbon cycle offorest ecosystems, and yet our ability to obtain this information especially at a large spatial scale (e.g., regionalto global) is extremely limited. Several studies attempted to relate fine-root biomass and production with variousaboveground variables that can be measured more easily so that fine-root biomass and production could be es-timated 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 biomassat the stand level, we analysed fine-root data at the tree level. Fine-root biomass of overstory trees in stand wasfirst separated from that of understory and standardized to a common fine-root definition of < 2 mm or < 5 mmdiameter. Afterwards, we calculated fine-root biomass per tree for a ‘representative’ tree size of mean basal areafor each stand. Statistically significant correlations between the fine-root biomass per tree and the diameter at theground surface were found for all four boreal and cool temperate spruce, pine, fir and broadleaf forest types, and soallometric equations were developed for each group using a total of n = 212 measurements. The stand-level fine-root biomass of trees estimated using the allometric equations agrees well with the measurements, with r2 valuesof 0.64 and 0.57 (n = 171), respectively, for fine-roots < 2 mm and < 5 mm diameter. This study further estimatedfine-root production as the product of fine-root turnover rate and fine-root biomass, and determined the turnoverrate as a function of fine-root biomass, stand age, and mean annual temperature. The estimates of tree fine-rootproduction agree well with reported values, with r2 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 temporaldynamics of forest productivity is essential for sus-tainable forest management, and may play a role inthe management of forest carbon stores for mitigatingglobal 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.E-mail: wenjun.chen@ccrs.nrcan.gc.ca

forest ecosystems are regulated by fine-root biomassand production (McClaugherty et al., 1982; Pregitzeret al., 1995; Dixon et al., 1994; Chen et al., 2002).Fine-roots control the uptake of water and nutrientsfrom the soil, and thus control photosynthesis ratebecause many of the world’s forest ecosystems, partic-ularly at high latitudes, are nutrient-limited (van Cleveet al., 1991). The amount of fine-root biomass alsostrongly influences the rate of autotrophic respiration,which may consume up to 80% of gross photosyn-thesis (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 r2 = 0.10, and (b) basal area atthe breast height for a dataset compiled in this study with n = 124and r2 = 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 eco-systems, defined as the difference between the grossphotosynthesis rate and the autotrophic respiration rate(Vogt et al., 1996; Gower et al., 1997). All these factspoint to the need for spatially explicit estimation offine-root biomass and production if one wishes to ob-tain reliable spatial estimates of forest productivity andcarbon cycling.

Several attempts have been made to investigate thelarge-scale dynamics of fine-root biomass in relationto easily measurable variables (e.g., Kurz et al., 1996;Cairns et al., 1997; Vanninen and Mäkela, 1999; Sant-antonio 1989; Vogt et al., 1996; Li et al., 2003). Theproblem of course is that because roots are gener-ally buried, our ability to accurately estimate fine-rootbiomass and production over extensive areas is ex-tremely limited. For example, Kurz et al. (1996) andCairns et al. (1997) found the ratio of fine-root bio-mass to total root biomass, rather than root biomassitself, was well correlated with total root biomass. For

16 data points compiled by Kurz et al. (1996), thevalue of square of linear correlation coefficient (r2)for the relationship between the fine-root biomass andthe total root biomass was 0.28. Using a larger data-base (n = 88) compiled by Vogt et al. (1996), weobtained an r2 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 offine-root biomass for Scots pine stands, after the dataare stratified according to site quality. To stratify alarge data set compiled from many studies accordingto site quality, however, is impractical because mostsite quality assessments are (by definition) qualitativeand expressed in comparison to other sites in a spe-cific study. In some other studies, site quality is notreported at all. Consequently, a site labelled as ‘good’in one study can potentially be labelled as ‘poor’ inanother. Hence, without stratification, the correlationbetween fine-root biomass and basal area is likely tobe poor. For the datasets compiled in this study (n =124), the r2 value was 0.21, indicating a weak thoughstatistically significant relationship (Figure 1b). Sant-antonio (1989) found a linear relationship betweenfine-root and foliage biomass for several conifer spe-cies, but data from other studies indicated that thisrelationship was variable (e.g., Vanninen and Mäkela1999). Vogt et al. (1996) investigated a wide rangeof other relationships between fine-root biomass andabiotic/biotic factors, including soil texture, annualprecipitation, annual air temperature (mean, maximumor minimum), levels of N, P, or K in abovegroundlitterfall, and the mean residence time of forest floorN. The latter researchers looked at both the entire dataset and grouped the data according to climatic foresttypes (i.e., boreal needleleaf evergreen, cool temperatebroadleaf deciduous, cool temperate needleleaf ever-green, etc.). At the climatic forest type scale, whichis close to that of the present study, they found thatthe value of r2 was less than 0.3 for all factors whenn > 10. Thus, extrapolation of fine-root biomasssample data to regional or larger scales needs furtherinvestigation.

If estimation of fine-root biomass is a challengingproblem, estimating fine-root production is even moredifficult (Vogt et al., 1996; Nadelhoffer and Raich,1992). Vogt et al. (1996) analyzed fine-root productionagainst variables similar to those used in their fine-root biomass analysis aforementioned, and found thatr2 < 0.2 with n > 10 for all factors at the climaticforest type scale. While investigating the relationshipsbetween fine-root production and aboveground litter-

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fall at the global scale, Na delhoffer and Raich (1992)found contradictory results depending upon the meth-ods of deriving fine-root production. When estimatedusing the ecosystem N budget method, fine-root pro-duction was positively correlated with litterfall (r2 =0.36, n = 16). However, for estimates using the sumof seasonal changes in fine-root biomass (sequentialcore method), the difference between annual max-imum and minimum fine-root biomass (maximum-minimum method), or root growth into root-free cores(ingrowth core method) was not correlated with lit-terfall (Nadelhoffer and Raich, 1992). For all datasets combined, there was no significant correlationbetween fine-root production and litterfall (r2 = 0.05,n = 59).

