Phytosociology and ecological framework of forest tree species in the Toormang Valley, Dir Lower, Hindu Kush Range, Pakistan

Fitossociologia e enquadramento ecológico de espécies de árvores florestais no Vale Toormang, Dir Lower, Cordilheira Hindu Kush, Paquistão

Abstract

The phytosociological survey was conducted during 2018-2020. The research area was classified into five ecological zones based on habitat, physiognomy and species composition. Pc-Ord software was used for cluster analysis and four vegetation communities were established. The Quercus baloot-Quercus incana community is situated in Sair at an altitude of 1196 (mean ± SE) m altitude with a 14.1 ± 0 slope angle and contains eleven tree species. The Pinus wallichiana- Ailanthus altissima community had a relatively small number of tree species reported in Shakawlie at 1556 (mean ± SE) with a 17.5 ± 0 slope angle. The Pinus wallichiana- Quercus incana community is distributed in Wali Kandao and Mangi Kandao at altitudes of 2030.5 (mean ± SE) m and the slope angle was 19.2 ± 1.4. This community possesses a total of twenty-one tree species and is highly diverse. Similarly, the Populus alba - Platanus orientalis group was present in Banr Pate, with an altitude of 1613 (mean ± SE) m and a 16.3 slope angle. The principal component analysis (PCA) and non-metric multidimensional scaling (NMS) ordination methods were applied to study the relationships between ecological and soil variables with trees species. The NMS ordination of axis 1 was significantly correlated with Sand% (p<0.2), Nitrogen% (p<0.1) and Pb (mg/kg) (r= 0.876751, p<0.05), while the ordination of axis 2 was significantly correlated with Silt% (p<0.2), Sand% (p<0.2), Organic matter% (p<0.2), K (mg/kg) (r=0.882433, p<0.02), Fe (mg/kg)(r=0.614833, p<0.2), Ca (mg/kg) (r=0.721712, p< 0.2) and Zn (mg/kg) (r=0.609545, p<0.2). Similarly, the PCA ordination of axis 1 revealed that it was significantly correlated with phosphorus, calcium and slope angle, while that of axis 2 was significantly correlated with altitude, zinc and manganese.

Keywords:
tree species; environmental variables; cluster analysis; PCA & NMS ordination; Toormang Valley; Lower Dir; Pakistan

Resumo

O levantamento fitossociológico foi realizado durante o período 2018-2020. A área de pesquisa foi classificada em cinco zonas ecológicas com base no habitat, na fisionomia e na composição de espécies. O software Pc-Ord foi utilizado para análise de agrupamento e quatro comunidades de vegetação foram estabelecidas. A comunidade Quercus baloot-Quercus incana está situada em Sair, a uma altitude de 1.196 m (média ± SE) de altitude, com um ângulo de inclinação de 14,1° ± 0, e contém 11 espécies de árvores. A comunidade Pinus wallichiana-Ailanthus altissima teve um número relativamente pequeno de espécies de árvores relatadas em Shakawlie, em 1.556 m (média ± SE) de altitude, com um ângulo de inclinação de 17,5° ± 0. A comunidade de Pinus wallichiana-Quercus incana está distribuída em Wali Kandao e Mangi Kandao, em altitude de 2.030,5 m (média ± SE) e ângulo de inclinação de 19,2° ± 1,4. Esta comunidade possui um total de 21 espécies de árvores e é altamente diversificada. Da mesma forma, o grupo Populus alba-Platanus orientalis esteve presente em Banr Pate, com altitude de 1.613 m (média ± SE) e ângulo de inclinação de 16,3°. Os métodos de análise de componentes principais (PCA) e ordenação de escala multidimensional não métrica (NMS) foram aplicados para estudar as relações entre variáveis ecológicas e de solo com espécies arbóreas. A ordenação NMS do eixo 1 foi significativamente correlacionada com as porcentagens de Areia (p<0,2), Nitrogênio (p<0,1) e Chumbo (mg/kg) (r=0,876751, p<0,05), enquanto a ordenação do eixo 2 foi significativamente correlacionada com as porcentagens de Silte (p<0,2), Areia (p<0,2), Matéria Orgânica (p<0,2), Potássio (mg/kg) (r=0,882433, p<0,02), Ferro (mg/kg) (r=0,614833, p<0,2), Cálcio (mg/kg) (r=0,721712, p< 0,2) e Zinco (mg/kg) (r=0,609545, p<0,2). Da mesma forma, a ordenação PCA do eixo 1 revelou que estava significativamente correlacionada com fósforo e cálcio, e ângulo de inclinação, enquanto a do eixo 2 estava significativamente correlacionada com zinco e manganês, e altitude.

