63 Forests are one of the biggest terrestrial carbon pools. Forests (vegetation and soil) store 60% of the world’s terrestrial carbon (Iturbide et al., 2020). Therefore, sustainable forest management is recognized as one of the best climate change mitigation measures (Arasa- Gisbert et al., 2018). However, the ever-growing human population and its impacts on forests such as deforestation and forest degradation are posing a great challenge to the very existence and the vitality of forest ecosystems. Studies have indicated that anthropogenic pressures have indiscriminately degraded the forest ecosystems over the past few decades (Sundriyal & Sharma, 1996; Dhyani et al., 2019). In a forest ecosystem, carbon is stored in various pools such as above and below-ground living biomasses, including standing stems, branches, foliage and roots; and necromasses, including litter, woody debris, soil organic matter and forest products (Riutta et al., 2021). Among others, trees and soil are the main pools that store more carbon than the other pools (Amir et al., 2018). Currently, forests store around 45% of the organic carbon on land in their biomass and soils (Bonan, 2008). About 2 gigatonnes (GtC) of carbon are absorbed annually by existing old- growth and regenerating forests collectively, which significantly contributes to the terrestrial carbon sink (Pugh et al., 2019). About 40% of the global soil organic carbon (SOC) stock resides in the forest ecosystems (Eswaran et al., 1999). The current global stock of soil organic carbon is estimated to be 1,443 ± 141 Pg C and 3,153 ± 312 Pg C in top soils and subsoils respectively, Banko Janakari, Vol 32 No. 2, 2022 Pp 63‒76https://doi.org/10.3126/banko.v32i2.50896 Tree carbon stock in middle mountain forest types: A case study from Chandragiri hills, Kathmandu, Nepal The forest carbon stock usually depends on the forest types, forest density, age of forest, size of trees, site quality, wood density, annual precipitation, and species composition. This research aims to analyze the relationship among tree carbon stock, species richness, soil chemical properties such as soil organic carbon and soil pH in the Forests of Chandragiri Hills, Kathmandu, Central Nepal. Along this forest, five square plots (20 × 20 m2) each were established along the two transects at a maximum interval of 100 m. Carbon stock of each tree was estimated by using allometric equation based on measured tree height and DBH. The mean tree carbon stock was found to be highest in Mixed Forest (87.13 t/ha) followed by Oak Forest (52.75 t/ha), and Pine Forest (22.5 t/ha). The tree carbon stock showed significant negative correlation with tree species richness (r = -0.56, p = 0.001). The tree carbon stock showed significant positive correlation with soil organic carbon (r = 0.57, p = 0.001) and soil pH (r = 0.37, p = 0.05). Tree carbon was found positively highly significant correlation with altitude, Soil organic carbon, pH and Shannon diversity index. Keywords: Mixed forest, oak forest, pine forest, soil organic carbon, soil pH, species richness, tree biomass R. Gurung 1, H. S. Adhikari 1, R. S. Dani 1,2, and C. B. Baniya 1* Received: 8, August 2022 Revised: 9, November 2022 Accepted: 14, December 2022 Published: 31, December 2022 1 Central Department of Botany, Tribhuvan University, Kirtipur, Nepal, * Email: cbbaniya@gmail.com 2 Trichandra Multiple Campus, Ghantaghar, Kathmandu, Nepal https://orcid.org/0000-0003-4265-622X https://orcid.org/0000-0003-1472-1469 https://orcid.org/0000-0002-6206-0274 https://orcid.org/0000-0002-8746-7601 Banko Janakari, Vol 32 No. 2 64 Gurung et al. totaling to be 4,596 Pg C ± 453 Pg C to a depth of 1 m (Georgiou et al., 2022). Tropical forests that cover 7% of the earth’s total land surface and that are among the major carbon sinks play a significant role in global carbon cycle (Nascimento & Laurance, 2004). Studies have shown that forests have a tremendous role in lowering the net Green House Gases (GHGs) emissions to the atmosphere and mitigating the adverse impacts of climate change (Creutzig, 2015; Moomaw et al., 2020). However, clearing tropical forests for non-forestry uses is destroying globally important carbon sinks that are vital for sequestering CO2and future climate stabilization (Stephens et al., 2007). Globally, it is estimated that tropical deforestation accounts for annual emission of about 1.7 billion tons of carbon (Nakicenovic et al., 2000). The rate of emission depends on the types of disturbance such as logging, understory fires, edge effects etc. as well as the intensity and the frequency of disturbance events (Barlow et al., 2012; Sullivan et al., 2017). Iturbide et al.(2020) has shown that reducing deforestation and forest degradation lowers GHG emissions (high confidence), with an estimated technical mitigation potential of 0.4–5.8 GtCO2 yr -1 highlighting an important role of Reducing Emissions from Deforestation and Forest Degradation (REDD+) in mitigating climate change. In Nepal, forests cover about 44.74% of the country’s total land area. Nepalese forests could play an important role in the mitigation of global climate change (Ghimire et al., 2018). Carbon stock estimation reflects the potentiality of forests to mitigate climate change (Ghimire et al., 2018). The forest carbon stock usually depends on the forest types, forest density, and age of forest, size of trees, site quality, wood density, annual precipitation, and species composition. Furthermore, understanding the relationship between forest carbon stock and tree-species diversity and soil properties will be critical in maintaining carbon stocks of forests over the long term and improving our understandings of species-level management (Kaushal & Baishya, 2021). In this context, this research aims to analyze the relationship between tree carbon stock and tree species diversity and soil chemical properties in the mountain forests of Nepal. Estimating carbon stocks is highly desirable in different forest types, and the community forestry program of Nepal should promote it. Conifer- dominated Forest types store more carbon than broad-leaf-dominated forest types (Sharma et al., 2010; Aryal et al., 2013) while Shrestha & Devkota (2013) has found the higher carbon stock in Oak Forest (90.37 MgCha-1) than that in Pine Forest (24.82 MgCha-1). By sequestering atmospheric carbon in the growth of wood biomass through the process of photosynthesis, trees store carbon by raising the level of soil organic carbon (Brown & Pearce, 1994). Pradhan et al. (2012) has shown that the tree carbon stock and Soil organic carbon was higher in Schima-Castanopsis (Mixed Forest) than in pine forest. Based on the existing literature, it can be hypothesized that higher species richness and better soil properties will have higher tree carbon stock. Materials and methods Study site The study was conducted in the three forests [(Mixed Forest (MF), Oak Forest (OF), and Pine Forest (PF)] of Chandragiri Hills, which lies in Kathmandu district, Central Nepal (Figure 1). The selected forests were managed by three different community forest user groups (CFUGs) (Table 1). The geographic location of the study site extends from 27°27′E to 27°49′E longitude and 85°10′N to 85°32′N latitude. It ranges in elevation from 1600 to 2400m a.s.l. The study site has a sub-tropical to temperate climate with rainy summer and dry winter. The weather data recorded at the nearest weather station (Panipokhari weather station, provide coordinates here) showed that the average annual minimum and maximum temperature of the study site are 7.70°C and 14.12°C respectively and the site receives an annual precipitation of about 1,559.25 mm (Figure 2). Banko Janakari, Vol 32 No. 2 65 Gurung et al. Figure 1: Map of Kathmandu district showing the plots of studied sites of different forest types Table 1: Overview of study forests. The forest types, major tree species, the names of community forest user