Next Article in Journal
Methodological Proposal for Recognition Systems in Sustainable Freight Transport
Next Article in Special Issue
From Seed to Seedling: Influence of Seed Geographic Provenance and Germination Treatments on Reproductive Material Represented by Seedlings of Robinia pseudoacacia
Previous Article in Journal
Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States
Previous Article in Special Issue
Topographic Effects on the Spatial Species Associations in Diverse Heterogeneous Tropical Evergreen Forests
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Forest Structure and Composition under Contrasting Precipitation Regimes in the High Mountains, Western Nepal

Faculty of Forest Science and Forest Ecology, Georg-August-Universität, 37077 Goettingen, Germany
Faculty of Environmental Sciences, Technische Universität Dresden, 01737 Dresden, Germany
Center for International Forestry Research (CIFOR), Jalan CIFOR, Situ Gede, Bogor 16115, Indonesia
School of Ecosystem and Forest Sciences, University of Melbourne, Parkville, VIC 3010, Australia
Federation of Community Forestry Users, Nepal (FECOFUN), Duwakot 44800, Nepal
Forest Research and Training Centre, Babar Mahal, Kathmandu 44600, Nepal
Author to whom correspondence should be addressed.
Sustainability 2021, 13(13), 7510;
Submission received: 2 June 2021 / Revised: 24 June 2021 / Accepted: 29 June 2021 / Published: 5 July 2021
(This article belongs to the Special Issue Forest Biodiversity, Conservation and Sustainability – Series II)


The high mountains stretch over 20.4% of Nepal’s land surface with diverse climatic conditions and associated vegetation types. An understanding of tree species and forest structural pattern variations across different climatic regions is crucial for mountain ecology. This study strived to carry out a comparative evaluation of species diversity, main stand variables, and canopy cover of forests with contrasting precipitation conditions in the Annapurna range. Firstly, climate data provided by CHELSA version 1.2, were used to identify distinct precipitation regimes. Lamjung and Mustang were selected as two contrasting precipitation regions, and have average annual precipitation of 2965 mm and 723 mm, respectively. Stratified random sampling was used to study 16 plots, each measuring 500 m2 and near the tree line at an elevation range of 3000 to 4000 m across different precipitation conditions. In total, 870 trees were identified and measured. Five hemispherical photos using a fisheye lens were taken in each plot for recording and analyzing canopy cover. Margalef’s index was used to measure species richness, while two diversity indices: the Shannon–Wiener Index and Simpson Index were used for species diversity. Dominant tree species in both study regions were identified through the Important Value Index (IVI). The Wilcoxon rank-sum test was employed to determine the differences in forest structure and composition variables between the two precipitation regimes. In total, 13 species were recorded with broadleaved species predominating in the high precipitation region and coniferous species in the low precipitation region. Higher species richness and species diversity were recorded in the low precipitation region, whereas the main stand variables: basal area and stem density were found to be higher in the high precipitation region. Overall, an inverse J-shaped diameter distribution was found in both precipitation regions signifying uneven-aged forest. A higher proportion of leaning and buttressed trees were recorded in the high precipitation region. However, similar forest canopy cover conditions (>90%) were observed in both study regions. The findings of this research provide a comprehensive narrative of tree species and forest structure across distinct precipitation regimes, which can be crucial to administrators and local people for the sustainable management of resources in this complex region.

1. Introduction

Forest structure, composition, and diversity patterns are crucial ecological features that correlate significantly with prevailing environmental and anthropogenic components [1,2,3]. Stand structure and species composition are essential for forest biodiversity, and an understanding of these is the basis of sustainable forest management [4]. Forest structure and composition also have a vital role in the global carbon budget as they act as huge C-pools [5]. Tropical forests are hotspots of biodiversity, and their geographical variation depends on their evolutionary history and climatic conditions [6,7]. Comparisons in tropical forests have illustrated that mountain forests are usually shorter and less diverse than forests in the lowlands [8,9]. In addition to altitudinal gradient, regional climate plays a major role in influencing forest structure. It is usually inferred that forests in higher precipitation and temperature regions have taller trees and more biomass [10,11]. Furthermore, precipitation has been demonstrated to have a positive effect on tree diameter and the basal area of forests [12,13]. The shaping and configuration of forests are thus largely affected by changes in climate variables [14]. Over the past decades, upward shifts in tree species and tree lines have been documented owing to rises in temperature [6,15].
The relationship between species diversity and climatic effects has been analyzed in recent studies [16,17]. Field et al. [16] developed a model that describes the relationship between climate and plant richness, while Francis and Currie [17] developed a model for angiosperms. Higher species richness has been recorded with increasing temperatures up to a certain point, where richness diminishes due to water deficiency [16,18]. While Goldie et al. [19] found that in arid regions, water availability plays an important role in the evolutionary processes of woody plants and these processes are diminished by persistent drought, around 63% of global variability in angiosperm richness and 68% in woody plant family richness, explained by precipitation [16,17,20]. Similarly, a positive relationship was established between species richness and precipitation up to 4000 m in a neotropical region [21]. However, the relationship between species richness and temperature is found to be negative under limited water availability [18].
Due to the complex and diverse topographies and rain-shadow effects of high mountains, the Himalayan region of Nepal is characterized by significant local variations in climate [22]. The orographic effect of the Himalayan range plays an important role in determining the distribution of precipitation in this area. At a large scale, precipitation has been vital for determining species richness [23], composition [24], and distribution [25,26,27,28]. Vegetation monitoring allows in-depth analyses of components like moisture and temperature, and delivers knowledge on subtle monsoon variations for the Himalayas [29]. Likewise, species–environmental interactions can be applied as indicators of environmental conditions, and the diversity and forest patterns can be used to explain ecological phytogeography [12,30]. The fragile ecology of Himalayan forests is well-known [31]. However, fundamental knowledge of the structure and composition of Himalayan forests is limited in many regions [32]. Moreover, an understanding of how forest structure and diversity vary between different precipitation regimes is still lacking [33,34,35,36].
The high mountain region of Nepal has the highest forest proportion with forests covering 37.81% of the total land area [37]. Moreover, this region has been characterized by strong contrasts in precipitation regimes and forests, which are influenced by the effects of climate and land-use change. A better understanding of environmental factors influencing the distribution, abundance, and co-existence of tree species is crucial in forest ecology. Therefore, in this study, we examined the species diversity, species distribution, and stand structure of forests in contrasting precipitation regimes of the high mountain. The study aims to provide a better understanding of phytogeography in this complex region. This study may provide better insights on composition and structure in the mountain forests and would be highly applicable to several mountainous countries for the sustainable management of forest resources. The main objective of our study is to assess the forest structure and composition of high mountain forests in two contrasting precipitation conditions. To achieve the main objective of this study, we strived to address two main questions: (1) Are there any variations on forest composition and structure across contrasting precipitation conditions? (2) If so, how do the forest composition and structure vary with contrasting precipitation conditions?

2. Materials and Methods

2.1. Study Area

The high mountains area stretches from longitudes of 80°30′4″ to 88°07′04″ E and latitudes of 26°59′15″ to 30°06′47″ N, covering approximately 20.4% of the total land area of Nepal [37]. This study was carried out in the Annapurna Mountain range of Nepal. The Annapurna range or Annapurna massif (Figure 1) lies in the north-central part of Nepal and covers several peaks, including Annapurna (8091 m), the tenth highest mountain in the world [38]. The range covers five districts of Nepal, namely: Kaski, Lamjung, Manang, Mustang and Myagdi. It covers a total area of around 11,930 km2 with 36% forest cover (see Appendix A) [39].
The region is bounded by the Marshyangdi valley in the east, the Kali Gandaki river in the west, the dry alpine desert of Mustang in the north, and the valleys and foothills of Pokhara in the south [40]. The presence of the Annapurna massif has created strong variations in climate across the region. Spanning 120 km with altitudes of below 1000 m up to 8000 m, it has two distinct climatic regions [40]. The southern belt of this range—the Pokhara region—receives the highest precipitation, while the northern belt—the trans-Himalayan region—receives the lowest precipitation in Nepal [41]. Nepal’s largest conservation area, the Annapurna Conservation Area (ACA), covers most of this range and is situated between 83°34′ to 84°25′ E and 28°15′ to 28°50′ N, covering an area of 7629 km2. ACA is rich in biodiversity, harbors 29 ecosystem types [42,43] and has a wide range of habitats, from Shorea robusta to perennial snow forests, harboring 22 different forest types. Schimwa wallichi, Castanopsis indica, Alnus nepalensis, Pinus wallichiana and Betula utilis are the region’s major tree species [44]. The primary type of disturbances in this region are grazing, timber cutting, firewood collection, leaflitter collection and collection of other non-timber forest products [40].
As this study strives to compare forest composition and structure between two different climatic conditions, intensive study sites were selected by analyzing precipitation conditions over the ACA region using the CHELSA (Climatologies at high resolution for the Earth’s land surface areas) version 1.2 global climate dataset [45] (, (accessed on 21 September 2020)). The dataset provides monthly and annual precipitation and temperature patterns for the period from 1979 to 2013. Other climate analysis studies in Nepal, e.g., [41,46,47,48,49] were also used as references for study area selection. Two intensive study sites (Table 1, Figure 2) were selected for the contrasting precipitation regimes, caused by the strong orographic effects of Annapurna massif. Bhujung, Lamjung lies on the windward side, while Kobang, Mustang is on the leeward side of the Annapurna range. Climatic conditions in the intensive study sites are presented in Section 2.2.

2.2. Climatic Conditions in the Intensive Study Sites

Average annual precipitation in the high precipitation region (Lamjung) is 2965 mm, as depicted through the climate diagram [50] (Figure 3a). The average temperature in Lamjung is 3.9 °C, with a maximum average temperature of 14.3 °C in July and a minimum of −12.3 °C in January. The region receives higher precipitation from June to September, with an average of more than 500 mm.
The climate diagram for the low precipitation region (Mustang) (Figure 3b) shows average annual precipitation of around 723 mm and an average temperature of 6.8 °C. In Mustang, the maximum average temperature is 17.4 °C in July with a minimum of −10.2 °C in January. The four months from June to September receive the highest precipitation with averages above 100 mm.
Seasonal analyses of precipitation for both study sites were carried out based on the four seasons prevalent in Nepal: pre-monsoon (March–May), monsoon (June–September), post-monsoon (October–December) and winter (January–February) [48]. In both study sites, around 75% of precipitation occurred during the monsoon season (June–September) (see Table 2). Winter precipitation contributes more to total annual rainfall in the drier Mustang region. The greatest variation in precipitation for both study sites was in the post-monsoon season (coefficient of variation-CV: 74.1% for Lamjung, and 69.29% for Mustang).

