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Effects of forest fragmentation on organic carbon pool densities in the Mongolian forest-steppe
Forest Ecology and Management 433 (2019) 780–788
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Effects of forest fragmentation on organic carbon pool densities in the Mongolian forest-steppe
T
Choimaa Dulamsurena,b, Michael Klingec, Banzragch Bat-Enerela, Tumurbaatar Ariunbaatard, Daramragchaa Tuyae a
Plant Ecology, Albrecht von Haller Institute for Plant Sciences, Georg August University of Goettingen, Untere Karspüle 2, 37073 Göttingen, Germany Applied Vegetation Ecology, University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany c Physical Geography, Institute of Geography, University of Goettingen, Goldschmidtstraße 5, 37077 Göttingen, Germany d Institute of General and Experimental Biology, Mongolian Academy of Sciences, Zhukov Street 77, 13330 Ulan Bator, Mongolia e Tarvagatai Nuruu National Park, Tosontsengel Sum, Zavkhan Aimag, Mongolia b
ARTICLE INFO
ABSTRACT
Keywords: Boreal forest Carbon stock density Organic layer Siberian larch Stand size Stand isolation
The hypothesis was tested that the size and the degree of isolation of Larix sibirica forests in the forest-steppe ecotone of Mongolia affects aboveground and belowground carbon pool densities. The research question was based on the fact that both microclimate and the drought sensitivity of stemwood production were earlier shown to differ with stand size and isolation in this ecotone. Contrary to our hypothesis, we did not find significant differences in the organic carbon stock densities of the tree biomass and the mineral soil. The depth, carbon content and carbon stock density of the organic layer increased with stand size, but was not a major determinant of total ecosystem carbon stock density. Nevertheless, the increasing depth and the increasing humus content of the organic layer with stand size could be significant by improving moisture availability and, thus, promoting forest regeneration. Furthermore, reduced organic layer thickness and humus content and thus water storage capacity could be one out of several causes of the previously observed higher drought vulnerability of stemwood formation in small forest stands of the Mongolian forest-steppe. A mean carbon stock density of 237 Mg C ha−1 for total ecosystem organic carbon stock density matches with earlier estimates for Mongolia’s boreal forest corroborating the view that the ecosystem carbon pool density at the southern edge of the boreal forest is lower compared to forests at higher latitudes with even colder climate and deeper and more widespread permafrost.
1. Introduction
The productivity and vitality of boreal forests is strongly impacted by global climate warming (Lloyd and Bunn, 2007; Tei and Sugimoto, 2018). While most boreal forests were traditionally limited by low summer temperatures and low nitrogen availability (Jarvis and Linder, 2000), climate warming-induced summer drought has gained in importance as the key limiting factor in parts of the boreal forest biome since the late 20th century (Buermann et al., 2014). Drought limitation has caused growth depressions as recorded by tree-ring analysis (Barber et al., 2000; Dulamsuren et al., 2010a, 2013) and has increased tree mortality (Peng et al., 2011; Liu et al., 2013) at many places of the boreal forest biome. By reducing productivity and increasing tree mortality, global climate warming is likely to reduce the capacity of the boreal forest to act as a significant carbon sink and thus to mitigate greenhouse gas-induced climate warming. In addition to reduced biomass production, climate warming is capable of increasing soil temperatures and soil respiration and ultimately turning forests from carbon sinks into sources (Lindroth et al., 1998). In the cold boreal
The global boreal forests play an important role in the protection of the earth’s climate, as they store large amounts of organic carbon. Pan et al. (2011) estimated the total organic carbon pool in the boreal forest biome at approximately 270 Pg C and thus one third of the total carbon pool in the world’s forests. Carbon stock density is similar as in tropical forests, but the distribution within the ecosystems is different with most carbon being allocated in the soil of boreal forests, but in the vegetation biomass in the tropics (Cao and Woodward, 1998; Pan et al., 2011). Under the present climatic conditions, the boreal forest biome is a net organic carbon sink with a net uptake of 0.5 Pg C a−1 from 1990 to 2007 (Pan et al., 2011). Given annual anthropogenic CO2 emissions of 9.5 Pg C in 2011 (IPCC, 2013), which have continued rising since then, this means that roughly 5% of the carbon from anthropogenic sources, which is emitted as CO2 to the atmosphere, is absorbed by boreal forests.
E-mail address: [email protected] (Ch. Dulamsuren). https://doi.org/10.1016/j.foreco.2018.10.054 Received 10 September 2018; Received in revised form 21 October 2018; Accepted 25 October 2018 Available online 13 December 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved.
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forest biome, permafrost plays a crucial role at conserving soil organic carbon (SOC). This role is increasingly reduced by progressive climate warming-driven permafrost melt (Goulden et al., 1998; Zimov et al., 2006; Helbig et al., 2016). Nevertheless, even drought-limited boreal forests can store substantial amounts of carbon. For Mongolia’s boreal forest, Dulamsuren et al. (2016) estimated the mean organic carbon stock density at 215 Mg C ha−1, which is c. 85% of the global average for boreal forests (Cao and Woodward, 1998). Widespread drought limitation is well documented for Mongolia’s forests and especially for forests of Siberian larch (Larix sibirica Ledeb.), which is the most dominant boreal tree species in this region (De Grandpré et al., 2011; Dulamsuren et al., 2011; Khishigjargal et al., 2014). Even climate warming-related tree mortality has become widespread in the Mongolia’s forest-steppe (Liu et al., 2013; Khansaritoreh et al., 2018). The organic carbon stock density in southern boreal forests, however, is much lower than that of drought-limited oroboreal coniferous mountain forests at the northeastern edge of the Tibetan Plateau at lower latitudes in Inner Asia; Wagner et al. (2015) found here a total organic carbon stock density of 348 MG C ha−1. Apart from climate warming, the potential of forest ecosystems to accumulate organic carbon can be impacted by land use. Logging affects the climate sensitivity of forests by changing the age and size distribution of trees and the stand structure. Intense logging reduced the climate signal in tree-ring series of L. sibirica in terms of mean sensitivity, but reinforced growth depressions induced by high summer temperatures (Khansaritoreh et al., 2017a). A unique feature of foreststeppes at the southern edge of the boreal forest is that forests are naturally fragmented, since they occupy the moistest sites, which are basically north-facing mountain slopes, whereas dry sites are covered with grassland (Hilbig, 1995; Dulamsuren, 2004). While the size and the degree of isolation of forest stands varies naturally depending on the physio-geographical setting, an ongoing deforestation causes not only forest cover losses (Hansen et al., 2013), but also the progressive fragmentation of the remaining forest area. Because of this specific setting at the southern edge of the boreal forest, the objective of the present study was to analyze how habitat fragmentation affects the organic carbon stocks in these forests. An effect of forest fragmentation was conceivable, because forests were earlier shown to differ in microclimate dependent on stand size and stand isolation (Khansaritoreh et al., 2017b). Furthermore, dendrochronological analyses showed that L. sibirica is more vulnerable to climate warming in small and isolated compared to large forest stands in forest-dominated landscape (Khansaritoreh et al., 2017b). Small and isolated forests are exposed to more extreme temperatures than continuous forests (Saunders et al., 1991; Khansaritoreh et al., 2017b), which might limit biomass production and increase soil respiration. Further, small forests have a higher edge-to-interior ratio and are therefore more likely to be affected by livestock grazing as well as sporadic logging and fuelwood collection by mobile pastoralists settling in the grasslands of the forest-steppe ecotone (Lkhagvadorj et al., 2013a,b; Dulamsuren et al., 2014). Dulamsuren et al. (2016) compared the organic carbon stock density of forest edges (i.e. the outermost 30 m of the forest at the forest line to the steppe) with that of forest interiors in the Mongolian forest-steppe and found that carbon stock density at the edge was only 87% of that in the interior (188 vs. 215 Mg C ha−1). Therefore, the hypothesis was tested that both aboveground and belowground carbon stock density decrease with progressive forest fragmentation, i.e. decreasing stand size and increasing stand isolation.
2. Materials and methods 2.1. Study area Field work was carried out in the vicinity of Tosontsengel (Zavkhan provinve, 48°45′ N, 98°16′ E, 1700 m a.s.l. in northern Mongolia, c. 630 km W of Ulan Bator and 550 km SW of Lake Baikal (Fig. 1). The region is located in the forest-steppe, which forms the transition between the Eurosiberian boreal forest region in the north and the Mongolian-Chinese steppe region of the Central Asian steppes in the south (Pfeiffer et al., 2018). Tree growth in most of Mongolia’s forest-steppe and the neighboring East Kazakh forest-steppe region is limited by summer drought (Dulamsuren et al., 2010a,b, 2013) and this includes the studied forests, where Khansaritoreh et al. (2017b) demonstrated a dominant influence of summer rainfall on annual stemwood increment. Siberian larch (Larix sibirica Ledeb.) occupies roughly 75–80% of Mongolia’s boreal forest area of c. 73,800 km2 (Tsogtbaatar, 2004; Dulamsuren et al., 2016). Industrial timber harvest was widespread in the study area in the second half of the twentieth century, before it was abandoned in 1990 and replaced by unsystematic selective logging by the rural population. The forest-steppe area is home to mobile pastoralists, who keep mixed herds of sheep, goats, cattle, yak, and horses on common pastures. Livestock is not much herded and animals preferentially graze on grassland, but also penetrate into the forests along the edges and further into the interior, when the forest islands are small (Lkhagvadorj et al., 2013b). The dominant bedrock type in the study region is siliceous rock, including granite and metamorphic rock (e.g. schist). Cover beds of up to several meters thick aeolian sand occur at lower slope positions. The prevailing forest soils are Cambisols and Leptosols. The study area is located in the zone of discontinuous permafrost (Sharkhuu and Sharkhuu, 2012). 2.2. Climate The climate in Mongolia’s forest-steppe is highly continental and coined by dry cold winters and short warm summers, where most of the annual precipitation is received. Mean annual temperature in Tosontsengel (data available since 1964) was −5.8 °C (July 14.8 °C, January −31.2 °C). Mean annual precipitation (data since 1968) was 224 mm. While there was no trend for increase or decrease of annual precipitation since 1968, there was a shift in the distribution from autumn to spring. Temperature has increased at a rate of 0.44 K decade−1 since 1964. 2.3. Study design The study design included the variation of forest stand size and forest stand isolation (Table 1, Fig. 2). Stand size was varied by separating between four different size classes with ascending size from class F1/G1 (< 0.1 km2) to F4 (> 5.0 km2). The realized stand sizes in these classes ranged from 0.07 to 24 km2 as detected by remote sensing analysis of satellite imagery. Forest stand isolation was controlled by separating between a subregion with high forest-to-grassland ratio (classes F1 to F4; Fig. 1) and another subregion with low forest-tograssland ratio (class G1). Stand size was only varied in the forestdominated subregion (Fig. 2). Forest stands of classes F1 to F4 were selected in clusters that included all four size classes. This way a potential effect of physiographic heterogeneity within the study area should be avoided. The clusters were evenly distributed over the forest-dominated subregion (Fig. 1),
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Fig. 1. Study area near Tosontsengel, Mongolia with distribution of clusters of forest stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4, south-eastern part of the study area) or low (G1, north-western part) forest-to-grassland ratio. Forest area changes between 1986 (Landsat 5, July 23), 2002 (Landsat 7, June 9), and 2013 (Landsat 8, September 19) are indicated by different signatures. The last digit in the stand numbers specifies plot clusters 1–6.
