Variation in Biomass and Carbon Allocation in Various Components of Tree Species Along Different Forest Types in High Mountain Regions

Tropical Ecology 59(3): 457–472, 2018 ISSN 0564-3295 © International Society for Tropical Ecology www.tropecol.com

Variation in biomass and carbon allocation in various components of tree species along different forest types in high mountain regions

1 1,2* 1 1 1 RAM KRISHAN , ASHISH K. MISHRA , OM PRAKASH TIWARI , Y. S. RANA & C. M. SHARMA

1Department of Botany, HNB Garhwal University, Srinagar Garhwal-246174, Uttarakhand, India 2Department of Environmental Science, Babasaheb Bhimrao Ambedkar (Central) University, Vidya Vihar Raebareli Road, Lucknow-226025, Uttar Pradesh, India

Abstract: Live component utility of carbon discrimination play a vital role in forest carbon cycle through photosynthesis and actively help in mitigation of global warming. The present study was conducted in different mountain forests (situated between 1428-3460 m asl) in Garhwal Himalaya. The aim of this study is to present an assessment of the carbon stock variation in tree biomass purposing recommendations for future forest management. Eight temperate forest types viz., Abies spectabilis forest, Betula utilis forest, Cedrus deodara forest, mixed coniferous forest, Pinus wallichiana forest, Quercus floribunda forest, Q. leucotrichophora, and Q. semecarpifolia forests were studied to enumerate its growing stocks, biomass density and carbon stocks of the area. The mean Growing Stock Volume Density (GSVD) was calculated as 488±39.11 m3 ha-1, which ranged from 366.68±71.27 m3 ha-1 (Q. floribunda forest) to 676.38±155.19 m3 ha-1 (Cedrus deodara forests). The values of total biomass density (TBD) was recorded the highest for Abies forests (636.3±64.6 Mg ha-1) and the lowest for Betula forests (286.37±29.9 Mg ha-1). Highest total carbon density was recorded for Abies spectabilis forest (292.7±29.72 Mg C ha-1) followed by Q. floribunda forest (265.19±18.41 C Mg ha-1) and the lowest in Betula forest (131.73±13.75 Mg C ha-1). The study revealed that the conifer-dominated forest types had higher carbon storage potential than broadleaf-dominated forest types. Therefore, results will be useful for conservation practices and their plantation which should be necessary for controlling the greenhouse effect and climate change.

Key words: Climate change; forest carbon; Garhwal Himalaya; live tree biomass.

Introduction (IPCC 2003). Quantification of biomass and carbon storage has recently become important all over the Concern over global climate change related to world and is presently recognised as an important increasing carbon dioxide (CO2) and the enhanced component in the implementation of the emerging C greenhouse effect has encouraged the detail study of credit market mechanism (Chave et al. 2004). The carbon (C) balance in the ecosystem. The increase in quantity of biomass in a forest determines the greenhouse gases is projected to lead to 1–3.5 ºC in potential amount of C that can be added to the global mean surface temperature by 2100 (IPCC atmosphere and managed for meeting emission 1996). However, a common framework and good targets (Brown et al. 1999). Forests have a unique practice guidance for C reporting through biomass status in the whole scenario of climate change, as equation has been recognised by United Nations they act as a sink as well as source of carbon and

*Corresponding Author; e-mail: [email protected] 458 BIOMASS AND CARBON ALLOCATION IN TREE SPECIES IN HIGH MOUNTAIN FORESTS are valued globally for the services they provide to to assess the role of climate change in predicting the society. Forest ecosystems contain the majority effect on future species coexistence and shift in (approximately 60%) of the C stored in terrestrial Himalayan range (Sharma et al. 2016a, b). ecosystems (IPCC 2000). The main carbon pools in In earlier studies from Uttarakhand, various forests include plant biomass (above and below researchers from Kumoun Himalaya (Adhikari et ground), coarse woody debris, litter and soil. In al. 1995; Chaturvedi & Singh 1987; Rawat & most forested ecosystems, the majority of the carbon Singh 1988) and Garhwal Himalaya (Gairola et al. in biomass is stored below ground as dead organic 2011; Sharma et al. 2010, 2011) have attempted to matter (DOM). Within forest biomes as a whole, predict biomass and carbon stocks. Biomass 68% of the total forest carbon is in the DOM pool i.e. content of different components of tree were earlier 50% in tropical forests, 63% in temperate forests, studied from Kumaun division but not reported and 84% in the boreal forests (Kimble et al. 2003). A from Garhwal region. In addition to this, an up-to- better understanding of C changes is critical for date quantification of growing stock is necessary projecting future atmospheric CO2 growth and for better management, planning and decision guiding the design and implementation of making, as forests are source of timber and non mitigation policies. timber forest products (NTFPs). This paper aims In India total 34651 km2 (about 4.49% of the at (i) the assessment of tree diversity, growing country) areas occupied by forest cover of stocks volume, component wise contribution of Uttarakhand, while 38.52% of forest area is located biomass accumulation of tree species and their in temperate zone which is a major portion in the carbon stocks in the different forests of temperate Garhwal Himalaya (FSI 2013). Hence region of Uttarkashi district of Garhwal Himalaya, understanding of biomass and carbon potential of and at the assessment of (ii) altitudinal and these forests is seems to be essential. Although, the climatic effects on these forests. carbon pool of a Himalayan forest ecosystems varies Material and methods within the different forest (Sharma et al. 2010; Zhang et al. 2013) and along altitude (Zhu et al. Study area 2010). The Garhwal Himalayas contains a wide The Uttarakhand state is located in North- range of rich vegetation from dry tropical to West part of the country. Its geographical area is subalpine forests. However, conifer and oak forests 53,483 km2 which constitutes 1.63% of total area of dominantly occupy ridges, which constituted the the country (FSI 2013). This study was conducted major part of total forest area. In forest ecosystems, in several mountain ranges in temperate zone of trees produce and maintain the overall physical Bhagirathi catchment area (between 1428–3460 m structure of habitats, and thus, define funda- asl) in Uttarkashi district of Garhwal Himalaya mentally the templates for structural complexity (India). The district Uttarkashi is the largest and environmental heterogeneity (Jones et al. district of Uttarakhand occupying an area of about 1997). Various factors like climate, stand age, 8016 km2 and is situated at latitude 30º13’-30º14’ disturbance regimes, and edaphic conditions are N and longitude 78º47’–78º43’ E (Fig. 1). Out of sturdily affected the carbon pools in forest total area, only 39.23% of area covered with forest ecosystems (Pregitzer & Euskirchen 2004). But the cover (FSI 2013). The average temperature of the most important predictors of aboveground biomass study area is 18.8 ºC and the average rainfall is (AGB) of a tree are, in the order of less importance; 1693 mm, the maximum rainfall was recorded in its trunk diameter, wood specific gravity, total the months of June to August. The climatic height and forest type (Chave et al. 2005). Globally, variables including temperature and rainfall of the temperate forest is a widely distributed forest type, area observed during the year were estimated by and its carbon flux has significantly altered by the data published by Indian Meteorological changing climate (Dai et al. 2013). However, the Department, Uttarkashi for the year 1901-2002 carbon flux of coniferous forests has been more (Fig. 2). After the reconnaissance survey of the strongly influenced by climate change as compared study area, we selected eight dominant forest to deciduous forests (Ma et al. 2014). types and named according to the classification Composition of forest vegetation and its given by Champion & Seth (1968), viz. analysis can effectively predict the influence of climate change on migration of woody species. (i) Abies spectabilis forest (AF) - West Hence, quantification of the current forest Himalayan Sub-alpine Fir Forest (14/C1a) composition and carbon dynamics is crucial in order (ii) Betula utilis mixed forest (BF) - West Hima- RAM KRISHAN et al. 459