2.3. Geology and Soil in the Intensive Study Area

Our study sites: Lamjung and Mustang lie in two upper most tectonic plates namely Greater Himalayan Sequence (GHS) and Tethyan Himalayan Sequence (THS) respectively [53]. The underlying rocks in both study sites are mainly Gneisses, migmatite and some parts with limestone, shales, and sandstone in northern Mustang [54,55]. The soil sample (0–15 cm deep) from the center of each plot was collected to determine the physical and chemical properties. Both study sites were characterized by acidic soil conditions and high level of soil nutrients, whereas medium range of soil organic matter (Table 3). The method used to test the soil properties and the ranking chart used by the Soil Management Directorate Nepal [56] is presented in Appendix B.

2.4. Data Collection

Forest inventories for 16 plots (8 plots at each study site) were employed to acquire information on forest composition and structure and assess them under different climatic conditions. Systematic random sampling was employed for this study. The first plot was established randomly and remaining plots in a tentative straight line in the same direction with 100 m distances between plots. Rectangular sampling plots, each with an area of 500 m2 (25 m × 20 m) were established at each study site based on Nepal’s National Inventory Guideline [57]. Slope correction was carried out for plots on slopes with gradients of >10%, as slope correction is mainly applied for slopes exceeding 10% [58]. Slope angle was measured using a clinometer. The true horizontal distance was calculated using the formula:
L = Ls × cos S
where ‘L’ is the true horizontal distance, ‘Ls’ is the measured distance along the slope, and ‘S’ is the slope in degrees.
The area was then calculated using the true horizontal distance, and adjustments to plot area were made during analysis. Sampling plots were established near the tree line to determine tree line species in the Annapurna range. Total enumeration was done during forest inventory as most trees in higher elevations were dwarf trees. The brief research design framework is illustrated in Figure 4. In Lamjung, the sample plots lay at elevations of 3700 to 4000 m in southern aspect with average steepness of 38° while in Mustang they were at 3000 to 3100 m in northern aspect with average steepness of 42° near the tree line. Tree lines in the Annapurna region lie between 3600 and 3700 m on southern slopes, while tree line elevation increases considerably, entering high mountain massifs, i.e., 4000 to 4100 m [59,60]. With an increment in distance from Annapurna, the timberline elevation decreases again [60]. In the southwestern part of Mustang, tree lines dominated by Abies spectabilis and Pinus wallichiana can be found at elevations from 2900 to 3500 m [61].
All trees (stems) inside the research plots were measured other than seedlings. In total, 870 trees were measured in the two study sites: 549 in Lamjung and 321 in Mustang. Species name, DBH (diameter at breast height) and total height were recorded for each tree. Diameter tape was used for DBH measurement and a Suunto height meter for height measurement with measurement accuracy of 0.1 cm and 0.1 m, respectively. To analyze mountain forest canopy cover, hemispherical photographs were taken of each plot. In total, 80 hemispherical photographs (five for each plot) were recorded. In each plot, one photograph was taken at the center of the plot, and the remaining four were taken 5 m inside each border, at a 5 m distance from plot corners (Figure 5). A DSLR (Digital Single Lens Reflex) camera (Nikon, Model-D5300) and fisheye lens (Sigma Circular Fisheye 4.5 mm 1:2:8 lens with a view angle of 180°) were used for this purpose. The hemispherical photographs were taken according to the Beckschäfer method [62] at a height of 1.5 m during windless weather and standard overcast condition [63]. In addition, to assess the variability of mountain forest leaf sizes in two distinct precipitation conditions, 50 leaves/leaflets were collected for each species (a total of 650 leaves/leaflets) maintaining a representation of all three layers of crown: lower, middle, and top. For coniferous species, the size of a single needle was measured, considering the leaflet. The areas of around 576 leaves/leaflets were measured: 400 in Mustang and 176 in Lamjung. The remaining samples were deemed unacceptable due to shape distortion. The Leaf Byte app [64] iOS (iPhone Operating System) version was used for measuring leaf area.

2.5. Data Analysis

Firstly, a list of tree species recorded in both study sites was developed. The Shapiro–Wilk test [65] was used to assess the normality assumption which showed the collected field data were not normally distributed. General stand variables, such as basal area (m2 ha−1), quadratic mean diameter (cm), stem density (stems ha−1), mean canopy height (m), volume (m3 ha−1) were calculated using descriptive statistics. Species diversity, species richness [66], and species evenness [67] were generated using the vegan [68] package in R-studio [52]. Species diversity was measured using two indices: the Shannon–Wiener Index [69] and Simpson′s Index [70]. Additionally, a boxplot in R-studio [52] was used to visualize diversity indices, species richness, and species evenness. A tree diameter distribution graph was prepared using the inventory data for both study sites, which were crucial for describing forest structures and functions [71]. In simple terms, the histograms of frequencies of individual stems per hectare divided into diameter classes determined the tree distribution patterns in stands [72]. The R-studio [52], tidyverse [73] and ggplot2 [74] packages were used to visualize diameter frequency distribution. Important Value Index (IVI) was calculated for each species in both study sites to get an overview of important (dominant) species. The IVI was calculated by quantifying three components of each species: relative density, relative dominance, and relative frequency.
IVI = relative density + relative dominance + relative frequency
where: density = number of individuals per ha, dominance = basal area per ha, and frequency = occurrence of certain species in respective sample plots:
relative density = Number   of   individuals   of   the   species total   number   of   individuals   of   all   species   × 100 %
relative dominance = Total   basal   area   of   the   species Total   basal   area   of   all   species × 100 %
relative frequency=   percent   of   sample   plots   occupied   by   the   species percent   of   the   occurence   of   all   species × 100 %
Each of these values is expressed as a percentage ranging from 0% to 100%. The IVI is the sum of these three components and can range from 0 to 300 (Adapted from [75]). The hemispherical photos were analyzed in ImageJ [76] using the Beckschäfer method [77]. At first, the hemispherical photos were converted to binary pictures and the pixel values of gap fraction and canopy cover were recorded. The pixel values of the canopy divided by the total pixel value provided the percentage of canopy cover. The Wilcoxon rank-sum test, also called the Mann–Whitney U test [78], was used for the statistical analysis in this study. In R-studio [52], the wilcox.test was used to examine the statistical significance of differences observed in inventory analysis findings between the study sites.

3. Results

3.1. Forest Composition

3.1.1. Species Recorded and Their Main Features

In total, 13 species were recorded near the tree lines during the field studies (Table 4). In Lamjung, five species: Betula utilis, Juniperus indica, Rhododendron campanulatum, Salix nepalensis and Sorbus microphylla were recorded, while in Mustang eight species: Abies spectabilis, Acer campbellii, Cotoneaster microphyllus, Elaeagnus parviflora, Ilex dipyrena, Pinus wallichiana, Rhododendron arboreum, and Taxus wallichiana were recorded.

3.1.2. Species Evenness, Richness, and Diversity

Before assessing the species diversity indices of our study sites, species evenness and richness were analyzed (Figure 6a). Mustang had higher species evenness (0.76 ± 0.03) and species richness (0.84 ± 0.06) than Lamjung (evenness- 0.47 ± 0.03 and richness- 0.48 ± 0.02). Similarly, statistically significant differences were observed between the two study sites in terms of species evenness (W = 7, p-value: 0.006) and species richness (W = 9, p-value: 0.014). The higher Shannon index and Simpson index values for forests in Mustang indicate higher species diversity in comparison to Lamjung. A significant difference was observed between the two study sites for species diversity based on the two diversity indices (W = 8, p-value: 0.01). Shannon index values varied from 0.53 ± 0.01 to 1.01 ± 0.03 between Lamjung and Mustang. Similarly, the Simpson index value was 0.28 ± 0.01 for Lamjung and 0.55 ± 0.06 for Mustang (Figure 6b).

3.1.3. Species Distribution

In Lamjung, Rhododendron campanulatum was found to be the dominant species (Table 5). It had an abundance of 1100 stems ha−1, a basal area of 16.4 m2 ha−1, and a frequency of 100%. The least dominant tree species was Juniperus indica with an abundance of 43 stems ha−1, and a frequency of only 13% in the study site. This study site was dominated by broadleaved species with few undergrowth of coniferous species. In Mustang, Abies spectabilis was the most dominant species (Table 6). It had an abundance of 163 stems ha−1, a basal area of 13.4 m2 ha−1, and a frequency of 50%. Ilex dipyrena was the least dominant species in Mustang with a stem density of 8 stems ha−1 and a frequency of only 13%. The study site in Mustang was found to be dominated by two coniferous species: Abies and Pinus in addition to the broadleaved species Rhododoendron arboreum, which had a frequency of 100%, signifying its presence in all research plots.

3.2. Forest Structure

3.2.1. Diameter Frequency Distribution

The diameter frequency distributions of all species in the total study area and in the precipitation, conditions differentiated study sites signifies the presence of natural forest in the region (Figure 7). Except for trees below 10 cm diameter, both study sites showed an inverse J-shaped curve, indicating that numbers of trees decrease as diameters increase. The highest proportion of trees belonged to the 10–20 cm diameter class (around 51% in Lamjung and 37% in Mustang). The >80 cm diameter class accounted for only 0.18% of trees in Lamjung and 0.37% in Mustang. The inverse J-shaped curve was more pronounced for Lamjung. Diameters of measured trees varied from 3.9 to 96 cm in Lamjung, and 2.1 to 84 cm in Mustang.

3.2.2. Main Stand Variables and Health Attributes of Trees

Both horizontal and vertical stand variables were derived in this study. The main stand variables, such as basal area, quadratic mean diameter (QMD), stem density, mean tree height, and tree volume were generated for both study sites (Table 7). The forest in Lamjung had a higher basal area and stem density than forest in Mustang. Average basal area was approximately 28 m2 ha−1 for forest in Lamjung, and 25 m2 ha−1 for Mustang with no significant difference between mean values. There was significant difference in stem density between the two study sites, where Lamjung and Mustang had stem densities of 1373 stems ha−1 and 806 stems ha−1, respectively. QMD values were 16.12 cm for the forest in Lamjung, and 21.53 cm for Mustang, with no statistically significant difference between the two study sites. Average tree height was roughly double in Mustang (10.2 m) compared to Lamjung (5.2 m), with a statistically significant difference. Stem volume ranged from 102.68 m3 ha−1 in Lamjung to 282.47 m3 ha−1 in Mustang. The higher volume in Mustang might be due to the higher QMD and mean tree height values in the area, though no statistically significant difference was observed in mean values for volumes between the study sites. Moreover, analyzing the hemispherical photographs (Figure 8a,b) of both study sites showed similar canopy cover, i.e., > 90% (Figure 8c) with no statistically significant difference (W = 24, p-value: 0.43).
Comparisons of five health and morphological attributes, namely: dead/dying trees, the presence of buttresses, leaning trees, crooked trees, and trees with broken crowns, were made between the two study sites (Figure 9). Lamjung had higher proportions of leaning (>60%) and buttressed trees (39%) in comparison to Mustang. The forest in Lamjung had the highest values for all attributes except for broken crowns, while crooked trees were absent from the forest in Mustang.

