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Article

Influence of Edaphic Properties in Determining Forest Community Patterns of the Zabarwan Mountain Range in the Kashmir Himalayas

by
Shiekh Marifatul Haq
1,2,
Aqil Tariq
3,4,*,†,
Qingting Li
5,*,†,
Umer Yaqoob
6,
Muhammad Majeed
7,
Musheerul Hassan
1,
Sammer Fatima
7,
Manoj Kumar
8,
Rainer W. Bussmann
2,
Muhammad Farhan Ul Moazzam
9 and
Muhammad Aslam
10
1
Clybay Research Private Limited-560114, Bangalore, India
2
Department of Ethnobotany, Institute of Botany, Ilia State University, 0105 Tbilisi, Georgia
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
4
Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS 39762, USA
5
Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
6
Zonal Educational office, Vehil, Shopian, Jammu and Kashmir 192303, India
7
Department of Botany, University of Gujrat, Hafiz Hayat Campus, Gujrat 50700, Pakistan
8
GIS Centre, Forest Research Institute, PO New Forest, Dehradun 248006, India
9
Department of Civil Engineering, College of Ocean Science, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea
10
School of Computing Engineering and Physical Sciences, University of West of Scotland, Paisley G72 0LH, UK
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2022, 13(8), 1214; https://doi.org/10.3390/f13081214
Submission received: 28 April 2022 / Revised: 2 July 2022 / Accepted: 8 July 2022 / Published: 1 August 2022
(This article belongs to the Special Issue Mountain Treelines: Tree Growth and Plant Ecology under Climate Change)
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Abstract

:
The significance of edaphic factors in describing forest vegetation patterns is becoming more well acknowledged, with significant implications for the description of biogeographical regions and biome classification, as well as abundance and growth patterns at regional levels. The current study examines the vegetation association in the Zabarwan mountain range of the Western Himalayas and its association with edaphic factors. To collect data on forest types, we employed a systematic random sampling strategy in 60 plots (0.1 ha) across five forest types. We investigated data using ordination and cluster analysis approaches after calculating the important value index (henceforth IVI) for each plant species and edaphic data from forests. In total, 76 plant species from 39 different families were found in the area. The Rosaceae family was the most numerous, followed by Fabaceae and Asteraceae. Scrub forest types have lower diversity indices, while broad-leaved forest types have greater diversity indices. Two-way cluster analyses classified the forest vegetation of the Zabarwan mountain range into two plant communities on the basis of indicator plant species. The ordination analysis (canonical correspondence analysis) indicated that vegetation association tended to be influenced differently by distinct levels of soil parameters. The soil pH and calcium content were the main factors influencing the species distribution in the different forest types. The phytosociological features (basal area) were higher in coniferous forest type (74.49 m2ha−1) compared to broad-leaved (58.63 m2ha−1) and scrub forest type (15.4 m2ha−1). Overall, the goal of this research is to gain a better understanding of the impact of soil elements on forest composition and associations in order to develop scientifically based management options for forest ecosystem protection in the Himalayan region.