Fig. 1. Map of district Uttarkashi with studied forest types.

(iv) Pure Pinus wallichiana forest (PF) - Pine Forest (9/C1b) (v) Mixed conifer forest (MCF) - West Himalayan mixed coniferous forests (12/C2) (vi) Quercus floribunda forest (QFF) - Moru Oak Forest (12/C1b) (vii) Quercus leucotrichophora forest (QLF) - Banj Oak forest (12/C1a) (viii) Quercus semecarpifolia forest (QSF) - West Himalayan Upper Oak Forest (12/C2b) Selected forest types were undisturbed and exist on inherently different types of woody plants in sub-alpine vegetation. Fig. 2. Meteorological data of district Uttarkashi Vegetation analysis (1901-2002) (source: Indian Meterological Department, Uttarkashi). To analyse the forest vegetation on different ridge tops, 10 sample plots of 0.1 ha each were laid layan Sub-Alpine Birch/Fir Forest (14/C1b) out in eight selected ridge tops of each forest types (iii) Cedrus deodara forest (CF) - Dry Deodar (10 plots (2 plots in each ridge top × 5 ridge tops) × Forest (13/C2b) 08 forest types = total 80 sample plots). All indivi- 460 BIOMASS AND CARBON ALLOCATION IN TREE SPECIES IN HIGH MOUNTAIN FORESTS

Table 1. Details of elevation, SR, TBC, Density, GSVD, TBD and TCD values in different forest types.

Forest types Elevation SR TBC Density GSVD TBD TCD (m asl.) (m2 ha-1) (ha-1) (m3 ha-1) (Mg ha-1) (Mg C ha-1) Abies spectabilis 2580–3460 10 20.73 778±89.97 602.28±69.93 636.30±64.6 292.70±29.72 Betula utilis 3220–3460 6 13.44 498±36.11 406.39±28.67 286.37±29.9 131.73±13.75 Cedrus deodara 2425–3125 9 21.18 592±94.41 676.38±155.19 366.13±59.1 168.42±27.19 Mixed Coniferous 2072–2737 14 22.73 610±48.68 550.83±76.03 570.82±102.5 262.58±47.15 Pinus wallichiana 2577–3252 13 14.69 560±69.93 446.51±70.86 355.38±30.6 163.47±14.08 Quercus floribunda 2530–2690 12 14.61 662±18.81 366.68±71.21 589.30±40.9 265.19±18.41 Q. leucotrichophora 1428–2578 10 18.51 616±40.69 415.63±57.06 466.27±59.6 209.82±26.82 Q. semecarpifolia 2638–3523 10 17.88 574±45.56 418.16±35.69 510.87±43.4 229.89±19.53 Note- SR- Species richness, TBC: Total Basal Cover, GSVD- Growing Stock Volume Density; TBD- Total Biomass Density; TCD- Total Carbon Density. duals ≥ 10 cm diameter at breast height (DBH = Where, Y represents the weight of tree 1.37 m from ground) were considered as tree in each component in kilograms, and X represents the tree sample plot. The DBH and height of all the trees circumference in centimetres measured at breast falling within the sample plot were measured by height (CBH), a and b are the intercept and slope tree Calliper and Ravi multimeter, respectively. of the particular tree species, respectively. The The tree height by different slope positions was total C density (TCD) was computed by using the measured according to the ways of MacDicken et al. following formula: (1991). The slope correction was employed for the Carbon (C Mg ha-1) = Biomass (Mg ha-1) × sample plots which are located on a slope > 10%, so Carbon % that the adjustment can be made to the plot area at The C percentage of 46% was used for the forest the time of analysis. The slope angle was measured types, where all conifers together constituted more by clinometer. than 50%. For forest types where conifers and broad-leaved species occurred in similar proportion, Biomass and Carbon where broadleaved species constituted more than 50% in upper zone above 1500 m asl, the C The growing stock volume density (GSVD) was percentage was taken as 45% (Manhas et al. 2006; estimated using volume tables or volume Negi et al. 2003). The individual tree total biomass equations based on the records of Forest Research and carbon contents in a quadrat were summed to Institute (FRI) and Forest Survey of India (FSI) obtain total biomass and carbon storage. Variations for Himalayan region (FSI 1996). In a few cases, among the ecological attributes for different studied where the volume tables or volume equations for forest types of high mountains were tested with the desired species were not available, the volumes Analysis of Variance (ANOVA) and Tukey's post hoc of those species were calculated per convention by test using SPSS software version 22 (SPSS Inc., using volume tables/equations of similar species Chicago, IL, USA). To know the variations in having similar height, form, taper, and growth species composition, association and diversity rate. across different forest stands were performed by The biomass of the tree species was calculated mean of Detrended correspondence analysis (DCA) by regression equations. The tree components (bole, ordination plot using Canaco 5 software. bole bark, branches, twigs, leaves, stump roots, lateral roots and fine roots) were calculated by Results various equations given by various researchers The mean values of total basal cover, (Adhikari et al. 1995; Garkoti & Singh 1992; Rawat elevations and stem density in different studied & Singh 1988). Biomass equations for tree forests are shown in table 1. The occurrence of components were developed to relate oven dry different forests over elevational ranges is shown weight to tree circumference (measured at breast in figure 3. QFF forests are seen within short height i.e. 1.37 m). The form of the allometric elevational ranges, whereas QSF and QLF are function of the equation is: found in larger ranges. Conifers forests except Ln Y= a + b*Ln X MCF are found above 2500 m asl. A total of 29 tree RAM KRISHAN et al. 461