3.2.3. Leaf Sizes of Mountainous Tree Species

Leaf sizes of the species recorded during the study differed significantly between the forests with different precipitation conditions (Table 8). In the high precipitation region, the dominant species, Rhododendron campanulatum, had the biggest leaf size at 40.94 ± 2.30 cm2, while Sorbus microphylla had the smallest leaf size at 2.48 ± 0.15 cm2. The only recorded coniferous species: Juniperus indica in Lamjung had the needle/leaflet size at 0.44 ± 0.03 cm2. The sparsely recorded broadleaved species in Mustang i.e., Rhododendron arboreum, was found to have the largest leaf size at 31.29 ± 1.80 cm2. Among the coniferous species recorded in Mustang, Pinus wallichiana had the largest needle/leaflet size at 0.65 ± 0.02 cm2.

4. Discussion

Climate variables, especially precipitation, strongly influence vegetation distribution and composition through impacts on water availability and local weather conditions [95,96]. The spatiotemporal patterns of vegetation at upper tree lines in mountains are largely determined by soil moisture [97,98] and patterns differ between slopes due to differences in the presence of permafrost [99,100]. The two study sites varied only in terms of slope direction and precipitation while all other factors slope inclination, type of soil were almost similar. The most common species recorded near the tree line in Nepal are Abies spectabilis, Rhododendron campanulatum, Pinus wallichiana [84], Betula utilis [101], Sorbus microphylla, Salix spp. and Juniperus spp. [102], which supports our findings on the species encountered during this study. Dense Rhododendron campanulatum forest in the Lamjung site has also been documented in the Annapurna range by Schickhoff’s study [59] on the timberline of the Hindu-Kush Himalayas. In this study, broadleaf forest with Rhododendron campanulatum and Betula utilis was found in the higher precipitation zone, whereas needle-leaved forest with Abies spectabilis and Pinus wallichiana was found in the lower precipitation zone. The predominance of Rhododendron campanulatum, Sorbus spp. in mesic sites has been documented by Singh and Singh [103]. The evergreen species and large shrubs of Rhododendron spp. in areas dominated by monsoon precipitation have also been reported by Schickhoff [59]. The dominance of Pinus wallichiana in south-facing dry slopes, and Abies spectabilis and Rhododendron campanulatum in mesic slopes in the Himalayas of Nepal have been reported by many studies [22,59,104,105]. However, the presence of Abies spectabilis in a dry area like Mustang contradicts many studies [61,81]. The area near Kobang (Lete region) is estimated to receive more precipitation and therefore has a wider distribution of this species [106].
Many studies, e.g., [107,108,109] have mentioned climate variables, mainly precipitation and temperature, and their interactions as the main factors for variation in species richness. Species richness and species diversity were higher in Mustang than in Lamjung, which depicts the negative correlation between precipitation and diversity. The higher species richness and diversity in Mustang than Lamjung could be due to the differences in study plot aspects. The higher diversity recorded on the north-facing slopes of Mustang than the south-facing forest of Lamjung in this study concurs with other findings in Nepal [110,111] and other parts of the globe [112,113]. In the northern hemisphere, south-facing slopes are usually warmer due to their higher levels of irradiance, which support drought-resistant vegetation and restrict tree growth, while north-facing slopes are cold and humid with higher soil moisture, which support a larger number of species [111]. Another reason for the higher number of species recorded in Mustang might be due to the higher precipitation in our study area (Kobang/Lete region) at 723 mm compared to the average of 200 mm for the Mustang region [48]. Increases in precipitation have been found to enhance species richness and plant diversity markedly by promoting soil moisture variability, especially in semi-arid and arid regions [114,115,116]. The difference in species richness and diversity between Mustang and Lamjung could be also due to the different elevations of the study sites, as species richness is believed to decrease with increases in elevation [117,118]. Though this is open to debate with other studies showing a hump-shaped relationship in the Himalayas [104,119].
The vague relationship between species richness and precipitation has been determined by different studies. A study in Eastern Himalaya in Bhutan showed the nil effect of precipitation on species richness. However, in different moisture regimes, temperature had a significant effect on species richness [120]. The non-significant effect of precipitation on species diversity is also reported by Stan and Sanchez-Azofeifa [121]. In contrast, the study by Kushwaha and Nandy [122] recorded lower species richness in dry forest than moist forests, stating that moisture availability affects the regeneration of tree species. In lower-elevation tropical forests, higher species diversity and richness were recorded in high precipitation regions than dry regions in Myanmar by Khaine et al. [123], and in mangrove forest [124] and sub-Saharan Africa [125] as well. Regarding forest structure, most studies [1,32,126,127,128] have focused on Himalayan vegetation patterns along altitudinal gradients, though few have mentioned the importance of climate variables in predicting forest stand structure [21,129,130]. Considering forest structure varies with water availability [131], and environmental and biological factors control forest structure at higher elevations [132,133], this study tried to assess differences in forest structure along a precipitation gradient in the Annapurna range. The higher stem density and basal area in Lamjung than Mustang signified a positive relationship between precipitation and forest structure. Similar to our findings, Khaine et al. [123] reported the strong influence of precipitation on forest structure in Myanmar. They found increases in basal area and stem density with precipitation increasing from 843 to 2035 mm. Muñoz Mazón et al. [131] also found an increase in basal area with increasing precipitation and decreasing temperature along the Atlantic slope of Talamanca Mountain. Higher basal area and stem density were observed in humid versus dry areas of forest in Brazil, and were attributed to precipitation seasonality [134]. Moreover, a strong relationship between climate variables and forest structure was observed in the Eastern Himalayan of India [135]. Restricted tree growth due to low water availability during long dry seasons is documented by Hiltner et al. [136]. Structural changes owing to changes in precipitation were also forecast by Hiltner et al. [136]. Similarly, Kushwaha and Nandy [122] discovered a strong relationship between precipitation and forest structure in West Bengal, India. The influence of annual precipitation on forest structure in tropical regions has also been documented by Beard [137]. A study by Duchesne and Ouimet [138] mentions the significant effects of precipitation on the structural development of forests over time. Basal area and density increased from younger to medium age stands, then decreased in older stands in sites with higher precipitation, whereas basal area showed a linear relationship with stand age in sites with lower precipitation [138]. According to Hiltner et al. [136], precipitation affects forest structure through its impact on moisture availability and therefore drought-tolerant species would show no significant change.
Furthermore, the inverse J-shaped pattern documented for overall size class distribution in this study is similar to the findings of Bhutia et al. [32] who found the highest number of individuals in the smallest DBH class of 3 to 13 cm and least in the highest class in Eastern Himalayan, India. This pattern is further supported by the findings of Shrestha et al. [139], Dar et al. [140], Pandey et al. [127], and Schwab [141]. More than 90% of trees were found in lower diameter classes in the Himalayan forest of India [140]. Pandey et al. [127] recorded maximum percentages of trees in the DBH range of 10 to 29.9 cm in the trans-Himalayan region of Nepal. Most tree species in the Krummholz tree line of Rolwaling Himal, Nepal in the lower DBH range i.e., 0–14 cm were Abies spectabilis, Betula utilis, Sorbus microphylla and Rhododendron campanulatum [141]. This type of diameter distribution suggests an uneven-aged forest with enough young recruitment to replenish mature forest stands [142]. Although canopy cover analysis of mountain forests using fisheye lenses are rarely documented, the percentage of canopy cover recorded by this study is higher than observations made by Uniyal et al. [143] in the Garhwal Himalayan forest in India, and by Måren and Sharma [144] in the Langtang area of Nepal. The canopy cover of high-elevation forest was recorded at >60% in Garhwal, India [143] and around 65 to 77% in protected and government forest in the mountains of Nepal [144]. Woody life forms such as buttresses are common features of tropical forest communities [145] but may also be present in sub-tropical and higher elevation forests [146,147]. The presence of different woody life forms has been documented in lower proportions at higher elevations [148,149], which supports our findings on different health and morphological attributes in the study sites. Although there is no satisfactory theory to describe life form development, it might be due to the influence of the humid environment on root tension [145]. However, quantitative studies of forest structure, including life forms, are rarely documented in higher elevation forests [150].
The size of leaves varied according to the species. Since the species in two study sites were completely different, it was difficult to compare leaf size to the precipitation. However, the Rhododendron species in high precipitation region had larger leaves than low precipitation region. The larger leaf sizes in cold and wet climates were also recorded by Peppe et al. [151]. A reduction in leaf size along lower soil phosphorus and rainfall was also recorded by McDonald et al. [152]. In our study, soil properties were uniform in both areas, therefore, the change in leaf size could be associated with precipitation. The study of other morphological traits in leaves along different precipitation region is recommended. This study is based on a small number of sample plots as the number of sample plots is determined by topographical and climatic factors as well as time constraints [153]. Roughly 21–108 trees were recorded per plot, which included almost all types and species of trees available in the area. Even though this study is based on few plots, this gives reliable information about structure and composition of mountain forest along contrasting precipitation conditions. In small forest area, the smaller sample size and resulting higher level of sampling error is usually accepted [154]. However, research on large-scale forest monitoring certainly requires the larger sample size for an appropriate precision level [154].

5. Conclusions

This study analyzed the variation in the forest structure specially stand variables and forest composition that include species richness, evenness, and diversity in two sites with similar topographic and edaphic factors but different precipitation conditions. The mountain forest in the high precipitation region is dominated by broadleaved forest, whereas in the low precipitation region, coniferous forest is dominant. The mountain forest was characterized by the uneven-aged stand and uneven diameter distribution signifying natural forest condition. The precipitation had a positive impact on forest structure, but had a negative impact on species diversity. However, precipitation had no effect on canopy cover, whereas the leaf area depended on the nature of plant species. Conclusively, precipitation is an important parameter in defining the structure and composition of the forest stand. Although the findings of this study were based on a smaller sample size, they give a clear indication of the importance of this research in understanding species composition and forest structure on two contrasting sides of the mountain range. Although it would be desirable to validate and quantify the findings of this study with more research in this area, it may nevertheless prove very useful in understanding high-elevation forest and in implementing a sustainable management approach.

Author Contributions

Conceptualization, K.P.B. and A.A.; methodology, K.P.B. and A.A.; software, K.P.B., A.K.A. and A.A.; validation, H.B.; formal analysis, K.P.B., A.K.A., A.A. and C.P.; investigation, K.P.B. and S.K.; resources, K.P.B., S.K. and H.B.; data curation, K.P.B., C.P., R.D. and S.K.; writing—original draft preparation, K.P.B., A.A. and H.B.; writing—review and editing, A.A. and H.B. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.


