Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization

Forest Ecology and Management xxx (2013) xxx–xxx

Contents lists available at SciVerse ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization Päivi Merilä a,⇑, Kaisa Mustajärvi b, Heljä-Sisko Helmisaari c,1, Sari Hilli d, Antti-Jussi Lindroos c, Tiina M. Nieminen c, Pekka Nöjd c, Pasi Rautio d, Maija Salemaa c, Liisa Ukonmaanaho c a

Finnish Forest Research Institute (Metla), Oulun yliopisto, P.O. Box 413, 90014 Oulu, Finland Metla, Parkano, Kaironiementie 15, 39700 Parkano, Finland c Metla, Vantaa, P.O. Box 18, 01301 Vantaa, Finland d Metla, Rovaniemi, P.O. Box 16, 96301 Rovaniemi, Finland b

a r t i c l e

i n f o

Article history: Available online xxxx Keywords: Nitrogen pools Picea abies Pinus sylvestris Roots Understory vegetation Whole-tree harvest

a b s t r a c t Nitrogen (N) is typically the growth-limiting factor in boreal forest ecosystems. Therefore, knowledge on forest N stocks and fluxes is crucial in order to predict and evaluate the effects of different anthropogenic factors (e.g. climate change, air pollutant deposition, forest management practices) on the condition, development and sustainability of boreal forests. In this study, we evaluated the amount and distribution of N and biomass in different compartments of forest ecosystem, including not only tree stand and soil, but also such rarely reported N stocks as litter layer, ground vegetation and fine and small roots. We also calculated the theoretical export of N in three forest harvest regimes of different intensity (stem-only harvest, whole-tree harvest, whole-tree harvest and stump uplifting) and assessed the time required for N deposition to compensate the N losses occurring in them. The study included seven Scots pine and eight Norway spruce dominated stands belonging to the UN-ECE ICP Forests Level II programme in Finland. The average effective temperature sum and stand age of the sites ranged 658–1351 d.d. and 55–200 yrs, respectively. Among the study sites, the total biomass (needles, living and dead branches, stems, bark, stumps, coarse roots, fine and small roots, understory, litter, humus and mineral soil layers) ranged from 178 Mg ha1 to 541 Mg ha1, the respective range for N stock being 1890–7530 kg ha1. The two largest pools of N in forest ecosystem were mineral soil (depth 0–40 cm; mean = 70%) and humus layer (mean = 16%). The largest living biomass N stock was in stems in pine stands (88 kg ha1) and in needles in spruce stands (134 kg ha1). Nstored in tree biomass accounted for 7–19% of the total ecosystem N stock. The proportion of N stored in potential logging residues or biofuel (needles, living and dead branches, stumps and coarse roots) was 67 ± 4% and 53 ± 5% of the tree N stock in northern spruce stands and in southern pine stands, respectively. The understory vegetation N stock was the largest in northern spruce stands, and the lowest in southern spruce stands. Our results supported the hypothesis that in boreal coniferous forests, inputs of N by deposition accumulating during the following rotation period will be able to replenish the export of N caused by conventional stem-only-harvest in final cutting, but the sustainability of the site productivity will be challenged when more intense whole tree harvest regimes are practiced, especially in Norway spruce stands. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction The effects of global warming pose a serious threat to humankind and nature (IPCC, 2007). As an international mitigation action

of global warming, Kyoto protocol to the United Nations Framework Convention on Climate Change (UNFCC) has set binding obligations on the industrialized countries to reduce their fossil fuel emissions of greenhouse gases. One way to cut down the emissions

⇑ Corresponding author. Tel.: +358 50 3914061. E-mail addresses: paivi.merila@metla.fi (P. Merilä), kaisa.mustajarvi@ramboll.fi (K. Mustajärvi), helja-sisko.helmisaari@helsinki.fi (H.-S. Helmisaari), sari.hilli@metla.fi (S. Hilli), antti.lindroos@metla.fi (A.-J. Lindroos), tiina.nieminen@metla.fi (T.M. Nieminen), pekka.nojd@metla.fi (P. Nöjd), pasi.rautio@metla.fi (P. Rautio), maija.salemaa@metla.fi (M. Salemaa), liisa.ukonmaanaho@metla.fi (L. Ukonmaanaho). 1 Present address: Forest Sciences Building, Viikki, P.O. Box 27, 00014 University of Helsinki, Finland. 0378-1127/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2013.06.029

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

2

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

is to substitute the consumption of fossil energy with renewable energy. Accordingly, the EU pursues to increase its average share of energy consumption from renewable sources to 20% by 2020; the respective target settled for Finland is 38% (Directive 2009/ 28/EC). As an action to achieve this goal, Finland strives to intensify the utilization of forest bioenergy, traditionally regarded as carbon neutral energy source, because the CO2 emissions released into the atmosphere in forest bioenergy combustion are captured back to biomass in forest regrowth. The target has been set to increase the annual use of forest residues from the current volume of 6–7 million m3 to 13.5 million m3 by 2020 (Ministry of Employment and the Economy, 2010), which in practice requires utilization of the logging residues on nearly every clear-cut area. In the conventional stem-only harvesting (SOH) the logging residues, treetops, branches and foliage, are left on the site. In contrast, the whole-tree harvesting (WTH) practiced for bioenergy purposes implies the removal of these residues from the site (Röser et al., 2008). Their removal inevitably increases the export of nitrogen (N), because these compartments, needles especially, in many cases contain more N than stemwood (Blanco et al., 2005; Mälkönen, 1974; Palviainen and Finér, 2012; Ukonmaanaho et al., 2008). In addition to the aboveground tree biomass compartments, also the tree stumps with their coarse roots may be removed from the site to be used as bioenergy. Given that N is generally the growth limiting nutrient in boreal forest ecosystems, the sustainability of such intensive harvest regimes can be challenged, as the depletion of N may result in the degradation of long-term site productivity (Helmisaari et al., 2011; Jacobson et al., 2000; Kellomäki and Seppälä, 1987; Mälkönen, 1974; Olsson et al., 1996) and in a decrease in the amount of soil organic matter within the forest ecosystem (meta-analysis by Johnson and Curtis, 2001), thus leading to a decreased carbon (C) sequestration potential of forest ecosystem (Repo et al., 2011; Schulze et al., 2012; Tamminen et al., 2012; review by Thiffault et al., 2011). Reliable judgment of the sustainability of more intensive bioenergy harvest regime requires precise knowledge on the stocks and distribution of N in different compartments of diverse forest ecosystems. Information achieved in some earlier studies (e.g. Finér et al., 2003; Helmisaari et al., 2002; Mälkönen, 1974; Raulund-Rasmussen et al., 2008) is sparse and not sufficient to allow us to draw general conclusions on the sustainability of WTH regimes in boreal forests at an ecosystem level. In addition, predicting and modeling the effects of global warming, e.g. on C sequestration, require information on the fluxes and pools of N, because C sequestration potential is tightly linked to N availability (Magnani et al., 2007; Tamm, 1991). In this study, we examined the biomass and N stocks in boreal forest ecosystems in Finland, based on data gathered from the plots belonging to the intensive monitoring programme of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests operating under the UN-ECE Convention on Long-range Transboundary Air Pollution (ICP Forests Level II). The plots include xeric–sub-xeric and mesic–herb rich heath forests dominated by Scots pine and Norway spruce, respectively (Salemaa et al., 2008). The objective was to gain more detailed knowledge on the differences in distribution of N in forest compartments (above- and below-ground tree biomass, understory vegetation, litter and humus layers, mineral soil), and to evaluate the significance of each compartment as a N stock in sites of different species composition and biogeographical location. By utilizing this data, we then applied the N balance approach and calculated the theoretical export of N in three forest harvest regimes of different intensity (SOH, WTH, WTH + stumps) for each site and assessed the time required for N deposition to compensate the N losses occurring in them. We hypothesized that in boreal coniferous forests, inputs of N by deposition (Mustajärvi et al., 2008)

accumulating during a rotation period would be able to replenish the export of N caused by conventional stem-only-harvest in final cutting, but the sustainability of the site productivity will be challenged when more intense whole-tree harvest regimes are practiced.

2. Materials and methods 2.1. Sample plots The data were collected in seven Scots pine (Pinus sylvestris (L.)) and eight Norway spruce (Picea abies (L.) Karst.) forests during 1998–2009 (Table 1 and Fig. 1). The sites belong to the European intensive forest monitoring network (Level II), established under the UN-ECE ICP Forests monitoring programme (Derome et al., 2007). Twelve of the plots are located in semi-natural stands under conventional forest management and three in protected conservation areas where management has not been carried out during the last 50 yr. The sample plots are located along a latitudinal gradient in Finland and were classified as northern (middle or northern boreal) and southern (southern boreal; Ahti et al., 1968; Salemaa et al., 2008; Fig. 1, Table 1) plots. Most of the pine plots are located on sorted glaciofluvial material, and the spruce plots on till soils (Table 1). Each plot consists of three subplots (30  30 m) and a surrounding mantle. The design of the monitoring plot and the monitoring network are described in detail in Derome et al. (2007).

