Introduction

The Indian Himalayan Region (IHR), with a geographical coverage of over 5.37 lakh km2, constitutes a significantly large portion of the Himalayan Biodiversity Hotspot. It covers 16.2% of total geographical area of the country. The temporal and spatial variations caused by diverse geological orogeny have resulted in marked differences in its climate and physiography, thus contributing greatly to the richness and representativeness of its biodiversity components at all levels (Anonymous 2009). These variations provide a suitable opportunity for studying species distribution patterns in relation to different environmental variables (Rawat 2014).

Field ecological investigations seem incomplete without understanding the patterns and processes of vegetation along environmental gradients. Vegetation heterogeneity and variation in species diversity across history, habitat, climate, productivity and biotic interactions have been well investigated along environmental gradients (Givinish 1999; Willig 2003; Currie and Francis 2004; Gonzalez-Espinosa 2004; Qian and Ricklefs 2004; Bhattarai and Vetaas 2006; Gairola et al. 2008; Acharya et al. 2011). Environmental features have been proven genuine towards underlining plant distribution and compositions (Keenan and Kimmins 1993). In the current global change context, prediction of species distribution and its assemblage with space and time across environmental gradients has been given the utmost importance to investigate the fate of biodiversity (Dubuis et al. 2011). Among other, influence of climate on the species distribution has been well observed using topographical aspects along with climatic parameters (Woodcock et al. 2002). Hence, the diversity in climate and topography is cause of distinct forest types. Spatiotemporal patterns act as function to which plant community of a region respond, however, environmental factors play a role in the formation of plant communities and their compositions (Kharkwal et al. 2005). These habitat factors not only influence the general vegetation composition but also play an important role in the distribution of native and alien flora (Arevalo et al. 2005; Khuroo et al. 2011). Distribution of distinct biological communities with high level of endemism in mountain ecosystems has been suggested through the study of topography and historical background of these ecosystems (Gentry 1993). Further, effect of slope on spatial heterogeneity and distribution of soil parameters has been investigated (Mohammad 2008). Apart from the influence of environmental factors on vegetation, human disturbance has been proven a major cause for disruptive changes in vegetation structure and its function (Naeem 2002). Effects of human disturbance on vegetation distribution, regeneration and functional diversity have been well documented (Biswas and Malik 2010; Fakhry et al. 2020).

Several ecological studies have been carried out on the high altitude of Indian western Himalaya including phytosociology, species composition, species distribution, climate change (Gairola et al. 2008; Rawal et al. 1991; Rawa and Pangtey 1994; Rawal and Dhar 1997; Joshi 2002; Rawat et al. 2015a, 2015b). However, studies explaining current vegetation distribution along environmental and other influential parameters human disturbance are meager (Gairola et al. 2008; Rawat 2014). As a result, there is limited information available about the distribution of vegetation and its underlying control in the Himalaya. Considering above, the present study is focused on the species diversity and distribution patterns of mid to high altitude vegetation in relation to different environmental parameters and human disturbance.

Methods

Study area and site selection

Nanda Devi Biosphere Reserve (NDBR) lies between 30°06′ and 31°04′ North latitude and 79°13′ and 80°17′ East longitudes (Fig. 1). Having an area of 6407.03 km2, NDBR includes core zone with and area of 712.12 km2 followed by buffer zone (5148.57 km2) and transition zone (546.34 km2). Two intensive study transects Pindari–Sunderdhunga–Kafni (PSK) in Kumaun and Lata–Tolma–Phagti (LTP) in Garhwal region was identified for the present study. Compositional patterns were recorded/observed across altitudinal range from 1800 m asl to 4000 m asl which covers temperate to sub-alpine forest communities in the region. 30 forest stands in each transects were investigated for the collection of data. Details of forest stands and site characteristics are presented for both sites in Tables 1 and 2. Extensive visits to these sites were made during 2012–2014. Thirty forest stands were further categorized into 11 forest communities in Pindari–Sunderdhunga–Kafni (PSK) and 8 in Kumaun and Lata–Tolma–Phagti (LTP). The detailed community characteristics along with soil properties are presented in Table 3.