Palavras-chave:
espécies de árvores; variáveis ambientais; análise de cluster; ordenação PCA e NMS; Vale Toormang; Lower Dir; Paquistão

1. Introduction

Phytosociology is related with plant community composition, structure and distribution (Magray et al., 2022). It is important to use phytosociological methods to study the different kinds of species that live in a certain area (Lovett et al., 2006). Phytosociological research is essential for the maintenance of natural plant communities and biodiversity, as well as for understanding past and future changes (Soni and Namdeo, 2022). Many scientists have shown that the climate and edaphic factors affect the Himalayan and Hindukush regions of Pakistan (Ahmed et al., 2006). The goal of phytosociological studies is to develop a conservation strategy for a wide variety of plant species (Badshah et al., 2016; Hussain et al., 2019).

Forest species supply resin, fuel wood, fodder, and timber to local communities. The increasing demand for forest products and growing populations have put considerable stress on subtropical forest stands, leading to the loss of species and disruption of communities (Timilsina et al., 2007; Gairola et al., 2008). During the course of the last century, over 60% of the forest cover in the Himalayan region's has been eradicated (Todaria et al., 2010). Pakistan is in danger because less than 4% of its land is covered by forests, and 3% of its forests are being cut down every year. Over the last three decades, Pakistan has lost more than a quarter of its forests (Ahmad et al., 2010; Shaheen and Shinwari, 2012).

Plant communities are established by species that share structural and floristic traits at a particular level of ecological tolerance (Magray et al., 2022). The distribution of plant communities is influenced by ecological factors, species competition and human activities (Ilyas et al., 2020). The existence and development of plant communities provide information about the type of plant and the environmental conditions that led to their development (Hussain et al., 2015). The vegetation and plant communities of various habitats are changing gradually or quickly due to human disturbances (Iqbal et al., 2008; Naz et al., 2017). The plant population size and the structure of the vegetation can provide information about the species' ability to tolerate particular ecological characteristics (Sher et al., 2014).The levels of micronutrients and macronutrients in the soil can have a significant impact on plant communities (Hussain et al., 2019). The topography and elevation of mountainous regions have a significant impact on community structure, species richness and species diversity (Khan et al., 2015).

Cluster analysis techniques are used to evaluate ecological groups whereas, ordinations provide specific patterns for plant communities with respect to environmental gradients (Ali et al., 2022a). Research revealed that advanced multivariate statistical analysis was initially limited to use in highly developed countries. However, in recent decades, multivariate statistical analyses have been frequently used to examine vegetation communities and investigate the effects of environmental factors on plant distribution patterns in various parts of the world (Hou et al., 2017). The objective of this study was to analyze the vegetation of the Toormang Valley, Dir Lower, Pakistan, in relation to different environmental and ecological factors. It is predicted that the altitude, slope angle, and aspect of the mountain influence vegetation structure and biodiversity. The literature shows that there is no research on the vegetation of the Toormang Valley. Therefore, the current study is considered necessary for investigating the pattern and features of vegetation in the Toormang Valley, Dir Lower, Pakistan.

2. Materials and Methods

2.1. Study area

The Toormang Valley is located in the lower Dir district of Khyber Pakhtunkhwa, Pakistan. The valley is present in the northwestern corner of Khyber Pakhtunkhwa and is bounded on all sides by the massive Koh-e-Hindukush Range. It is located between 34°54'50” and 34°52'29” N and between 72°01'12” and 72°06'00” E of the Equator. A map showing the various study sites (is shown in Figure 1). Summer is short and pleasant, while winter is harsh. The high mountains receive snow from December to March (Khan et al., 2010).

Figure 1
Map of the study area.

2.2. Data collection

The data were collected on a regular basis from 2018 to 2020. Based on habitat, species composition, and physiognomy, the valley was divided into five ecological zones. A variety of quantitative methods were applied to study the phytosociology of tree species using a quadrat size of 10m×10 m. At each site, ten quadrats were established for each tree species (Ali and Khan 2022). Ecological parameters such as density, frequency and cover for tree species were calculated (Rahman et al., 2023). A GPS device was used to measure the altitudes and geographical coordinates of each quadrat. The aspect was determined using a magnetic compass, while the slope angle was calculated using a clinometer (Ali et al., 2022b). At each sampling site, soil was collected from three different regions up to a depth of 15 cm and mixed to form a composite sample (Ali et al., 2017). Soil texture was evaluated using the hydrometer method at the Land Resource Research Institute, NARC, Islamabad (Elfaki et al., 2016). The acid neutralization method was used to assess soil organic matter and lime levels (Amin et al., 2022). The pH of the soil was measured in a 1:5 water-soil solution (Cheng et al., 2019).