2.2. Estimation of stand biomass Stand measurements were made on all three sub-plots of each plot during autumn 2004–spring 2005. Tree species, diameter (at 1.3 m above ground level), tree height and crown length were measured on all trees with a breast height diameter of at least 4.5 cm. The biomasses of individual trees were estimated according to Repola et al. (2007) except for needle biomass, which was calculated according to Repola (2009). We used functions using breast-height diameter and tree height as predictors to estimate the biomass of bark, stem wood, roots and dead branches. The functions used for estimating needle biomass and living branches also include crown length or crown ratio as a predicting variable. The biomasses of individual trees were summed to obtain total stand biomass (kg ha1 dry weight).

2.3. Sampling of stem wood, bark, living and dead branches for N analysis To determine the N concentrations in the above-ground tree compartments (stem wood, bark, living and dead branches; for N concentrations see Ukonmaanaho et al., 2008), five randomly selected trees from the dominant canopy layer were harvested on each of the sites in 2006 (Ukonmaanaho et al., 2008) and discs were cut from each trunk at 1.3 m height above ground level and bark, sapwood and heartwood were separated to samples. The living canopy of each tree was divided into four sectors by height. Three sections (length 5 cm) of the main branches and four annual shoots of the main branches were randomly selected from each sector. The sections from the main branches as well as the samples of the collateral branches were pooled and separately analyzed by tree. One to five dead branches were taken from each felled tree or if not available, from the surrounding trees of the plot and combined to form a bulk sample for the plot. The tree biomass samples (stem wood, bark, living and dead branches) were dried at 40 °C and milled.

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

3

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Table 1 Site and soil characteristics of the stands. Effective temperature sum equals the sum of differences between daily mean temperatures and the threshold of +5 °C. For soil bulk density at 20–40 cm depth, the mean and standard deviation (in parentheses) are presented (n = 3). Plot nr.

Name

Lat. °N

Eff. Prec.a Stem Stand Mean height age temp. (mm) nr. (m) suma (ha1) (y)s (d.d.)

Long. °E

Stoniness Organic (%) layer thickness (cm)

10.1

75

1.2

UVET

Xeric

Sorted

1.5 (0.06)

0.5

1.7

Subxeric Subxeric

Sorted

1.3 (0.17)

0.5

3.0

Sorted

1.3 (0.15)

0.5

3.6

Albic arenosol Rustic podzol Haplic arenosol Albic arenosol

Sorted

1.2 (0.13)

2.2

2.9

Sorted

1.5 (0.11)

8.8

2.8

Sorted

1.5 (0.02)

6.3

3.0

Sorted

1.4 (0.19)

5.5

2.6

Orsteinic podzol Albic arenosol Albic arenosol

Till

1.3 (0.06)

24.6

3.2

Till

1.0 (0.07)

16.0

3.5

Sandy loam

1.3 (0.18)

17.3

3.5

Endogleyic regosol Haplic arenosol Haplic regosol Rustic podzol Haplic arenosol

Till

1.4 (0.10)

5.2

7.8

Till

1.0 (0.07)

32.3

2.8

Till

1.2 (0.20)

17.9

5.0

Sandy loam Till

1.0 (0.15)

49.0

3.0

1.0 (0.16)

18.4

4.3

419

370

6

66°210 26°440 885

537

1748 55

11.1

127

6.0

EMT

63°09 30°42 1066

623

588

130

16.9

311

8.5

EVT

South Scots pine 10 Juupajoki

61°520 24°130 1163

614

378

80

21.1

182

3.8

VT

16

61°460 29°200 1280

593

956

80

21.4

306

11.1

VT

Kivalo Lieksa

Punkaharju

0

0

0

0

200

Subxeric Subxeric Xeric

18

Miehikkälä

60°42 27°50 1351

629

415

120

19.4

159

4.3

CT

13

Tammela

60°370 23°500 1275

627

604

60

19.2

203

10.5

VT

Subxeric

480

1107 140

7.5

66

1.8

HMT

Mesic

0

0

5

Kivalo

66°20 26°38 825

539

1663 70

9.6

117

3.1

HMT

Mesic

21

Oulanka

66°180 29°300 774

554

1742 170

8.8

176

2.7

HMT

Mesic

South Norway spruce 23 Uusikaarlepyy 63°330 22°290 1131

492

970

55

18.9

334

13.9

OMT

61°510 24°180 1140

615

867

80

19.8

339

10.4

OMT

70

26.1

348

8.8

OMT

Herbrich Herbrich Herbrich Herbrich Mesic

11

c

Soil Soil bulk structure density depth 20– 40 cm (g cm3)

69°350 28°540 658

North Norway spruce 3 Pallasjärvi 67°600 24°140 683

a

Soil typec Forest Site Stem typeb type of volume heath growth forests (m3 ha1 1 yr )

North Scots pine 1 Sevettijärvi

20

b

Stem volume (m3 ha1)

Juupajoki

0

0

17

Punkaharju

61°48 29°19 1289

594

378

19

Evo

61°140 25°040 1209

601

1295 170

18.5

650

4.2

OMT

12

Tammela

60°380 23°480 1253

625

678

20.0

273

8.9

MT

60

Rustic podzol Albic arenosol Albic arenosol

Data for effective temperature sum and precipitation from the Finnish Meteorological Institute for the period 1961–1990. According to Cajander (1949), abbreviations explained in Salemaa et al. (2008). According to IUSS Working group WRB (2006).

2.4. Sampling and pretreatment of needles for N analysis

2.5. Understory vegetation and litter layer

Sample branches were collected in late October–early December 2007 from 10 predominant or dominant sample trees on each plot. Needle samples (all present green needle year classes) were collected from the bottom part of the uppermost third of the living crown (between the 7th and the 15th whorls) with a pruning device. The branches were stored in a freezer (18 °C) during the period between sampling and pretreatment. In the pretreatment procedure, the branches were cut into separate shoot sections bearing different needle year classes, and shoots with the same needle-year class of each tree were pooled. The shoots were dried at 60 °C for 10 days and the needles were then removed from the shoots and weighed to obtain the biomass of each needle year class. Composite plotwise sample for each needle year class was formed by weighing the same mass of dry needles (generally 5 g, minimum 2 g) per needle year class of each 10 sample trees of each plot. Composite samples were formed for those needle year classes present in P50% of the sample trees. The dry needles were milled using an ultracentrifugal mill (mesh size 1 mm). In calculation of the needle N pool we utilized the mean N concentration of all needle year classes present in P50% of the sample trees of each plot, weighted by their share of biomass.

Aboveground biomass of understory vegetation was harvested from 28 squares (30 30 cm2 each) located systematically along the sides of the vegetation monitoring subplots of the study plots in July–September 2009. The total area of the squares on each plot was 2.52 m2. The litter (L) layer was collected from ten plots in 2002–2003 (Hilli et al., 2008a) and from one plot in 2009 using the corresponding sampling design. Missing data for understory vegetation (one plot) and litter biomass (five plots) were modeled. The living biomass and necromass of understory vegetation were separated by plant species in each sample. The necromass of vascular plants was not included in this data, and lower parts of mosses and lichens were recorded as litter. The L layer was sorted into the following fractions: needle litter, coarse tree litter, dead parts of dwarf shrubs, grasses and herbs and lower (decomposing and dead) parts of mosses and lichens (Hilli et al., 2008a). The biomass fractions were dried at 60 °C and weighed separately. The total living biomass of the understory vegetation per plot was calculated as a sum of all species. The total mass of the L layer was obtained by summing the mass of the individual litter fractions. For determination of the N concentrations, 1–8 joint samples were formed for each plant species or litter fraction in a plot. The N concentrations were determined from the homogenized samples

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

4

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

including mycorrhizal root tips, and small roots (diameter 2– 5 mm). Prior to N analysis, root samples were dried at 70 °C for 48 h, weighed and milled. Ash content of fine roots was determined to estimate the extent to which mineral soil, still remaining on the fine roots after washing, affected the dry mass values. The ash content was always less than 6% (Helmisaari et al., 2007). Fine and small root biomass in each of the volumetric mineral soil samples was corrected for the presence of stones based on the stoniness index (Viro, 1952; Tamminen, 1991). 2.7. Soil sampling and analysis

Fig. 1. Location of the study plots. The plots were divided in northern (northern or middle boreal) and southern (southern boreal) plots (Ahti et al., 1968).