while the individual forest stands of the different size classes were randomly selected from the cluster areas. The plots in the grasslanddominated subregion were randomly selected among the available forest stands of the smallest size class. Selection of clusters and plots was based on remote sensing analysis of time series of forest distribution in the study region. Forest stands that recently had changed their size class were not selected as sample plots. Sample plots had a size of 20 m × 20 m and were selected in the interior of each forest stand. The geographic position was determined by GPS. Although selection was by random, moist depressions, which are not characteristic for most of the forest area, were avoided. In addition, recently burned and logged forest stands were excluded, because forest fires and logging usually leads to the release of substantial amounts of soil carbon to the atmosphere and would, thus, interfere with potential fragmentation signals in the data. The outermost 30 m of the forests were excluded from the plot search to avoid bias by direct edge effects. Six replicate plots were selected per forest classes yielding a total of 30 plots. Sampling was conducted during the summers of 2014 and 2015.
2.4. Estimation of tree biomass Tree biomass was estimated from stem diameter and tree height data after the methodology of Dulamsuren et al. (2016) using allometric regression equations. For all tree individuals exceeding a height of 4 m, the diameter at breast height (dbh) was deduced from measurements of the stem circumference with a measuring tape. Tree height was measured with a Vertex IV ultrasonic clinometer and T3 transponder (Haglöf, Långsele, Sweden). Sapling-sized trees of ≤4 m were assigned to five height classes (0–0.5, 0.6–1, 1.1–2, 2.1–3, 3.1–4 m) and counted. Mean stem diameter, which was measured at the base, if the trees were below 2 m high, was determined for randomly selected individuals of each height class. Allometric functions to calculate stem and branch biomass were taken from Dulamsuren et al. (2016) (‘estimate 1’) and Battulga et al., 2013 (‘estimate 2’) and mean values from these two estimates were calculated. The regression equations used in the present study are compiled in Table 2. The allometric functions of Dulamsuren et al.
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Table 1 Stand characteristics of Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-to-grassland ratio near Tosontsengel, Mongolia.
Forest size class (km2) Forest-to-grassland ratio Realized stand size (km2) Elevation (m a.s.l.) Trees (ha−1) Saplings (ha−1) Tree basal area (m2 ha−1)
F1
F2
F3
F4
G1
< 0.1 High 0.07 ± 0.02 1914 ± 53 1310 ± 181 a 121 ± 57 a 37.8 ± 3.8 ab
0.1–1.0 High 0.28 ± 0.09 1910 ± 26 1508 ± 181 a 211 ± 69ab 43.0 ± 3.6 a
1.1–5.0 High 2.36 ± 0.48 1936 ± 30 1656 ± 140 a 547 ± 283 ab 43.5 ± 3.1 a
> 5.0 High 23.8 ± 5.4 1997 ± 22 1375 ± 158 a 925 ± 423b 35.8 ± 3.5 ab
< 0.1 Low 0.08 ± 0.01 2018 ± 29 1702 ± 164 a 239 ± 174 ab 28.1 ± 1.5b
Within a row, means sharing the same letter, do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel,
(2016) and Battulga et al. (2013) are based on harvested L. sibirica trees from the Khangai and Altai Mountains, respectively, in western Mongolia and thus derive from environments with similar site conditions as in our study area; the similarity in climatic conditions is higher for estimate 1 than 2. Needle biomass was derived from branch biomass using linear regression (Dulamsuren et al., 2016). Root biomass was assessed from stem diameter and tree height using an allometric function that was established with the help of excavated L. sibirica trees from the Chinese Altai Mountains (Han and Liang, 1997; Wang et al., 2005). The tree biomass was converted into organic carbon mass by assuming an organic carbon content of 47% based on earlier measurements in L. sibirica wood from trunks and branches as well as needles from the Mongolian forest-steppe, which all yielded a mean carbon content of 47% (Dulamsuren et al., 2016). The mean carbon content of deadwood was also assumed to be 47%. The volume of deadwood pieces was assessed based on length and diameter measurements. To compensate for the conical shape of most deadwood pieces, we calculated with a low wood density of 0.2 g cm−3.
2.6. Remote sensing analysis of forest distribution Time series of forest distribution in the study region were studied with ArcGIS 3.2 (ESRI, Redlands, California, USA). The recent forest distribution at the time of biomass and soil sampling was determined by supervised classification of a Spot 6 multispectral satellite image of September 14, 2014. The spatial resolution of this image of 1.6 m × 1.6 m enabled a detailed delineation of forest stands and isolated trees. The classification result was visually corrected and transformed into vectored data. The size of the single polygons bordering the closed forests was used to calculate the forest areas. To analyze for the temporal dynamics of forest cover during the last 30 years, a change detection analysis was performed using three different Landsat satellite images: Landsat 5 TM of July 23, 1986; Landsat 7 ETM+ from June 9, 2002; Landsat 8 OLT/TIRS of September 19, 2013. Initially the forest distribution of every satellite image was delineated by supervised classification. The computed forest areas of every time slice were subtracted from each other to analyze potential area changes. The spatial resolution of Landsat images of 30 m × 30 m induces a minor inaccuracy depending on the relative portion of trees in one pixel. Therefore, single trees could not be detected and the borders of closed forests could slightly alternate between the different classifications. However, closed forests were generally satisfactorily distinguished. Although there was forest disturbance by fire in the surrounding region, a significant change in forested area since 1986 can be ruled out for the studied forests.
Soil sampling included the organic layer and the mineral soil at 0–1 m depth at 5 sampling points per sample plot. The five sampling points were established in the center and at the corners of each sample plot. Soil profiles were dug and soil samples were taken in the center of five depth intervals (0–20, 20–40, 40–60, 60–80, 80–100 cm) with a steel cylinder of 240 cm3. Soil was dried at 70 °C and sifted (2 mm mesh size) to remove stones and roots, which were weighted afterwards. Total carbon was determined with the C/N analyzer (Vario EL III Elementar, Hanau, Germany) in a subsample of the soil. In another subsample, the organic carbon was released by combustion at 600 °C and the remaining inorganic carbon was measured with the C/N analyzer. Organic carbon was determined as the difference of total minus inorganic carbon content. Another subsample of the soil was dried at 105 °C until weight constancy to determine dry weight. In the organic layer (> 30% of organic substance), the carbon content was analyzed. The organic layer carbon content was excluded from the calculation of SOC pool density, but is included in the sum for total belowground
2.7. Statistical analysis Arithmetic means ± standard errors are presented throughout the paper. All data were tested for normal distribution with the ShapiroWilk test. Multiple comparisons of normally distributed data were made using Duncan's multiple range test; degrees of freedom (df) are given as dfmodel, dferror. All statistical analyses were computed with SAS 9.4 software.
Forest-dominated subregion F 0.1-1.0 km² F2
1.1-5.0 km² F3
4, 25).
organic carbon density.