2.3. Statistical analysis of the data

PC-Ord (version 5.10) was used for the data analysis. The quantitative data associated with ecological and edaphic variables were subjected to software to reveal core clusters and significant patterns in vegetation structure (McCune and Mefford, 2005). Ward’s cluster analysis was used for classification, whereas non-metric multidimensional scaling (NMS) and principal component analysis (PCA) were used for coordination (Gauch and Whittaker, 1981). Then, the IVI data of 28 tree species and 19 parameters were subjected to cluster analysis and ordination at 75%. Furthermore, descriptive statistics were used to calculate the mean and standard error of IVI data collected from different communities of tree species. After that, the soil data from the different study areas were correlated with the various values of the NMS and PCA axes (Gupta et al., 2008).

3. Results

3.1. Ward’s cluster analysis of tree species

In the present research, Pc-Ord software (version 5.2) was used to perform cluster analysis on the IVIs of 28 tree species. The resulting data were divided by 75% of the remaining information about the individual species into 4 vegetation groups. The one-way cluster dendrogram and two-way cluster dendrogram of the tree species are shown in (Figures 2 and 3). The Quercus baloot -Quercus incana vegetation group was present in the sair at an altitude of 1196 (mean ± SE) m and the slope angle was 14.10°. This group was dominated by Quercus baloot (IVI = 15.11 ± 0%) and Quercus incana (IVI = 13.53 ± 0%) while Ailanthus altissima (IVI =12.53 ± 0%), Juglans regia (IVI =11.53 ± 0%) and Phoenix sylvestris (IVI =11.14 ± 0%) were the major associates. The total density (426 individuals/ha) and overall basal area (13822 cm² cover/ha) were recorded (Table 1). The soil in this vegetation group was sandy loam in texture, calcareous and slightly alkaline in nature, with a pH value of 7.6 ± 0. The organic matter content was low, the nitrogen level was low, the phosphorus was moderate and the potassium content was appropriate (Table 2).

Figure 2
Tree species cluster dendrogram based on IVI.

Figure 3
Cluster dendrogram (two-way) of tree species.

Table 1
Overstory vegetation Ward’s cluster analysis and IVI values, density/ha and cover/ha in different vegetation groups.