1. Introduction

Mountain regions have played a key role in the conservation of biodiversity throughout history, and they will serve an even bigger role in future initiatives to combat climate change [1]. Mountain ecosystems not only supply direct and indirect ecological resources for human life, but they also have a much broader impact since they provide ecological services to lowland ecosystems and humans [2]. Plants have long been used directly for medical purposes and as a source of fuel and fodder for cattle in the western Himalayan region [3,4,5]. Mountains are critical for the survival of human populations who rely on large river water bodies for domestic and agricultural water resources in this region, as well as throughout the south Asian countries [6,7]. The Himalayas are the source of several major rivers, and the economies of many south Asian countries rely significantly on their flow, which ensures food security by providing irrigation water for wheat and rice, two of the world’s most important staple cereals [8]. The shrubby flora of these high-altitude mountain ecosystems also controls the avalanche movements and prevents soil erosion [9]. High-altitude biodiversity and habitats are now at risk of biodiversity loss as a result of global warming, which result in both geographical range reduction and the risk of extinction of mountain-top ecosystems [10]. Adequate assessment of biodiversity for resource management decisions that affect forest wealth is one of the most critical factors for the successful management of forest resources in protected areas [11].
Changes in population structure, diversity, abundance and distribution in the Himalayan-protected forest ecosystem are complex due to topographic heterogeneity (e.g., altitude, slope), forest productivity, biological interactions of the forest and evolutionary competition between different species [12,13]. The interaction of all these variables defines the unique environmental conditions of each group, including species richness, architecture and spatial association patterns, and thus can help with the assessment and quantification of vegetation [12]. The discontinuous distribution of many biotic/abiotic processes that operate on various geographical and temporal dimensions determines the structure and diversity of vascular plants [14]. Abiotic factors such as topography and soil composition have a substantial impact on plant physiognomic differentiation in a variety of habitats. For effective forest management and conservation of biodiversity, identifying these crucial characteristics is critical [15].
The importance of soil in explaining ecological patterns in forests is becoming more widely recognized, with important implications for biogeography domain and biome characterization [16] as well as abundance and growth patterns at regional and community levels [17]. The interaction between plant and soil at the small scale is linked to a wide range of important ecological processes in which conditions and resources can alter the community’s features [16,17]. Through these interactions, plant populations are exposed to biogeochemical and hydrological cycles influenced by factors such as water availability, pH, growth necessary nutrients (N, K, C, P, Mg, Ca, S) and possibly toxic elements (Al, Pb, Mn, among others) [18]. Several studies suggest that local occurrence and variability filters can influence how resources become available for plant survival and growth. The soil may impact the successional process and functional/phylogenetic diversities through these influences, as well as play a part in species selection from the regional pool, as well as their patterns of establishment and growth [17,18].
The structure and function of habitats are influenced by a wide range of elements such as soil structure, erosion rates, terrain and hydrology, among others. Soil nutrient quality has been proven to influence tree height and basal area, and hence the composition of plant communities, among the edaphic influences [16]. Plant species diversity is positively linked to soil productivity as shown in several studies [17,18], while some studies published contradictory findings [19], necessitating the conduct of in-depth studies in diverse ecosystems. The soil–vegetation interaction in this diversified environment is critical for conservation biology because it specifies habitat choice, plant structure and diversity supported by each type of soil and habitat formation, that is, the habitat with the richest plant and soil [17].
While the Zabarwan mountain range of the Kashmir Himalayan region supports a diverse forest type [20], ecologists and foresters have generally overlooked this region for multivariate phytosociological analysis. Despite the fact that scientific understanding of protected forests is growing, significant information gaps around the world remain, particularly in the Global South. The Zabarwan mountain range in the Kashmir Himalayan region is a dry temperate Himalayan forest that has received little academic attention. As a result, broad-scale classification is critical for understanding regional dynamics of plant associations and habitat types, as well as forest conservation, planning and management. The current study was developed to answer the following research issues since the Zabarwan mountain range is so essential for biodiversity protection. (1) What is the floral composition of the Zabarwan mountain range’s forest types? (2) What relationship do edaphic characteristics have on forest vegetation association? Our findings will help guide forest ecosystem management and conservation in the Himalayan region by providing a better understanding of the influence of soil variables on forest vegetation composition and association.

2. Materials and Methods

2.1. Study Area

In the union territory of Jammu and Kashmir, India, the Zabarwan Range is a short (32 km) sub-mountain range located between the Pir-panjal and the Great Himalayan Ranges in the central part of the Kashmir Valley. The Zabarwan mountain range possesses great Himalayan features of rich forests in Dachigam National Park (DNP). DNP has a total size of 141 km2 and is located between 34°05′ N and 34°11′ N and 74°54′ E and 75°09′ E. (Figure 1). The park has been a protected area since 1910, when it was first under the jurisdiction of the Maharaja of Jammu and Kashmir and later promoted and proclaimed a national park in 1981. The climate in DNP is temperate, with warm and pleasant summers and severe and harsh winters. The average precipitation in the area is 660 mm, but there is no such thing as a rainy season, as there is in other parts of the world. The average maximum temperature in the summer is 27 °C, while it is 2 °C in the winter (minimum). The park is dominated by deciduous and coniferous forests, with riparian vegetation intermingled (for a more detailed description of the vegetation and region, see [21].

2.2. Sampling and Data Analysis

Several field exploration investigations and botanical exploration trips were conducted in the Zabarwan Range between 2018 and 2021 to know more about the vegetation composition, topography, distribution and approachability of various forest types. Pinus wallichiana forest (PNFT), broad-leaved forest (BLFT), acacia forest (ACFT), oak forest (OKFT) and scrub forest (SRFT) are the main types of forests in the Zabarwan Range [21,22]. Voucher plant specimens were collected for the identification and future study following standard taxonomic procedures [23]. In each of the forest types, random vegetation sampling was carried out and twelve square plots of 0.1 ha were spread out in four different directions. Within each (0.1 hectare) plot, shrubs were sampled in four subplots (5 × 5 m2). Finally, five 1 m2 subplots were sampled for herbaceous diversity, one in each plot’s corner and one in the center. In total, 60 (0.1 ha) plots for trees, 300 (60 plots × 5 forest types = 300) plots (1 m2) for herbs and 120 (24 plots × 5 forest types = 120) plots (5 m2) for shrubs were sampled in the current study. We calculated the importance value index (hereinafter IVI) for each plant species using the abundance, cover and the number of species from each quadrant (frequency). The IVI was chosen since it is a widely used ecological technique for determining the dominance of plant species in a given habitat [24].
From each (0.1 ha) plot, four different random soil samples were collected to study the different physicochemical parameters of the forest types for further analysis. Soil samples from each plot were taken and sieved via a 2 mm mesh screen. The pH was calculated using pH meter (Mettler Toledo pH meter) and electrical conductivity and salinity were calculated using electro meter (Conductivity TDS Tester–HI98129) meter (Conductivity TDS Tester–HI98129). The total nitrogen was estimated by modified Kjeldahl’s method, phosphorous by Olsen’s method and carbon by the Walkley and Black method [25]. The soil types are orthods, belonging to the spodosol suborder, with a coarse texture, being typically acidic and infertile, with reddish-brown or black subsoil.

