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Article

Species Diversity, Biomass Production and Carbon Sequestration Potential in the Protected Area of Uttarakhand, India

by
Geetanjali Upadhyay
1,
Lalit M. Tewari
1,*,
Ashish Tewari
2,
Naveen Chandra Pandey
1,
Sheetal Koranga
1,
Zishan Ahmad Wani
3,
Geeta Tewari
4,* and
Ravi K. Chaturvedi
5,*
1
Department of Botany, Kumaun University, D.S.B. Campus, Nainital 263001, Uttarakhand, India
2
Department of Forestry and Environment, Kumaun University, D.S.B. Campus, Nainital 263001, Uttarakhand, India
3
Terrestrial Ecology and Modelling (TEaM), Department of Environmental Science and Engineering, SRM University-AP, Amravati 522240, Andha Pradesh, India
4
Department of Chemistry, Kumaun University, D.S.B. Campus, Nainital 263001, Uttarakhand, India
5
Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, Jinghong 666303, China
*
Authors to whom correspondence should be addressed.
Plants 2025, 14(2), 291; https://doi.org/10.3390/plants14020291
Submission received: 12 December 2024 / Revised: 28 December 2024 / Accepted: 16 January 2025 / Published: 20 January 2025
(This article belongs to the Special Issue Plant Functional Diversity and Nutrient Cycling in Forest Ecosystems)
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Abstract

:
Ecosystem functioning and management are primarily concerned with addressing climate change and biodiversity loss, which are closely linked to carbon stock and species diversity. This research aimed to quantify forest understory (shrub and herb) diversity, tree biomass and carbon sequestration in the Binsar Wildlife Sanctuary. Using random sampling methods, data were gathered from six distinct forest communities. The study identified 271 vascular plants from 208 genera and 74 families. A notable positive correlation (r2 = 0.085, p < 0.05) was observed between total tree density and total tree basal area (TBA), shrub density (r2 = 0.09), tree diversity (D) (r2 = 0.58), shrub diversity (r2 = 0.81), and tree species richness (SR) (r2 = 0.96). Conversely, a negative correlation was found with the concentration of tree dominance (CD) (r2 = 0.43). The Quercus leucotrichophora, Rhododendron arboreum and Quercus floribunda (QL-RA-QF) community(higher altitudinal zone) exhibited the highest tree biomass (568.8 Mg ha−1), while the (Pinus roxburghii and Quercus leucotrichophora) PR-QL (N) community (lower altitudinal zone) in the north aspect showed the lowest (265.7 Mg ha−1). Carbon sequestration was highest in the Quercus leucotrichophora, Quercus floribunda and Rhododendron arboreum (QL-QF-RA) (higher altitudinal zone) community (7.48 Mg ha−1 yr−1) and lowest in the PR-QL (S) (middle altitudinal zone) community in the south aspect (5.5 Mg ha−1 yr−1). The relationships between carbon stock and various functional parameters such as tree density, total basal area of tree and diversity of tree showed significant positive correlations. The findings of the study revealed significant variations in the structural attributes of trees, shrubs and herbs across different forest stands along altitudinal gradients. This current study’s results highlighted the significance of wildlife sanctuaries, which not only aid in wildlife preservation but also provide compelling evidence supporting forest management practices that promote the planting of multiple vegetation layers in landscape restoration as a means to enhance biodiversity and increase resilience to climate change. Further, comprehending the carbon storage mechanisms of these forests will be critical for developing environmental management strategies aimed at alleviating the impacts of climate change in the years to come.

Graphical Abstract

1. Introduction

Globally, natural reserved forests serve as habitats rich in biodiversity, housing a wide array of plant and animal species [1,2]. As a crucial element of the natural world, forests play a key role in stabilizing the climate [3]. They serve multiple functions: regulating ecosystems, safeguarding biodiversity, playing an essential part in the carbon cycle, supporting human livelihoods and offering goods and services that contribute to sustainable development [4]. Human activities pose a significant threat to forests [5]. The primary factors endangering biodiversity and the environment include population growth, agricultural expansion, intensification and infrastructure development, which contribute to increased atmospheric CO2 levels [6,7,8]. Evaluating forest community composition and structure is crucial for comprehending tree population dynamics and diversity [9,10]. Himalayan forests, typically governed by low temperatures, are particularly vulnerable to the pronounced effects of climate change [11]. Comparing current vegetation with historical patterns allows for tracking ongoing changes in Himalayan forests. Consequently, quantitative analysis of these forests is essential to evaluate climate change’s impact on future species coexistence, establish baseline data for long-term monitoring and assess species shifts [10,12].
Moreover, issues such as global climate change, habitat fragmentation, land degradation, land-use alterations and sudden canopy gaps due to intensive logging significantly affect forest tree populations and their diversity [2,13,14,15]. This is particularly relevant in countries like India, where forests play a crucial role in rural communities’ food security [16,17,18]. Situated in the foothills of the Himalayan Mountain ranges, Uttarakhand State constitutes the central portion of the Indian Himalayan Region (IHR). The state is also distinguished by its significant forest carbon reserves, ranking fourth in India with 62.77 Mg ha−1 of aboveground biomass [7]. An investigation by Tolangay and Moktan (2020) revealed that the Indian Himalayan region (IHR) alone sequesters 65 million tons of carbon annually, emphasizing the potential of Himalayan forests in mitigating climate change and addressing global warming [19]. Numerous studies underscore the importance of assessing structure, diversity, carbon storage and sequestration in various protected areas, including wildlife sanctuaries [8,20,21,22,23,24,25,26,27]. Protected areas play a key role in carbon (C) sequestration by rapidly accumulating carbon [28,29], thus significantly contributing to global warming mitigation [30]. Previous studies in the Binsar Wildlife Sanctuary primarily focused on the phytosociological characteristics of tree species [27,31,32]. Limited research has been conducted on other life forms, such as shrubs and herbs [33,34]. Furthermore, Joshi et al. (2024) have examined the carbon stock of the trees in this study site [27]. Therefore, the current research aims to quantify forest understory (shrub and herb) diversity, tree biomass and carbon sequestration potential of the forests in the Binsar Wildlife Sanctuary (BWLS), Uttarakhand, India, to find the answers tothe following questions:(1) How does density and total basal area vary in different forest communities? (2) How much carbon do different forest communities sequester annually? (3) Do altitudes influence the tree density, biomass and carbon sequestration potential of forests? (4) What is the relationship between vegetation attributes and carbon stock?

