Forest communities and factors responsible for vegetation pattern in the legally protected areas of the Kashmir Himalaya, India

The protected areas (PAs) of any region are contributing people’s livelihoods and are proving to be the backbone of all forms of biodiversity conservation. The reforms in protection rules at global level and legal protection at local level has contributed a lot to the conservation of forests and other associated biodiversity. However, due to various anthropogenic activities and other climatic changes, protected areas and other species rich sites are being exposed to a continuous threat. Realizing the future perspective of potential and economic value of these protected forests, the phytosociological investigations were carried out in protected forests of Jammu and Kashmir in Kashmir Himalaya. and plants from and was lower than that of the coniferous forest and other types. The average tree density of 890 individual’s ha − 1 was recorded throughout the study area.


Introduction
Over the recent past, the science of conservation biology has developed a lot, its stress has changed drastically from the conservation and management of particular species within habitats to its emphasis on management of total species living within Protected areas (PAs), (Gaston et al. 2001;Summerville et al. 2003). Moreover, PAs encompass valuable landscapes and are key indicators of the accelerated climate changes and may interact with other human-induced changes (i.e., changes in land use patterns and climate) and degradation (Hannah et al. 2007; Gaston et al. 2008). Although, the conservation measures inside PAs are enjoying the special level of protection in various parts of the world, despite diversity of living organisms continues to decline (Butchart et al. 2010). However, there is not any proper system of classi cation in which the PAs are placed for the planning of utilisation of land resources at the local level. As a consequence of lack of land use planning, there is escalating level of heterogeneity in habitats, decreasing the quantity of large areas, resulting in diminishing the dimensions of the accessible habitation for core zone of plant types, which normally require well-built neighbouring areas of a reasonably untouched habitat (Debinski and Holt 2000).In addition, there is inadequate information related to important factors that would be associated with any stable program for conservation of biodiversity in the protected forests of Himalaya region, particularly Kashmir Himalaya.
However, it is imperative to understand the situations through which the protected areas provide conservation bene ts is vital for conservation advocates and policy makers (Margules and Pressey 2000). One of the important factors for effective forest resource management in the protected areas is the appropriate assessment of biodiversity for resource management actions that have an impact on the forest wealth (Foxcroft et al. 2013).In Himalayan protected forests ecosystem, the complex of differences in community structure, diversity, richness, and distribution are owed topographic heterogeneity (e.g. elevation, slope, soil composition) Khan et al. 2017;Saima et al. 2018) forest productivity, biotic-forest interaction and evolutionary competition between different species (Criddle et al. 2003). Combination of all these variables determines the ecological conditions unique for each community attributes such as architecture, species richness and the spatial association patterns and hence can assist in vegetation evaluation and quanti cation . The identi cation of these key variables is of extreme signi cance in e cient diversity conservation in case of forests (Ehrlich 1996).The use of multivariate analysis and statistical techniques for investigating vegetationenvironment relationships enabled ecologists to get results for a large dataset in more e cient way with expense of less time and labour (Massberg et al. 2002;Hair et al. 2006). However, these methods are very less in practice for forest community classi cation in particularly protected area of Kashmir Himalayan region.
Kashmir Himalayan part of the Indian Himalayan arc supports a huge oristic diversity  however; this area is neglected for multivariate phytosociological investigations by ecologists and foresters. Although the accumulated scienti c knowledge on the protected forest is growing fast, yet there are substantial knowledge gaps across the world, especially in the global south. The important protected areas of the North-western part of the Himalayan, such as the Kashmir Himalaya have received little research attention. Thus, the broad-scale classi cation is important for understanding regional patterns of plant associations and habitat types for management, planning and the use of forest based natural resources (Rahman et al. 2017;Haq et al. 2017). Based on these stimuli, we selected protected areas in the Dachigam National Park of Kashmir Himalaya. This investigation will highlight the essential protocols for understanding the present scenario and guide lines for potential conservation efforts required for these protected forest ecosystems.

