Biomass dynamics of central Siberian Scots pine forestsfollowing surface fires of varying severity

Elena A. KukavskayaA,E, Galina A. IvanovaA, Susan G. ConardB,Douglas J. McRaeC and Valery A. IvanovD

ARussian Academy of Sciences, Siberian Branch, V. N. Sukachev Institute of Forest,

50/28 Akademgorodok, Krasnoyarsk, 660036, Russia.BUS Forest Service Rocky Mountain Research Station, Fire Sciences Laboratory,

5775 W US Highway 10, Missoula, MT 59808, USA.CNatural Resources Canada, Canadian Forest Service, 1219 Queen Street East,

Sault Ste Marie, ON, P6A 2E5, Canada. [Retired]DSiberian State Technological University, 82 Mira Street, Krasnoyarsk, 660049, Russia.ECorresponding author. Email: kukavskaya@ksc.krasn.ru

Abstract. In 2000–2002 nine 4-ha prescribed fires of various severities were conducted on experimental plots in matureScots pine forest in the central Siberian taiga, Russia. Total above-ground living biomass decreased after low- andmoderate-severity fires by 10 and 15%, whereas high-severity fire reduced living above-ground biomass by 83%. We

monitored changes in fuel structure and biomass for 6–8 years following these fires. By 6–8 years after burning the groundfuel loading had recovered to 101, 96 and 82% of pre-fire levels after fires of low-, moderate- and high-severity. Downwoody fuel loading increased by 0.18� 0.04 kgm�2 year�1. We developed regressions relating time since fire to changes

in above-ground biomass components for fires of different severity for feather moss–lichen Scots pine forest of Siberia.Our results demonstrate the importance of both burn severity and composition of pre-fire surface vegetation in determiningrates and patterns of post-fire vegetation recovery on dry Scots pine sites in central Siberia.

Additional keywords: biomass accumulation, boreal forest, fire severity, fuels, Pinus sylvestris.

Received 21 March 2013, accepted 21 March 2014, published online 27 June 2014

Introduction

Boreal forests account for 33% of the global forest area andcover,1.2� 109 ha in North America and Eurasia (FAO 2001).

Russia contains 2/3 of the world’s boreal forests. Fire is themainecological disturbance controlling forest development (Furyaev1996) and redistributing biomass between the forest floor and

living trees (Wardle et al. 2003). Projected warmer and dryerclimates are expected to lead to increases in extreme fireweather, with resulting greater fire frequency and larger annualburned area in boreal forests (Flannigan et al. 2009).

Fire leads to changes in forest species composition andstructure and in carbon storage, as well as direct emissions ofgreenhouse gases and aerosols to the atmosphere. Large areas

(up to 15–20� 106 ha) in Siberia are burned every year (Sojaet al. 2004; Vivchar 2011; Kukavskaya et al. 2013a). Scots pine(Pinus sylvestris) is the dominant tree species in ,30% of

Siberian forests (Forest Fund of Russia 1999). The open foreststructure, flammable moss and lichen surface vegetation, andsummer drought typical for Scots pine forests lead to a fire

regime of moderately frequent surface fires with occasionalcrown fires (Sannikov 1973, 1992). Mean fire return intervals inSiberia’s Scots pine forests range from 10 to 60 years anddecrease from north to south as fire season length and

anthropogenic impact increase (Arbatskaya and Vaganov1997; Ivanova et al. 2002, 2010). Fire severity varies widelydepending on weather and pre-fire ecosystem conditions (e.g.

stand age, tree species, understorey composition, forest floorvegetation) (Furyaev 1996).

Fuel loading in Siberian forests is determined in large

part by ecosystem characteristics and the time since themost recent fire (Kurbatsky 1970). Although there are somedata on fuel structure and loading (biomass per unit area)in different forest types of Siberia (e.g. Lashinsky 1981;

Evdokimenko 1983; Atkin and Atkina 1985; Ivanova 2005),there are only a few published measurements of post-firedynamics of different biomass components (Cherbakov

et al. 1979; Evdokimenko 1979; Furyaev 1996), and datarelating post-fire recovery to fire behaviour and first-orderfire effects are largely lacking.

Although temperate forests with surface fire regimes, such asponderosa pine in the western US, can recover quickly after fire(Keeley et al. 2009), boreal pine forests require more time to

develop adequate and continuous fuel loads to carry fire(Gorshkov et al. 2005). Post-fire vegetation recovery andbiomass accumulation in forest ecosystems are influenced bypre-fire ecosystem characteristics, fire severity and climate

CSIRO PUBLISHING

International Journal of Wildland Fire

http://dx.doi.org/10.1071/WF13043

Journal compilation � IAWF 2014 www.publish.csiro.au/journals/ijwf

(Landhausser and Wein 1993; Mack et al. 2008). After high-severity crown fires in Siberian Scots pine forests Polytrichum

moss species begin to appear in 3–4 years, whereas lichens beginto reappear in 4–8 years and the major forest moss species(e.g. Pleurozium schreberi and Dicranum undulatum) invade

only after 10–15 years. After surface fires, living surfacevegetation (grasses, small shrubs and mosses) typically beginsto recover within 1 year (Korchagin 1954; Sannikov and

Sannikova 1985). Gorshkov (2001), working in EuropeanRussia, found that the relative cover of mosses had stabilisedby 50 years after stand-replacing fires in moist pine–moss foresttypes, whereas it took ,120–140 years for relative cover and

height of lichens to stabilise in the dryer pine–lichen foresttypes. In the northern taiga pine–lichen forests, duff reached itspre-fire depth in 120 years, whereas in lichen–feather moss and

moss–pine forests recovery took 175–190 years (Gorshkov et al.2005). Post-fire duff recovery in forests with low tree mortalityis more rapid than where high-severity fire has resulted in total

tree mortality (Buryak et al. 2003).Biomass accumulation and stand productivity have a strong

influence on carbon balance during post-fire succession. In a

study of four chronosequences after crown fire in Siberian Scotspine,Wirth et al. (2002) found that stands acted as a net source ofcarbon up to 40 years post-fire, whereas middle-aged stands hadmaximum rates of biomass accumulation, and mature stands

(.100 years) continued to sequester carbon but at lower rate(10–25% of the sequestration rate in middle-aged stands).However, they studied only sites that had experienced stand-

replacing crown fires, whereas the majority of fires in Russianboreal forests (,80% in typical years) burn as surface fires,where overstorey tree mortality is typically low. Extensive

crown fires occur in Siberia only under severe drought condi-tions, when they may account for up to 50% of burned area(Korovin 1996).

The goal of this study was to describe changes in above-ground structure and biomass of different fuel components

(duff, litter, dead and down woody debris, ground vegetation,young regeneration, trees) in dry Scots pine forest over the firstseveral years after surface fires of various severities. Other

studies of post-fire biomass changes in Siberia have typicallybeen conducted following wildfires of varying severity. In suchretrospective studies fire behaviour and pre-fire fuel loads can

only be inferred, and it is difficult to find a range of firebehaviour conditions or fire effects on sites that supportedsimilar pre-fire stands. In this study, we monitored recoveryon plots that were burned under a range of environmental

conditions by experimental fires with known parameters,enabling us to evaluate relationships among fire behaviour, firstorder fire effects, fire weather and post-fire recovery.

Materials and methods

Description of study area and plot design

This study was conducted in mature pure Scots pine (Pinussylvestris) stands with feather moss (Pleurozium schreberi)and lichens (Cladina rangiferina, C. stellaris, C. arbuscula,

C. cetraria) as the dominant ground vegetation. Sparse shrubunderstorey included Rosa acicularis Lindl. and Salix caprea L.Vaccinium species dominated among small shrubs. Nine 4-ha

(200� 200-m) plots were laid out across a 200-ha study area,which was a large island surrounded by bogs and lakes, west ofthe Yenisey River in the Krasnoyarsk krai (Fig. 1). The site was

on the Sym plain, a vast alluvial plain in central Siberia(608380N; 898410E), and Scots pine forests of this type are typicalin the region. Average diameter at breast height (DBH) across

our study plots was 30.9� 1.1 cm and height was 19.2� 0.4m.Tree density amounted to 333.6� 45.0 stems ha�1 and standbasal area was 23.9� 2.2m2 ha�1. The climate was continental,

Borders of Russia

N

Large rivers

Location of study area 0 500 1000 2000Kilometres

Fig. 1. Map of Russia showing the location of the study region in central Siberia. Insert is a map of the island

study site surrounded by bogs and lakes, west of the Yenisey River in the Krasnoyarsk krai (courtesy of Google

Earth) with sample plots (,4 ha) shown by square boxes.

B Int. J. Wildland Fire E. A. Kukavskaya et al.

with annual air temperatures ranging from �3.2 to �5.78C and400 to 500mm in annual precipitation. Soils were sandy pod-zols. To coordinate data collection and distribute sampling

across the study plots, a 25� 25-m sampling grid (normally 49grid points) was established on each plot (McRae et al. 2006).Experimental plots were burned over 3 consecutive years (Plots

13 and 14 in 2000, Plots 2, 3, 6, 19 in 2001 and Plots 4, 20, 21 in2002). Detailed data on weather, fire behaviour characteristics,first-order fire effects and pre-fire vegetation and fuels were

collected on each plot (results and methods are described inMcRae et al. 2006).

