SEARCH

SEARCH BY CITATION

Keywords:

  • forest C ecosystem dynamics;
  • root dynamics;
  • CBM-CFS3;
  • belowground processes;
  • Canada's managed forest

Abstract

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

[1] Canada's forests play an important role in the global carbon cycle through carbon (C) storage and C exchange with the atmosphere. While estimates of aboveground biomass have been improving, little is known about belowground C storage in root biomass. Here we estimated the contribution of roots to the C budget of Canada's 2.3 × 106 km2 managed forests from 1990 to 2008 using the empirical modeling approach of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) driven by detailed forestry data sets from the National Forest C Monitoring, Accounting and Reporting System. The estimated average net primary production (NPP) during this period was 809 Tg C yr−1 (352 g C m2 yr−1) with root growth and replacement of turnover contributing 39.8 % of NPP. Average heterotrophic respiration (Rh) was 738 Tg C yr−1 (321 g C m−2 yr−1), which resulted in a net ecosystem production (NEP) value of 31 g C m−2 yr−1(71 Tg C yr−1), and on average only 8.7% of NPP remained in the system as NEP. Estimated average root C stocks were 2.38 Pg (1235 g C m−2), mostly in coarse roots (≥ 5 mm diameter), and had an average root to shoot percentage (belowground to aboveground biomass) of 25.6%. Detailed monitoring of C exchange between forests and the atmosphere and an improved understanding of the belowground processes and their response to environmental changes are needed to improve our understanding of the terrestrial C budget.

Introduction

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

[2] Globally, forests are important carbon (C) sinks, sequestering ~30% of anthropogenic CO2 emissions annually [Canadell et al., 2007; Pan et al., 2011] but can also act as an important regional net CO2 sources when disturbed, deforested, or degraded [Kurz et al., 2008; van der Werf et al., 2009]. The obvious manifestation of this C sequestration is the accumulation of aboveground tree biomass, but a substantial proportion of forest net primary production (NPP) is directed toward maintenance and accumulation of belowground biomass [Cairns et al., 1997; Heath et al., 2005; Mokany et al., 2006; van der Werf et al., 2009]. Despite the importance of belowground C allocation to the functioning of plant and soil communities, controls on belowground C allocation and the release of C from roots into soil are relatively poorly understood [Jones et al., 2004]. Consequently, our ability to predict how belowground C flows will respond to environmental change remains rudimentary [Giardina et al., 2005].

[3] Belowground net primary productivity (NPPBG) cannot be measured over large scales using straightforward methods, and therefore it is typically estimated as a function of more readily observable aboveground vegetation dynamics [Running et al., 2004] using relatively simple quantitative relationships. Root turnover and respiration are equally challenging to measure [Kurz and Kimmins, 1987; Steele et al., 1997; Vogt et al., 1986], and representation of these processes is typically highly simplified in terrestrial ecosystem models. Root biomass is also simplified in models by using root to shoot ratios or more complex functional relationships to estimate belowground biomass C stocks as a function of more easily measured aboveground biomass C.

[4] Advances in Earth observation of land cover dynamics and terrestrial ecosystem C modeling are far outpacing advances in understanding of belowground C dynamics, despite the importance of belowground C flows to the terrestrial C budget [Chapin et al., 2009]. As this knowledge gap widens, it becomes increasingly important to report and evaluate model predictions of belowground C stocks and flows so that these may be critically examined and used to advance our knowledge of these important components of the terrestrial C budget.

[5] We evaluated national-scale estimates of root biomass C stocks and flows for Canada's managed forests using the Carbon Budget Model of the Canadian Forest Sector, version 3 (CBM-CFS3). The CBM-CFS3 is an inventory-based model of forest C dynamics that consists of a linked set of submodels for live biomass, plant detritus and soil C stocks, forest management, land-use change, and disturbance [Kurz et al., 2009]. This model is used to estimate greenhouse gas emissions and removals from Canada's managed forests, a subset of the entire forest area of Canada [Environment Canada, 2009; Stinson et al., 2011]. Root biomass and turnover parameters in the CBM-CFS3 were developed by Kurz et al. [1996] and Li et al. [2003] using empirical regressions to determine root biomass as a function of aboveground biomass and forest type, allocation of root biomass into fine and coarse roots, and fine root turnover rates.

[6] The objectives of this study were to (1) develop an updated inventory-based estimate of C in root biomass within Canada's managed forest, (2) examine root to shoot ratios for Canadian forest types, and (3) estimate the contribution of roots to NPP and heterotrophic respiration (Rh). These estimates are described in a manner that is intended to facilitate comparison with field measurements and encourage critical review with the aim of stimulating future advances in simulation modeling of belowground C flows. A final objective was to examine the sensitivity of predictions of dead organic matter (defined as plant detritus and humified organic matter pools including soil C) stocks and flows to changes in root-related model parameters and to assess the impact of altering root parameters on national-scale production and Rh estimates.

Methods

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

Model Overview: The CBM-CFS3

[7] The CBM-CFS3 was developed to serve as the core modeling component of Canada's National Forest Carbon Monitoring Accounting and Reporting System [Kurz and Apps, 2006; Stinson et al., 2011], and as a decision support tool for operational foresters in Canada [Kull et al., 2011; Kurz et al., 2002], and can be used to explore research or management questions at smaller spatial scales [Bernier et al., 2010; Trofymow et al., 2008]. See the article by Kurz et al. [2009, and references therein] for a full description of the model, evaluation procedures, and stand and regional-scale applications.

[8] For time step t, root biomass was determined using a root to shoot ratio for softwoods

  • display math(1)

and a root development equation for hardwoods

  • display math(2)

where B is the biomass and subscripts AG and BG refer to aboveground and belowground, respectively. For mixed stands, the root biomass was estimated separately for each component and then summed. These equations were developed by Kurz et al. [1996] and refined by Li et al. [2003] using 340 pairs of aboveground and root biomass data for softwood species, and 103 pairs for hardwood species from studies in boreal and temperate forest ecosystems. It was assumed that aboveground and belowground biomass has a 50% carbon concentration [Lamlom and Savidge, 2003; Matthews, 1993].

[9] In the model, roots were separated into fine roots (< 5 mm diameter) and coarse roots (≥ 5 mm diameter). The fine root proportion, Pf, was estimated as

  • display math(3)

based on an empirical fit of 90 pairs of total root and fine root biomass with diameter thresholds between 1 and 5 mm [Li et al., 2003]. Annual detritus from fine root turnover (representing root mortality) was estimated as the fine root biomass multiplied by a turnover rate

  • display math(4)

where the 0.5 represents a 50% carbon concentration, DFR is the fine root detritus, and the annual fine root turnover rate, TFR, is 64.1%, based on an empirical fit of 102 pairs of fine root biomass and fine root production data [Li et al., 2003]. The annual coarse root detritus was estimated in a similar manner, and the turnover rate was assumed to be 2% [Kurz et al., 1996].

[10] C from dead fine roots and dead coarse roots was transferred into the corresponding dead organic matter pools (Figure 1). There are 11 dead organic matter pools in CBM-CFS3, and pools are named and numbered according to their speed of decay, with pool 11, the very fast pool within the mineral soil having the fastest decay rate, and pool 1, the slow pool within the mineral soil having the slowest decay rate (Table 1). C from dead fine roots was split equally between two very fast pools one of which was in the soil organic horizon and the other was within the mineral soil. Similarly, C from dead coarse roots was split equally into two quickly decaying pools called the fast pools. The model simulates decomposition as an annual flow of C which releases most of the C to the atmosphere, and transfers a percentage of decayed C to the slowly decaying pools to simulate C which has been stabilized. Decay parameters and dead organic matter dynamics are described in detail by Kurz and Apps [1999], Kurz et al. [2009], Smyth et al. [2011], and Smyth and Kurz [2013] and are described briefly below.

image

Figure 1. Schematic of 6 of the 11 dead organic matter pools in the CBM-CFS3 that include root-derived C. Shaded boxes indicate pools located within the mineral soil. Half of the root detritus is allocated to pools within the soil organic horizon, and the other half is allocated to pools within the mineral soil. Decayed carbon from the very fast and fast pools transfers 81.5% to 83% to the atmosphere (CO2) as heterotrophic respiration and transfers the remainder (17% to 18.5%) to the slow pools. Detritus from other sources and dead organic matter pools 3 through 7 are not shown.