2.5. Soil sampling, chemical analysis and calculation of SOC
<0.1 km² F1
error =
Grassland-dominated subregion G >5.0 km² F4
<0.1 km² G1
F11 F12 F13 F21 F22 F23 F31 F32 F33 F41 F42 F43 F14 F15 F16 F24 F25 F26 F34 F35 F36 F44 F45 F46
G11 G12 G13 G14 G15 G16
Stand isolation
Stand size Replicate stands
Fig. 2. Plot design for studying the effect of stand isolation (forest-dominated vs. grassland-dominated area) and, within the forest-dominated subregion, stand size on Larix sibirica stands. Two plots of 20 m × 20 m were studied for each of the 6 replicate stands; data per stand were averaged. 783
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Table 2 Allometric regression equations used for estimating the biomass (in kg dry weight) of Larix sibirica from stem diameter (D) and tree height (H) data. Plant organ
Model b
Stem (estimate 1) Stem (estimate 2) Branches (estimate 1) Branches (estimate 2) Needles Roots
c
y = aD H y = (D2 H)/(a + bD) y = aDb y = aDb y = a + bx y = a(D2H)b
Parameters
Reference
a = 0.0188, b = 1.6055, c = 1.3350 a = 39.908, b = 0.9081 a = 0.0031, b = 2.8556 a = 0.0180, b = 2.4268 a = 0.7098, b = 0.1900; x = branch biomass a = 0.006984, b = 0.9724
Dulamsuren et al. (2016) Battulga et al. (2013) Dulamsuren et al. (2016) Battulga et al. (2013) Dulamsuren et al. (2016) Wang et al. (2005)
Table 3 Tree biomass (in Mg ha−1) in Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-to-grassland ratio. F1
F2
F3
F4
G1
Stem
Estimate 1 Estimate 2 Mean
117 ± 16 ab 130 ± 16 ab 124 ± 16 ab
142 ± 17 a 154 ± 16 a 148 ± 16 a
143 ± 10 a 157 ± 10 a 150 ± 10 a
117 ± 17 ab 128 ± 17 ab 123 ± 17 ab
78.4 ± 9.3b 90.0 ± 8.6b 84.2 ± 8.9b
Branches
Estimate 1 Estimate 2 Mean
25.9 ± 4.6 ab 30.8 ± 3.3 a 28.3 ± 3.9 ab
31.8 ± 4.4 a 35.2 ± 3.1 a 33.5 ± 3.7 a
30.0 ± 4.7 a 35.2 ± 2.7 a 32.6 ± 3.6 a
25.0 ± 3.2 ab 29.2 ± 3.0 ab 27.1 ± 3.1 ab
16.0 ± 2.0b 22.1 ± 1.3b 19.0 ± 1.6b
Needles
Estimate 1 Estimate 2 Mean
5.8 ± 0.8 ab 6.4 ± 0.5 ab 6.1 ± 0.6 ab
7.1 ± 0.9 a 6.9 ± 0.5 a 7.0 ± 0.7 a
6.9 ± 0.9 a 7.0 ± 0.5 a 6.9 ± 0.7 a
5.7 ± 0.6 ab 5.8 ± 0.5 ab 5.8 ± 0.5 ab
4.2 ± 0.4b 5.1 ± 0.2b 4.7 ± 0.2b
Aboveground
Estimate 1 Estimate 2 Mean
148 ± 21 ab 168 ± 20 ab 158 ± 20 ab
181 ± 20 a 196 ± 19 a 189 ± 20 a
180 ± 15 a 199 ± 13 a 189 ± 14 a
148 ± 21ab 163 ± 20 ab 156 ± 21 ab
98.6 ± 11.4b 117 ± 10b 108 ± 11b
Roots
–
46.2 ± 6.7 ab
56.1 ± 6.4 a
54.8 ± 4.5 a
46.1 ± 6.5 ab
30.4 ± 3.6b
Total
Estimate 1 Estimate 2 Mean
194 ± 28 ab 214 ± 27 ab 204 ± 27 ab
237 ± 27 a 253 ± 25 a 245 ± 26 a
234 ± 19 a 254 ± 18 a 244 ± 18 a
194 ± 28 ab 209 ± 27 ab 202 ± 27 ab
129 ± 15b 148 ± 13b 138 ± 14b
Biomass ( ± SE) for all trees of > 4 m height. Within a row, means sharing the same letter, do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel, error = 4, 25).
3. Results
0.8 Mg C ha−1 in F1).
3.1. Tree biomass and carbon stock densities
3.2. Soil organic carbon
Aboveground tree biomass ranged from 156 to 189 Mg ha−1 in the forest-dominated subregion (stands F1-F4), but amounted to only 108 Mg ha−1 in the forest stands of the grassland-dominated subregion (G1) (Table 3). Tree root biomass was estimated at 46–56 Mg ha−1 in the forest-dominated subregion and 30 Mg ha−1 in the grasslanddominated subregion. This yielded a total tree biomass between 202 and 245 Mg ha−1 in the forest-dominated subregion, but of only 138 Mg ha−1 in the grassland-dominated subregion of the study area. Even though the numerical values for mean tree biomass appeared quite different between the two subregions, this difference was only significant between G1, on the one hand, and F2 and F3, on the other hand (Table 3). This finding corresponded to a similar pattern for tree basal area, whereas stand density did not differ between the plots (Table 1). Tree biomass did not show a consistent trend for increase or decrease with stand size (from F1 to F4). The carbon stock density in the aboveground biomass of living mature trees varied between 73 and 89 Mg C ha−1 in the forest-dominated subregion and was 51 Mg C ha−1 in the forest stands of the grassland-dominated subregion (Table 4). The contribution of forest regeneration to the total biomass and carbon stock density was negligible, but nevertheless steadily increased with forest stand size from 0.02 Mg C ha−1 in F1 to 0.12 Mg C ha−1 in F4 (Table 4). Deadwood also accounted for only a small proportion of the organic carbon stock density (Table 4); its significance was especially low in the smallest forest stands of less than 0.1 km2 in size (0.2 Mg C ha−1 in G1 and
The density of organic carbon in the mineral soil (soil organic carbon, SOC) at 0–1 m depth varied between 75 and 110 Mg C ha−1, but was not influenced by the stand type (Table 4). The mean SOC density of all study sites amounted to 98 ± 6 Mg C ha−1. The vertical distribution of SOC density in the soil profile shows that, on average, 43 ± 3% of the total organic carbon in the mineral soil was found within the uppermost 20 cm (Fig. 3). Roughly 50% of the total organic carbon in the 1 m profile occurred within the uppermost 30 cm, leaving the other half of the total SOC for the soil depth between 30 and 100 cm. While in the average over all soil profiles, there was a nonlinear (exponential) decrease of SOC density with increasing soil depth (Fig. 3), a sharp increase in SOC density was sporadically observed at the bottom of some individual profiles (Fig. 4). The depth (Fig. 5a) as well as the organic carbon content (Fig. 5c) of the organic layer increased with forest stand size (from F1 to F4), but were not different between small forests in the forest-dominated (F1) and grassland-dominated (G1) subregions. The matter density in the organic layer (Fig. 5b) showed an insignificant trend for lower density in the large (F3-F4) than in the small forests (F1-F2, G1). Albeit this trend was not significant, it influenced the between-site differences in organic carbon stock density resulting in the lowest carbon stock density (Fig. 5d) in the organic layer of stand type F3 (at relatively high organic layer depth and carbon content, but low matter density) and the highest carbon stock density in F4 (at low matter density, but high depth and carbon content). 784
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Table 4 Aboveground and belowground organic carbon pool densities (in Mg C ha−1) in Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-to-grassland ratio.
Aboveground Mature trees
Estimate Estimate Mean – – Estimate Estimate Mean
Regeneration Deadwood Subtotal aboveground
Belowground Tree roots Organic layer SOC (0–1 m) Subtotal belowground Total
1 2
1 2
– – – – Estimate 1 Estimate 2 Mean
F1
F2
F3
F4
G1
69.7 ± 9.8 ab 78.8 ± 9.4 ab 74.2 ± 9.6 ab 0.02 ± 0.01 a 0.8 ± 0.5 a 70.4 ± 9.9 ab 79.5 ± 9.5 ab 75.0 ± 9.7 ab
85.2 ± 9.6 a 92.3 ± 9.0 a 88.8 ± 9.2 a 0.03 ± 0.01 ab 1.7 ± 0.2 ab 85.2 ± 9.6 a 92.3 ± 9.0 a 88.8 ± 9.2 a
84.4 ± 7.0 a 93.7 ± 6.3 a 89.0 ± 6.6 a 0.05 ± 0.02 ab 4.3 ± 0.7b 84.4 ± 7.0 a 93.7 ± 6.3 a 89.0 ± 6.6 a
69.6 ± 9.9 ab 76.8 ± 9.5 ab 73.2 ± 9.7 ab 0.12 ± 0.05b 2.2 ± 1.8 ab 69.6 ± 9.9 ab 76.8 ± 9.5 ab 73.2 ± 9.7 ab
46.3 ± 5.4b 55.1 ± 4.6b 50.7 ± 5.0b 0.05 ± 0.03 ab 0.2 ± 0.2 a 46.3 ± 5.4b 55.1 ± 4.6b 50.7 ± 5.0b
21.7 ± 3.1 ab 39.4 ± 2.9 ab 102 ± 15 a 163 ± 18 a 233 ± 22 a 242 ± 21 a 238 ± 21 a
26.4 ± 3.0 a 42.0 ± 6.1 ab 96.0 ± 12.9 a 164 ± 10 a 251 ± 15 a 259 ± 14 a 255 ± 15 a
25.7 ± 2.1 a 32.9 ± 5.3 a 75.3 ± 12.6 a 134 ± 16 a 223 ± 21 a 232 ± 20 a 227 ± 20 a
21.7 ± 3.0 ab 49.5 ± 5.6b 110 ± 17 a 181 ± 17 a 253 ± 14 a 260 ± 14 a 257 ± 14 a
14.3 ± 1.7b 25.6 ± 2.2 ab 106 ± 13 a 156 ± 14 a 202 ± 11 a 211 ± 12 a 207 ± 12 a
Arithmetic means ± SE. Within a row, means sharing the same letter, do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel,
SOC (%) 0
A
Soil depth (cm)
20
SOC (%)
50
100 0
B
F1
SOC (%)
50
100 0
C
F2
SOC (%)
50
100 0
4, 25).
SOC (%)
50
D
F3
error =
100 0
50
E
F4
100
G1
40 60 80 100 0
20
40
60
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
SOC (%)
Fig. 3. Depth profiles of SOC density in the mineral soil of forest stand types (A) F1, (B) F2, (C) F3, (D) F4), and (E) G1 (for the definition of stand types see Table 1 and Fig. 2) in the Larix sibirica forest. Numbers at the y-axis represent the lower border of 20 cm deep soil segments. Gray lines with open symbols show the accumulated percentage of SOC at the relevant soil depth related to the total SOC at 1 m depth.