We would like to thank the Department of National Parks and Wildlife Conservation (DNPWC), Kathmandu and Annapurna Conservation Area Project (ACAP), Pokhara for allowing us to conduct this study. We specially acknowledge CGIAR research program on Forests, Trees and Agroforestry for the partial support to publish this work. We extend our thanks to Department of Tropical Silviculture and Forest Ecology, University of Goettingen, and German Academic Exchange service (PZ 91706208) for the support throughout the study. We express our special gratitude to Ashok Subedi, Rukmangat Subedi, Suman Ghimire, Tulsasi Prasad Dahal, Basu Dev Neupane, Rajesh Gupta, Nabin Bishwokarma, Deepak Pandit, Rijan Tamrakar, Ramesh Prasad Bhatta, Rishi Baral, and Nirmal Magar, as well as the ACAP Bhujung Unit and ACAP Jomsom Unit for their support during our field studies.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Land cover pattern in the Annapurna range (Source: EU-Copernicus, 2020).
Table A1. Land cover pattern in the Annapurna range (Source: EU-Copernicus, 2020).
Land CoverArea (km2)Percentage
Sparse vegetation3095.0725.94
Herbaceous vegetation2781.3523.32
Built up area49.920.42
Permanent inland water18.930.16
Herbaceous wetland7.080.06
Figure A1. Land cover map of the Annapurna range prepared based on information provided by the European Union Copernicus Land Monitoring Service, 2020.
Figure A1. Land cover map of the Annapurna range prepared based on information provided by the European Union Copernicus Land Monitoring Service, 2020.
Sustainability 13 07510 g0a1

Appendix B

Table A2. Method used for testing of soil’s physical chemical properties at Soil lab in Nepal.
Table A2. Method used for testing of soil’s physical chemical properties at Soil lab in Nepal.
Soil TextureHydrometer method [155]
pH1:2 soil water suspension [156]
Organic matter content (OM, %) Walkely and Black [157]
Total Nitrogen content (N, %) Kjeldahl method [158]
Available Phosphorus (P205, kg ha−1) Olsen′s bicarbonate [159]
Available Potassium (K20, kg ha−1) Flame photometry [160]
Table A3. Rating of soil chemical properties provided by Soil Management Directorate Nepal.
Table A3. Rating of soil chemical properties provided by Soil Management Directorate Nepal.
pHIF > 7.5, “Alkaline”, IF > 6.4, “Neutral”, “Acidic”
O.M.IF > 5, “High”, IF > 2.4, “Medium”, “Low”
N.IF > 0.2, “High”, IF > 0.1, “Medium”, “Low”
P2O5 IF > 55, “High”, IF > 31, “Medium”, “Low”
K2OIF > 280, “High”, IF> 110, “Medium”, “Low”