Download figure to PowerPoint

Table 1. CBM-CFS3 Dead Organic Matter Pools and Associated Parameters
CBM-CFS3 PoolDescriptionDecay ParametersaIPCC GPG Poolb
kb (% yr−1)Qτ (%)
  1. a

    Decomposition parameters included the base decay rate (kb) at a reference temperature of 10°C, the temperature quotient (Q), and the percentage of decayed C transferred to the atmosphere (τ).

  2. b

    Corresponding pools described in the Good Practice Guidance (GPG) of the Intergovernmental Panel on Climate Change (IPCC) [Intergovernmental Panel on Climate Change, 2003].

C1Slow decaying C in the mineral soil0.331100Soil organic matter
C2Slow decaying C in the soil organic horizon1.52.65100Litter
C3Softwood stem snag C1.87283Dead wood
C4Hardwood stem snag C1.87283Dead wood
C5Medium decaying C3.74283Dead wood
C6Softwood branch snag C7.18283Dead wood
C7Hardwood branch snag C7.18283Dead wood
C8Fast decaying C in the soil organic horizon14.35283Litter
C9Fast decaying C in the mineral soil14.35283Dead wood
C10Very fast decaying C in the soil organic horizon35.52.6581.5Litter
C11Very fast decaying C in the mineral soil50283Soil organic matter

[11] For time step t, the C stock in the very fast pool within the mineral soil, C11(t), was estimated as

  • display math(5)

where half of the fine root detritus, 0.5DFR, was added to the stock from the previous time step and then decayed with a decay rate of k11, at a mean annual air temperature of T. Similarly, the C stock in the fast pool within the mineral soil, C9(t), was estimated as

  • display math(6)

where DCR is the coarse root turnover and k9 is the decay rate.The decay rate for each pool, ki, was estimated from a base decay rate defined at a reference temperature of 10°C, kbi, modified by a temperature quotient, Qi,

  • display math(7)

where T is the mean annual air temperature and the subscript i represents the pool number.

[12] In estimating the fluxes, the model used a conservation of mass approach. All C entering the system was accounted for as NPP, and C can leave the system through Rh, oxidation in burning, or through harvest transfers out of the forest. The CBM-CFS3 did not provide estimates of gross primary production or autotrophic respiration (Ra).

[13] NPP was estimated by adding the C gains associated with net biomass increment to the C uptake that was required to replace losses from biomass turnover:

  • display math(8)

where D is the detritus from biomass pools. Fluxes were expressed from the ecosystem perspective, where positive values denote net ecosystem C uptake or gain [Kurz et al., 2009; Li et al., 2002]. NPP did not include inputs from bryophytes or other nonwoody vegetation. Net ecosystem production (NEP) was estimated as NPP-Rh.

[14] Rh from the very fast pool within the mineral soil was estimated as

  • display math(9)

where decayed C from the pool was multiplied by the percentage transferred to the atmosphere, τ11. Rh was estimated in a similar manner for all dead organic matter pools, and fluxes from dead coarse roots and dead fine roots in the mineral soil were further examined. Rh from dead roots in the soil organic horizon was included but could not easily be examined because emissions from pools with multiple sources of detrital inputs cannot be attributed to the source of the original biomass.

[15] The CBM-CFS3 used a spin-up procedure to initialize C stocks in the soil organic horizon and mineral soil [Kurz et al., 2009]. The model simulated each stand through repeated cycles of growth followed by stand-replacing disturbance (usually fire) until the soil pools reached a quasi-steady state—slow C pools were within 0.1% at the end of two successive rotations. The model then applied one more rotation terminated by the last known stand-replacing disturbance and then grew each stand to its inventory age. The size and composition of dead organic matter and soil C pools thus reflects the disturbance history, the last stand-replacing disturbance, and stand dynamics to the age recorded in the forest inventory.

Managed Forest Simulations

[16] The C budget of Canada's 2.3 × 106 km2 managed forests was estimated from 1990 to 2008 using the inventory and disturbance data as described by Stinson et al. [2011]. A brief description of the model simulations is given here, and interested readers are referred to the article by Stinson et al. [2011, and references therein] for more information.

[17] The managed forest was defined using an area-based approach [Intergovernmental Panel on Climate Change, 2003] and included (i) lands managed for the sustainable harvest of wood, (ii) lands under intensive protection from natural disturbances (e.g., fire and insect suppression to protect forest resources), and (iii) protected areas, such as national and provincial parks that are managed to conserve forest ecological values.

[18] Inputs to the model included inventory data which were used in detailed wood supply analyses by the forest sector as provided by provincial and territorial agencies or data were compiled from the Canadian National Forest Inventory [Power and Gillis, 2006]. Inventory data included (1) stand attributes—age, location, and species types—and (2) merchantable volume yield tables for each of the hardwood and softwood components. Results are reported at three scales: (1) national, (2) for the 15 ecologic regions (ecozones), and (3) histograms based on percentages of area distributions.

[19] Model simulations included annual forest management activities and natural disturbances from 1990 to 2008. Clear-cut and partial harvesting were based on volume harvest statistics reported in Canada's National Forestry Database [Canadian Council of Forest Ministers, 2009]. Clear-cut harvesting was simulated as a transfer of a range of 85% to 97% of the gross merchantable stem biomass in harvested stands to the forest product sector. The remaining merchantable stem biomass was assumed left on site as logging residue along with 100% of tops, branches, stumps, foliage, roots, and submerchantable trees. All partial harvesting was simulated as a transfer of 30% of gross merchantable stem biomass in harvested stands to the forest product sector, leaving 70% to continue growing.

[20] Disturbances were a major source of movement of C throughout the forest system. Natural disturbance information was compiled from monitoring data [de Groot et al., 2007; Fraser et al., 2000; GeoConnections, 2004; Simpson and Coy, 1999; Stocks et al., 2002]. Wildfires annually disturbed an average of 6578 km2, which released 4 Tg C yr−1 from biomass to the atmosphere, transferred 27 Tg C yr−1 from biomass to dead organic matter, and released 19 Tg C yr−1 from dead organic matter pools to the atmosphere [Stinson et al., 2011]. Windthrow disturbances were not included in the simulation with the exception of one hurricane event. Insect outbreaks annually disturbed an average 28,061 km2, with higher disturbed areas from 2000 to 2008. Insect disturbances transferred 25 Tg C yr−1 to dead organic matter with mortality rates ranging from 2% to 70%, depending on severity of impact (light, moderate, severe, or very severe) and type of insect (mountain pine beetle (Dendroctonus ponderosae Hopkins), spruce beetle (Dendroctonus rufipennis Kirby), eastern hemlock looper (Lambdina fiscellaria fiscellaria Guenee), or aspen defoliators (forest tent caterpillar (Malacosoma disstria Hubner) and large aspen tortrix (Choristoneura conflictama Walker)).

Ground Plot Sensitivity Study

[21] Two sensitivity studies have been conducted using the CBM-CFS3. The first, by White et al. [2008], assessed the impact of varying 21 parameters on dead organic matter stocks and stock changes using Bayesian statistics for three simulated stands. The second sensitivity study by Smyth and Kurz [2013] assessed the impact of varying 36 parameters on dead organic matter stocks using simulations of 597 ground plots. Plot data are described by Smyth et al. [2011] and include information on forest floor and mineral soil C, tree species, mean annual temperature, and plot location [Ecological Land Classification Group, 2005; Shaw et al., 2005]. These data do not contain any root measurements.

[22] The second sensitivity study was expanded here to include six additional model parameters relating to the C percentage of roots, annual root turnover rates, and allocation of roots into soil organic horizon and mineral soil versions of the very fast and fast dead organic matter pools. The 36 parameters in the original sensitivity analysis included 11 base decay rates, 11 temperature quotients, 10 percentages transferred to the slow pools (Table 1 and Figure 1), and four fire retention percentages of the dead organic matter pools in the soil organic horizon used during model spin-up.