Fig. 1
figure 1

Location map of study area–Nanda Devi Biosphere Reserve (NDBR) (bottom left) and target site Pindari–Sunderdhunga–Kafni (bottom right) and Lata–Tolma–Phagti (up right)

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Table 1 Details of representative forest stands in Pindari–Sunderdhunga–Kafni (PSK) site
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Table 2 Details of representative forest stands in Lata–Tolma–Phagti (LTP) site
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Table 3 Details of representative forest communities Pindari–Sunderdhunga–Kafni (PSK) and Lata–Tolma–Phagti (LTP) sites in Nanda Devi Biosphere Reserve (NDBR)
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Sampling and data collection

Stratified random sampling was adopted to investigate forest communities in study areas. To perform quadrat study, standard ecological methods were adopted (Misra 1968; Muller-Dombois and Ellenberg 1974; Dhar et al. 1997). Trees and saplings were investigated in ten (10 × 10 m) quadrats. Seedlings and shrubs were recorded in five (2 × 2 m) random sub quadrats within each 10 × 10 m quadrat. Herbs were enumerated in ten (1 × 1 m) quadrat within each 10 × 10 m quadrat. Based on the circumference at breast height (at 1.37 m from ground level) recorded for everyone, tree (> 31 cm CBH), sapling (11–30 cm CBH) and seedling (< 10 cm) were defined. Shrubs were considered as woody taxa based on branching from the base (Saxena and Singh 1982). A detailed sampling scheme has been presented in Fig. 2. The vegetation composition and species distribution parameters have already been attempted earlier (Rawat et al. 2015a), therefore, the highlights of compositional parameters in the result section are referred from Rawat et al. (2015a).

Fig. 2
figure 2

Sampling scheme adopted for survey and vegetation analysis in Nanda Devi Biosphere Reserve (NDBR)

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Taxonomic identification: natives and endemic species

The plant specimens collected during field investigations were preserved (Jain and Rao 1977) and identified with the help of regional and national flora (Hooker 1872–1897; Naithani 1984; Sharma and Balakrishnan 1993; Hajra and Balodi 1995; Kumar and Panigrahi 1995; Gaur 1999), monograph (Mukherjee and Constance 1993; Dikshit and Panigrahi 1998) and checklists (Uniyal et al. 2007).

The nativity of the species was assigned following Samant et al. (1998). Species originated from Himalaya and having distribution in neighboring countries/states were considered as natives. Endemism of the species was determined based on the extent of geographical distribution (Dhar and Samant 1993; Dhar et al. 1996; Dhar 2002).

Soil sampling and analysis

From each stand, five samples, preferably one from centre and four from corners were collected. Twenty-centimeter depth was used to core up soil and mixed to make a composite sample. The homogenized composite soil samples were brought to the laboratory in air-tight polythene bags. The fresh soil was used for pH measurement. Rest of samples were shade dried and passed through 2 mm sieve. Dried samples were analysed for organic carbon, organic matter, and total nitrogen.

Soil pH was determined by pH meter (LI-127, ELICO) and organic carbon and organic matter by rapid titration method (Walkley and Black 1934). Total nitrogen was calculated by sample digestion following Kjeldhal technique (Novosamsky et al. 1983) and distillation in Kjeltec unit (Teator, Kjeltec Auto 1030 analyser). Different soil parameters and their analysis are presented in Table 3.

Statistical analysis

Simple Linear Correlation (SPSS statistics for windows version 17.0) was performed to evaluate changes to species richness across elevational and the environmental properties. Level of significance of data was checked at P < 0.01 and P < 0.05 level. The Correlation analysis data is finally combined, and species composition values are presented against environmental parameters (Table 4). Further, to interpret the combined effect environmental properties on species richness values and other compositional values, Canonical Correspondence Analysis (PC-ORD for windows version 4.2) was performed. For CCA, variables were considered strongly related to an axis when scores or factors loadings were greater than 0.5 for a given axis (Norusis 1994). Both the analyses were based on 5 environmental variables [i.e., altitude (ALT), soil pH, soil total organic carbon (OC), soil total nitrogen (N), slope of the stands (SLP)] and, human disturbance (disturbance in terms of grazing/lopping % (DST)) and their underlying control on different forest structural components [i.e., total canopy cover % (CNP), total basal area of each stand (TBA), species richness at tree (RT), herbs (HB), shrubs (SH), native species (NAT), non-native species (NN) and endemic species (E) level].