2.3. Data Analysis

Plant IVI and stand-level soil data were calculated and analyzed using ordination techniques for multivariate analysis. To compare the relationships between the forest vegetation, we used detrended correspondence analysis (DCA), an ordination technique based on reciprocal averaging. Species scores represent the relative position of taxa in the reduced space in terms of how they change over time. The significance of DCA axes is then inferred by examining species’ relative positions in relation to what is known about their distribution in environmental parameters. This is a completely qualitative process and its major purpose, as with any ordination approach, is to establish the environmental significance of the axes, and hence define the ecological area indicated by the ordination axes. Following that, the space specified by species is used to ordinate samples, and by plotting axis scores stratigraphically, it is feasible to deduce how the stated environmental factors evolved through time. We investigated the relationship between plant species and soil variables using canonical correspondence analysis (CCA) by extracting key gradients among combinations of explanatory variables. Following CCA, the Monte Carlo test was used to evaluate the influence of explanatory variables on vegetation composition [26]. PCORD version 5 was used to perform two-way cluster analysis on the presence/absence data [27]. The Rényi diversity profile was used with PAST software version 3.14 [28] to highlight disparities in diversity curves for all five forest types. PAST software version 3.14 was used to generate the generally used diversity indices [29]: Shannon–Wiener [30], Simpson [31], Margalef richness index, dominance index and evenness index [32].

3. Results

3.1. Plant Composition and Distribution

In this study, we have documented 76 plants belonging to 63 genera and 39 families. Herbaceous life forms have the most species (60%), followed by trees (24%), shrubs (13%) and climbers (3%) (Table 1). The perennial was the most common life span group, accounting for 87 percent of all species, followed by annual (9%) and biannual (4%) species (Table 1). Half of the collected plants belonged to just seven families: Rosaceae, Fabaceae, Asteraceae, Poaceae, Asparagaceae and Lamiaceae, while the other half belonged to thirty-two (32) families. The majority of the families (25) were monotypic (Table 1).

3.2. Diversity and Phytosociological Attributes

Shannon diversity indices ranged from a high of 3.66 to a low of 3.092 for broad-leaved and scrub forest types. The Rényi diversity profiles revealed that the BLFT has much higher diversity than other forest types, as evidenced by the values (Figure 2). The decreasing order of species richness in forest types was as BLFT > ACFT > PNFT > OKFT > SRFT. The phytosociological features (basal area) were higher in coniferous forest type (74.49 m2ha−1) compared to broad-leaved (58.63 ± 21.57 m2ha−1) and scrub forest type (15.4 m2ha−1). In the case of tree density, the scrub forest type (1197.5 ± 199.56 N ha−1) was denser than acacia forest (850 ± 204.61 N ha_1) and coniferous (707.5 ± 148.18 N ha−1) forest type (Table 2).

3.3. Vegetation Ordination Approaches

3.3.1. DCA Ordination

We found that the 76 plant species found in the five forest types grouped differentially on the positive and negative sides of the DCA axis during DCA ordination (Figure 3). The maximal species had a positive connection with both axis 1 and 2. The following plant species, Asplenium ofelia, Conyza canadensis, Dioscoreadeltoidea, Dryopteris barbigera, Fragaria nubicola, Impatiens glandulifera, Geranium nepalense, Quercusrobur, Trifloium repens, Rubus ulmifolius, Pteris cretica, and Viburnum grandiflorum. Delphinium roylei, Aesculus indica, Cynodon dactylon, Celtis australis, Geranium wallichianum, Morus alba, Populus alba, Rosa webbiana, Pinus wallichiana, Ulmus wallichiana and Prunus tomentosa, were among the species clusters that showed differences in forest types on the negative side of both axes in ordination space. Table 3 shows a detailed summary of the total inertia (sum of all eigenvalues).

3.3.2. Role of Soil Parameters in Vegetation Patterns

The CCA ordination indicated that species were differently distributed along different soil variables (Figure 4). The species that are sensitive to Ca include Asyneuma thomsonii, Crataegus songarica, Celtis australis, Delphinium roylei, Desmodium elegans, Oplismenus burmannii, Pinus wallichiana and Viburnum grandiflorum. Other elements such as N, K, EC and P have an impact on species distribution; however plant species that are positively connected with their values include Carpesium abrotanoides, Parrotiopsisjacquemontiana, Verbascum thapsus and Polygonatum acuminatifolium. The species impacted by pH include Artemisia vulgaris, Acer caesium, Conyza canadensis, Berberis lyceum, Fragarianubicola, Digitalis purpurea, Geum urbanum, Hypericum perforatum, Salix alba, Populus alba, Juglans regia and Ulmus wallichiana. The species that were found to be sensitive to OC include Asparagus filicinus, Prunella vulgaris and Quercus robur (Figure 5). In the species data, the total variation (inertia) was 4.10. All axes had pseudo-canonical correlations of 0.98. For all axes, the Monte Carlo test yielded an eigenvalue of 0.75, a F-ratio of 0.977 and a p-value of 0.028 (Table 4).