3.3. Ecosystem carbon pool density in variation of stand size and isolation
large continuous forests (Khansaritoreh et al., 2017b), this enhanced climate sensitivity due to forest fragmentation has not yet resulted in any curtailing of organic carbon stock density. This can be explained by the high share of SOC in the total ecosystem carbon pool as everywhere in the boreal forest biome (Cao and Woodward, 1998). The organic carbon stock density in the mineral soil (SOC stock density) was demonstrated to be not different between L. sibirica forests and surrounding grasslands by Dulamsuren et al. (2016) and is, therefore, not likely to be affected by deforestation. The absence of any deforestation signal in the SOC stock density is attributable to lower mean and minimum temperatures in the highly continental forest-steppe of Mongolia in open than forested landscape, which outweighs the higher temperature maxima and thus potentially faster organic matter decomposition in the short summer (Lee et al., 2011). Moreover, the meadow steppe grasslands near forest edges of the forest-steppe ecotone are relatively well water-supplied and often highly productive and densely permeate the upper soil with roots promoting the transfer of carbon to the soil (Dulamsuren and Hauck, 2008; Fan et al., 2008; Pfeiffer et al., 2018). In contrast to the mineral soil, the organic layer responded to forest stand size. The steady increase in the depth and carbon concentration of
The total ecosystem carbon stock density was neither significantly influenced by stand size nor stand isolation (Table 4). Total carbon stock density varied between 227 and 257 Mg C ha−1 in the forestdominated subregion (F1 to F4) and 207 Mg C ha−1 in the grasslanddominated subregion (G1). Since a significant difference between the individual stand types was absent, the mean organic carbon stock density across all stand types could be calculated as 237 ± 9 Mg C ha−1. 4. Discussion Contrary to our expectations, neither the size nor the degree of isolation of a forest stand exerted a significant influence on the organic carbon pool density of L. sibirica forests in the forest-steppe ecotone of Mongolia. The lack of a significant difference referred to both tree biomass and the mineral soil (SOC) and, thus, to the total ecosystem carbon pool density. Even though the stemwood production of larch trees from small and isolated forests in the same study area was shown to be more vulnerable to drought limitation compared to trees from 785
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Fig. 4. Examples for soil profiles with increasing SOC density at the bottom of the soil profile: (A, B) forest stands of < 0.1 km2 (F1), (C) 0.1–1 km2 (F2), (D) > 5 km2 (F4) in the forest-dominated subregion and of (E, F) < 0.1 km2 in the grassland-dominated subregion (G1).
the organic layer with stand size at constant organic carbon stock densities in the tree biomass and in the SOC suggests that the organic layer is the most sensitive component of the larch forest ecosystem with respect to forest fragmentation. The variation of the gravimetric density of the organic layer only showed an insignificant trend for higher values in the forest stands of ≤1 km2 size (F1, F2) compared to the stands of > 1 km2 (F3, F4). However, this insignificant trend deserves attention, because it produced the non-steady increase of the organic layer carbon stock density with stand size. The trend for higher gravimetric density in the small forests is probably due to soil compaction by livestock that frequently browses forest margins and small forests, but usually avoids the interior of large forest stands (Lkhagvadorj et al., 2013a,b). The combination of intermediate organic layer depth and carbon concentration and relatively low matter density causes that the lowest carbon stock density was found in forests of intermediate size (F3). Regional differences in livestock density apparently leave an imprint in the organic layer depth and carbon stock density. In the Mongolian Altai and the western Khangai Mountains of western Mongolia, which were the field sites of Dulamsuren et al. (2016), higher livestock densities (Lkhagvadorj et al., 2013a,b) led to a degradation of the organic layer and thus carbon stock density. At high livestock density, browsing animals penetrate more deeply into forest stands and compact and degrade the organic layer (Khishigjargal et al., 2013). In our study area near Tosontsengel, livestock density was much lower than at the field sites of Dulamsuren et al. (2016) due to fewer nomads because of alternative job opportunities in the timber industry since the mid-20th century (Khansaritoreh et al., 2017a). In addition to livestock density, the much colder winter climate in Tosontsengel (mean January temperature of −31 °C) compared to the Mongolian Altai and the western Khangai (−21 to −24 °C; Dulamsuren et al., 2016) might explain the regional differences in the carbon stock density of the organic
layer, since soil respiration increases with temperature even below the freezing point (Mikan et al., 2002). In contrast to SOC stock density, organic layer carbon density was also found to contribute to the total loss of ecosystem carbon stock density, when L. sibirica forests are converted into grasslands (Dulamsuren et al., 2016). While the variation in the traits of the organic layer was of subordinate significance for the forests’ total carbon stock density, reduced organic layer depth and humus content as well as the trend for increased compaction in small forests are likely to exert a negative effect on forest regeneration and probably also on the diversity and activity of decomposers. High humus concentrations are beneficial for tree seedlings and saplings, since they increase soil water retention (Vereecken et al., 1989). Soil moisture is critical for seedling emergence and survival in L. sibirica in the drought-prone Inner Asian forest-steppes (Dulamsuren et al., 2008). Reduced organic layer depth and humus content could also contribute to the increased drought sensitivity of mature larch trees in small forest stands of our study area (Khansaritoreh et al., 2017b). Detrimental effects on forest regeneration due to organic layer degradation in small forests would add to strong reductions in the regeneration success that are caused by high and increasing numbers of livestock, and goats in particular (Khishigjargal et al., 2013). The carbon stock density in the aboveground biomass of L. sibirica stands in the forest-dominated subregion of 73–89 Mg C ha−1 exceeded that of the larch forests in the Mongolian Altai and the western Khangai (55–72 Mg C ha−1; Dulamsuren et al., 2016), but match with estimates from southern Siberia (Thurner et al., 2014). An estimate of Shvidenko and Shepaschenko (2014) for southern Siberia, however, is more in the range found by Dulamsuren et al. (2016). We cannot explain the differences in the estimates for southern Siberia, but in the case of Mongolia, they are attributable to the higher stand basal area in the forests 786
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Fig. 5. Traits of the organic layer in Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-tograssland ratio: (a) depth, (b) organic matter density, (c) organic carbon content, and (d) carbon stock density. Means ( ± SE) sharing a common letter do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel, error = 4, 25).
of the present study 36–44 m2 ha−1 than found by Dulamsuren et al. (2016) (33–36 m2 ha−1). The low basal area in the forest stands of the grassland-dominated subregion of 28 m2 ha−1 in the present study matches with the low aboveground carbon stock density of 51 Mg C ha−1. These comparisons highlight the importance of stand density for the aboveground biomass and carbon stock density. In Mongolia’s boreal forests, which are subjected to extensive land use by pastoralists and the unsystematic logging of trees for the supply of local lumber mills, the density and structure of forest stands and, thus, basal area are strongly controlled by increased logging activities for timber and fuel harvest since the political transformation from planned to market economy in the early 1990s (Dulamsuren et al., 2014) as well as by the suppression of forest regeneration by recently increased livestock numbers (Khishigjargal et al., 2013; Lkhagvadorj et al., 2013b). Hence, land use intensity is a major determinant of the aboveground organic carbon stocks. Deadwood, which can store large amounts of carbon in natural forests (Pregitzer and Euskirchen, 2004; Pan et al., 2011; Jacob et al., 2013), does not play a significant role in today’s forests of Mongolia, since it is often removed as fuel wood. The total ecosystem organic carbon stock densities of 212–257 Mg C ha−1 found for the individual forest stand types were in the range of estimates of 211–218 Mg C ha−1 published by Dulamsuren et al. (2016) for the Mongolian forest-steppe based on sample plots from the Altai Mountains and the Khangai Mountains 170–700 km west and southwest of our study area in northern Mongolia. The mean organic carbon stock density calculated over all stand types irrespective of stand size and degree of stand isolation exceeded that specified by Dulamsuren et al. (2016) by 10%, which is attributable to differences in stand density and organic layer carbon stocks. In an over-regional comparison, our carbon pool density figures corroborate the view that the ecosystem carbon stock density in the Inner Asian forest-steppes is below the global average of boreal forests due to below-average SOC density. Our mean SOC density of 99 Mg C ha−1 is, for example,
roughly 10% below the estimate for the global boreal forests (109 Mg C ha−1) by DeLuca and Boisvenue (2012). This lower SOC density in the forest-steppe can be attributed to the warmer climate and the lack of continuous permafrost compared to higher latitudes. The vertical distribution of SOC density with roughly, half of the SOC in the 1 m profiles found below 30 cm soil depth confirms the importance of studying not only the upper few decimeters of the soil, but at least 1 m for the assessment of the organic carbon pool in boreal forests. The increase in SOC density towards the bottom of some of the 1 m profiles corroborates this view; this increase is probably due to the presence of permafrost, which inhibits soil respiration (Davidson and Janssens, 2006; Ping et al., 2015). 5. Conclusions The ongoing habitat fragmentation in Mongolia’s forest-steppe ecotone widely leading to the reduction of forest stand sizes and the progressive isolation of forests in the grassland had no effect on organic carbon stock density in the remaining forests in our study. This means that even small forest remnants deserve protection with regard to their function to store organic carbon and thereby to protect the global climate. The function of the forests for the unique biodiversity of the Inner Asian forest-steppe reinforces the need for the conservation even of small forests (Hauck et al., 2014). Deforestation and conversion into grassland is known to reduce the organic carbon stock density in the Mongolian forest-steppe by c. 40% due to the removal of vegetation biomass and the reduction of the organic layer (Dulamsuren et al., 2016). In contrast to the density of tree biomass and SOC, the organic layer responds sensitively to stand size, which has not much influence on the total ecosystem pool, but might be crucial for soil moisture and thus forest regeneration and the drought sensitivity of mature trees, which is known to increase with decreasing stand size (Khansaritoreh et al., 2017b). 787
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Acknowledgments
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Effects of forest fragmentation on organic carbon pool densities in the Mongolian forest-steppe
T
Choimaa Dulamsurena,b, Michael Klingec, Banzragch Bat-Enerela, Tumurbaatar Ariunbaatard, Daramragchaa Tuyae a
Plant Ecology, Albrecht von Haller Institute for Plant Sciences, Georg August University of Goettingen, Untere Karspüle 2, 37073 Göttingen, Germany Applied Vegetation Ecology, University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany c Physical Geography, Institute of Geography, University of Goettingen, Goldschmidtstraße 5, 37077 Göttingen, Germany d Institute of General and Experimental Biology, Mongolian Academy of Sciences, Zhukov Street 77, 13330 Ulan Bator, Mongolia e Tarvagatai Nuruu National Park, Tosontsengel Sum, Zavkhan Aimag, Mongolia b
ARTICLE INFO
ABSTRACT
Keywords: Boreal forest Carbon stock density Organic layer Siberian larch Stand size Stand isolation
The hypothesis was tested that the size and the degree of isolation of Larix sibirica forests in the forest-steppe ecotone of Mongolia affects aboveground and belowground carbon pool densities. The research question was based on the fact that both microclimate and the drought sensitivity of stemwood production were earlier shown to differ with stand size and isolation in this ecotone. Contrary to our hypothesis, we did not find significant differences in the organic carbon stock densities of the tree biomass and the mineral soil. The depth, carbon content and carbon stock density of the organic layer increased with stand size, but was not a major determinant of total ecosystem carbon stock density. Nevertheless, the increasing depth and the increasing humus content of the organic layer with stand size could be significant by improving moisture availability and, thus, promoting forest regeneration. Furthermore, reduced organic layer thickness and humus content and thus water storage capacity could be one out of several causes of the previously observed higher drought vulnerability of stemwood formation in small forest stands of the Mongolian forest-steppe. A mean carbon stock density of 237 Mg C ha−1 for total ecosystem organic carbon stock density matches with earlier estimates for Mongolia’s boreal forest corroborating the view that the ecosystem carbon pool density at the southern edge of the boreal forest is lower compared to forests at higher latitudes with even colder climate and deeper and more widespread permafrost.