Table 4 Effect of environmental variables on different vegetation parameters in Pindari–Sunderdhunga–Kafni (PSK) and Lata–Tolma–Phagti (LTP) sites
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Results

Compositional pattern

The study transects in NDBR comprised of a total 451 plant species belonging to 94 families. Among all, herb with 318 species showed the maximum contribution (70.51%) followed by shrubs with 80 and trees with 53 species. Pindari–Sunderdhunga–Kafni site shares 73.6% and Lata–Tolma–Phagti site 54.9% of total species (Rawat et al. 2015). Maximum number of tree species (23 spp.) was recorded in Mixed Abies pindrow-Quercus spp. and Quercus semecarpifolia communities. In sapling and seedling layer, Q. floribunda showed highest contribution with 18 species of sapling and 19 species of seeding. In all cases, minimum species richness was observed in Hippophae salicifolia community. Maximum shrub and herb species were recorded in Q. semecarpifolia (23 spp.) and Mixed Quercus-deciduous spp. (96 spp.) communities, respectively. In Lata–Tolma–Phagti site, species richness varies greatly from 4 to 18 species in tree, 10–29 species in shrubs and 18–107 species in herbs. Highest tree species was recorded from P. wallichiana community with 18 species and lowest was recorded from Mixed Taxus wallichiana-Abies pindrow with 4 species. Maximum shrub (29 spp.) and herb (107 spp.) were recorded from P. wallichiana community (Rawat et al. 2015a).

Species diversity along environmental gradients

Simple Linear Correlation explains interrelationship between species richness parameters and environmental parameters in both the study sites (Table 4). In Pindari–Sunderdhunga–Kafni site species richness of tree, shrub, native and endemic species richness are significantly correlated variedly with one or another environmental parameter. Total species richness, species richness of herbs and canopy cover remains unaffected from influence of environmental parameters. Species richness of non-native species, disturbance and TBA are showed correlation with elevation. A significant decrease in disturbance (P < 0.01), TBA (P < 0.05), non-native species richness (P < 0.05) and increase in endemic species richness (P < 0.01) has been observed across elevation. Species richness of tree (P < 0.01), native (P < 0.01) shrubs (P < 0.05) and TBA (P < 0.05) show significant positive correlation with pH. Native (P < 0.01) and endemic (P < 0.05) species richness has positive correlation with organic carbon. Nitrogen has significant relationship with shrub (P < 0.05), native (P < 0.05) and endemic (P < 0.05) species richness. Slope has good positive relationship with tree (P < 0.05), shrub (P < 0.01), native (P < 0.05) and endemic (P < 0.05) species richness. While in Lata–Tolma–Phagti site, canopy and TBA did not show significant correlation with any environmental parameters. However, all species richness parameters (total- P < 0.01; tree- P < 0.01; herb- P < 0.01; shrub-P < 0.01; native- P < 0.01; non-native- P < 0.01; endemic- P < 0.01) showed significant correlation with organic carbon. Disturbance showed good negative (P < 0.01) and species richness of endemic showed positive correlation (P < 0.05) with elevation. pH shows significant increase with increasing disturbance (P < 0.05). Slope shows good impact on species richness of herb (P < 0.05), native (P < 0.01), non-native (P < 0.05) and endemic (P < 0.01). Level of disturbance (P < 0.01) is less prominent in higher slopes (Table 4).

Further, the datasets obtained from Simple Linear Correlation analysis has supported ordination analysis to establish combines effects of each environmental parameter on species richness and other compositional values. In the CCA of environmental data with species richness, most variances for species richness (43%—Pindari–Sunderdhunga–Kafni site; 55.6%—Lata–Tolma–Phagti site) were explained by the first three axes, which all had eigenvalues > 0.5 (Table 5); only the first two axes are plotted (Fig. 3). Most factors were strongly related to axis 1 and 2 having factor loadings greater than 0.5 in both the sites.

Fig. 3
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CCA plots of factor loadings along the first two axes for the subsets of vegetation units and environmental variables in NDBR; (a) Pindari–Sunderdhunga–Kafni site; (b) Lata–Tolma–Phagti site

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Table 5 Eigenvalues, cumulative % variance, and factor loadings for environmental and ecological variables along axes using CCA in Nanda Devi Biosphere Reserve, India
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As depicted in Fig. 3a, in Pindari–Sunderdhunga–Kafni site, low elevational areas are highly disturbed supporting high total basal area and species richness particularly of non-native species. These areas are represented by moderate slopes. While, in higher elevations, good canopy cover in steep slopes supports good numbers of native, herbs and considerable numbers of endemic species. Native and endemic species are abundant in nutrient rich soil with higher amount of organic carbon, nitrogen, and pH (Fig. 3a). Low elevational areas in Lata–Tolma–Phagti site under high influence of disturbance but this has no impact on species richness variables except tree species richness. Good numbers of non-native species in moderate slopes are characteristics of these areas. High elevational zones are not influenced by disturbances, thus support highly nutrient rich soil and species richness of endemic and native species (Fig. 3b).