groups managing the forests, their area and the years the forests were handed over for community management Forest Types Major tree species Name of Community Forest User Groups (CFUGs) and their addresses Area (ha) Handover Year (AD) Mixed Forest Schima wallichii, Myrica esculenta, Castanopsis indica, and Myrsine sp. Laglagepakha CFUG, Thankot 24.509 1994 Oak Forest Quercus semecarpifolia and Rhododendron arboreum Gumalchoki CFUG, Chandragiri 80.5 2000 Pine Forest Pinus roxburghii Bosan CFUG, Kirtipur 57 1994 Banko Janakari, Vol 32 No. 2 66 Gurung et al. Figure 2: Ombrothermic diagram showing mean monthly temperature and precipitation from years 2011-2020 of Panipokhari weather station, Kathmandu (Source: Department of Hydrology and Meteorology Kathmandu Nepal, DHM, 2021). Daily average Maximum temperature at station was 20.9˚C and total monthly average annual rainfall was 1559 mm. Sampling design and data collection The transect method was used for vegetation survey and soil sample collection. A total of six transects, two transects per study forest were laid out. A distance of 100m was maintained between two transects. Five square plots of size 20 × 20m2 were established in each transect maintaining a distance of 100m between the plots (Figure 3). The geographic locations (latitude, longitude and elevation) of plots were recorded using the Garmin eTrex GPS. Within each plot, the height and diameter at breast height (DBH = 1.37 m) of individual trees (DBH > 5 cm) were measured using a clinometer and a DBH tape (FRTC, 2022), and tree species having diameter less than 5cm were also used for vegetation analysis. The plant specimens were identified using standard literature (Malla et al., 1986; Press et al., 2000) and by tallying with Tribhuvan University Central Herbarium (TUCH) specimens. The world flora online (http://www.worldfloraonline.org/) was followed for the specimen nomenclature. Figure 3: Sampling design used for vegetation survey and soil sample collection. Two transects with five square plots (20 × 20m2) each separated by a distance of 100 m were laid out for vegetation survey. Soil samples were collected from five points (four corners and center) of each plot. The soil samples were collected from five points (four corners and center) of each plot. They were collected from the depth of 10 cm. One composite soil sample of 200gm per plot was prepared by mixing soil samples from five points. The soil samples were air-dried in the shade for a week and were taken for laboratory analysis. Vegetation analysis Frequency, density, basal area, and importance value index (IVI) were calculated by using the methods and equations (1-7) provided by Zobel et al.(1987), which are given below. Frequency (%)= ……….. (1) Density (trees/ha)= …….(2) Basal Area (BA) of tree (m2) = ……. (3) Where, DBH = diameter at breast height (m), and = 3.14 IVI=Relative frequency + Relative density + Relative basal area ……… (4) Banko Janakari, Vol 32 No. 2 67 Gurung et al. Where, Relative frequency (%) = ×100 ……………….(5) Relative density (%) = ×100 ……………………(6) Relative basal area (%) = ×100 …………………….. (7) Simpson’s diversity index and Shannon index were calculated using standard equations provided by Magurrun (2004) to estimate species diversity of the study forests. Interpretation of forest regeneration by size class distribution is better than seedling counts because the former represents longer periods (Maren & Vetaas, 2007). Trees recorded in all plots were divided into DBH classes of 5 cm interval. Then the size class distribution graph was prepared to analyze the regeneration status of the study forests. Biomass and carbon stock estimation The total above-ground tree biomass (AGTB) was calculated using the equation (model, Equation 8) developed by Chave et al. (2005). For moist forest types, AGTB = 0.0509x ρD2H …… (8) where, AGTB = above-ground tree biomass (kg); ρ = wood specific gravity (g cm-3); D = tree diameter at breast height (cm); H = tree