Estimation of biomass and production for above-ground components, however, has been much moresuccessful (Ter-Mikaelian and Korzukhin, 1997;Gower et al., 1999; Chen et al., 2002). Is there anylesson we can learn from the aboveground studies inorder to improve the estimation of belowground fine-root biomass and production? In this study, we firstcompared the methodologies available for estimatingbiomass of fine-roots and aboveground components,and then proposed and tested a new approach toimprove fine-root biomass and production estimations.

Material and methods

A new approach for estimating fine-root biomass

The most important methodological difference betweenestimates of aboveground and fine-root biomass isthat the biomass of aboveground components is usu-ally estimated at the tree level (Ter-Mikaelian andKorzukhin, 1997) whereas fine-root biomass is typic-ally estimated at the stand level (e.g., Kurz et al., 1996;Cairns et al., 1997; Vogt et al., 1996). For abovegroundcomponents, researchers typically sample trees over awide-range of tree sizes and measure the biomass ofthese components for each tree. Tree-level allometricequations are then developed by relating the biomassof these components to easily measurable variablessuch as tree diameter at breast height (DBH). Bio-mass of all these aboveground components can thenbe estimated at the stand-level using these allometricequations in combination with the tree size distribu-tion (e.g., Baskerville, 1965; Chen, 2002). In contrast,measurements of fine-root biomass generally repres-ent a stand – level average instead of that of a specific

Figure 2. Diagram showing a new approach for estimating fine-rootbiomass and production, where Bf r is the fine-root biomass perhectare at the stand level, Bf rt is the fine-root biomass per tree, Tis annual mean temperature, A is stand age, ρf r is the fine-rootturnover rate, and Pf r is the fine-root production. Pf r may beestimated using the measured Bf r (solid line) or estimated Bf r

(broken line).

tree because linking fine-roots to a specific tree isvery difficult – if not impossible (Vogt et al., 1998).Direct tracing of fine-roots to individual trees is onlypossible under some particular situations such as asub-arctic lichen woodland where fine-roots tend tooccur directly below the lichen mat and penetrate thesoil humus layer only slightly if at all (Rencz andAclair, 1980).

The second methodological difference is thataboveground estimation based on allometric equa-tions generally deals with a clearly definable overstory,while fine-root biomass estimation at a large scale gen-erally does not distinguish overstory from understorylayers (e.g., Kurz et al., 1996; Cairns et al., 1997; Vogtet al., 1996). Thus, it is very difficult to quantify therelationship between fine-root biomass and other moreeasily measurable variables (commonly aboveground)such as DBH and basal area. For instance, total rootbiomass and basal area would be low for a youngstand, yet the fine-root biomass of the understory herbsand shrubs could be extremely high.

The third methodological difference is that thereare many definitions for fine-roots, ranging from <

0.5 mm to < 10 mm (Vogt et al., 1996), whilethere is potentially little confusion in defining theaboveground components.

These comparisons suggest that we may be able toimprove fine-root biomass estimation by (1) standard-

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izing fine root definitions by convert fine root biomassmeasurements to the commonly used < 2 mm or< 5 mm; (2) partitioning fine-roots into overstory andunderstory vegetation layers; and (3) analysing fine-root biomass at the tree-level instead of at the moreconventional stand-level. A new, tree-level approachfor estimating fine-root biomass is thus proposed (Fig-ure 2). In this new approach, we first separate thefine-roots of overstory and understory, and we thenstandardize fine-root definition in order to conductcross-site analyses. Because trees within a stand typ-ically have a range of sizes, it is essential to knowthe tree size distribution so that an appropriate meantree size representative of the stand can be determined(Chen, 2002). Once the representative tree size is iden-tified, the corresponding fine-root biomass per treecan be estimated by dividing total fine-root biomass(kg ha−1) by stand density (stem ha−1). Allomet-ric equations can then be used to estimate fine-rootbiomass per tree for the ‘representative’ tree size.

Method for estimating fine-root production

As discussed in the introduction, previous studies haveshown that directly correlating fine-root productionto other factors has given unsatisfactory results (Vogtet al., 1996; Nadelhoffer and Raich, 1992). Vogt et al.(1996) also showed that factors correlated with fine-root biomass usually did not correlate with fine-rootproduction, and vice verse. This indicates that the dy-namics of fine-root biomass production and turnoverrate are different, and should be investigated separ-ately. Given the fact that fine-root biomass can beestimated independently, we only need to determinefine-root turnover rate in order to quantify fine-rootproduction. At a measurement site, fine-root turnoverrate (ρf r ) is given by Pf r/Bf r , where Pf r is the fine-root production and Bf r the fine-root biomass. Thefine-root turnover rate is then correlated with othervariables (Figure 2). There are two commonly useddefinitions of ρf r in the literature. Aber et al. (1985)and Aerts et al. (1992) used annual mean Bf r in theequation. An alternative definition by Gill and Jackson(2000) calculated ρf r as Pf r divided by the annualmaximum of Bf r . Nevertheless, the annual mean andmaximum of Bf r ’s were found to be well correlated(Gill and Jackson, 2000), so ρf r can likely be estim-ated from either. In this study, we use the mean valuebecause most fine-root biomass data were averagedover a year.

Table 1. Number of data points, n, for which the ratio ofoverstory tree fine-root biomass to total fine-root biomass(both < 2 mm and < 5 mm) was measured in boreal andcool temperate shade-tolerant, shade-intolerant needleleaf,and shade-intolerant broadleaf forest stands

n Reference∗

Shade-tolerant 16 1–6

Shade-intolerant needleleaf 38 4, 7–11

Shade-intolerant broadleaf 10 3–5, 12

∗(1) Smith et al., 2000; (2) Liu and Tyree, 1997; (3) Bauhusand Messier, 1999; (4) Steele et al., 1997; (5) Finer et al.,1997; (6) Grier et al., 1981; (7) Vogt et al., 1987; (8) Gholzet al., 1986; (9) Haland and Brekke, 1989; (10) Makkonenand Helmisaari, 1998; (11) Finer and Laine, 1998; (12) Ru-ark and Bockheim, 1988.

A number of studies have examined the relation-ships between ρf r and various biophysical factors(e.g., Gill and Jackson, 2000; Persson, 1992; Haynesand Gower, 1995; Nadelhoffer, 2000; Joslin et al.,2000). At global scale, Gill and Jackson (2000) foundstrong exponential relationships between mean annualtemperature and ρf r in shrublands (r2 = 0.55) andgrasslands (r2 = 0.48), but only a weak relationship inforests (r2 = 0.17). Other studies have demonstratedthat age, tree species, carbon economy, nutrient avail-ability, water status, soil toxicity, and allelopathy mayall affect fine-root turnover rate (e.g., Persson, 1992;Haynes and Gower, 1995; Nadelhoffer, 2000; Joslinet al., 2000). For instance, ρf r tends to be higher inyounger stands, and at sites where nutrient and waterregimes are less favourable (Persson, 1992). Becauseof the difficulty in quantitatively defining site qualitybased on information provided in literature for eachsites, fine-root biomass density (kg m−2) is used asa proxy for site quality (Vogt at al., 1996). Clearly,many factors may affect ρf r and a multiple linear re-gression seems necessary to adequately represent ρf r

using these factors. In this study, we focused on the ex-amination of the relationships between ρf r and annualtemperature, age, and fine-root biomass.

Data sources

Data were collected from the literature for a number ofstudies that measured fine-root biomass of both over-story tree species and understory herb and shrub veget-ation (Table 1). We limited our data collection to stud-ies conducted in the boreal and cool temperate regionssince our primary research interest is Canada’s forestecosystems (Liu et al., 1999; Bauhus and Messier,

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Table 2. Number of data points, n, for which fine-root biomass of different sizes was measured in borealand cool temperate forests (dominated, respectively, by spruce, pine, fir, and broadleaf species). Factors usedto convert fine-root biomass between different size classes (e.g., f1−2 = Bf r,<1mm/Bf r,<2mm) are alsopresented where applicable

f1−2 n Reference∗ f2−5 n Reference∗ f5−10 n Reference∗

Spruce 0.3044 20 6–8 0.4672 15 6

Pine 0.6315 5 1–2 0.7146 34 3, 7, 9–14 0.7532 5 3, 14

Fir 0.6260 5 3–4 0.6813 13 3, 15–19 0.8474 27 3, 23

Broadleaf 0.6852 2 3, 5 0.4729 13 3, 7–8, 20–22 0.8581 2 3, 20

∗(1) Gholz et al., 1986; (2) Finer and Laine, 1998; (3) Finer et al., 1997; (4) Santantonio and Hermann, 1985;(5) Joslin and Henderson, 1987; (6) Rencz and Auclair, 1980; (7) Steele et al., 1997; (8) Ruess et al., 1996;(9) Vanninen and Mäkela, 1999; (10) Vanninen et al., 1996; (11) Makkonen and Helmisaari, 1998; (12) Widenand Majdi, 2001; (13) Retzlaff et al., 2001; (14) Gholz et al., 1986; (15) Bauhus and Messier, 1999; (16) Grieret al., 1981; (17) Vogt et al., 1982; (18) McDowell et al., 2001; (19) Keyes and Grier, 1981; (20) Joslin andHenderson, 1987; (21) Liu and Tyree, 1997; (22) Liu et al., 1997; (23) Santantonio et al., 1977.

1999; Price et al., 1997, 1999; Chen et al., 2000a, b,c, 2002; Chen, 2002). A total of 64 such data pointswere collected for boreal and cool temperate forests,including 16 of them dominated by shade-tolerant spe-cies, 38 by shade-intolerant needleleaf species, and 10by shade-intolerant broadleaf species.

A wide range of root diameters have been usedto define fine-roots in different studies, from as smallas 0.5 mm or less, up to 10 mm (Vogt et al., 1996).In order to standardize these fine-root biomass val-ues measured under different diameter definitions toa common, we collected data points that have fine-root biomass values measured using different diameterdefinitions at the same site from the literature. A totalof 141 such data points were collected for the borealand cool temperate forest types forests (Table 2).

A total of 212 data points, each of which has meas-urements of fine-root biomass and tree size variables(i.e., stand density in trees per hectare, DBH or basalarea), were collected to carry out tree-level fine-rootbiomass analysis from the literature and unpublishedsources (unpublished data, JB) (Table 3). Three typesof fine-root biomass measurement were reported in theliterature. The most common is to core the soil to aspecified depth (e.g., 30 cm) several times during ayear (e.g., monthly), with several samples (perhaps 20or more) being taken each time from each site (Vogtet al., 1998). While this type of measurement prob-ably gives the most reliable annual mean estimate atthe stand level, errors will occur if the sampling depthis too shallow, the sampling intervals are too long orthe replications too few. A total of 171 data pointsof this type of fine-root biomass measurements wascompiled. The second type is direct measurement offine-root biomass at the tree level using whole tree

harvesting (e.g., Rencz and Auclair, 1980). This typeis limited to specific, and relatively unusual, condi-tions, such as in the sub-arctic lichen woodland whereremoval of the total root system is greatly facilitatedby the tendency of fine-roots to occur directly belowthe lichen mat and penetrate the soil humus layer onlyslightly (if at all) (Rencz and Auclair, 1980). As well,it is likely to be an extremely labour-intensive method,and therefore used rarely even where it can be doneat all. Undoubtedly, when performed properly, thismethod is likely to give relatively accurate tree-levelfine-root biomass at the specific time when the tree isharvested, though it may not be representative of theannual mean. A total of 30 data points of that type ofmeasurement was collected (Rencz and Auclair, 1980;Foster, 1985). The third type used a method developedby Santantonio et al. (1977). Fine-root biomass of atree is determined by soil coring in the ‘polygon ofoccupancy’ that surrounds the tree. With this method,errors are likely because the assumed ‘polygon ofoccupancy’ does not necessarily represent the realfine-root distribution for a given individual tree, al-though if repeated for several neighbouring trees ina stand, such errors will be largely self-cancelling. Atotal of 22 data points for this type were found fine-root for cool temperate fir forests (Santantonio et al.,1977).

To estimate fine-root turnover and production, wecompiled data of 162 measurements that included in-formation on fine-root production, fine-root biomass,stand age, and annual mean temperature, for borealand cool temperate forests (Table 4). Many methods tomeasure fine-root production have been tried, includ-ing sequential coring, ingrowth cores, and maximum-minimum method, carbon budget, N budget, and

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Table 3. Number of measurements, n, of fine-root biomass inboreal and cool temperate spruce, pine, fir, and broadleaf foresttypes

n Reference∗

Spruce 33 1–7

Pine 63 3, 8–22

Fir 84 23–35

Broadleaf 32 3–4, 14, 20, 23–24, 26, 36–43

∗(1) Rencz and Auclair, 1980; (2) Smith et al., 2000; (3) Steeleet al., 1997; (4) Ruess et al., 1996; (5) Helmisaari and Hall-bachen, 1999; (6) Nisbet and Mullins, 1986; (7) Lytle and Cronan,1998; (8) Vanninen and Mäkela, 1999; (9) Vanninen et al., 1996;(10) Haland and Brekke, 1989; (11) Makkonen and Helmisaari,1999; (12) Widen and Majdi, 2001; (13) Finer and Laine, 1998;(14) Retzlaff et al., 2001; (15) Santantonio and Grace, 1987;(16) Arneth et al., 1998; (17) Gholz et al., 1986; (18) Haynes andGower, 1995; (19) Coleman et al., 2000; (20) McClaugherty et al.,1982; (21) Pearson et al., 1983; (22) Comeau and Kimmins, 1989;(23) Finer et al., 1997; (24) Bauhus and Messier, 1999; (25) Foster,1985; (26) Xu et al., 1997; (27) Grier et al., 1981; (28) Vogt et al.,1982; (29) McDowell et al., 2001; (30) Santantonio and Hermann,1985; (31) Keyes and Grier, 1981; (32) Santantonio et al., 1977;(33) Curt et al., 2001; (34) Gower et al., 1992; (35) Vogt et al.,1987; (36) Ruark and Bockheim, 1988; (37) Joslin and Hender-son, 1987 (38) Kelting et al., 1995; (39) Coleman et al., 2000;(40) Garkoti and Sigh, 1995; (41) Symbula and Day, 1988; (42) Liuand Tyree, 1997; (43) Wilmot et al., 1995.

minirhizotron (Vogt and Persson, 1991; Santantonioand Grace, 1987; Hendrick and Pregitzer, 1993; Steeleet al., 1997; Vogt et al., 1998). As yet there is nouniversally accepted standard method, and differentmethods applied to the same site have been shown toproduce estimates of fine-root production differing byan order of magnitude (Vogt et al., 1998). There is alsoan ongoing debate on whether the most commonlyused sequential core method underestimates or over-estimates root production (Kurz and Kimmins, 1987;Santantonio and Grace, 1987; Publicover and Vogt,1993; Steele et al., 1997; Sala et al., 1988). In general,fine-root production may be underestimated becauseother carbon losses from roots are not accounted for(e.g., exudation), and because fine-root growth andmortality typically occur simultaneously (Kurz andKimmins, 1987; Santantonio and Grace, 1987; Pub-licover and Vogt, 1993; Steele et al., 1997). In contrast,Sala et al. (1988) suggested that belowground pro-duction is always overestimated because of samplingerrors that accumulate as the frequency of samplingincreases during a year. For forest ecosystems, manyof the positive changes in fine-root biomass betweensampling dates are typically not significant (Goweret al., 1992) so this type of error accumulation is not

Table 4. Number of measurements, n, of fine-root biomass andproduction at the same site of boreal and cool temperate spruce,pine, fir, and broadleaf forest types

n Reference∗

Spruce 43 1–10

Pine 46 9, 12–27

Fir 21 28–40

Broadleaf 52 1, 3–4, 6, 9, 19–23, 27, 32-34, 41–48

∗(1) Ruess et al., 1996; (2) Arthur and Fahey, 1992; (3) DeAn-gelis et al., 1981; (4) Cole and Rapp 1981; (5) Kimmins andHawkes, 1978; (6) Nadelhoffer et al., 1985; (7) Helmisaari andHallbachen, 1999; (8) Alexander and Fairley, 1983; (9) Steeleet al., 1997; (10) Ford 1982; (11) Jiangping et al., 1993;(12) Comeau and Kimmins, 1989; (13) Gholz et al., 1986;(14) Kinerson et al., 1977; (15) Santantonio and Santantonio,1987; (16) Santantonio and Grace, 1987; (17) Arneth et al.,1998; (18) Haynes and Gower, 1995; (19) Aber et al., 1985;(20) Pastor et al., 1984; (21) McClaugherty et al., 1982; (22) Mc-Claugherty et al., 1984; (23) Vogt et al., 1986; (24) Persson,1978; (25) Axelsson and Brakenhielm, 1980; (26) Finer andLaine, 1998; (27) Fredericksen and Zedaker, 1995; (28) Grieret al., 1981; (29) Merier 1981; (30) Vogt et al., 1982; (31) Vogt,1991. (32) Gomez and Day, 1982; (33) Powell and Day 1991;(34) Finer et al., 1997; (35) Santantonio and Hermann, 1985;(36) Cole and Gessel, 1968; (37) Vogt et al., 1990; (38) Goweret al., 1992; (39) Keyes and Grier, 1981; (40) McDowell et al.,2001; (41) Bauhus and Messier, 1999; (42) Yin et al., 1989;(43) Kelting et al., 1995; (44) Hendrick and Pregitzer, 1993;(45) Burke and Raynal, 1995; (46) Fahey and Hughes, 1994;(47) Harris et al., 1975; (48) Joslin and Henderson, 1987.

common if only significant differences are examined.Because a standard method is so far lacking, we de-cided to use values as reported in various studies.Being aware that there is large uncertainty associatedwith different methods of measuring fine-root produc-tion, we expect the resulting estimates to represent an‘average’ value given by various methods applied tothe same site and to provide state-of-the art estimationat large spatial scales, but it should be emphasized thatthe results do not necessarily agree well when compar-ing site-level measurements using a specific method.The dataset of fine-root biomass and production com-piled in this study is probably one of the largest for theboreal and cool temperate forest biomes (Vogt et al.,1996; Gill and Jackson, 2000; Li et al., 2003).

Results

Fine-root biomass partition

In forest ecosystems, it is often assumed that the com-petitive effects of taller overstory trees determine, in

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Figure 3. Fraction of tree fine-root biomass to total fine-root bio-mass (Fot ), as a function of basal area at the ground surface, Abg ,for three boreal and cool temperate forest types: shade intolerantneedle-leaf, shade intolerant broadleaf, and shade tolerant. Errorbars show one standard deviation within a bin. Class size intervalsare 5 m2 ha−1 for basal area < 20 m2 ha−1 and 10 m2 ha−1 forbasal area > 20 m2 ha−1. Data sources are listed in Table 1.

large part, the distribution and abundance of under-story layers (e.g., shrubs and herbs). For example,strong and predictable relationships between herb-aceous and woody plant layers have been describedduring early succession (Halpern and Franklin, 1990)and during stand closure when severe light limitationcan lead to dramatic loss of understory plants (Ala-back, 1982; Klinka et al., 1996). Therefore, it isreasonable to expect that a decrease in overstory ve-getation distribution and abundance will also reducethe mass ratio of overstory to ecosystem total fine-rootbiomass. Figure 3 confirms this hypothesis, showinghow the ratio of tree fine-root to total fine-root bio-mass (Fot ) varies with basal area at the ground surface(Abg). No significant difference in the values of Fot forfine roots < 2 mm (Fot,<2mm) and for that < 5 mm(Fot,<5mm) was detected, based on the 10 data pairsthat measure both Fot,<2mm and Fot,<5mm. From these10 data pairs, we have Fot,<5mm = 0.98 Fot,<2mm, andr2 = 0.85 (data not shown). These paired measure-ments suggested that there is no need to separate Fot

into < 2 mm and < 5 mm, and all data with Fot werethus used equally. Nevertheless, the total number ofFot data numbers is still quite small (n = 22), and so

we limited the analysis to three broad categories basedon shade tolerance and foliage form of the tree layer:shade tolerant, shade-intolerant needleleaf, and shade-intolerant broadleaf. The ratios were then groupedaccording to Abg. Basal area at ground level was usedin preference to basal area determined at breast height,so measurements in very young stands could also beincluded in the analysis. The bin size was chosen tobe 5 for Abg < 20 m2 ha−1, and 10 for Abg >

20 m2 ha−1. The results indicate that young stands,with small basal area, have larger fractions of under-story fine-root biomass under shade-intolerant over-story trees. This is presumably because the canopyform of shade-intolerant trees (which typically dom-inate in early successional stages) allows more lightto penetrate to ground level–particularly for shade-intolerant broadleaves. Shade-tolerant species usuallyachieve canopy dominance during later successionalstages, and form canopies that allow less light to passthrough to ground level. Consequently, Fot of shade-tolerant overstory trees is generally higher than that ofshade-intolerant overstory trees at the same basal area.When basal area (Abg) exceeds 40 m2 ha−1, Fot ap-proaches to 1.0 for all three tree categories. The linesof best least-square fit for the data points in Figure 3as follows:

Fot = 1

1 + 8.9483A−0.9896bg

, r2 = 0.57,

n = 7 for shade - tolerant,(1)

Fot = 1

1 + 39.683A−1.0818bg

, r2 = 0.54,

n = 10 for shade - intolerant needleleaf,(2)

and

Fot = 1

1 + 262998.4A−3.6199bg

, r2 = 0.97,

n = 5 for shade - intolerant broadleaf.(3)

Standardization of fine-root definition

In order to conduct a cross-site analysis, it is essen-tial to standardize the definition of ‘fine-roots’. Whilea wide range of upper limits have been used, twocommonly used definitions of fine-roots are diameter< 2 mm and < 5 mm (Vogt et al., 1996), which aregenerally well-correlated (Figure 4). We divided theentire data set into four forest categories: namely fir,pine, spruce, and broadleaf, as a compromise betweenthe desire of deriving species specific information and

38

Figure 4. Relationships between fine-root biomass < 2 mm(Bf r,<2mm) and that of < 5 mm (Bf r,<5mm) for four types ofboreal and cool temperate forests (dominated by spruce, pine, fir,and broadleaved species, respectively). Data sources and ratio ofBf r,<2mm/Bf r,<5mm are given in Table 2.

the limited data availability as well as the need forsufficient data points to perform statistical analyses.Table 2 lists these data sets and conversion factorsrelating biomass in the two fine-root size classes.

Allometric equations for fine-root biomass

After separating the fine-root biomass of overstorytrees from that of understory vegetation using Eqs. (1–3), and converting the fine-root biomass of overstorytrees to a standard definition (i.e., 2 mm or 5 mm)using the conversion factors in Table 2, we calculatedfine-root biomass per tree (Bfrt ) for each stand listedin Table 3 as follows:

Bf rt = Bf r

S, (4)

(4) where Bf r is the fine-root biomass of trees atthe stand level derived from Eqs. 1–3, and S is thestand density (number of trees per hectare). In orderto develop a fine-root biomass allometric equation,we need also to know the size of the ‘representat-ive’ tree corresponding to the calculated Bf rt . In mostforest stands, tree diameters vary widely, so a detaileddescription of the size distribution is necessary to ac-curately quantify biomass. As an approximation, a treeof mean basal area is generally the best choice for rep-

resenting the stand when biomass must be estimatedfrom the dimensions of a single ‘representative’ tree(Baskerville, 1965; Chen, 2002).

Estimated values of Bf rt were then plotted againstdiameter at the ground surface of the ‘representative’tree (Dgs) for the four forest types (Figure 5). Lin-ear relationships between ln(Bf rt ) and ln(Dgs) werefound for fine-root biomass defined both as < 2 mmand < 5 mm classes (Figure 5). Least-squares fittingto these data produced the following set of fine-rootbiomass allometric equations:

Bf rt,<2mm = 0.002D2.1191gs , r2 = 0.75, n = 33,

CF = 1.46 for spruce forests,(5)

Bf rt,<2mm = 0.0072D1.7353gs , r2 = 0.89, n = 63,

CF = 1.175 for pine forests,(6)

Bf rt,<2mm = 0.0052D1.8515gs , r2 = 0.91, n = 83,

CF = 1.23 for fir forests,(7)

Bf rt,<2mm = 0.0072D1.9634gs , r2 = 0.66, n = 32,

CF = 1.256 for broadleaf forests,(8)

for fine-roots < 2 mm, and

Bf rt,<5mm = 0.011D1.9748gs , r2 = 0.80, n = 33,

CF = 1.29 for spruce forests,(9)

Bf rt,<5mm = 0.012D1.737gs , r2 = 0.84, n = 63,

CF = 1.28 for pine forests,(10)

Bf rt,<5mm = 0.0077D1.8529gs , r2 = 0.91, n = 83,

CF = 1.23 for fir forests,(11)

Bf rt,<5mm = 0.0113D2.0711gs , r2 = 0.81, n = 32,

CF = 1.125 for broadleaf forests,(12)

for fine-roots < 5 mm, where CF is the correctionfactor required when we transform the computed bio-mass from logarithmic form back to arithmetic units(Sprugel, 1983). These statistically significant correl-ations indicate that at the tree level, fine-root biomasscan be reliably estimated using allometric equations,in the same fashion as for aboveground components(e.g. Ter-Mikaelian and Korzukhin, 1997).

Fine-root biomass

Using Eqs. (5–12), values of Bf rt can be calculatedfor a given Dgs of the ‘representative’ tree. Figure 6

39

Figure 5. Allometric equations of < 2 mm and < 5 mm fine-root biomass, Bf r , against the diameter at the ground surface of the ‘represent-ative’ tree of mean basal area for a stand, Dgs , for four type of boreal and cool temperate spruce, pine, fir, and broadleaf forest types. Datasources are listed in Table 3 and details of the allometric equations are given in the text.

shows the one-to-one comparison of estimated andmeasured Bf rt per tree for the datasets that combinedall four forest types (n = 212). The values of estim-ated Bf rt agree well with those of measured Bf rt ,with r2 = 0.86 and 0.85, for fine-roots < 2 mmand < 5 mm, respectively. As shown in Figure 7,reasonable agreement was found between measuredand estimated Bf r at the stand level, with r2 = 0.64(n = 171) and 0.57 (n = 171), for fine-roots < 2 mmand < 5 mm, respectively.

Fine-root turnover rate

As discussed previously, fine-root turnover rateof fine-roots is most commonly calculated fromρf r,<2mm = Pf /Bf r,<2mm, or ρf r,<5mm =Pf r/Bf r,<5mm for 2 mm and 5 mm size classes, re-spectively. Because most measurements reported totalPf r , the same value of Pf r was used in this studyfor both definitions of ρf r . Table 4 lists data for thisanalysis. As shown in Table 5, ρf r,<5mm is correlated

Table 5. Correlation coefficients (and number of data samplesin brackets) between turnover rate for fine root < 5 mm,ρf r,<5mm, and biophysical factors: (a) temperature index,

IT (= 1.4(T −10)/10, where the value of 1.4 was chosen basedon results of Gill and Jackson, 2000), which represents the ef-fect of annual mean air temperature, T ; (b) stand age, A; and(c) < 5 mm fine root biomass, all in natural log-log scales. Resultsfor < 2 mm fine roots are similar (not shown). Here ∗ denotestatistically significance at 0.1 level and ∗∗ at 0.01 level

Spruce Fir Pine Broadleaf

It 0.22∗ (43) 0.19∗ (21) 0.01 (46) 0.11 (52)

A 0.13 (43) 0.23∗ (21) 0.24∗ (46) 0.29∗ (52)

Bfr 0.74∗∗ (43) 0.08 (21) 0.60∗∗ (46) 0.30∗ (52)

with temperature index (IT = 1.4(T−10)/10, where T

is annual mean air temperature), stand age (A), and< 5 mm fine-roots biomass (Bfr,<5mm) significantlyin some cases, but very weak or not at all in othercases. The r2 for broadleaf may be the result of a few

40

Figure 6. Comparison of measured and estimated fine-root biomass(< 2 mm or < 5 mm) per tree, Bf rt , for the combined dataset

compiled in this study with n = 212 and r2 = 0.86 for fine-roots< 2 mm, and n = 212, and r2 = 0.85 for fine-roots < 5 mm.

data points for young stands that have high leverage.Obviously, more data of fine-root biomass and produc-tion are needed, especially at young broadleaf stands.These results indicate there is no single factor that canbe used to satisfactorily describe the fine-root turnoverrate for all types of forests. Therefore, a multiple linearregression was used to explore the fine-root turnoverrate, and we have the following least-square regressionequations for fine-roots with diameters < 5 mm:

ρf r,<5mm = 2.0436B−0.6982fr,<5mmA−0.198I 0.7498

T ,

r2 = 0.75, n = 43, CF = 1.14 for spruce forests,

(13)

ρf r,<5mm = 1.4626B−0.7277fr,<5mmA0.021I 0.1577

T ,

r2 = 0.77, n = 46, CF = 1.12 for pine forests,(14)

ρf r,<5mm = 3.4356B−0.1217fr,<5mmA−0.314I 1.3787

T ,

r2 = 0.24, n = 21, CF = 1.09 for fir forests,(15)

ρf r,<5mm = 1.4161B−0.2081fr,<5mmA−0.2294I 1.069

T ,

r2 = 0.59, n = 52, CF = 1.23 for broadleaf forests.

(16)

and for fine-roots with diameters < 2 mm:

Figure 7. Comparison of measured and estimated fine-root biomass(< 2 mm or < 5 mm) of overstory trees at the stand level, Bf t ,for the combined dataset compiled in this study with n = 171 andr2 = 0.64 for fine-roots < 2 mm, and n = 171, and r2 = 0.57 forfine-roots < 5 mm.

ρf r,<2mm = 5.865B−0.6992fr,<2mmA−0.1937I 0.2826

T ,

r2 = 0.69, n = 43, CF = 1.174 for spruce forests,

(17)

ρf r,<2mm = 1.6805B−0.6834fr,<2mmA0.1295I 0.2644

T ,

r2 = 0.71, n = 46, CF = 1.09 for pine forests,(18)

ρf r,<2mm = 5.758B−0.152fr,<2mmA−0.3249I 1.4887

T ,

r2 = 0.31, n = 21, CF = 1.087 for fir forests,(19)

ρf r,<2mm = 1.807B−0.5299fr,<2mmA0.0429I−0.2818

T ,

r2 = 0.53, n = 52, CF = 1.197 for broadleaf forests.

(20)

Again, here CF is the correction factor for trans-forming computed fine-root turnover rate from logar-ithmic form back to arithmetic units. Figure 8 showsthe one-to-one comparison of measured and estimatedfine-root turnover rates ρf r,<2mm and ρf r,<5mm, us-ing Eqs. (13–20). The combination of Bf r , A, and IT

explains about 59% of the variation in ρf r,<2mm, and70% of the variation in ρf r,<5mm for all values of rootturnover in the fine-root dataset (n = 162).

41

Figure 8. Comparison of measured and estimated turnover rate of< 2 mm fine-roots, ρf r,<2mm and < 5 mm fine-roots, ρf r,<5mm,for the combined dataset compiled in this study with n = 162 andr2 = 0.59 for fine roots < 2 mm, and n = 162, and r2 = 0.70 forfine roots < 5 mm.

Fine-root production

Fine-root production of overstory trees (Pf r,<2mm =S×ρf r,<2mm or Pf r,<5mm = S×ρf r,<5mm) could thenbe calculated from the estimated values of ρf r,<2mm orρf r,<5mm and the stand density (S). Figure 9 showsthe one-to-one comparison of measured and estim-ated fine-root production. The estimated Pf r,<2mm

explained 53% of the variation in fine-root production,while estimated Pf r,<5mm explained 54%, fine-rootfor all forest types contained in the combined data-set (n = 162). The corresponding SEEs for fine-rootproduction were 0.55 and 0.54, respectively (Table 6).

Discussion

Estimation errors in fine-root biomass

In comparison with other approaches (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 new approach presented here provides a substan-

Figure 9. Comparison of measured and estimated fine-root produc-tion (a) using < 2 mm fine-root biomass, and (b) using < 5 mmfine-root biomass for the combined dataset compiled in this studywith n = 162 and r2 = 0.53 for fine roots < 2 mm, and n = 162,and r2 = 0.54 for fine roots < 5 mm.

tially improved capability for estimating fine-root bio-mass over a large area. For example, as discussedin the introduction, the approach of direct correlationbetween fine-root biomass and total root biomass givesan r2 value of 0.1 for the database of n = 88 com-piled by Vogt et al. (1996). The approach of directcorrelation between fine-root biomass and basal areawithout stratification according to site quality, givespoor results as well, with r2 = 0.21 for the datasetscompiled in this study (n = 124). Using the new ap-proach proposed in this study, however, we were ableto increase the stand level r2 values to 0.64 (n = 171)and 0.57 (n = 171), for tree fine-root size classes of< 2 mm and < 5 mm, respectively.