Fuel biomass sampling

We sampled both living and dead biomass on each experimentalplot. Living biomass components included the overstorey treestand, tree regeneration, lichen-moss and grass-small shrub

layers. Dead organic matter components included dead standingtrees, downwoody debris, litter and duff. Stand structure of treesgreater than 10-cm DBH was measured using the point-centred

quarter (PCQ) method (Cottam and Curtis 1956; Morisita 1957)with PCQplots offset by 1m from alternate grid sampling points(25 sample grid points; 100 trees per plot). For the nearest tree in

each quadrant we measured distance from point to tree, DBH,tree height and height to live crown. Tree age was determinedboth from core samples on selected trees and by counting thenumber of tree rings on the stumps of trees harvested for

developing biomass regressions. All pockets of young Scotspine regeneration (up to 10-cm DBH and .1.3-m height) wereinventoried with DBH and height measured. Above-ground tree

biomass was calculated using PCQ data on tree distribution bydifferent DBH classes and destructive sampling of model treesacross the range of size classes.

We sampled dead surface fuels (dead and downwoody debrisabove the litter layer) with the line intersect method developedby VanWagner (1968) as adapted byMcRae et al. (2006). Each5-m transect was installed at right angles to the gridline and ran

from the grid point pin to a second metal pin. Up to 49 transectswere sampled on each plot. Fuel pieces intersected by thevertical plane of this line were tallied by diameter class (0.0–

0.49, 0.5–0.99, 1.0–2.99, 3.0–4.99 and 5.0–6.99-cm size clas-ses) using a ‘go–no-go’ gauge and then converted to loadingsusing multiplication factors. Woody pieces larger than 7.0 cm in

diameter were measured using callipers and converted to loads(McRae et al. 1979, 2006).

To quantify living surface vegetation and ground fuel loads,

we sampled twenty-five 20� 25-cm quadrats laid out ,1mfrom the uneven-numbered grid points on each plot. First, livingsurface vegetation within the frame was harvested to estimatefuel loads of grasses and small shrubs (e.g. Vaccinium vitis-

idaea, Ledum palustre, Calamagrostis arundinacea). To deter-mine ground fuel loads of litter, mosses, lichens and duff layers,each sample was removed from the forest floor by cutting

through the layers with a knife along the inside edge of thesampling frame. After the litter layer (foliage, bark, cones, curedgrasses and easily recognisable plant parts) was collected, the

moss and duff (fermentation and humus layers) were sectionedhorizontally into 2-cm thick layers down to mineral soil. Lichenusually was collectedwhole because of its friability. Forest floor(litter, moss, lichen and duff) depth for each sample quadrat was

determined by averaging 4 measurements per quadrat (one ineach corner). All samples were taken to the laboratory todetermine oven-dry weights.

We sampled fuel loading on the nine burned plots for 6–8years after fires, depending on the year the plot was burned.Post-fire measurements were taken every year after fire through

2005 and in 2008. We were unable to revisit plots in 2006 and2007 because of logistical problems.

To estimate post-fire mortality and changes in above-ground

live and dead tree biomass, the condition of the trees (live v.

dead, standing v. fallen, percentage of green crown, insectabundance) in PCQ plots was monitored every year. Thediameter of young regeneration found in pockets at the experi-

mental plots was measured to determine the effect of diameteron post-fire tree mortality. Changes in above-ground treebiomass in the first 8 post-fire years were estimated based on

existing data relating annual tree increment to tree diameter andage in dry Scots pine forest. To determine annual increment ofScots pine needles, we assumed a needle life of 5 years (Onuchin

and Spitsina 1995). Tree branch increment was estimated basedon the total percentage of tree biomass that was in branches,using 1.45% for overstorey trees and 11.7% for young regener-

ation, based on data from similar stands in the study region(Vedrova 1998).

Surface and ground fuel loads immediately after fire werecalculated based on pre-fire fuel load and fuel consumption data

(McRae et al. 2006). The same transects were used to measurepre-fire dead and down woody material, fuel consumption andpost-fire debris accumulation for 6–8 years after fire. For

sampling of ground fuels after fire, we used a procedure similarto that for pre-fire sampling, (20� 25 cm; 25 samples per plot),except that living moss and lichen were separated from the duff

layer, and duff was sampled in a single unit from the surface ofthe duff layer down to mineral soil. Litter was sampled the sameway as before fire. Moss and duff depths were measured at eachcorner of the sample quadrats (Kurbatsky 1970). Owing to the

destructive sampling methods, these plots had to be in newlocations every year. Sample plots were always located nearbythose in previous years and in the same type of surface fuel and

overstorey conditions. Litter was sorted in the laboratory by type(foliage, bark, cones, cured herbs and grasses) and livingvegetation by species. All samples were dried at 1008C until

they reached constant weight to give oven-dry weights. Duringour 8-year study, we collected ,4500 samples of groundmaterial and made repeat measurements of down woody mate-

rial on 2200m of fuel transects.

Relating post-fire biomass dynamics to fire characteristics

Experimental fires were carried out in June and July, which

corresponds to the main fire season for this region. All plotswere burned using line ignition along the windward side tomimic equilibrium fire behaviour conditions. Fires were sur-

face fires, but they ranged widely in fire behaviour and in fireseverity. We developed regressions relating post-fire dynam-ics both to fireline intensity and to fire severity. Byram’s

(1959) equation calculates fireline intensity as a product ofrate of spread, dry weight of consumed fuel and heat of com-bustion of consumed fuel. Average fireline intensity on ourexperimental plots ranged from 700 to 4745 kWm�1 (Table 1)

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire C

(McRae et al. 2006; updated by D. J. McRae, unpubl. data). We

quantified fire severity on the plots with the following SeverityIndex (SI) (S. G. Conard, unpubl. data):

SIi ¼RoSi

RoSmax

þ DoBi

DoBmax

þ ABi

ABmax

� �

3ð1Þ

where RoS is rate of spread (mmin�1), DoB is depth of burn(cm), AB is plot area that experienced fire (%), i is average valuefor the specific plot, max is maximum value obtained in thecourse of 13 experimental fires conducted in Scots pine stands of

central Siberia (4 of these fires were on other study sites notreported here). This index provided good separation amongthree distinct groups of burned plots at this site and related well

to fire effects and to post-fire responses. Plots 3, 4 and 19 (SI0.41–0.44) were ranked as low-severity fires, Plots 2, 6, 13, 20and 21 (SI 0.57–0.67) as moderate-severity and Plot 14 (SI 0.76)

as high-severity (Table 1). Several indices with different sets ofvariables were explored in developing this Severity Index, andthey all provided identical rankings of fire severity among the

plots, and similar separation between low-, moderate- and high-severity plots. This form of the index was selected because thethree variables are mathematically independent from each otherand represent different factors that affect overall fire effects on

the ecosystem (S. G. Conard, unpubl. data).

Results

Pre-fire biomass characteristics on the sample plots

Total pre-fire biomass varied from 13.14 to 20.95 kgm�2, with

57–76% of this in living trees. Biomass of standing dead treesvaried from zero (on four of the nine burn plots) to 0.18 kgm�2

on Plot 4. Patches of tall conifer regeneration (sapling) from 4.0to 7.6m in height, of the type that can promote transition of

surface fires into the canopy, accounted for up to 0.02 kgm�2

and occupied from 0.01 to 0.08 ha (0.25 to 2.0% of area) on thesample plots. Combined surface and ground fuel loading varied

from 4.00 to 6.24 kgm�2 on our plots. Down woody debris wasnon-uniformly distributed across the plots, with plot-levelaverages ranging from 0.44 to 1.69 kgm�2. Living biomass

(9.56–17.27 kgm�2) dominated the above-ground biomass, anddead organic matter made up ,30% of the total live and deadloading.

Post-fire tree mortality and biomass changes of overstoreytrees and conifer regeneration

Although our experimental fires were surface fires, trees weredamaged to varying degrees by the influence of the fire heat fluxon overstorey crowns, roots and stems. Post-fire stand condi-

tions were controlled largely by fire severity (Fig. 2) as pre-firestand conditions were similar across the study area. Insectinfestations resulting from post-fire loss of tree vigour alsocontributed to tree mortality on our plots (Oreshkov and

Shishikin 2003). One year after fire, overstorey tree mortalityranged from 0 to 81% (Table 1). Tree mortality was closelycorrelated with average fireline intensity (r¼ 0.83, P, 0.005 1

year after fire). Most of the tree mortality occurred during thefirst 2 to 3 years after fire (2002–2005 depending on the year ofthe fire). On all but low-severity burn plots, tree mortality had

levelled off by ,4 years post-fire.The highest tree mortality occurred on Plot 14, following a

high-severity fire that scorched nearly all of the tree canopies.Although this was not a crown fire, the high tree mortality

of overstorey and understorey vegetation makes it a stand-replacement fire. Four years post-fire, tree mortality on this plothad increased to 89.3% (Table 1). Living trees were generally

along the edges of the plot. Most of the dead trees were stillstanding 8 years after the fires. These trees lost foliage, and someof their bark and branches (Fig. 2).