2. Results

2.1. Phytocoenology Study

The current study identified 271 vascular plant species from 208 genera and 74 families. The flora comprised 214 herbaceous plants, 31 shrubs and 26 trees. Four gymnosperm species were recorded, namely Picea smithiana, Cupressus torulosa, Cedrus deodara and Pinus roxburghii. Asteraceae emerged as the most dominant family with 38 species, followed by Poaceae (21 species), Fabaceae (18 species), Lamiaceae (17 species), Rosaceae (10 species), Apiaceae, Cyperaceae, and Orchidaceae (7 species each) (Figure 1). Of the 74 recorded families, 29 were represented by a single species namely Anacardiaceae, Apocynaceae, Aquifoliaceae, Begoniaceae, Betulaceae, Brassicaceae, Celasteraceae, Corariaceae, Cucurbitaceae, Cupressaceae, Daphniphyllaceae, Droseraceae, Euphorbiaceae, Hypericaceae, Linaceae, Lythraceae, Malvaceae, Menispermaceae, Moraceae, Myricaceae, Myrtaceae, Onagraceae, Orobanchaceae, Phyllanthaceae, Rutaceae, Sapindaceae, Saxifragaceae, Scrophulariaceae and Thymeleaceae. Geranium and Erigeron were the dominant genera with four species (Figure 2). Pinus roxburghii and Quercus leucotrichophora (PR-QL) mixed community was recorded in both south and north aspects at 1600–1900 m and 1900–2100 m altitudinal range. Pinus roxburghii (PR) community was recorded at an elevation range of 1600–1900 m in the south aspect, Quercus leucotrichophora and Rhododendron arboreum (QL-RA) mixed community was found ranging between 1900 and 2100 in north aspect; Quercus leucotrichophora, Rhododendron arboreum and Quercus floribunda (QL-RA-QF) mixed community was mostly abundant between 2100 and 2400 m in north aspect, whereas, Quercus leucotrichophora, Quercus floribunda and Rhododendron arboreum (QL-QF-RA) mixed community was recorded between 2100 and 2400 m elevation in south aspect.
Among the identified forest communities, the minimum and maximum number of trees were recorded in the PR community (7) and QL-RA-QF (18). The overall tree density within the identified forest communities ranged from 663 to 1066 individuals ha−1 and the total basal area from 40.24 to 71.20 m2 ha−1 (Figure 3). A significant positive correlation (r2 = 0.085, p < 0.05) was found between total tree density and total tree basal area, shrub density (r2 = 0.09), tree diversity (r2 = 0.58), shrub diversity (r2 = 0.81) and tree species richness (r2 = 0.96) and showed negative correlation with concentration of dominance of tree (r2 = 0.43) (Figure 4).
Shrub density was maximum in the QL-RA community (6640 individuals ha−1) and minimum in the PR-QL (S) community (3786 individuals ha−1) (Table 1). The maximum concentration of dominance was recorded in the PR-QL (N) community (0.24), and the minimum was recorded from the QL-RA-QF community (0.11) and showed an inverse relation with Shannon diversity H′ ranged between 1.98 in PR-QL (N) community and 2.44 in the QL-RA-QF community. The maximum species diversity was recorded from PR and QL-QF-RA communities (14), and the minimum was recorded from the PR-QL community in the south (10 species). Margalef index of species richness was found to be maximum in the PR community (2.26) and minimum in the PR-QL community in the south aspect (1.09). Equitability was recorded at the maximum in the QL-RA-QF community (0.95) and minimum in the QL-RA community (0.803). Eighty-five percent of the species exhibited continuous dispersal (Table 2).
The herb density was analyzed for the three different seasons: rainy, winter and summer. During the rainy season, maximum density was recorded in the PR-QL (S) community (63.58 individuals m−2) and minimum in the QL-RA community (42.45 individuals m−2). The maximum species diversity was recorded in the PR-QL community in the north aspect (78), and the minimum was recorded in the QL-RA community (35). The QL-RA-QF community exhibited a maximum concentration of dominance (0.094), while the QL-RA community showed the lowest (0.017). Interestingly, this pattern was inversely related to Shannon diversity (H′), with the QL-RA community displaying the maximum diversity (3.99) and the QL-RA-QF community showing the lowest (2.61) (Table 3). Equitability was recorded as maximum in QL-RA-QF (0.024) and minimum in QL-RA community (0.005). Margalef species richness index was recorded as maximum in the PR community (11.58), and the minimum was recorded in the PR-QL community in the south aspect (4.90).
During the winter season, maximum density was recorded in PR-QL (S) community (9.29 individuals m−2) and minimum in QL-RA-QF community (3.72 individuals m−2). Maximum species diversity was recorded in the PR-QL community in the north aspect (16), and the minimum was recorded in the QL-RA-QF community (8). The maximum concentration of dominance was recorded in the QL-RA-QF community (0.168), and the minimum was recorded in the PR community (0.091), which showed an inverse relation with the Shannon diversity H′ (maximum was recorded in the PR community; 2.55 and minimum was recorded in QL-RA-QF community; 2.11) (Table 4). Equitability was recorded as maximum in the QL-RA-QF community (0.058) and minimum in the PR community (0.035). The maximum Margalef index of species richness was recorded in the QL-QF-RA community (2.49), and the minimum was recorded in the QL-RA-QF community (1.78).
During the summer season, maximum density was recorded in the QL-QF-RA community (40.51 individuals m−2) and minimum in the PR-QL (N) community (6.31 individuals m−2). Maximum species diversity was recorded in the QL-QF-RA community (64), and the minimum was recorded in the PR-QL community in the north aspect (11). The minimum concentration of dominance was recorded in the QL-RA community (0.046), and the maximum was recorded in the PR community (0.192). Notably, Shannon diversity (H′) followed an inverse trend, with the QL-RA community exhibiting the maximum diversity (3.27) and the PR community displaying minimum diversity (1.99) (Table 5). The maximum equitability was recorded in the PR-QL community in the north aspect (0.065) and the minimum in the QL-RA and PR-QL (S)communities (0.013). The maximum Margalef index of species richness was recorded in the QL-QF-RA community (5.47), and the minimum was recorded in the PR community (1.85).

2.2. Tree Biomass

Among the identified forest communities, first-year tree biomass varied from 256.8 Mg ha−1 to 554.2 Mg ha−1. Maximum biomass was recorded in the QL-RA-QF community, and minimum was recorded in PR-QL in the north aspect. The proportion of below and aboveground biomass was recorded at 22.2% and 77.8% in the PR-QL (N) community, 19.0% and 81.0% in the PR community, 22.5% and 77.5% in QL-RA community, 18.5% and 81.5% in (PR-QL) S community, 20% and 80% in QL-RA-QF community, and 21.1% and 78.9% QL-QF-RA community. The maximum biomass of the second year was recorded in the QL-RA-QF community (568.8 Mg ha−1), and the minimum was recorded in the PR-QL community in the north aspect (265.7 Mg ha−1) (Table 6). The proportion of aboveground and belowground was recorded 77.6% and 22.4% in the PR-QL (N) community, 81.1% and 18.9% inthe PR community, 77.9% and 22.1% in the QL-RA community, 81.6% and 18.4% in the (PR-QL) S community, 79.8% and 20.2% in the QL-RA-QF community, and 79.4% and 20.6% in the QL-QF-RA community. The biomass of the tree showed a strong positive correlation with the tree density (r2 = 0.60), total basal area of the tree (r2 = 0.63), tree species richness (r2 = 0.67) and total tree diversity (r2 = 0.65) (Figure 5).

2.3. Tree Carbon Stock and Carbon Sequestration

Among the identified forest communities, first-year tree carbon stock varied from 121.99 Mg C ha−1 to 263.27 Mg C ha−1. Maximum biomass was recorded for the QL-RA-QF community, and the minimum was recorded in PR-QL for the north aspect. In the second year, maximum biomass was recorded from the QL-RA-QF community (270.17 Mg C ha−1), and minimum was recorded from the PR-QL community in the north aspect (127.49 Mg C ha−1) (Table 7).The maximum carbon sequestered in the QL-QF-RA community (7.48 Mg C ha−1 yr−1), followed by the PR-QL (S) community (7.27 Mg C ha−1 yr−1) and QL-RA-QF (6.91 Mg C ha−1 yr−1), and the minimum was recorded for PR-QL community in south aspect (5.5 Mg C ha−1 yr−1).

2.4. Relationship Between Diversity and Carbon Stock

Correlation and regression analysis of carbon metrics and variables viz. basal area, density and diversity indices of trees are presented in Figure 5 and Table 8. The density of tree (r2 = 0.783), TBA (r2 = 0.790), D (r2 = 0.817) and SR (r2 = 0.824) was significantly (p <0.01) and positively associated with CS. The concentration of dominance of trees was observed to be significantly and negatively correlated with the C stock of trees (r2 = −0.756; p < 0.01). Tree biomass and diversity indices were also positively (p < 0.01) correlated.
A principal component analysis (PCA) ordination plot was constructed using eleven variables of different ecological attributes. The PCA ordination plot revealed that the first PCA axis accounted for 61.10% of the variability in species composition, while the second PCA axis explained 28.58% of this variation. According to the PCA ordination plot between PC 1 and PC 2, three forest communities (QL-RA, QL-RA-QF AND QL-QF, RA) were adjacent to each other; however, PR-QL (N), PR-QL (S) were not adjacent to each other (Figure 6). Herb density variables (e.g., RS herb density, SS herb density, WS herb density) and tree-related metrics (tree density, diversity of tree) contribute positively to both PCA 1 and PCA 2. The total basal area of the tree, the diversity of the tree, and the total carbon sequestration of the tree are clustered closely, suggesting these variables are highly correlated. PR-QL (S) is in the upper region and has a strong influence on PCA 2. QL-QF-RA and QL-RA-QF are located on the far right, indicating strong positive contributions to PCA 1.