3.3.3. Vegetation Classification

From the investigations of five forest types and 76 plant species, two-way clustering resulted in the establishment of two major plant groups. The BLFT and ACFT forest types are more similar and make up one leg of the cluster, whereas the OKFT, PNFT and SRFT forest types are similar in composition and make up the second. The white boxes represent the absence of a plant species in the forest types, whereas the black boxes represent the presence of a plant species (Figure 6).

4. Discussion

Mountains are unquestionably the world’s most rugged, yet fragile ecosystems and biodiversity-rich areas. However, these fragile environments are particularly vulnerable, and even the tiniest disturbance can place many species that live in these areas at risk. As a result, it is important to learn about the flora of these delicate mountain habitats so that conservation efforts can be prioritized. In this investigation, we identified 76 plants from five forest types in the Zabarwan mountain range. The number of plant species found in the research area is comparable to those found in previous studies in the Himalayan region and elsewhere. For example, Shaheen et al. [33] and Bokhari et al. [34] recorded 72 and 75 species from Pakistan’s Himalayan woodlands, respectively. Deka et al. [35] and Borah et al. [36] found 71 and 88 species, respectively, in Assam’s woodlands. From the forests of Jammu, India, Sharma and Kant [37] and Sharma and Raina [38] identified 112 and 63 species, respectively.
The distribution of plants in specific families reflects the underlying effects of abiotic and biotic processes. Species composition and abundance, on the other hand, appear to be linked to environmental plant traits. The preponderance of Rosaceae and Fabaceae groups shows that the studied region has a less disturbed environment. Similar observations were made by Haq et al. [23,39,40], who reported Rosaceae as the leading family in the Kashmir Himalayas, India.
The floristic analysis reported that the species richness values of the present study are more or less similar with several phytosociological related investigations in the Himalayas. Similar results were noticed by Gairola et al. [41] where the authors reported species richness ranges between 31 and 58 from the Western Himalayas. Shaheen and Shinwari [42] reported 29 to 38 species from Chitral, Hindukush Himalayas. Ummara et al. [43] reported 19–32 species from the vegetation of the Shogran valley, Pakistan. Comparatively low species richness of 10–17 was reported by Nazir et al. [44] from phytosociological studies from the Pakistani Himalayas. The greater species richness was observed in broad-leaved and coniferous-dominated forest types compared to scrub forest type. A similar pattern was observed by Sharma and Kant [37] and Dar and Sundarapandian [45] from forest communities of the Western Himalayas. The phytosociological attributes of coniferous forest types were greater than those of broad-leaved and scrub forest types. The coniferous forest has a higher basal area due to slow-growing, long-lived tree species and old natural forest stands [2].
The tree basal area was reported to be between 15.4 and 74.49 m2ha–1. Dar and Sundarapandian [45] (19.4–51.9 m2ha–1) and Haq et al. [46] (71–92 m2ha–1) from India, Shaheen and Shinwari [40] (42.3–105.2 m2ha–1) from Pakistani Himalayas and Haq et al. [47] (6.7–104 m2ha–1) from the central Himalayas all agreed on the current findings. Scrub forest types were thicker than broad-leaved and coniferous forest types in terms of tree density. Because the research region is a protected area, the high density of the scrub forest is a result of no deforestation. The tree density was observed in a range of 640 to 1197 N ha–1. The current findings matched those published by Ahmed et al. [48] from the Pakistani Himalayas 530–940 N ha–1. Sreejith et al. [49] and Supriyadevi and Yadava [50] found tree density of 625–850 N ha–1 in Northern Kerala and 534–620 N ha–1 in Northeast India, respectively. It is conceivable that this is owed to the fact that no human activity is permitted within the park. As a result, protected forests, as hypothesized, have a greater tree density due to less anthropogenic disturbances.
Multivariate analyses (two-way cluster analyses, DCA and CCA) were adopted for the classification and ordination of plant associations in forest types. Two-way cluster analysis classified the forest vegetation of the Zabarwan mountain range into two plant communities on the basis of indicator plant species. Similar classifications were also carried out by previous researchers such as Siddiqui et al. [51], Rahman et al. [52] and Bano et al. [53] from the Pakistani Himalayas, Shahid and Joshi [54] and Shahid and Joshi [55] from the Garhwal Himalayas, India, three plant groups by Wang et al. [56] from China, Moradi and Vacik [57] from the southern forests of Iran and Sainge et al. [58] from the montane forest in Cameroon. The CCA diagram (bi-plot) revealed that variations in environmental and biotic interactions were reflected in the diversity, distribution and relationship of plant species. In addition, each change in soil characteristics has a major impact on plant population growth [12].
The results of this study demonstrated that the soil physicochemical parameters of different forest types differ significantly. Due to terrain, climate, weathering processes, plant cover and microbiological activity [59,60], as well as a range of other biotic and abiotic factors [61,62,63,64], forest soil physicochemical parameters fluctuate through time and location. Soil quality varies over short distances based on parent rocks, vegetation cover and land use. In Himalayan landscapes, bioclimatic conditions fluctuate fast and can vary over short distances, resulting in a remarkable variety of soil types and their chemical, physical and biological properties [65,66,67,68], as well as fluctuating vegetation patterns [68,69,70,71]. We found that, in addition to climactic factors such as temperature and precipitation, edaphic elements such as soil texture and chemistry emerged as key determinants of plant community composition. Other studies have proposed similar relationships between soil edaphic properties and plant species composition, which can be explained by the fact that local edaphic properties affect the resource availability of water and nutrients in different soil types, thereby selecting plant communities with varied ecological functions [69,70,71,72,73].
Other mountain forest habitats around the world have also discovered the role of soil structure on species zonation [69,70,71,72,73,74]. These findings differ from ours in that they were conducted in non-protected woodland habitats. Furthermore, it was revealed that the mildly acidic pH of the soil has an impact on the growth of diverse plant species in this habitat. The first axes were largely associated with soil pH and Ca, while the second axes were mostly connected with phosphorus, electrical conductivity and potassium contents, according to CCA’s ecological gradient processes for both forest types and species. These findings match those of Khan et al. [75] and Khan et al. [12], all of whom conducted their research in Pakistan’s Himalayan forests. The CCA bi-plot also showed that organic carbon, electric conductivity and phosphorus were determinants in shaping the composition, diversity and distribution of flora, as species are highly sensitive to these soil parameters. Hussain et al. [76,77], Majeed et al. [78], Rahman et al. [79] and Malik et al. [80] recorded a positive association between edaphic factors and plant structure and distribution trends, which supports our findings. The results reveal the potential role of edaphic parameters in shaping the various forest communities at a regional scale.