forest types are shown in Table 2. Among the different aboveground components of tree, tree bole had stored largest biomass accumulation, whereas foliage stored lowest amount of biomass but higher than fine roots of the tree. In case of the bole, biomass estimated in the range of 112.25±13.5 (BF) to 327.97±32.5 Mg ha-1 (QF) with mean biomass density (bole) was calculated as 253.55±25.97 Mg ha-1. Biomass of tree branches in different forest types showed mean value of 78.31±9.1 Mg ha-1. Biomass of branches in the QLF (109.77±13.9 Mg ha-1) showed higher value, whereas PF (41.86±5.2 Mg ha-1) and CF (40.54±5.2 Mg ha-1) showed lower values among these studied forests. Biomass in twigs ranged between 12.95±2 (CF) to 36.14±3.1 Mg ha-1 (QLF), whereas foliage

biomass ranged between 7.65±0.7 (BF) to Fig. 3. Distribution of different forest types along an 17.37±1.6 Mg ha-1 (AF). In case of belowground elevational gradient. biomass, stump roots have biomass ranged between 30.46±3.5 to 85.04±10.3 Mg ha-1 with its species were occurred in eight studied forest types, maximum value calculated in QLF (85.04±10.3 Mg out of which 24 species were recorded in conifer ha-1), whereas the lowest value was estimated from dominated forests and 22 species were found in CF (30.46±3.5 Mg ha-1). Similarly, lateral roots broad-leaved forests. The value of total basal cover biomass was varied between 6.23±1.7 (BF) to varied from 13.44 (BF) to 22.73 m2 ha-1 (MCF). The 38.19±3.8 Mg ha-1 (AF) and fine roots biomass was highest stem density was calculated for AF calculated in the range between 1.47±0.4 (PF) to (778±89.97 trees ha-1), whereas lowest value was 27.12±15.9 Mg ha-1 (QFF). recorded in BF (498±36.11 trees ha-1). The carbon stocks in these studied forest types The values of biomass and growing stocks were observed in the range of 131.73 ± 13.75 Mg C varied considerably in different studied forest types. ha-1 (BF) to 292.7±29.72 Mg C ha-1 (Table 1). Abies The mean of GSVD in different forest types was forests (292.7±29.72 Mg C ha-1) showed the highest calculated as 485.38±39.11 m3 ha-1, which ranged value of carbon stock followed by MCF between 366.68±71.21 to 676.38±155.19 m3ha-1. The (262.58±47.15 Mg C ha-1) and CF (168.42±27.19 highest GSVD value was recorded is 676.38±155.19 Mg C ha-1), PF (163.47±14.08 Mg C ha-1) showed m3ha-1 in CF while QLF had the lowest value the lowest carbon assimilation in case of conifer (366.68±71.21 m3ha-1) of GSVD among the studied dominated forests. In case of broad-leaved forests, forests. The results demonstrated that the highest QFF (265.19±18.41 Mg C ha-1) forests were shown TBD was found in AF (636.3±64.6 Mg ha-1) followed the highest carbon stocks value, whereas QSF and by QFF (589.3±40.9 Mg ha-1), whereas lowest value QLF had showed carbon stocks in the range of of TBD was calculated in BF (286.37±29.9 Mg ha-1). 229.89±19.53 Mg C ha-1 and 209.82±26.82 Mg C Aboveground biomass density (AGBD) was ha-1, respectively. ANOVA results showed that the calculated in the range of 243.13±25.1 (BF) to vegetation composition, biomass production, and 504.59±51.9 Mg ha-1 (AF), whereas below ground carbon storage were observed to be significantly biomass density (BGBD) was calculated in the different among studied forest types (Table 3). range of 43.24±5 (BF) to 131.71±12.9 Mg ha-1 (AF). Biomass of tree components was observed to be The value of AGBD and BGBD was calculated in significantly different in the studied forest types (P the ratio of 85: 15 in PF, whereas QSF forests had < 0.05) except fine root biomass had not shown ratio 76: 24. significant difference (P = 0.09). Tree density (P = Proportion of tree biomass was calculated by 0.03) and elevation (P < 0.01) also showed categorizing into different parts: aerial growth significant difference during the study. (bole, bole bark, branch, twigs, and foliage) and The patterns of variations in species undergrowth including stump, lateral and fine composition in different forest types across roots. The biomass accumulation in the different different elevational ranges is presented by components of different plant species in different mean of DCA diagram (Fig. 4). The distance (dots) 462 BIOMASS AND CARBON ALLOCATION IN TREE SPECIES IN HIGH MOUNTAIN FORESTS

Table 2. Details of biomass and their allocation in various components of trees species in different forest types in the study area.