on a LECO CHN-600 analyzer. The plot-specific N stock representing the whole understory vegetation was calculated as a sum of N quantities of individual species (dry weight of a species N concentration). Similarly, the N stock of the whole litter layer per plot was calculated as a sum of N quantities of different litter fractions (dry weight of a fraction N concentration). 2.6. Fine and small roots Fine (diameter < 2 mm) and small (diameter 2–5 mm) root samples were taken over three weeks (July 20–August 12, 1998). From each stand, 12 root cores were taken with a cylindrical soil corer (diameter 40 mm; Helmisaari et al., 2007). The cores were divided into sections comprising the organic layer and the 0–5, 5–10, 10–20 and 20–30 cm mineral soil layer. Both understory and tree roots were separated from the soil by washing, and sorted into living and dead fractions based on elasticity and toughness (Persson, 1983). The roots were identified to Scots pine, Norway spruce, birch and other broad-leaved species roots and understory (mainly dwarf shrubs and grasses) roots and rhizomes based on microscopic morphology and color. The roots were further sorted into fine roots (diameter < 2 mm) (Persson, 1983; Vogt et al., 1983),

Soil samples (organic layer and mineral soil layers 0–5, 5–10, 10–20 and 20–40 cm) were taken at 24 systematically located points on the 30 30 m2 sample plot in 2006. The samples were bulked to give three composite samples per layer and dried in an oven (40 °C). The organic layer samples were milled and sieved (<1 mm) and mineral soil samples sieved through a 2 mm sieve. The organic matter content was determined as the loss in weight on ignition (dry ashing for 3 h at 550 °C in a muffle furnace). Bulk density of the organic layer and mineral soil on the plots was determined by taking soil samples using steel cylinders (diameter 5.80 cm, length 2.90 cm for organic layer; diameter 5.64 cm, length 6.14 cm for mineral soil). Diameter of the cylinders were considered to be large enough to prevent compaction of the samples, i.e. in the range of 50–75 mm (Costantini, 1995), and the samples were taken as carefully as possible. Bulk density of the organic layer was determined by taking three composite samples each containing 20 subsamples from systematically located points on the plot. The thickness of the organic layer was measured at these points. For the mineral soil, the cylinder was pushed into the vertical face of soil pits at depths of 0–5, 5–10, 10–20, and 20–40 cm. There were three pits, excavated halfway along three sides of the 30 30 m2 plot. The fresh weight of the sample was determined and the sample then dried in a convection oven at 105 °C until the weight had stabilized. The dry weight of the sample was determined. The bulk density was determined as the average of the bulk density of corresponding samples (n = 3). Stone content of the soil was determined using Viro’s rod penetration method (Tamminen and Starr, 1994) and it was taken into account in the calculation of the soil pools of organic matter and N. 2.8. Determination of N concentrations, calculation of N stocks and N budget for a rotation period Total N concentrations of all compartments were measured with a CHN analyzer (LECO). The N pool ha1 stored in each forest ecosystem compartment, (including needles, living and dead branches, stems, bark, stumps, coarse roots, fine and small roots, understory vegetation, litter and humus layer, and mineral soil (0–40 cm)) was calculated by multiplying the mass ha1 of each compartment by its N concentration. As N concentrations of the coarse roots and stumps were not available, their N stock was estimated by using the N concentration in stems. This may slightly underestimate the total N stock of coarse roots as their N concentration is higher than in stems (Helmisaari, 1991; Helmisaari and Siltala, 1989; Hellsten et al., 2013). The time required to compensate the export of N caused by different harvest regimes in final cutting (SOH, WTH and WTH + stumps) by N deposition (Mustajärvi et al., 2008) was calculated taking into account the N leaching from the site (Mustajärvi et al., 2008) (Table 2). We calculated the results for 100% and 70% removal of the residues in WTH and WTH + stumps. The sampling and analysis methods for measurement of N deposition and leaching are described in detail in Mustajärvi et al. (2008). Briefly, N deposition was measured as bulk deposition (BD) in an

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

5

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Table 2 Annual bulk deposition (DEPO) and leaching of total nitrogen and their balance (Net input), export of N in stem-only harvest (SOH), 100% and 70% whole-tree harvest (WTH) and 100% and 70% WTH with stump uplifting (WTH + stumps) in final cutting, and the time in years required to compensate these exports by net input of N. Plot

DEPOa

Leachinga

Net input

kg N ha1 yr1 Northern Scots pine 1 0.87 6 2.26 20 3.09

WTH 70%

WTH + stumps 100%

WTH + stumps 70%

Export, kg N ha1

SOH

WTH 100%

WTH 70%

WTH + stumps 100%

WTH + stumps 70%

Compensation time, yr

0.32 0.03 1.92

57 101 82

146 219 177

102 153 124

183 241 198

128 169 139

– – 43

– – 92

– – 65

– – 103

– – 72

1.67 1.62 1.78 0.83 1.51

1.60 1.31 2.11 3.15 1.39

104 172 63 184 109

210 324 165 343 226

147 227 116 240 158

236 359 179 388 255

165 251 125 272 179

65 131 30 58 65b

132 247 78 109 132b

92 173 55 76 114b

148 274 85 123 147b

103 192 59 86 128b

Northern Norway spruce 3 1.53 1.01 5 2.28 1.3 21 1.43 0.18

0.52 0.98 1.25

57 82 86

212 276 230

148 193 161

238 306 259

167 214 181

109 84 69

406 283 184

285 197 129

456 313 207

320 219 145

2.63 2.68 2.15 2.71 3.48 2.05

204 215 265 268 204 173

560 553 564 624 480 437

392 387 395 437 336 306

616 613 644 685 538 487

431 429 451 480 377 341

78 80 123 99 59 88

213 206 262 230 138 240

149 144 184 161 97 149

234 228 299 252 154 268

164 160 210 177 108 166

pine 3.27 2.93 3.89 3.98 2.90

Southern Norway spruce 23 3.33 11 3.27 17 2.93 19 3.42 12 3.86 2.76 Norway spruce mean a

WTH 100%

1.2 2.29 1.17

Southern Scots 10 16 18 13 Scots pine mean

b

SOH

0.70 0.58 0.77 0.70 0.38 0.70

Mustajärvi et al. (2008). Plots nr. 1 and 6 excluded due to negative net input of N.

open area adjacent to the corresponding forest plot using three precipitation collectors and a 2-week sampling interval during the snow-free period and two snow collectors and a 4-week sampling interval during winter. Soil percolation water was collected at 4-week intervals during the snow free period using five zero tension lysimeters located at depth of 40 cm. The water flux was estimated by sulfate (SO4) budget method (Nilsson et al., 1998). In this approach, sulfate is considered to be a conservative anion i.e., it is assumed that the annual amount of SO4 deposited on the forest floor in stand throughfall is equal to the amount of SO4 leached from the surface layers in percolation water. Using this mass balance assumption, the annual water output flux (mm a1) in percolation water was calculated on the basis of the annual input of water (throughfall, mm a1), the mean annual SO4 concentration (mg l1) in throughfall, and the mean annual SO4 concentration (mg l1) in percolation water (Mustajärvi et al., 2008). We used the SO4 budget method to calculate the water fluxes in percolation water (mm yr1) instead of the actual volume of water collected by the zero-tension lysimeters for two main reasons: firstly, only 5 replicate lysimeters (total surface area 0.16 m2) cannot be considered to give a reliable estimate of the water flux on a 30 30 m plot, and secondly, during snowmelt in the spring the amount of percolation water is normally considerably greater than the amount of water that can be stored in the collection bottle located below the lysimeter (Mustajärvi et al., 2008). As a result, the water flux in the spring will clearly be underestimated if measured by lysimeters. The annual N deposition in BD was calculated by multiplying the corresponding total N concentrations with the sample water volume and summing the annual sampling periods. The annual N leaching was estimated by multiplying the annual mean total N concentrations with annual estimated water flux. The mean of

annual total N fluxes over 1998–2004 were used as estimated annual input (BD) and output (soil water) fluxes of N.

2.9. Statistical methods We used linear mixed models in order to detect possible differences in biomass (or in the case of humus and mineral soil layers, organic matter content) and N stocks between different compartments, tree species and north/south locations. In the models, biomass and N stocks were dependent variables, and compartment, species and location north/south (and their interactions) were factors. As the compartments were not (spatially) independent, compartment was defined as a repeated (within subject) factor, whereas species and location were between subjects factors. As subjects we used plots. For the repeated factor, the diagonal covariance structure was selected on the basis of Akaike’s information criterion. Due to statistically significant complex interactions between the above factors (Tables 3 and 4), we dissected the analysis into compartment-wise comparisons of species and locations. This was done by means of generalized linear models with biomass (Table 5) and nitrogen stocks (Table 6) as dependent variable, and species and location (and their interactions) as factors. Depending on the distribution of biomass and N stocks in different compartments we used in the analysis either normal distribution with identity link function, or gamma distribution with log link function. The selection between distributions (and consequent link functions) was based on Akaike’s information criterion. The approach described above was also applied to test if N stock ratios differ between species and locations, and if these have interactions (Table 7). If log link function was used, this is indicated (by L) in Tables 5–7.

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

6

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Table 3 Results of linear mixed model for biomass in different compartments. F-tests with df (numerator/denominator) and significance for the species (pine vs. spruce), compartment (Comp), location (Loc: north vs. south) and their interactions. Source

df

F

Sig.