3. Discussion

Bioclimatic conditions in Himalayan ecosystems fluctuate rapidly over short distances due to the highly fragmented landscape [35,36]. This variability can significantly impact vegetation types and their roles in these delicate ecosystems [8]. The current research identified 271 vascular plant species (214 herbs, 31 shrubs, and 26 trees) from 208 genera and 74 families. This finding surpasses previous studies, including those by Mandal and Joshi (2014) [37], who reported 66 species in Indian forests; Shaheen et al. (2016) [38], who documented 65 plant species in Pakistan; and Khera et al. (2001) [39], who identified 77 species in Central Himalayan forests in India. Additionally, the present study exceeds the species counts reported by Ilyas (1998) [33] from Binsar Wildlife Sanctuary (141 species), Majilla and Kala (2010) (51 species) [34], Rawat et al. (2013) [31] (147 species), Khan and Arya (2017) (23 species) [32], and Tamta (2017) (114 species) [40]. However, Wani and Pant (2023b) [41] documented a higher number of 364 species in the Gulmargh Wildlife Sanctuary. Asteraceae was the dominant family in the present study, which was similar to the findings reported by Shaheen et al. (2012) [42], Rawat et al. (2013) [31] and Singh and Pusalkar (2020) [43].
The total shrub density observed in the present study area (3786–6640 individuals ha−1) was higher than the total shrub density reported by Tripathi et al. (1991) [44] and Singh et al. (2014) [45] in the Kumaun Himalaya (1005–3050 individuals ha−1). However, it was comparable to the range of 3455–6685 individuals ha−1 reported by Majilla and Kala (2010) in the Binsar Wildlife Sanctuary [34]. The herb density across all three seasons, ranging from 3.72 to 63.58 individuals m−2, exceeded the figures reported by Tripathi et al. (1991) [44] while lower than reported by Kumar (2020) from Kalatop-Khajjiar Wildlife Sanctuary [46], Himachal Pradesh, and Wani and Pant (2023a) from Gulmargh wildlife sanctuary [25]. In the tree and shrub strata, a contagious distribution pattern was most frequently observed. The distribution pattern of woody species followed the order of contagious > random > regular, aligning with the findings of Pathak et al. (1993) for forest vegetation along an altitudinal gradient in the Kumaun Himalaya [47].
The forest communities exhibited high species diversity and low concentration of dominance for shrubs and herbs, which is characteristic of natural forests [48]. Hart and Chen (2006) emphasized that high diversity is crucial for maintaining various ecological services and regulating forest composition dynamics [49]. The concentration of dominance for shrubs in this study (0.11–0.24) was lower than that reported by Maletha et al. (2023) (0.10–0.44) in the Nanda Devi Biosphere Reserve, Western Himalaya [50], Dar and Sundarapandian (2016) (0.43–0.75) from seven temperate forest types of Western Himalaya [51] but higher (0.08–0.19) than Malik and Bhatt (2015) [52] from Kedarnath Wildlife Sanctuary. Cd of herbs of all three seasons ranged from 0.017 to 0.192, which is again lower than Dar and Sundarapandian (2016) [51] (0.08–0.35). Within the forest community, the diversity of shrubs (H′) ranged from 1.98 to 2.44, which is again lower than Dar and Sundarapandian (2016) [51] from seven temperate forest types of Western Himalaya. The herb layer, which often displays the greatest species diversity among forest strata, plays a vital role in forest biodiversity [53]. In this study, the herb layer diversity ranged from 1.99 to 3.27 for the summer season, 2.11 to 2.55 for the winter season, and 2.61 to 3.99 for the rainy season. Horvitz et al. (1998) suggested that competitive dynamics in the herbaceous layer may influence the initial success of plants in upper strata, particularly affecting the regeneration of dominant tree species [54]. In the present study, species richness for shrubs ranged from 10 to 14, which was comparable with Rana and Gairola (2009) (10–12) [55] and lower than Dar and Sundarapandian (2016) (3–9) [51]. Species richness of herbs of all three seasons ranged from 8 to 78, which was lower than Rawat et al. (2015) [56] from Nanda Devi Biosphere Reserve (18–107) and Dar and Sundarapandian (2016) [51] from temperate forests of Kashmir Himalaya (20–84). The study also revealed that the Margelf index of species richness varied across different altitudes, ranging from 1.09 to 2.61 for shrubs, 4.90–11.58 for rainy season herbs, 1.78–2.71 for winter season herbs and 1.45 to 5.47 for summer season herbs. The dominance of herbaceous plant forms observed in this research aligns with findings from various other studies conducted across different regions of the Indian Himalayan Region (IHR) [57,58,59]. This phenomenon can be attributed to the fact that herbs are typically the most common growth forms in mountainous areas, owing to their ability to adapt to diverse environmental conditions [60].
In our first-year investigations, the total biomass ranged from 256.8 Mg ha−1 to 554.2 Mg ha−1, while in the second year of investigation, the tree biomass was recorded to be in the range of 265.7 to 568.8 Mg ha−1. The QL-RA-QF community in the north aspect showed the maximum biomass, while PR-QL exhibited the minimum. These values were higher than those reported by Negi et al. (1983) [61]; Pandey et al. (1987) [62]; Rawat and Singh (1988) [63]; Negi et al. (1995) [64]; Gairola et al. (2010) [65]; Gossain et al. (2015) [66]; Pant and Tewari (2020) [67] and Joshi et al. (2024) [27]. However, they were lower than the figures reported by Rana et al. (1989) [68], Adhikari et al. (1995) [69], Pant and Tewari (2013) [70], Dimri et al. (2016) [71] and Karki et al. (2017) [72] from various Kumaun Himalayan forests. The possible reason for the higher biomass observed in our study, compared to other Himalayan forest studies, is the fact that the present research area is a protected wildlife sanctuary where species diversity could be on the higher side. A mature ecosystem has a significantly complex structure and trophic network, such as a natural forest or a well-established protected area. It has a substantial amount of biomass distributed among numerous taxa and individuals. Numerous factors, such as type of species, CBH, density, disturbance and climatic change, influence the contribution of different components in any given forest [73]. The proportion of below and aboveground biomass was observed to range from 18.40% to 22.25% and 77.66% to 81.02%, correspondingly, which was consistent withthe values reported for Indian forests (79% and 21%) [74]. The diversity indices and the overall tree biomass and carbon stock showed a statistically positive correlation. The positive relationship observed between overall tree biomass and carbon stock supports previous research findings [75]. Sharma et al. (2010) [76] showed a significant negative correlation between total carbon density and species diversity in their study involving twenty major Garhwal Himalayan forest types. Forests with higher tree diversity are reported to maintain better ecosystem productivity and help mitigate the effects of global warming by acting as potential carbon sinks [77]. Tree carbon stock of the present study was 127.49–269.93 Mg C/ha−1 and was higher than reported by Lodhiyal and Lodhiyal (2012) [78], Gossain et al. (2015) [66] and Joshi et al. (2024) [27] but lower than recorded by Jina et al. (2008) [79], Zhu et al. (2010) [80], Sharma et al. (2011) [81] and Lal and Lodhiyal (2016) [82] from natural reserved forest. The carbon storage values varied across different ranges of the study area, which could be due to variations in tree species compositions, diversity, disturbances and forest management history [83]. The carbon sequestration of the present study ranged between 5.5 and 7.48 Mg C ha−1 yr−1 and was recorded higher than reported by Jina et al. (2008) [79] and Gosain et al. (2015) [66] but lower than recorded by Rawat (1983) [84] and Rana et al. (1989) [68]. Our analysis revealed an increase in biomass and carbon density within oak-dominated communities (QL-RA-QF and QL-QF-RA) as elevation increased, indicating that high-altitude mountain ridge-top oak forests are more effective at carbon sequestration. This finding aligns with previous research supporting the notion that biomass production and carbon assimilation rise with increasing altitude [71,75,76]. At a given altitude, the combined effects of topography, aspect, slope inclination, and soil type exert significant control over plant diversity, morphometric characteristics, soil nutrient status, and community distribution [85,86]. Our research also identified key tree species that are the most common and noticeable within a specific ecosystem [87]. These prevalent species typically play a significant role in the ecosystem’s population density, stand diversity and biomass carbon storage [88,89].

4. Materials and Methods

4.1. Study Area

The Binsar Wildlife Sanctuary is a protected zone encompassing a mountain range with elevations between 1600 and 2400 m above sea level. Situated in Uttarakhand’s Kumaun region, it lies 30 km northeast of Almora district. The sanctuary spans 47.67 km2 and is positioned between 29°39′–29°44′ N and 79°41′–79°49′ E. (Figure 7). In 1988, this area was designated as a wildlife sanctuary to preserve and safeguard the diminishing broadleaf Quercus forests. Known for its exceptional natural beauty, the sanctuary boasts a unique landscape that attracts tourists. The area primarily features three major forest types: pine forests, pine–oak mixed forests, and oak forests. Located in the inner lesser Himalayan zone, the sanctuary’s geology is dominated by rocks from the Paleozoic Korlgroup, including quartzite, shale–quartzite, graphite, schist, granite and granodiorite [90]. The sanctuary is home to a diverse array of flora and fauna, including various bird species [40].

4.2. Phytosociological Analysis

The study was conducted from 2021 to 2023. The research area was segmented into three distinct altitudinal zones (1600–1900 m, lower altitudinal zone; 1900–2100 m, middle altitudinal zone; and 2100–2400 m, higher altitudinal zone) on both northern and southern aspects after reconnaissance survey in the region (Table 9). Within each elevation band, 100 × 100 m plots were established, with 30 randomly placed 10 × 10 m quadrats for tree sampling on both north and south-facing slopes. Tree circumference at breast height (cbh) was recorded at 1.37 m above ground level. For shrub and herb sampling, 10 quadrats of 5 × 5 m (25 m2) and 20 quadrats of 1 m × 1 m (1 m2) were nested within the same plot, adhering to standard sampling protocols [91,92]. Herbaceous species were examined across all three seasons (rainy, winter, summer). Plant specimens were identified using the relevant literature [93,94,95,96], and voucher specimens were deposited at the Department of Botany, D.S.B Campus, Kumaun University, Nainital.
In the forest zone, communities were identified following the importance value index (IVI). IVI was calculated as the sum of relative density, relative frequency and relative basal area [92]. When one species accounts for 50% or more of the total IVI, those sites are classified as pure communities of that species; when two or more species account for 50% or more of the total IVI, those sites are classified as mixed communities of those species. The total number of species in a given community was used to calculate species richness using the Margalef index [97]. Simpson’s Index [98] was used to calculate the concentration of dominance, and Shannon–Wiener’s information statistic (H′) [99] was used to determine species diversity and to determine species evenness; the method given by Magurran (1988) was used [100].