Tree mortality following fires of low- and moderate-severitywas generally low (2–14% by year 4) and typically the smallest(12-cm DBH) and the largest (52–56-cm DBH) trees were

killed, as well as trees weakened by earlier fire scars. Treemortality after the high severity fire affected trees regardless ofdiameter.

Tree biomass was not re-distributed noticeably after low- to

moderate-severity fires. The greatest increase in dead treebiomass occurred during the first 2–3 years following thesefires (1–12% of the total stand biomass). Following the high-

severity fire dead biomass increased to 88% of the total treebiomass by the eighth post-fire year (Table 1). The decrease ofdead tree biomass over the observation time on some of the

studied plots (e.g. Plot 3) was a result of trees falling on theground and their transition to the category ‘downwoody debris’.

Surface fires killedmost tall Scots pine regeneration (2–5-cmDBH and 1.5–12-m height); however, some of these trees

Table 1. Cumulative post-fire overstorey mortality (percentage of pre-fire live trees, first value) and dead tree biomass (percentage of the total

standing tree biomass, second value)

Note: tree mortality not estimated for a particular year is marked with an en-dash

Plot Fire severity SI Fireline intensity

(kWm�1)

Time since fire (years)

1 2 3 4 5 6 7 8

14 High 0.76 4745 81.0; 81 86.9; 87 88.1; 87 89.3; 88 89.3; 88 – – 89.3; 88

6 Moderate 0.59 1581 12.0; 7 14.0; 10 14.0; 10 14.0; 9 – – 16.0; 11 –

2 0.65 2297 4.2; 1 5.2; 1 8.3; 2 8.3; 2 – – 8.3; 2 –

20 0.67 2995 4.0; 4 4.0; 4 7.0; 5 – – 8.0; 6 – –

13 0.61 1296 2.4; 1 4.8; 4 4.8; 4 4.8; 4 4.8; 4 – – 4.8; 4

21 0.57 1970 0; 0 1.0; 0.4 1.0; 0.4 – – 2.0; 1 – –

3 Low 0.44 700 0; 8 0; 7 1.0; 5 1.9; 5 – – 3.8; 7 –

4 0.44 892 2.0; 8 5.0; 11 7.0; 12 – – 12.0; 14 – –

19 0.41 1347 4.8; 12 5.8; 12 5.8; 11 6.7; 11 – – 8.6; 12 –

D Int. J. Wildland Fire E. A. Kukavskaya et al.

survived because of patchy burning patterns (Table 2). From 71

to 100% of saplings of the lowest diameter size class (2 cm) died,except on Plot 3 where 77% of this regeneration survived,perhaps because the forest floor was dominated by moss that

had fairly high moisture content at the time of the fire due torecent rain (425% compared to 15–44% on other experimentalburns; Kukavskaya 2009). Small trees (up to 4-cm DBH) hadvery low survival. For larger trees (5–10-cmDBH)mortalitywas

significantly correlated with fireline intensity. The correlationcoefficients between fireline intensity and sapling tree mortalitywere 0.73 for.5–7-cmDBH trees (P, 0.05), and 0.97 for trees

of.7–10-cm DBH (P, 0.0005). The high severity surface fireon Plot 14 killed all Scots pine regeneration (Table 2). Somepost-fire sapling survival occurred on seven plots (2, 3, 4, 13, 14,

19, 21), and post-fire biomass of these saplings was up to0.01 kgm�2. The biomass killed by experimental fires variedfrom 3 to 100% of the total sapling living biomass depending on

fire severity. The total (both live and dead) biomass of saplingdecreased by 3–17% compared to pre-fire data.

Post-fire surface and ground fuel biomass changes

Although pre-fire forest floor depth varied among the plots from7.3� 0.3 to 9.4� 0.6 cm, after fire it decreased by 30–75% and

ranged from 2.5� 0.3 to 5.5� 0.4 cm. Surface and ground fuelloads immediately after fire were 26–66% less than pre-fire

values (Fig. 3) whereas in 6–8 years they reached 103–133 and86–152% of the respective pre-fire values after fires of low andmoderate severity (Table 3).

As might be expected, in the first year after fire, ground fuel

load (litter, moss, duff) was highly correlated with the fireseverity index (SI; Fig. 4). By 4 years post-fire the R2 betweenSI and ground fuel loads decreased to 0.61, P, 0.05 due to

differences in rates of post-fire recovery. By 6–8 years after firethe ground fuel load averaged 87–101, 62–96 and 71–82% of itsrespective pre-fire value after fires of low, moderate and high

severity (Fig. 5; Table 3). Ground fuel loading was 93� 3% ofpre-fire values by the third year after low-severity fires.Although ground fuel decreased significantly after the high-

severity fire, the rate of accumulation was rapid compared tothat after low- and moderate-severity fires. Fuel loads aftermoderate-severity fires were 10–20% lower than after low-severity fires over the entire sampling period.

Surface non-tree vegetation experienced the greatest post-fire changes of all surface and ground fuel components. Grassesand small shrubs were almost completely consumed in the fires,

but they quickly re-established and gradually increased in coverafter fire (Fig. 2). Low- to moderate-severity fires did not affectspecies composition of small shrubs, but did reduce their cover

and density (live fuel loading of small shrubs had decreased 70and 80% 1 year after respective low- and moderate-severityfires). After high-severity fire, however, fuel loading of small

shrubs decreased 97%. The highest biomass increment ofgrasses and small shrubs was observed in the second to thirdyears, when it reached 50, 70 and 80% of pre-fire levels afterrespective high-, moderate- and low-severity fires. Small shrub

loading had recovered to the pre-fire level after 6–8 years onmost plots. The loading on Plot 3 recovered more rapidlybecause of patchy burning. The highest post-fire biomass of

grasses and small shrubs was on Plot 14 (high-severity fire),where after 5 years it was 35% higher than it had been pre-fire (Fig. 3; Table 3). Invading post-fire (pyrogenic) species,

Table 2. Dieback of conifer regeneration over 1.5m tall (%) immedi-

ately after fire

Note: NA, no regeneration of this diameter size class found on the plot

Plot Fire severity Diameter (cm)

2 4 6 8 10

14 High 100 100 100 100 NA

21 Moderate 100 100 100 NA NA

13 100 100 64 0 0

2 100 58 73 17 0

4 Low 97 37 0 0 0

19 71 65 0 0 0

3 23 0 0 0 0

(a)

I

II

(b) (c) (d ) (e)

Fig. 2. Scots pine forest (a) before, (b) immediately after, and (c) 3, (d ) 5 and (e) 8 years after fire of (I)moderate (Plot 13) and (II) high (Plot 14) severity.Note

that after moderate-severity fire trees are alive and composition of herb–shrub layer did not change, whereas high-severity fire resulted in almost total tree

mortality and in colonisation by the grasses Calamagrotis arundinacea and Chamerion angustifolium.

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire E

primarily fireweed (Chamanerion angustifolium) and the tall

grass Calamagrostis arundinacea, accounted for up to 32% ofthe post-fire grass and small shrub biomass on this plot.

Lichens were generally completely consumed by all fires,

whereas consumption of the feather moss layer (which typicallyhas higher moisture content) was patchy and varied among plots(Table 3). Where patches of feather moss had remained intact

after fire (Plots 3, 4, 6, 13, 19), their biomass did not changesignificantly (P. 0.05) during the 6–8 years after burning, asfeather moss grows quite slowly. Common haircap moss (Poly-trichum commune) began to invade exposed patches of mineral

soil where lichen had dominated 4–5 years after fire. On Plot 14this species was 4.5-cm tall, with biomass of 0.04� 0.02 kgm�2

(40% of the living surface fuel biomass) by 8 years after fire

(Table 3). Some areas where fire had burned down to mineralsoil were still bare 6–8 years following burning.

Our experimental fires generally consumed all litter except

for Scots pine cones, whose post-fire loadswere up to 15%of thepre-fire value. As overstorey damage increased in response tofire severity, surface litter from trees also increased. Post-fire

litter loading was closely correlated with fireline intensity(r¼ 0.78, P, 0.01 1 year post-fire and r¼ 0.76, P, 0.012 years post-fire). The needle fall 1 year after high-intensity fire

(Plot 14)was five times that of low intensity fires (Plots 3, 4, 19),

where fresh needle litter was just a little more than the averagefor undisturbed stands (Fig. 6).