Species Comp Loc Species  Comp Species  Loc Comp  Loc Species  Comp  Loc

1/39.9 11/23.9 1/39.9 11/23.9 1/39.9 11/23.9 11/23.9

17.1 122.7 48.7 20.3 11.3 16.1 4.9

<0.001 <0.001 <0.001 <0.001 0.002 <0.001 0.001

Table 4 Results of linear mixed model for N-stocks in different compartments. F-tests with df (numerator/denominator) and significance for the species (pine vs. spruce), compartment (Comp), location (Loc: north vs. south) and their interactions. Source

df

F

Sig.

Species Comp Loc Species  Comp Species  Loc Comp  Loc Species  Comp  Loc

1/29.3 11/23.8 1/29.3 11/23.8 1/29.3 11/23.8 11/23.8

13.8 171.0 14.0 15.7 3.8 15.7 6.3

0.001 <0.001 0.001 <0.001 0.061 <0.001 <0.001

Table 5 Result of generalized linear models for biomass stocks in different compartments. Wald chi-square statistics and statistical significance for species (pine vs. spruce), location of the plots (north vs. south) and their interactions. Compartments marked with (L) were tested using log-link function. Significance values <0.05 are indicated in bold. Compartment

Mineral soil Humus (L) Litter Understory (L) Fine and small roots Coarse roots Stumps (L) Bark (L) Stems Dead branches Living branches Needles Total

Species

Species*Location

Location

Wald

Sig.

Wald

Sig.

Wald

Sig.

2.68 9.21 10.7 9.61 11.65 10.48 4.22 15.9 1.08 0.013 42.98 123.8 10.69

0.102 0.002 0.001 0.002 0.001 0.001 0.04 <.001 0.298 0.91 <.001 <.001 0.001

23.5 0.304 24.97 22.57 17.9 13.46 19.1 17.23 19.16 24.16 22.87 17.29 30.48

<.001 0.582 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001

0.04 2.78 0.73 4.68 0.06 9.88 5.89 10.61 3.88 8.26 17.05 12.07 7.05

0.842 0.096 0.394 0.03 0.801 0.002 0.015 0.001 0.049 0.004 <.001 0.001 0.008

Table 6 Result of generalized linear models for nitrogen stock in different compartments. Wald chi-square statistics and statistical significance for species (pine vs. spruce), location of the plots (north vs. south) and their interactions. Compartments marked with (L) were tested using log-link function. Significance values <0.05 are indicated in bold. Compartment

Mineral soil Humus Litter (L) Understory Fine and small roots Coarse roots Stumps Bark (L) Stems Dead branches Living branches (L) Needles Total (L)

Species

Relationship between N stock and organic matter content in mineral soil was examined by means of Spearman’s rank correlation coefficient (rS). All statistical analyses were performed using IBM SPSS Statistics software (ver. 20.0.0). 3. Results 3.1. Biomass stocks The total site biomass ranged from 178 to 541 Mg ha1, found in the northernmost pine plot nr. 1 and in southern spruce plot nr. 19, respectively (Table 8). Basically, all tested factors (tree species, ecosystem compartment, location north/south) and their interactions showed a statistically significant effect on the biomass stocks (Table 3). The largest biomass/organic matter stocks were the mineral soil (28–37%) and the stems (19–32%; Table 8, Fig. 2a). These two compartments and dead branches were the only ones showing no significant difference between pine and spruce plots, though the interaction term Species*Location was significant for stems and dead branches (Table 5), indicating that the magnitude of difference between species differs in the south and the north. The spruce plots had 41% more total biomass than the pine plots. This difference was statistically significant (Table 5), but it was apparent only between southern stands (Fig. 2a), as indicated by a significant Species*Location interaction (Table 5). Consistently, biomasses of most compartments were significantly larger in spruce than in pine plots (Tables 5 and 8). The exceptions were understory and litter layers, which had larger biomass in pine than in spruce plots. The northern sites had on an average 40% less biomass than the southern sites. Humus layer was the only compartment showing no significant difference between northern and southern sites. However, the interaction Species*Location was nearly significant (p = 0.096). Within the pine plots, the biomass stock in humus layer tended to be even slightly larger in the northern sites. In addition, even if the mean of biomass stock was clearly higher in the south than in the north, the variation within the southern spruce plots was large. The northern plots had on an average 2.1 times more understory biomass than southern plots and significantly more fine root biomass than southern plots (Tables 5 and 8). In other compartments, the biomasses were higher in south than in north. The difference in total biomass between northern and southern plots was more pronounced within spruce plots, as the southern spruce plots had on average nearly twice as much biomass as the northern ones. This difference was mostly due to a larger stem biomass in southern stands, though the southern spruce sites had more biomass in most compartments (Fig. 2a and Table 5). 3.2. N stocks

*

Location

Species Location

Wald

Sig.

Wald

Sig.

Wald

Sig.

8.05 8.8 3.36 1.1 1.82 16.02 9.55 18.45 2.57 0.13 82.58 93.26 12.99

0.005 0.003 0.067 0.294 0.177 <.001 0.002 <.001 0.109 0.716 <.001 <.001 <.001

8.35 6.02 60.7 7.85 0.48 16.21 20.61 23.38 27.68 9.44 44.34 36.19 13.12

0.004 0.014 <.001 0.005 0.489 <.001 <.001 <.001 <.001 0.002 <.001 <.001 <.001

1.24 2.68 6.59 8.03 2.6 12.42 9.55 12.88 4.32 0.46 15.46 17.34 1.65

0.266 0.101 0.01 0.005 0.107 <.001 0.002 <.001 0.038 0.499 <.001 <.001 0.199

The southern spruce sites had the largest total N stocks, while the northern pine sites had the smallest (Fig. 2b and Table 9). As with biomass stocks, all tested factors (tree species, ecosystem compartment, location) and their interactions showed a significant effect on the N stocks (0.001 < p 6 0.061; Table 4). On an average, the spruce plots had 61% more N than the pine plots and southern sites on average 59% more N than the northern sites. The two largest pools of N in forest ecosystem were mineral soil (mean = 70% of total N) and humus layer (mean = 16%). There was a significant correlation between the mass of organic matter in mineral soil and mineral soil N stock (Spearman rs = 0.896, p < 0.001, n = 15). In pine stands, the stems were the major living biomass N stock, while in spruce stands it was the needle biomass (Table 9, Fig. 2c).

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

7

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Table 7 Results of generalized linear models for N stock ratios of potential logging residues without (LOGRES) and with stump uplifting (LOGRESSTUMP) to total tree and ecosystem N stocks, and N stock ratios aboveground:belowground in trees and in ecosystem. Wald chi-square statistics and statistical significance for species (pine vs. spruce), location of the plots (north vs. south) and their interactions. Parameters marked with (L) were tested using log-link function. Significance values <0.05 are indicated in bold. Parameter

N N N N N N

ratio ratio ratio ratio ratio ratio

Species

LOGRES : total tree LOGRESSTUMP : total tree LOGRES : ecosystem (L) LOGRESSTUMP : ecosystem (L) tree above:belowground ecosystem aboveground:belowground (L)

Species*Location

Location

Wald

Sig.

Wald

Sig.

Wald

Sig.

23.9 22.9 4.99 3.58 0.11 0.25

<.001 <.001 0.025 0.058 0.736 0.618

3.0 6.6 0.72 0.65 6.83 1.91

0.085 0.010 0.398 0.420 0.009 0.168

2.2 0.89 0.41 0.69 0.0003 0.30

0.136 0.345 0.521 0.408 0.985 0.581

Table 8 Distribution of biomass (kg ha1 dwt.) in forest vegetation compartments, and organic matter content as loss on ignition (kg ha1 dwt.) in the humus layer and in mineral soil. Distribution of biomass as percentages of total are indicated in italics so that they would not become mixed with the absolute biomass values. Plot Northern Scots pine 1 6 20 North pine mean (%) of total North SD Southern Scots pine 10 16 18 13 South pine mean (%) of total South SD Scots pine total mean

