Introduction

Vegetation biomass is a key component of forest ecosystem [1] and represents habitats which support majority of species and provide valuable ecosystem services for human use [2]. Climate change has a significant impact on global forest biodiversity, and it is likely to be the leading cause of biodiversity loss in the near future [3]. Among all worldwide ecosystems, Himalayan forest ecosystem undergoes greater modifications than other ecosystems [4]. The elevated atmospheric level of carbon dioxide can be mitigated by increasing the area under forest cover [5]. To reduce the imminent increase in biodiversity loss and climate risks in the Zabarwan sub-mountains range, a National Park was established in 1981. In the sub-urban landscapes, National Parks (NPs) are currently employed as principal tool for maintaining biodiversity and mitigate climate change [6]. Furthermore, the landscapes surrounded by NPs are the main indicators of the speedy climatic changes which can interfere with anthropogenic disturbances (viz. climate changes and modifications in the land use patterns) and other deteriorations [7]. Thus, these events make forest conservation critically important for mitigating climate change [8].

In community ecology, the major challenge is to understand the factors that influence the productivity, spatial variation, growth, composition and other ecological attributes of woody trees species [9]. As the large-scale environmental change is predicted to intensify the consequences of land-use change [10], the managers and conservation scientists ought to pick out the handiest methods to hold the integrity of biodiversity and ecosystem services and other processes [11]. The response of the net exchange of CO2 to climate depends on the response of carbon uptake growing stock volume density (GSVD) and on the carbon residence times resides in terrestrial ecosystems [12]. In Indian Himalayas, the status of NPs is very special, where human intervention is only allowed to perform a particular activity. In NPs, natural factors have some consequences on the growth, biomass and structure of wood trees, and the effect of these factors can be easily assessed in these areas. These natural drivers affect the spatial variation of productivity in the NPs, indicating the productivity of a specific part in accordance with the standing biomass at a given period of time. Managers, policymakers, and conservation activists needed to understand the conditions under which NPs offer conservation benefits. The accurate assessment of carbon of the most critical factors for sustainable forest resource management in protected areas. Numerous studies revealed the competency of NPs in reducing the deforestation rate [13]. However, in order to assess the role of existing NPs under REDD + , a thorough assessment of their ability to secure forest carbon is critical.

Within the mountain ecosystems field, the Indian Himalayan Region (IHR) is of particular importance. Despite the fact that the Himalayan mountain range accounts for only 18% of India's total geographical area, it is home to more than 50% of India's forest cover and more than 40% of the endemic species found on the Indian subcontinent [14]. Some critical regions of IHR, such as the Zabarwan sub-mountain range, have received little attention in terms of ecological research. Dachigam National Park (DNP) is a significant feature of the range, which is why its various forests were chosen for the current study. Despite the fact that our research was conducted over a small-scale distance between selected forest types, the diversity of tree species in these forests varied, with different dominant species. These forests support a rich diversity of wildlife and provide important ecosystem carbon sequestration and storage functions [15]. This region therefore provides an ideal place to evaluate changes in carbon stock. This study specifically addresses the research questions on (i), to evaluate the tree C stocks in the forests of Zabarwan range in Kashmir Himalaya, (ii) what is the contribution of different tree species in the total C stock? Additionally, all the collected data will be useful in devising new management framework policies, the diversely vegetative landscapes could be turned into vast C sequesters which could prove critical role in mitigating the regional (Zabarwan sub-mountains range) as well as global climatic crises.

Materials and Methods

Study Area

The central part of Kashmir valley located in the territory of Jammu and Kashmir, India, consists of Zabarwan Range, a sub-mountain range of 32 km long and is located in between Himalayan range and Pir Panjal. The Zabarwan mountain range is enriched with wildlife and acquires great Himalayan features. One of the significant features of the range is Dachigam National Park (DNP), and that is the reason for the selection of its different forests for the current study [15]. DNP covers an area of 141 km2 which extends between 34° 05′ N–34° 11′ N and 74° 54′ E–75° 09′ E (Fig. 1). The study area has an altitude range of 5704 to 6334 feet. The details of sampled forest types are shown in Table 1. The average rainfall in the area is 660 mm, but like other regions of the country, there is no any such particular rain season. The maximum reported average temperature is 2 °C in the winter and 27 °C in the summer. According to Dachigam Management Plan 2011–2016, the entire Dachigam National Park is mountainous, with crystalline rocks such as granite, phyllites, and schists, as well as lime stone buried in them. The Dachigam National Park is surrounded by a fold of the Zanskar range, which forms the core of the range. The majority of the sediments that make up these ranges date from the Cambrian through the Tertiary periods. The soil depth at Dachigam is less than 25 cm on the slope from lower to middle reaches, putting it in the category of very shallow soils [16]. The most dominant forests of the park are broadleaved and coniferous, in between these forests are the meadows [17].