Discussion

Compositional pattern

Among total species richness, maximum was recorded from herb layer (70.51%). Herb layer contributes remarkable to overall plant species richness in any plant community and thus holds control over tree species regeneration thorough competitive and facilitative interactions (Gilliam 2007). In addition, overall forest productivity is depending on the herb layer contribution (Mölder et al. 2008). Among sites, maximum species richness contribution comes from Pindari–Sunderdhunga–Kafni site (73.6%) in Nanda Devi Biosphere Reserve. More diverse forest communities in Pindari–Sunderdhunga–Kafni site contribute maximum species richness (Rawat et al. 2015a). Changes in climatic and environmental parameters are more pronounced in diverse types of forest and these forests having a direct bearing on the habitat types and species diversity (Singh et al. 1996; Muller 1982). Therefore, more climatic fluctuations and more mesic (moist) conditions in Pindari–Sunderdhunga–Kafni site than Lata–Tolma–Phagti site are possible reason of greater variations in community types and species richness in Pindari–Sunderdhunga–Kafni site. Lata–Tolma–Phagti site, having comparatively more dry conditions than Pindari–Sunderdhunga–Kafni site, is more suitable for growth of coniferous forests (Joshi 2002).

Species diversity along environmental gradients

Simple Linear Correlation and CCA based analysis showed that higher elevations (cool temperate to sub-alpine) in both the sites are associated with higher slopes, high organic carbon, and nitrogen. Plant distribution and composition is well known to be determined by local environmental features (Keenan and Kimmins 1993). Among others, soil characteristics such as organic carbon, nitrogen and pH have been reported to strongly correlate with vegetation (Wales 1967; Pregitzer and Barnes 1982; Pregitzer et al. 1983). Increase in soil organic carbon, organic matter and nitrogen with increase in elevation has already been predicted (Sims and Nielsen 1986; Garten et al. 1999; Xu et al. 2010; Dai and Huang 2006; Proll et al. 2011). Besides, high altitude communities (sub-alpine) also support moderate canopy cover, richness of native, endemic and shrub species. Distribution of native species along altitude has also been demonstrated (Khuroo et al. 2011). Low-mid altitudinal zones (cool temperate) support high richness of tree and seedlings and high TBA in moderately disturbed areas with open canopy. The open canopy sites supported good density of recruitment and shrub species (Mishra et al. 2003; Mishra et al. 2004). Further, species and community responses to the climatic gradients and atmospheric gases are well established (Aguado-Santacruz and Garcia-Moya 1998; Vasseur and Potvin 1998).

In general, both the study sites showed good species richness in mid elevational zones where levels of disturbance and steepness of slopes are moderate. The open canopy in Lata–Tolma–Phagti site and closed canopy in Pindari–Sunderdhunga–Kafni site in mid elevational zones supporting high species richness may be attributed to the physiognomic types in the regions (> 60% broadleaf forest in Pindari–Sunderdhunga–Kafni site; >60% coniferous forest in Lata–Tolma–Phagti site).

Conclusions

Present study highlights the forest species distribution in relation to different associated factors influencing the growth and establishment of species/communities in the west Himalayan region. Of five environmental variables that were measured, elevation, slope and organic carbon were found to be the most influencing variable. However, nitrogen and pH showed their impact on certain compositional parameters. In the western Himalayan forest, some site variable like soil components and non-native proliferation may act as a baseline data of past disturbance regime and can be utilized for assessing the recovery and regenerative potential of disturbed sites. Changes in land use and habitat alterations because of human pressure are very intense in Himalayan regions, needs initiatives towards mitigating effects of non-native invasive species. As a result of such land use changes, habitat alterations and approach to new sites due to dispersal, non-native species in lower elevations can be enriched in range towards higher elevations in the future that needs an immediate attention in the reserve. This is amongst the few studies depicting the distribution pattern of forest species as well as communities in relation to different associated factors responsible for the growth and establishment of species/communities in NDBR, west Himalaya. This scenario, therefore, calls for similar studies on various attributes highlighted in present study in other Himalayan biosphere reserves (HBRs).