1. Introduction
The productivity and vitality of boreal forests is strongly impacted by global climate warming (Lloyd and Bunn, 2007; Tei and Sugimoto, 2018). While most boreal forests were traditionally limited by low summer temperatures and low nitrogen availability (Jarvis and Linder, 2000), climate warming-induced summer drought has gained in importance as the key limiting factor in parts of the boreal forest biome since the late 20th century (Buermann et al., 2014). Drought limitation has caused growth depressions as recorded by tree-ring analysis (Barber et al., 2000; Dulamsuren et al., 2010a, 2013) and has increased tree mortality (Peng et al., 2011; Liu et al., 2013) at many places of the boreal forest biome. By reducing productivity and increasing tree mortality, global climate warming is likely to reduce the capacity of the boreal forest to act as a significant carbon sink and thus to mitigate greenhouse gas-induced climate warming. In addition to reduced biomass production, climate warming is capable of increasing soil temperatures and soil respiration and ultimately turning forests from carbon sinks into sources (Lindroth et al., 1998). In the cold boreal
The global boreal forests play an important role in the protection of the earth’s climate, as they store large amounts of organic carbon. Pan et al. (2011) estimated the total organic carbon pool in the boreal forest biome at approximately 270 Pg C and thus one third of the total carbon pool in the world’s forests. Carbon stock density is similar as in tropical forests, but the distribution within the ecosystems is different with most carbon being allocated in the soil of boreal forests, but in the vegetation biomass in the tropics (Cao and Woodward, 1998; Pan et al., 2011). Under the present climatic conditions, the boreal forest biome is a net organic carbon sink with a net uptake of 0.5 Pg C a−1 from 1990 to 2007 (Pan et al., 2011). Given annual anthropogenic CO2 emissions of 9.5 Pg C in 2011 (IPCC, 2013), which have continued rising since then, this means that roughly 5% of the carbon from anthropogenic sources, which is emitted as CO2 to the atmosphere, is absorbed by boreal forests.
E-mail address: [email protected] (Ch. Dulamsuren). https://doi.org/10.1016/j.foreco.2018.10.054 Received 10 September 2018; Received in revised form 21 October 2018; Accepted 25 October 2018 Available online 13 December 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved.
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forest biome, permafrost plays a crucial role at conserving soil organic carbon (SOC). This role is increasingly reduced by progressive climate warming-driven permafrost melt (Goulden et al., 1998; Zimov et al., 2006; Helbig et al., 2016). Nevertheless, even drought-limited boreal forests can store substantial amounts of carbon. For Mongolia’s boreal forest, Dulamsuren et al. (2016) estimated the mean organic carbon stock density at 215 Mg C ha−1, which is c. 85% of the global average for boreal forests (Cao and Woodward, 1998). Widespread drought limitation is well documented for Mongolia’s forests and especially for forests of Siberian larch (Larix sibirica Ledeb.), which is the most dominant boreal tree species in this region (De Grandpré et al., 2011; Dulamsuren et al., 2011; Khishigjargal et al., 2014). Even climate warming-related tree mortality has become widespread in the Mongolia’s forest-steppe (Liu et al., 2013; Khansaritoreh et al., 2018). The organic carbon stock density in southern boreal forests, however, is much lower than that of drought-limited oroboreal coniferous mountain forests at the northeastern edge of the Tibetan Plateau at lower latitudes in Inner Asia; Wagner et al. (2015) found here a total organic carbon stock density of 348 MG C ha−1. Apart from climate warming, the potential of forest ecosystems to accumulate organic carbon can be impacted by land use. Logging affects the climate sensitivity of forests by changing the age and size distribution of trees and the stand structure. Intense logging reduced the climate signal in tree-ring series of L. sibirica in terms of mean sensitivity, but reinforced growth depressions induced by high summer temperatures (Khansaritoreh et al., 2017a). A unique feature of foreststeppes at the southern edge of the boreal forest is that forests are naturally fragmented, since they occupy the moistest sites, which are basically north-facing mountain slopes, whereas dry sites are covered with grassland (Hilbig, 1995; Dulamsuren, 2004). While the size and the degree of isolation of forest stands varies naturally depending on the physio-geographical setting, an ongoing deforestation causes not only forest cover losses (Hansen et al., 2013), but also the progressive fragmentation of the remaining forest area. Because of this specific setting at the southern edge of the boreal forest, the objective of the present study was to analyze how habitat fragmentation affects the organic carbon stocks in these forests. An effect of forest fragmentation was conceivable, because forests were earlier shown to differ in microclimate dependent on stand size and stand isolation (Khansaritoreh et al., 2017b). Furthermore, dendrochronological analyses showed that L. sibirica is more vulnerable to climate warming in small and isolated compared to large forest stands in forest-dominated landscape (Khansaritoreh et al., 2017b). Small and isolated forests are exposed to more extreme temperatures than continuous forests (Saunders et al., 1991; Khansaritoreh et al., 2017b), which might limit biomass production and increase soil respiration. Further, small forests have a higher edge-to-interior ratio and are therefore more likely to be affected by livestock grazing as well as sporadic logging and fuelwood collection by mobile pastoralists settling in the grasslands of the forest-steppe ecotone (Lkhagvadorj et al., 2013a,b; Dulamsuren et al., 2014). Dulamsuren et al. (2016) compared the organic carbon stock density of forest edges (i.e. the outermost 30 m of the forest at the forest line to the steppe) with that of forest interiors in the Mongolian forest-steppe and found that carbon stock density at the edge was only 87% of that in the interior (188 vs. 215 Mg C ha−1). Therefore, the hypothesis was tested that both aboveground and belowground carbon stock density decrease with progressive forest fragmentation, i.e. decreasing stand size and increasing stand isolation.
2. Materials and methods 2.1. Study area Field work was carried out in the vicinity of Tosontsengel (Zavkhan provinve, 48°45′ N, 98°16′ E, 1700 m a.s.l. in northern Mongolia, c. 630 km W of Ulan Bator and 550 km SW of Lake Baikal (Fig. 1). The region is located in the forest-steppe, which forms the transition between the Eurosiberian boreal forest region in the north and the Mongolian-Chinese steppe region of the Central Asian steppes in the south (Pfeiffer et al., 2018). Tree growth in most of Mongolia’s forest-steppe and the neighboring East Kazakh forest-steppe region is limited by summer drought (Dulamsuren et al., 2010a,b, 2013) and this includes the studied forests, where Khansaritoreh et al. (2017b) demonstrated a dominant influence of summer rainfall on annual stemwood increment. Siberian larch (Larix sibirica Ledeb.) occupies roughly 75–80% of Mongolia’s boreal forest area of c. 73,800 km2 (Tsogtbaatar, 2004; Dulamsuren et al., 2016). Industrial timber harvest was widespread in the study area in the second half of the twentieth century, before it was abandoned in 1990 and replaced by unsystematic selective logging by the rural population. The forest-steppe area is home to mobile pastoralists, who keep mixed herds of sheep, goats, cattle, yak, and horses on common pastures. Livestock is not much herded and animals preferentially graze on grassland, but also penetrate into the forests along the edges and further into the interior, when the forest islands are small (Lkhagvadorj et al., 2013b). The dominant bedrock type in the study region is siliceous rock, including granite and metamorphic rock (e.g. schist). Cover beds of up to several meters thick aeolian sand occur at lower slope positions. The prevailing forest soils are Cambisols and Leptosols. The study area is located in the zone of discontinuous permafrost (Sharkhuu and Sharkhuu, 2012). 2.2. Climate The climate in Mongolia’s forest-steppe is highly continental and coined by dry cold winters and short warm summers, where most of the annual precipitation is received. Mean annual temperature in Tosontsengel (data available since 1964) was −5.8 °C (July 14.8 °C, January −31.2 °C). Mean annual precipitation (data since 1968) was 224 mm. While there was no trend for increase or decrease of annual precipitation since 1968, there was a shift in the distribution from autumn to spring. Temperature has increased at a rate of 0.44 K decade−1 since 1964. 2.3. Study design The study design included the variation of forest stand size and forest stand isolation (Table 1, Fig. 2). Stand size was varied by separating between four different size classes with ascending size from class F1/G1 (< 0.1 km2) to F4 (> 5.0 km2). The realized stand sizes in these classes ranged from 0.07 to 24 km2 as detected by remote sensing analysis of satellite imagery. Forest stand isolation was controlled by separating between a subregion with high forest-to-grassland ratio (classes F1 to F4; Fig. 1) and another subregion with low forest-tograssland ratio (class G1). Stand size was only varied in the forestdominated subregion (Fig. 2). Forest stands of classes F1 to F4 were selected in clusters that included all four size classes. This way a potential effect of physiographic heterogeneity within the study area should be avoided. The clusters were evenly distributed over the forest-dominated subregion (Fig. 1),
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Fig. 1. Study area near Tosontsengel, Mongolia with distribution of clusters of forest stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4, south-eastern part of the study area) or low (G1, north-western part) forest-to-grassland ratio. Forest area changes between 1986 (Landsat 5, July 23), 2002 (Landsat 7, June 9), and 2013 (Landsat 8, September 19) are indicated by different signatures. The last digit in the stand numbers specifies plot clusters 1–6.