The greatest increase in litter was in the first year after fire on

Plot 14 (high-severity fire; Fig. 7, Table 3). Litter loading on thisplot then decreased by 40% between the first and third yearsafter fire due to decomposition of surface litter and decreases in

fresh litter fall (as the majority of the trees had died and droppedtheir needles within the first year). The litter composition on thisplot changed over time. Scots pine cones dominated beforeburning, needle foliage dominated (70–84%) for the first 3-post-

fire years, and by the fourth and fifth post-fire years, tree barkmade up 41% of the litter (Fig. 8), leading to another increasein total litter. By 8 years the load of needles had decreased by

up to 10%.Although litter loading increased 36–131% in the first 2 years

after moderate-severity fires (Plots 2, 6, 13, 20, 21), due

primarily to tree mortality, it decreased by 30–48% followinglow-severity fires (Plots 3, 4, 19). On the low-severity fire plots,litter loading had returned nearly to the pre-fire levels 2–4 years

after fire (Fig. 7).Litter composition exhibited similar trends after low- and

moderate-severity fires. Post-fire litter was mainly needle

0

1

2

3

4

5

Beforefire

Immediatelyafter fire

Load

ing

(kg

m–

2 )

0

1

2

3

4

5

1 2 3 4 5 6 7

Beforefire

Immediatelyafter fire

Time since fire (year)

1 2 3 4 5 6 7

8

Herbs, small shrubs

Down woody debris

Litter

Lichen

Moss

Duff

(a)

(b)

Fig. 3. Surface and ground fuel biomass dynamics following fire of high (Plot 14) (a) and low (Plot 19) (b) severity. These

plots had similar pre-fire fuel loads.

F Int. J. Wildland Fire E. A. Kukavskaya et al.

Table3.

Changes

inpost-firesurface

andgroundfuelbiomass

Fuelloadsarein

kilogramsper

squaremetre

�standarderror.Note:fuelsamplesnotrecorded

inaparticularyeararemarked

withan

en-dash

Fueltype

Tim

esince

fire

(years)

12

34

56

78

Plot2

Groundvegetation

0.01�0.003

0.02�0.004

0.02�0.004

0.02�0.004

––

0.03�0.01

–

Downwoodydebris

1.29�0.36

1.37�0.36

1.34�0.36

1.74�0.33

––

2.18�0.38

–

Litter

0.19�0.04

0.21�0.01

0.22�0.03

0.19�0.03

––

0.23�0.04

–

Moss

andlichen

00

00

––

0.04�0.02

–

Duff

1.73�0.20

1.81�0.17

1.98�0.19

2.17�0.22

––

1.63�0.17

–

Total

3.22

3.41

3.56

4.12

––

4.11

–

Plot3

Groundvegetation

0.01�0.002

0.02�0.004

0.02�0.01

0.03�0.005

––

0.04�0.01

–

Downwoodydebris

0.89�0.28

0.98�0.32

1.15�0.35

1.37�0.42

––

1.42�0.36

–

Litter

0.13�0.03

0.12�0.02

0.19�0.02

0.28�0.06

––

0.24�0.05

–

Moss

andlichen

0.08�0.04

0.11�0.06

0.10�0.04

0.12�0.06

––

0.10�0.06

–

Duff

1.71�0.19

1.72�0.18

1.89�0.16

2.04�0.19

––

1.83�0.21

–

Total

2.82

2.95

3.35

3.84

––

3.63

–

Plot4

Groundvegetation

0.02�0.004

0.03�0.004

0.03�0.01

––

0.04�0.01

––

Downwoodydebris

0.95�0.29

1.28�0.37

1.34�0.35

––

1.93�0.42

––

Litter

0.09�0.01

0.19�0.02

0.28�0.03

––

0.18�0.04

––

Moss

andlichen

0.13�0.06

0.12�0.06

0.16�0.08

––

0.19�0.07

––

Duff

2.36�0.21

2.57�0.23

2.62�0.22

––

2.78�0.22

––

Total

3.55

4.19

4.43

––

5.12

––

Plot6

Groundvegetation

0.005�0.001

0.03�0.005

0.03�0.01

0.02�0.01

––

0.04�0.01

–

Downwoodydebris

0.86�0.27

0.95�0.28

0.94�0.26

1.19�0.34

––

1.31�0.33

–

Litter

0.12�0.02

0.25�0.02

0.28�0.03

0.33�0.03

––

0.26�0.04

–

Moss

andlichen

0.10�0.06

0.09�0.06

0.08�0.05

0.08�0.05

––

0.06�0.04

–

Duff

2.81�0.14

2.94�0.22

2.92�0.23

2.84�0.18

––

3.48�0.45

–

Total

3.90

4.26

4.25

4.46

––

5.15

–

Plot13

Groundvegetation

0.01�0.002

0.02�0.05

0.04�0.01

0.04�0.01

0.04�0.01

––

0.05�0.01

Downwoodydebris

1.31�0.84

3.00�0.92

3.23�0.98

3.25�0.97

3.20�0.81

––

3.38�1.05

Litter

0.25�0.03

0.19�0.02

0.18�0.02

0.27�0.03

0.27�0.03

––

0.18�0.03

Moss

andlichen

0.01�0.006

0.01�0.008

0.01�0.01

0.01�0.01

0.03�0.02

––

0.03�0.02

Duff

1.60�0.20

1.82�0.13

1.98�0.15

2.51�0.19

2.52�0.19

––

3.19�0.32

Total

3.18

5.04

5.44

6.08

6.06

––

6.83

(Continued)

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire G

Table3.

(Continued)

Fueltype

Tim

esince

fire

(years)

12

34

56

78

Plot14

Groundvegetation

0.002�0.001

0.01�0.002

0.03�0.01

0.08�0.01

0.09�0.01

––

0.06�0.01

Downwoodydebris

0.53�0.02

0.98�0.31

1.37�0.36

1.19�0.30

1.55�0.34

––

2.13�0.45

Litter

0.33�0.02

0.27�0.02

0.20�0.03

0.32�0.04

0.28�0.03

––

0.17�0.03

Moss

andlichen

00

00.002�0.001

0.002�0.001

––

0.04�0.02

Duff

0.71�0.14

1.18�0.15

1.67�0.15

2.27�0.22

2.24�0.18

––

2.01�0.15

Total

1.57

2.44

3.27

3.86

4.16

––

4.41

Plot19

Groundvegetation

0.02�0.005

0.02�0.01

0.03�0.01

0.02�0.004

––

0.03�0.01

–

Downwoodydebris

0.91�0.34

0.88�0.31

1.04�0.29

1.14�0.32

––

1.29�0.29

–

Litter

0.09�0.01

0.16�0.02

0.22�0.02

0.25�0.03

––

0.18�0.04

–

Moss

andlichen

0.26�0.03

0.28�0.03

0.35�0.08

0.29�0.05

––

0.30�0.05

–

Duff

2.31�0.18

2.44�0.32

2.50�0.32

2.53�0.19

––

2.44�0.25

–

Total

3.59

3.78

4.14

4.23

4.24

Plot20

Groundvegetation

0.002�0.001

0.01�0.002

0.01�0.003

––

0.03�0.01

––

Downwoodydebris

0.48�0.14

0.71�0.16

0.83�0.20

––

1.33�0.32

––

Litter

0.18�0.02

0.22�0.02

0.36�0.04

––

0.25�0.03

––

Moss

andlichen

00

0–

–0

––

Duff

2.30�0.14

2.51�0.22

2.54�0.24

––

2.58�0.22

––

Total

2.96

3.45

3.74

––

4.19

––

Plot21

Groundvegetation

0.01�0.004

0.01�0.004

0.02�0.01

––

0.02�0.01

––

Downwoodydebris

0.68�0.23

0.69�0.22

0.72�0.21

––

1.05�0.27

––

Litter

0.16�0.02

0.24�0.02

0.29�0.03

––

0.31�0.06

––

Moss

andlichen

0.14�0.10

0.14�0.11

0.07�0.04

––

0.15�0.10

––

Duff

2.13�0.22

2.40�0.19

2.65�0.35

––

2.19�0.32

––

Total

3.12

3.48

3.75

––

3.72

––

H Int. J. Wildland Fire E. A. Kukavskaya et al.

foliage (Fig. 8). During the first 3 years following fire the dry

weight of needles in the litter layer increased by 30–350% overpre-fire levels; the amount was directly related to tree mortalitylevels on individual plots. Then it decreased and remained

relatively constant (41–46% of the total litter loading) for upto 8 years after fire. Because overstorey tree mortality was low,the proportion of Scots pine bark in the litter did not tend to

increase after fire and remained fairly constant (14–30% of thetotal litter) over 3–5 years.

Dead grass and small shrub loading depended greatly uponthe time of sampling and did not exceed 4% of litter, except onPlot 14, where the high fire severity favoured grass establish-

ment. Cured grass accounted for 21% of the litter load 8 yearsfollowing burning on this plot, with the loading of otherfractions tending to decrease (Fig. 8). Cured grasses and small

shrubs exhibited the greatest post-fire variability (coefficient ofvariation among the plots is up to 245%) because of theirextremely low average cover and spotty distribution. The loadof cones in the litter also was highly variable after burning

(coefficient of variation – 90–130%).Down woody fuel loading decreased by 5–50% immediately

after fire. Post-fire tree mortality led to increased accumulation

of down woody fuels (Fig. 9; Table 3). The highest increase wason Plot 13, which was burned by a slowly spreading moderate-severity fire. Two years after fire, the woody debris load was

,180% of its pre-fire value. Several large-diameter trees(30–60-cm DBH) that fell after the fire accounted for 94–97%of the woody debris loads on this plot. Among all other plots, the

largest increase in downwoody fuel occurred after high-severityfire (Table 3). Over the first 6–8 years post-fire average downwoody fuel across all plots increased 0.18� 0.04 kgm�2 annu-ally. The highest amount of down woody fuel accumulation

occurred in the first 3 years after fire (53% of the total debrisincrement on our sample plots over the study period). Thegreatest proportion of down woody elements over 7-cm dia-

meter (80–97% of the total debris loading) occurred in the first2 years after burning on all sample plots because the largematerial was only partially burned in the fires, whereas smaller

branches on the forest floor were generally burned completely.By the third year post-fire the proportion of fuels .7-cmdiameter decreased, whereas that of smaller fallen branchesincreased, largely due to post-fire tree mortality.