height (m). The global database developed by Zanne et al. (2009) was used for the wood specific gravity. For some tree species, for which wood specific gravity information were unavailable in Zanne et al. (2009), information in Penman et al. (2003) were used. Below-ground tree biomass (BGTB) was estimated by assuming that it constitutes 15% of AGTB (MacDicken, 1997). The total tree biomass (only living) was calculated by adding the above and below-ground biomass of the trees. Finally, the living tree carbon stock was calculated by multiplying the total tree biomass with the default carbon fraction of 0.47 (Eggleston et al., 2006). Soil analysis Soil samples were analyzed in the laboratory of the Agricultural Technology Centre, Jwagal, Lalitpur, Nepal. The soil properties such as pH and water holding capacity (WHC) were estimated using which methods. Soil organic carbon (SOC) was estimated using the rapid titration method developed by Walkey & Black (1934). Data analysis Descriptive statistics were used to calculate means, range, and standard errors. ANOVA was used to test the difference between the forest types. Correlation analysis and scatter plots were used to analyse the relationship between carbon stock and species diversity and soil properties. All the analyses were done using Microsoft-Excel 2007 and R version 4.1.2 (R Core Team, 2022). Results Tree community attributes of forest types Altogether 19 tree species belonging to 16 families were recorded in mixed forest. Schima wallichii (59.71) had the highest IVI, followed by Pinus roxburghii and Castanopsis tribuloides (Table 2.).On the other hand, only two species (2 families) and three species (3 families) were recorded in oak and pine forests respectively. Quercus semecarpifolia (254.19) and Pinus roxburghii (228.57) had the highest IVIs in the Oak and Pine forests respectively (Table 2). Banko Janakari, Vol 32 No. 2 68 Gurung et al. Table 2: Tree community attributes (BA, Basal area; RBA, Relative basal area; D, Density; RD, Relative Density; F, Frequency; RF, Relative Frequency: and IVI, Importance Value Index) of Mixed, Oak and Pine Forests of Chandragiri Hills, Kathmandu, Central Nepal. Forest SN Tree species Family BA (cm2) RBA (%) D (/m2) RD (%) F (%) RF (%) IVI (%) Mixed Forest 1 Schima wallichii Choisy Theaceae 31961.15 24.56 208.00 25.24 10.00 9.90 59.71 2 Pinus roxburghii Sarg Pinaceae 43199.00 33.20 86.00 10.44 9.00 8.91 52.55 3 Castanopsis tribuloides A.DC Fagaceae 15888.57 12.21 139.00 16.87 10.00 9.90 38.98 4 Myrica esculenta Buch.- Ham. Myricaceae 13480.02 10.36 104.00 12.62 9.00 8.91 31.89 5 Castanopsis indica (Roxb. ex Lindl.) A.DC. Fagaceae 11510.34 8.85 86.00 10.44 9.00 8.91 28.19 6 Rhododendron arboreum Sm. Ericaceae 2537.78 1.95 47.00 5.70 8.00 7.92 15.58 7 Myrsine semiserrata Wall. Myrsinaceae 2588.06 1.99 52.00 6.31 7.00 6.93 15.23 8 Symplocos pyrifolia Wall. ex G. Don Symplocaceae 1522.51 1.17 33.00 4.00 8.00 7.92 13.10 9 Syzygium cumini (L.) Skeels Myrtaceae 1851.52 1.42 19.00 2.31 6.00 5.94 9.67 10 Eurya acuminata DC. Pentaphylacaceae 1088.74 0.84 17.00 2.06 6.00 5.94 8.84 11 Fraxinus floribunda Wall. Oleaceae 741.33 0.57 13.00 1.58 6.00 5.94 8.09 12 Rhus javanica L. Anacardiaceae 281.60 0.22 8.00 0.97 3.00 2.97 4.16 13 Engelhardtia spicata Lechen ex Blume Juglandaceae 867.27 0.67 4.00 0.49 3.00 2.97 4.12 14 Persea gamblei (King ex Hook. f.) Kosterm. Lauraceae 405.78 0.31 2.00 0.24 2.00 1.98 2.53 15 Albizia lebbeck (L.) Benth. Leguminosae 1460.97 1.12 2.00 0.24 1.00 0.99 2.36 16 Betula alnoides Buch.- Ham. ex D.Don Betulaceae 373.25 0.29 1.00 0.12 1.00 0.99 1.40 17 Rhus succedanea L. Anacardiaceae 248.85 0.19 1.00 0.12 1.00 0.99 1.30 18 Semecarpus anacardium L.f Anacardiaceae 56.75 0.04 1.00 0.12 1.00 0.99 1.16 19 Pyrus pashia Buch.