Study area
The Dachigam National Park (DNP) which is falling underneath Zabarwan hill range in the North-western Himalayan region of India ( Fig. 1), which being a part of J&K state. DNP is geographically situated between the latitude of 34°05 -34°11 N and the longitude of 74°54 -75°09 E, and occupy a total area of 141 Sq. Km. Since 1910, this forest region is under protection of J&K government, and later declared as a National Park in 1981. Initially, it was protected to ensure clean drinking water supply for Srinagar city, the summer capital town of J&K. DNP is predominantly inhabited by peculiar subtropical deciduous trees with semi-evergreen to evergreen broad-leaved forests  Sampling design and measurements The forest working arrangement of the Dachigam National Park was consulted for validating administrative jurisdiction, topographical position and forest vegetations patterns. Several eld reconnaissance surveys and botanical expedition tours were carried out during 2017-2018 to have an idea about the landscape pattern, species diversity, accessibility and distribution of various forest forms. Major forms of forests present in Dachigam National Park include Acacia forest (ACFT), Broad leaved forest (BLFT), Coniferous forest (PNFT), Oak forest (OKFT) and Scrub (Parrotiopsis jacquemontiana) (SRFT) forest type. Forest types were studied considering the authentic reference of Champion and Seth (1968).During eld investigation, the relevant data pertaining to the plant specimens were recorded and the standard herbarium techniques were used as per Jain and Rao (1977) in this study. The vouchers were identi ed by comparing the housed herbarium samples at the Centre for Biodiversity and Taxonomy, University of Kashmir and consulting appropriate taxonomic literatures (Stewart 1972;eFloras). The specialized taxonomic database of The Plant List (www.theplantlist.org) was referred for the updated nomenclature of recorded species from Dachigam National Park.
The data collection related to oristic diversity of the study area was done by quadrant method of vegetation sampling . In each of the selected forest types, four sample plots were laid for trees sampling in all the four directions i.e. NE, NW, SW and SE each of 31.6 × 31.6 m (≅ 0.1 ha) size respectively. The density of live stem in each sample plot was recorded. Within each mentioned plot, the shrub density data was recorded from its 2 sub-plots of 5 × 5 m size. For recording herbaceous diversity, ve plots of 1 × 1 m size (one in the center and four in corners) have been laid down. In total, sixty forest stand (12 × 5 = 60) plots were sampled in the present study. Twenty-four (5 × 5 m 2 ) quadrants for shrubs and sixty (1 × 1 m 2 ) quadrants for herbs were randomly laid for understory diversity at each forest type. In total, three hundred (60 × 5 = 300) quadrants (1 × 1 m 2 ) for herbs and one hundred twenty (24 × 5 = 120) plots (5 × 5 m 2 ) for shrubs were sampled in the present study. Within these four sample plots (0.1 ha), four soil samples in all four direction and two replicate soil samples were taken for analysis. The 2 mm mesh screen was used to sieve soil samples. The soil conductivity and pH were determined in 1:2 ratio by digital conductivity &pH meter, OC by wet digestion, ascorbic acid method and kelpluskjeldahl nitrogen method were used to calculate available P and nitrogen respectively. Other physic-chemical parameters of soil are determined by Gupta et al. (2000) and Jackson (1973).
In order to minimize any sampling bias, as far as possible, it was ensured to accommodate the differences in vegetation growth caused by variation in slope and aspect. The clinometer was used to measure slope angle. The GPS devices were used to study physiographic factors, viz altitude and other aspects across different forest sites (Garmin, GPS map76cs).

Data analysis
The Importance Value Index (IVI) of plant species was used to determine the dominance plant species. The IVI of herb and shrub layers (Curtis and McIntosh 1951) and of tree species (Naidu and Kumar, 2016) were determined as the sum of relative density, relative abundance and relative frequency respectively.

Vegetation diversity and composition
The IVI along with environmental data of stands were used for multivariate analysis. For diversity analyses, the following indexes were used: Shannon-Wiener (1948), Simpson (1949), Margalef richness index, Evenness Index (Pielou 1969), and Dominance index.

Impacts of environmental variables on vegetation composition
The Canonical Correspondence Analysis (CCA) were utilized to examine vegetation samples and allocation of species with respect to their ecological factors by utilizing the CANOCO software version 4.5 (Ter Braak and Smilauer, 2002;Hejcmanova and Hejcman, 2006) after the utilization of PCORD version 5 . We also utilized the detrended correspondence analysis (DCA) to notice length the gradient length and the correlation among different vegetation types (Ter-Braak, 1986;Haq et al. 2017).
CCA was additionally applied to draw and comprehend the relationship of each natural variable on quantitative traits of the plant species and relationship in the region. Presence/nonappearance information were investigated utilizing cluster and two-way cluster examinations by means of PCORD version 5 (Leps and Smilauer 2003).

Vegetation composition and distribution of plant species
The vegetation composition of the study plot reported a sum of 84 species belonging to 39 families and 71 genera. The ora of the region based on the plant habits, can be classi ed into herbs 54 (64%), trees 18 (22%), shrubs 10 (12%) and climbers 2(2%) respectively. The oristic analysis relieved the perennial with 71 species (84%) was dominant life span category, followed by annual 10 (12%) and biennial 3 (4%).