Low-severity fires caused minimal changes in down woodyfuel diameter class distribution, with a small increase in theproportion of large size classes and a decrease in the proportion

of twigs 0–0.49 cm in diameter. After moderate-severity fires,small twig loading also decreased. However, the fuel load ofdown woody debris from 5 to 7 cm in diameter had increased by

an average of 350% 6–7 years after fire. Down woody fuelaccumulation in the year after fire was strongly correlated withfire intensity (r¼ 0.75, P, 0.01). This correlation grew weaker

with each following year because of increasing influence of

0.1

0.2

0.3

y � 5 � 10�5x � 0.0045

R2 � 0.89, P � 0.005

00 1000 2000 3000 4000 5000

Nee

dle

load

ing

(kg

m�

2 )

Fireline intensity (kW m�1)

Fig. 6. Fallen needle biomass 1 year after fires of varying intensity. The

bars indicate standard error.

Beforefire

1 2 3 4 5 6 7 8

Time since fire (year)

High-severity fire

Moderate-severity fire

Low-severity fire

0.4

0.3

0.2

0.1

0

Litte

r lo

adin

g (k

g m

�2 )

Fig. 7. Litter loading changes after surface fires of varying severity. The

bars indicate standard error.

0.30

Fire severity index

0.600.500.40 0.800.70

y ��111.5x � 130.0 R 2 � 0.69, P � 0.005

0

20

40

60

80

100

Gro

und

fuel

load

ing

(per

cent

age

of p

re-f

ire v

alue

)

Fig. 4. Relationship between ground fuel load 1 year after fire and fire

severity index (SI). SI is a unitless index that ranges from 0 to 1. The bars

indicate standard error.

y � 82.0x0.11

R2 � 0.94

y � 68.4x0.12

R2 � 0.93

y � 38.1x0.40

R2 � 0.95

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8

Load

ing

(per

cent

age

of p

re-f

ire v

alue

)

Time since fire (year)

Low-severity fire

Moderate-severity fire

High-severity fire

Fig. 5. Recovery of ground fuel loading after fires of low (Plots 3, 4, 19),

moderate (Plots 2, 6, 13, 20, 21) and high severity (Plot 14). The bars indicate

standard error.

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire I

environmental factors (e.g. precipitation, temperature, soil

moisture) and stand characteristics. With a lapse of time weexpect large diameter dead and down woody debris to increasesubstantially (especially on Plot 14) once the trees that werekilled in the fire begin to fall.

Total post-fire above-ground biomass dynamics

The above-ground live biomass before fire was dominated bythe overstorey trees, which contained an average of 82–93%

of the total live biomass. Live biomass continued to dominateabove-ground loading after low- and moderate-severity fires, asit had before fire.We observed average decreases in fuel loads of

live biomass of 10% (low severity) and 15% (moderate severity)

compared to pre-fire amounts. The high-severity fire on Plot 14

reduced living above-ground biomass by 83%, as most treesdamaged by fire died in the first 2 years following this fire(Fig. 10). Post-fire dead organic matter accumulated on all plots

y � 19.2x � 91.50R2 � 0.90

y � 8.6x � 100.86R2 � 0.88

0

100

200

300

0 1 2 3 4 5 6 7 8

Load

ing

(per

cent

age

of p

re-f

ire v

alue

)

Time since fire (year)

Low-severity fireModerate- to high-severity fire

Fig. 9. Changes in down woody fuel loads for 8 years after fires on our

sample plots. Regressions are shown for combined high- and moderate-

severity fires and for low-severity fires. The bars indicate standard error.

y � 69.28x�0.01

R2 � 0.73

y � 17.30x�0.52

R2 � 0.89

y � 24.45x0.17

R2 � 0.85

y � 16.84ln(x)�55.8R2 � 0.91

0 1 2 3 4 5 6 7 8

0 1 2 3 4 5 6 7 8

Bio

mas

s (p

erce

ntag

e of

pre

-fire

val

ue)

0

20

40

60

80

100

0

20

40

60

80

100

Time since fire (year)

Living organic matter Dead organic matter

(a)

(b)

Fig. 10. Changes in above-ground biomass after fires of (a) low- to

moderate-severity or (b) high-severity in central Siberian Scots pine forest.

The bars indicate standard error.

1 2 3 4 5

Time since fire (year)Before fire

Litte

r lo

adin

g (%

)

6 7 80

20

40

60

80

100(a) (b) (a) (b) (a) (b) (a) (b) (a) (b) (a) (b)(a) (a)

NeedlesBarkConesCured herbs and grasses

Fig. 8. Changes in litter load fractions following fires of (a) moderate- to low- or (b) high-severity. There were

no data on litter composition 6 and 7 years after fire of high severity.

J Int. J. Wildland Fire E. A. Kukavskaya et al.

with time as standing snags fell down and needles dropped fromtrees damaged by fire. The total above-ground living biomassdid not change significantly following low-severity fires. Living

ground vegetation was represented by small amounts of grassesand small shrubs. No substantial changes of biomass distributionamong the ecosystem components occurred after moderate-

severity fires either, except that they caused higher tree mor-tality (up to 14% of all trees) and resulted in greater reductions inground and surface fuel loading, than low-severity fires. The

biggest changes in above-ground biomass distribution werefound after the high-severity fire.

Ground cover biomass tended to increase after fires, with theincrease depending substantially on fire severity during the first

2–3-post-fire years. After these early post-fire years the influ-ence of specific site characteristics on organic matter accumu-lation increased.

Discussion

Most studies on the influence of fire on fuels structure and

biomass in Russia have either compared burns of different ages,or were conducted on sites burned by wildfires of differentseverity (Smirnov 1970; Kurbatsky and Ivanova 1987; Furyaev

1996; Buryak et al. 2003). These studies estimated fire severitybased on indirect indicators, such as forest floor depth, tree stemchar height and tree mortality. The data obtained in our studyallowed us to quantify fire parameters, estimate loads of dif-

ferent ecosystem components before burning, and monitor theirchanges multiple times following fire. We found that both post-fire fuel structure and accumulation rate of fuel loadings in Scots

pine sites of the central taiga depended substantially on fireseverity. The regressions developed allowed us to relate timesince fire to changes in above-ground biomass components for

fires of different severity for a feather moss–lichen Scots pineforest of Siberia.

Although low- to moderate-severity surface fires, such asthose on all but one of our experimental plots, dominate the

natural fire regime in dry Scots pine forests, stand replacementfires, like that on one plot, occur approximately every 200 yearson most sites (Ivanova 2005). Whereas the lower severity

surface fires do not kill many trees (up to 16% of the totalpre-fire live trees on our experimental plots), high-severitystand-replacement fires result in substantial redistribution of

above-ground biomass with large increases in dead standingbiomass. Even in low to moderate severity fires most treesaplings are usually killed, except in areas of patchy burning.

The pockets of tall conifer regeneration can be an importantladder fuel to initiate torching or crown fire in the canopy treesunder high wind conditions (Kurbatsky 1962).

Ground fuel loading after low- and moderate-severity fires

often had high within-plot spatial variability (coefficient ofvariation up to 40%) due to non-uniform fuel consumption.This resultedmainly from small-scale variations in soil moisture

regimes. Local microdepressions were wetter than surroundingareas and often supported species of Sphagnum and Ledum;many of these areas did not burn. On mesic microsites that were

covered with feather moss before fire, often just the top mosslayer was consumed, and the remaining forest floor was rela-tively intact and up to 15 cm deep. Drier lichen-dominated areaswere often burned down to the mineral soil. Other studies have

reported an increase in the error estimates of individual groundfuel components as their proportion of the fuel load decreasesand the non-uniformity of their distribution increases

(Kurbatsky 1970; Furyaev and Baranov 1972; Sofronov andVolokitina 1990).

Litter is one of the main sources of post-fire increases in

surface organic matter. The ratio between increases in organicmatter and losses due to mineralisation varies depending pri-marily on hydrothermal regime (Kobak 1988). Thus, litter can

be used as an indicator of forest floor changes. In addition, litteris one of the main components of available fuel that influencefire spread. We found a significant variation of litter composi-tion and its loading depending on fire severity. The largest

increase in litter was observed after high-severity fire due toscorching of needles and high tree mortality. After low-severityfires amount of litter decreased in the first post-fire years due to

near complete consumption during fire combined with lack ofneedle drop from the overstorey trees. Evdokimenko (1979)reported a 300% increase in the amount of fresh litter 1 year after

high-severity fire compared to unburned stands in a more mesicrhododendron pine forest of the Baikal region. Akunovich(2003) reported a 500% increase in the loading of needles in

the litter layer after high-severity surface fires in Scots pineforests with a heather (Calluna vulgaris) understorey. Althoughwe observed bark peeling 4 years after fire, crown fires in Scotspine forest often cause bark to slough off even 1 year after

burning (Ilyichev et al. 2003). During our observation periodlitter loads and composition returned to nearly pre-fire levelafter low- and moderate-severity fires. We expect that the

amount of litter will continue to decrease in the future on Plot14 burned by high-severity fire, because the majority of treeshave died and there will be no sources of fresh foliage until the

post-fire tree regeneration begins to achieve dominance.Rapid increase of duff load after high-severity fire (Plot 14) is

attributable to the large amount of fresh needle litter and fallenbranch material from dead and dying trees. We hypothesise that

these components decomposed quite rapidly because of highdaytime temperatures (Tarasov et al. 2008) that were the resultof heavy thinning of the overstorey by fire. A decrease in post-

fire C :N ratio to 34.1 on Plot 14 compared to 45.2 on anunburned control plot also suggests higher organic matterdecomposition rates (Bezkorovaynaya et al. 2006). Other

researchers have also reported increasing rates of physical andchemical processes, growing soil redox potential and biologicalactivity, and decreasing soil acidity following high-severity fire

in Scots pine (Popova 1980; Gorbachev et al. 1982; Gorshkovet al. 2005). Post-fire increases in soil nitrogen on sandypodzolic soils (Gorbachev et al. 1982) resulted in invadingfireweed that is a common invader on clearcuts and areas burned

by severe fires (Sannikov and Sannikova 1985; Ilyichev et al.

2003).The presence of higher ground fuel loading after low-severity

fires due to incomplete ground cover consumption was alsoreported by Furyaev (1976). Buryak et al. (2003) examined largeburned areas in grass pine forest and concluded that forest floor

decreased significantly after high-severity fires with total treemortality and that forest floor loading did not recover even after25 years. According to Furyaev (1974) ground fuel loading infeather moss–small shrub pine forest after low-severity fires

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire K

reached 20, 39 and 46%of its respective pre-fire value in 1, 2 and3 years. These are somewhat moister sites than those in ourstudy. Our results are more similar to those of Popova (1975)

who found that ground fuels recovered in 3–4 years aftermoderate-severity fires. In mixed larch–feather moss forestsof Siberian southern taiga, pre-fire ground and surface fuel loads

are 25% higher than on our drier pine sites (Ivanova et al. 2011).These larch sites are characterised by larger productivity and ahigher rate of recovery of fuel loads after surface fires of low-

and moderate-severity compared to our sites. After high-severityfire in larch stand fuel loads did not reach pre-fire values in fiveyears (Zhila 2013) that differs from our sites. Repeated firesoften significantly decrease fuel loads, and theymay not recover

for many years. Thus, repeated fires in narrow bands of pineforests that occur on the southern border of forest-steppeecozone decreased fuel loads by 75–95% and resulted in

insufficient densities of regeneration for forest to re-establishafter fire (Buryak et al. 2011; Kukavskaya et al. 2013b).Although low-severity fires on moist soils may result in prolif-

eration of Sphagnum species that depress tree biomass produc-tion (Lecomte et al. 2006), we did not observe this processbecause our experimental plots were on dry sandy podzols. Post-

fire recovery of mosses, which usually begins to occur inmicrodepressions, on rotten tree stumps, and fallen tree stems,is expected to take 25–50 or more years (Gorbachev et al. 1982;Popov 1982).

Our data on post-fire down woody fuel loading after high-severity surface fire are similar to those reported for burned birchforest with 100% tree mortality following fires in the Russian

Far East in 1998. However, woody debris loading on our plots is65% less of that in larch forest following the same fire inKhabarovsk region (Tarasov and Ryabinin 2002) and 30–40%

less than loads after fires inmore productive southern taiga larchforest of the Krasnoyarsk region (Zhila 2013). By 10–15 yearsafter low- to moderate-severity burning we expect a decrease inaccumulation of freshly fallen small diameter debris, whereas

the dead trees killed in high-severity fire will begin to fall over,producing high fuel loads of large-diameter branch and bolematerial. In 2005, 12 years after an experimental high-severity

surface and crown fire (FIRESCAN Science Team 1996) on avery similar dry pine site,20 km from the current study site, weobserved that almost all dead trees had fallen to the ground.

Although the typical historic fire return interval in dry Scotspine stands such as those studied here is 35–50 years (Ivanova2005), the accumulated post-fire loading of surface and ground

fuels found on our plots after 6–8 years would likely besufficient to support fire spread under severe droughts if therewere an ignition source (lightning, anthropogenic factor). Suchdroughts are projected to increase in duration and severity as

climate changes (Flannigan et al. 2009). High-severity fireresulted in intensive colonisation by pyrogenic herbaceousspecies, such as fireweed and Calamagrostis, both of which

die back in the winter and provide highly flammable surface fuelin early spring following snowmelt. Where frequent repeat fireshave been observed in Scots pine forest, they can result in total

consumption of ground biomass and the dieback of all trees,which drastically changes the succession patterns and canprolong or prevent recovery of the ecosystem to the pre-firestate (Popov 1982; Buryak et al. 2011).

Difficulties in predicting influence of fires of varying severityon tree mortality are a major source of error in post-fireaboveground biomass estimates. Tree mortality in surface fire

and mixed fire regimes can vary widely depending on environ-mental conditions, fuel condition, weather patterns and foreststand species composition (Sofronov 1967; Matveyev 1992;

Reinhardt et al. 1997; Stephens andMoghaddas 2005). Post-fireinsect damage can also be a factor, as observed by Breece et al.(2008) after prescribed fires on ponderosa pine sites in the south-

western US. We have observed similar effects on experimentalburn sites in Scots pine (Oreshkov and Shishikin 2003). Ifvariations in fire severity are not taken into account, there issignificant potential for error in estimates of fire effects, recov-

ery trajectories and changes in carbon stocks. The data andmodels presented here are representative of fire responses over arange of conditions in dry Scots pine lichen–feather moss stands

on sandy soils. Results would be different for sites with differentoverstorey or understorey vegetation, and other types of soil.

Conclusions

Fire severity was a key factor influencing all ecosystem com-

ponents and controlling post-fire biomass accumulation in dryScots pine forests in central Siberia. Following our experimentalburns, we found large differences in post-fire fuel loads, andvegetation structure and composition that were related to fire

severity or fireline intensity.Accumulation rates of both ground and surface fuels after

fires were a function of fire severity and damage to the over-

storey, whichwas strongly correlatedwith fireline intensity. Theaccumulation rate of organic matter and recovery of surfacevegetation were highest in the first 2–3 years after fire. The level

of overstorey damage on the plots greatly influenced the annuallitterfall and litter composition. Data and models on levels oftree mortality after fire are clearly important for projecting post-fire litter dynamics and carbon stocks. High-severity fire

resulted in substantial redistribution of above-ground biomass.Dead standing biomass increased, primarily during the first2 years following fires, because of tree mortality.

Quantifying the amount of biomass in various fuel sizeclasses and ecosystem components in dry Siberian Scots pineforests and other forest types with surface or mixed-severity

fire regimes is critical because of the potential for changing fireregimes to affect carbon stocks in the vast boreal forest areas ofSiberia under changing global climate (Alexander et al. 2012)

and changing human influences, such as logging (Kukavskayaet al. 2013b). Our study provides a basis for improvedmodellingof how current fire regimes and projected future changes in firefrequency and severity might impact long-term fuel loads and

carbon sequestration in one of the most frequently burned foresttypes in Siberia. Several global and regional models have beendeveloped that incorporate or estimate fuel loads, carbon stocks,

fire emissions and effects of fire on carbon balance in borealforests (e.g. van der Werf et al. 2006; Balshi et al. 2007;Wiedinmyer et al. 2011; Potter et al. 2012) where ground-based

data such as that reported here could be used for verifying orimproving model inputs and assumptions. There is a criticalneed for ecosystem-specific data and models relating fuelconsumption and post-fire fuel and vegetation dynamics to fire

L Int. J. Wildland Fire E. A. Kukavskaya et al.

behaviour, fire severity, fire weather and fuel conditions.Further studies focussing on biomass dynamics after variousseverity fires in other locations and for different boreal forest

types of Siberia are needed.

Acknowledgements

The authors gratefully acknowledge financial support for this research from

the National Aeronautics and Space Administration (NASA) Land Cover

Land Use Change (LCLUC) Science Program; the Russian Foundation for

Basic Research; theUnited StatesDepartment ofAgriculture (USDA)Forest

Service; Natural Resources Canada, Canadian Forest Service and the

Russian Academy of Sciences, Siberian Branch. The authors thank

Dr Eugenia Krasnoshchekova and students of the Siberian State Techno-

logical University for their assistance in collecting fuel samples. The authors

are also thankful to the anonymous reviewers for their helpful comments and

useful suggestions.