5. Conclusions

The present study brings new contributions about the soil impacts on forest plant communities within the Zabarwan mountain range of the Kashmir Himalayas, which are under threat from a variety of factors and are regarded as one of the world’s most endangered forests. In this investigation, we recorded 76 plants, the majority of which (60%) are found in herbaceous life forms. According to the Rényi diversity profiles, the BLFT has much higher diversity than other forest types. The sequence of decreasing species richness in forest types was BLFT > ACFT > PNFT > OKFT > SRFT. The phytosociological characteristics (basal area) of coniferous forest type were greater than those of broad-leaved forest type and scrub forest type. Based on multiple-factor categorization and multivariate analyses, these findings reveal the link between forest types and soil parameters. As expected, species diversity and composition varied across spatial scales with higher values in BLFT. Soil variables such as Ca, N, K, EC, P and soil pH, on the other hand, tended to influence vegetation association in diverse ways. Furthermore, the conservation biology model has changed to put a greater focus on multi-scale approaches to biodiversity preservation; certain abiotic influences were involved in deciding what processes work at a given spatial scale to induce variations in population structure and species diversity. Our findings will provide a better understanding of forest community composition and related soil characteristics, which will guide forest ecosystem management and conservation in the Himalayan region.

Author Contributions

Conceptualization, S.M.H.; methodology, S.M.H., U.Y., Q.L., M.H. and A.T.; software, S.M.H.; validation, S.M.H., A.T. and M.M.; formal analysis, S.M.H. and M.H.; investigation, M.M., M.F.U.M., A.T. and R.W.B.; resources, S.F.; data curation, S.F. and A.T.; writing—original draft preparation, S.M.H. and U.Y.; writing—review and editing, S.M.H., U.Y., A.T., M.A., M.F.U.M., S.F., R.W.B., A.T., M.K. and M.M.; visualization, S.M.H., A.T. and M.M.; supervision, A.T.; project administration, Q.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by China high-resolution earth observation system (grant no. 03-Y30F03-9001-20/22) and National Natural Science Foundation of China (grant no. 42071321).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the obtained data are provided in the research article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of the Zabarwan mountain range in Kashmir Himalayas, India and point showing the sampling forest types in Dachigam National Park.
Figure 1. Map of the Zabarwan mountain range in Kashmir Himalayas, India and point showing the sampling forest types in Dachigam National Park.
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Figure 2. Rényi diversity profiles of the forest types in the Zabarwan mountain range of the Kashmir Himalayas. Broad-leaved forest (BLFT), Oak Forest (OKFT), Pinus wallichiana Forest (PNFT), Acacia Forest (ACFT), and Scrub Forest (SRFT).
Figure 2. Rényi diversity profiles of the forest types in the Zabarwan mountain range of the Kashmir Himalayas. Broad-leaved forest (BLFT), Oak Forest (OKFT), Pinus wallichiana Forest (PNFT), Acacia Forest (ACFT), and Scrub Forest (SRFT).
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Figure 3. DCA vegetation ordination in the Kashmir Himalayan Zabarwan mountain range.
Figure 3. DCA vegetation ordination in the Kashmir Himalayan Zabarwan mountain range.