4.3. Biomass Estimation

To evaluate annual diameter growth, all measured trees were marked with yellow paint at breast height (1.37 m) in September 2021 (B1). The equation y = a + blnx was employed, where y represents the component’s dry weight (kg), x is the CBH (cm), a denotes the intercept, b signifies the regression coefficient, and ln stands for natural logarithm. Component-specific biomass (above ground—bole, bark, branch, twigs, foliage; below ground—stump root, lateral root and fine root) was determined using allometric equations of Chaturvedi and Singh (1983) [101], Adhikari et al. (1995) [69], Adhikari et al. (1998) [102] and Rawat and Singh (1988) [63]. Following CBH re-measurement in September 2022 (B2), the two-year net biomass change was calculated. Annual biomass accumulation was defined as the change in biomass (ΔB = B2 − B1), with B2 and B1 representing second and first-year biomass, respectively. The sum of ΔB values across components yielded the trees’ net biomass accretion.

4.4. Carbon Stock and Carbon Sequestration

Carbon stock was calculated by multiplying species biomass by 0.475, as per Magnuseen and Reed (2004) [103]. Initial carbon stock (C1) was determined for each forest in 2021. After one year, the increased carbon stock (C2) was estimated using the enhanced biomass value for each stand/site. The net change in carbon stock (ΔC = C2 − C1) provided the annual carbon accumulation.

4.5. Statistical Analysis of the Data

Regression analysis (linear) was performed in Microsoft Excel Windows11 to ascertain the association between the density of trees and other ecological attributes. At the 95% significance level, the p values were tested at less than 0.05 (significant) and 0.01 (highly significant). Using Past software (version 3), the Pearson correlation analysis was per formed to determine the linear correlation (significant, p < 0.05) between the various ecological attributes. Principal component analysis (PCA) was performed to summarize the compositional differences between various sites and was analyzed using PAST software [104].

5. Conclusions

The current study’s results highlighted the significance of wildlife sanctuaries, which not only aid in wildlife preservation but also play a substantial role in carbon sequestration and support a wider range of biodiversity. Temperate forests account for greater biomass and C stock than subtropical forests. Research findings indicated that forests dominated by Quercus species contained the highest levels of biomass and carbon storage within protected areas, which is particularly relevant given current climate change concerns. We found that structural attributes governed the biomass carbon stock in selected forest communities, mainly tree density, total tree basal area, tree species richness and overall tree diversity. Elevation showed significant positive correlations as, with increasing altitude, the biomass and carbon stock increased, indicating that the entire elevational range has a huge carbon stock. Furthermore, few dominant species with large diameters account for the majority of the C stock of these forests. Hence, the cutting and felling of these species must be regulated for long-term ecosystem sustainability. Given the ecological importance of oak species, it is advised to utilize smaller trees or those outside forested areas for fuel and timber purposes. To ensure the future stability of the carbon supply, these species must be used responsibly and allowed to regenerate naturally. Protecting oak species is crucial for effective land management, as they constitute the majority of the area’s carbon stock and sequestration potential. To reduce the pressure on natural forests from human activities, fast-growing, high-density tree species or fuel wood plantations could be established along village corridors. This kind of dynamic study of carbon storage will help develop effective environmental management strategies to mitigate the effects of climate change, improve conservation efforts and support the sustainable utilization of forest resources. Afforestation programs using the Assisted Natural Regeneration (ANR) technique should be initiated in the forests. These afforestation programs could also contribute to the Paris Climate Agreement, the Trillion Trees initiative, and the Bonn Challenge, which aims to restore 350 million ha of degraded and deforested areas by 2030.

Implications for Climate Change Mitigation

Forest management sustainability on a global scale was evaluated using two key indicators: species diversity and carbon. However, forest managers may face challenges in simultaneously preserving diversity and enhancing carbon storage. These factors have a significant impact that goes beyond their physical presence, affecting the overall ecological balance of forest environments. The present study emphasizes the need to implement multispecies and multi-layered eco-restoration strategies, prioritizing the inclusion of shrubs and herbs alongside tree species to maintain overall biodiversity and ensure the continued delivery of ecosystem services and goods. Although tree biomass and carbon stock contributed 554.2 Mg ha−1 and 270.17 Mg C ha−1, respectively, they are crucial for maintaining the ecological stability and functionality of forest ecosystems. Consequently, biomass must be recognized as a critical component of forest ecosystems and preserved accordingly. In the BWLS landscapes, the Quercus-dominated forest type should be prioritized for protection and restoration due to its higher carbon content compared to other forest types. Given the increasing focus on global ecological restoration, forest vegetation should be considered a fundamental element in forest conservation and restoration efforts. The planting of broadleaved trees should be promoted in the area, as they enhance forest cover, mitigate soil erosion, sustain soil fertility, improve water infiltration and aeration, and enrich soil nutrients.