Plant species Bole Bole bark Branch Twig Foliage AGBD Stump root Lateral root Fine root BGBD TBD Abies forest Abies pindrow 45.05±45.1 5.83±5.8 9.48±9.5 4±0 1.95±1.9 66.31±66.3 9.01±9 4.7±4.2 1.67±1.7 15.38±15.4 81.69±81.7 Abies spectabilis 204.31±72.1 35.67±9.6 55.83±15.2 24.96±6.7 13.06±3.4 333.83±107 51.43±14.2 27.7±7.5 12.07±3.1 91.21±24.8 425.04±131.8 Acer acuminatum 10.62±5.8 0.7±0.4 3.05±1.7 1.01±0.6 0.32±0.2 15.7±8.6 3.09±1.7 0.87±0.5 0.24±0.1 4.2±2.3 19.9±10.9 Acer caesium 1.51±1.5 0.1±0.1 0.42±0.4 0.14±0.1 0.05±0 2.22±2.2 0.44±0.4 0.12±0.1 0.03±0 0.59±0.6 2.81±2.8 Aesculus indica 1.26±1.3 0.28±0.3 0.49±0.5 0.11±0.1 0.05±0.1 2.21±2.2 0.42±0.4 0.23±0.2 0.07±0.1 0.72±0.7 2.93±2.9 Betula utilis 5.51±2.1 - 9.19±3.8 0.78±0.3 0.47±0.2 15.95±6.3 2.51±0.9 - - 2.51±0.9 18.45±7.2 Cedrus deodara 3.25±3.2 0.16±0.2 0.42±0.4 0.11±0.1 0.07±0.1 4.01±4 0.32±0.3 0.09±0.1 0.01±0 0.42±0.4 4.44±4.4 Pinus wallichiana 12.65±12.5 - 2.49±2.5 0.62±0.6 0.28±0.3 16.03±15.8 1.86±1.8 0.6±0.6 0.04±0 2.5±2.5 18.53±18.3 Quercus 33.89±20.8 2.98±1.8 8.81±5.4 1.35±0.8 1.09±0.7 48.12±29.5 8.79±5.4 3.84±2.4 1.46±0.9 14.1±8.6 62.22±38.1 semecarpifolia Taxus wallichiana 0.08±0.1 - 0.05±0.1 0.04±0 0.03±0 0.21±0.2 0.04±0 0.03±0 - 0.08±0.1 0.29±0.3 Grand total 318.13±33.3 45.73±4.7 90.23±10.1 33.13±3 17.37±1.6 504.59±51.9 77.91±8 38.19±3.8 15.61±1.3 131.71±12.9 636.3±64.6 Betula utilis forest Abies spectabilis 53.42±19.9 7.16±2.7 11.28±4.2 5±1.9 2.6±1 79.45±29.6 10.45±3.9 5.59±2.1 2.39±0.9 18.43±6.9 97.88±36.5 Acer caesium 2.14±1.3 0.14±0.1 0.61±0.4 0.2±0.1 0.06±0 3.15±1.9 0.62±0.4 0.17±0.1 0.05±0 0.84±0.5 4±2.4 Betula utilis 44.6±5.5 - 83.97±10.7 6.21±0.8 4.05±0.5 138.82±17 19.97±2.5 - - 19.97±2.5 158.8±19.3 Lyonia ovalifolia 4.73±3 0.35±0.2 3.26±2 1.76±1.1 0.48±0.3 10.58±6.6 2.01±1.3 0.18±0.1 0.1±0.1 2.29±1.4 12.87±8.1 Pinus wallichiana 4.37±3.8 - 0.75±0.7 0.21±0.2 0.12±0.1 5.45±4.7 0.68±0.6 0.21±0.2 0.02±0 0.9±0.8 6.35±5.5 Populus indica 2.99±3 - 1.74±1.7 0.58±0.6 0.34±0.3 5.66±5.7 0.71±0.7 0.08±0.1 - 0.8±0.8 6.46±6.5 Grand total 112.25±13.5 7.65±2.4 101.61±10.4 13.97±0.8 7.65±0.7 243.13±25.1 34.44±2.8 6.23±1.7 2.56±0.8 43.24±5 286.37±29.9 Cedrus forest Cedrus deodara 179.1±62.9 8.71±2.7 24.85±4.4 8.12±1.9 5.59±1.3 226.38±67.6 18.85±3.3 6.79±1.5 0.85±0.2 26.5±4.9 252.88±69.6 Lyonia ovalifolia 0.39±0.4 0.03±0 0.28±0.3 0.15±0.1 0.05±0 0.9±0.9 0.2±0.2 0.02±0 0.01±0 0.23±0.2 1.12±1.1 Picea smithiana 30.68±18.1 1.55±0.9 4.98±2.8 1.66±0.9 1.14±0.6 40.01±23.2 3.78±2.1 1.38±0.7 0.17±0.1 5.34±2.9 45.35±26.1 Pinus wallichiana 16.45±9.4 - 3.01±1.9 0.8±0.5 0.42±0.2 20.68±11.9 2.49±1.4 0.78±0.4 0.06±0 3.33±1.8 24.01±13.7 Populus ciliata 3.38±3.4 0.52±0.5 0.55±0.5 0.18±0.2 0.43±0.4 5.05±5 0.39±0.4 0.45±0.5 0.06±0.1 0.9±0.9 5.95±6

Contd... RAM KRISHAN et al. 463 Table 2. Continued.