S.N	Species	GROUP 1			GROUP 2			GROUP 3			GROUP 4
		Mean ± SE			Mean ± SE			Mean ± SE			Mean ± SE
		IVI	Density/ha	Cover/ha	IVI	Density/ha	Cover/ha	IVI	Density/ha	Cover/ha	IVI	Density/ha	Cover/ha
1	Quercus baloot Griff.	15.11 ± 0	90 ± 0	1620 ± 0	6.49 ± 0	18 ± 0	1298 ± 0	7.44 ± 3.39	16 ± 6	1180 ± 1180	6.06 ± 0	16 ± 0	1100 ± 0
2	Quercus incana Bartram.	13.53 ± 0	12 ± 0	520 ± 0	8.22 ± 0	28 ± 0	590 ± 0	15.42 ± 6.87	62 ± 10	1340.2 ± 1.4	9.68 ± 0	84 ± 0	580 ± 0
3	Ailanthus altissima (Mill.)Swingle.	12.53 ± 0	4 ± 0	3380 ± 0	18.2 ± 0	20 ± 0	1360 ± 0	3.61 ± 3.65	4 ± 2	1130.3 ± 510.2	0 ± 0	0 ± 0	0 ± 0
4	Platanus orientalis	5.24 ± 0	16 ± 0	702 ± 0	0 ± 0	0 ± 0	0 ± 0	1.98 ± 1.56	2 ± 2	370 ± 370	13.42 ± 0	96 ± 0	1705 ± 0
5	Phoenix sylvestris (L.) Roxb.	11.14 ± 0	98 ± 0	1860 ± 0	0 ± 0	0 ± 0	0 ± 0	1.15 ± 1.15	2 ± 2	110 ± 110	8.06 ± 0	8 ± 0	1400 ± 0
6	Acacia modesta Wall.	9.87 ± 0	80 ± 0	580 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	10.12 ± 0	4 ± 0	3330 ± 0
7	Celtis caucasica Willd.	6.62 ± 0	92 ± 0	1702 ± 0	0 ± 0	0 ± 0	0 ± 0	6.27 ± 1.47	14 ± 4	2500.2 ± 280	0 ± 0	0 ± 0	0 ± 0
8	Diospyros lotus L.	4.57 ± 0	8 ± 0	560 ± 0	0 ± 0	0 ± 0	0 ± 0	4.05 ± 1.36	6 ± 2	1406.3 ± 550	0 ± 0	0 ± 0	0 ± 0
9	Juglans regia	11.53 ± 0	4 ± 0	1120 ± 0	0 ± 0	0 ± 0	0 ± 0	5.27 ± 1.47	10 ± 4	1800.2 ± 426.2	0 ± 0	0 ± 0	0 ± 0
10	Diospyros kaki L.f.	3.57 ± 0	4 ± 0	488 ± 0	0 ± 0	0 ± 0	0 ± 0	2.5 ± 0.12	6 ± 0	1950.2 ± 210	0 ± 0	0 ± 0	0 ± 0
11	Acacia nilotica (L.) Delile.	6.24 ± 0	18 ± 0	1290 ± 0	0 ± 0	0 ± 0	0 ± 0	1.34 ± 1.34	2 ± 2	130 ± 130	0 ± 0	0 ± 0	0 ± 0
12	Quercus dilatata Royle.	0 ± 0	0 ± 0	0 ± 0	10.18 ± 0	4 ± 0	3360 ± 0	2.24 ± 0.55	2 ± 0	790 ± 790	0 ± 0	0 ± 0	0 ± 0
13	Ziziphus jujuba Mill.	0 ± 0	0 ± 0	0 ± 0	7.61 ± 0	12 ± 0	1726 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0
14	Eriobotrys japonica (Thunb.) Lindl.	0 ± 0	0 ± 0	0 ± 0	9.49 ± 0	80 ± 0	540 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0
15	Salix tetrasperma Roxb.	0 ± 0	0 ± 0	0 ± 0	8.81 ± 0	32 ± 0	1470 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0
16	Melia azedarach L.	0 ± 0	0 ± 0	0 ± 0	6.99 ± 0	160 ± 0	1708 ± 0	2.02 ± 2.02	2 ± 0	430 ± 430	4.16 ± 0	4 ± 0	1306 ± 0
17	Pinus wallichiana. Jacks.	0 ± 0	0 ± 0	0 ± 0	24.01 ± 0	192 ± 0	1790 ± 0	20.1 ± 5.33	118 ± 14	1520 ± 12.2	3.78 ± 0	4 ± 0	1160 ± 0
18	Morus macroura Miq.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	2.56 ± 1.56	2 ± 2	540 ± 540	7.16 ± 0	12 ± 0	1700 ± 0
19	Ficus carica L	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	9.25 ± 0	80 ± 0	566 ± 0
20	Populus alba	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	13.54 ± 0	92 ± 0	1696 ± 0
21	Ziziphus sativa Gaertn.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	4.05 ± 4.05	6 ± 6	1415.2 ± 590	4.23 ± 0	8 ± 0	580 ± 0
22	Pyrus baccata L.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	8.15 ± 0	28 ± 0	1410 ± 0
23	Celtis australis L.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	1.72 ± 1.02	2 ± 2	330 ± 330	2.38 ± 0	8 ± 0	282 ± 0
24	Punica granatum L	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	2.3 ± 2.37	2 ± 4	140 ± 140	0 ± 0	0 ± 0	0 ± 0
25	Eucalyptus camaldulensis Dehnh.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	8.98 ± 6.98	20 ± 10	1750 ± 65.3	0 ± 0	0 ± 0	0 ± 0
26	Salix alba L.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	2.2 ± 2.37	14 ± 14	565 ± 565	0 ± 0	0 ± 0	0 ± 0
27	Pinus roxburghii Sarg.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	3.02 ± 2.02	2 ± 2	735 ± 735	0 ± 0	0 ± 0	0 ± 0
28	Pyrus pashia D. Don.	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	0 ± 0	1.69 ± 1.69	0 ± 0	320 ± 320 ±	0 ± 0	0 ± 0	0 ± 0
Total			426	13822		546	13842		294	20451		444	16815

• Key: (SE) standard error = the standard error is the approximate standard deviation of a statistical sample population.

Table 2
Soil nutrients and topographic and edaphic factors related to Overstory species.

Parameters	GROUP 1			GROUP 2			GROUP 3			GROUP 4
	Mean	±	SE	Mean	±	SE	Mean	±	SE	Mean	±	SE
Altitude	1196	±	0	1556	±	0	2030.5	±	232.5	1613	±	0
Slope	14.1	±	0	17.5	±	0	19.2	±	2.2	16.3	±	0
Clay %	18	±	0	13	±	0	23	±	2	23	±	0
Silt %	8	±	0	42	±	0	14	±	2	4	±	0
Sand %	74	±	0	45	±	0	63	±	4	73	±	0
PH	7.6	±	0	7.7	±	0	7.3	±	0.1	7.2	±	0
Lime	6.75	±	0	7	±	0	6.625	±	0.375	7.5	±	0
O.M%	0.79	±	0	0.93	±	0	0.755	±	0.035	0.86	±	0
N %	0.039	±	0	0.04	±	0	0.0378	±	0.0012	0.043	±	0
P (mg/kg)	6.2	±	0	5.4	±	0	25.55	±	18.15	6.5	±	0
K (mg/kg)	70	±	0	110	±	0	95	±	5	85	±	0
Fe (mg/kg)	0.893	±	0	1.14	±	0	1.05	±	0.171	1.33	±	0
Zn (mg/kg)	0.272	±	0	0.911	±	0	0.513	±	0.375	0.288	±	0
Ca(mg/kg)	0.88	±	0	0.3166	±	0	0.239	±	0.006	0.327	±	0
Cu (mg/kg)	0.222	±	0	0.344	±	0	0.388	±	0.176	0.4312	±	0
Mn(mg/kg)	3.232	±	0	2.345	±	0	3.5865	±	1.6005	4.786	±	0
Pb(mg/kg)	1.23	±	0	1.12	±	0	1.06	±	0.08	1.9	±	0
Mg (mg/kg)	2.243	±	0	2.987	±	0	2.839	±	0.707	3.786	±	0