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Figure 4. In the Kashmir Himalayan Zabarwan mountain range, a CCA diagram depicts the distribution of soil characteristics. P = Phosphorus, K = Potassium, Sal = Salinity, N = Available Nitrogen, Ca = Calcium, EC = Electrical Conductivity, OC = Organic Carbon and Sal = Salinity.
Figure 4. In the Kashmir Himalayan Zabarwan mountain range, a CCA diagram depicts the distribution of soil characteristics. P = Phosphorus, K = Potassium, Sal = Salinity, N = Available Nitrogen, Ca = Calcium, EC = Electrical Conductivity, OC = Organic Carbon and Sal = Salinity.
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Figure 5. In the Kashmir Himalayan Zabarwan mountain range, a CCA diagram depicts the distribution of plant species along soil characteristics. P = Phosphorus, K = Potassium, Sal = Salinity, N = Available Nitrogen, Ca = Calcium, EC = Electrical Conductivity and OC = Organic Carbon.
Figure 5. In the Kashmir Himalayan Zabarwan mountain range, a CCA diagram depicts the distribution of plant species along soil characteristics. P = Phosphorus, K = Potassium, Sal = Salinity, N = Available Nitrogen, Ca = Calcium, EC = Electrical Conductivity and OC = Organic Carbon.
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Figure 6. Two-way cluster analysis of 76 plant species and 5 forest types in the Kashmir Himalayan Za-barwan mountain range based on Sorenson’s similarity index.
Figure 6. Two-way cluster analysis of 76 plant species and 5 forest types in the Kashmir Himalayan Za-barwan mountain range based on Sorenson’s similarity index.
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Table
Table 1. Database of plant species with family, growth form, life span and IVI values recorded in forests in the Zabarwan mountain range of the Kashmir Himalayas.
Table 1. Database of plant species with family, growth form, life span and IVI values recorded in forests in the Zabarwan mountain range of the Kashmir Himalayas.
FamilyBotanical NameAbbreviationLife SpanGrowth FormACFTBLFTOKFTPWFTSRFT
AcanthaceaeStrobilanthes attenuate Nees (SMH 186)Str_attPerennialHerb006.0300
Strobilanthes wallichii Nees (SMH 188)Str_walPerennialHerb0010.615.984.32
AdoxaceaeViburnum grandiflorum Wall. ex DC. (SMH 492)Vib_graPerennialShrub77.8930.6253.5183.570
AmaranthaceaeAchyranthes bidentata Blume (SMH 264)Ach_bidPerennialHerb0022.4218.8333.14
AsteraceaeArctium lappa L. (SMH 185)Arc_lapBiennialHerb6.396.272.9800
Artemisia vulgaris L. (SMH 190)Art_vulPerennialHerb4.854.47000
Carpesiumabrotanoides L. (SMH 194)Car_abrPerennialHerb4.433.992.983.594.85
Erigeron canadensis L. (SMH 192)Eri_canAnnualHerb5.175.5404.543.53
AsparagaceaeAsparagus filicinus Buch.-Ham. ex D.Don (SMH 210)Asp_filPerennialHerb002.9900
Asparagus officinalis L. (SMH 211)Asp_offPerennialHerb0004.373.53
Polygonatum biflorum (Walter) Elliott (SMH 442)Pol_bifPerennialHerb005.974.275.15
Polygonatum verticillatum (L.) All. (SMH 445)Pol_verPerennialHerb0002.353.21
AspleniaceaeAsplenium ofeliae Salgado, A.E. (SMH 435)Asp_ofePerennialHerb3.753.278.052.192.801
ApiaceaeDaucus carota L. (SMH 198)Dau_carBiennialHerb5.785.62000
Selinum wallichianum (DC.) Raizada& H.O. Saxena (SMH 212)Sel-walPerennialHerb2.252.14000
AraliaceaeHedera nepalensis K.Koch (SMH 216)Hed_nepPerennialClimber11.2411.7113.1214.0916.51
BalsaminaceaeImpatiens glandulifera Royle (SMH 305)Imp_glaAnnualHerb14.3113.0516.069.1510.91
BerberidaceaeBerberis lyceum Royle (SMH 306)Ber_lycPerennialShrub59.0776.29000
CaprifoliaceaeLonicera webbiana Wall. ex DC. (SMH 326)Lon_webPerennialShrub69.290000
CampanulaceaeAsyneuma thomsonii (C.B.Clarke) Bornm. (SMH 328)Asy_thoPerennialHerb0002.352.36
CannabaceaeCeltis australis L. (SMH 440)Cel_ausPerennialTree33.7142.6815.2845.7546.75