  1. Gairola, S.; Rawal, R.S.; Todaria, N.P. Forest vegetation patterns along an altitudinal gradient in sub-alpine zone of west Himalaya, India. Afr. J. Plant Sci. 2008, 2, 042–048. [Google Scholar]
  2. Timilsina, N.; Ross, M.S.; Heinen, J.T. A community analysis of sal (Shorea robusta) forests in the western Terai of Nepal. For. Ecol. Manag. 2007, 241, 223–234. [Google Scholar] [CrossRef]
  3. Ahmad, I.; Ahmad, M.S.A.; Hussain, M.; Ashraf, M.; Ashraf, M.Y. Spatio-temporal variations in soil characteristics and nutrient availability of an open scrub type rangeland in the sub-mountainous Himalayan tract of Pakistan. Pak. J. Bot. 2011, 43, 565–571. [Google Scholar]
  4. Gutiérrez, A.G.; Huth, A. Successional stages of primary temperate rainforests of Chiloé Island, Chile. Pers. Plant. Ecol. Evol. Syst. 2012, 14, 243–256. [Google Scholar] [CrossRef]
  5. Canadell, J.G.; Le Quéré, C.; Raupach, M.R.; Field, C.B.; Buitenhuis, E.T.; Ciais, P.; Conway, T.J.; Gillett, N.P.; Houghton, R.A.; Marland, G. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl. Acad. Sci. USA 2007, 104, 18866–18870. [Google Scholar] [CrossRef] [Green Version]
  6. Khaine, I.; Woo, S.Y. An overview of interrelationship between climate change and forests. For. Sci. Technol. 2014, 11, 11–18. [Google Scholar] [CrossRef]
  7. Sullivan, M.J.P.; Talbot, J.; Lewis, S.L.; Phillips, O.L.; Qie, L.; Begne, S.K.; Chave, J.; Cuni-Sanchez, A.; Hubau, W.; Lopez-Gonzalez, G.; et al. Diversity and carbon storage across the tropical forest biome. Sci. Rep. 2017, 7, 39102. [Google Scholar] [CrossRef] [Green Version]
  8. Grubb, P.J. Control of forest growth and distribution on wet tropical mountains: With special reference to mineral nutrition. Annu. Rev. Ecol. Syst. 1977, 8, 83–107. [Google Scholar] [CrossRef]
  9. Lieberman, D.; Lieberman, M.; Peralta, R.; Hartshorn, G.S. Tropical forest structure and composition on a large-scale altitudinal gradient in Costa Rica. J. Ecol. 1996, 84, 137–152. [Google Scholar] [CrossRef]
  10. Koch, G.W.; Sillett, S.C.; Jennings, G.M.; Davis, S.D. The limits to tree height. Nature 2004, 428, 851–854. [Google Scholar] [CrossRef]
  11. Chave, J.; Réjou-Méchain, M.; Búrquez, A.; Chidumayo, E.; Colgan, M.S.; Delitti, W.B.; Duque, A.; Eid, T.; Fearnside, P.M.; Goodman, R.C.; et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 2014, 20, 3177–3190. [Google Scholar] [CrossRef] [PubMed]
  12. Toledo, M.; Poorter, L.; Peña-Claros, M.; Alarcón, A.; Balcázar, J.; Leaño, C.; Lianque, O.; Vroomans, V.; Zuidema, P.; Bongers, F. Climate is a stronger driver of tree and forest growth rates than soil and disturbance. J. Ecol. 2011, 99, 254–264. [Google Scholar] [CrossRef]
  13. Walther, G.R. Community and ecosystem responses to recent climate change. Phil. Trans. Ro. Soc. B Biol. Sci. 2010, 365, 2019–2024. [Google Scholar] [CrossRef]
  14. Beckage, B.; Osborne, B.; Gavin, D.G.; Pucko, C.; Siccama, T.; Perkins, T. A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont. Proc. Natl. Acad. Sci. USA 2008, 105, 4197–4202. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Kullman, L.; Öberg, L. Post-Little Ice Age tree line rise and climate warming in the Swedish Scandes: A landscape ecological perspective. J. Ecol. 2009, 97, 415–429. [Google Scholar] [CrossRef]
  16. Field, R.; ÓBrien, E.M.; Whittaker, R.J. Global models for predicting woody plant richness from climate: Development and evaluation. Ecology 2005, 86, 2263–2277. [Google Scholar] [CrossRef] [Green Version]
  17. Francis, A.P.; Currie, D.J. A globally consistent richness-climate relationship for angiosperms. Am. Nat. 2003, 161, 523–536. [Google Scholar] [CrossRef]
  18. Gillman, L.N.; Wright, S.D. Species richness and evolutionary speed: The influence of temperature, water and area. J. Biogeogr. 2014, 41, 39–51. [Google Scholar] [CrossRef]
  19. Goldie, X.; Gillman, L.; Crisp, M.; Wright, S. Evolutionary speed limited by water in arid Australia. Phil. Trans. Ro. Soc. B Biol. Sci. 2010, 277, 2645–2653. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. ÓBrien, E. Water-energy dynamics, climate, and prediction of woody plant species richness: An interim general model. J. Biogeogr. 1998, 25, 379–398. [Google Scholar] [CrossRef]
  21. Gentry, A.H. Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann. Mo. Bot. Gard. 1988, 75, 1–34. [Google Scholar] [CrossRef]
  22. Shrestha, K.B.; Hofgaard, A.; Vandvik, V. Recent tree line dynamics are similar between dry and mesic areas of Nepal, central Himalaya. J. Plant. Ecol. 2015, 8, 347–358. [Google Scholar] [CrossRef] [Green Version]
  23. Ter Steege, H.; Pitman, N.C.; Phillips, O.L.; Chave, J.; Sabatier, D.; Duque, A.; Molino, J.F.; Prévost, M.F.; Spichiger, R.; Castellanos, H.; et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 2006, 443, 444–447. [Google Scholar] [CrossRef] [PubMed]
  24. Hall, J.B.; Swaine, M. Classification and ecology of closed-canopy forest in Ghana. J. Ecol. 1976, 64, 913–951. [Google Scholar] [CrossRef]
  25. Bongers, F.; Poorter, L.; Van Rompaey, R.S.A.R.; Parren, M. Distribution of twelve moist forest canopy tree species in Liberia and Cote d’Ivoire: Response curves to a climatic gradient. J. Vegt. Sci. 1999, 10, 371–382. [Google Scholar] [CrossRef]
  26. Engelbrecht, B.M.; Comita, L.S.; Condit, R.; Kursar, T.A.; Tyree, M.T.; Turner, B.L.; Hubbell, S.P. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 2007, 447, 80–82. [Google Scholar] [CrossRef]
  27. Swaine, M.D. Precipitation and soil fertility as factors limiting forest species distributions in Ghana. J. Ecol. 1996, 84, 419–428. [Google Scholar] [CrossRef]
  28. Toledo, M.; Peña-Claros, M.; Bongers, F.; Alarcón, A.; Balcázar, J.; Chuviña, J.; Claudio, L.; Licona, J.C.; Poorter, L. Distribution patterns of tropical woody species in response to climatic and edaphic gradients. J. Ecol. 2012, 100, 253–263. [Google Scholar] [CrossRef]
  29. Mainali, J.; All, J.; Jha, P.K.; Bhuju, D.R. Responses of montane forest to climate variability in the central Himalayas of Nepal. Mt. Res. Dev. 2015, 35, 66–77. [Google Scholar] [CrossRef]
  30. Diekmann, M. Species indicator values as an important tool in applied plant ecology—A review. Basic. Appl. Ecol. 2003, 4, 493–506. [Google Scholar] [CrossRef]
  31. Karpouzoglou, T.; Dewulf, A.; Perez, K.; Gurung, P.; Regmi, S.; Isaeva, A.; Foggin, M.; Bastiaensen, J.; Van Hecken, G.; Zulkafli, Z.; et al. From present to future development pathways in fragile mountain landscapes. Environ. Sci. Pol. 2020, 114, 606–613. [Google Scholar] [CrossRef]
  32. Bhutia, Y.; Gudasalamani, R.; Ganesan, R.; Saha, S. Assessing forest structure and composition along the altitudinal gradient in the state of Sikkim, Eastern Himalayas, India. Forests 2019, 10, 633. [Google Scholar] [CrossRef] [Green Version]
  33. Kalacska, M.; Sanchez-Azofeifa, G.A.; Calvo-Alvarado, J.C.; Quesada, M.; Rivard, B.; Janzen, D.H. Species composition, similarity and diversity in three successional stages of a seasonally dry tropical forest. For. Ecol. Manag. 2004, 200, 227–247. [Google Scholar] [CrossRef]
  34. Feroz, S.M.; Kabir, M.E.; Hagihara, A. Species composition, diversity, and stratification in subtropical evergreen broadleaf forests along a latitudinal thermal gradient in the Ryukyu Archipelago, Japan. Glob. Ecol. Conserv. 2015, 4, 63–72. [Google Scholar] [CrossRef] [Green Version]
  35. Johnson, C.; Chhin, S.; Zhang, J. Effects of climate on competitive dynamics in mixed conifer forests of the Sierra Nevada. For. Ecol. Manag. 2017, 394, 1–12. [Google Scholar] [CrossRef]
  36. Nlungu-Kweta, P.; Leduc, A.; Bergeron, Y. Climate and disturbance regime effects on aspen (Populus tremuloides Michx.) stand structure and composition along an east–west transect in Canada’s boreal forest. Forestry Int. J. For. Res. 2017, 90, 70–81. [Google Scholar]
  37. Department of Forest Research and Survey (DFRS). High Mountains and High Himal Forests of Nepal; Forest Resource Assessment (FRA) Nepal, Department of Forest Research and Survey: Kathmandu, Nepal, 2015. Available online: (accessed on 10 October 2020).
  38. Carter, H.A. Classification of the Himalaya. Am. Alp. J. 1985, 27, 127–129. [Google Scholar]
  39. European Union, Copernicus Land Monitoring Service (Eu-Copernicus). Copernicus Global Land Service; European Union: Brussels, Belgium, 2020. Available online: (accessed on 10 October 2020).
  40. Annapurna Conservation Area (ACA). Management plan of Annapurna Conservation Area. Nepal Trust for Nature Conservation, 2009–2012; Nepal Trust for Nature Conservation: Kathmandu, Nepal, 2009.
  41. Dhar, O.N.; Nandargi, S. Areas of heavy precipitation in the Nepalese Himalayas. Weather 2005, 60, 354–356. [Google Scholar] [CrossRef]
  42. Biodiversity Profiles Project (BPP). Biodiversity Assessment of Forest Ecosystems of the Western Mid-Hills of Nepal; Biodiversity Profiles Project, Publication No. 7; Department of National Parks and Wildlife Conservation: Kathmandu, Nepal, 1995.
  43. Biodiversity Profiles Project (BPP). Biodiversity Assessment of Forest Ecosystems of the Central Mid-Hills of Nepal; Biodiversity Profiles Project, Publication No. 8; Department of National Parks and Wildlife Conservation: Kathmandu, Nepal, 1995.
  44. Department of National Parks and Wildlife Conservation (DNPWC). Protected Areas of Nepal; Ministry of Forests and Environment. Department of National Parks and Wildlife Conservation: Kathmandu, Nepal, 2018. Available online: (accessed on 12 September 2020).
  45. Karger, D.N.; Conrad, O.; Böhner, J.; Kawohl, T.; Kreft, H.; Soria-Auza, R.W.; Zimmermann, N.E.; Linder, H.P.; Kessler, M. Climatologies at high resolution for the Earth’s land surface areas. Sci. Dat. 2017, 4, 170122. [Google Scholar] [CrossRef] [Green Version]
  46. Bohner, J. General climatic controls and topo climatic variations in Central and High Asia. Boreas 2006, 35, 279–295. [Google Scholar] [CrossRef]
  47. Government of Nepal, Ministry of Forests and Soil Conservation (GoN/MoFSC). Nepal National Biodiversity Strategy and Action Plan 2014–2020; Government of Nepal, Ministry of Forests and Soil Conservation: Kathmandu, Nepal, 2014. Available online: (accessed on 16 September 2020).
  48. Karki, R.; Hasson, S.; Schickhoff, U.; Scholten, T.; Böhner, J. Rising Precipitation Extremes across Nepal. Climate 2017, 5, 4. [Google Scholar] [CrossRef] [Green Version]
  49. Karki, R.; Talchabhadel, R.; Aalto, J.; Baidya, S.K. New climatic classification of Nepal. Theor. Appl. Climatol. 2016, 125, 799–808. [Google Scholar] [CrossRef]
  50. Walter, H.; Lieth, H. World Atlas of Climate Diagrams; Part 3; VEB Gustav Fischer Verlag: Jena, Germany, 1967. [Google Scholar]
  51. Guijarro, J.A. Climatol: Climate Tools (Series Homogenization and Derived Products). 2019. Available online: (accessed on 16 September 2020).
  52. R Studio Team. RStudio: Integrated Development for R; Version 1.3; R Studio: Boston, MA, USA, 2021; Available online: (accessed on 10 January 2021).
  53. Parsons, A.J.; Law, R.D.; Searle, M.P.; Phillips, R.J.; Lloyd, G.E. Geology of the Dhaulagiri-Annapurna-Manaslu Himalaya, Western Region, Nepal. 1: 200,000. J. Map. 2016, 12, 100–110. [Google Scholar] [CrossRef]