Despite this substantial improvement, the standarderror of estimation (SEE) of fine-root biomass was stillquite large (Table 5). For the combined datasets ofn = 171, the SEE of fine-root biomass was 0.64 forfine-roots < 2 mm and 0.63 for fine-roots < 5 mm.There are several potential sources of error, including:(1) separation of overstory (tree) and understory fine-roots; (2) lack of standardization in the definition of

42

Table 6. Standard error of estimation (SEE) for fine-root biomass and production (indiameter classes of < 2 mm and < 5 mm) of boreal and cool temperate spruce, pine, fir,and broadleaf forest types, as well as their combination. Number of data points n, is alsolisted. Here ‘fine-root production’ is defined as the total production of fine-roots in eachof the two diameter classes

Forest types SEE for fine-root biomass SEE for fine-root production

< 2 mm < 5 mm n < 2 mm < 5 mm n

Spruce 0.57 0.52 18 0.60 0.55 43

Pine 0.62 0.78 63 0.43 0.52 46

Fir 0.60 0.63 67 0.44 0.42 21

Broadleaf 0.59 0.49 24 0.63 0.59 52

All types 0.65 0.64 172 0.55 0.54 163

‘fine-roots’; (3) errors in the allometric relationshipsused to develop the estimates of fine-root biomass andproduction; and (4) several problems in the field meas-urements of fine-root biomass. Measurement errorsfine-root may arise from several sources. First, thereis likely to be considerable seasonal variation in fine-root biomass, so a large error will occur if the siteis visited too infrequently. Second, there is generallylarge spatial heterogeneity in fine-root distribution, sosufficient samples are required to capture the rangeof spatial variation. Third, differences in samplingdepth between studies will likely introduce additionalinconsistencies. Finally, it may also be possible thatsome errors are introduced in the processes of sorting,drying, and weighing in laboratory.

Clearly, measurement errors fine-root are likely tobe the most critical because they influence all othersources of error. The approach presented here, how-ever, is better equipped to reduce the first three errorsources, and is not capable to address the last one.Because of the high labour cost and other effortsare required to conduct field fire-root measurements,number of fine-root measurements, particularly whenspecific region and species are concerned as in the cur-rent study, is generally small. This fact makes eachof these reported fine-root measurements being highlyvaluable and so we cannot afford to select data accord-ing to measurement methods. We, however, stronglyrecommended the experimenters in their future field-works to take these error sources into considerationand to measure aboveground variables such as tree sizedistribution as well, so that their data will be moreuseful to large-scale studies like this one.

Estimation errors in fine-root production

Previous studies indicated that fine-root productionestimation is an even more difficult problem thanfine-root biomass estimation (e.g., Vogt et al., 1996;Nadelhoffer and Raich, 1992). For example, Vogtet al. (1996) obtained r2 < 0.2 for all relationshipsbetween fine-root production and several abiotic andbiotic factors at the climatic species scale. Nadelhofferand Raich (1992) found that there was also no sig-nificant correlation between fine-root production andlitterfall when all data points in their study were in-cluded (r2 = 0.05, n = 59). In comparison, themultiple linear regression method proposed in thisstudy was able to explain much of the observed vari-ation, using fine-root biomass, age, and annual meantemperature as independent variables, regardless of themeasurement methods, with significantly lower SEE(Figure 9, Table 6). The values of SEE varied amongforest types, but the magnitudes were similar. For thedatasets compiled in this study, there was little dif-ference whether fine-root biomass diameter classes of< 2 mm or < 5 mm were used.

We recognize that errors may be introduced inthis new approach due to uncertainties in fine-rootbiomass, age, annual air temperature, and their re-lationships with fine-root production, but much ofthe SEE in the fine-root production estimates couldstill be due to methodological differences betweenthe individual studies. Vogt et al. (1998) comparedfive different methods to estimate fine-root productionat a single forest stand (sequential core maximum-minimum method using significant differences; de-cision matrix method; sequential core � positive bio-mass change and overestimation corrected; carbonbudget method; and N budget method). They found

43

differences of an order of magnitude between thelowest and highest estimates (Vogt et al., 1998).

By combining data from studies performed usingdifferent methods, the fine-root production estimatesof this study may not necessarily agree well whencomparing site-level measurements using a specificmethod, instead they represent an ‘average’ valuegiven by various methods applied to the same site.Because a standard method is so far lacking, the mul-tiple linear relationships of this study may represent apractical method to obtain ‘state-of-the art’ and ‘con-sensus’ means and variances fine-root production overan extensive region. We emphasis that the results ofthe approach being used here does not provide dataon how a system will respond to a specific disturb-ance (drought, El Nino etc) and how roots respondto factors such as climate change but will allow anassessment of current conditions.

Acknowledgements

We thank Dr Robert Landry for internally reviewingthis manuscript. Inspiring and constructive discussionswith Kurt Pregitzer, John King, Don Zak, WernerKurz, Fred Beall and Pierre Bernier are greatly ap-preciated. Louise Blouse helped with literature re-search and data analysis. The constructive commentsby Dr Kristiina Vogt and an anonymous reviewer sig-nificantly strengthened this manuscript. This projectis financially supported by the Program for EnergyResearch and Development (PERD), Climate ChangeAction Plan 2000, and the Canadian Space Plan (CSP).

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Section editor: P. Ryser