DioscoreaceaeDioscorea deltoidea Wall. ex Griseb (SMH 441)Dio_delPerennialClimber10.9110.469.865.216.16
DryopteridaceaeDryopteris barbigera (T.Moore ex Hook.) Kuntze SMH (443)Dry_barPerennialHerb6.086.316.8712.1912.26
FabaceaeRobiniapseudoacacia L. (SMH 219)Rob_psePerennialTree87.8914.91000
Desmodium elegans DC. (SMH 449)Des_elePerennialShrub000063.62
Indigofera hebepetala Baker (SMH 243)Ind_hebPerennialShrub000097.27
Trifolium pratense L. (SMH 507)Tri_praPerennialHerb14.7316.59000
Trifolium repens L. (SMH 508)Tri_repPerennialHerb11.6610.645.6200
FagaceaeQuercus robur L. (SMH 516)Que_robPerennialTree16.460200.975.920
GeraniaceaeGeranium nepalense Sweet (SMH 517)Ger_nepPerennialHerb9.979.4310.494.625.48
Geranium pratense L. (SMH 356)Ger_praPerennialHerb3.534.342.984.173.07
Geranium wallichianum D.Don ex Sweet (SMH 357)Ger_walPerennialHerb005.011.692.29
HamamelidaceaeParrotiopsis jacquemontiana (Decne.) Rehder (SMH 138)Par_jacPerennialShrub065.93116.73170.91154.44
HypericaceaeHypericum perforatum L. (SMH 175)Hyp_perPerennialHerb2.752.63000
IridaceaeIris hookeriana Foster (SMH 382)Iri_hooPerennialHerb3.513.279.5700
JugalandaceaeJuglans regia L. (SMH 326)Jug_regPerennialTree25.2410.15000
LamiaceaePerilla frutescens (L.) Britton (SMH 320)Per_fruAnnualHerb7.717.67000
Salvia moorcroftiana Wall. ex Benth. (SMH 321)Sal_mooPerennialHerb002.232.353.21
Prunella vulgaris L. (SMH 322)Pru_vulPerennialHerb002.0200
MoraceaeMorus alba L. (SMH 334)Mor_albPerennialTree18.0121.9425.465.990
Morus nigra L. (SMH 335)Mor_nigPerennialTree26.4421.956.9500
OrchidaceaeCypripedumcordigerum D.Don (SMH 328)Cyp_corPerennialHerb003.873.22.81
OxalidaceaeOxalis acetosella L. (SMH 329)Oxa_acePerennialHerb0006.5510.06
PinaceaePinus wallichiana A.B.Jacks. (SMH 330)Pin_walPerennialTree000184.440
PoaceaeCynodon dactylon (L.) Pers. (SMH 331)Cyn_dacPerennialHerb00038.9621.17
Oplismenus burmannii f. cristata (J. Presl) Hier. ex Peter (SMH 332)Opl_burAnnualHerb54.1952.3630.489.0783.31
Poa bulbosa L. (SMH 339)Poa_bulAnnualHerb23.2822.6614.3300
Sorghum halepense (L.) Pers. (SMH 480)Sor_halPerennialHerb006.8600
Stipa sibirica (L.) Lam. (SMH 481)Sti_sibPerennialHerb35.4839.1459.118.310.02
PolygonaceaeFagopyrum esculentum Moench (SMH 483)Fag_escAnnualHerb5.816.993.3900
Polygonum amplexicaule D.Don (SMH 482)Pol_ampPerennialHerb1.741.643.1900
Polygonum hydropiper L. (SMH 489)Pol_hydAnnualHerb3.513.27000
PlantaginaceaeDigitalis purpurea L. (SMH 486)Dig_purPerennialHerb6.085.57000
Plantago major L. (SMH 479)Pla_majPerennialHerb5.356.263.394.174.99
PteridaceaePteris cretica L. (SMH 460)Pte_crePerennialHerb4.193.8403.824.18
RosaceaeCrataegus monogyna Jacq. (SMH 462)Cra_monPerennialTree04.76.325.10
Prunus persica (L.) Batsch (SMH 463)Pru_perPerennialTree013.48000
Prunus armeniaca L. (SMH 340)Pru_armPerennialTree11.653.1206.3514.1
Prunus avium (L.) L. (SMH 370)Pru_aviPerennialTree21.2616.72000
Prunus cerasus L. (SMH 371)Pru_cerPerennialTree5.1813.71000
Rosa webbiana Wall. ex Royle (SMH 204)Ros_webPerennialShrub052.1240.100
Rubus ulmifolius Schott (SMH 350)Rub_ulmPerennialShrub93.730000
Sorbaria tomentosa (Lindl.) Rehder (SMH 385)Sor_tomPerennialShrub000069.24
Fragaria nubicola (Lindl. ex Hook.f.) Lacaita(SMH 450)Fra_nubPerennialHerb6.175.743.486.255.83
Geum aleppicum Jacq. (SMH 472)Geu_alePerennialHerb2.643.639.54.884.18
Geum urbanum L. (SMH 473)Geu_urbPerennialHerb10.219.776.224.635.12
Prunus tomentosa Thunb. (SMH 474)Pru_tomPerennialShrub075.0389.6345.510
RanunculaceaeDelphinium roylei Munz (SMH 475)Del_royPerennialHerb0002.424.83
SalicaceaePopulus alba L. (SMH 476)Pop_albPerennialTree047.26000
Salix alba L. (SMH 477)Sal_albPerennialTree4.1130.31000