while the individual forest stands of the different size classes were randomly selected from the cluster areas. The plots in the grasslanddominated subregion were randomly selected among the available forest stands of the smallest size class. Selection of clusters and plots was based on remote sensing analysis of time series of forest distribution in the study region. Forest stands that recently had changed their size class were not selected as sample plots. Sample plots had a size of 20 m × 20 m and were selected in the interior of each forest stand. The geographic position was determined by GPS. Although selection was by random, moist depressions, which are not characteristic for most of the forest area, were avoided. In addition, recently burned and logged forest stands were excluded, because forest fires and logging usually leads to the release of substantial amounts of soil carbon to the atmosphere and would, thus, interfere with potential fragmentation signals in the data. The outermost 30 m of the forests were excluded from the plot search to avoid bias by direct edge effects. Six replicate plots were selected per forest classes yielding a total of 30 plots. Sampling was conducted during the summers of 2014 and 2015.
2.4. Estimation of tree biomass Tree biomass was estimated from stem diameter and tree height data after the methodology of Dulamsuren et al. (2016) using allometric regression equations. For all tree individuals exceeding a height of 4 m, the diameter at breast height (dbh) was deduced from measurements of the stem circumference with a measuring tape. Tree height was measured with a Vertex IV ultrasonic clinometer and T3 transponder (Haglöf, Långsele, Sweden). Sapling-sized trees of ≤4 m were assigned to five height classes (0–0.5, 0.6–1, 1.1–2, 2.1–3, 3.1–4 m) and counted. Mean stem diameter, which was measured at the base, if the trees were below 2 m high, was determined for randomly selected individuals of each height class. Allometric functions to calculate stem and branch biomass were taken from Dulamsuren et al. (2016) (‘estimate 1’) and Battulga et al., 2013 (‘estimate 2’) and mean values from these two estimates were calculated. The regression equations used in the present study are compiled in Table 2. The allometric functions of Dulamsuren et al.
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Table 1 Stand characteristics of Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-to-grassland ratio near Tosontsengel, Mongolia.
Forest size class (km2) Forest-to-grassland ratio Realized stand size (km2) Elevation (m a.s.l.) Trees (ha−1) Saplings (ha−1) Tree basal area (m2 ha−1)
F1
F2
F3
F4
G1
< 0.1 High 0.07 ± 0.02 1914 ± 53 1310 ± 181 a 121 ± 57 a 37.8 ± 3.8 ab
0.1–1.0 High 0.28 ± 0.09 1910 ± 26 1508 ± 181 a 211 ± 69ab 43.0 ± 3.6 a
1.1–5.0 High 2.36 ± 0.48 1936 ± 30 1656 ± 140 a 547 ± 283 ab 43.5 ± 3.1 a
> 5.0 High 23.8 ± 5.4 1997 ± 22 1375 ± 158 a 925 ± 423b 35.8 ± 3.5 ab
< 0.1 Low 0.08 ± 0.01 2018 ± 29 1702 ± 164 a 239 ± 174 ab 28.1 ± 1.5b
Within a row, means sharing the same letter, do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel,
(2016) and Battulga et al. (2013) are based on harvested L. sibirica trees from the Khangai and Altai Mountains, respectively, in western Mongolia and thus derive from environments with similar site conditions as in our study area; the similarity in climatic conditions is higher for estimate 1 than 2. Needle biomass was derived from branch biomass using linear regression (Dulamsuren et al., 2016). Root biomass was assessed from stem diameter and tree height using an allometric function that was established with the help of excavated L. sibirica trees from the Chinese Altai Mountains (Han and Liang, 1997; Wang et al., 2005). The tree biomass was converted into organic carbon mass by assuming an organic carbon content of 47% based on earlier measurements in L. sibirica wood from trunks and branches as well as needles from the Mongolian forest-steppe, which all yielded a mean carbon content of 47% (Dulamsuren et al., 2016). The mean carbon content of deadwood was also assumed to be 47%. The volume of deadwood pieces was assessed based on length and diameter measurements. To compensate for the conical shape of most deadwood pieces, we calculated with a low wood density of 0.2 g cm−3.
2.6. Remote sensing analysis of forest distribution Time series of forest distribution in the study region were studied with ArcGIS 3.2 (ESRI, Redlands, California, USA). The recent forest distribution at the time of biomass and soil sampling was determined by supervised classification of a Spot 6 multispectral satellite image of September 14, 2014. The spatial resolution of this image of 1.6 m × 1.6 m enabled a detailed delineation of forest stands and isolated trees. The classification result was visually corrected and transformed into vectored data. The size of the single polygons bordering the closed forests was used to calculate the forest areas. To analyze for the temporal dynamics of forest cover during the last 30 years, a change detection analysis was performed using three different Landsat satellite images: Landsat 5 TM of July 23, 1986; Landsat 7 ETM+ from June 9, 2002; Landsat 8 OLT/TIRS of September 19, 2013. Initially the forest distribution of every satellite image was delineated by supervised classification. The computed forest areas of every time slice were subtracted from each other to analyze potential area changes. The spatial resolution of Landsat images of 30 m × 30 m induces a minor inaccuracy depending on the relative portion of trees in one pixel. Therefore, single trees could not be detected and the borders of closed forests could slightly alternate between the different classifications. However, closed forests were generally satisfactorily distinguished. Although there was forest disturbance by fire in the surrounding region, a significant change in forested area since 1986 can be ruled out for the studied forests.
Soil sampling included the organic layer and the mineral soil at 0–1 m depth at 5 sampling points per sample plot. The five sampling points were established in the center and at the corners of each sample plot. Soil profiles were dug and soil samples were taken in the center of five depth intervals (0–20, 20–40, 40–60, 60–80, 80–100 cm) with a steel cylinder of 240 cm3. Soil was dried at 70 °C and sifted (2 mm mesh size) to remove stones and roots, which were weighted afterwards. Total carbon was determined with the C/N analyzer (Vario EL III Elementar, Hanau, Germany) in a subsample of the soil. In another subsample, the organic carbon was released by combustion at 600 °C and the remaining inorganic carbon was measured with the C/N analyzer. Organic carbon was determined as the difference of total minus inorganic carbon content. Another subsample of the soil was dried at 105 °C until weight constancy to determine dry weight. In the organic layer (> 30% of organic substance), the carbon content was analyzed. The organic layer carbon content was excluded from the calculation of SOC pool density, but is included in the sum for total belowground
2.7. Statistical analysis Arithmetic means ± standard errors are presented throughout the paper. All data were tested for normal distribution with the ShapiroWilk test. Multiple comparisons of normally distributed data were made using Duncan's multiple range test; degrees of freedom (df) are given as dfmodel, dferror. All statistical analyses were computed with SAS 9.4 software.
Forest-dominated subregion F 0.1-1.0 km² F2
1.1-5.0 km² F3
4, 25).
organic carbon density.