• Key: (O.M) organic matter. Soil organic matter is the portion of soil composed of plant or animal material at different phases of decomposition.

Vegetation group-II was situated in Shakawlie at 1556 (mean ± SE) m altitude with a 17.50 slope angle. This vegetation group is less diverse than other communities. Among them Pinus wallichiana (IVI =24.01 ± 0%) and Ailanthus altissima (IVI =18.2 ± 0%), were recognized as the leading species, followed by Quercus dilatata (IVI =10.18 ± 0%) and Eriobotrys japonica (IVI =9.49 ± 0%). The soil in this vegetation group was mostly loamy and calcareous, with a pH of 7.7 ± 0; the nitrogen and organic matter contents were low; the phosphorus content was moderate; and the potassium content was reportedely high compared to that in other communities. In this community, the overall basal area (13842 cm² cover/ha) and the highest density per hectare, with 546 individuals, were observed in vegetation group II (Table 1).

Overstory vegetation group III, consists of twenty-one different species and is more diverse than the other communities. This vegetation group is situated in Wali Kandao and Mangi Kandao at an elevation of 2030.5 ± 232.5 (mean ± SE) m and a slope angle of 19.2 ± 1.4°. Pinus wallichiana was the dominant species (IVI =20.1 ± 5.33%) followed by Quercus incana (IVI=15.42 ± 6.87), Eucalyptus camaldulensis (IVI=8.98 ± 6.98) and Quercus baloot (IVI=7.44 ± 3.39). The soil was sandy loam in texture with a slightly alkaline pH (7.3 ± 0.1) and was calcareous. Organic matter, nitrogen, and potassium were less abundant, while phosphorus was found in high amounts in the soil (Table 2). The density and basal area of the dominant species were (118 ± 0 density/ha and 1520 ± 12.2 cm² cover/ha) respectively whereas the overall basal area was greater than that of the other vegetation groups.

Populus alba (IVI =13.54 ± 0%) and Platanus orientalis (IVI =13.42 ± 0%) dominated the fourth vegetation group and were found in Banr pate at 1613 (mean ± SE) m altitude and 16.3 ± 0 slope angle. Acacia modesta (IVI =10.12 ± 0%) and Quercus incana (IVI =9.68 ± 0%) were the major associates with the dominant species. The soil of this community was sandy loam and calcareous, with less organic matter and a pH value of 7.2 ± 0. The nitrogen content was high, the phosphorus content was moderate, and the potassium content was low in the soil (Table 2). This vegetation group was reported as the second most diverse community in terms of the total density (444 individuals/ha) and overall basal area (16815 cm² cover/ha), as shown in Table 1.

3.2. Factors influencing species distribution patterns

The relationships of environmental variables, physiochemical characteristics, and micro and macronutrients with tree species were examined by using NMS and PCA ordination. The results show that different groups of tree species were obtained by using Ward cluster analysis and were also subjected to NMS and PCA ordination.

3.3. NMS ordination of the tree vegetations

Figure 4 shows that in the NMS ordination plot, the Quercus baloot-Quercus incana vegetation group located at the bottom of the plot toward axis 1, was generated due to the effects of the high amount of sand (74 ± 0) and low amount of lime (6.75 ± 0). The Pinus wallichiana-Ailanthus altissima community on the upper side toward axis 2 developed under the influence of high pH (7.7 ± 0) and low silt concentration (13 ± 0). The Pinus wallichiana-Quercus incana located in the left region along axis 2 are characterized by a high clay content (23 ± 2) and a low concentration of organic matter (0.755 ± 0.035). Similarly, the Populus alba-Platanus orientalis group on the right side of axis 1 was assembled under the influence of a high nitrogen concentration (0.043 ± 0) and low pH (7.2 ± 0)

Figure 4
The occurrence of tree species between. Axis 1 and Axis 2 in the NMS ordination graph.

3.4. NMS ordination axis correlation with the soil and environmental variables

The results showed that the NMS ordination of axis 1 was significantly correlated with sand % (p< 0.2), nitrogen % (p< 0.1) and pb (mg/kg) (r= 0.876751, p<0.05), while the NMS ordination of axis 2 was significantly correlated with silt% (p<0.2), sand % (p<0.2), organic matter % (p<0.2), K (mg/kg) (r=0.882433, p<0.02), Fe (mg/kg)(r=0.614833, p<0.2), Ca (r=0.721712, p<0.2) (mg/kg) and Zn (r=0.609545, p<0.2) (mg/kg) (Table 3; Figure 4). The correlations of NMS ordination axes 1 and 2 with the remaining parameters were determined non-significant. Current research has revealed that the primary parameters affecting the distribution of tree species are altitude, organic matter, nitrogen, phosphorus, calcium, magnesium and lead.

Table 3
Correlation between NMS ordination axes 1 and 2 with soil and environmental variables.

Parameters	Axis 1		Axis 2
	Multiple R	Probability level	Multiple R	Probability level
Altitude	0.582739	Non significant	0.309706	Non significant
Slope	0.345	Non significant	0.412765	Non significant
Clay %	0.115103	Non significant	0.357328	Non significant
Silt %	0.572639	Non significant	0.693407	p<0.2
Sand %	0.670957	p<0.2	0.727211	p<0.2
PH	0.170988	Non significant	0.04095	Non significant
Lime	0.607827	Non significant	0.406289	Non significant
O.M %	0.226227	Non significant	0.654887	p<0.2
N %	0.745442	p<0.1	0.291178	Non significant
P (mg/kg)	0.434446	Non significant	0.16645	Non significant
K (mg/kg)	0.109396	Non significant	0.882433	p<0.02
Fe (mg/kg)	0.287441	Non significant	0.614833	p<0.2
Zn (mg/kg)	0.483008	Non significant	0.609545	p<0.2
Ca(mg/kg)	0.493294	Non significant	0.721712	p<0.2
Cu (mg/kg)	0.054042	Non significant	0.414328	Non significant
Mn(mg/kg)	0.374698	Non significant	0.043376	Non significant
Pb(mg/kg)	0.876751	p<0.05	0.108986	Non significant
Mg (mg/kg)	0.275011	Non significant	0.394547	Non significant

3.5. PCA ordination of tree vegetation

PCA ordination was used to study the associations of tree vegetation with edaphic, topographic, soil micro and macronutrients. Ward’s cluster analysis revealed four groups of tree species, which were superimposed on PCA ordination axes 1, 2; 1, 3 and axes 2, 3. According to the results, PCA ordination and Ward’s cluster analysis are separate methodologies that are used for the same objectives. In the PCA ordination, community 1 is present on the left side of axis 2, while community 2 is on the right of axis 1. Group 3 is present at the left of axis 2, and group 4 is present on the upper side towards axis 2. The PCA ordination axes 1,2; 2,3 and 1,3 are shown in (Figures 5, 6 and 7) respectively.

Figure 5
PCA ordination (axis 1 and axis 2) of tree species.

Figure 6
PCA ordination (axis 2 and axis 3) of tree sspecies.

Figure 7
PCA ordination (axes 1 and 3) of tree species.

3.6. Association of the PCA ordination axis with topographic, edaphic and soil nutrients

The PCA ordination of axis 1 revealed that it was significantly correlated with slope (p< 0.20), clay percentage (r=0.754, p< 0.10), P concentration (R=0.785, p< 0.10) (mg/kg) and Ca concentration (r=0.687, p< 0.20)(mg/kg). PCA ordination axis 2 showed a significant correlation with altitude (p< 0.10), K (r= 0.803, p< 0.10), Zn (r= 0.637, p< 0.20) and Mn (0.893, p< 0.02), while PCA ordination axis 3 showed no significant correlation (Table 4).

Table 4
Correlation of PCA ordination axis 1 and 2 with soil and environmental factors.