- Ham. ex D.Don Rosaceae 50.27 0.04 1.00 0.12 1.00 0.99 1.15 Oak Forest 1 Quercus semecarpifolia Sm. Fagaceae 613993.12 99.68 495.00 92.01 10.00 62.50 254.19 2 Rhododendron arboreum Sm. Ericaceae 2001.28 0.32 43.00 7.99 6.00 37.50 45.81 Pine Forest 1 Pinus roxburghii Sarg Pinaceae 274066.75 95.09 380.00 80.85 10.00 52.63 228.57 2 Schima wallichii Choisy Theaceae 13057.06 4.53 76.00 16.17 7.00 36.84 57.54 3 Castanopsis indica (Roxb. ex Lindl.) A.DC. Fagaceae 1106.82 0.38 14.00 2.98 2.00 10.53 13.89 Regeneration status of forest types The DBH class distribution curve of Mixed Forest showed a reverse J-shaped distribution. Whereas that of Oak Forest showed a U-shaped distribution. Meanwhile, Pine Forest showed a bell-shaped distribution with a higher number of individuals in the middle DBH classes (Figure 4). Figure 4: Tree diameter at breast height (DBH) class distribution of Mixed, Oak and Pine Forests of Chandragiri Hills, Kathmandu, central Nepal Banko Janakari, Vol 32 No. 2 69 Gurung et al. Variation in tree carbon stock with forest types The mean living tree carbon stock was found to be highest in Mixed Forest (87.13 t/ha) followed by Oak Forest (52.75 t/ha) and Pine Forest (22.50 t/ha). The differences in living tree carbon stock among the three forests types were significant at a 95% confidence interval (Figure 5). Regarding the species contribution to the living tree carbon stock, Albizia lebbeck contributed the most (34.93%)to the living tree carbon stock in Mixed Forest followed by Betula alnoides (12.33%), Pinus roxburghii (12.25%), Myrsine semiserrata (9.31%). The least contribution was made by Pyrus pashia (0.29%, Table 3). Figure 5: Mean living tree carbon stock of Mixed Forest (MF), Oak Forest (OF) and Pine Forest (PF of Chandragiri Hills, Kathmandu, central Nepal. Differences between the forest types were tested using ANOVA. Bar diagrams with same letters at the top are not significantly different while those with different letters at the top are significantly different (name which post-hoc test was used, P<0.05). Error bars shows uncertainty in the estimation. Table 3: Tree species contribution to the living tree carbon stock of Mixed Forest, Oak Forest and Pine Forest of Chandragiri Hills, Kathmandu, Central Nepal. Mean diameter at breast height (DBH), mean height (HT), total biomass (TB), and total carbon stock (CS) Forests SN Tree Species DBH (cm) HT (m) TB (Mg) CS (t/ha) CSC (%) Mixed Forest 1 Albizia lebbeck (L.) Benth. 29.20 19.85 647.50 30.43 34.93 2 Betula alnoides Buch.-Ham. ex D.Don 21.80 13.70 228.67 10.75 12.33 3 Pinus roxburghii Sarg 22.84 11.79 227.05 10.67 12.25 4 Myrsine semiserrata Wall. 19.75 12.00 172.51 8.11 9.31 5 Persea gamblei (King ex Hook. f.) Kosterm. 16.05 9.90 90.59 4.26 4.89 6 Myrica esculenta Buch.-Ham 12.14 9.55 66.31 3.12 3.58 7 Schima wallichiiChoisy 13.21 8.88 60.20 2.83 3.25 8 Syzygium cumini (L.) skeels 10.80 9.87 57.33 2.69 3.09 9 Rhus succedanea L. 17.80 6.70 54.67 2.57 2.95 10 Engelhardtia spicata Lechen ex Blume 15.70 6.93 53.79 2.53 2.90 11 Castanopsis indica (Roxb. ex Lindl.) A.DC. 12.41 7.27 50.16 2.36 2.71 12 Castanopsis tribuloides A.DC 11.44 9.01 49.09 2.31 2.65 13 Fraxinus floribunda Wall. 8.25 8.26 22.45 1.06 1.21 14 Semecarpus anacardiumL.f 8.50 7.60 19.28 0.91 1.04 15 Eurya acuminata DC. 8.64 7.42 19.23 0.90 1.04 16 Symplocos pyrifolia Wall. ex G. Don 7.09 5.54 11.88 0.56 0.64 17 Rhododendron arboreum Sm. 7.82 4.86 10.09 0.47 0.54 18 Rhus javanica L. 6.48 6.10 7.02 0.33 0.38 19 Pyrus pashia Buch.-Ham. ex D.Don 8.00 2.70 6.07 0.29 0.33 Total 264.73 174.05 1867.26 87.13 100.00 Oak Forest 1 Quercus semecarpifolia Sm. 29.12 13.03 1110.26 52.18 98.93 2 Rhododendron arboreum Sm. 7.40 7.17 11.98 0.56 1.07 Total 36.52 20.20 1122.23 52.75 100.00 Pine Forest 1 Pinus roxburghii Sarg 29.47 14.00 370.36 17.41 77.38 2 Schima wallichii Choisy 14.35 10.58 74.60 3.51 15.59 3 Castanopsis indica (Roxb. ex Lindl.) A.DC. 9.37 8.79 33.66 1.58 7.03 Total 53.19 33.36 478.62 22.50 100.00 Banko Janakari, Vol 32 No. 2 70 Gurung et al. Variation of soil properties with forest types The soil organic carbon (SOC) was found to be significantly higher in Oak Forest (5.57±1.18) than in Mixed Forest (2.54±0.86) and Pine Forest (2.82 ± 0.48, Table 4). All three forest types were found to have acidic soil. The soil pH of Mixed Forest (6.11±0.5) was found to be significantly more acidic than that of Oak Forest (6.57±0.3) and Pine Forest (6.4±0.25). In comparison to Mixed Forest and Oak Forest, Pine Forest (63.4±9.82) was found to have significantly lower water-holding capacity (WHC, Table 4). Table 4: Soil properties (Mean ± SD) of Mixed Forest, Oak Forest and Pine Forest of Chandragiri Hills, Kathmandu, Central Nepal. Differences between the forest types were tested using ANOVA. Values with same letters in superscript are not significantly different while those with different letters in superscript are significantly different (name which post-hoc test was used, P < 0.05). Forests SOC (%) pH WHC (%) Mixed 2.54±0.86a 6.11±0.5a 80.1±7.33a Oak 5.57±1.18b 6.57±0.3b 79±7.29a Pine 2.82±0.48a 6.4±0.25b 63.4±9.82b F-value 35.63 3.9 12.88 p-value <0.0001 0.033 <0.0001 Relationship between tree carbon stock and tree species diversity and soil properties The living tree carbon stock showed a strong negatively significant (-0.56) relationship with the species richness of trees in forests. Meanwhile, such a relation with altitude was strong and positively significant (0.83). Correlation analysis (Figure 6) showed that living tree carbon stock was found to have a positively significant (0.57) relationship with Soil organic carbon. The living tree carbon stock has a fair positively significant (0.37) relationship with the pH of the soil. And in the case of the water-holding capacity of the soil, there was no significant (0.084) relationship with the carbon stock of the tree. Discussions Tree community attributes, tree species diversity and regeneration status of forest types The result of the tree community structure indicates the ecological success of dominant species, and their good regeneration potential utilizing most of the forest area and resources (Shameem & Kangroo, 2011) in the study area. Thus, the high IVI of Schima wallichii, Quercus semecarpifolia, Pinus roxburghii and associated species might be due to available resources such as low tree density, sufficient rainfall, good light availability etc. In the present study, a J-shaped DBH class distribution curve structure in Mixed and Oak Forests showed a higher number of trees with a smaller DBH class. This indicates a good natural regeneration state of these forests which are still in evolving stage (Campbell et al., 1992; Basyal et al., 2011). The reversed J-shaped DBH class distribution curve of Pine trees in Pinus Figure 6: Correlation coefficient matrix among different variables (Shan- Shannon Diversity Index, Tree_richnes – Tree species richness, Indi- Individuals, cartphec- Carbon stock ton per hectare, SOC – Soil organic carbon, pH – Soil pH, WHC – Water holding capacity of soil) Each value inside the box represented the correlation coefficient value, Star/s (*) indicated the level of significance. Three stars (***) indicated p < 0.000, two stars (**) indicated p < 0.001 and a single star (*) indicated p < 0.05. Banko Janakari, Vol 32 No. 2 71 Gurung et al. forest indicated artificial regeneration. The mature status of the Pine Forest at the present study site is similar to the result inferred by Dar et al. (2017) and Sharma et al. (2020). This may have been accomplished by minimizing the disturbances and shifting management regimes (Bhatt et al., 2015, Dar et al., 2017). Relationship of living tree carbon stock with species structure and soil properties The forest carbon stock is mainly determined by the nature of vegetation composition of the forest where the seedling and saplings have significantly less contribution (Hu et al., 2015). Higher living tree carbon stock in Mixed Forest and lower in Pine Forest may be due to different factors such as forest types, forest age, size and density of trees, degree of disturbance, species composition and allometric equation used