Species-family relationship
Page 6/23 The species grouping patterns across the families were unequal with 8 families contributes half of the species, while as 31 families represents remaining half and large numbers of families 23are monotypic.

Diversity and phytosociological attributes
The species richness recorded at the DNP ranged from 38 to 55 with maximum 55 at BLFT and minimum 38 at SRFT. Shannon-Wiener diversity index value of SRFT was statistically lower than that of the PNFT and other forest types; Simpson's diversity index, ranging from 0.923 at SRFT to 0.966 at BLFT. Dominance index ranging from 0.033 at BLFT to 0.076 at SRFT; Species evenness ranged between a minimum of 0.502 at PNFT to a maximum of 0.709 at BLFT. The highest values of Fisher's alpha value and Margalef value (16.63; 8.88) was observed in BLFT and lowest value (14.89; 7.17) in OKFT respectively (Table 2).An average tree density of 890 Nha − 1 was recorded overall. Minimum was 640 ± 140.95Nha _1 at OKFT and maximum of 1197.5 ± 199.56 N ha _1 at SRFT. Forests stands showed an average basal area of 47.35 m 2 ha _1 . SRFT had the least, 15.4 m 2 ha _1 but 74.49 m 2 ha _1 was found at PNFT (Table 1).

Cluster analyses
The2 clusters were distinguished from the 5 different forest types through Cluster Analyses by pruning the dendrogram at 22% information remaining (Fig. 3). The dendrogram generated 2 distinctly separate clusters based on oristic similarity. The forest type's viz. BLFT and ACFT forms one limb cluster. OKT, PNFT and SRFT second limb of cluster. The cluster forest types that grouped in one limb are more similar in species composition and close proximity to each other.
The Two-Way Cluster Analyses of 5 forest types transect (elevation classes) including 84 species result 2 major plant communities. In diagram the white boxes display absence whereas the black one shows presence of a plant species in the forest types (Fig. 4).

DCA ordination
Along with the supplementary variables, in DCA ordination, the maximum 6.84 gradient length was recorded for axis 1 with eigen value 0.81. The minimum gradient length for axis 4 was 2.07 with eigen value 0.04; total inertia (sum of all eigenvalues) was 4.61. The different species clustered in ordination space in DCA ordination ( Table 2). The DCA ordination displayed that most of the species were positively correlated with both axes 1 and 2. These plant species include Dryopteris barbigera, Rubusulmifolius, Prunuspersica, Quercus robur, Tri oium repens, Aspleniumo feliae, Dioscorea deltoidea, Conyza canadensis, Fragaria nubicola, Geranium nepalense, Pteris cretica, Impatiens glandulifera and Viburnum grandi orum etc.

Environmental gradient and vegetation
In CCA ordination, the maximum eigenvalues were recorded for axis 1 (0.73) followed by axis 2 and 3 viz (0.44) and (0.38) respectively. The proportion of variance observed for axis 1, 2 and 3 were 17.92, 28.78 and 38.23, respectively. The sum of variance (inertia) in data related to species were 4.10, illustrative factors represent 100%. Pseudo-canonical correlations for all axes were 0.98. Theresults from Monte Carlo test revealed that all axes were having eigenvalue0.75; F-ratio 0.977, P-value 0.028 ( The other factors such as N, P, K and EC is having a great in uence over species distribution; on the other hand, the plant species, which signi cantly correlated with its value includes Achyranthes aspera, Carpesium abrotanoides, Strobilanthes urticifolia, Polygonatum aconitifolium, Parrotiopsis jacquemontiana and Verbascum Thapsus etc (Fig. 6).

Discussion
All over the globe, the forest ecosystems generally have varied community assemblages because of its rapidly altering microclimate, edaphic factors, topography, landscape, and geomorphological attributes (Martijn and Herben 2003;Fosaa 2004;Khan et al. 2011;Khan et al. 2017). In this current observation, the oristic composition and distribution comprises 84 species, which belong to 39 families, this value was found to be within the range as reported by earlier researchers in the different forests of Himalayas (Shaheen et al. 2011). The number of species observed in the present study area was higher than the number found by several other workers. Nazir et al. (2012) recorded a total of 40 species in Kotli district of Azad Jammu and Kashmir in Pakistan. Similarly, Shahid and Joshi (2016) recorded total of 35 species in Shiwalik hills of lower Himalayas. Further the total species reported from the current study was lesser than the species reported by several other similar studies. In addition to this, Qureshi and Bhatti (2010) reported a presence of 93 species from Pai forest region of Nawab Shah, in Sindh Pakistan. Sharma and Kant (2014) reported total of 112 species in Jammu hills, India.
In case of oristic distribution patterns, the present ndings could be compared with rest of the observations from mountain regions in the Himalayas, where oristic groups like Poaceae, Asteraceae, Rosaceae, Fabaceae, and Lamiaceae were the most dominant distributed families Rahman et al. 2017).The inline results were observed by Rahman et al. (2018) in Manoor valley, Pakistan andGairolaet al. (2010) in Uttarakhand, Garhwal Himalaya, India. Similarly, Suyal et al. (2010) in Uttarakhand, Garhwal Himalaya, India observed Lamiaceae as the prevailing family and Khan et al. (2015) in Kabal (Swat), Pakistan. On the other hand, Singh et al. (2018) in Nandini Wildlife Sanctuary in Western Himalaya, reported Fabaceae as dominant family. The oristic analyses revealed the unequal distribution of species across families and with large numbers of families are monotypic. These values are in line with the earlier reported values from different region of Himalaya (Gairolaet al. 2010;Rahman et al. 2018).
The parameters like tree density and basal area are key phytosociological characteristics that contribute to the structure of forests (Yam and Tripathi 2016). The average basal area reported in all forests stands was 47.35 m 2 ha − 1 ; (ranged between 15.4 m 2 ha − 1 and 74.49 m 2 ha − 1 ). These values are more or less comparable with the earlier reports from Garhwal .46 m 2 ha − 1 ), from temperate forests of Northern Kashmir .98 m 2 ha − 1 ), from moist tropical montane of the Himalaya by Malik and Bhatt (2015), 492 Nha − 1 in subtropical forest of Pakistan Himalaya by Shaheen et al. (2016), 578 Nha − 1 from Western Himalaya by Dar and Sahu (2018), 390-433 Nha − 1 from Saptasajya hill range, India, by Sahu et al. (2019) and higher than other region of western Himalayas 90-302 Nha − 1 by Shaheen et al. (2012). Similarly, the average 90.99 Nha − 1 was reported by Shaheen et al. (2016) in forest of Kashmir Himalaya and 149.99 Nha − 1 was reported by Akash and Bhandari (2019) from Garhwal Himalaya, India, respectively.
The rough terrain, uneven topography and remote area make it intricate to carry exhaustive vegetation sampling in the Kashmir Himalayan region. Maximum phytosociological studies performed so far in the Himalayan region have often used conventional protocols of community classi cations, where the names of plant types were characterized on the basis of dominant species having high importance value index. In the present study, new statistical approaches have been adopted for ordination and classi cation of plant species. The DCA and CCA bi-plot diagram revealed that diversity, distribution and association of plant species were the expressions of the differences in the environmental and biotic interactions. Besides, any variation in soil parameters causes considerable impact in the development of plant communities . The effect of soil composition in species pattern was also found in other mountain forest ecosystems around the globe (Hegazy et al. 1998;Wang and Singh 2006;Davies et al. 2008;Khan et al. 2012). These studies carried out vary from the present study as they were carried out in the un-protected forest ecosystems. Further, it was found that the pH of the soil, which was slightly acidic, played an important role in the growth of particular plant species in this ecosystem. The determination of natural inclination methodology through CCA both for stations and species advocates that the rst axes was basically connected with soil pH and Ca; the second axes were associated mostly with phosphorous, electrical conductivity and potassium contents. These ndings are in agreement with Khan et al. (2017), where carrying out their studies in the Thandiani forests of the Western Himalayas, Pakistan. The CCA bi-plot also revealed that species were highly sensitive to organic carbon, electric conductivity and phosphorous. The current ndings corroborate with Hussain et al. (2019) who also reported the positive correlation between edaphic factors, vegetation structure and its distribution patterns.

Conclusions
The composition and diversity of various plant species differed across spatial scales. On the other hand, phytosociological parameters gave off an impression of being differentially impacted by discrete levels within soil parameters. Plant community composition varied signi cantly within forest types even when total species richness does not reveal much alteration. Furthermore, the prototype of conservation biology has got shifted towards updated patter of biodiversity safeguarding by highlighting various multi-scale approaches. There are some critical abiotic factors, which prove very much helpful in understanding the mechanisms operating at a speci c level to create variation in species diversity as well as in community composition. The upcoming and promising tools like diversity partitioning is making us competent in