Parameters	axis 1		axis 2		axis 3
	Multiple R	Probab. Level	Multiple R	Probab. Level	Multiple R	Probab. Level
Altitude	0.453	Non significant	0.745	P < 0.10	0.132	Non significant
Slope	0.576	P < 0.20	0.043	Non significant	0.243	Non significant
Clay %	0.754	P < 0.10	0.543	Non significant	0.465	Non significant
Silt %	0.532	Non significant	0.365	Non significant	0.165	Non significant
Sand %	0.453	Non significant	0.253	Non significant	0.376	Non significant
pH	0.432	Non significant	0.352	Non significant	0.254	Non significant
Lime	0.564	Non significant	0.601	Non significant	0.436	Non significant
O.M %	0.235	Non significant	0.412	Non significant	0.0243	Non significant
N %	0.376	Non significant	0.054	Non significant	0.534	Non significant
P mg/kg	0.785	P < 0.10	0.476	Non-significant	0.297	Non significant
K mg/kg	0.326	Non significant	0.803	P < 0.10	0.184	Non significant
Fe mg/kg	0.0436	Non significant	0.576	Non significant	0.0476	Non significant
Zn mg/kg	0.234	Non significant	0.637	P < 0.20	0.574	Non significant
Ca mg/kg	0.687	P < 0.20	0.587	Non significant	0.376	Non significant
Cu mg/kg	0.476	Non significant	0.351	Non significant	0.265	Non significant
Mn mg/kg	0.597	Non significant	0.893	P < 0.02	0.042	Non significant
Pb mg/kg	0.472	Non significant	0.574	Non significant	0.265	Non significant
Mg mg/kg	0.564	Non significant	0.0453	Non significant	0.065	Non significant

4. Discussion

The climate of the lower Dir is moist and cold (Irfan et al., 2018). However, the differences in plant communities at various altitudes are due to significant changes in physiographic, edaphic, and climatic conditions (Ahmed, 1986). The nutrients in the soil, soil moisture, and amount of rainfall all have a significant impact on the establishment of forest communities, as do other environmental factors (Timilsina et al., 2007).The Quercus baloot-Quercus incana community was established in Sair at an altitude of 1196 m above sea level with a 14.1 slope angle. The dominant tree species were Quercus baloot and Quercus incana. The woody species that are associated with this species are Ailanthus altissima, Juglans regia, Phoenix sylvestris, Acacia modesta, and Celtis caucasica. These findings are consistent with those of Rahman et al. (2022), who investigated Quercus-dominated forests in the Swat district. The Quercus baloot was also reported by Khan et al. (2010) from District Chitral. Khan (2012) reported the Quercus baloot-Quercus dilitata community from Dir Upper. Ali (2016) described an Abies pindrow-Quercus incana community from Chail Valley district swat. In Quercus-dominated communities, the degree of verifiability is mostly determined by the aspect and altitude of the landscape (Coop and Givnish, 2007).

The Pinus wallichiana- Ailanthus altissima community was found in Shakawlie at 1556 m altitude with a 17.5 slope angle. This community is less diverse than other tree communities. These results are in agreement with those of Zaman and Badshah (2020), who also reported that due to the extensive use of wood in the area, tree species were quite scarce. The presence of insufficient trees and rather thick plants in the vicinity of the lower sections demonstrated the disturbed nature of the ecosystem, which is under enormous biotic pressure. The expansion of the population and the construction of new residential units are the main causes of species extinction in this area. The lower number of tree species could be attributed to the increased demand for fuel, timber wood, infrequent fires, and the clearing of the forest for porches to be built on top. A decrease in forest area indicates a variety of environmental and economic problems (Ullah, 2013; Sharifullah et al.,2020). Pinus wallichianaw was the most common species with 24.01% IVI, followed by Ailanthus altissima, Quercus dilatata and Eriobotrys japonica. Pinus wallichiana was also reported by Ilyas et al. (2015) as the dominant species with Quercus incana from Swat. Ahmed et al. (2006) and Akbar (2013) reported similar observations in their investigations, which supports the current findings. Khan et al. (2012) also investigated Pinus wallichiana as a dominating species in the dry temperate zone of Chitral. Some species present in this community were also documented in a Quercus baloot-dominated community (Khan et al., 2012).

Populus alba and Platanus orientalis communities were found in Banr Pate. This community consisted of thirteen different species. The Pinus wallichiana- Quercus incana community was reported from Wali Kandao and Mangi Kandao at an altitude of 2030.5 m; this community has a total of twenty-one different species and is more diverse than other communities. On the basis of IVI, the leading species in this community was Pinus wallichiana, followed by Quercus incana and Eucalyptus camaldulensis. These findings are consistent with those of Jackson (2005), who investigated woodlands dominated by Pinus wallichiana, while Rahman et al. (2017) reported Pinus wallichiana-dominated communities in the district of Swat. The Picea smithiana-Pinus wallichiana community was also reported by Wahab et al. (2010) from Dir. Pinus wallichiana has previously been reported in both pure communities and in combination with other species, demonstrating its wide ecological range (Ahmad et al., 2010; Khan et al., 2013). In these forests, the climate is of the moist temperate type, which indicates that the temperature is low and the amount of rainfall is abundant. Therefore, the plant communities that have been reported might be the remains of a temperate forest that once covered the land (Hussain et al., 1992; Malik, 2005).