for the estimation of carbon stock (Mandal et al., 2013; Berenguer et al., 2014; Biswas et al., 2020; Saimun et al., 2021). The variation of living tree carbon stock among the forests with different vegetation compositions is more or less supported by Ikraoun et al. (2022), Poudel et al. (2020), Sharma et al. (2020), Verma and Garkoti (2019), Shrestha et al. (2016), Aryal et al. (2013) and Joshi et al. (2013). In these forests, all silvicultural practices (thinning, pruning, singling, litter collection, plantation, fodder collection for cattle, etc.) may have been executed, which might also be the cause of the significant variation in the species-specific contribution to the carbon stock (Forrester & Baker, 2012; Marden et al., 2021). Soil organic Carbon (SOC) was also found varied in the different forest types which might be due to the forest stand, vegetation composition, soil moisture, soil organic matter (Zhang et al., 2021). Higher SOC in Oak Forest than that in Pine and Mixed Forest in the present study is comparable with the results inferred by Aryal et al. (2013), Shrestha et al. (2016), Aryal et al. (2018) and Kumar et al. (2021). In the present study, the soil pH of the all forests of different vegetation composition was acidic in nature. This may be the consequence of basic ions in the muddy soil being washed out, which led to H+ rich ions in the soil and more acid being generated by the decay of organic matter. Since Yu et al. (2019) suggested that soil pH doesn’t play a significant role in the accumulation of SOC, which strongly supports the present study. Soil organic carbon with Oak Forest found a strong positive correlation with the other two forest types which could be result from better nutrient input through litterfall and an increase in regenerating oak trees. The findings of the present study are comparable with various studies by Khanal et al. (2010), Gairola et al. (2012), Joshi & Negi (2015), and Pandey et al. (2019). Similar to this inference, in the present study, living tree carbon stock and biomass were higher for Mixed Forest than that for Oak and Pine forests. Present study would be an excellent model to demonstrate to other communities that the more expansive, global conservation policies, strategies, and carbon market mechanism of REDD+ can offer significantly more protection to the forest and enhance economic benefit. Conclusions The living tree carbon stock of forest depends upon the different vegetation compositions. The average living tree carbon stock was found to be higher in Mixed Forest (87.13 t/ha) and lower in Pine Forest (22.50 t/ha). Furthermore, the living tree carbon stock was found to be positively correlated with forest stand, altitude, soil organic carbon, soil pH, and WHC of soil whereas, it was found to be negatively correlated with Shannon and Weiner Index, tree species richness. Mixed Forest and Oak Forest were in good regeneration condition whereas the Pine Forest was in mature state. Furthermore, the Tree carbon stock was found to be positively correlated with forest stand, altitude, soil organic carbon, soil pH, and WHC of soil whereas, it was found to be negatively correlated with Shannon and Weiner Index, tree species richness. These findings imply that forests might be included in the REDD+ program, which would then help for better forest management. Acknowledgments We are grateful to the community forest user groups (CFUG) of Laglagepakha, Gumalchoki, Banko Janakari, Vol 32 No. 2 72 Gurung et al. and Bosan Community Forest for their cooperation during the fieldwork. We are thankful to Professor Dr. Ram Kailash Prasad Yadav, Head of Central Department of Botany, T. U. for providing necessary facilities. Thanks also goes to Mr. Basudev Poudel, Mrs. Hira Shova Shrestha, Ms. Bidhya Shrestha, and Ms. Pristi Dangol for their contribution to this research. We are highly grateful to all anonymous reviewers for manuscript. 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