  54. Hodges, K.V.; Parrish, R.R.; Searle, M.P. Tectonic evolution of the central Annapurna range, Nepalese Himalayas. Tectonics 1996, 15, 1264–1291. [Google Scholar] [CrossRef]
  55. Dahal, J.; Chidi, C.L.; Mandal, U.K.; Karki, J.; Khanal, N.R.; Pantha, R.H. Physico-chemical properties of soil in Jita and Taksar area of Lamjung district, Nepal. Geo. J. Nepal. 2018, 11, 45–62. [Google Scholar] [CrossRef] [Green Version]
  56. Pandey, S.; Bhatta, N.P.; Paudel, P.; Pariyar, R.; Maskey, K.H.; Khadka, J.; Thapa, T.B.; Rijal, B.; Panday, D. Improving fertilizer recommendations for Nepalese farmers with the help of soil-testing mobile van. J. Crop. Dev. 2018, 32, 19–32. [Google Scholar] [CrossRef] [Green Version]
  57. Department of Forests (DoF). Community Forest Inventory Guideline; Department of Forests: Kathmandu, Nepal, 2004.
  58. Kleinn, C.; Traub, B.; Hoffmann, C. A note on the slope correction and the estimation of the length of line features. Can. J. Forest Res. 2002, 32, 751–756. [Google Scholar] [CrossRef]
  59. Miehe, G. Vegetationsgeographische Untersuchungen im Dhaulagiri-und Annapurna-Himalaya. Diss. Bot. 1982, 66, 1–2. [Google Scholar]
  60. Schickhoff, U. The Upper Timberline in the Himalayas, Hindu Kush and Karakorum: A Review of Geographical and Ecological Aspects. In Mountain Ecosystems; Broll, G., Keplin, B., Eds.; Springer: Berlin/Heidelberg, Germany, 2005; pp. 275–354. [Google Scholar]
  61. Udas, E. The influence of climate variability on growth performance of Abies spectabilis at tree line of West-Central Nepal. Master’s Thesis, University of Greifswald, Greifswald, Germany, 2009. [Google Scholar]
  62. Beckschäfer, P.; Seidel, D.; Kleinn, C.; Xu, J. On the exposure of hemispherical photographs in forests. iForest-Biogeosci. For. 2013, 6, 228–237. [Google Scholar] [CrossRef] [Green Version]
  63. Černý, J.; Pokorný, R.; Haninec, P.; Bednář, P. Leaf area index estimation using three distinct methods in pure deciduous stands. J. Vis. Exp. 2019, 150, e59757. [Google Scholar] [CrossRef]
  64. Getman-Pickering, Z.L.; Campbell, A.; Aflitto, N.; Grele, A.; Davis, J.K.; Ugine, T.A. Leaf Byte: A mobile application that measures leaf area and herbivory quickly and accurately. Meth. Ecol. Evol. 2020, 11, 215–221. [Google Scholar] [CrossRef] [Green Version]
  65. Razali, N.M.; Wah, Y.B. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2011, 2, 21–33. [Google Scholar]
  66. Margalef, R. Information theory in ecology. Gen. Syst. Bul. 1958, 3, 36–71. [Google Scholar]
  67. Pielou, E.C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 1966, 13, 131–144. [Google Scholar] [CrossRef]
  68. Oksanen, J.; Guillaume Blanchet, F.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D.; Minchin, P.R.; ÓHara, R.B.; Simpson, G.L.; Solymos, P.; et al. Vegan: Community Ecology Package. 2019. Available online: (accessed on 10 September 2020).
  69. Shannon, C.E.; Wiener, W. The Mathematical Theory of Communication, 1st ed; Urban University Illinois Press: Champaign, IL, USA, 1963; 125p. [Google Scholar]
  70. Simpson, E.H. Measurement of diversity. Nature 1949, 163, 688. [Google Scholar] [CrossRef]
  71. Gadow, K.V.; Zhang, C.Y.; Wehenkel, C.; Pommerening, A.; Corral-Rivas, J.; Korol, M.; Myklush, S.; Hui, G.Y.; Kiviste, A.; Zhao, X.H. Forest structure and diversity. In Continuous Cover Forestry; Pukkala, T., von Gadow, K., Eds.; Springer: Dordrecht, The Netherlands, 2012; pp. 29–83. [Google Scholar]
  72. Lima, R.B.; Bufalino, L.; Alves Junior, F.T.; Silva, J.A.D.; Ferreira, R.L. Diameter distribution in a Brazilian tropical dry forest domain: Predictions for the stand and species. Anais da Academia Brasileira de Ciências 2017, 89, 1189–1203. [Google Scholar] [CrossRef] [Green Version]
  73. Wickham, H. Tidyverse: Easily Install and Load the ‘Tidyversé. 2019. Available online: (accessed on 11 September 2020).
  74. Wickham, H.; Chang, W.; Henry, L.; Pedersen, T.L. ggplot2: Create Elegant Data Visualizations Using the Grammar of Graphics. 2019. Available online: (accessed on 20 September 2020).
  75. Curtis, J.T.; Mcintosh, R.P. The interrelations of certain analytic and synthetic phytosociological characters. Ecology 1950, 31, 434–455. [Google Scholar] [CrossRef]
  76. Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, S.; Rueden, C.; Saalfeld, S.; Schmid, B.; et al. Fiji: An open-source platform for biological-image analysis. Nat. Meth. 2012, 9, 676–682. [Google Scholar] [CrossRef] [Green Version]
  77. Beckschäfer, P. Hemispherical_2. 0–Batch Processing Hemispherical and Canopy Photographs with ImageJ–User Manual; University of Göttingen: Göttingen, Germany, 2015; Available online: (accessed on 12 August 2020).
  78. Wilcoxon, F. Individual comparisons by ranking methods. Biom. Bull. 1945, 1, 80–83. [Google Scholar] [CrossRef]
  79. Zobel, D.B.; Singh, S.P. Himalayan forests and ecological generalizations. Bioscience 1997, 47, 735. [Google Scholar] [CrossRef]
  80. Tree Improvement and Silviculture Component (TISC). Forest and Vegetation Types of Nepal; Natural Resource Management Sector Assistance Program Nepal, Tree Improvement and Silviculture Component; Document Series 105; Tree Improvement and Silviculture Component: Kathmandu, Nepal, 2002. [Google Scholar]
  81. Miehe, G.; Pendry, C.; Chaudhary, R.P. Nepal: An Introduction to the Natural History, Ecology and Human Environment of the Himalayas: A Companion Volume to the Flora of Nepal; Royal Botanic Garden Edinburgh: Edinburgh, Scotland, 2015. [Google Scholar]
  82. Rajbhandari, K.R.; Watson, M. Rhododendrons of Nepal (Fascicle of Flora of Nepal); Department of Plant Resources: Kathmandu, Nepal, 2005; Volume 5.
  83. Pradhan, S.; Saha, G.K.; Khan, J.A. Ecology of the red panda Ailurus fulgens in the Singhalila National Park, Darjeeling, India. Biol. Conserv. 2001, 98, 11–18. [Google Scholar] [CrossRef]
  84. Tiwari, A.; Jha, P.K. An overview of tree line response to environmental changes in Nepal Himalaya. Trop. Ecol. 2018, 59, 273–285. [Google Scholar]
  85. Chhetri, P.K. Dendrochronological Analyses and Climate Change Perceptions in Langtang National Park, Central Nepal. In Climate Change and Disaster Impact Reduction; Aryal, K.R., Gadema, Z., Eds.; Northumbria University: Newcastle, UK, 2008. [Google Scholar]
  86. District Development Committee (DDC). Resource Mapping Report of Mustang District; District Development Committee: Jomsom, Mustang, 2014.
  87. Christensen, M.; Heilmann-Clausen, J. Forest biodiversity gradients and the human impact in Annapurna Conservation Area, Nepal. Biodvers. Conserv. 2009, 18, 2205–2221. [Google Scholar] [CrossRef]
  88. Jnawali, S.R.; Baral, H.S.; Lee, S.; Acharya, K.P.; Upadhyay, G.P.; Pandey, M.; Shrestha, R.; Joshi, D.; Lamichhane, B.R.; Griffiths, J.; et al. The Status of Nepal’s Mammals: The National Red List Series-IUCN; IUCN-SSC and National Trust of Nature Conservation: Kathmandu, Nepal, 2011. [Google Scholar]
  89. Bista, D.; Shrestha, S.; Sherpa, P.; Thapa, G.J.; Kokh, M.; Lama, S.T.; Khanal, K.P.; Thapa, A.; Jnawali, S.R. Distribution and habitat use of red panda in the Chitwan-Annapurna Landscape of Nepal. PLoS ONE 2017, 12, e0178797. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Bhattarai, S.; Chaudhary, R.P.; Taylor, R.S. The use of plants for fencing and fuelwood in Mustang District, Trans-Himalayas, Nepal. Sci. World 2009, 7, 59–63. [Google Scholar] [CrossRef]
  91. Chapagain, N.R.; Chetri, M. Biodiversity Profile of Upper Mustang; National Trust for Nature Conservation, Annapurna Conservation Area Project, Upper Mustang Biodiversity Conservation Project: Kathmandu, Nepal, 2006. [Google Scholar]
  92. Dobremez, J.F. Nepal: Ecology and Biogeography; Éditions du Centre National de la Recherche Scientifique: Paris, France, 1976. [Google Scholar]
  93. Stainton, J.D.A. Forest of Nepal; John Murrey: London, UK, 1972. [Google Scholar]
  94. Bhuju, S.; Gauchan, D.P. Taxus wallichiana (Zucc.), an Endangered Anti-Cancerous Plant: A Review. Int. J. Res. 2018, 5, 10–21. [Google Scholar]
  95. Leilei, L.; Jianrong, F.; Yang, C. The relationship analysis of vegetation cover, precipitation and land surface temperature based on remote sensing in Tibet, China. In Proceedings of the IOP Conference Series: Earth and Environmental Science (Vol. 17, No. 1, p. 012034), International Symposium on Remote Sensing of Environment (ISRSE35), Beijing, China, 22–26 April 2013. [Google Scholar]
  96. Joshi, N.; Gyawali, P.; Sapkota, S.; Neupane, D.; Shrestha, S.; Shrestha, N.; Tuladhar, F.M. Analyzing the effect of climate change (precipitation and temperature) on vegetation cover of Nepal using time-series MODIS images. Ann. Photogram. Remote Sens. Spatial Inf. Sci. 2019, 209–216. [Google Scholar]
  97. Daniels, L.D.; Veblen, T.T. Spatiotemporal influences of climate on altitudinal tree line in northern Patagonia. Ecology 2004, 85, 1284–1296. [Google Scholar] [CrossRef] [Green Version]
  98. Elliott, G.P.; Kipfmueller, K.F. Multi-scale influences of climate on upper tree line dynamics in the southern Rocky Mountains, USA: Evidence of intraregional variability and bioclimatic thresholds in response to twentieth century warming. Ann. Assoc. Am. Geogr. 2011, 101, 1181–1203. [Google Scholar] [CrossRef]
  99. Danby, R.K.; Hik, D.S. Variability, contingency, and rapid change in recent subarctic alpine tree line dynamics. J. Ecol. 2007, 95, 352–363. [Google Scholar] [CrossRef]
  100. Elliott, G.P.; Kipfmueller, K.F. Multi-scale influences of slope aspect and spatial pattern on ecotonal dynamics at upper tree line in the southern Rocky Mountains, USA. Arct. Antarct. Alp. Res. 2010, 42, 45–56. [Google Scholar] [CrossRef] [Green Version]
  101. Paudel, P.K.; Bhattarai, B.P.; Kindlmann, P. An overview of the biodiversity in Nepal. In Himalayan Biodiversity in the Changing World; Kindalmann, P., Ed.; Springer: Dordrecht, The Netherlands, 2012; pp. 1–40. [Google Scholar]
  102. Bhuju, D.R.; Gaire, N.P. Tree-Rings and Tree Lines of Nepal Himalaya; Research Synopsis, Commemorating Symposium Himalayan Tree-Line; Tree-Ring Society of Nepal: Katmandu, Nepal, 2017. [Google Scholar]
  103. Singh, J.S.; Singh, S.P. Forest vegetation of the Himalaya. The Bot. Rev. 1987, 53, 80–192. [Google Scholar] [CrossRef]
  104. Vetaas, O.R.; Grytnes, J.A. Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal. Glob. Ecol. Biogeogr. 2002, 11, 291–301. [Google Scholar] [CrossRef]
  105. Paudel, S.; Vetaas, O.R. Effects of topography and land use on woody plant species composition and beta diversity in an arid Trans-Himalayan landscape, Nepal. J. Mt. Sci. 2014, 11, 1112–1122. [Google Scholar] [CrossRef]
  106. Kharal, D.K.; Meilby, H.; Rayamajhi, S.; Bhuju, D.; Thapa, U.K. Tree ring variability and climate response of Abies spectabilis along an elevation gradient in Mustang, Nepal. Banko Janakari 2014, 24, 3–13. [Google Scholar] [CrossRef] [Green Version]
  107. Kharkwal, G.; Mehrotra, P.; Rawat, Y.S.; Pangtey, Y.P.S. Phytodiversity and growth form in relation to altitudinal gradient in the Central Himalayan (Kumaun) region of India. Curr. Sci. 2005, 873–878. [Google Scholar]