[23] Parameter impact was assessed by estimating the absolute percent difference, |δ|, in the total dead organic matter C stocks for an arbitrary ±5% change in parameter value. The percent difference, δ, was estimated as the adjusted C stock minus the original C stock all divided by the original C stock times 100%. Each plot was simulated 42 times, with one parameter adjusted in each simulation. For the additional six root parameters, the C percentage of fine and coarse roots was adjusted by ±5%, annual root turnover rates were adjusted by ±5%, and the proportion of dead fine roots allocated soil organic horizon pools was adjusted by ±5%. In the case of parameter dependencies, two parameters were simultaneously adjusted, e.g., an increase in the fine root allocation in the soil organic horizon caused a corresponding decrease in the mineral soil allocation.

National-Scale Impacts Assessment

[24] The two sensitivity studies indicated the uncertainty in stocks and stock changes for a prescribed or fixed range of parameter uncertainty and specific conditions (simulated landscapes and plot data), but the impact of a parameter adjustment can only be assessed by reestimating the national-scale stocks and fluxes using an adjusted parameter. Three national-scale runs were completed to assess the impact of adjusting three parameters found to be important from the ground plot sensitivity analysis and other previously published studies [Riley et al., 2009; White et al., 2008]. These three parameters included the annual fine root turnover (TFR), the annual coarse root turnovers (TCR), and the percentage transferred to the atmosphere from decayed dead fine roots within the mineral soil (τ11). The transfer percentage is directly related to the recalcitrant percentage, (100-τ11), of decayed dead fine roots.

[25] The first simulation reduced TFR from 64.1% (default) to 51% based on the results by Yuan and Chen [2010] for 17 boreal fine root turnover studies with diameter thresholds between 2 and 5 mm. The second simulation decreased τ11 from 83% to 67.3% based on observations that the recalcitrant percentage of dead fine roots was 32.7% [Currie et al., 2010]. The third simulation increased TCR from 2% to 4.24%, based on 11 boreal forest studies by Gill and Jackson [2003] which had a median turnover of 4.24% and an average turnover rate of 4.4%. Four estimates of coarse root turnover from peaty forests [Finér et al., 1997; Finér and Laine, 1998] were excluded from the average because the observed turnovers exceeded 50%.

[26] For each of the three national-scale simulations, one parameter was adjusted and all other parameters were set to default values. The impact assessment determined the change in fluxes and total dead organic matter stocks for the three adjusted runs as compared to the results of the original run. Impacts were expressed as Δ, the difference in magnitude (adjusted minus original), and δ, the percent difference (100Δ/original) for indicators NPP, Rh, NEP, and the slow C pool stock within the mineral soil (C1). Significance was tested by comparing the probability from a t-test to an alpha level of 0.05 (SAS Institute Inc., v.9.2, 2008). A p-value less than 0.05 rejected the null hypothesis that the means were equal.

Results

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

Managed Forest: Stocks, NPP, NEP, and Rh

[27] The CBM-CFS3 estimated the root C stocks for the managed forests of Canada to contain 2838 Tg C or an average C density of 1235 g C m−2, averaged over 1990 to 2008. The C density of root carbon stocks differed among regions (Figure 2), reflecting the differences in aboveground biomass C stock density. Forests along the Pacific coast supported the highest root C stocks (2985 g C m−2) because of this area's high site quality, low disturbance frequency, and dominance of long-lived tree species. Forests in high northern latitudes and in semiarid prairie regions had some of the lowest root C stocks (<525 g C m−2), consistent with low C in aboveground C stocks.

image

Figure 2. Root C stocks for Canada's managed forest regions spatially averaged over broad reporting units.

Download figure to PowerPoint

[28] As expected, fine root C stocks comprised a relatively small percentage (2.7%) of the total biomass C (Figure 3). Coarse roots had a modest percentage of the total biomass C (17.1%), and the majority of C was contained within the aboveground biomass components: merchantable biomass C pool contained 44.9%, foliage contained 6.9%, and the remainder was in the other biomass C pool (28.4%).

image

Figure 3. (a) Contribution to biomass stocks and NPP from fine roots, coarse roots, foliage, other (nonmerchantable-sized trees, tops, stumps, and branches), and C from merchantable-sized trees. (b) Histograms based on area distributions of Figure 5b of belowground (BG) and aboveground (AG) biomass (in g C m−2) and (c) root to shoot percentage expressed as a percentage of total area. Vertical lines indicate the area-weighted average (Ave.) and estimates from Mokany et al. [2006] (M), Cairns et al. [1997] (C), and Gower et al. [2001] (G).

Download figure to PowerPoint

[29] The softwood component had a root to shoot percentage of 22.2% as prescribed by equation ((1)). The hardwood component had an area-weighted root to shoot average of 34.1%. Estimates of the root to shoot percentage from the CBM-CFS3 for Canada's managed forest were 31.2% for low aboveground biomass (≤7500 g C m−2) and 24.7% for high aboveground biomass (>7500 g C m−2). The overall area-weighted average root to shoot percentage was 25.6%.

[30] Average NPP from 1990 to 2008 was 352 g C m−2 yr−1 (809 Tg C yr−1), of which 84% was due to replacement of turnover and 16% was due to net biomass increment. The highest contribution to NPP from the five biomass pools came from the fine roots (31%), even though this biomass pool only constituted 2.7% of the total biomass (Figure 3). Foliage and the other pool accounted for 24% each to the total NPP, and the merchantable pool, which had the majority of the biomass stock, accounted for only 12% to the total NPP. Belowground NPP was 140 g C m−2 yr−1 (322 Tg C yr−1), which accounted for 39.8% of the total NPP. Most of NPPBG was used to replace biomass turnover (92%), rather than biomass increment (8%), and fine root replacement accounted for most (83%) of the total biomass replacement (Figure 4).

image

Figure 4. Estimated ecosystem C stocks and flows for Canada's managed forest during 1990–2008. All flows are national landscape averages over the whole time period. Biomass C stocks were separated into aboveground and roots stocks and further separated into CBM-CFS3 biomass pools. Net primary production (NPP) was estimated as the sum of net biomass increment and replacement of turnover. Root turnover was further separated into coarse root (CR) turnover and fine root (FR) turnover.

Download figure to PowerPoint

[31] Average Rh was 321 g C m−2 yr−1 (738 Tg C yr−1), which resulted in an NEP value of 31 g C m−2 yr−1(71 Tg C yr−1), and on average only 8.7% of NPP remained in the system as NEP. Most of this NEP was subsequently lost through fire and harvest disturbances transferring C out of the ecosystem. Insect outbreaks also reduced NEP in later years by reducing NPP and increasing Rh [Kurz et al., 2008; Stinson et al., 2011].

[32] Rh estimates for each dead organic matter pool are simulated in the model, but attributing the respiration to specific detrital source was difficult when dead organic matter pools received C from more than one source. However, two pools within the mineral soil received detrital inputs only from roots; pools 9 and 11 (Figure 1). For these two pools, the emissions were 45.2 g C m−2 yr−1 from decaying fine roots (very fast pool within the mineral soil) and 13.1 g C m−2 yr−1 from decaying coarse roots (fast pool within the mineral soil).

[33] The slow pools had emissions of 20.7 g C m−2 yr−1 and 27.8 g C m−2 yr−1 within the soil organic horizon and the mineral soil, respectively. A portion of these emissions would be caused by the decay of the recalcitrant portion that was transferred from the decay of roots. Overall, the model predicted that root-derived heterotrophic respiration accounted for about 40% of total heterotrophic respiration.

Sensitivity Analysis Using Ground Plot Data

[34] The top 20 most sensitive parameters from the 42 sets of simulations, ranked by their impact on estimates of total dead organic matter C stocks, are listed in Table 2. Three of the six root-related coefficients ranked in the top 12: cFR and cCR the C content (percentage) of biomass of fine and coarse roots, respectively, and TFR, the annual fine root turnover. The coarse root turnover coefficient ranked 16th, and the allocation of roots ranked 18th and 27th for the fine roots and coarse roots, respectively. Of course, some of the other highly ranked parameters were also influenced by root dynamics; for instance, the slow pool stocks within the mineral soil contain C transferred from decayed fine roots and coarse roots which influence the slow pool's stock size and associated decay parameters (kb1 and Q1).

Table 2. The 20 Model Parameters With the Largest Impact on Model Stocks
RankaParameterDefault ValueDescriptionδ+δ|δ|b
  1. a

    Ranking of the top 20 most sensitive model parameters from a ±5% sensitivity study of 46 model parameters for the 597 ground plots included in the analysis.

  2. b

    The percent difference in total dead organic matter C stocks for a + 5% change, δ+, a −5% change, δ, and the average absolute value, |δ|.

  3. c

    Fire retention coefficients vary by ecozone.

  4. d

    A national-scale model run was performed with this parameter adjusted.

  5. e

    The coarse root allocation parameter, ACR, ranked 27th.

1kb10.33 % yr−1Slow (mineral soil) base decay rate2.72−2.452.58
2Q11Slow pool (mineral soil) temperature quotient−2.222.222.22
3cFR50 %Percentage of C in fine root biomass−1.351.351.35
4TFRd64.1 %Fine root turnover−1.331.331.33
5R2VariescSlow pool (soil organic horizon) retention coefficient−1.201.241.22
6τ1081.5 %Very fast pool (soil organic horizon) transfer percentage to the atmosphere−1.091.091.09
7kb21.5 % yr−1Slow pool (soil organic horizon) base decay rate1.09−1.041.07
8Q22.65Slow (soil organic horizon) temperature quotient−0.920.870.89
9τ11d83 %Very fast pool (mineral soil) transfer percentage to the atmosphere−0.750.750.75
10τ883 %Fast pool (soil organic horizon) transfer percentage to the atmosphere−0.700.700.70
11τ20.6 %Slow pool (soil organic horizon) transfer percentage−0.610.590.60
12cCR50 %Percentage of C in coarse root biomass−0.520.520.52
13kb53.74 % yr−1Medium pool base decay rate0.53−0.490.51
14τ583 %Medium pool transfer percentage−0.470.470.47
15Q52Medium pool temperature quotient−0.440.430.43
16TCRd2 % yr−1Coarse roots turnover−0.320.320.32
17τ983 %Fast pool (mineral soil) transfer percentage to the atmosphere−0.270.270.27
18AFRe50 %Fine root allocation0.23−0.230.23
19kb814.35 % yr−1Fast pool (soil organic horizon) base decay rate0.22−0.200.21
20Q82Fast pool (soil organic horizon) temperature quotient−0.180.180.18

National-Scale Impacts Assessment

[35] For all three simulations in which the root turnover and recalcitrant percentage were adjusted, the changes in NPP and Rh were small (<7%), and the change in NEP was less than 4% (Table 3). Adjustment of the root annual turnovers changed NPP and Rh indicators by roughly 22 g C m−2 y−1 (Figure 5a) with increased fluxes for an increased coarse root turnover and reduced fluxes for a reduced fine root turnover. Changing the fine root recalcitrant percentage, (100-τ11), from 17% to 32.7% increased the estimate of the C contained in the mineral soil by 28.3% but had no impact on the NPP estimates, as expected, and increased NEP estimates by only 1.8%.

Table 3. National-Scale Impact Assessment Results: Percent Differences (δ) for Flux and Slow Pool (Mineral Soil) (C1) C Stock Indicators
ParameterDefault Value (% yr−1)Adjusted Value (% yr−1)δ
NPP (%)Rh (%)NEP (%)C1 (%)
  1. a

    Indicates that the means were significantly different.

TFR64.151.0−6.2−6.5−2.8−8.6
τ118367.30.0−0.21.828.3a
TCR2.04.26.66.93.68.4
image

Figure 5. Differences in (a) fluxes and (b) pool-specific heterotrophic respiration for national-scale simulations using three adjusted parameter simulations as compared to respiration estimates using default parameters. The three simulations adjusted the fine root turnover rate (TFR), the percentage transferred to the atmosphere from the very fast pool within the mineral soil (τ11), and the coarse root turnover rate (TCR).

Download figure to PowerPoint

[36] Decreasing fine root turnover reduced heterotrophic respiration from the very fast pools (Rh10 and Rh11) by a total of 17.8 g C m−2 yr−1 and decreased respiration from the soil pools (Rh1 and Rh2) by 3.3 g C m−2 yr−1(Figure 5b). Increasing the coarse root turnover had the opposite response of raising heterotrophic respiration from the fast pools (Rh8 and Rh9) by a total of 19.1 g C m−2 yr−1 and respiration from the soil pools (Rh1 and Rh2) increased by 3.2 g C m−2 yr−1.

[37] Increasing the recalcitrant percentage lowered heterotrophic respiration from the very fast pool within the mineral soil by 21% or 8.7 g C m−2 yr−1 and increased the respiration from the slow pool within the mineral soil by a similar amount (28.4%, or 7.6 g C m−2 yr−1). The overall impact on total Rh emissions of changing the fine root recalcitrant percentage was small. However, C stocks in the slow pool within the mineral soil were increased by 28.3% when the recalcitrant percentage increased, as compared to a change of 8.4% to 8.6% when the turnovers were adjusted.

Discussion

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

[38] Using empirically derived equations of root biomass and turnover along with detailed stand-level inventories from provincial and territorial sources, we have estimated the contribution of roots to national-scale C stocks and flows. Our results have shown that roots have a relatively small contribution to the total biomass stocks, but have a larger contribution to NPP and Rh, mainly associated with replacement of ephemeral fine root biomass. The impacts of adjusting root turnover parameters using recently published values were assessed through national-scale simulations, and the effects on NPP, Rh, and NEP were found to be small (<7%), although estimates of mineral soil stocks were affected.

[39] In this discussion, we compare our modeling assumptions and stock and flux estimates to published experimental results and identify potential areas of improvement for modeling capabilities as well as data needs. Where possible, modeled results were compared to published studies, which included biome-level or national-scale results, but where these data are lacking, we relied on published case studies.

[40] Estimated root to shoot percentages compared favorably with values from the literature. For the Boreal region, Mokany et al. [2006] found a slightly higher root to shoot percentage of 39% ± 6% versus the model's predicted value of 31.2% for low aboveground biomass (<7500 Mg m−2) but was consistent with the root to shoot percentage for high aboveground biomass (>7500 Mg m−2), 24% ± 2% versus the model's predicted value of 24.7%. Estimates of root to shoot percentages by Cairns et al. [1997] and Gower et al. [2001] for the boreal region were 27% ± 1% and 28 ± 11%, respectively, which were similar to the overall root to shoot percentage of 25.6% predicted by the national-scale run.

[41] Our results found that the fine root C biomass density was 168 g C m−2, or 2.7% of the total biomass C, which was consistent with Vogt et al. [1996], who found that fine roots were less than 5% of the total biomass. The predicted coarse root biomass density was 1067 g C m−2, which was comparable to the lower bound of boreal forest estimates by Yuan and Chen [2010] of 1090 g C m−2 (average 1230 g C m−2, standard error of 140, n = 128) assuming a 5 mm diameter threshold, and 16% smaller than the observed value of 1296 g C m−2 by Lavigne and Krasowski [2007].

[42] The CBM-CFS3 estimate of NPP for the managed forest of Canada, 352 g C m2 yr−1, compared favorably with previous estimates by Chen et al. [2000a], Kang et al. [2006], Li et al. [2003], and Zheng et al. [2003] (Table 4) (see Stinson et al. [2011, Table 4]). Our estimate was lower than the estimate of 422 ± 45 g C m2 yr−1 (standard error) in a review of nine boreal studies in 24 stands [Gower et al., 2001] and within the range of NPP values (271 g C m2 yr−1 to 536 g C m2 yr−1) for the boreal [Luyssaert et al., 2007]. The NPPBG estimate of 140 g C m2 yr−1 for the CBM-CFS3 was very close to the NPPBG by Gower et al. [2001] of 144 ± 25 g C m2 yr−1(standard error) and within the range of boreal forest NPPBG values by Luyssaert et al. [2007] of 69 g C m2 yr−1 to 166 g C m2 yr−1. The ratio of NPPBG/NPP estimated by the CBM-CFS3 was 39.8%, which was similar to the estimates by Gower et al. [2001] of 33% ± 3% (Table 4). Fine root NPP was 31%, which was similar to a global estimate of 33% by Jackson et al. [1997].