Fig. 1
figure 1

Map showing Jammu & Kashmir, India (a), and Dachigam National Park (b) in the Zabarwan range of the Kashmir Himalayan biogeographic zone

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Table 1 General details of sampled forest types in the Zabarwan range of the Kashmir Himalaya
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Sampling and Data Analysis

Several field surveys and tours of Zabarwan range were carried out during 2018–2019 to investigate the landscape, trees composition, species distribution and approachability of various forest types of the range. The main forests of the range include Broad-leaved forest (BLFT), Acacia forest (ACFT); Oak forest (OKFT), Pinus wallichiana forest (PWFT), and Scrub forest (SRFT) [17, 18]. The details of the sampled forest types are shown in Table 1. Random floral sampling was performed for all selected forest types, and 12 square plots of 0.1 ha were laid in four different directions: north-east, south-east, north-west and south-west. In the current study, total, 60 plots (0.1 ha) were sampled. Slope angle and tree height were measured with clinometers and Ravi multimeter, respectively.

At each site, the height and dbh (diameter at breast height, 1.37 m above the ground) were measured for each individual tree falling inside the sampling plot. The trees only with dbh ≥ 10 cm were individually sampled [19]. Growing stock volume density (GSVD) was calculated via the volume equations set by the Forest Survey of India (FSI 1996) and further consultation with recent studies [20,21,22]. For the tree species where the volume equations were unavailable, same the congeneric equations were used with same plant height, growth, form and canopy. The values for aboveground biomass density (AGBD) of tree components (stem, branches, twigs and leaves) were extracted from GSVD, which was calculated by multiplying biomass expansion factor (BEF) with the GSVD of tree species [23]. The equations by Sharma et al. [24] and Haq et al. [25] were used for calculating the GSVD of hardwood tree species. For the belowground biomass density, the equation by Cairns et al. [26] was used. BGBD (fine and coarse roots) was estimated for each tree species as BGBD = exp {− 1.059 + 0.884 × ln (AGBD) + 0.284}. The total biomass density (TBD) was calculated by summation of AGBD and BGBD. The total C density (TCD) was computed using formula as Carbon (CMgha−1) = Biomass (Mg ha−1) × Carbon %. For the forest types, with dominancy of all coniferous constituted greater than 50%, the C percentage used was as 46%. In forests, where conifers and broadleaved species are equally distributed or broadleaved species counted higher than 50%, the C percentage used was as 45% [24, 27].

Data Analysis

Associational analyses among ecological variables and plant compositions were carried out with clustering analysis [28]. Sorensen’s (Bray–Curtis) distance was used to identify significant differences among the different forest types and microclimatic similarities [29] with the help of PAST software ver.3.14. (https://www.techworld.com/download/office-business/past-314-3330821/linkid=163338).