References

Akunovich EG (2003) Surface fire impact on forest floors in heather pine

forest. Proceeding of the Byelorussian State Technological University

1(11), 108–110. [In Russian]

Alexander HD, Mack MC, Goetz S, Loranty MM, Beck PSA, Earl K,

Zimov S, Davydov S, Thompson CC (2012) Carbon accumulation

patterns during postfire succession in Cajander larch (Larix cajanderi)

forests of Siberia. Ecosystems 15(7), 1065–1082. doi:10.1007/S10021-

012-9567-6

Arbatskaya MK, Vaganov EA (1997) Long-term variability of fire fre-

quency and pine increment in central Siberian taiga.Ecology 5, 330–336.

[In Russian]

Atkin AS, Atkina LI (1985) Ground fuel loading in pine forest. In ‘Forest

Fires and Their Consequences’. pp. 92–101. (Institute of Forest and

Wood, Siberian Branch of the Academy of Sciences of USSR:

Krasnoyarsk) [In Russian]

Balshi MS, McGuire AD, Zhuang Q, Melillo J, Kicklighter DW,

Kasischke E, Wirth C, Flannigan M, Harden J, Clein JS, Burnside TJ,

McAllister J, Kurz WA, Apps M, Shvidenko A (2007) The role of

historical fire disturbance in the carbon dynamics of the pan-boreal

region: a process-based analysis. Journal of Geophysical Research 112,

G02029. doi:10.1029/2006JG000380

Bezkorovaynaya IN, Tarasov PA, Krasnoshekova EN (2006) Postfire

ecological state of soils after fires in central taiga pine forests of

Krasnoyarsk region. Krasnoyarsk State Agricultural University Herald

13, 178–183. [In Russian]

Breece CR, Kolb TE, Dickson BG, McMillin JD, Clancy KM (2008)

Prescribed fire effects on bark beetle activity and tree mortality in

southwestern ponderosa pine forests. Forest Ecology and Management

255, 119–128. doi:10.1016/J.FORECO.2007.08.026

Buryak LV, Luzganov AG, Matveev PM, Kalenskaya OP (2003) ‘Impact of

Surface Fires on the Formation of Light-Coniferous Forests of Southern

Central Siberia.’ (Siberian State Technological University: Krasnoyarsk)

[In Russian]

Buryak LV, Sukhinin AI, Kalenskaya OP, Ponomarev EI (2011) Con-

sequences of fire events in the ribbon-like pine forests of the south of

Siberia. Contemporary Problems of Ecology 3, 331–339. [In Russian]

Byram GM (1959) Combustion of forest fuels. In ‘Forest Fire: Control and

Use’. (Ed. KP Davis) pp. 61–89 (McGraw-Hill: New York)

Cherbakov IP, Zabelin OF, Karpel BA, Michaleva VM, Chugunova RV,

Yakovlev AP, Averinsky AI, Korochodkina VG (1979) ‘Forest Fires in

Yakutia and Their Impact on Forest.’ (Nauka Publications: Novosibirsk)

[In Russian]

Cottam G, Curtis JT (1956) The use of distance measures in phytosociolo-

gical sampling. Ecology 37, 451–460. doi:10.2307/1930167

Evdokimenko MD (1979) Microclimate of stands and hydrothermal state of

soils in pine forests of Transbaikalia after surface fires. In ‘Burning and

Fires in a Forest. Part II. Forest Fires and their Consequences’. pp. 130–

139. (Institute of Forest and Wood, Siberian Branch of the Academy of

Sciences of USSR: Krasnoyarsk) [In Russian]

Evdokimenko MD (1983) Forest floor dynamics after surface fires in pine

forests of Transbaikalia. In ‘Proceedings: The Role of the Forest Floor in

the Forest Biogeocenosis’, 14–16September 1983,Krasnoyarsk, Russia.

p. 60. (Nauka Publications: Moscow) [In Russian]

FAO (2001) Global Forest Resources Assessment 2000. Main report. FAO

Forestry Paper 140. (FAO, Rome)

FIRESCAN Science Team (1996) Fire in ecosystems of boreal Eurasia: The

Bor Forest Island fire experiment, Fire Research Campaign Asia-North

(FIRESCAN). In ‘Biomass Burning and Global Change’. (Ed. JS

Levine) pp. 848–873. (MIT Press: Cambridge, MA)

Flannigan MD, Krawchuk MA, de Groot WJ, Wotton BM, Gowman LM

(2009) Implications of changing climate for global wildland fire.

International Journal of Wildland Fire 18, 483–507. doi:10.1071/

WF08187

Forest Fund of Russia (1999) Reference Manual (based on data of state

account of forest fund by 1 January 1998). (All-Russian Research and

Information Center for Forest Resources: Moscow) [In Russian]

Furyaev VV (1974) Prescribed burning for formation of fire-resistant pine

forest. In ‘Forest Fire Science Problems’. pp. 241–261. (Institute of

Forest and Wood, Siberian Branch of the Academy of Sciences of

USSR: Krasnoyarsk) [In Russian]

Furyaev VV (1976) Postfire dynamics of fuel loading at pine forests of

Angara region. In ‘Present Investigations of Forest Typology and Fire

Science’. pp. 122–126. (Arkhangelsk Institute of Forest and Forest

Chemistry: Arkhangelsk) [In Russian]

Furyaev VV (1996) ‘Role of Fire in Forest Development.’ (Eds NP

Kurbatsky, AI Buzikin) (Nauka Publications: Novosibirsk) [In Russian]

Furyaev VV, Baranov NM (1972) About precision of ground forest fuel

material estimation. In ‘Forest Fire Science Problems’. pp. 164–170.

(Institute of Forest and Wood, Siberian Branch of the Academy of

Sciences of USSR: Krasnoyarsk) [In Russian]

Gorbachev VN, Popova EP, Sorokin ND, Dmitrienko VK (1982) ‘Soil-

Ecological Investigations in Forest Biogeocenosis.’ (Nauka Publica-

tions: Novosibirsk) [In Russian]

Gorshkov VV (2001) Postfire restoration of pine forests of European North.

PhD(Biol.) thesis, V.L. Komarov Botanical Institute of the Russian

Academy of Sciences, St Petersburg. [In Russian]

Gorshkov VV, Stavrova NI, Bakkal IY (2005) Postfire dynamics of forest

floor restoration in boreal pine forests.Russian Forest Sciences 3, 37–45.

[In Russian]

Ilyichev YuN, Bushkov NT, Tarakanov VV (2003) ‘Natural Forest Restora-

tion on Burnt Lands of the Mid-Ob Millet Grass.’ (Ed. VV Furyaev)

(Nauka Publications: Novosibirsk) [In Russian]

Ivanova GA (2005) Vegetation zone-specific characteristics of Scots pine

forest fires in central Siberia. PhD(Biol.) thesis, V. N. Sukachev Institute

of Forest, Siberian Branch of the Russian Academy of Sciences:

Krasnoyarsk. [In Russian]

Ivanova GA, PerevosnikovaVD, Conard SG (2002) Impact of the fire on the

carbon budget in pine forests of central Siberia. In ‘Proceedings of

IBFRA 2000 Conference: The Role of Boreal Forests and Forestry in

the Global Carbon Budget’, 8–12 May 2000, Edmonton, AB. (Eds CH

Shaw, MJ Apps) Canadian Forest Service, Natural Resources Canada,

Northern Forestry Centre, pp. 279–288. (Edmonton, AB)

Ivanova GA, Ivanov VA, Kukavskaya EA, Soja AJ (2010) Forest fire

frequency in Scots pine stands of Tuva, Russia.Environmental Research

Letters 5, 015002. doi:10.1088/1748-9326/5/1/015002

Ivanova GA, Conard SG, Kukavskaya EA, McRae DJ (2011) Fire impact

on carbon storage in light conifer forests of the Lower Angara region,

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire M

Siberia. Environmental Research Letters 6, 045203. doi:10.1088/1748-

9326/6/4/045203

Keeley JE, Aplet GH, Christensen NL, Conard SG, Johnson EA, Omi PN,

Peterson DL, Swetnam TW (2009) Ecological foundations for fire

management in North American forest and shrubland ecosystems.

USDA Forest Service, Pacific Northwest Research Station, General

Technical Report PNW-GTR-779. (Portland, OR)

Kobak KI (1988) ‘Biotic components of the carbon cycle.’ (Gidrometioiz-

dat: Leningrad) [In Russian]

Korchagin AA (1954) Fire impact on forest vegetation and its restoration

after fires in European North. Geobotanika 3(9), 75–149. [In Russian]

Korovin GN (1996) Analysis of the distribution of forest fires in Russia. In

‘Fire in Ecosystems of Boreal Eurasia’. (Eds JG Goldammer,

VV Furyaev) pp. 112–128. (Kluwer Academic Publishers: Dordrecht)

Kukavskaya EA (2009) Forest fire influence on carbon budget of central

taiga Scots pine stands of the Yenisei-Side plain. PhD thesis, V. N.