Table 4. NPPAG (Aboveground) and NPPBG (Belowground)
DescriptionSpatial ExtentNPPNPPAGNPPBGNPPBG/NPP
(g C m−2 yr−1)(%)
  1. a

    n is the number of samples.

  2. b

    Estimate of the standard error.

  3. c

    Assumed that wood was 50% C and that foliage and fine roots were 45% C.

This studyCanada's managed forest35221214039.8
Luyssaert et al. [2007]Boreal271 (n = 38)a 334 (n = 14) 536 (n = 6) 69 (n = 36) Conifer/humid 166 (n = 14) Conifer/semiarid 112 (n = 5) Deciduous/semiarid 
Li et al. [2003]Prairies  13847
Gower et al. [2001]Boreal422 ± 45b278 ± 30144 ± 25 (n = 24)33 ± 3
Steele et al. [1997]Boreal  113 (n = 4) Conifer 57 (n = 2) Deciduous 
Gower et al. [1997]cBoreal273 ± 23 474 ± 90 102 ± 70 Conifer 105 ± 270 Deciduous39 ± 3 21 ± 3

[43] Most of the fine root NPP was associated with the growth of ephemeral fine roots. Roots are generally partitioned into ephemeral fine roots and structural coarse roots using a diameter threshold (e.g., < 5 mm), although consensus on a specific diameter threshold has not been reached [Ruark and Bockheim, 1987; Steele et al., 1997]. Gill and Jackson [2000] have shown that turnover decreases with diameter, and the inconsistencies of defining “fine” make it difficult to compare turnover estimates across studies. In addition, the measurement method influences the estimate of fine root turnover [Hendricks et al., 2006; Steele et al., 1997; Strand et al., 2008] which further complicates study comparisons.

[44] Fine roots are sensitive to site and stand characteristics such as successional stage, stand composition, and nutrient and moisture availability [Ares and Peinemann, 1992; Brassard et al., 2009; Pinno et al., 2010; Pritchett, 1986; Vogt et al., 1986], and these conditions are inherently poorly known and difficult to model. There is some evidence to suggest that softwood species have a lower fine root turnover rate than hardwoods due to different strategies employed for moisture and nutrient uptake. For example, root longevity in softwood species has been reported to be increased by mycorrhizae due to the physical protection, better access to nutrients and water from the bulk soil, and protection from pathogens [Bauhus and Messier, 1999; Bloomfield et al., 1996; Trofymow and Lalumière, 2011]. A recent review of fine root turnover by Yuan and Chen [2010] found that fine root turnover for roots less than 2 mm diameter was 51% larger for hardwood species than for softwood species.

[45] In comparison to recent fine root turnover estimates in the literature, the CBM-CFS3 value of 64.1% [Li et al., 2003] is high but is still within the range of recent measurements [Olesinski et al., 2012a; Olesinski et al., 2012b]. Reviews of fine root turnover studies have found lower fine root turnover values of 40% [Gill and Jackson, 2000] and 51% ± 11% [Yuan and Chen, 2010]. Given the lack of consistency in defining “fine”, the uncertainty in the fine root turnover estimate and the demonstrated small impact on national-scale NPP, Rh, and NEP estimates, the CBM-CFS3's fine root turnover value does not warrant adjustment at this time. Additional data are needed to refine the fine root turnover rate to be forest-type specific, and these turnover rates could then be employed in the CBM-CFS3.

[46] Determination of the appropriate value of the coarse root turnover to compare to the model's default value of 2% yr−1 was difficult, because data on coarse root necromass or turnover rate are seldom reported in the literature, and the few estimates that are available for boreal forests are quite diverse. An earlier review by Gill and Jackson [2000] found an average coarse root turnover of 4.4% ± 0.7% yr−1 for 11 boreal forest estimates excluding peaty forests. A more recent review by Yuan and Chen [2010] found a coarse root turnover of 30% ± 8% yr−1 for 20 boreal forest estimates. Coarse root turnover estimates in models range from 2% yr−1 [Newton, 2006; Rasse et al., 2001] to less than 5% yr−1 (Chen et al. [2000a] used 2.7% yr−1 for softwood and 4.5% yr−1 for hardwood), to 15% yr−1 [Hunt et al., 1999]. Other estimates include a coarse root turnover of 28% yr−1 based on the best fit between a fractal model and field observations of root architecture from two young trees [Nygren et al., 2009] and a turnover of 1.5% yr−1 assuming the turnover rate was similar to that of branches [Peltoniemi et al., 2006]. It is difficult to determine why the coarse root turnover rates are so diverse. Coarse root senescence over years may arise because of adverse growing seasons, herbivory, pathogens, and defoliation. However, most of the coarse root biomass is concentrated near the stump, and while branches die as the canopy lifts with increasing tree height, most coarse roots simply increase in diameter to provide structural support for the tree. Annual coarse root turnover as a proportion of coarse root biomass is thus likely at the lower end of the range of estimates in the literature. Given the variability in the measured coarse root turnover estimates and the limited impact on national-scale NPP and Rh estimates, the CBM-CFS3's coarse root turnover rate does not warrant adjustment, but it would be beneficial to have additional data on coarse root turnover rates.

[47] The sensitivity analysis revealed that carbon stocks were sensitive to reducing the C concentration of roots from 50% to 47.5%. The default value of the carbon percentage of biomass is 50% in the CBM-CFS3, which is consistent with the default value of 50% found in the Good Practice Guidance of the Intergovernmental Panel on Climate Change [2003] and many other studies, but does not account for some of the observed range of C concentration [Thomas and Martin, 2012]. Both higher (52%) and lower (47.1%) carbon concentrations have been observed in roots [Green et al., 2005], and fine root C concentration has been shown to vary with stand age, soil depth, and ectomycorrhizal association [Trofymow and Lalumière, 2011]. Measured values of the root C concentration could be included in the model if data for Canadian species were available.

[48] The CBM-CFS3 allocates 50% of the dead roots to C pools within the soil organic horizon and 50% to C pools within the mineral soil, but there is insufficient information to evaluate this assumption. Studies of rooting depth in the boreal generally find shallow rooting depths, with 83% of the fine root biomass located in the upper 30 cm of the soil profile [Jackson et al., 1997]. Root profiles can vary by broad species groups [Finér et al., 1997], moisture and soil conditions [Ares and Peinemann, 1992], and successional stage [Gale and Grigal, 1987; Yuan and Chen, 2010]. Modeling forest C stocks at the national scale excludes such details as the soil moisture content and successional status, but future regional applications of the model could employ alternative allocations of the root C within the soil organic horizons and mineral soil.

[49] The decay rate of fine roots has recently been estimated from a long-term litterbag study by Currie et al. [2010]. Mass remaining time series of three types of fine roots had an asymptotic decay form, with an exponential decay rate of 0.81 yr−1 or 0.56 yr−1 when expressed as a power series decay. This decay rate was similar to the model's power series base decay rate of 0.5 yr−1 at 10°C for dead roots in the very fast pool within the mineral soil. Decay rates from litterbag studies may underestimate the actual decay rate because roots are removed from in situ decay conditions and rhizosphere associations [Dornbush et al., 2002] and because the litterbag mesh can reduce colonization by decay organisms and subsequent decay rates [Setälä et al., 1996]. The decay rate of fine roots has been found to be affected by age [Ruess et al., 2003], species [Finér et al., 1997], soil manganese concentration [Borken et al., 2007], and climate and initial nitrogen concentration [Currie et al., 2010].

[50] The decay of coarse roots has been estimated from chronosequence studies. Ludovici et al. [2002] found that exponential decomposition rates were 0.053 yr−1 while Chen et al. [2001] found that coarse root decomposition rates were 0.03 to 0.11 yr−1 for the woody portion of the root. These observed decay rates are smaller than the model's base decay rate of 0.1435 yr−1, even after adjustments for temperature and functional form are applied. Additional data on coarse root decay are needed to estimate a national-scale base decay rate and temperature response.

[51] The recalcitrant portion of dead fine root decay was found to be 32.7% in a long-term litterbag study by Currie et al. [2010], which was higher than the 17% assumed by the CBM-CFS3. A national-scale run with the recalcitrant percentage increased to 32.7% found that the C stocks in the slow pool within the mineral soil were increased by 28.3%. This large change in C stocks would necessitate a recalibration of slow pool decay parameters because mineral soil stocks have been calibrated to reproduce observed C stocks in ground plot data. However, it is beyond the scope of the present study to recalibrate the model to quantitatively predict the change in mineral soil respiration. Based on the results of the present sensitivity study, and that of White et al. [2008], it is clear that the recalcitrant percentage of decayed fine roots has broader impacts and requires further study when more data become available. However, despite the large impacts on estimates of C stocks, the impacts of changing the recalcitrant percentage on estimates of fluxes were very small, with NEP estimates increasing by 1.8%.

[52] Results presented in this study are national in scale and utilize relationships derived from root study compilations to represent stocks and turnover. However, there are several potential changes to the CBM-CFS3 which could improve predictions at a regional scale. Root productivity, turnover, and decay rates are site and species specific [Bauhus and Messier, 1999; Currie et al., 2010; Finér et al., 1997; González-Molina et al., 2011; Gower et al., 2001; Stover et al., 2007; Yuan and Chen, 2010], but these fine-scale dynamics are presently unaccounted for in the model. Root diseases have not been included in the model, but these could alter mortality, root to stem biomass proportions, and reduce long-term site potential [Cruickshank et al., 2011]. Additionally, the model does not consider impacts on roots from changes in atmospheric carbon dioxide [González-Molina et al., 2011; Heath et al., 2005; Iversen, 2010; Robert B. Jackson et al., 2009; Norby et al., 2004; Stover et al., 2007; Trueman and Gonzalez-Meler, 2005] or temperatures [Eissenstat and Volder, 2005; Gill and Jackson, 2000] or drought [W. Chen et al., 2000b; Hogg et al., 2008], nor does it consider survival of roots after a disturbance for clonal species [Frey et al., 2003].

Summary and Conclusions

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

[53] In this paper we examined the national-scale contribution of roots to C stocks and flows using empirically driven equations of root biomass and turnover along with detailed stand-level inventories from provincial and territorial sources. For the 2.3 × 106 km2 of managed forest that were simulated, root C biomass stock accounted for 2.8 Pg C, predominantly (86.4%) from coarse roots. Overall, the root to shoot percentage was 25.6% and roots accounted for almost 40% of the net primary productivity, mostly due to the replacement of ephemeral fine roots, rather than an increase in biomass.

[54] A 5% sensitivity analysis found that the model was sensitive to root C concentration, the recalcitrant percentage of decayed fine roots and annual root turnover rates. Using published values to guide alternate parameter values, three national-scale runs were performed with adjusted parameters, and the impacts on NEP estimates were found to be small.

[55] The present CBM-CFS3 modeling of root C stocks is based on simple empirical relationships to represent belowground biomass as a function of more readily observable aboveground biomass. An empirical relationship is also used to partition fine roots and coarse roots and to estimate the annual fine root turnover rate. Additional modeling capabilities that would improve estimates of present root C stocks and flows would be to allow root productivity, turnover rates, and decay rates to vary by species, but at present, insufficient empirical data exist to implement such refinements.

[56] One of the concerns in modeling root dynamics is that the models can become increasingly sophisticated, but the scarcity of data prevents adequate evaluation of the simulated root dynamics. Despite the importance of belowground carbon allocation to the global carbon budget, there are not enough data against which to evaluate some of our model assumptions and parameters, and research funds tend to be directed toward estimates of aboveground biomass and total productivity fluxes. Additional data which would improve model accuracy include estimates of fine and coarse root turnover rates for various forest types and/or species, estimates of the recalcitrant percentage of decayed fine roots, and the allocation of roots within the soil organic horizons and mineral soil.

Acknowledgments

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

[57] This study would not have been possible without the strong cooperation between provincial, territorial, and federal government agencies, and we thank all members of the National Forest Sinks Committee and their colleagues. We thank M. Lavigne, T. Trofymow, M. Cruickshank, and two anonymous reviewers for their helpful and constructive comments.

[58] Thanks also to G. Zhang, M. Fellows, S. Morken, and M. Magnan for assistance with CBM-CFS3 and data preprocessing software tools. Funding for this study was provided by the Government of Canada's Clean Air Agenda.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Summary and Conclusions
  8. Acknowledgments
  9. References
  • Ares, A., and N. Peinemann (1992), Fine-root distribution of coniferous plantations in relation to site in southern Buenos Aires, Argentina, Can. J. For. Res., 22, 15751582.
  • Bauhus, J., and C. Messier (1999), Soil exploitation strategies of fine roots in different tree species of the southern boreal forest of eastern Canada, Can. J. For. Res., 29, 13.
  • Bernier, P. Y., L. Guindon, W. A. Kurz, and G. Stinson (2010), Reconstructing and modelling 71 years of forest growth in a Canadian boreal landscape: A test of the CBM-CFS3 carbon accounting model, Can. J. For. Res., 40, 109118.
  • Bloomfield, J., K. A. Vogt, and P. M. Wargo (1996), Tree root turnover and senescence, in Plant Roots: The Hidden Half, edited by W. Yoav et al., pp. 1136, Marcel Dekker, New York.
  • Borken, W., G. Kossmann, and E. Matzner (2007), Biomass, morphology and nutrient contents of fine roots in four Norway spruce stands, Plant Soil, 292, 7993.
  • Brassard, B. W., H. Y. H. Chen, and Y. Bergeron (2009), Influence of environmental variability on root dynamics in northern forests, Crit. Rev. Plant Sci., 28, 179197.
  • Cairns, M. A., S. Brown, E. H. Helmer, and G. A. Baumgardner (1997), Root biomass allocation in the world's upland forests, Oecologia, 111, 111.
  • Canadell, J. G., C. Le Quéré, M. R. Raupach, C. B. Field, E. T. Buitenhuis, P. Ciais, T. J. Conway, N. P. Gillett, R. A. Houghton, and G. Marland (2007), Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks, Proc. Natl. Acad. Sci. U. S. A., 104, 18,86618,870.
  • Canadian Council of Forest Ministers (2009), National Forestry Database, in Canadian Council of Foresty Ministers, http://nfdp.ccfm.org.
  • Chapin, F. S. III, J. McFarland, D. McGuire, E. Euskirchen, R. Ruess, and K. Kielland (2009), The changing global carbon cycle: Linking plant-soil carbon dynamics to global consequences, J. Ecol., 97, 840850.
  • Chen, J., W. Chen, J. Liu, J. Cihlar, and S. Gray (2000a), Annual carbon balance of Canada's forests during 1895–1996, Global Biogeochem. Cycle, 14, 839849.
  • Chen, W., J. Chen, J. Liu, and J. Cihlar (2000b), Approaches for reducing uncertainties in regional forest carbon balance, Global Biogeochem. Cycle, 14, 827838.
  • Chen, H., M. E. Harmon, and R. P. Griffiths (2001), Decomposition and nitrogen release from decomposing woody roots in coniferous forests of the Pacific Northwest: A chronosequence approach, Can. J. For. Res., 31, 246260.
  • Cruickshank, M. G., D. J. Morrison, and A. Lalumière (2011), Site, plot, and individual tree yield reduction of interior Douglas-fir associated with non-lethal infection by Armillaria root disease in southern British Columbia, For. Ecol. Manage., 261, 297307.
  • Currie, W. S., M. E. Harmon, I. C. Burke, S. C. Hart, W. J. Parton, and W. Silver (2010), Cross-biome transplants of plant litter show decomposition models extend to a broader climatic range but lose predictability at the decadal time scale, Global Change Biol., 16, 17441761.
  • Dornbush, M. E., T. M. Isenhart, and J. W. Raich (2002), Quantifying fine-root decomposition: An alternative to buried litterbags, Ecology, 83, 29852990.
  • Ecological Land Classification Group (2005), Ontario Terrestrial Assessment Program. Ontario Ministry of Natural Resources, Sault Ste. Marie, Ont.
  • Eissenstat, D. M., and A. Volder (2005), The efficiency of nutrient acquisition over the life of a root, in Nutrient Acquisition by Plants, edited by H. Bassirirad, pp. 347 , Springer, Berlin, Heidelberg.
  • Environment Canada (2009), National Inventory Report: 1990–2007, Greenhouse Gas Sources and Sinks in Canada, edited by Greenhouse Gas Division, pp 661, Ottawa, Ont.
  • Finér, L., and J. Laine (1998), Root dynamics at drained peatland sites of different fertility in southern Finland, Plant Soil, 201, 2736.
  • Finér, L., C. Messier, and L. De Grandpre (1997), Fine-root dynamics in mixed boreal conifer - broad-leafed forest stands at different successional stages after fire, Can. J. For. Res., 27, 304314.
  • Fraser, R. H., Z. Li, and J. Cihlar (2000), Hotspot and NDVI differencing synergy (HANDS): A new technique for burned area mapping over boreal forest, Remote Sens. Environ., 74, 362376.
  • Frey, B. R., V. J. Lieffers, S. M. Landhausser, P. G. Comeau, and K. J. Greenway (2003), An analysis of sucker regeneration of trembling aspen, Can. J. For. Res., 33, 11691179.
  • Gale, M. R., and D. F. Grigal (1987), Vertical root distributions of northern tree species in relation to successional status, Can. J. For. Res., 17, 829834.
  • Geoconnections (2004), Insect monitoring datasets for 1980–2000. Available at: http://geodiscover.cgdi.ca (accessed November 2004).
  • Giardina, C. P., M. D. Coleman, D. Binkley, J. E. Hancock, J. S. King, E. A. Lilleskov, W. M. Loya, K. S. Pregitzer, M. G. Ryan, and C. C. Trettin (2005), The response of belowground carbon allocation in forests to global change, in Tree Species Effects on Soils: Implications for Global Change, edited by D. Binkley et al., pp. 358, Kluwer Academic Publishers, Dordrecht, Netherlands.
  • Gill, R. A., and R. B. Jackson (2000), Global patterns of root turnover for terrestrial ecosystems, New Phytol., 147, 1331.
  • Gill, R. A., and R. B. Jackson (2003), Global Distribution of Root Turnover in Terrestrial Ecosystems, pp. 3 , National Laboratory Distributed Active Archive Center, Oak Ridge, Tenn.
  • González-Molina, L., J. D. Etchevers-Barra, F. Paz-Pellat, H. Díaz-Solis, M. H. Fuentes-Ponce, S. Covaleda-Ocón, and M. Pando-Moreno (2011), Performance of the RothC-26.3 model in short-term experiments in Mexican sites and systems, J. Agric. Sci., 149, 10.
  • Gower, S. T., J. G. Vogel, J. M. Norman, C. J. Kucharik, S. J. Steele, and T. K. Stow (1997), Carbon distribution and aboveground net primary production in aspen, jack pine, and black spruce stands in Saskatchewan and Manitoba, Canada, J. Geophys. Res., 102, 29,02929,041.
  • Gower, S. T., O. Krankina, R. J. Olson, M. Apps, S. Linder, and C. Wang (2001), Net primary production and carbon allocation patterns of boreal forest ecosystems, Ecol. Appl., 11, 13951411.
  • Green, C., B. Tobin, M. O'Shea, E. Farrell, and K. Byrne (2005), Above- and belowground biomass measurements in an unthinned stand of Sitka spruce ( Picea sitchensis (Bong) Carr.), Eur. J. For. Res., 126, 179188.
  • de Groot, W. J., et al. (2007), Estimating direct carbon emissions from Canadian wildland fires, Int. J. Wildland Fire, 16, 593606.
  • Heath, J., E. Ayres, M. Possell, R. D. Bardgett, H. I. J. Black, H. Grant, P. Ineson, and G. Kerstiens (2005), Rising atmospheric CO2 reduces sequestration of root-derived soil carbon, Science, 309, 17111713.
  • Hendricks, J. J., R. L. Hendrick, C. A. Wilson, R. J. Mitchell, S. D. Pecot, and D. Guo (2006), Assessing the patterns and controls of fine root dynamics: An empirical test and methodological review, J. Ecol., 94, 4057.
  • Hogg, E. H., J. P. Brandt, and M. Michaelian (2008), Impacts of a regional drought on the productivity, dieback, and biomass of western Canadian aspen forests, Can. J. For. Res., 38, 13731384.
  • Hunt, E. R., M. B. Lavigne, and S. E. Franklin (1999), Factors controlling the decline of net primary production with stand age for balsam fir in Newfoundland assessed using an ecosystem simulation model, Ecol. Model., 122, 151164.
  • Intergovernmental Panel on Climate Change (2003), Good Practice Guidance for Land Use, Land-Use Change and Forestry, edited by J. Penman et al., pp. 632, Institute for Global Environmental Strategies, Hayama, Japan.
  • Iversen, C. M. (2010), Digging deeper: Fine-root responses to rising atmospheric CO2 concentration in forested ecosystems, New Phytol., 186, 346357.
  • Jackson, R. B., H. A. Mooney, and E. D. Schulze (1997), A global budget for fine root biomass, surface area, and nutrient contents, Proc. Natl. Acad. Sci. U. S. A., 94, 73627366.
  • Jackson, R. B., C. W. Cook, J. S. Pippen, and S. M. Palmer (2009), Increased belowground biomass and soil CO2 fluxes after a decade of carbon dioxide enrichment in a warm-temperate forest, Ecology, 90, 33523366.
  • Jones, D. L., A. Hodge, and Y. Kuzyakov (2004), Plant and mycorrhizal regulation of rhizodeposition, New Phytol., 163, 459480.
  • Kang, S., J. S. Kimball, and S. W. Running (2006), Simulating effects of fire disturbance and climate change on boreal forest productivity and evapotranspiration, Sci. Total Environ., 362, 85102.
  • Kull, S. J., G. J. Rampley, S. Morken, J. Metsaranta, E. T. Neilson, and W. A. Kurz (2011), Operational-scale Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) Version 1.2: User's Guide, Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB.
  • Kurz, W. A., and M. J. Apps (1999), A 70-Year retrospective analysis of carbon fluxes in the Canadian forest sector, Ecol. Appl., 9(2), 526547.
  • Kurz, W. A., and M. J. Apps (2006), Developing Canada's national forest carbon monitoring, accounting and reporting system to meet the reporting requirements of the Kyoto Protocol, Miti. Adapt. Strat. Global Change, 11, 3343.