Results and Discussion

The basal area, density, BGBD, AGBD, biomass and C stock of different forest types are shown in Table 2. The present study results indicate that coniferous dominated forests types had higher C stocks than the broad-leaved forest types. Carbon budget of forest ecosystem is affected by many factors which include biotic (structure and function of tree species and forest type) and abiotic factors like soil property, droughts, forest floor, its humidity, aridity and temperature [30]. The average total carbon stock (TCD) was estimated to be 174.49 MgCha−1 (range 56.68–258.46 MgCha−1), the maximum TCD was recorded for P. wallichiana forest (258.46 Mg C ha−1), and the least TCD of 56.68 Mg C ha−1 was reported for Scrub forest type. Forests capture atmospheric CO2 via the process of carbon sequestration, there serves as an excellent tool for mitigation. The extent of atmospherically captured and storage carbon can be quantified by knowing the total carbon stock in forests. Here, we presented the results of an assessment of the potential forest carbon availability in the protected forests of Kashmir Himalaya and we provided insight into how these forest carbon potentials are spatially distributed across the forest types. According to our calculations, total forest carbon stock in the study was 872.45 Mg C ha−1 and could store an average of 174.49 Mg C ha−1. The overall values of the carbon stock of the forests of the current study are in the range reported by several workers in Himalaya, e.g., TCD values of 107.8 to 234.1 MgCha−1 and 92.06–245.31 MgCha−1 were reported by [24] and [27] from mid to high altitude forests in the Garhwal Himalaya, India; [28] reported an average TCD value of 151.38 Mg C ha-1 from Pakistan, Himalaya. The trend in carbon pattern in the present study was as P. wallichiana forest > Broad-leaved forest > Acacia forest > Oak forest > Scrub forest. Similar pattern of carbon stocks in forests of Himalaya was reported by [24, 27] further supported our findings. The most dominant trees species in terms of C was Populus alba contributing of 57.08 Mg C ha-1 in BLFT, P. wallichiana in 233.56 Mg C ha-1 in PWFT, Quercus robur in 131.83 Mg C ha-1 in OKFT, Robinia pseudoacacia in 65.21 Mg C ha-1 in ACFT, Parrotiopsis jacquemontiana 29.41 Mg C ha-1 in SRFT, and the contribution of remaining co-dominant tree species in respective forest type is shown in Table.3. When compared to the rest of the tree species, P. wallichiana, Q. robur, Celtis australis, and R. pseudoacacia store the most C stock. Similar results were reported by Sajad et al. & Haq et al. in northern part of Kashmir Himalayas. Any attempt to discuss of tree carbon stock must essentially consider soil carbon and other site-specific factors scrutinized carefully. However, our study focused primarily on assessing tree carbon stock of forest types. It is suggested that any study regarding carbon storage should consider forest structure as well as soil carbon in future.

Table 2 Basal area, density, belowground biomass density (BGBD), aboveground biomass density (AGBD), total biomass density (TBD), total C density (TCD) in different forest types of the Zabarwan range of the Kashmir Himalaya
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Table 3 Species wise C stock contribution of tree in different forest types of the Zabarwan range of the Kashmir Himalaya
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In the study area, there were a total of 20 tree species belonging to 15 genera and 12 families. The individual tree contribution of each tree species to C stock in different forest types is shown in Table 3. We distinguished 4 clusters from the 20 tree species through cluster analyses using Bray–Curtis neighborhood clustering. Based on C stock values, distinct grouping of species in clearly segregated clusters was reported in cluster analysis. The four tree communities were identified through cluster analysis of 20 tree species using PAST software. On the dendrogram, Q. robur was grouped separately on the extreme left, while as on the extreme right P. wallichiana and Crataegus monogyna form individual branches with minimum similarity (Fig. 2). Based on species coexistence at sites closely related in geographical occurrence, all other species were clustered in single distinct groups. Morus Alba, Prunus avium, Populus Alba, Prunus persica, Ulmus wallichiana, Salix alba, C. australis, Acer caesium formed a cluster with maximum similarity. Similarly, P. jacquemontiana, Ulmus villosa, Prunus armeniaca displayed maximum resemblance at cut level 2. It was due to their presence within same forest type which significantly separated these from the rest at maximum similarity (Fig. 2). Cluster analysis distinguished 4 clusters from the 20 tree species using Bray–Curtis neighborhood clustering. The cluster analysis based on location, biomass and C stock values clearly segregated the tree species into distinct clusters. Similar classifications were also carried out by previous worker from Pakistan Himalayas [28].

Fig.2
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The cluster dendrogram of 20 tree species showing 4 plant community types in the Zabarwan range of the Kashmir Himalaya

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Conclusion

The study clued that in the Zabarwan range, the forest shows a good signal toward an opportunity in effectively conserving the carbon storage in regional forests of Kashmir Himalayas by checking destruction of lush green woody forests. Compared to afforestation, safeguarding mature forests is a wiser step in reducing carbon emission which is home to a huge quantity of C stocks. Thus, our results underscore the significant role of protected forest area in biodiversity and regional C storage. With these framework policies, the diversely vegetative landscapes could be turned into vast C sequesters which could prove critical role in mitigating the regional (Kashmir Himalayas) as well as global climatic crises.