Author Contributions

Conceptualization, L.M.T. and A.T.; Data curation, N.C.P. and S.K.; Formal analysis, G.U., N.C.P., Z.A.W. and R.K.C.; Methodology, G.U.; Supervision, L.M.T. and A.T.; Visualization, L.M.T. and A.T.; Writing—original draft, G.U.; Writing—review and editing, G.U., L.M.T., Z.A.W. and G.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors are thankful to the Head of the Department of Botany, D.S.B. Campus, Nainital, and IERP, GBPNIE, Kosi, Katarmal, for providing the necessary facilities. The authors express gratitude to the Forest Department of Almora, Uttarakhand, for granting permission to conduct this study. RKC thanks National Natural Science Foundation of China (NSFC) (grant No. 31750110466), and the Central Laboratory, Xishuangbanna Tropical Botanical Garden, China.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Kutnar, L.; Nagel, T.A.; Kermavnar, J. Effects of disturbance on understory vegetation across Slovenian forest ecosystems. Forests 2019, 10, 1048. [Google Scholar] [CrossRef]
  2. John, E.; Bunting, P.; Hardy, A.; Roberts, O.; Giliba, R.; Silayo, D.S. Modelling the impact of climate change on Tanzanian forests. Divers. Distrib. 2020, 26, 1663–1686. [Google Scholar] [CrossRef]
  3. Grassi, G.; House, J.; Dentener, F.; Federici, S.; Den Elzen, M.; Penman, J. The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Change 2017, 7, 220–226. [Google Scholar] [CrossRef]
  4. Thompson, I.; Mackey, B.; McNulty, S.; Mosseler, A. CBD Technical Series No. 43: A Synthesis of the Biodiversity/Resilience/Stability Relationship in Forest Ecosystems. Available online: https://www.learningfornature.org/en/cbd-technical-series-no-43-a-synthesis-of-the-biodiversity-resilience-stability-relationship-in-forest-ecosystems/ (accessed on 6 July 2023).
  5. Htun, N.Z.; Mizoue, N.; Yoshida, S. Tree species composition and diversity at different levels of disturbance in Popa Mountain Park, Myanmar. Biotropica 2011, 43, 597–603. [Google Scholar] [CrossRef]
  6. Baboo, B.; Sagar, R.; Bargali, S.S.; Verma, H. Tree species composition, regeneration and diversity of an Indian dry tropical forest protected area. Trop. Ecol. 2017, 58, 409–423. [Google Scholar]
  7. Bisht, S.; Bargali, S.S.; Bargali, K.; Rawat, G.S.; Rawat, Y.S.; Fartyal, A. Influence of anthropogenic activities on forest carbon stocks-a case study from Gori Valley, Western Himalaya. Sustainability 2022, 14, 16918. [Google Scholar] [CrossRef]
  8. Bisht, S.; Rawat, G.S.; Bargali, S.S.; Rawat, Y.S.; Mehta, A. Forest vegetation response to anthropogenic pressures: A case study from Askot Wildlife Sanctuary, Western Himalaya. Environ. Dev. Sustain. 2023, 26, 10003–10027. [Google Scholar] [CrossRef]
  9. Singh, S.; Malik, Z.A.; Sharma, C.M. Tree species richness, diversity, and regeneration status in different oak (Quercus spp.) dominated forests of Garhwal Himalaya, India. J. Asia Pac. Biodivers. 2016, 9, 293–300. [Google Scholar] [CrossRef]
  10. Dash, S.S.; Panday, S.; Rawat, D.S.; Kumar, V.; Lahiri, S.; Sinha, B.K.; Singh, P. Quantitative assessment of vegetation layers in tropical evergreen forests of Arunachal Pradesh, Eastern Himalaya, India. Curr. Sci. 2021, 120, 850–858. [Google Scholar] [CrossRef]
  11. Wester, P.; Mishra, A.; Mukherji, A.; Shrestha, A.B. The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People; Springer Nature Switzerland AG: Cham, Switzerland, 2019. [Google Scholar]
  12. Sharma, C.M.; Tiwari, O.P.; Rana, Y.S.; Krishan, R.; Mishra, A.K. Plant diversity, tree regeneration, biomass production and carbon storage in different oak forests on ridge tops of Garhwal Himalaya. J. Environ. Sci. 2016, 32, 329–343. [Google Scholar] [CrossRef]
  13. Hassaballah, K.E.A. Land Degradation in the Dinder and Rahad Basins: Interactions Between Hydrology, Morphology and Ecohydrology in the Dinder National Park, Sudan; Final Report; Hassan, T.T., Ed.; Delft University of Technology: Delft, The Netherlands, 2015. [Google Scholar]
  14. Beltrán-Rodríguez, L.; Valdez-Hernández, J.I.; Saynes-Vásquez, A.; Blancas, J.; SierraHuelsz, J.A.; Cristians, S.; Martínez-Ballesté, A.; Romero-Manzanares, A.; LunaCavazos, M.; De la Rosa, M.A.B.; et al. Sustaining medicinal barks: Survival and bark regeneration of Amphipterygiumadstringens (Anacardiaceae), a tropical tree under experimental debarking. Sustainability 2021, 13, 2860. [Google Scholar] [CrossRef]
  15. Mohammed, E.M.I.; Hamed, A.M.E.; Ndakidemi, P.A.; Treydte, A.C. Illegal harvesting threatens fruit production and seedling recruitment of Balanites aegyptiaca in Dinder Biosphere Reserve, Sudan. Glob. Ecol. Conserv. 2021, 29, e01732. [Google Scholar] [CrossRef]
  16. Fahmi, M.K.M.; Dafa-Alla, D.A.M.; Kanninen, M.; Luukkanen, O. Impact of agroforestry parklands on crop yield and income generation: Case study of rainfed farming in the semi-arid zone of Sudan. Agrofor. Syst. 2018, 92, 785–800. [Google Scholar] [CrossRef]
  17. Neya, T.; Neya, O.; Abunyewa, A.A. Agroforestry parkland profiles in three climatic zones of Burkina Faso. Int. J. Biol. Chem. Sci. 2019, 12, 2119. [Google Scholar] [CrossRef]
  18. Mohammed, E.M.I.; Hammed, A.M.E.; Minnick, T.J.; Ndakidemi, P.A.; Treydte, A.C. Livestock browsing threatens the survival of Balanites aegyptiaca seedlings and saplings in Dinder Biosphere Reserve, Sudan. J. Sustain. For. 2021, 41, 1046–1063. [Google Scholar] [CrossRef]
  19. Tolangay, D.; Moktan, S. Trend of studies on carbon sequestration dynamics in the Himalaya hotspot region: A review. J. Appl. Nat. Sci. 2020, 12, 647–660. [Google Scholar] [CrossRef]
  20. Yam, G.; Tripathi, O.P. Tree diversity and community characteristics in Talle Wildlife Sanctuary, Arunachal Pradesh, Eastern Himalaya, India. J. Asia-Pac. Biodivers. 2016, 9, 160–165. [Google Scholar] [CrossRef]
  21. Dar, D.A.; Sahu, P. Assessment of biomass and carbon stock in temperate forests of Northern Kashmir Himalaya, India. Proc. Int. Acad. Ecol. Environ. Sci. 2018, 8, 139–150. [Google Scholar]
  22. Hossain, M.K.; Alim, A.; Hossen, S.; Hossain, A.; Rahman, A. Diversity and conservation status of tree species in Hazarikhil Wildlife Sanctuary (HWS) of Chittagong, Bangladesh. Geol. Ecol. Landsc. 2020, 4, 298–305. [Google Scholar] [CrossRef]
  23. Samant, S.S.; Devi, K.; Puri, S.; Singh, A. Diversity, distribution pattern and traditional knowledge of sacred plants in Kanawar Wildlife Sanctuary (KWLS), Himachal Pradesh, Northwestern Himalaya. Indian J. Tradit. Knowl. (IJTK) 2020, 19, 642–651. [Google Scholar]
  24. Malik, Z.A.; Bhat, J.A.; Youssouf, M.; Bhatt, A.B. Diversity and regeneration of tree species in Western Himalayas: A case study from Kedarnath Wildlife Sanctuary. In Diversity and Dynamics in Forest Ecosystems; Apple Academic Press: Palm Bay, FL, USA, 2021; pp. 279–321. [Google Scholar]
  25. Wani, Z.A.; Pant, S. Tree diversity and regeneration dynamics in Gulmarg Wildlife Sanctuary, Kashmir Himalaya. Acta Ecol. Sin. 2023, 43, 375–381. [Google Scholar] [CrossRef]
  26. Haq, S.M.; Waheed, M.; Darwish, M.; Siddiqui, M.H.; Goursi, H.U.; Kumar, M.; Song, L.; Bussmann, R.W. Biodiversity and carbon stocks of the understory vegetation as indicators for forest health in the Zabarwan Mountain Range, Indian Western Himalaya. Ecol. Indic. 2024, 159, 111685. [Google Scholar] [CrossRef]
  27. Joshi, V.C.; Sundriyal, R.C.; Chandra, N.; Arya, D. Unlocking nature’s hidden treasure: Unveiling forest status, biomass and carbon wealth in the Binsar Wildlife Sanctuary, Uttarakhand for climate change mitigation. Environ. Chall. 2024, 14, 100825. [Google Scholar] [CrossRef]
  28. Wheeler, C.E.; Omeja, P.A.; Chapman, C.A.; Glipin, M.; Tumwesigye, C.; Lewis, S.L. Carbon sequestration and biodiversity following 18 years of active tropical forest restoration. For. Ecol. Manag. 2016, 373, 44–55. [Google Scholar] [CrossRef]
  29. Haq, S.M.; Rashid, I.; Waheed, M.; Khuroo, A.A. From forest floor to treetop: Partitioning of biomass and carbon stock in multiple strata of forest vegetation in Western Himalaya. Environ. Monit. Assess. 2023, 195, 812. [Google Scholar] [CrossRef]