Plant species Bole Bole bark Branch Twig Foliage AGBD Stump root Lateral root Fine root BGBD TBD Quercus floribunda 13.39±13.4 0.8±0.8 4.18±4.2 0.96±1 0.91±0.9 20.25±20.2 2.72±2.7 1.47±1.5 0.46±0.5 4.64±4.6 24.89±24.9 Quercus 3.52±3.5 - 2.04±2 0.51±0.5 0.25±0.2 6.31±6.3 1.33±1.3 0.22±0.2 0.02±0 1.57±1.6 7.88±7.9 leucotrichophora Rhododendron 1.61±1.6 0.05±0 0.67±0.7 0.57±0.6 0.14±0.1 3.04±3 0.7±0.7 0.28±0.3 0.04±0 1.02±1 4.06±4.1 arboreum Grand total 248.53±57.9 11.65±1.9 40.54±5.2 12.95±2 8.93±1.2 322.6±59 30.46±3.5 11.4±1.6 1.67±0.5 43.53±5.4 366.13±59.1 Mixed Conifer forest Abies pindrow 145.33±66.4 18.9±8.5 30.59±14 13.02±5.9 6.4±2.8 214.25±97.6 28.98±13.4 15.17±6.9 5.56±2.4 49.71±22.7 263.95±120.2 Acer acuminatum 0.37±0.4 0.02±0 0.1±0.1 0.03±0 0.01±0 0.53±0.5 0.11±0.1 0.03±0 0.01±0 0.14±0.1 0.67±0.7 Alnus nepalensis 1.31±1.3 - 6.58±6.6 0.34±0.3 0.28±0.3 8.5±8.5 0.55±0.5 - - 0.55±0.5 9.05±9 Betula alnoides 0.37±0.4 - 0.58±0.6 0.04±0 0.03±0 1.02±1 0.16±0.2 - - 0.16±0.2 1.18±1.2 Ilex dipyrena 0.97±0.5 0.05±0 0.36±0.2 0.06±0 0.04±0 1.48±0.7 - 0.12±0.1 0.05±0 0.18±0.1 1.66±0.8 Lyonia ovalifolia 0.91±0.9 0.06±0.1 0.62±0.6 0.34±0.3 0.09±0.1 2.02±2 0.36±0.4 0.03±0 0.02±0 0.41±0.4 2.43±2.4 Neolitsea cuipala 0.88±0.9 0.04±0 0.29±0.3 0.07±0.1 0.04±0 1.33±1.3 0.31±0.3 0.22±0.2 4.79±4.8 5.32±5.3 6.65±6.7 Picea smithiana 17.74±14.1 0.9±0.7 2.92±2.1 0.96±0.6 0.66±0.4 23.18±17.9 2.22±1.6 0.8±0.5 0.1±0.1 3.12±2.1 26.3±20 Pinus wallichiana 64.27±39.3 - 11.1±6.9 3.09±1.9 1.69±1 80.15±49.1 9.9±6 3.06±1.9 0.23±0.1 13.19±8 93.34±57.1 Prunus cornata 5.35±5.4 - 3.16±3.2 1.09±1.1 0.64±0.6 10.25±10.3 1.35±1.4 0.15±0.1 0.01±0 1.51±1.5 11.76±11.8 Quercus 6.3±6.3 - 3.73±3.7 1.08±1.1 0.51±0.5 11.62±11.6 2.82±2.8 0.48±0.5 0.04±0 3.35±3.3 14.96±15 leucotrichophora Quercus 50.48±33.5 4.44±2.9 13.12±8.7 2.02±1.4 1.63±1.1 71.69±47.6 13.1±8.8 5.72±3.8 2.18±1.5 21±14 92.7±61.7 semecarpifolia Rhododendron 7.37±3.7 0.22±0.1 2.81±1.4 2.49±1.2 0.59±0.3 13.47±6.7 2.55±1.3 1.09±0.6 0.14±0.1 3.78±1.9 17.25±8.7 arboreum Taxus wallichiana 18.44±12.7 0.95±0.7 3.48±2.7 1.28±1 0.89±0.7 25.04±17.7 2.65±2 1.06±0.9 0.14±0.1 3.85±3 28.9±20.7 Grand total 320.08±60.1 25.59±8.9 79.45±12.4 25.92±3.3 13.5±1.8 464.54±81.9 65.07±11.5 27.95±6.9 13.26±6.8 106.28±23.4 570.82±102.5 Pinus Forest Abies pindrow 7.39±7.4 0.98±1 1.56±1.6 0.68±0.7 0.34±0.3 10.95±11 1.46±1.5 0.77±0.8 0.31±0.3 2.53±2.5 13.49±13.5 Acer acuminatum 4.05±4.1 0.27±0.3 1.16±1.2 0.39±0.4 0.12±0.1 5.99±6 1.18±1.2 0.33±0.3 0.09±0.1 1.6±1.6 7.59±7.6 Acer caesium 5.32±5.3 0.35±0.4 1.52±1.5 0.51±0.5 0.16±0.2 7.86±7.9 1.55±1.5 0.43±0.4 0.12±0.1 2.1±2.1 9.96±10 Cedrus deodara 10.61±10.6 0.53±0.5 1.59±1.6 0.51±0.5 0.35±0.3 13.58±13.6 1.2±1.2 0.42±0.4 0.05±0.1 1.68±1.7 15.26±15.3 Juglans regia 5.2±5.2 0.92±0.9 0.65±0.6 0.19±0.2 0.09±0.1 7.04±7 1.05±1 0.32±0.3 - 1.37±1.4 8.42±8.4 Lyonia ovalifolia 1.02±1 0.07±0.1 0.7±0.7 0.38±0.4 0.1±0.1 2.28±2.3 0.42±0.4 0.04±0 0.02±0 0.47±0.5 2.75±2.8 Persea duthiei 1.01±1 * 0.62±0.6 0.24±0.2 0.14±0.1 2.01±2 0.31±0.3 0.03±0 - 0.34±0.3 2.36±2.4 Contd... 464 BIOMASS AND CARBON ALLOCATION IN TREE SPECIES IN HIGH MOUNTAIN FORESTS

Table 2. Continued.

Plant species Bole Bole bark Branch Twig Foliage AGBD Stump root Lateral root Fine root BGBD TBD Picea smithiana 9.44±8.7 0.5±0.5 2.19±2 0.84±0.7 0.58±0.5 13.55±12.4 1.67±1.5 0.69±0.6 0.09±0.1 2.45±2.2 16.01±14.6 Pinus wallichiana 186.76±21.3 - 30.82±3.1 8.95±1 5.53±0.9 232.06±26.2 29.43±3.6 8.9±1 0.72±0.1 39.05±4.7 271.11±30.9 Populus ciliata 1.44±1.4 0.22±0.2 0.23±0.2 0.08±0.1 0.18±0.2 2.15±2.1 0.17±0.2 0.19±0.2 0.02±0 0.38±0.4 2.53±2.5 Rhododendron 0.99±1 0.03±0 0.38±0.4 0.33±0.3 0.08±0.1 1.81±1.8 0.34±0.3 0.15±0.1 0.02±0 0.5±0.5 2.31±2.3 arboreum Symplocos 0.29±0.3 0.02±0 0.11±0.1 0.04±0 0.02±0 0.47±0.5 0.1±0.1 0.01±0 0.01±0 0.12±0.1 0.59±0.6 paniculata Taxus wallichiana 2.06±2.1 0.1±0.1 0.33±0.3 0.1±0.1 0.07±0.1 2.65±2.7 0.25±0.2 0.08±0.1 0.01±0 0.34±0.3 2.99±3 Grand total 235.59±16.4 3.99±2 41.86±5.2 13.23±1.9 7.76±0.7 302.42±24.9 39.11±4 12.38±1.5 1.47±0.4 52.96±5.8 355.38±30.6 Quercus floribunda forest Abies pindrow 36.08±21.3 4.91±2.9 7.63±4.5 3.44±2 1.82±1.1 53.88±31.7 6.98±4.1 3.79±2.2 1.69±1 12.45±7.3 66.33±39.1 Cupressus torulosa 0.1±0.1 0.01±0 0.09±0.1 0.04±0 0.03±0 0.27±0.3 0.04±0 0.04±0 0.01±0 0.09±0.1 0.36±0.4 Ilex dipyrena 0.86±0.4 0.05±0 0.32±0.2 0.05±0 0.03±0 1.31±0.7 - 0.11±0.1 0.05±0 0.16±0.1 1.47±0.7 Lyonia ovalifolia 1.37±0.7 0.1±0.1 0.95±0.5 0.52±0.3 0.15±0.1 3.09±1.6 0.62±0.3 0.05±0 0.03±0 0.7±0.4 3.8±1.9 Neolitsea cuipala 3.19±2.8 0.16±0.1 1.02±0.9 0.26±0.2 0.16±0.1 4.8±4.3 1.21±1.1 0.89±0.8 18.26±16.2 20.37±18 25.17±22.3 Persea duthiei 0.19±0.2 - 0.13±0.1 0.06±0.1 0.03±0 0.41±0.4 0.08±0.1 0.01±0 - 0.09±0.1 0.5±0.5 Pinus wallichiana 0.56±0.6 - 0.07±0.1 0.03±0 0.03±0 0.68±0.7 0.1±0.1 0.03±0 - 0.13±0.1 0.81±0.8 Quercus floribunda 247.33±58.8 13.77±3.3 69.61±16.6 12.31±3 12.21±3 355.22±84.5 35.91±8.7 18.34±4.5 5.69±1.4 59.93±14.6 415.15±98.8 Quercus 0.87±0.9 - 0.52±0.5 0.16±0.2 0.07±0.1 1.62±1.6 0.42±0.4 0.07±0.1 0.01±0 0.5±0.5 2.11±2.1 leucotrichophora Quercus 28.18±17.7 2.47±1.6 7.33±4.6 1.1±0.7 0.89±0.6 39.97±25.1 7.21±4.5 3.16±2 1.19±0.7 11.56±7.2 51.53±32.3 semecarpifolia Rhododendron 9.18±3.7 0.27±0.1 3.59±1.4 3.15±1.3 0.75±0.3 16.94±6.8 3.4±1.3 1.43±0.6 0.19±0.1 5.02±1.9 21.97±8.7 arboreum Symplocos 0.05±0.1 - 0.02±0 0.01±0 - 0.08±0.1 0.01±0 - - 0.02±0 0.1 ± 0.1 paniculata Grand total 327.97±32.5 21.74±1.5 91.27±9.9 21.13±1.9 16.18±1.9 478.29±45.7 55.98±2.5 27.92±1.3 27.12±15.9 111.01±12.9 589.3±40.9 Q. leucotrichophora forest Alnus nepalensis 0.83±0.8 - 1.02±1 0.12±0.1 0.06±0.1 2.03±2 0.4±0.4 - - 0.4±0.4 2.42±2.4 Betula alnoides 0.93±0.9 - 1.35±1.3 0.14±0.1 0.08±0.1 2.5±2.5 0.43±0.4 - - 0.43±0.4 2.93±2.9 Lyonia ovalifolia 2.19±0.9 0.17±0.1 1.52±0.6 0.82±0.4 0.24±0.1 4.94±2.1 1±0.4 0.09±0 0.05±0 1.14±0.5 6.08±2.6 Neolitsea cuipala 0.16±0.2 0.01±0 0.05±0 0.01±0 0.01±0 0.24±0.2 0.06±0.1 0.05±0 0.94±0.9 1.06±1.1 1.29±1.3 Contd... RAM KRISHAN et al. 465 Table 2. Continued.