Needle

Living branch

Dead branch

Stem

Bark

Stump

Coarse root

Finea and smallb roots

Understory vegetation

Litter layer

Humus layer

Soil 0– 40 cm

Total

2600 5000 2500 1.6

9400 10,700 9700 4.6

2300 3600 4200 1.5

23,500 62,600 90,900 25.3

4200 6000 5600 2.4

3000 5000 6800 2.2

17,600 12,900 24,100 8.3

7000 14,300 11,900 5.0

4600 2100 3100 1.6

4300 4900 4600d 2.1

28,300 30,600 35,600 14.5

71,400 50,200 78,900 30.9

178,200 207,900 278,000 100

0.8

1.0

0.2

10.6

0.5

0.4

1.9

1.6

0.9

0.4

1.6

8.3

3100 5000 2900 4300 1.3

9400 12,700 8700 12,300 3.6

3600 5600 3200 4400 1.4

77,900 142,100 65,600 97,300 31.8

4600 8300 4300 6400 2.0

5500 8500 5000 6800 2.2

17,900 25,500 15,500 21,200 6.7

6500 6700 7900 5000 2.3

2600 2100 2700c 1100 0.8

5700 5500 5600d 6100 2.0

34,100 23,600 22,700 24,400 9.0

118,200 86,200 88,000 145,500 37.1

289,100 331,800 232,300 334,800 100

0.2 3600

0.2 10,400

0.2 3800

7.4 80,000

0.4 5600

0.3 5800

0.7 19,200

0.8 8500

0.3 2600

0.4 5200

2.2 28,500

7.7 91,200

264,600

11,000 15,200 13,000 5.5

1300 2500 2800 0.9

26,800 49,700 58,000 18.6

4000 6700 6600 2.4

3200 5000 5800 2.0

13,200 20,800 21,600 7.7

12,000 16,400 14,300 6.0

3800 1800 2800 1.2

3100 3800 3500d 1.5

36,900 40,100 41,500 16.8

79,600 97,100 63,400 33.9

202,600 270,800 239,400 100

0.1

0.3

5.5

0.4

0.4

1.3

0.1

0.6

0.1

1.7

6.6

Northern Norway spruce 3 7700 5 11,500 21 6200 North spruce mean 3.6 (%) of total North SD 0.8 Southern Norway spruce 23 14,400 11 14,000 17 13,600 19 13,400 12 12,500 South spruce mean 3.1 (%) of total South SD 0.5 Norway spruce 11,700 total mean

d

22,800 22,900 24,900 31,300 20,700 5.4

5800 5200 5000 6700 4600 1.2

132,100 126,400 129,300 210,900 109,400 31.1

13,900 13,200 12,700 23,800 11,100 3.3

10,800 10,200 11,000 18,200 8900 2.6

40,700 37,700 38,100 60,500 32,400 9.2

9700 8300 9400 13,200 10,500 2.3

500 1200 900 700 1200 0.2

5100 4000 6500 5100d 4700 1.1

118,100 35,200 61,300 29,800 40,300 12.4

149,400 101,800 125,500 127,200 127,800 28.2

523,200 380,100 438,200 540,700 384,100 100

0.7 20,200

0.1 4200

5.3 105,300

0.7 11,500

0.5 9100

1.4 33,100

0.3 11,700

0.1 1600

0.2 4600

6.5 50,400

3.5 109,000

372,400

a

Diameter < 2 mm. Diameter 2–5 mm. Modeled from cover data 2003 (Salemaa and Hamberg, 2007) using conversion factors between cover% and biomass (kg/ha) 44.7 for dwarf shrubs, 20.4 for mosses and 52.3 for lichens. d Estimate (group mean). b

c

The N stored in tree biomass (above and belowground) ranged from 7% to 19% of the total N stock of the sites. Consistently with the total N stock, spruce had significantly higher N stock in all compartments than pine with the exception of litter, understory, fine and small roots, stems and dead branches (Tables 6 and 9). Forest compartment N stocks were significantly higher in south than in north, with the exception of the N stock in fine and small roots, which did not differ significantly between north and south (Tables 6 and 9). The differences in N stocks between northern and southern spruce sites were mostly due to differences in biomass, as the N concentrations in different forest compartments

were statistically higher for southern sites only for needles, humus layer and bark (data not shown). The ratio of N stock stored in tree aboveground vs. belowground compartments was significantly higher in southern (7.9 ± 1.8) than in northern plots (5.5 ± 1.9; Table 7). The ratio of ecosystem N stock aboveground:belowground ranged 0.08–0.22 and did not differ significantly either between spruce and pine or between northern and southern sites. The forest compartments potentially forming logging residues to be used as biofuel (LOGRES: needles, living and dead branches; LOGRESSTUMP with stumps and coarse roots) form a larger N stock

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

8

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Fig. 2. (a–c) The stocks of (a) biomass and (b) nitrogen in northern Scots pine and Norway spruce sites (n = 3 in both) and in southern Scots pine and Norway spruce sites (n = 4 and 5, respectively) studied. In order to elicit the stock of N in the other compartments, N stocks in mineral soil and humus layers are excluded in c).

than stems and bark in all plots (Table 2). The proportion of LOGRES of the total tree N stock was significantly higher in spruce plots (mean = 53%) than in pine plots (mean = 44%; Table 7). The proportion of LOGRESSTUMP of the total tree N stock was higher in northern sites than in southern sites, as shown by a significant Species*Location interaction (Table 7). The proportions of LOGRES and LOGRESSTUMP N stocks from the total forest ecosystem N stock ranged 3–9% and 4–11%, respectively, and were higher in spruce than in pine plots (Table 7). In spruce, the N stock of understory vegetation was in the northern plots twice as large as in the south (Table 9). Such a difference was not apparent between northern and southern pine plots.

3.3. N budget during one rotation period The cumulative net N input (deposition – leaching; Mustajärvi et al., 2008) was high enough to compensate the theoretical N export caused by final cut carried out as stem-only harvest (SOH) during one rotation period in most cases (Table 2). The number of annual net inputs needed to gain the loss caused by SOH was lower than the duration of a customary rotation period (70– 100 yr), excluding only one Scots pine plot (nr. 16) and one Norway spruce plot (nr. 17) in southern Finland (Table 2). In contrast, when the most intensive harvest regimes, 100% whole-tree harvest (WTH) and WTH with stump lifting are applied, the N balance would remain negative still after the time needed for the next rotation period, excluding only one northern Scots pine plot (nr. 20) and another Scots pine plot in the south (nr. 18). However, a reduction of the N export of WTH and WTH + stumps to 70% of total made the N losses compensable by the net N input during a rotation period in three of the seven Scots pine plots, while the N losses in Norway spruce plots would still not be compensated.

4. Discussion 4.1. Export of N by final harvests of different intensities The time required to compensate the export of N by accumulating N deposition was calculated for SOH, WTH and WTH + stumps harvest regimes in final cutting taking into account the N leaching previously assessed for our sites (Mustajärvi et al., 2008). The export of N in SOH was found to become replenished by accumulating N deposition on an average in 65 yr in Scots pine plots and in 88 yr in Norway spruce plots, thus in a period close to the conventional rotation period in boreal forests in Finland (70–100 yr). However, the export of N was not completely compensated by N deposition in case of 100% WTH and WTH + stumps, the compensation potential of N deposition being inadequate especially in Norway spruce plots. These results thus supported our hypothesis that the sustainability of site productivity is questionable under whole-tree harvest regimes. Our results are well in line with several other studies carried out in boreal region concerning the effects of WTH regimes on nutrient balance and growth (Jacobson et al., 2000; Helmisaari et al., 2011; Olsson et al., 1996; Palviainen and Finér, 2012; Raulund-Rasmussen et al., 2008; review by Thiffault et al., 2011). Generally, in areas under low or moderate N deposition inputs WTH regimes are prone to result in overexploitation of N, but not in all cases. Thus, the sensitivity to WTH is sitespecific, determined by factors such as soil fertility, tree species and climate (Thiffault et al., 2011). Therefore, competent and practical indicators and criteria for determining the sustainable harvesting intensity are urgently needed. Current guidelines for bioenergy harvesting include recommendation of leaving 30% of canopy biomass on the harvested site in order to avoid excess loss of nutrients (Hakkila, 2002). Our results indicated that even after such a reduction in logging residue and stump removal the N balance of Norway spruce sites remained

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

9

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Table 9 N stocks (kg ha1) in forest compartments. Distribution of biomass as percentages of total are indicated in italics so that they would not become mixed with the absolute biomass values. Plot Northern Scots pine 1 6 20 North pine mean (%) of total North SD Southern Scots pine 10 16 18 13 South pine mean (%) of total South SD Scots pine total mean