  30. Reyer, C.; Guericke, M.; Ibisch, P.L. Climate change mitigation via afforestation, reforestation and deforestation avoidance: And what about adaptation to environmental change. New For. 2009, 38, 15–34. [Google Scholar] [CrossRef]
  31. Rawat, B.; Negi, V.S.; Rawat, J.; Tewari, L.M.; Rawat, L. The potential contribution of wildlife sanctuary to forest conservation: A case study from Binsar Wildlife Sanctuary. J. Mt. Sci. 2013, 10, 854–865. [Google Scholar] [CrossRef]
  32. Khan, A.H.; Arya, D. Analysis of Forest Vegetation in Binsar Wildlife Sanctuary, Kumaun Himalaya, Uttarakhand, India. Am.-Eurasian J. Agric. Environ. Sci. 2017, 17, 336–342. [Google Scholar]
  33. Ilyas, O. People and Protected Areas: The case of Binsar Wildlife Sanctury; World Wide Fund for Nature India: New Delhi, India, 1998. [Google Scholar]
  34. Majilla, B.S.; Kala, C.P. Forest structure and regeneration along the altitudinal gradient in the Binsar Wildlife Sanctuary, Uttarakhand Himalaya, India. Russ. J. Ecol. 2010, 41, 75–83. [Google Scholar] [CrossRef]
  35. Baumler, R. Soils. In Nepal: An Introduction to the Natural History, Ecology and Human Environment in the Himalayas-A Companion to the Flora of Nepal, 1st ed.; Miehe, G., Pendry, C.A., Chaudhary, R., Eds.; The Royal Botanical Garden: Edinburgh, UK, 2015; pp. 125–134. [Google Scholar]
  36. Bargali, K.; Manral, V.; Padalia, K.; Bargali, S.S.; Upadhyay, V.P. Effect of vegetation type and season on microbial biomass carbon in Central Himalayan forest soils, India. Catena 2018, 171, 125–135. [Google Scholar] [CrossRef]
  37. Mandal, G.; Joshi, S.P. Analysis of vegetation dynamics and phytodiversity from three dry deciduous forests of Doon Valley, Western Himalaya, India. J. Asia-Pac. Biodivers. 2014, 7, 292–304. [Google Scholar] [CrossRef]
  38. Shaheen, H.; Khan, R.W.A.; Hussain, K.; Ullah, T.S.; Nasir, M.; Mehmood, A. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot. 2016, 48, 2351–2357. [Google Scholar]
  39. Khera, N.; Kumar, A.; Ram, J.; Tewari, A. Plant biodiversity assessment in relation to disturbances in mid-elevational forest of Central Himalaya, India. Trop. Ecol. 2001, 42, 83–95. [Google Scholar]
  40. Tamta, P. Population Dynamics and Species Diversity of Entomofauna in Binsar Wildlife Sanctuary. Thesis, Kumaun University, Nainital, India, 2017. [Google Scholar]
  41. Wani, Z.A.; Pant, S. Assessment of floristic diversity and community characteristics of Gulmarg Wildlife sanctuary, Kashmir Himalaya. Geol. Ecol. Landsc. 2023, 1–20. [Google Scholar] [CrossRef]
  42. Shaheen, H.; Ullah, Z.; Khan, S.M.; Harper, D.M. Species composition and community structure of western Himalayan moist temperate forests in Kashmir. For. Ecol. Manag. 2012, 278, 138–145. [Google Scholar] [CrossRef]
  43. Singh, D.K.; Pusalkar, P.K. Floristic Diversity of the Indian Himalaya. In Biodiversity of the Himalaya: Jammu and Kashmir State; Topics in Biodiversity and Conservation; Dar, G., Khuroo, A., Eds.; Springer: Heidelberg, Germany, 2020; Volume 18. [Google Scholar]
  44. Tripathi, B.C.; Rikhari, H.C.; Bargali, S.S.; Rawat, Y.S. Species composition and regeneration in disturbed forest sites in the oak zone in and around Nainital. Proc. Indian Natl. Sci. Acad. 1991, 57, 381–390. [Google Scholar]
  45. Singh, N.; Tamta, K.; Tewari, A.; Ram, J. Studies on vegetational analysis and regeneration status of Pinus roxburghii, Roxb. and Quercus leucotrichophora forests of Nainital Forest Division. Glob. J. Sci. Front. Res. 2014, 14, 41–47. [Google Scholar]
  46. Kumar, A. Ecological Assessment Valuation and Conservation Prioritization of Floristic Diversity in Kalatop Khajjiar Wildlife Sanctuary of Himachal Pradesh, Northwestern Himalaya. Ph.D. Thesis, Kumaun University, Uttarakhand, India, 2020. [Google Scholar]
  47. Pathak, M.C.; Bargali, S.S.; Rawat, Y.S. Analysis of woody vegetation in a high elevation oak forest of central Himalaya. Indian For. 1993, 119, 722–731. [Google Scholar]
  48. Spies, T.A. Ecological concepts and diversity of old-growth forests. J. For. 2004, 102, 14–20. [Google Scholar] [CrossRef]
  49. Hart, S.A.; Chen, H.Y. Understory vegetation dynamics of North American boreal forests. Crit. Rev. Plant Sci. 2006, 25, 381–397. [Google Scholar] [CrossRef]
  50. Maletha, A.; Maikhuri, R.K.; Bargali, S.S. Population structure and regeneration pattern of Himalayan birch (Betula utilis D. Don) in the timberline zone of Nanda Devi Biosphere Reserve, Western Himalaya, India. Geol. Ecol. Landsc. 2023, 7, 248–257. [Google Scholar]
  51. Dar, J.A.; Sundarapandian, S. Patterns of plant diversity in seven temperate forest types of Western Himalaya, India. J. Asia-Pac. Biodivers. 2016, 9, 280–292. [Google Scholar] [CrossRef]
  52. Malik, Z.A.; Bhatt, A.B. Phytosociological analysis of woody species in Kedarnath Wildlife Sanctuary and its adjoining areas in Western Himalaya, India. J. For. Environ. Sci. 2015, 31, 149–163. [Google Scholar] [CrossRef]
  53. Gilliam, F.S.; Turill, N.L.; Adams, M.B. Herbaceous-layer and overstory species in clear-cut and mature central Appalachian hardwood forests. Ecol. Appl. 1995, 5, 947–955. [Google Scholar] [CrossRef]
  54. Horvitz, C.C.; Pascarella, J.B.; McMann, S.; Freedman, A.; Hofstetter, R.H. Functional roles of invasive non-indigenous plants in hurricane-affected subtropical hardwood forests. Ecol. Appl. 1998, 8, 947–974. [Google Scholar] [CrossRef]
  55. Rana, C.S.; Gairola, S. Forest community structure and composition along an elevational gradient of Parshuram Kund area in Lohit district of Arunachal Pradesh, India. J. Nat. Sci. 2009, 1, 44–52. [Google Scholar]
  56. Rawat, B.; Gairola, S.; Rawal, R.S. Assessing conservation values of forest communities in Nanda Devi Biosphere Reserve: Plant diversity, species distribution and endemicity. J. Mt. Sci. 2015, 12, 878–890. [Google Scholar] [CrossRef]
  57. Sharma, N.; Behera, M.D.; Das, A.P.; Panda, R.M. Plant richness pattern in an elevation gradient in the Eastern Himalaya. Biodivers. Conserv. 2019, 28, 2085–2104. [Google Scholar] [CrossRef]
  58. Haq, S.M.; Shah, A.A.; Yaqoob, U.; Hassan, M. Floristic quality assessment index of the Dagwan Stream in Dachigam National Park of Kashmir Himalaya. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 2021, 91, 657–664. [Google Scholar] [CrossRef]
  59. Rawat, B. Spatial prediction of plant species richness and density in high-altitude forests of Indian west Himalaya. Trees For. People 2021, 6, 100132. [Google Scholar] [CrossRef]
  60. Ahmad, M.; Sharma, P.; Rathee, S.; Singh, H.P.; Batish, D.R.; Lone, G.R.; Kaur, S.; Jaryan, V.; Kohli, R.K. Niche width analyses facilitate identification of high-risk endemic species at high altitudes in western Himalayas. Ecol. Indic. 2021, 126, 107653. [Google Scholar] [CrossRef]
  61. Negi, K.S.; Rawat, Y.S.; Singh, J.S. Estimation of biomass and nutrient storage in a Himalayan moist temperate forest. Can. J. For. Res. 1983, 13, 1185–1196. [Google Scholar] [CrossRef]
  62. Pandey, P.; Rawat, Y.S.; Singh, S.P. Structure and function of ringal (Arundinaria falcata Nees.) community in oak forest in Kumaun Himalaya. In River Gaula Catchment (Central Himalaya) Ecodevelopment Project; Final Tech. Report; Singh, S.P., Ed.; Ministry of Environment and Forests, Govt. of India: New Delhi, India, 1987; pp. 231–297. [Google Scholar]
  63. Rawat, Y.S.; Singh, J.S. Structure and function of oak forests in Central Himalaya. I. Dry Matter Dynamic. Ann. Bot. 1988, 62, 397–411. [Google Scholar] [CrossRef]
  64. Negi, M.S.; Tandon, V.N.; Rawat, R.S. Biomass and nutrient distribution in young teak (Tectona grandis Linn. F.) plantations in Tarai region of Uttar Pradesh. Indian For. 1995, 121, 455–464. [Google Scholar]
  65. Gairola, S.; Sharma, C.M.; Rana, C.S.; Ghildiyal, S.K.; Suyal, S. Phytodiversity (Angiosperms and Gymnosperms) in Mandal-Chopta Forest of Garhwal Himalaya, Uttarakhand, India. Nat. Sci. 2010, 8, 1–17. [Google Scholar]