Plant species Bole Bole bark Branch Twig Foliage AGBD Stump root Lateral root Fine root BGBD TBD Pyrus pashia 0.42±0.4 - 0.28±0.3 0.13±0.1 0.07±0.1 0.9±0.9 0.17±0.2 0.02±0 - 0.19±0.2 1.09±1.1 Quercus floribunda 20.52±14.7 1.13±0.8 5.72±4.1 0.98±0.7 0.98±0.7 29.33±21 2.87±2 1.45±1 0.45±0.3 4.78±3.3 34.11±24.3 Quercus 156.4±26.2 - 93.32±15.5 28.65±4.5 13.42±2.1 291.79±48.3 74.83±11.9 13.03±2 1.12±0.2 88.98±14.1 380.76±62.3 leucotrichophora Rhododendron 14.53±7.3 0.43±0.2 5.39±2.7 4.82±2.4 1.12±0.6 26.3±13.2 4.67±2.4 2.04±1 0.24±0.1 6.96±3.6 33.25±16.8 arboreum Swida oblonga 1.45±1.4 - 0.9±0.9 0.37±0.4 0.22±0.2 2.94±2.9 0.48±0.5 0.05±0.1 - 0.54±0.5 3.48±3.5 Toona ciliate 0.35±0.3 - 0.22±0.2 0.09±0.1 0.05±0.1 0.71±0.7 0.12±0.1 0.01±0 - 0.13±0.1 0.84±0.8 Grand total 197.77±28.7 1.74±0.9 109.77±13.9 36.14±3.1 16.24±1.9 361.67±47.4 85.04±10.3 16.75±2.1 2.81±1.1 104.6±12.4 466.27±59.6 Q.semecarpifolia forest Abies spectabilis 0.97±1 0.13±0.1 0.21±0.2 0.09±0.1 0.05±0 1.46±1.5 0.19±0.2 0.1±0.1 0.05±0 0.34±0.3 1.79±1.8 Ilex dipyrena 0.45±0.3 0.02±0 0.18±0.1 0.03±0 0.02±0 0.69±0.5 - 0.06±0 0.03±0 0.09±0.1 0.78±0.5 Lyonia ovalifolia 1.49±1.1 0.11±0.1 1.02±0.7 0.55±0.4 0.15±0.1 3.32±2.4 0.62±0.4 0.06±0 0.03±0 0.7±0.5 4.02±2.9 Neolitsea cuipala 2.46±2.5 0.12±0.1 0.81±0.8 0.2±0.2 0.12±0.1 3.72±3.7 0.87±0.9 0.63±0.6 13.37±13.4 14.86±14.9 18.58±18.6 Picea smithiana 5.29±5.3 0.28±0.3 1.02±1 0.34±0.3 0.24±0.2 7.17±7.2 0.78±0.8 0.29±0.3 0.04±0 1.1±1.1 8.27±8.3 Pinus wallichiana 3.91±3.9 - 0.66±0.7 0.19±0.2 0.11±0.1 4.86±4.9 0.61±0.6 0.19±0.2 0.01±0 0.81±0.8 5.67±5.7 Quercus floribunda 5.01±5 0.29±0.3 1.45±1.5 0.28±0.3 0.27±0.3 7.3±7.3 0.8±0.8 0.41±0.4 0.13±0.1 1.34±1.3 8.64±8.6 Quercus 234.38±38.1 20.59±3.4 60.93±9.9 9.31±1.7 7.52±1.3 332.73±54.4 60.52±10.4 26.47±4.5 10.04±1.8 97.03±16.7 429.76±71.1 semecarpifolia Rhododendron 14.03±6.2 0.41±0.2 5.42±2.4 4.77±2.1 1.13±0.5 25.76±11.3 5.01±2.2 2.13±0.9 0.27±0.1 7.41±3.2 33.17±14.5 arboreum Symplocos 0.09±0.1 - 0.04±0 0.01±0 - 0.15±0.1 0.04±0 - - 0.04±0 0.19±0.2 paniculata Grand total 268.08±31.2 21.96±3 71.73±6.6 15.78±1.4 9.61±0.8 387.15±41.1 69.42±7.7 30.33±3.2 23.97±12.1 123.72±8.6 510.87±43.4

Note – all above values of biomass are shown in Mg ha-1.