Sukachev Institute of Forest, SiberianBranch of the RussianAcademyof

Sciences, Krasnoyarsk. [In Russian]

Kukavskaya EA, Soja AJ, PetkovAP, Ponomarev EI, IvanovaGA, Conard

SG (2013a) Fire emissions estimates in Siberia: evaluation of uncer-

tainties in area burned, land cover, and fuel consumption. Canadian

Journal of Forest Research 43, 493–506. doi:10.1139/CJFR-2012-0367

Kukavskaya EA, Buryak LV, Ivanova GA, Conard SG, Kalenskaya OP,

Zhila SV, McRae DJ (2013b) Influence of logging on the effects

of wildfire in Siberia. Environmental Research Letters 8, 045034.

doi:10.1088/1748-9326/8/4/045034

Kurbatsky NP (1962) ‘Technique and Tactics of Forest Firefighting.’

(Goslesbumizdat: Moscow) [In Russian]

Kurbatsky NP (1970) Investigating forest fuel loading and properties. In

‘Forest Fire Science Problems’. (Ed. NP Kurbatsky) pp. 5–58. (V. N.

Sukachev Institute of Forest Publications: Krasnoyarsk) [In Russian]

Kurbatsky NP, Ivanova GA (1987) ‘Forest-Steppe Scots Pine Stand Fire

Danger and Ways of its Reduction.’ (V. N. Sukachev Institute of Forest

Publications: Krasnoyarsk) [In Russian]

Landhausser SM, Wein RW (1993) Recovery and tree establishment at the

arctic treeline: climate-change–vegetation-response hypotheses. Jour-

nal of Ecology 81(4), 665–672. doi:10.2307/2261664

Lashinsky NP (1981) ‘Structure and dynamics of pine forests of Lower

Angara region.’ (Nauka Publications: Novosibirsk) [In Russian]

Lecomte N, Simard M, Fenton N, Bergeron Y (2006) Fire severity and

long-term ecosystem biomass dynamics in coniferous boreal forests of

Eastern Canada. Ecosystems 9, 1215–1230. doi:10.1007/S10021-004-

0168-X

MackMC, Treseder KK, Manies KL, Harden JW, Schuur EAG, Vogel JG,

Randerson JT, Chapin FS (2008) Recovery of aboveground plant

biomass and productivity after fire in mesic and dry black spruce forests

of interior Alaska. Ecosystems 11(2), 209–225. doi:10.1007/S10021-

007-9117-9

Matveyev PM (1992) Fire effects at larch forest ecosystems at permafrost.

PhD(Biol.) thesis, AM Gorky Polytechnic Institute, Yoshkar-Ola.

[In Russian]

McRae DJ, Alexander ME, Stocks BJ (1979) ‘Measurement and description

of fuels and fire behavior on prescribed burns: a handbook. Environment

Canada, Canadian Forest Service, Great Lakes Forestry Research

Centre, Information Report O-X-287. (Sault Ste Marie, ON)

McRae DJ, Conard SG, Ivanova GA, Sukhinin AI, Baker SP, Samsonov

YN, Blake TW, Ivanov VA, Churkina TV, HaoWM, Koutzenogij KP,

Kovaleva N (2006) Variability of fire behavior, fire effects and emis-

sions in Scotch Pine forests of central Siberia.Mitigation andAdaptation

Strategies for Global Change 11, 45–74. doi:10.1007/S11027-006-

1008-4

Morisita M (1957) A new method for the estimation of density by

spacing method applicable to nonrandomly distributed populations.

Physiology and Ecology 7, 134–144. [In Japanese, available as USDA

Forest Service translation number 11116, USDA Forest Service,

Washington, DC]

OnuchinAA, Spitsina NT (1995) Regularities of changes of needle biomass

in coniferous stands. Russian Forest Sciences 5, 48–58. [In Russian]

Oreshkov DN, Shishikin AS (2003) Dynamics of animal populations after

fires of various severities in central taiga Scots pine forests of Central

Siberia. Contemporary Problems of Ecology 6, 743–748. [In Russian]

Popov LV (1982) ‘South Taiga Forests of Central Siberia.’ (Irkutsk

University Publications: Irkutsk) [In Russian]

Popova EP (1975) Composition and physical properties of forest floor at

uneven-aged burned area of Angara region pine forest. In ‘Agrophysical

Investigations of Central Siberia Soils’. pp. 83–88. (V. N. Sukachev

Institute of Forest and Wood, Siberian Branch of the Academy of

Science of USSR: Krasnoyarsk) [In Russian]

Popova EP (1980) Soil formation peculiarities in forest biogeocenosis of

Angara region depending on time since fire. In ‘Genesis and Geography

of Forest Soils’. pp. 40–52. (Nauka Publications: Moscow) [In Russian]

Potter C, Klooster S, Genovese V, Hiatt C, Boriah S, Kumar V, Mithal V,

Garg A (2012) Terrestrial ecosystem carbon fluxes predicted from

MODIS satellite data and large-scale disturbancemodeling. Internation-

al Journal of Geosciences 3(3), 469–479. doi:10.4236/IJG.2012.33050

Reinhardt ED, Keane RE, Brown JK (1997) First Order Fire Effects Model:

FOFEM 4.0, User’s Guide. USDA Forest Service, Intermountain Forest

and Range Experiment Station, General Technical Report INT-344.

(Ogden, UT)

Sannikov SN (1973) Forest fires as evolutional and ecological factor of pine

regeneration in Zayralie. In ‘Burning and Fires in a Forest’. (Ed. NP

Kurbatsky) pp. 236–277. (V. N. Sukachev Institute of Forest Publica-

tions: Krasnoyarsk) [In Russian]

Sannikov SN (1992) ‘Ecology and geography of Scots pine natural regener-

ation’. (Nauka Publications: Moscow) [In Russian]

Sannikov SN, Sannikova NS (1985) ‘Ecology of natural pine restoration

under forest canopy.’ (Nauka Publications: Moscow) [In Russian]

Smirnov AV (1970) Impact of anthropogenic factors on forest vegetation

components of southern territory of central Siberia. PhD (Agricult.)

thesis, Krasnoyarsk. [In Russian]

Sofronov MA (1967) ‘Forest fires in mountains of south Siberia.’ (Nauka

Publications: Moscow) [In Russian]

Sofronov MA, Volokitina AV (1990) ‘Fire zoning at taiga forest.’ (Nauka

Publications: Novosibirsk) [In Russian]

Soja AJ, Sukhinin AI, Cahoon DR, Shugart HH, Stackhouse PW (2004)

AVHRR-derived fire frequency, distribution and area burned in Siberia.

International Journal of Remote Sensing 25(10), 1939–1960.

doi:10.1080/01431160310001609725

Stephens SL, Moghaddas JJ (2005) Experimental fuel treatment impacts on

forest structure, potential fire behavior, and predicted tree mortality in a

California mixed conifer forest. Forest Ecology and Management

215, 21–36. doi:10.1016/J.FORECO.2005.03.070

Tarasov ME, Ryabinin BN (2002) Woody debris and duff carbon loading at

burned areas of Khabarovsk krai. In ‘Forest Protection from Fires

at Present Time: Proceedings of International Theoretical and Practical

Conference’, 27–29 March 2002, Khabarovsk, Russia. pp. 287–291.

(KPB Publications: Khabarovsk) [In Russian]

Tarasov PA, Ivanov VA, Ivanova GA (2008) Soil temperature regime of

central taiga pine forests burned by surface fires. Coniferous Trees of

Boreal Zone 3–4, 300–304. [In Russian]

van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Kasibhatla PS,

Arellano AF (2006) Interannual variability in global biomass burning

emissions from 1997 to 2004. Atmospheric Chemistry and Physics 6,

3423–3441. doi:10.5194/ACP-6-3423-2006

Van Wagner CE (1968) The line intersect method in forest fuel sampling.

Forest Science 1, 20–26.

Vedrova EF (1998) Carbon cycle in pine forests of taiga zone of

Krasnoyarsk krai. Russian Forest Sciences 6, 3–11. [In Russian]

N Int. J. Wildland Fire E. A. Kukavskaya et al.

VivcharA (2011)Wildfires inRussia in 2000–2008: estimates of burnt areas

using the satellite MODIS MCD45 data. Remote Sensing Letters 2(1),

81–90. doi:10.1080/01431161.2010.499138

Wardle DA, Hornberg G, Zackrisson O, Kalela-Brundin M, Coomes DA

(2003) Long-term effects of wildfire on ecosystem properties across an

island area gradient. Science 300, 972–975. doi:10.1126/SCIENCE.

1082709

Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA,

Orlando JJ, Soja AJ (2011) The Fire Inventory from NCAR (FINN): a

high resolution global model to estimate the emissions from open

burning. Geoscientific Model Development 4, 625–641. doi:10.5194/

GMD-4-625-2011

WirthC, Schulze E-D, Luhker B, Grigoriev S, SiryM, HardesG, ZieglerW,

Backor M, Bauer G, Vygodskaya NN (2002) Fire and site type

effects on the long-term carbon and nitrogen balance in pristine Siberian

Scots pine forests. Plant and Soil 242, 41–63. doi:10.1023/

A:1020813505203

Zhila SV (2013) Postfire phytomass transformation in light-coniferous

forests of the Lower Angara region. PhD(Biol.) thesis, V. N. Sukachev

Institute of Forest Publications, Krasnoyarsk. [In Russian]

www.publish.csiro.au/journals/ijwf

Post-fire biomass dynamics in Siberian pine forest Int. J. Wildland Fire O