Needle

Living branch

Dead branch

Stem

Bark

Stump

Coarse root

Finea and smallb roots

Understory vegetation

Litter layer

Humus layer

Soil 0– 40 cm

Total

32 53 31 1.8

53 57 56 2.6

4 8 8 0.3

42 78 62 2.8

15 23 20 0.9

6 6 5 0.3

31 16 16 1.0

20 30 11 1.0

27 18 26 1.1

43 40 37 1.9

316 361 318 15.4

1567 1202 1953 71.0

2156 1892 2543

0.8

0.4

0.1

1.1

0.3

0.1

0.4

0.6

0.2

0.4

3.4

6.8

42 68 38 66 2.0

56 68 57 82 2.4

8 16 7 11 0.4

86 145 46 154 4.0

18 27 17 30 0.9

6 9 3 11 0.3

20 26 11 34 0.8

16 13 14 15 0.5

28 24 28 15 0.9

51 58 60 67 2.2

499 341 348 382 14.2

2334 1177 1972 2804 71.4

1.0 47

0.7 61

0.3 9

2.4 88

0.4 21

0.1 7

0.4 22

0.1 17

0.4 24

0.6 51

3.0 366

7.9 1858

68 83 79 2.7

3 7 5 0.2

43 57 61 1.9

14 25 25 0.8

5 6 6 0.2

21 24 23 0.8

16 28 15 0.7

37 20 28 1.0

30 43 39 1.3

420 488 401 15.5

2159 2424 1588 72.0

0.6

0.1

0.6

0.3

0.0

0.2

0.2

0.4

0.3

1.5

3.3

147 156 135 186 125 3.3

14 15 7 13 6 0.2

141 157 211 150 152 3.5

63 58 54 118 52 1.5

12 13 18 15 12 0.3

44 47 62 46 46 1.1

24 21 25 16 24 0.5

10 16 13 10 19 0.3

77 60 90 77 71 1.6

2498 458 982 477 665 18.4

4302 2411 3399 2686 3091 65.7

1.2 122

0.1 9

1.0 122

0.9 51

0.1 11

0.3 39

0.1 21

0.1 19

0.3 61

8.7 799

5.0 2758

Northern Norway spruce 3 84 5 104 21 60 North spruce mean 2.9 (%) of total North SD 0.3 Southern Norway spruce 23 195 11 167 17 157 19 157 12 145 South spruce mean 3.5 (%) of total South SD 0.8 Norway spruce 134 total mean a b

3164 1972 2601 3671

2571

2900 3309 2330

7527 3579 5153 3951 4408

4145

Diameter < 2 mm. Diameter 2–5 mm.

negative. In Scots pine plots the 30% reduction in biomass removal corrected the balance in a part, but not in all of the cases. Thus, our results agree with the conclusion of Palviainen and Finér (2012) suggesting that if the nutrient losses are pursued to be minimized in WTH regimes, they should be implemented in mature Scots pine stands. Since needle biomass is the greatest N stock stored in living biomass accounting for as much as one third of the aboveground tree N stock in Norway spruce plots, leaving as great proportion of foliar biomass as possible on the site improves the sustainability of WTH and is, in actual fact at least partly, also applied in the current WTH harvesting practices. In our calculation on forest site N balance we took into account the potential of N deposition, or actually, that of net input of N (N deposition – leaching) to compensate N losses due to biomass removals in final cutting for SOH, WTH and WTH + stumps harvest regimes. N deposition was measured as bulk deposition (Mustajärvi et al., 2008), which is a conservative estimate of total N deposition, as it includes wet deposition and a part of dry deposition in the open area. Korhonen et al. (2012) estimated that the share of wet N deposition in the bulk deposition was 57% on a Scots pine forest in Hyytiälä, southern Finland. In a study on N balance of this site (Korhonen et al., 2013) the dry N deposition rate of inorganic N was estimated as a mean of those obtained by four different models (Flechard et al., 2011). Using these assumptions, the bulk deposition of inorganic N and organic N were 91% and 75%, respectively,

of the estimated total deposition of these forms of N (Korhonen et al., 2013). On our monitoring plots the long-term mean of organic N bulk deposition varied 0.18–0.71 kg N ha1 yr1 (Mustajärvi et al., 2008), and was thus considerably lower than that (1.5 kg N ha1 yr1) reported by Korhonen et al. (2013). Therefore, we assume that also the dry deposition of organic N on our plots may be clearly lower than 1.1 kg N ha1 yr1 reported by Korhonen et al. (2013). The values obtained for the N leaching are strongly affected by the method used in calculating the water fluxes. We used the N leaching estimates published by Mustajärvi et al. (2008), who applied the sulfate ðSO2 4 ) budget method for estimating the water flux. This method was chosen because chloride (Cl), which is often considered to be a conservative ion in forest ecosystems and is therefore generally used in mass balance calculations, showed extremely low concentrations and lacked a logical increase with increasing depth in our sites (Mustajärvi et al., 2008). Consistently, results of Svensson et al. (2012) suggested that in sites with low Cl deposition (<6 kg ha1 yr1) i.e., as in our case, Cl may actually not be conservative. Further, Cl and SO2 4 mass balance methods have been shown to give comparable and strongly inter-correlated results (Nilsson et al., 1998). Moreover, our water flux estimates obtained by SO2 budget method agree with those reported by 4 Starr and Ukonmaanaho (2004) using the WATBAL model on plots in eastern Finland.

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

10

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

The net N input applied has been measured for our study plots holding mature tree stands in relatively undisturbed conditions (Mustajärvi et al., 2008). It is however, important to bear in mind that during a rotation period, several other anthropogenic and natural factors may contribute either negatively (N export via thinnings, increasing N leaching after harvests, possible denitrification causing gaseous outputs) or positively (biological N2 fixation) to N balance of the sites (Jacobson et al., 2000; Kreutzweiser et al., 2008). For example, biological N2 fixation associated in bryophyte carpets of boreal forests may contribute as much as 1.5–2 kg ha1 yr1 to N balance in northern forests (DeLuca et al., 2002). In a recent study carried out using samples taken from ten of our fifteen study plots, the N2 fixation rate of symbiont cyanobacteria on feather moss was found to be higher in the northern than in southern Finland (Leppänen et al., 2013). Despite the uncertainties of the rather simplistic N balance calculations applied in our study, this approach offers a useful tool to evaluate the effect of biomass removal on N stocks in the ecosystem level and can be further utilized e.g. for modeling purposes (Raulund-Rasmussen et al., 2008). 4.2. Nitrogen and biomass allocation to different ecosystem compartments Overall, our results on biomass and N stocks in boreal coniferous forests emphasize the importance of mineral soil (0–40 cm layer) as a large long-term storage of organic matter (28–37% of total biomass) and N (ca. 70% of total) in these ecosystems. The accumulation of organic matter in mineral soil indicates a positive balance between organic matter production (including input in above ground litter), decomposition and leaching. An important mechanism for the stratification of organic matter in the mineral soil is related to podzolisation processes, which result in the accumulation of dissolved organic matter and N in the enrichment layer (B-horizon) of podzolic soil profile (e.g. Lindroos et al., 2008). The organic matter in forest mineral soil originates from the organic matter inputs by the aboveground and belowground parts of forest vegetation. It mostly consists of resistant, slowly decomposing humic substances (Cresser et al., 1993; Hilli et al., 2012). Thus, even if the N stock in forest mineral soil is large, it cannot be considered to be as significant N storage as the aboveground parts from the point of view of tree nutrition, since the nitrogen in mineral soil is bound in organic substances that are not readily mineralizable or degradable to forms of organic N available to plants and their symbiotic mycorrhizas (Inselsbacher and Näsholm, 2012; Olsson et al., 2012). Lower inputs of organic matter on soil surface as a result of intensified harvesting regimes will presumably result in a decrease in soil N and organic matter reserves, but due to large stocks and high spatial variation the changes in these stocks may be difficult to determine (Tamminen et al., 2012; Thiffault et al., 2011). After mineral soil, humus layer formed the second largest organic matter (13% of total biomass) and N stock (16% of total) in our study plots. As with most other compartments, both stocks were higher in spruce than in pine plots. Humus layer was the only compartment showing no significant differences in biomass stocks between the northern and southern sites, as this stock tended to be even slightly larger in the northern pine sites. In addition, although the mean of the organic matter stock in humus layer within the spruce plots was higher in the south than in the north, the difference was not significant because of the large variation within the southern plots. However, as the N concentration of the humus layer was lower in the northern sites, the N stocks in the humus layer were higher in the south than in the north. The differences between northern and southern humus layers may be more apparent in terms of quality, as reported by Hilli et al. (2008a,b). They found concentrations of the recalcitrant fraction (acid insolubles)