  108. Manish, K.; Telwala, Y.; Nautiyal, D.C.; Pandit, M.K. Modelling the impacts of future climate change on plant communities in the Himalaya: A case study from Eastern Himalaya, India. Model. Earth Syst. Environ. 2016, 2, 92. [Google Scholar] [CrossRef] [Green Version]
  109. Sharma, N.; Behera, M.D.; Das, A.P.; Panda, R.M. Plant richness pattern in an elevation gradient in the Eastern Himalaya. Biodivers. Conserv. 2019, 28, 2085–2104. [Google Scholar] [CrossRef]
  110. Ghimire, B.; Mainali, K.P.; Lekhak, H.D.; Chaudhary, R.P.; Ghimeray, A.K. Regeneration of Pinus wallichiana AB Jackson in a trans-Himalayan dry valley of north-central Nepal. Himal. J. Sci. 2010, 6, 19–26. [Google Scholar]
  111. Måren, I.E.; Karki, S.; Prajapati, C.; Yadav, R.K.; Shrestha, B.B. Facing north or south: Does slope aspect impact forest stand characteristics and soil properties in a semiarid trans-Himalayan valley? J. Arid. Environ. 2015, 121, 112–123. [Google Scholar] [CrossRef] [Green Version]
  112. Olivero, A.M.; Hix, D.M. Influence of aspect and stand age on ground flora of Southeastern Ohio forest ecosystems. Plant Ecol. 1998, 139, 177–187. [Google Scholar] [CrossRef]
  113. Schickhoff, U. Contributions to the synecology and syntaxonomy of West Himalayan coniferous forest communities. Phytoeco. 1996, 26, 537–581. [Google Scholar] [CrossRef]
  114. Zavaleta, E.S.; Shaw, M.R.; Chiariello, N.R.; Thomas, B.D.; Cleland, E.E.; Field, C.B.; Mooney, H.A. Grassland responses to three years of elevated temperature, CO2, precipitation, and N deposition. Ecol. Monogr. 2003, 73, 585–604. [Google Scholar] [CrossRef] [Green Version]
  115. Stevens, M.H.H.; Shirk, R.; Steiner, C.E. Water and fertilizer have opposite effects on plant species richness in a mesic early successional habitat. Plant. Ecol. 2006, 183, 27–34. [Google Scholar] [CrossRef]
  116. Yang, H.; Li, Y.; Wu, M.; Zhang, Z.H.E.; Li, L.; Wan, S. Plant community responses to nitrogen addition and increased precipitation: The importance of water availability and species traits. Glob. Change. Biol. 2011, 17, 2936–2944. [Google Scholar] [CrossRef]
  117. Yoda, K. A preliminary survey of forest vegetation of eastern Nepal. J. Coll. Art. Sci. 1967, 5, 99–140. [Google Scholar]
  118. Stevens, G.C. The elevational gradient in altitudinal range: An extension of Rapoport’s latitudinal rule to altitude. Am. Nat. 1992, 140, 893–911. [Google Scholar] [CrossRef]
  119. Bhattarai, K.R.; Vetaas, O.R. Can Rapoport’s rule explain tree species richness along the Himalayan elevation gradient, Nepal? Divers. Distb. 2006, 12, 373–378. [Google Scholar] [CrossRef]
  120. Kluge, J.; Worm, S.; Lange, S.; Long, D.; Böhner, J.; Yangzom, R.; Miehe, G. Elevational seed plants richness patterns in Bhutan, Eastern Himalaya. J. Biogeogr. 2017, 44, 1711–1722. [Google Scholar] [CrossRef]
  121. Stan, K.; Sanchez-Azofeifa, A. Tropical dry forest diversity, climatic response, and resilience in a changing climate. Forests 2019, 10, 443. [Google Scholar] [CrossRef] [Green Version]
  122. Kushwaha, S.P.S.; Nandy, S. Species diversity and community structure in sal (Shorea robusta) forests of two different precipitation regimes in West Bengal, India. Biodivers. Conserv. 2012, 21, 1215–1228. [Google Scholar] [CrossRef]
  123. Khaine, I.; Woo, S.Y.; Kang, H.; Kwak, M.; Je, S.M.; You, H.; Lee, T.; Jang, J.; Lee, H.K.; Lee, E.; et al. Species diversity, stand structure, and species distribution across a precipitation gradient in tropical forests in Myanmar. Forests 2017, 8, 282. [Google Scholar] [CrossRef] [Green Version]
  124. Osland, M.J.; Feher, L.C.; Griffith, K.T.; Cavanaugh, K.C.; Enwright, N.M.; Day, R.H.; Stagg, C.L.; Krauss, K.W.; Howard, R.J.; Grace, J.B.; et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 2017, 87, 341–359. [Google Scholar] [CrossRef] [Green Version]
  125. Staver, A.C.; Archibald, S.; Levin, S. Tree cover in sub-Saharan Africa: Rainfall and fire constrain forest and savanna as alternative stable states. Ecology 2011, 92, 1063–1072. [Google Scholar] [CrossRef]
  126. Sharma, C.M.; Ghildiyal, S.K.; Gairola, S.; Suyal, S. Vegetation structure, composition, and diversity in relation to the soil characteristics of temperate mixed broad-leaved forest along an altitudinal gradient in Garhwal Himalaya. Indian J. Sci. Technol. 2009, 2, 39–45. [Google Scholar] [CrossRef]
  127. Pandey, K.P.; Adhikari, Y.P.; Weber, M. Structure, composition and diversity of forest along the altitudinal gradient in the Himalayas, Nepal. Appl. Ecol. Environ. Res. 2016, 14, 235–251. [Google Scholar] [CrossRef]
  128. Bhat, J.A.; Kumar, M.; Negi, A.K.; Todaria, N.P.; Malik, Z.A.; Pala, N.A.; Kumar, A.; Shukla, G. Altitudinal gradient of species diversity and community of woody vegetation in the Western Himalayas. Glob. Eco. Cons. 2020, 24, e01302. [Google Scholar]
  129. Maçaneiro, J.P.D.; Oliveira, L.Z.; Seubert, R.C.; Eisenlohr, P.V.; Schorn, L.A. More than environmental control at local scales: Do spatial processes play an important role in floristic variation in subtropical forests? Acta Bot. Bras. 2016, 30, 183–192. [Google Scholar] [CrossRef] [Green Version]
  130. Sevegnani, L.; Uhlmann, A.; de Gasper, A.L.; Meyer, L.; Vibrans, A.C. Climate affects the structure of mixed rain forest in southern sector of Atlantic domain in Brazil. Acta Oecol. 2016, 77, 109–117. [Google Scholar] [CrossRef]
  131. Muñoz Mazón, M.; Klanderud, K.; Finegan, B.; Veintimilla, D.; Bermeo, D.; Murrieta, E.; Delgado, D.; Sheil, D. How forest structure varies with elevation in old growth and secondary forest in Costa Rica. For. Ecol. Manag. 2020, 469, 118191. [Google Scholar] [CrossRef]
  132. Komárková, V.; Webber, P.J. An Alpine Vegetation Map of Niwot Ridge, Colorado. Arct. Alp. Res. 1978, 10, 1–29. [Google Scholar] [CrossRef]
  133. Chaurasia, O.P.; Brahma, S. Cold Desert Plants; Volume III-Changthang Valley; Field Research Laboratory, DRDO: Leh, Jammu; Kashmir, India, 1997; Volume 3, p. 85. [Google Scholar]
  134. Terra, M.D.C.N.S.; Santos, R.M.D.; Prado Júnior, J.A.D.; de Mello, J.M.; Scolforo, J.R.S.; Fontes, M.A.L.; Schiavini, I.; dos Reis, A.A.; Bueno, I.T.; Magnago, L.F.S.; et al. Water availability drives gradients of tree diversity, structure, and functional traits in the Atlantic–Cerrado–Caatinga transition, Brazil. J. Plant. Ecol. 2018, 11, 803–814. [Google Scholar] [CrossRef] [Green Version]
  135. Sinha, S.; Badola, H.K.; Chhetri, B.; Gaira, K.S.; Lepcha, J.; Dhyani, P.P. Effect of altitude and climate in shaping the forest compositions of Singalila National Park in Khangchendzonga Landscape, Eastern Himalaya, India. J. Asia-Pac. Biodivers. 2018, 11, 267–275. [Google Scholar] [CrossRef]
  136. Hiltner, U.; Bräuning, A.; Gebrekirstos, A.; Huth, A.; Fischer, R. Impacts of precipitation variability on the dynamics of a dry tropical montane forest. Ecol. Model. 2016, 320, 92–101. [Google Scholar] [CrossRef]
  137. Beard, J.S. Historical and ecological development of evergreen broadleaved forest of Australia. Vegt. Sci. For. 1995, 12a, 179–187. [Google Scholar]
  138. Duchesne, L.; Ouimet, R. Relationships between structure, composition, and dynamics of the pristine northern boreal forest and air temperature, precipitation, and soil texture in Quebec (Canada). Intl. J. For. Res. 2009, 2009, 398389. [Google Scholar] [CrossRef] [Green Version]
  139. Shrestha, B.B.; Ghimire, B.; Lekhak, H.D.; Jha, P.K. Regeneration of tree line birch (Betula utilis D. Don) forest in a trans-Himalayan dry valley in central Nepal. Mt. Res. Dev. 2007, 27, 259–267. [Google Scholar] [CrossRef]
  140. Dar, J.A.; Sundarapandian, S. Patterns of plant diversity in seven temperate forest types of Western Himalaya, India. J. Asia-Pac. Biodivers. 2016, 9, 280–292. [Google Scholar] [CrossRef] [Green Version]
  141. Schwab, N. Sensitivity and Response of Alpine Tree Lines to Climate Change-Insights from a Krummholz Tree Line in Rolwaling Himal, Nepal. 2019. Available online: (accessed on 24 October 2020).
  142. Vetaas, O.R. The effect of environmental factors on the regeneration of Quercus semicarpifolia Sm. in central Himalaya, Nepal. Plant. Ecol. 2000, 146, 137–144. [Google Scholar] [CrossRef]
  143. Uniyal, P.; Pokhriyal, P.; Dasgupta, S.; Bhatt, D.; Todaria, N.P. Plant diversity in two forest types along the disturbance gradient in Dewalgarh Watershed, Garhwal Himalaya. Curr. Sci. 2010, 98, 938–943. [Google Scholar]
  144. Måren, I.E.; Sharma, L.N. Managing biodiversity: Impacts of legal protection in mountain forests of the Himalayas. Forests 2018, 9, 476. [Google Scholar] [CrossRef] [Green Version]
  145. Richards, P.W.; Frankham, R.; Walsh, R.P.D. The Tropical Rain Forest: An Ecological Study; Cambridge University Press: Cambridge, UK, 1996. [Google Scholar]
  146. Francis, W.D. The Development of Buttresses in Queensland Trees; Government Printer: Pretoria, South Africa, 1924.
  147. Nicoll, B.C.; Ray, D. Adaptive growth of tree root systems in response to wind action and site conditions. Tree Physiol. 1996, 16, 891–898. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  148. Hernández, L.; Dezzeo, N.; Sanoja, E.; Salazar, L.; Castellanos, H. Changes in structure and composition of evergreen forests on an altitudinal gradient in the Venezuelan Guayana Shield. Revista de Biología Tropical 2012, 60, 11–33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  149. Baur, G.N. The Ecological Basis of Rainforest Management; Library AN: 113414, 1965; Food and Agricultural Organization: Rome, Italy, 1965; Available online: (accessed on 1 September 2020).
  150. Berry, P.E.; Holst, B.K.; Yatskievych, K.; Manara, B. Flora of Venezuelan Guayana. Springer 1998, 53, 1017–1018. [Google Scholar]
  151. Peppe, D.J.; Royer, D.L.; Cariglino, B.; Oliver, S.Y.; Newman, S.; Leight, E.; Wright, I.J.; Enikolopov, G.; Fernandez-Burgos, M.; Herrera, F.; et al. Sensitivity of leaf size and shape to climate: Global patterns and paleoclimatic applications. New. Phyt. 2011, 190, 724–739. [Google Scholar] [CrossRef] [Green Version]
  152. McDonald, P.G.; Fonseca, C.R.; Overton, J.M.; Westoby, M. Leaf-size divergence along rainfall and soil-nutrient gradients: Is the method of size reduction common among clades? Funct. Ecol. 2003, 17, 50–57. [Google Scholar] [CrossRef] [Green Version]
  153. Forest and Agriculture Organization (FAO). Sustainable Forest Management (SFM) Toolbox, Forest Inventory; Forest and Agriculture Organization of United Nations, Rome, Italy. 2021. Available online: (accessed on 18 June 2021).
  154. Fischer, C.; Kleinn, C.; Fehrmann, L.; Fuchs, H.; Panferov, O. A national level forest resource assessment for Burkina Faso–A field based forest inventory in a semiarid environment combining small sample size with large observation plots. For. Ecol. Manag. 2011, 262, 1532–1540. [Google Scholar] [CrossRef]
  155. Bouyoucos, G.J. Directions for making mechanical analyses of soils by the hydrometer method. Soil. Sci. 1936, 42, 225–230. [Google Scholar] [CrossRef]
  156. Jackson, M.L. Soil chemical analysis, pentice hall of India Pvt. Ltd., New Delhi, India 1973, 498, 151–154. [Google Scholar]