The plant communities in any zone are based on the types of plants and the environmental conditions in which they grow and flourish (Malik, 1986). Climatic, physiographic and soil variables cause considerable shifts in plant communities (Ahmed, 1986). In the present study, NMS and PCA ordination were used to investigate the associations between vegetation and edaphic, topographic and soil macro and micronutrients. Similar NMS and PCA techniques were used by Rahman et al. (2016); who identified four Isodon rugosus-dominated communities in Khwazakhela using cluster analysis and NMS ordination.

Tavili et al. (2009) investigated environmental vegetation relationships in the rangelands of Southern Khorasan and applied cluster analysis as well as CCA ordination. Khan (2012) reported three communities of Quercus baloot from the Upper Dir district using cluster analysis and DCA coordination. Haq et al. (2015) used CCA and DCA ordination and recorded four communities from Nandiar Khuwar, while Irshad et al. (2016) also reported four plant communities through cluster analysis and NMS ordination. et al., (2010) applied CCA ordination to investigate the vegetation of the Middle Kunlun Mountains in China. Khan et al. (2017) reported the DCA and CCA ordination of tree vegetation from the Tandiana forest in the Western Himalayas (Pakistan). A similar approach was taken by Akbar (2013) who explored tree vegetation via cluster analysis and principal component analysis (PCA). Therefore, my results are in line with those of the above authors.

In the present study, the correlation between NMS ordination axes 1, 2 and the others studied parameters were not significant. The main factors affecting tree species distribution are altitude, nitrogen, soil organic matter, calcium, and lead concentrations. Rahman et al. (2022) agreed with our findings, claiming that most climatic factors have little or no effect on vegetation patterns. Rahman et al. (2016) reported that CCA ordination axes were significantly correlated with soil pH, organic matter, phosphorus, and potassium concentrations in the soil, while the NMS ordination axes showed a significant correlation with several environmental and edaphic parameters, as reported by Oswalt et al. (2006). According to the results, PCA ordination and Ward’s cluster analysis are separate methodologies that are used for the same objectives. Akbar et al. (2011) studied the tree vegetation data of Gilgit Baltistan through PCA ordination, while DCA ordination was used by Ilyas et al. (2015) for investigating the Kabal Valley vegetation of Swat district. Ahmed et al. (2011) showed a significant correlation between ordination axes and a variety of environmental factors such as altitude, soil pH, organic matter, magnesium, and nitrogen concentration. Akbar et al. (2011) showed a strong correlation of PCA ordination axes with magnesium, iron, nitrogen, manganese, and zinc concentrations in soil related to tree vegetation. In the present study, the majority of the analyzed parameters were not significantly correlated with the PCA ordination. This may be due to strong climatic changes that affected natural processes as documented by Khan et al. (2013), Wahab (2011) and Gui et al. (2010).

In the present study, the density/ha and cover/ha of the tree species differed among the various sampling sites in the study area. It was found that different tree species had low density/ha and cover/ha. The lower contribution of such tree species may be attributed to the rising demand for firewood and other uses, as reported by Ullah (2013). The highest density/ha of trees was found in community group 2, followed by community group 3 and community 1. Some important tree species that show the highest density/ha in different communities are Pinus wallichiana, Melia azedarach, Phoenix sylvestris, Celtis caucasica, Quercus baloot, Quercus incana, Acacia modesta, Eriobotrys japonica and Salix tetrasperma.

The mean cover/ha of the tree species in the different communities ranged from 110-3380 cm² basal area/h. The highest cover/ha was shown by Ailanthus altissima, followed by Quercus dilatata, Celtis caucasica, Diospyros kaki, Phoenix sylvestris, Quercus baloot, Eucalyptus camaldulensis, and Pinus wallichiana. Our results are in line with recorded values of 83-5947 cm² basal area/h from isodon rugosus dominated communities at Khwazakhela swat (Rahman et al., 2016).

5. Conclusion

The vegetation of Toormang valley, Hindukush ranges were analyzed with the help of PC-Ord. The cluster analysis classified the vegetation into four different vegetation groups, namely, Quercus baloot-Quercus incana, Pinus wallichiana-Ailanthus altissima, Pinus wallichiana-Quercus incana and Populus alba-Platanus orientalis. These vegetation groups faced different environmental (altitude, slope, aspect) and edhapic (texture, organic matter, nutrients etc) conditions. Both the environmental and edaphic variables were important in determining the composition, structure, and vegetation distribution in the research area.

Acknowledgements

The present work is a part of Ph. D research project of Tahir Hameed. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors are thankful to the local inhabitants of the Toormang Valley for their cooperation during data collection.

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Publication Dates

• Publication in this collection
  02 Sept 2024
• Date of issue
  2024

History

• Received
  04 Feb 2024
• Accepted
  30 Apr 2024