of organic matter to be higher and those of more easily degradable fractions (water-soluble extractives and non-polar extractives) to be lower in the south than in north. Both N and biomass stocks were largest in the southern spruce plots. This was expected because the spruce sites, as in general, were more fertile than the pine sites, resulting from the fact that Scots pine is well adapted to conditions of low water and nutrient availability and has a higher volume production in sites of low fertility than Norway spruce (Ilvessalo 1927; Heiskanen and Mäkitalo, 2002). However, there was a greater variance in N stocks between north and south in spruce plots than in pine plots. This can be due to the fact that pine, with a more northern growth regime is more tolerant to the northern harsher climatical conditions and grows in less fertile soil conditions also in the south. Therefore, the differences in growth conditions between southern and northern forests are steeper for spruce than for pine. The great differences between northern and southern spruce plots may however, partly be due to southern plot nr. 23 where N stocks in humus layer and mineral soil were exceptionally high. The site is located on fertile soil (C:N in organic layer = 27), the humus layer on this site is thicker than on the other plots, and the local N deposition is considerably high due to ammonia emissions from nearby fur farms. N bulk deposition on this plot is ca. 6 kg ha1 yr1, whereas the average to other southern spruce plots is ca. 3 kg ha1 yr1. However, the reported differences between pine and spruce stands were mostly statistically significant even when plot nr. 23 was excluded. The differences in the N stocks can be explained mostly due to variation in biomass stocks, as the N concentrations differed significantly between northern and southern sites only in needle, bark and humus layer compartments. Tree-related biomass estimates were based on modeling and thus include some inaccuracy. However, the input variables for the biomass estimation were taken from detailed tree measurements in the study plots – DBH, tree height and crown length – were measured from each tree on the plots. Further, the biomass functions applied are based on extensive empirical data and are developed especially to Finnish conditions (Repola, 2009). Regardless of tree species of the site, the allocation of biomass and N to roots was proportionally higher in the north than in the south. This reflects the fact that northern forests are less fertile and less dense than the southern forests and thus the competition for nutrients is more severe than competition for light. In southern spruce forests, the tree crowns effectively overshadow the forest floor, which is reflected in the lowest understory biomass and N stocks in these plots. In contrast, in the thin northern forests the ground layer receives more light resulting in higher biomass and N in understorey vegetation compared to the southern forests. In addition, in southern spruce forests greater proportion of the understory vegetation is formed by grasses and herbs having lower biomass than perennial dwarf shrubs which are more abundant in less fertile pine plots and in northern spruce plots. The N stocks in the fine and small roots accounted for a small proportion of the N stocks in stand biomass. According to Helmisaari et al. (2002) however, this pool accounts for a major part of the N used for annual biomass production (45 kg ha1, 45–63% of annual demand for growth in mature pine stands). The N stocks reported here were comparable to those observed by Helmisaari et al. (2002) for a mature pine stand in Finland. In a study of Helmisaari et al. (2007), which was carried out on the same sample plot network as the present study, the fine root biomass of trees and understory plant species was larger in northern Finland in both spruce and pine stands, resulting in a negative relationship between fine root biomass and the temperature sum and in a positive relationship between fine root biomass and the C:N ratio of the soil organic layer. Further, they found that pine had more fine roots in the north than in the south, whereas such a difference was not

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

found for spruce. They concluded that the lack of difference in tree fine root biomass between northern and southern spruce stands may result from more pronounced root competition with abundant understory in the north, as two of the stands in north were old and pristine (Oulanka nr. 21) or relatively unmanaged (Pallasjärvi nr. 3) forests with well-developed root systems. 5. Conclusions Our results supported the hypothesis that in boreal coniferous forests, inputs of N by deposition accumulating during the following rotation period are able to replenish the export of N caused by conventional stem-only-harvest in final cutting, but if more intense whole-tree harvest regimes are practiced, the sustainability of the site productivity will be challenged, especially in the Norway spruce stands. The study provided detailed information on the amount and distribution of N and biomass in different compartments of boreal forest ecosystem, including not only tree stand and soil, but also such rarely reported N stocks as litter layer, ground vegetation, and fine and small roots. The comprehensive approach of the study was enabled by a large data set provided by several studies and monitoring activities carried out in the long-term integrated forest monitoring network in Finland. Acknowledgements Besides Metla’s resources, a part of the data utilized in the study was collected with co-funding provided within the frameworks of the EU/Forest Focus (EC) No 2152/2003 and EU/Life+ FutMon programmes. The contribution of Metla’s field and laboratory stuff is gratefully acknowledged. References Ahti, T., Hämet-Ahti, L., Jalas, J., 1968. Vegetation zones and their sections in northwestern Europe. Ann. Bot. Fenn. 5, 169–211. Blanco, J.A., Zavala, M.A., Imbert, J.B., Castillo, F.J., 2005. Sustainability of forest management practices: evaluation through a simulation model of nutrient cycling. For. Ecol. Manage. 213, 209–228. Cajander, A.K., 1949. Forest types and their significance. Acta For. Fenn. 1, 1–175. Costantini, A., 1995. Sampling soil bulk density in the coastal lowlands of south-east Queensland. Aust. J. Soil Res. 33, 11–18. Cresser, M., Killham, K., Edwards, T., 1993. Soil chemistry and its applications. Cambridge Environmental Chemistry Series 5. Cambridge University Press, Cambridge. DeLuca, T.H., Zackrisson, O., Nilsson, M.-C., Sellstedt, A., 2002. Quantifying nitrogenfixation in feather moss carpets of boreal forests. Nature 419, 917–920. Derome, J., Lindgren, M., Merilä, P., Beuker, E., Nöjd, P., 2007. Forest Condition Monitoring under the UN/ECE and EU Programmes in Finland. Working Papers of the Finnish Forest Research Institute 45, 11–20, . Finér, L., Piirainen, S., Mannerkoski, H., Starr, M., 2003. Carbon and nitrogen pools in an old-growth, Norway spruce-mixed forest in eastern Finland and changes associated with clear-cutting. For. Ecol. Manage. 174, 51–63. Flechard, C.R., Nemitz, E., Smith, R.I., Fowler, D., Vermeulen, A.T., Bleeker, A., Erisman, J.W., Simpson, D., Zhang, L., Tang, Y.S., Sutton, M.A., 2011. Dry deposition of reactive nitrogen to European ecosystems: a comparison of inferential models across the NitroEurope network. Atmos. Chem. Phys. 11, 2703–2728. Hakkila, P., 2002. Operations with reduced environmental impact. In: Richardson, J., Björnheden, R., Hakkila, P., Lowe, A.T., Smith, C.T. (Eds.), Bioenergy from Sustainable Forestry. Guiding Principles and Practice. Kluwer, Dordrecht, pp. 244–261. Heiskanen, J., Mäkitalo, K., 2002. Soil water-retention characteristics of Scots pine and Norways spruce forest sites in Finnish Lapland. For. Ecol. Manage. 162, 137–152. Hellsten, S., Helmisaari, H.-S., Melin, Y., Skovsgaard, J.P., Kaakinen, S., Kukkola, M., Saarsalmi, A., Petersson, H., Akselsson, C., 2013. Nutrient concentrations in stumps and coarse roots of Norway spruce, Scots pine and silver birch in Sweden. Finland and Denmark. For. Ecol. Manage. 290, 40–48. Helmisaari, H.-S., 1991. Variation in nutrient concentrations of Pinus sylvestris roots. In: McMichel, B.L., Persson, H. (Eds.), Plant roots and their Environment. Developments in Agricultural and Managed-Forest Ecology, vol. 24. Elsevier Science Publishers, pp. 204–212.