466 BIOMASS AND CARBON ALLOCATION IN TREE SPECIES IN HIGH MOUNTAIN FORESTS

Table 3. Results of the ANOVA test on various ecological attributes in the eight different forest types.

Parameters F value P value r2 Elevation 7.52 0.00 0.62 Species richness 1.13 0.37 0.20 TBC 1.84 0.12 0.29 Density 2.55 0.03 0.36 Volume 2.02 0.08 0.31 S/R ratio 2.55 0.03 0.36 Bole 4.89 0.00 0.52 Bole bark 13.19 0.00 0.74 Branch 7.07 0.00 0.61 Twig 16.11 0.00 0.78 Foliage 8.43 0.00 0.65 AGBD 4.45 0.00 0.49 Stump root 8.40 0.00 0.65 Fig. 5. DBH wise distribution of biomass in different Lateral root 11.80 0.00 0.72 forest types. Fine root 1.96 0.09 0.30 BGBD 9.12 0.00 0.67 species in forests. This diagram shows very much TBD 5.65 0.00 0.55 similarity in the distributional patterns of plant TCD 5.70 0.00 0.56 species among PF – QFF – QSF forests. Similarly, MCF and CF have similar plant composition (Fig. 4). To study the forest structure, all trees were divided into different DBH classes. Biomass of all studied forests showed decrease in value with increasing DBH. DBH classes of 10–30 cm, 30–50 cm and 50–70 cm shows higher biomass production in comparison to other DBH classes (Fig. 5).

Discussion

Tree biomass in forest ecosystems varies with forest types, species composition, DBH class of trees, study area, precipitation, stand age, soil factors, and altitude (Gairola et al. 2011; Zhao et al. 2013). The values of total biomass density taken in different temperate forests of Garhwal Himalaya ranged between 286.33±71.21 and 636.3±155.19 Mg ha-1. These values are higher than earlier reported values from Garhwal Himalaya, compa-rative values of TBD and TCD from Uttarakhand and other parts of India which are presented in Table 4. The values of carbon stocks estimated in the study ranged between 131.73±13.75 Mg C ha-1 to Fig. 4. DCA ordination showing distribution of tree 292.7±29.72 Mg C ha-1. These values are higher species under different forest types. than those reported by Gairola et al. (2011). It may be attributed to the lack of disturbance in these approximates the dissimilarity of distribution of forest stands, but these values of carbon density relative abundance of those species across the were in accordance with those reported by Suwal et forests. The distance between forests (Circle) and al. (2014) and Jina et al. (2008). FSI (2013) also species (triangle) approximates the (predicted) reported the highest growing stock volume relative frequency (or probability of occurrence) of for Uttarakhand in India followed by Arunachal RAM KRISHAN et al. 467

Table 4. Comparison of biomass and carbon stocks with earlier reported values in different forest types of Garhwal Himalaya, Uttarakhand and other parts of India.

Forest Type Study area Elevation TBD TCD (m asl.) (Mg ha-1) (Mg C ha-1) Abies forests Uttarkashi, Uttarakhand [P] 2580–3460 636.3±64.6 292.7±29.72 Betula forests Uttarkashi, Uttarakhand [P] 3220–3460 286.37±29.9 131.73±13.75 Cedrus forests Uttarkashi, Uttarakhand [P] 2425–3125 366.13±59.1 168.42±27.19 Pine forests Uttarkashi, Uttarakhand [P] 2577–3252 355.38±30.6 163.47±14.08 Oak forests Uttarkashi, Uttarakhand [P] 1428–3400 522.15±47.97 234.97±21.58 Temperate forests Garhwal Himalaya [Sharma 2700–3675 283-464 127–208 et al. 2016] Sub-tropical Moist Doon Valley, Uttarakhand – 338.40–438.17 169.20–219.08 Deciduous Forests [Shahid et al. 2015] Cypress forest Nainital, Central Himalaya 2100–2400 178 and 431 89.07 and 206 [Rana et al. 2015] Tropical forest Nagaon, Assam [Borah et al. 250–270 135.3–146.2 67.64–73.21 2015] Temperate forests Anantnag, J&K [Dar & 1550–5425 100.8–294.8 112.5 to 205.7 Sundarapandian 2015] Androstachys Mecrusse, Gaza [Magalhães – 167.05 82.73 johnsonii forests & Seifert 2015] Sal forests Doon Valley, Uttarakhand – 392.25–597.41 184.26–280.78 [Mandal & Joshi 2015] Himalayan forests Langtang, Nepal [Suwal et al. 540–5144 248–444 167–284.5 2014] Ailanthus excelsa Dehradun, Uttarakhand [Giri 449 126.07 78.6 forest & Rawat 2013 ] Temperate forests Kashmir, J&K [Singh et al. 1700–4000 22.09–657.88 – 2012] Pinus kesiya forest Riat Laban, Meghalaya 1645–1660 460.5 283.1 [Baishya & Barik 2011] Moist temperate Chamoli, Uttarakhand 1500–2850 215.5 to 468.2 107.8 to 234.1 forests [Gairola et al. 2011] Sub-tropical & Pauri, Uttarakhand [Sharma 350–3100 129-533 59 -245 temperate forests et al. 2010] Temperate forests Changbai, Northeast China 500–2744 – 52–245 [Zhu et al. 2010] Oak forests Kumaun, Central Himalaya – – 242.56–290.62 [Jina et al. 2008] Temperate conifer India (1994) [Manhas et al. – 258.68–278.33 28.56 –65.03 forests 2006] Acer, Betula and Pindari, NDBR, uttarakhand 2750–3300 38.7–304.3 – Rhododendron [Garkoti & Singh 1995] forests

Contd... 468 BIOMASS AND CARBON ALLOCATION IN TREE SPECIES IN HIGH MOUNTAIN FORESTS

Table 4. Continued.