2.5. Soil sampling, chemical analysis and calculation of SOC
<0.1 km² F1
error =
Grassland-dominated subregion G >5.0 km² F4
<0.1 km² G1
F11 F12 F13 F21 F22 F23 F31 F32 F33 F41 F42 F43 F14 F15 F16 F24 F25 F26 F34 F35 F36 F44 F45 F46
G11 G12 G13 G14 G15 G16
Stand isolation
Stand size Replicate stands
Fig. 2. Plot design for studying the effect of stand isolation (forest-dominated vs. grassland-dominated area) and, within the forest-dominated subregion, stand size on Larix sibirica stands. Two plots of 20 m × 20 m were studied for each of the 6 replicate stands; data per stand were averaged. 783
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Table 2 Allometric regression equations used for estimating the biomass (in kg dry weight) of Larix sibirica from stem diameter (D) and tree height (H) data. Plant organ
Model b
Stem (estimate 1) Stem (estimate 2) Branches (estimate 1) Branches (estimate 2) Needles Roots
c
y = aD H y = (D2 H)/(a + bD) y = aDb y = aDb y = a + bx y = a(D2H)b
Parameters
Reference
a = 0.0188, b = 1.6055, c = 1.3350 a = 39.908, b = 0.9081 a = 0.0031, b = 2.8556 a = 0.0180, b = 2.4268 a = 0.7098, b = 0.1900; x = branch biomass a = 0.006984, b = 0.9724
Dulamsuren et al. (2016) Battulga et al. (2013) Dulamsuren et al. (2016) Battulga et al. (2013) Dulamsuren et al. (2016) Wang et al. (2005)
Table 3 Tree biomass (in Mg ha−1) in Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-to-grassland ratio. F1
F2
F3
F4
G1
Stem
Estimate 1 Estimate 2 Mean
117 ± 16 ab 130 ± 16 ab 124 ± 16 ab
142 ± 17 a 154 ± 16 a 148 ± 16 a
143 ± 10 a 157 ± 10 a 150 ± 10 a
117 ± 17 ab 128 ± 17 ab 123 ± 17 ab
78.4 ± 9.3b 90.0 ± 8.6b 84.2 ± 8.9b
Branches
Estimate 1 Estimate 2 Mean
25.9 ± 4.6 ab 30.8 ± 3.3 a 28.3 ± 3.9 ab
31.8 ± 4.4 a 35.2 ± 3.1 a 33.5 ± 3.7 a
30.0 ± 4.7 a 35.2 ± 2.7 a 32.6 ± 3.6 a
25.0 ± 3.2 ab 29.2 ± 3.0 ab 27.1 ± 3.1 ab
16.0 ± 2.0b 22.1 ± 1.3b 19.0 ± 1.6b
Needles
Estimate 1 Estimate 2 Mean
5.8 ± 0.8 ab 6.4 ± 0.5 ab 6.1 ± 0.6 ab
7.1 ± 0.9 a 6.9 ± 0.5 a 7.0 ± 0.7 a
6.9 ± 0.9 a 7.0 ± 0.5 a 6.9 ± 0.7 a
5.7 ± 0.6 ab 5.8 ± 0.5 ab 5.8 ± 0.5 ab
4.2 ± 0.4b 5.1 ± 0.2b 4.7 ± 0.2b
Aboveground
Estimate 1 Estimate 2 Mean
148 ± 21 ab 168 ± 20 ab 158 ± 20 ab
181 ± 20 a 196 ± 19 a 189 ± 20 a
180 ± 15 a 199 ± 13 a 189 ± 14 a
148 ± 21ab 163 ± 20 ab 156 ± 21 ab
98.6 ± 11.4b 117 ± 10b 108 ± 11b
Roots
–
46.2 ± 6.7 ab
56.1 ± 6.4 a
54.8 ± 4.5 a
46.1 ± 6.5 ab
30.4 ± 3.6b
Total
Estimate 1 Estimate 2 Mean
194 ± 28 ab 214 ± 27 ab 204 ± 27 ab
237 ± 27 a 253 ± 25 a 245 ± 26 a
234 ± 19 a 254 ± 18 a 244 ± 18 a
194 ± 28 ab 209 ± 27 ab 202 ± 27 ab
129 ± 15b 148 ± 13b 138 ± 14b
Biomass ( ± SE) for all trees of > 4 m height. Within a row, means sharing the same letter, do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel, error = 4, 25).
3. Results
0.8 Mg C ha−1 in F1).
3.1. Tree biomass and carbon stock densities
3.2. Soil organic carbon
Aboveground tree biomass ranged from 156 to 189 Mg ha−1 in the forest-dominated subregion (stands F1-F4), but amounted to only 108 Mg ha−1 in the forest stands of the grassland-dominated subregion (G1) (Table 3). Tree root biomass was estimated at 46–56 Mg ha−1 in the forest-dominated subregion and 30 Mg ha−1 in the grasslanddominated subregion. This yielded a total tree biomass between 202 and 245 Mg ha−1 in the forest-dominated subregion, but of only 138 Mg ha−1 in the grassland-dominated subregion of the study area. Even though the numerical values for mean tree biomass appeared quite different between the two subregions, this difference was only significant between G1, on the one hand, and F2 and F3, on the other hand (Table 3). This finding corresponded to a similar pattern for tree basal area, whereas stand density did not differ between the plots (Table 1). Tree biomass did not show a consistent trend for increase or decrease with stand size (from F1 to F4). The carbon stock density in the aboveground biomass of living mature trees varied between 73 and 89 Mg C ha−1 in the forest-dominated subregion and was 51 Mg C ha−1 in the forest stands of the grassland-dominated subregion (Table 4). The contribution of forest regeneration to the total biomass and carbon stock density was negligible, but nevertheless steadily increased with forest stand size from 0.02 Mg C ha−1 in F1 to 0.12 Mg C ha−1 in F4 (Table 4). Deadwood also accounted for only a small proportion of the organic carbon stock density (Table 4); its significance was especially low in the smallest forest stands of less than 0.1 km2 in size (0.2 Mg C ha−1 in G1 and
The density of organic carbon in the mineral soil (soil organic carbon, SOC) at 0–1 m depth varied between 75 and 110 Mg C ha−1, but was not influenced by the stand type (Table 4). The mean SOC density of all study sites amounted to 98 ± 6 Mg C ha−1. The vertical distribution of SOC density in the soil profile shows that, on average, 43 ± 3% of the total organic carbon in the mineral soil was found within the uppermost 20 cm (Fig. 3). Roughly 50% of the total organic carbon in the 1 m profile occurred within the uppermost 30 cm, leaving the other half of the total SOC for the soil depth between 30 and 100 cm. While in the average over all soil profiles, there was a nonlinear (exponential) decrease of SOC density with increasing soil depth (Fig. 3), a sharp increase in SOC density was sporadically observed at the bottom of some individual profiles (Fig. 4). The depth (Fig. 5a) as well as the organic carbon content (Fig. 5c) of the organic layer increased with forest stand size (from F1 to F4), but were not different between small forests in the forest-dominated (F1) and grassland-dominated (G1) subregions. The matter density in the organic layer (Fig. 5b) showed an insignificant trend for lower density in the large (F3-F4) than in the small forests (F1-F2, G1). Albeit this trend was not significant, it influenced the between-site differences in organic carbon stock density resulting in the lowest carbon stock density (Fig. 5d) in the organic layer of stand type F3 (at relatively high organic layer depth and carbon content, but low matter density) and the highest carbon stock density in F4 (at low matter density, but high depth and carbon content). 784
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Table 4 Aboveground and belowground organic carbon pool densities (in Mg C ha−1) in Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-to-grassland ratio.
Aboveground Mature trees
Estimate Estimate Mean – – Estimate Estimate Mean
Regeneration Deadwood Subtotal aboveground
Belowground Tree roots Organic layer SOC (0–1 m) Subtotal belowground Total
1 2
1 2
– – – – Estimate 1 Estimate 2 Mean
F1
F2
F3
F4
G1
69.7 ± 9.8 ab 78.8 ± 9.4 ab 74.2 ± 9.6 ab 0.02 ± 0.01 a 0.8 ± 0.5 a 70.4 ± 9.9 ab 79.5 ± 9.5 ab 75.0 ± 9.7 ab
85.2 ± 9.6 a 92.3 ± 9.0 a 88.8 ± 9.2 a 0.03 ± 0.01 ab 1.7 ± 0.2 ab 85.2 ± 9.6 a 92.3 ± 9.0 a 88.8 ± 9.2 a
84.4 ± 7.0 a 93.7 ± 6.3 a 89.0 ± 6.6 a 0.05 ± 0.02 ab 4.3 ± 0.7b 84.4 ± 7.0 a 93.7 ± 6.3 a 89.0 ± 6.6 a
69.6 ± 9.9 ab 76.8 ± 9.5 ab 73.2 ± 9.7 ab 0.12 ± 0.05b 2.2 ± 1.8 ab 69.6 ± 9.9 ab 76.8 ± 9.5 ab 73.2 ± 9.7 ab
46.3 ± 5.4b 55.1 ± 4.6b 50.7 ± 5.0b 0.05 ± 0.03 ab 0.2 ± 0.2 a 46.3 ± 5.4b 55.1 ± 4.6b 50.7 ± 5.0b
21.7 ± 3.1 ab 39.4 ± 2.9 ab 102 ± 15 a 163 ± 18 a 233 ± 22 a 242 ± 21 a 238 ± 21 a
26.4 ± 3.0 a 42.0 ± 6.1 ab 96.0 ± 12.9 a 164 ± 10 a 251 ± 15 a 259 ± 14 a 255 ± 15 a
25.7 ± 2.1 a 32.9 ± 5.3 a 75.3 ± 12.6 a 134 ± 16 a 223 ± 21 a 232 ± 20 a 227 ± 20 a
21.7 ± 3.0 ab 49.5 ± 5.6b 110 ± 17 a 181 ± 17 a 253 ± 14 a 260 ± 14 a 257 ± 14 a
14.3 ± 1.7b 25.6 ± 2.2 ab 106 ± 13 a 156 ± 14 a 202 ± 11 a 211 ± 12 a 207 ± 12 a
Arithmetic means ± SE. Within a row, means sharing the same letter, do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel,
SOC (%) 0
A
Soil depth (cm)
20
SOC (%)
50
100 0
B
F1
SOC (%)
50
100 0
C
F2
SOC (%)
50
100 0
4, 25).
SOC (%)
50
D
F3
error =
100 0
50
E
F4
100
G1
40 60 80 100 0
20
40
60
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
0
20
40
60
SOC (Mg C ha-1)
SOC (%)
Fig. 3. Depth profiles of SOC density in the mineral soil of forest stand types (A) F1, (B) F2, (C) F3, (D) F4), and (E) G1 (for the definition of stand types see Table 1 and Fig. 2) in the Larix sibirica forest. Numbers at the y-axis represent the lower border of 20 cm deep soil segments. Gray lines with open symbols show the accumulated percentage of SOC at the relevant soil depth related to the total SOC at 1 m depth.