11

Helmisaari, H.-S., Siltala, T., 1989. Variation in nutrient concentrations of Pinus sylvestris stems. Scand. J. For. Res. 4, 443–451. Helmisaari, H.-S., Makkonen, K., Kellomäki, S., Valtonen, E., Malkönen, E., 2002. Below- and above-ground biomass, production and nitrogen use in Scots pine stands in eastern Finland. For. Ecol. Manage. 165, 317–326. Helmisaari, H.-S., Derome, J., Nöjd, P., Kukkola, M., 2007. Fine root biomass in relation to site and stand characteristics in Norway spruce and Scots pine stands. Tree Physiol. 27, 1493–1504. Helmisaari, H.-S., Holt Hanssen, K., Jacobson, S., Kukkola, M., Luiro, J., Saarsalmi, A., Tamminen, P., Tveite, B., 2011. Logging residue removal after thinning in Nordic boreal forests: Long-term impact on tree growth. For. Ecol. Manage. 261, 1919– 1927. Hilli, S., Stark, S., Derome, J., 2008a. Carbon quality and stocks in organic horizons in boreal forest soils. Ecosystems 11, 270–282. Hilli, S., Stark, S., Derome, J., 2008b. Qualitative and quantitative changes in waterextractable organic compounds in the organic horizon of boreal coniferous forests. Boreal Environ. Res. 13 (Suppl. B), 107–119. Hilli, S., Stark, S., Willför, S., Smeds, A., Reunanen, M., Hautajärvi, R., 2012. What is the composition of AIR? Pyrolysis-GC–MS characterization of acid-insoluble residue from fresh litter and organic horizons under boreal forests in southern Finland. Geoderma 179–180, 63–72. Ilvessalo, Y., 1927. Suomen metsät. Tulokset vuosina 1921–1924 suotitetusta valtakunnan metsien arvioimisesta, (The forests of Finland. results of the general survey of the forests of the country carried out during the years 1921– 1924),. Metsätieteellisen koealaitoksen julkaisuja 11, 1–421 [+192pp. In Finnish with English summary]. Inselsbacher, E., Näsholm, T., 2012. The below-ground perspective of forest plants: soil provides mainly organic nitrogen for plants and mycorrhizal fungi. New Phytol. 195, 329–334. IPCC, 2007. Climate change 2007: impacts, adaptation and vulnerability. In: Parry, M.L., Canziani, P.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E. (Eds.), Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, p. 76. IUSS Working Group WRB, 2006. World Reference Base for soil resources 2006. second ed. World Soil Resources Reports No 103. FAO, Rome. Jacobson, S., Kukkola, M., Mälkönen, E., Tveite, B., 2000. Impact of whole-tree harvesting and compensatory fertilization on growth of coniferous thinning stands. For. Ecol. Manage. 129, 41–51. Johnson, D.W., Curtis, P.S., 2001. Effects of forest management on soil C and N storage: meta-analysis. For. Ecol. Manage. 140, 227–238. Kellomäki, S., Seppälä, M., 1987. Simulations on the effects of timber harvesting and forest management on the nutrient cycle and productivity of Scots pine stands. Silva Fenn. 21, 203–236. Korhonen, J.F.J., Pumpanen, J., Pihlatie, M., 2012. Total nitrogen deposition to a boreal forest – organic dry nitrogen deposition estimated. In: KulmalaM., Lappalainen H.K., Boy M., Brus M., Nieminen T. (Eds.), Proceedings of Finnish Center of Excellence in ‘‘Physics, Chemistry, Biology and Meteorology of Atmospheric Composition and Climate Change’’, and Nordic Center of Excellence in ‘‘Cryosphere-Atmosphere Interactions in a Changing Arctic Climate’’, Annual Meetings 2012. Report Series in Aerosol Science, vol. 134, 2012, pp. 375–379. Korhonen, J.F.J., Pihlatie, M., Pumpanen, J., Aaltonen, H., Hari, P., Levula, J., Kieloaho, A.J., Nikinmaa, E., Vesala, T., Ilvesniemi, H., 2013. Nitrogen balance of a boreal Scots pine forest. Biogeosciences 10, 1083–1095. Kreutzweiser, D.P., Hazlett, P.W., Gunn, J.M., 2008. Logging impact on the biogeochemistry of boreal forest soils and nutrient export to aquatic systems: a review. Environ. Rev. 16, 157–179. Leppänen, S.M., Salemaa, M., Smolander, A., Mäkipää, R., Tiirola, M., 2013. Nitrogen fixation and methanotrophy in forest mosses along a N deposition gradient. Environ. Exp. Bot. 90, 62–69. Lindroos, A.-J., Derome, J., Mustajärvi, K., Nöjd, P., Beuker, E., Helmisaari, H.-S., 2008. Fluxes of dissolved organic carbon in stand throughfall and percolation water in 12 boreal coniferous stands on mineral soils in Finland. Boreal Environ. Res. 13 (Suppl. B), 22–34. Magnani, F., Mencuccini, M., Borghetti, M., Berbigier, P., Berninger, F., Delzon, S., Grelle, A., Hari, P., Jarvis, P.G., Kolari, P., Kowalski, A.S., Lankreijer, H., Law, B.E., Lindroth, A., Loustau, D., Manca, G., Moncrieff, J.B., Rayment, M., Tedeschi, V., Valentini, R., Grace, J., 2007. The human footprint in the carbon cycle of temperate and boreal forests. Nature 447, 848–850. Mälkönen, E., 1974. Annual primary production and nutrient cycle in some Scots pine stands. Commun. Inst. For. Fenn. 84, 87. Ministry of Employment and the Economy, 2010. Finland’s National Action Plan for Promoting Energy from Renewable Sources Pursuant to Directive 2009/28/EC, 55p. (last accessed 29.01.13). Mustajärvi, K., Merilä, P., Derome, J., Lindroos, A.-J., Helmisaari, H.-S., Nöjd, P., Ukonmaanaho, L., 2008. Fluxes of dissolved organic and inorganic nitrogen in relation to stand characteristics and latitude in Scots pine and Norway spruce stands in Finland. Boreal Environ. Res. 13 (Suppl. B), 3–21, . Nilsson, S.I., Berggren, D., Westling, O., 1998. Retention of deposited NHþ 4 —N and NO 3 —N in coniferous forest ecosystems in southern Sweden. Scand. J. For. Res. 13, 393–401. Olsson, B.A., Staaf, H., Lundkvist, H., Bengtsson, J., Rosén, K., 1996. Carbon and nitrogen in coniferous forest soils after clear-felling and harvests of different intensity. For. Ecol. Manage. 82, 19–32.

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029

12

P. Merilä et al. / Forest Ecology and Management xxx (2013) xxx–xxx

Olsson, B.A., Hansson, K., Persson, T., Beuker, E., Helmisaari, H.-S., 2012. Heterotrophic respiration and nitrogen minrealisation in soils of Norway spruce, Scots pine and silver birch stands in contrasting climates. For. Ecol. Manage. 269, 197–205. Palviainen, M., Finér, L., 2012. Estimation of nutrient removals in stem-only and whole-tree harvesting of Scots pine, Norway spruce, and birch stands with generalized nutrient equations. Eur. J. For. Res. 131, 945–964. Persson, H., 1983. The distribution and productivity of fine roots in boreal forests. Plant Soil 71, 87–101. Raulund-Rasmussen, K., Stupak, I., Clarke, N., Callesen, I., Helmisaari, H.-S., Karltun, E., Varnagiryte-Kabasinskiene, I., 2008. Effects of very intensive forest biomass harvesting on short and long term site productivity. In: Röser, D., Asikainen, A., Raulund-Rasmussen, K., Stupak, I. (Eds.), Sustainable Use of Forest Bio mass for Energy: A Synthesis with Focus on the Baltic and Nordic Region. Springer Science + Business, Media B.V., pp. 29–78. Repo, A., Tuomi, M., Liski, J., 2011. Indirect carbon dioxide emissions from producing bioenergy from forest harvest residues. Glob. Change Biol. Bioenergy 3, 107– 115. Repola, J., 2009. Biomass equations for Scots Pine and Norway Spruce in Finland. Silva Fenn. 43, 625–647. Repola, J., Ojansuu, R., Kukkola, M., 2007. Biomass functions for Scots pine, Norway spruce and birch in Finland. Working Papers of the Finnish Forest Research Institute, vol. 53, 28p. . Röser, D., Asikainen, A., Raulund-Rasmussen, K., Stupak, I. (Eds.), 2008. Sustainable use of Forest Biomass for Energy. A Synthesis with Focus on the Baltic and Nordic Region. Springer, Dordrecht. Salemaa, M., Hamberg, L., 2007. Understorey vegetation on the Level II plots during 1998–2004. Working papers of the Finnish Forest Research Institute, vol. 45, pp. 69–80. Salemaa, M., Derome, J., Nöjd, P., 2008. Response of boreal forest vegetation to the fertility status of the organic layer along a climatic gradient. Boreal Environ. Res. 13 (Suppl. B), 48–66.

Schulze, E.-D., Körner, C., Law, B.E., Haberl, H., Luyssaert, S., 2012. Large-scale bioenergy from additional harvest of forest biomass is neither sustainable nor greenhouse gas neutral. Glob. Change Biol. Bioenergy 4, 611–616. Starr, M., Ukonmaanaho, L., 2004. Levels and characteristics of TOC in throughfall, forest floor leachate and soil solution in undisturbed boreal forest ecosystems. Water Air Soil Pollut. Focus 4, 715–729. Svensson, T., Lovett, G.M., Likens, G.E., 2012. Is chloride a conservative ion in forest ecosystems? Biogeochemistry 107, 125–134. Tamm, C.O., 1991. Nitrogen in Terrestrial Ecosystems. Springer, Berlin. Tamminen, P., 1991. Kangasmaan ravinnetunnusten ilmaiseminen ja viljavuuden alueellinen vaihtelu Etelä-Suomessa, (Expression of soil nutrient status and regional variation in soil fertility of forested site in southern Finland),. Folia Forestalia 777, 40 (In Finnish with English summary). Tamminen, P., Starr, M., 1994. Bulk density of forested mineral soils. Silva Fenn. 28, 53–60. Tamminen, P., Saarsalmi, A., Smolander, A., Kukkola, M., Helmisaari, H.-S., 2012. Effects of logging residue harvest in thinnings on amounts of soil carbon and nutrients in Scots pine and Norway spruce stands. For. Ecol. Manage. 263, 31– 38. Thiffault, E., Hannam, K.D., Paré, D., Titus, B.D., Hazlett, P.W., Maynard, D.G., Brais, S., 2011. Effects of forest biomass harvesting on soil productivity in boreal and temperate forests – a review. Environ. Rev. 19, 278–309. Ukonmaanaho, L., Merilä, P., Nöjd, P., Nieminen, T.M., 2008. Litterfall production and nutrient return to the forest floor in Scots pine and Norway spruce stands in Finland. Boreal Environ. Res. 13 (Suppl. B), 67–91, http://www.borenv.net/BER/ pdfs/ber13/ber13-B067.pdf. Viro, P.J., 1952. Kivisyyden määrittämisestä (On the determination of stoniness). Commun. Inst. For. Fenn. 40, 23 (In Finnish with English summary). Vogt, K.A., Grier, C.C., Meier, C.E., Keyes, M.R., 1983. Organic matter and dynamics in forest floors of young and mature Abies amabilis stands in Western Washington, as suggested by fine root input. Ecol. Monogr. 53, 139–157.

Please cite this article in press as: Merilä, P., et al. Above- and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization. Forest Ecol. Manage. (2013), http://dx.doi.org/10.1016/j.foreco.2013.06.029