  • Kurz, W. A., and J. P. Kimmins (1987), Analysis of some sources of error in methods used to determine fine root production in forest ecosystems: A simulation approach, Can. J. For. Res., 17, 909912.
  • Kurz, W. A., S. J. Beukema, and M. J. Apps (1996), Estimation of root biomass and dynamics for the carbon budget model of the Canadian forest sector, Can. J. For. Res., 26, 19731979.
  • Kurz, W. A., M. Apps, E. Banfield, and G. Stinson (2002), Forest carbon accounting at the operational scale, For. Chron., 78, 672679.
  • Kurz, W. A., C. C. Dymond, G. Stinson, G. J. Rampley, E. T. Neilson, A. L. Carroll, T. Ebata, and L. Safranyik (2008), Mountain pine beetle and forest carbon feedback to climate change, Nature, 452(7190), 987990.
  • Kurz, W. A., et al. (2009), CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards, Ecol. Model., 220, 480504.
  • Lamlom, S. H., and R. A. Savidge (2003), A reassessment of Carbon content in wood: Variation within and between 41 North American species, Biomass Bioenergy, 25(4), 381388.
  • Lavigne, M. B., and M. J. Krasowski (2007), Estimating coarse root biomass of balsam fir, Can. J. For. Res., 37, 991998.
  • Li, Z., M. J. Apps, E. Banfield, and W. A. Kurz (2002), Estimating net primary production of forests in the Canadian Prairie Provinces using an inventory-based carbon budget model, Can. J. For. Res., 32, 161169.
  • Li, Z., W. A. Kurz, M. J. Apps, and S. J. Beukema (2003), Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: Recent improvements and implications for the estimation of NPP and NEP, Can. J. For. Res., 33, 126136.
  • Ludovici, K. H., S. J. Zarnoch, and D. D. Richter (2002), Modeling in-situ pine root decomposition using data from a 60-year chronosequence, Can. J. For. Res., 32, 16751684.
  • Luyssaert, S., et al. (2007), CO2 balance of boreal, temperate, and tropical forests derived from a global database, Global Change Biol., 13, 25092537.
  • Matthews, G. (1993), The carbon content of trees, Rep. Technical Paper 4, 21 pp., Forestry Commission, Surrey, U. K.
  • Mokany, K., R. J. Raison, and A. S. Prokushkin (2006), Critical analysis of root: Shoot ratios in terrestrial biomes, Global Change Biol., 12, 8496.
  • Newton, P. F. (2006), Forest production model for upland black spruce stands—Optimal site occupancy levels for maximizing net production, Ecol. Model., 190, 190204.
  • Norby, R. J., J. Ledford, C. D. Reilly, N. E. Miller, and E. G. O'Neill (2004), Fine-root production dominates response of a deciduous forest to atmospheric CO2 enrichment, Proc. Natl. Acad. Sci. U. S. A., 101, 96899693.
  • Nygren, P., M. Lu, and H. Ozier-Lafontaine (2009), Effects of turnover and internal variability of tree root systems on modelling coarse root architecture: Comparing simulations for young Populus deltoides with field data, Can. J. For. Res., 39, 97108.
  • Olesinski, J., M. B. Lavigne, J. A. Kershaw Jr, and M. J. Krasowski (2012a), Fine-root dynamics change during stand development and in response to thinning in balsam fir (Abies balsamea L. Mill.) forests, For. Ecol. Manag., 286(0), 4858.
  • Olesinski, J., M. J. Krasowski, M. B. Lavigne, J. A. Kershaw, and P. Y. Bernier (2012b), Fine root production varies with climate in balsam fir (Abies balsamea), Can. J. For. Res., 42(2), 364374.
  • Pan, Y., et al. (2011), A large and persistent carbon sink in the world's forests, Science, 333, 988993.
  • Peltoniemi, M., T. Palosuo, S. Monni, and R. Makipaa (2006), Factors affecting the uncertainty of sinks and stocks of carbon in Finnish forests soils and vegetation, For. Ecol. Manage., 232, 7585.
  • Pinno, B. D., S. D. Wilson, D. F. Steinaker, K. C. J. Van Rees, and S. A. McDonald (2010), Fine root dynamics of trembling aspen in boreal forest and aspen parkland in central Canada, Ann. For. Sci., 67, 710.
  • Power, K., and M. Gillis (2006), Canada's Forest Inventory 2001, in Inf. Rep. BC-X-408, pp. 128, Natural Resources Canada, Canadian Forest Service, Victoria, BC.
  • Pritchett, W. L. (1986), Forest Soils, Wiley, New York.
  • Rasse, D. P., L. François, M. Aubinet, A. S. Kowalski, I. Vande Walle, E. Laitat, and J.-C. Gérard (2001), Modelling short-term CO2 fluxes and long-term tree growth in temperate forests with ASPECTS, Ecol. Model., 141, 3552.
  • Riley, W. J., J. B. Gaudinski, M. S. Torn, J. D. Joslin, and P. J. Hanson (2009), Fine-root mortality rates in a temperate forest: Estimates using radiocarbon data and numerical modeling, New Phytol., 184, 387398.
  • Ruark, G. A., and J. G. Bockheim (1987), Belowground biomass of 10-, 20-, and 32-year-old Populus tremuloides in Wisconsin, Pedobiologia, 30, 207217.
  • Ruess, R. W., R. L. Hendrick, A. J. Burton, K. S. Pregitzer, B. Sveinbjornssön, M. F. Allen, and G. E. Maurer (2003), Coupling fine root dynamics with ecosystem carbon cycling in black spruce forests of interior Alaska, Ecol. Monogr., 73, 643662.
  • Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto (2004), A continuous satellite-derived measure of global terrestrial primary production, BioScience, 54, 547560.
  • SAS Inc. (2008), SAS/STAT 9.2 user's guide. SAS Institute Inc., Cary, NC.
  • Setälä, H., V. G. Marshall, and J. A. Trofymow (1996), Influence of body size of soil fauna on litter decomposition and 15N uptake by poplar in a pot trial, Soil Biol. Biochem., 28, 16611675.
  • Shaw, C. H., J. S. Bhatti, and K. J. Sabourin (2005), An ecosystem carbon database for Canadian forests, in Inf. Rep. NOR-X-403, pp. 113 , Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB.
  • Simpson, R., and D. Coy (1999), An ecological atlas of forest insect defoliation in Canada: 1980–1996, in Inf. Rep. M-X-206E, pp. 15, Natural Resources Canada, Canadian Forest Service, Atlantic Forestry Centre, Fredricton, NB.
  • Smyth, C., and W. A. Kurz (2013), Forest soil decomposition and its contribution to heterotrophic respiration: A case study based on Canada, Soil Biol. Biochem., 67, 155165, in press.
  • Smyth, C. E., W. A. Kurz, and J. A. Trofymow (2011), Including the effects of water stress on decomposition in the Carbon Budget Model of the Canadian Forest Sector CBM-CFS3, Ecol. Model., 222, 10801091.
  • Steele, S. J., S. T. Gower, J. G. Vogel, and J. M. Norman (1997), Root mass, net primary production and turnover in aspen, jack pine and black spruce forests in Saskatchewan and Manitoba, Canada, Tree Physiol., 17, 577587.
  • Stinson, G., et al. (2011), An inventory-based analysis of Canada's managed forest carbon dynamics, 1990 to 2008, Global Change Biol., 17, 22272244.
  • Stocks, B. J., et al. (2002), Large forest fires in Canada, 1959–1997, J. Geophys. Res., 108(D1), FFR5.1FFR5.12, doi:10.1029/2001JD000484.
  • Stover, D. B., F. P. Day, J. R. Butnor, and B. G. Drake (2007), Effect of elevated CO2 on coarse-root biomass in Florida scrub detected by ground-penetrating radar, Ecology, 88, 13281334.
  • Strand, A. E., S. G. Pritchard, M. L. McCormack, M. A. Davis, and R. Oren (2008), Irreconcilable differences: Fine-root life spans and soil carbon persistence, Science, 319, 456458.
  • Thomas, S. C., and A. R. Martin (2012), Carbon content of tree tissues: A synthesis, Forests, 3(2), 332352.
  • Trofymow, J. A., and A. Lalumière (2011), Fine root density distribution and biomass in second- and third-growth Douglas-fir stands on Vancouver Island, British Columbia Rep., Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC.
  • Trofymow, J. A., G. Stinson, and W. A. Kurz (2008), Derivation of a spatially explicit 86-year retrospective carbon budget for a landscape undergoing conversion from old-growth to managed forests on Vancouver Island, BC, For. Ecol. Manage., 256, 16771691.
  • Trueman, R. J., and M. A. Gonzalez-Meler (2005), Accelerated belowground C cycling in a managed agriforest ecosystem exposed to elevated carbon dioxide concentrations, Global Change Biol., 11, 12581271.
  • Vogt, K. A., C. C. Grier, and D. J. Vogt (1986), Production, turnover and nutrient dynamics of above and belowground detritus of world forests, in Advances in Ecological Research, vol. 15, pp. 303377, Academic Press, Orlando, Fla.
  • Vogt, K. A., J. V. Daniel, A. P. Peter, B. Paul, O. H. Jennifer, and A. Heidi (1996), Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species, Plant Soil, 187, 159219.
  • van der Werf, G. R., D. C. Morton, R. S. DeFries, J. G. J. Olivier, P. S. Kasibhatla, R. B. Jackson, G. J. Collatz, and J. T. Randerson (2009), CO2 emissions from forest loss, Nat. Geosci., 2(11), 737738.
  • White, T., N. Luckai, G. R. Larocque, W. A. Kurz, and C. Smyth (2008), A practical approach for assessing the sensitivity of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), Ecol. Model., 219, 373382.
  • Yuan, Z. Y., and H. Y. H. Chen (2010), Fine root biomass, production, turnover rates, and nutrient contents in boreal forest ecosystems in relation to species, climate, fertility, and stand age: Literature review and meta-analyses, Crit. Rev. Plant Sci., 29, 204221.
  • Zheng, D. L., S. Prince, and R. Wright (2003), Terrestrial net primary production estimates for 0.5 degree grid cells from field observations—A contribution to global biogeochemical modeling, Global Change Biol., 9, 4664.