  66. Gossain, B.G.; Negi, G.C.S.; Dyani, P.P.; Bargali, S.S.; Saxena, R. Ecosystem services of forest: Carbon stock in vegetation and soil component in watershed of Kumaun Himalaya. Int. J. Environ. Sci. 2015, 41, 177–188. [Google Scholar]
  67. Pant, H.; Tewari, A. Green sequestration potential of Chir Pine forests located in Kumaun Himalaya. For. Prod. J. 2020, 70, 64–71. [Google Scholar] [CrossRef]
  68. Rana, B.S.; Singh, S.P.; Singh, R.P. Biomass and Net primary productivity in central Himalayan forest along an altitudinal gradient. For. Ecol. Manag. 1989, 27, 199–218. [Google Scholar] [CrossRef]
  69. Adhikari, B.S.; Rawat, Y.S.; Singh, S.P. Structure and function of high altitude forests of central Himalaya I. Dry Matter Dynamics. Ann. Bot. 1995, 75, 237–248. [Google Scholar] [CrossRef]
  70. Pant, H.; Tewari, A. Carbon sequestration potential of Chir Pine (Pinus roxburghii Sarg) forest on two contrasting aspects in Kumaun Central Himalaya between 1650 and 1860 m elevation. Appl. Ecol. Environ. Res. 2013, 1, 110–112. [Google Scholar] [CrossRef]
  71. Dimri, S.; Baluni, P.; Sharma, C.M. Biomass production and carbon storage potential of selected old-growth temperate forests in Garhwal Himalaya, India. 2016. Proc. Natl. Acad. Sci. India-Sect. B Bioogical Sci. 2016, 87, 1327–1333. [Google Scholar] [CrossRef]
  72. Karki, H.; Bargali, K.; Bargali, S.S.; Vibhuti, V.; Rawat, Y.S. Plant diversity, regeneration status and standing biomass under varied degree of disturbances in temperate mixed oak-conifer forest, Kumaun Himalaya. Int. J. Ecol. Environ. Sci. 2017, 43, 331–345. [Google Scholar]
  73. Awasthi, P. A Study on Structural and Functional Attributes in Coriaria nepalensis Wall. Shrubland in Kumaun Himalaya, India. Thesis, Kumaun University, Nainital, India, 2021. [Google Scholar]
  74. Chhabra, A.; Palria, S.; Dadhwal, V.K. Growing stock-based forest biomass estimate for India. Biomass Bioenergy 2002, 22, 187–194. [Google Scholar] [CrossRef]
  75. Gairola, S.; Sharma, C.M.; Ghildiyal, S.K.; Suyal, S. Live tree biomass and carbon variation along an altitudinal gradient in moist temperate valley slopes of the Garhwal Himalaya (India). Curr. Sci. 2011, 100, 1862–1870. [Google Scholar]
  76. Sharma, C.M.; Baduni, N.P.; Gairola, S.; Ghildiyal, S.K.; Suyal, S. Tree diversity and carbon stocks of some major forest types of Garhwal Himalaya, India. For. Ecol. Manag. 2010, 260, 2170–2179. [Google Scholar] [CrossRef]
  77. Con, V.T.; Thang, T.N.; Thanh-Ha, T.D.; Khiem, C.C.; Quy, H.T.; Lam, T.V.; Do, V.T.; Sato, T. Relationship between aboveground biomass and measures of structure and species diversity in tropical forests of Vietnam. For. Ecol. Manag. 2013, 310, 213–218. [Google Scholar] [CrossRef]
  78. Lodhiyal, N.; Lodhiyal, L.S. Tree layer composition and carbon content of Oak and Pine in Lohaghat forest of Kumaun Himalaya. J. Plant Dev. Sci. 2012, 91, 55–62. [Google Scholar]
  79. Jina, B.S.; Sah, P.; Bhatt, M.D.; Rawat, Y.S. Estimating carbon sequestration rates and total carbon stock pile in degraded and non-degraded sites of Oak and Pine forest of Kumaun Central Himalaya. Ecoprinting 2008, 15, 75–81. [Google Scholar] [CrossRef]
  80. Zhu, B.; Wang, X.; Fang, J.; Piao, S. Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China. J. Plant Res. 2010, 123, 439–452. [Google Scholar] [CrossRef] [PubMed]
  81. Sharma, C.M.; Gairola, S.; Baduni, N.P.; Ghildiyal, S.K.; Suyal, S. Variation in carbon stocks on different slope aspects in seven major forest types of temperate region of Garhwal Himalaya, India. J. Biosci. 2011, 36, 701–708. [Google Scholar] [CrossRef] [PubMed]
  82. Lal, B.; Lodhiyal, L.S. Stand Structure, Productivity and Carbon Sequestration Potential of Oak Dominated Forests in Kumaun Himalaya. Curr. World Environ. 2016, 11, 466–476. [Google Scholar] [CrossRef]
  83. Joshi, R.K.; Dhyani, S. Biomass, carbon density and diversity of tree species in tropical dry deciduous forests in Central India. Acta Ecol. Sin. 2019, 39, 289–299. [Google Scholar] [CrossRef]
  84. Rawat, Y.S. Plant Biomass Net Primary Production and Nutrient Cycling in Oak Forests. Ph.D. Thesis, Kumaun University, Nainital, India, 1983. [Google Scholar]
  85. Tadesse, G.; Bekele, T.; Demissew, S. Dryland woody vegetation along an altitudinal gradient on the eastern escarpment of Welo, Ethiopia. SINET Ethopian J. Sci. 2008, 31, 43–54. [Google Scholar] [CrossRef]
  86. Singh, B.; Kumar, M.; Cabral-Pinto, M.M.; Bhatt, B.P. Seasonal and altitudinal variation in chemical composition of Celtis australis L. tree foliage. Land 2022, 11, 2271. [Google Scholar] [CrossRef]
  87. Upadhyay, G.; Tewari, A.; Tewari, L.M.; Pandey, N.C. Diversity and regeneration status of tree species in Binsar Wildlife Sanctuary, Uttarakhand, India. Indian J. For. 2024, 46, 97–104. [Google Scholar] [CrossRef]
  88. Sundriyal, R.C.; Sharma, E. Anthropogenic pressure on tree structure and biomass in the temperate forest of Mamlay watershed in Sikkim. For. Ecol. Manag. 1996, 81, 113–134. [Google Scholar] [CrossRef]
  89. Kaushal, S.; Baishya, R. Stand structure and species diversity regulate biomass carbon stock under major Central Himalayan forest types of India. Ecol. Process. 2021, 10, 14. [Google Scholar] [CrossRef]
  90. Gansser, A. Geology of the Himalayas; John Wiley and Sons: London, UK, 1964. [Google Scholar]
  91. Curtis, J.T.; McIntosh, R.P. The interrelations of certain analytical and synthetic phytosociological characters. Ecology 1950, 31, 434–455. [Google Scholar] [CrossRef]
  92. Misra, R. Ecology Work Book; Oxford and IBH Publishing: Calcutta, India, 1968. [Google Scholar]
  93. Osmaston, A.E. A Forest Flora for Kumaun; International Book Distributors: Dehradun, India, 1927; p. 605. [Google Scholar]
  94. Jain, C.P.; Rao, R.R. A Handbook of Field and Herbarium Methods; Goyal Offsets: New Delhi, India, 1977. [Google Scholar]
  95. Champion, H.G.; Seth, S.K. A Revised Survey of the Forest Types of India; Manager of Publications: New Delhi, India, 1968; p. 404. [Google Scholar]
  96. Gaur, R.D. Flora of the District Garhwal Northwest Himalayas (With Ethnobotanical Notes); Transmedia: Srinagar, India, 1999; pp. 1–811. [Google Scholar]
  97. Margalef, R. Perspectives in Ecological Theory; University of Chicago Press: Chicago, IL, USA, 1968; p. 111. [Google Scholar]
  98. Simpson, E.H. Measurement of diversity. Nature 1949, 163, 688. [Google Scholar] [CrossRef]
  99. Shannon, C.E.; Weaver, W. The Mathematical Theory of Communications; University of Illinois Press: Urbana, IL, USA, 1963; p. 125. [Google Scholar]
  100. Magurran, A.E. Measuring Biological Diversity; Blackwell Publishing: London, UK; Malden, MA, USA, 2004; p. 256. [Google Scholar]
  101. Chaturvedi, O.P.; Singh, J.S. The structure and function of pine forest in central Himalaya. 1. Dry matter dynamics. Ann. Bot. 1983, 60, 237–252. [Google Scholar] [CrossRef]
  102. Adhikari, B.S.; Dhaila, S.; Rawat, Y.S. Structure of Himalayan moist temperate cypress forest at and around Nainital, Kumaun Himalayas. Oecologia Mont. 1998, 7, 21–31. [Google Scholar]
  103. Magnuseen, S.; Reed, D. Modelling for Estimation and Monitoring; FAOIUFRO, 2004. [Google Scholar]
  104. Hammer, Ø.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. J. Mt. Sci. 2001, 4, 9. [Google Scholar]
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Figure 1. Dominant families with number of taxa in Binsar Wildlife Sanctuary.
Figure 1. Dominant families with number of taxa in Binsar Wildlife Sanctuary.
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Figure 2. Dominant genera in Binsar Wildlife Sanctuary.
Figure 2. Dominant genera in Binsar Wildlife Sanctuary.
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Figure 3. Density (individuals ha−1) and total basal area (m2 h−2) of trees in BWLS.
Figure 3. Density (individuals ha−1) and total basal area (m2 h−2) of trees in BWLS.