Forest Type Study area Elevation TBD TCD (m asl.) (Mg ha-1) (Mg C ha-1) Abies and oak Almora, Uttarakhand 2000–3000 502–590 – forests [Adhikari et al. 1995] Tropical-temperate Central Himalaya [Singh et 1800–3300 500–600 – forests al. 1994] Pine forest Central Himalaya [Rana et 1700 200.8 – al. 1988] Temperate Oak Kumaun, Central Himalaya 1950–2240 294–467 – forests [Rawat & Singh 1988] Pine forest Central Himalaya 1650–1750 113–283 – [Chaturvedi & Singh 1987]

Note- TBD- Total Biomass Density; TCD- Total Carbon Density; P- Present study.

Pradesh. Negi et al. (2003) suggested that the species like B. utilis and R. arboreum having long forests have maximum C storage potential in the rotation periods as suggested by Singh et al. (1994) following order: conifers > deciduous > evergreen > who also reported that biomass remained high up to bamboos. The present study also recorded that C 2600 m altitude and then it declines sharply above stocks decrease in the order as follows: Abies forests 3100 m asl in Betula and Rhododendron forests. > mixed conifer forests > oak forests > Pine forests > The distribution of different forests on Cedrus forests > Betula forests. The proportion of different elevations is shown in figure 3. Along foliage biomass to the total aboveground biomass with the climatic variations, lower elevational varied from 2.48%–.60 % in studied forest types ridges had comparatively higher number of species which followed to the earlier studies (Negi et al. than lower number of species at higher elevational 1983; Rawat & Singh 1988). The bole bark biomass ridge tops, which imply the climatic adaptation of is major contributor of total biomass of an plant species. There is also seen exponential individual tree. It generally constitute of 63–78% of decline in biomass and carbon increment with AGB and about 50–66% of total tree biomass of an increasing DBH as observed in our study is individual trees in different forest types. Similarly, primarily related to the age of trees (Fig. 5). stump roots constitute about 50–80% of total BGB Forests in studied area were mature with higher and 8–13% of total tree biomass. girth values as they were undisturbed. According Both types of results have been reported by to Saxena et al. (1979), trees with higher girths researchers in many parts of the world that live tree indicate the best representation of a species in the biomass and carbon stocks decline with increasing particular forest in specific environmental altitude (Leuschner et al. 2007; Moser et al. 2008; conditions whereas, lower girths either indicate Zhu et al. 2010). However, in some other studies the chance occurrence of the species in that area or forest biomass and carbon stocks were reported to show presence of the biotic disturbance in the past. be positively correlated with increasing altitude Age-related declines in carbon stocks are widely (Alves et al. 2010). Biomass and carbon stocks documented (D’Amato et al. 2011; Bradford & generally increase with stand age (Pregitzer & Kastendick 2010). Moreover, the Indian forests Euskirchen 2004) and cool temperature combined had 6,865.1 Mt AGBD and 1,818.7 Mt BGBD with high precipitation favours carbon accumu- stocks, respectively, which had indicated a 79: 21 lation (Keith et al. 2009). Carbon stocks of different ratio (Chhabra et al 2002). In our study also we forest types in present study showed increase in the have found similar AGBD (78.7%) and BGBD value as we moves from lower to higher elevation (21.3%) ratio. In the present study, tree density in up to 3500 m asl, but decrease in carbon stocks was the different forests show changes as function of seen upon further increase in altitude only in case altitude, forest types, biotic and abiotic of Betula forests. This might be due to the broad- components of the forest ecosystem. Parthasarathy leaved upland hardwood tree species are dominated & Karthikeyan (1997) and Samant et al. (2002) in by lower diameter classes (young trees) and tree temperate Himalayan forests also reported RAM KRISHAN et al. 469 increase in density with increase in altitude. Conclusion On the basis of above facts, it is found that the temperate forests of Garhwal Himalaya were The study concluded that Abies spectabilis conifer-dominated stands, especially by Abies forest, mixed coniferous forest and oak forests spectabilis, Cedrus deodara and Pinus wallichiana stored more biomass and carbon stocks as compared at higher elevations and oak forests at lower to other forest types in Garhwal Himalaya. The elevations. Temperate forests has possessed study revealed that the conifer-dominated forest higher stand volume, long rotation period and types had higher carbon storage potential than have the more impact, per unit areas compared to broadleaf-dominated forest types. These results will other forest types. Hence, these forests can be be useful for conservation practices and their proved as a promising carbon sink and helps in plantation whereas it could be helpful for reduction of carbon emissions from deforestation controlling the global warming and carbon and other anthropogenic activities. Conservation of mitigation. Study results can be used in these forests is necessary for maintaining the rehabilitation of degraded forests, forest ecological balance in the Himalaya. Quantification afforestation, and enriching current existing forests of the current forest composition and carbon which enhanced to mitigate climate change as these dynamics is crucial in order to assess the role of actions support high carbon sequestration in biomass. The highest potential of C sinks can climate change in predicting effect on future mitigate the consequences of greenhouse effects as species coexistence and species shift in Himalayan well as energy demands in local areas. Carbon stock range. Along the climatic variation, lower variation in tree biomass will be helpful to elevational ridge tops had comparatively higher recommend future forest carbon management in the number of species and lower number of species at Himalayan range. higher elevational ridge tops, which suggests the selective climatic adaptation of few tree species. Acknowledgements The carbon reservoirs in this forest biomass are very large, highlighting the importance of The authors are thankful to Department of conserving natural forest, and eliminating Science and Technology, Government of India, agricultural practices and other anthropogenic New Delhi, India for providing financial support. disturbances which contribute to the deterioration (Project No. SERB/SR/SO/PS/14/2010) and Univer- of these reservoirs. Conserving forests is a means sity Grant Commission, India for providing fellow- of adapting to climate change. It provides ship under Rajiv Gandhi National fellowship for protection against surface erosion regulates water SC scheme. flows and limits landslides and rock falls. Forests at the coastline provide protection against wind References and water erosion as well as water and sand Adhikari, B. S., Y. S. Rawat & S. P. Singh. 1995. intrusion. All of these facts strengthen the needs of Structure and Function of High Altitude Forests of conservation of these forests for maintaining the Central Himalaya I. Dry Matter Dynamics. Annals ecological balance in the Himalaya and other of Botany 75: 237–248. forest ecosystems. Alves, L. F., S. A. Vieira, M. A. Scaranello, P. B. Camargo, As the climate change issues became F. A. M. Santos, C. A. Joly & L. A. 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(Received on 12.12.2016 and accepted after revisions, on 05.08.2018)