3.3. Ecosystem carbon pool density in variation of stand size and isolation
large continuous forests (Khansaritoreh et al., 2017b), this enhanced climate sensitivity due to forest fragmentation has not yet resulted in any curtailing of organic carbon stock density. This can be explained by the high share of SOC in the total ecosystem carbon pool as everywhere in the boreal forest biome (Cao and Woodward, 1998). The organic carbon stock density in the mineral soil (SOC stock density) was demonstrated to be not different between L. sibirica forests and surrounding grasslands by Dulamsuren et al. (2016) and is, therefore, not likely to be affected by deforestation. The absence of any deforestation signal in the SOC stock density is attributable to lower mean and minimum temperatures in the highly continental forest-steppe of Mongolia in open than forested landscape, which outweighs the higher temperature maxima and thus potentially faster organic matter decomposition in the short summer (Lee et al., 2011). Moreover, the meadow steppe grasslands near forest edges of the forest-steppe ecotone are relatively well water-supplied and often highly productive and densely permeate the upper soil with roots promoting the transfer of carbon to the soil (Dulamsuren and Hauck, 2008; Fan et al., 2008; Pfeiffer et al., 2018). In contrast to the mineral soil, the organic layer responded to forest stand size. The steady increase in the depth and carbon concentration of
The total ecosystem carbon stock density was neither significantly influenced by stand size nor stand isolation (Table 4). Total carbon stock density varied between 227 and 257 Mg C ha−1 in the forestdominated subregion (F1 to F4) and 207 Mg C ha−1 in the grasslanddominated subregion (G1). Since a significant difference between the individual stand types was absent, the mean organic carbon stock density across all stand types could be calculated as 237 ± 9 Mg C ha−1. 4. Discussion Contrary to our expectations, neither the size nor the degree of isolation of a forest stand exerted a significant influence on the organic carbon pool density of L. sibirica forests in the forest-steppe ecotone of Mongolia. The lack of a significant difference referred to both tree biomass and the mineral soil (SOC) and, thus, to the total ecosystem carbon pool density. Even though the stemwood production of larch trees from small and isolated forests in the same study area was shown to be more vulnerable to drought limitation compared to trees from 785
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Fig. 4. Examples for soil profiles with increasing SOC density at the bottom of the soil profile: (A, B) forest stands of < 0.1 km2 (F1), (C) 0.1–1 km2 (F2), (D) > 5 km2 (F4) in the forest-dominated subregion and of (E, F) < 0.1 km2 in the grassland-dominated subregion (G1).
the organic layer with stand size at constant organic carbon stock densities in the tree biomass and in the SOC suggests that the organic layer is the most sensitive component of the larch forest ecosystem with respect to forest fragmentation. The variation of the gravimetric density of the organic layer only showed an insignificant trend for higher values in the forest stands of ≤1 km2 size (F1, F2) compared to the stands of > 1 km2 (F3, F4). However, this insignificant trend deserves attention, because it produced the non-steady increase of the organic layer carbon stock density with stand size. The trend for higher gravimetric density in the small forests is probably due to soil compaction by livestock that frequently browses forest margins and small forests, but usually avoids the interior of large forest stands (Lkhagvadorj et al., 2013a,b). The combination of intermediate organic layer depth and carbon concentration and relatively low matter density causes that the lowest carbon stock density was found in forests of intermediate size (F3). Regional differences in livestock density apparently leave an imprint in the organic layer depth and carbon stock density. In the Mongolian Altai and the western Khangai Mountains of western Mongolia, which were the field sites of Dulamsuren et al. (2016), higher livestock densities (Lkhagvadorj et al., 2013a,b) led to a degradation of the organic layer and thus carbon stock density. At high livestock density, browsing animals penetrate more deeply into forest stands and compact and degrade the organic layer (Khishigjargal et al., 2013). In our study area near Tosontsengel, livestock density was much lower than at the field sites of Dulamsuren et al. (2016) due to fewer nomads because of alternative job opportunities in the timber industry since the mid-20th century (Khansaritoreh et al., 2017a). In addition to livestock density, the much colder winter climate in Tosontsengel (mean January temperature of −31 °C) compared to the Mongolian Altai and the western Khangai (−21 to −24 °C; Dulamsuren et al., 2016) might explain the regional differences in the carbon stock density of the organic
layer, since soil respiration increases with temperature even below the freezing point (Mikan et al., 2002). In contrast to SOC stock density, organic layer carbon density was also found to contribute to the total loss of ecosystem carbon stock density, when L. sibirica forests are converted into grasslands (Dulamsuren et al., 2016). While the variation in the traits of the organic layer was of subordinate significance for the forests’ total carbon stock density, reduced organic layer depth and humus content as well as the trend for increased compaction in small forests are likely to exert a negative effect on forest regeneration and probably also on the diversity and activity of decomposers. High humus concentrations are beneficial for tree seedlings and saplings, since they increase soil water retention (Vereecken et al., 1989). Soil moisture is critical for seedling emergence and survival in L. sibirica in the drought-prone Inner Asian forest-steppes (Dulamsuren et al., 2008). Reduced organic layer depth and humus content could also contribute to the increased drought sensitivity of mature larch trees in small forest stands of our study area (Khansaritoreh et al., 2017b). Detrimental effects on forest regeneration due to organic layer degradation in small forests would add to strong reductions in the regeneration success that are caused by high and increasing numbers of livestock, and goats in particular (Khishigjargal et al., 2013). The carbon stock density in the aboveground biomass of L. sibirica stands in the forest-dominated subregion of 73–89 Mg C ha−1 exceeded that of the larch forests in the Mongolian Altai and the western Khangai (55–72 Mg C ha−1; Dulamsuren et al., 2016), but match with estimates from southern Siberia (Thurner et al., 2014). An estimate of Shvidenko and Shepaschenko (2014) for southern Siberia, however, is more in the range found by Dulamsuren et al. (2016). We cannot explain the differences in the estimates for southern Siberia, but in the case of Mongolia, they are attributable to the higher stand basal area in the forests 786
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Fig. 5. Traits of the organic layer in Larix sibirica stands of different size (increasing from F1/G1 to F4) in subregions with high (F1 to F4) or low (G1) forest-tograssland ratio: (a) depth, (b) organic matter density, (c) organic carbon content, and (d) carbon stock density. Means ( ± SE) sharing a common letter do not differ significantly (P ≤ 0.05, Duncan’s multiple range test, dfmodel, error = 4, 25).
of the present study 36–44 m2 ha−1 than found by Dulamsuren et al. (2016) (33–36 m2 ha−1). The low basal area in the forest stands of the grassland-dominated subregion of 28 m2 ha−1 in the present study matches with the low aboveground carbon stock density of 51 Mg C ha−1. These comparisons highlight the importance of stand density for the aboveground biomass and carbon stock density. In Mongolia’s boreal forests, which are subjected to extensive land use by pastoralists and the unsystematic logging of trees for the supply of local lumber mills, the density and structure of forest stands and, thus, basal area are strongly controlled by increased logging activities for timber and fuel harvest since the political transformation from planned to market economy in the early 1990s (Dulamsuren et al., 2014) as well as by the suppression of forest regeneration by recently increased livestock numbers (Khishigjargal et al., 2013; Lkhagvadorj et al., 2013b). Hence, land use intensity is a major determinant of the aboveground organic carbon stocks. Deadwood, which can store large amounts of carbon in natural forests (Pregitzer and Euskirchen, 2004; Pan et al., 2011; Jacob et al., 2013), does not play a significant role in today’s forests of Mongolia, since it is often removed as fuel wood. The total ecosystem organic carbon stock densities of 212–257 Mg C ha−1 found for the individual forest stand types were in the range of estimates of 211–218 Mg C ha−1 published by Dulamsuren et al. (2016) for the Mongolian forest-steppe based on sample plots from the Altai Mountains and the Khangai Mountains 170–700 km west and southwest of our study area in northern Mongolia. The mean organic carbon stock density calculated over all stand types irrespective of stand size and degree of stand isolation exceeded that specified by Dulamsuren et al. (2016) by 10%, which is attributable to differences in stand density and organic layer carbon stocks. In an over-regional comparison, our carbon pool density figures corroborate the view that the ecosystem carbon stock density in the Inner Asian forest-steppes is below the global average of boreal forests due to below-average SOC density. Our mean SOC density of 99 Mg C ha−1 is, for example,
roughly 10% below the estimate for the global boreal forests (109 Mg C ha−1) by DeLuca and Boisvenue (2012). This lower SOC density in the forest-steppe can be attributed to the warmer climate and the lack of continuous permafrost compared to higher latitudes. The vertical distribution of SOC density with roughly, half of the SOC in the 1 m profiles found below 30 cm soil depth confirms the importance of studying not only the upper few decimeters of the soil, but at least 1 m for the assessment of the organic carbon pool in boreal forests. The increase in SOC density towards the bottom of some of the 1 m profiles corroborates this view; this increase is probably due to the presence of permafrost, which inhibits soil respiration (Davidson and Janssens, 2006; Ping et al., 2015). 5. Conclusions The ongoing habitat fragmentation in Mongolia’s forest-steppe ecotone widely leading to the reduction of forest stand sizes and the progressive isolation of forests in the grassland had no effect on organic carbon stock density in the remaining forests in our study. This means that even small forest remnants deserve protection with regard to their function to store organic carbon and thereby to protect the global climate. The function of the forests for the unique biodiversity of the Inner Asian forest-steppe reinforces the need for the conservation even of small forests (Hauck et al., 2014). Deforestation and conversion into grassland is known to reduce the organic carbon stock density in the Mongolian forest-steppe by c. 40% due to the removal of vegetation biomass and the reduction of the organic layer (Dulamsuren et al., 2016). In contrast to the density of tree biomass and SOC, the organic layer responds sensitively to stand size, which has not much influence on the total ecosystem pool, but might be crucial for soil moisture and thus forest regeneration and the drought sensitivity of mature trees, which is known to increase with decreasing stand size (Khansaritoreh et al., 2017b). 787
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Acknowledgments
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