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Figure 4. Correlation of tree density with total basal area of tree; tree diversity; shrub density; shrub diversity; tree species richness and concentration of dominance of trees.
Figure 4. Correlation of tree density with total basal area of tree; tree diversity; shrub density; shrub diversity; tree species richness and concentration of dominance of trees.
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Plants 14 00291 g005
Figure 5. Correlation of biomass and carbon stock: (A) tree diversity; (B) tree density; (C) total basal area; (D) species richness in the BWLS.
Figure 5. Correlation of biomass and carbon stock: (A) tree diversity; (B) tree density; (C) total basal area; (D) species richness in the BWLS.
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Figure 6. Principal component analysis (PCA) based on different ecological attributes of six forest communities. Abbreviations used: (RS: rainy season; WS: winter season; SS: summer season; TCSeq: total carbon sequestration; SR: species richness; TCS: total carbon stock).
Figure 6. Principal component analysis (PCA) based on different ecological attributes of six forest communities. Abbreviations used: (RS: rainy season; WS: winter season; SS: summer season; TCSeq: total carbon sequestration; SR: species richness; TCS: total carbon stock).
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Figure 7. Map of the study area.
Figure 7. Map of the study area.
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Table 1. Community-wise density of shrubs and herbs in BWLS.
Table 1. Community-wise density of shrubs and herbs in BWLS.
PR-L (N)PRQL-RAPR-QL (S)QL-RA-QFQL-QF-RA
Density (individuals ha−1)
Shrub581341066640378649205346
Density (individuals m−2)
Rainy season herb42.9944.1642.4563.5844.4553.94
Winter season herb6.018.26.479.293.724.52
Summer season herb6.3115.8122.3340.3037.9540.51
Table 2. Community-wise diversity–dominance indexes of shrub in the BWLS m2.
Table 2. Community-wise diversity–dominance indexes of shrub in the BWLS m2.
CommunitiesTaxaDominanceDiversityEvennessSpecies Richness
PR-QL (N)110.2401.980.821.15
PR140.1562.130.8072.26
QL-RA130.1732.060.8031.36
(PR-QL) S100.2202.050.891.09
QL-RA-QF130.1102.440.951.41
QL-QF-RA140.1202.390.901.51
Table 3. Community-wise diversity–dominance indexes of rainy season herb in BWLS.
Table 3. Community-wise diversity–dominance indexes of rainy season herb in BWLS.
CommunitiesTaxaDiversityDominanceEvennessSpecies Richness
PR-QL (N)783.380.0340.0086.45
PR503.650.0230.00611.58
QL-RA353.990.0170.0059.15
(PR-QL) S703.340.0470.0114.90
QL-RA-QF502.610.0940.0246.75
QL-QF-RA513.280.0490.0126.71
Table 4. Community-wise diversity–dominance indexes of winter season herb in BWLS.
Table 4. Community-wise diversity–dominance indexes of winter season herb in BWLS.
Communities TaxaDiversityDominanceEvennessSpecies Richness
PR-QL (N)162.210.1600.0582.09
PR132.550.0910.0352.71
QL-RA152.250.1470.0542.05
(PR-QL) S132.490.0960.0372.51
QL-RA-QF82.110.1680.0811.78
QL-QF-RA132.460.0980.0382.49
Table 5. Community-wise diversity–dominance indexes of summer season herb in BWLS.
Table 5. Community-wise diversity–dominance indexes of summer season herb in BWLS.
CommunitiesTaxaDiversityDominanceEvennessSpecies Richness
PR-QL (N)112.810.1560.0653.38
PR231.990.1920.0611.85
QL-RA343.270.0460.0134.59
(PR-QL) S383.250.0490.0134.41
QL-RA-QF573.070.0870.0225.31
QL-QF-RA642.840.0950.0235.47
Table 6. Community-wise first-year and second-year biomass (Mg ha−1) of trees BWLS.
Table 6. Community-wise first-year and second-year biomass (Mg ha−1) of trees BWLS.
First-Year BiomassSecond-Year Biomass
CommunitiesAGBBGBTBAGBBGBTB
PR-QL (N)199.757.1256.8205.760.0265.7
PR245.257.7302.9255.259.5314.7
QL-RA349.7101.6451.3363.4103.2466.6
(PR-QL) S356.081.04370369.083.2452.2
QL-RA-QF443.1111.1554.2454.1114.7568.8
QL-QF-RA435.6116.5552.1450.5117.1567.6
Abbreviations used: AGB: aboveground biomass; BGB: belowground biomass; TB: total biomass.
Table 7. Community-wise carbon stock (Mg C ha−1) and carbon sequestration (Mg ha−1 yr−1) of trees in BWLS.
Table 7. Community-wise carbon stock (Mg C ha−1) and carbon sequestration (Mg ha−1 yr−1) of trees in BWLS.
First-Year Carbon StockSecond-Year Carbon Stock
CommunitiesAGCSBGCSTCSAGCSBGCSTCSTCSeq
PR-QL (N)94.8827.12121.9999.5127.98127.495.50
PR116.4527.41143.86121.2328.28149.515.65
QL-RA166.3748.24214.61172.4649.00221.466.85
(PR-QL) S169.1038.46207.56175.2739.53214.807.24
QL-RA-QF210.4952.78263.27215.7054.47270.176.91
QL-QF-RA206.9055.35262.25214.0055.73269.737.48
Abbreviations used: AGB: aboveground carbon stock; BGB: belowground carbon stock; TB: total carbon stock; TCSeq: total carbon sequestration.
Table 8. Pearson’s correlation analysis among different variables.
Table 8. Pearson’s correlation analysis among different variables.
DensityTBADCDSRBCS
Density1
TBA0.2921
D0.762 **0.4621
CD−0.688 *−0.392−0.980 **1
SR0.980 **0.3580.867 **−0.799 **1
B0.781 **0.794 **0.813 **−0.751 **0.821 **1
CS0.783 **0.790 **0.817 **−0.756 **0.824 **0.999 *1
**: Significant at <1% level of significance; *: significant at <5% level of significance; TBA: total basal area of tree, D: diversity of tree, CD: concentration of dominance of tree, SR: species richness of trees; B: biomass of trees, CS: carbon stock of trees.
Table 9. Location and characteristics of studied sites.
Table 9. Location and characteristics of studied sites.
Forest ZoneAltitude
(m a.s.l)		AspectDominant Tree SpeciesAssociated Herb and Shrub
PR-QL (N)1600–1900
(Lower altitudinal zone)		NorthPinus roxburghii,
Quercus leucotrichophora		Berberis asiatica, Myrsine africana, Vincetoxicum glaucum, Achyranthes bidentata
PR1600–1900
(Lower altitudinal zone)		SouthPinus roxburghiiRubus ellipticus, Glochidion heyneanum, Ageratina adenophora, Theropogon pallidus
QL-RA1900–2100
(Middle altitudinal zone)		NorthQuercus leucotrichophora, Rhododendron arboreumDaphne papyracea, Coriaria napalensis, Dicliptera bupleuroides, Strobilanthes atropurpurea
PR-QL (S)1900–2100
(Middle altitudinal zone)		SouthPinus roxburghii, Quercus leucotrichophoraViburnum cotinifolium, Ototropis multiflora, Rostellularia diffusa, Pimpinella diversifolia
QL-RA-QF2100–2400
(Higher altitudinal zone)		NorthQuercus leucotrichophora, Rhododendron arboreum, Quercus floribundaBerberis aristata, Drepanostachyum falcatum, Strobilanthes atropurpurea, Valeriana hardwickei
QL-QF-RA2100–2400
(Higher altitudinal zone)		SouthQuercus leucotrichophora, Quecus floribunda, Rhododendron arboreumMyrsine africana, Berberis napaulensis, Stellaria patens, Origanum vulgare
Abbreviation used: PR: Pinus roxburghii; QL: Quercus leucotrichophora; RA: Rhododendron arboreum; QF: Quercus floribunda; N: north; S: south.
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Upadhyay, G.; Tewari, L.M.; Tewari, A.; Pandey, N.C.; Koranga, S.; Wani, Z.A.; Tewari, G.; Chaturvedi, R.K. Species Diversity, Biomass Production and Carbon Sequestration Potential in the Protected Area of Uttarakhand, India. Plants 2025, 14, 291. https://doi.org/10.3390/plants14020291

AMA Style

Upadhyay G, Tewari LM, Tewari A, Pandey NC, Koranga S, Wani ZA, Tewari G, Chaturvedi RK. Species Diversity, Biomass Production and Carbon Sequestration Potential in the Protected Area of Uttarakhand, India. Plants. 2025; 14(2):291. https://doi.org/10.3390/plants14020291

Chicago/Turabian Style

Upadhyay, Geetanjali, Lalit M. Tewari, Ashish Tewari, Naveen Chandra Pandey, Sheetal Koranga, Zishan Ahmad Wani, Geeta Tewari, and Ravi K. Chaturvedi. 2025. "Species Diversity, Biomass Production and Carbon Sequestration Potential in the Protected Area of Uttarakhand, India" Plants 14, no. 2: 291. https://doi.org/10.3390/plants14020291

APA Style

Upadhyay, G., Tewari, L. M., Tewari, A., Pandey, N. C., Koranga, S., Wani, Z. A., Tewari, G., & Chaturvedi, R. K. (2025). Species Diversity, Biomass Production and Carbon Sequestration Potential in the Protected Area of Uttarakhand, India. Plants, 14(2), 291. https://doi.org/10.3390/plants14020291

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