SapindaceaeAcer caesium Wall. ex Brandis (SMH 478)Ace_caePerennialTree3.365.8806.260
Aesculus indica (Wall. ex Cambess.) Hook. (SMH 110)Aes_indPerennialTree3.7615.3330.556.570
SimaraubaceaeAilanthus altissima (Mill.) Swingle (SMH 135)Ail_altPerennialTree37.0419.4114.465.946.84
ScrophulariaceaeVerbascum thapsus L. (SMH 511)Ver_thaBiennialHerb3.883.472.984.443.53
UlmaceaeUlmus wallichiana Planch. (SMH 525)Ulm_walPerennialTree5.8218.3707.6837.86
ViolaceaeViola odorata L. (SMH 512)Vio_odoPerennialHerb8.308.117.2620.2517.06
Table
Table 2. Multiple diversity, soil parameters and phytosociological attributes of different forest types in the Zabarwan mountain range of the Kashmir Himalayas.
Table 2. Multiple diversity, soil parameters and phytosociological attributes of different forest types in the Zabarwan mountain range of the Kashmir Himalayas.
Forest TypesACFTBLFTOKFTPNFTSRFT
Species Richness5155444638
Dominance0.0410.0330.0660.0750.076
Shannon3.5243.6643.263.143.092
Simpson0.9580.9660.9330.9240.923
Evenness0.6640.7090.5920.5020.579
Fisher Alpha16.3316.6314.8916.6215.03
pH6.566.55.95.516.66
Electrical Conductivity (µS/cm)384370660610396
Organic Carbon (%)4.145.185.444.55.04
Available Nitrogen(kg/ha)0.080.130.160.120.13
Phosphorus (μg/g)18.68.215.28.28.1
Potassium (μg/g)147129463230339
Calcium (μg/g)4.053.73.64.54.1
Salinity (ppm)23.724.683.121.313.3
Density (mean ± SD; trees/ha−1)850 ± 204.611057.5 ± 367.28640 ± 140.95707.5 ± 148.181197.5 ± 199.56
Basal Area (mean ± SD; m2ha−1)46.82 ± 14.7358.63 ± 21.5741.41 ± 3.8174.49 ± 12.0915.40 ± 6.20
Table
Table 3. Summary of the four axes of the DCA for vegetation data (using the importance value index) in the Kashmir Himalayan Zabarwan mountain range.
Table 3. Summary of the four axes of the DCA for vegetation data (using the importance value index) in the Kashmir Himalayan Zabarwan mountain range.
StatisticAxis 1Axis 2Axis 3Axis 4
Eigenvalues0.810.420.280.04
Accumulative explained variation17.626.832.933.9
Gradient length6.843.042.442.07
Total inertia4.603
Table
Table 4. CCA results of vegetation data according to soil variables included in the analysis of the Kashmir Himalayas’ Zabarwan mountain range.
Table 4. CCA results of vegetation data according to soil variables included in the analysis of the Kashmir Himalayas’ Zabarwan mountain range.
StatisticAxis 1Axis 2Axis 3Axis 4
Eigenvalues0.7360.4460.3880.347
Explained variation17.928.738.246.6
Pseudo-canonical correlation0.9840.9580.9800.962
Explained fitted variation31.550.667.282.7
Total inertia4.109
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Haq, S.M.; Tariq, A.; Li, Q.; Yaqoob, U.; Majeed, M.; Hassan, M.; Fatima, S.; Kumar, M.; Bussmann, R.W.; Moazzam, M.F.U.; et al. Influence of Edaphic Properties in Determining Forest Community Patterns of the Zabarwan Mountain Range in the Kashmir Himalayas. Forests 2022, 13, 1214. https://doi.org/10.3390/f13081214

AMA Style

Haq SM, Tariq A, Li Q, Yaqoob U, Majeed M, Hassan M, Fatima S, Kumar M, Bussmann RW, Moazzam MFU, et al. Influence of Edaphic Properties in Determining Forest Community Patterns of the Zabarwan Mountain Range in the Kashmir Himalayas. Forests. 2022; 13(8):1214. https://doi.org/10.3390/f13081214

Chicago/Turabian Style

Haq, Shiekh Marifatul, Aqil Tariq, Qingting Li, Umer Yaqoob, Muhammad Majeed, Musheerul Hassan, Sammer Fatima, Manoj Kumar, Rainer W. Bussmann, Muhammad Farhan Ul Moazzam, and et al. 2022. "Influence of Edaphic Properties in Determining Forest Community Patterns of the Zabarwan Mountain Range in the Kashmir Himalayas" Forests 13, no. 8: 1214. https://doi.org/10.3390/f13081214

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