  157. Walkley, A.; Black, I.A. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil. Sci. 1934, 37, 29–38. [Google Scholar] [CrossRef]
  158. Bremner, J.M.; Mulvaney, C.S. Nitrogen total. In Method of Soil Analysis; Agron. No. 9, Part 2: Chemical and Microbiological Properties, 2nd ed.; Page, A.L., Ed.; American Society of Agronomy: Madison, WI, USA, 1982. [Google Scholar]
  159. Olsen, S.R. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate; US Department of Agriculture: Washington, DC, USA, 1954.
  160. Toth, S.J.; Prince, A.L. Estimation of cation-exchange capacity and exchangeable Ca, K, and Na contents of soils by flame photometer techniques. Soil. Sci. 1949, 67, 439–446. [Google Scholar] [CrossRef]
Figure 1. Physiographic zones of Nepal and the Annapurna region.
Figure 1. Physiographic zones of Nepal and the Annapurna region.
Sustainability 13 07510 g001
Figure 2. Map showing the intensive study sites: Lamjung and Mustang.
Figure 2. Map showing the intensive study sites: Lamjung and Mustang.
Sustainability 13 07510 g002
Figure 3. Climate diagrams based on CHELSA data (1979–2013) for both study sites prepared using the Climatol package [51] in R-studio [52]. (a) Climate diagram for Lamjung where the light blue box for October signifies probable frost condition while definite frost conditions for six months from November to April are represented by dark blue boxes (b) Climate diagram for Mustang where the definite frost conditions for five months from November to March are indicated by the dark blue boxes while likely frost conditions in April is indicated by the light blue box. Letters in the x-axis of both (a) and (b) denote months.
Figure 3. Climate diagrams based on CHELSA data (1979–2013) for both study sites prepared using the Climatol package [51] in R-studio [52]. (a) Climate diagram for Lamjung where the light blue box for October signifies probable frost condition while definite frost conditions for six months from November to April are represented by dark blue boxes (b) Climate diagram for Mustang where the definite frost conditions for five months from November to March are indicated by the dark blue boxes while likely frost conditions in April is indicated by the light blue box. Letters in the x-axis of both (a) and (b) denote months.
Sustainability 13 07510 g003
Figure 4. Research design.
Figure 4. Research design.
Sustainability 13 07510 g004
Figure 5. Research design for capturing hemispherical photographs in each study plot.
Figure 5. Research design for capturing hemispherical photographs in each study plot.
Sustainability 13 07510 g005
Figure 6. (a) Comparison of species evenness and species richness; (b) Comparison of species diversity, between the study sites. The different letters (a,b) denote significant differences between mean values.
Figure 6. (a) Comparison of species evenness and species richness; (b) Comparison of species diversity, between the study sites. The different letters (a,b) denote significant differences between mean values.
Sustainability 13 07510 g006
Figure 7. Diameter frequency distribution for both study sites.
Figure 7. Diameter frequency distribution for both study sites.
Sustainability 13 07510 g007
Figure 8. Sample hemispherical photographs captured in Lamjung (a) and Mustang (b), and canopy cover of forests in Lamjung and Mustang (c) where a denotes non-significant difference between the mean values.
Figure 8. Sample hemispherical photographs captured in Lamjung (a) and Mustang (b), and canopy cover of forests in Lamjung and Mustang (c) where a denotes non-significant difference between the mean values.
Sustainability 13 07510 g008
Figure 9. Relative abundance of tree health and morphological attributes in both study sites.
Figure 9. Relative abundance of tree health and morphological attributes in both study sites.
Sustainability 13 07510 g009
Table 1. Descriptions of intensive study sites in the Annapurna range differentiated by contrasting precipitation regimes.
Table 1. Descriptions of intensive study sites in the Annapurna range differentiated by contrasting precipitation regimes.
Precipitation RegimeStudy SiteLocation
High/HumidBhujung, Lamjung (here after “Lamjung”)28°22′47″ N, 84°15′27″ E
Low/DryKobang, Mustang (here after “Mustang”)28°40′29″ N, 83°35′04″ E
Table 2. Average seasonal and annual precipitation and CV based on CHELSA data (1979–2013). Values in brackets are CV percentages.
Table 2. Average seasonal and annual precipitation and CV based on CHELSA data (1979–2013). Values in brackets are CV percentages.
Study SiteWinter
(January–February) (mm)
(March–May) (mm)
Table 3. Physical and chemical properties of soil for both study sites.
Table 3. Physical and chemical properties of soil for both study sites.
Study SiteSoil TextureAverage Soil pHAverage Soil Organic Matter (%)Soil Nutrients *
Average N (%)Average
(Kg ha−1)
Average K2O (Kg ha−1)
* Soil Nutrients: N- Nitrogen, P2O5- Phosphorus pentoxide, K2O- Potassium oxide
Table 4. Species recorded in both study sites along with elevations and main features.
Table 4. Species recorded in both study sites along with elevations and main features.
Study SiteSpecies Name, Recorded Range (Meter above Mean Sea Level)Number of Trees InventoriedMain Features
LamjungBetula utilis
3700–4000 m
21The only broadleaved species that dominates extensive areas in sub-alpine altitudes [79] and forms tree line vegetation in the Himalayas [80].
Juniperus indica
3900–4000 m
17Found in upper montane woodlands in pure stands or with Abies, Pinus, or in Betula woodland or alpine heath and grassland, these were also reported in the sunny slopes of Mustang [81].
Rhododendron campanulatum
3700–4000 m
440The major understory component of sub-alpine forest and forms pure stands above the tree line in the Himalayas of Nepal [82].
Salix nepalensis
3700–4000 m
26Salix spp. colonizes open soil patches after disturbance, and cattle trampling promotes Salix cover. It mainly occurs with alpine dwarf thickets such as Rhododendron [81].
Sorbus microphylla
3700–4000 m
45This is also called small leaf rowan and its berries are mainly consumed by the red panda (Ailurus fulgens) [83]. It commonly occurs with Betula utilis [81].
MustangAbies spectabilis
3100 m
65The dominant tree in the western and central Himalayas, it grows better in cool and moist north-facing slopes [84]. It occurs as a canopy dominant species along with different species of Rhododendron and Betula utilis [85].
Acer campbellii
3100 m
65The lower Mustang region has mixed forest of Acer, Pinus wallichiana, and Rhododendron spp. [86]. This is one of the less dominant species of the Annapurna region [87]. It forms good habitat for the red panda (Ailurus fulgens) [88] but evidence of red panda presence is unreported from Mustang district [89].
Cotoneaster microphyllus
3000–3100 m
11In the rain-shadow valley of the Himalayas, this species occurs along with the distribution range of Abies spp. between 2000 and 3500 m [81]. It is a shrub (0–5 m) and small tree (up to 15 m), acts as a good soil stabilizer [81] and is used for fuelwood, fencing, making tools, and for medicinal purposes in the Mustang region [90].
Elaeagnus parviflora
3000–3100 m
11This species commonly occurs with Ilex spp. [81], is reported at elevations of 2800–3000 m in Mustang and is mainly used for food [91].
Ilex dipyrena
3100 m
3An evergreen tree that occurs in sub-humid to sub-arid conditions. This species mainly occurs intermixed with Rhododendron arboreum and Taxus wallichiana [92] in [81].
Pinus wallichiana
3000 m
96Found in temperate to sub-alpine zones, typically in mountain screes and glacier forelands. It forms the tree line in relatively dry regions such as Manang [22].
Rhododendron arboreum
3000–3100 m
85It has the widest distribution range among all Himalayan species [93]. It mainly occurs on sunny slopes. It also occurs at the understory of Abies spectabilis and forms the second layer in mountains [81].
Taxus wallichiana
3100 m
21Like most conifers, it is an evergreen species belonging to Taxaceae. Also known as Himalayan Yew, it is slow-growing species and a major source of Taxol. This species occurs in the Annapurna range [94].
Table 5. IVI analysis of tree species for the study site in Lamjung.
Table 5. IVI analysis of tree species for the study site in Lamjung.
Species NameAbundance
[n ha−1]
Basal Area
[m2 ha−1]
Frequency [%]IVI
Rhododendron campanulatum110016.4100171.9
Sorbus microphylla1125.38856.1
Betula utilis535.66344.5
Salix nepalensis650.63819.5
Juniperus indica430.2138.0
Total137328.0 300
Table 6. IVI analysis of tree species for the study site in Mustang.
Table 6. IVI analysis of tree species for the study site in Mustang.
Species NameAbundance
[n ha−1]
Basal Area
[m2 ha−1]
Frequency [%]IVI
Abies spectabilis16313.45085.9
Pinus wallichiana2407.76376.0
Rhododendron arboreum2132.310060.7
Acer campbellii730.63820.6
Cotoneaster microphyllus280.26319.7
Taxus wallichiana530.92516.5
Elaeagnus parviflora280.15016.4
Ilex dipyrena80.1134.2
Total80625.2 300
Table 7. Main stand variables for both study sites generated from forest inventory data.
Table 7. Main stand variables for both study sites generated from forest inventory data.
Stand VariableLamjungMustangWilcoxon Test Statistics (W)p-Value
Basal Area (m2 ha−1)28.0325.19400.44
Stem density (stems ha−1)1373806520.037
Quadratic mean diameter (cm)16.1221.53200.23
Mean tree height (m)
Volume (m3 ha−1)102.68282.47170.13
Table 8. Average leaf areas of species recorded in the study sites.
Table 8. Average leaf areas of species recorded in the study sites.
Study SiteSpecies NameAverage Leaf Area ± se (cm2)Species Type
LamjungBetula utilis31.70 ± 2.95Broadleaved
Juniperus indica0.44 ± 0.03Coniferous
Rhododendron campanulatum40.94 ± 2.30Broadleaved
Salix nepalensis11.00 ± 0.70Broadleaved
Sorbus microphylla2.48 ± 0.15Broadleaved
MustangAbies spectabilis0.47 ± 0.12Coniferous
Acer cambellii21.11 ± 2.40Broadleaved
Cotoneaster microphyllus8.90 ± 0.76Broadleaved
Elaeagnus parviflora26.42 ± 2.99Broadleaved
Pinus wallichiana0.65 ± 0.02Coniferous
Ilex dipyrena25.27 ± 2.40Broadleaved
Rhododendron arboreum31.29 ± 1.80Broadleaved
Taxus wallichiana0.53 ± 0.03Coniferous
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Bhatta, K.P.; Aryal, A.; Baral, H.; Khanal, S.; Acharya, A.K.; Phomphakdy, C.; Dorji, R. Forest Structure and Composition under Contrasting Precipitation Regimes in the High Mountains, Western Nepal. Sustainability 2021, 13, 7510.

AMA Style

Bhatta KP, Aryal A, Baral H, Khanal S, Acharya AK, Phomphakdy C, Dorji R. Forest Structure and Composition under Contrasting Precipitation Regimes in the High Mountains, Western Nepal. Sustainability. 2021; 13(13):7510.

Chicago/Turabian Style

Bhatta, Kishor Prasad, Anisha Aryal, Himlal Baral, Sujan Khanal, Amul Kumar Acharya, Chanthavone Phomphakdy, and Rinzin Dorji. 2021. "Forest Structure and Composition under Contrasting Precipitation Regimes in the High Mountains, Western Nepal" Sustainability 13, no. 13: 7510.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop