Vegetation mapping and multivariate approach to indicator species of a forest ecosystem a case study from the thandiani sub forests division (ts fd) in the western himalayas

Ecological Indicators 71 (2016) 336–351
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Ecological Indicators
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Vegetation mapping and multivariate approach to indicator species of
a forest ecosystem: A case study from the Thandiani sub Forests
Division (TsFD) in the Western Himalayas
Waqas Khana
, Shujaul Mulk Khanb,∗
, Habib Ahmadc
, Zeeshan Ahmadb
, Sue Paged
a
Department of Botany, Hazara University, Mansehra, Pakistan
b
Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan
c
Department of Genetics, Hazara University, Mansehra, Pakistan
d
Department of Geography, University of Leicester, UK
a r t i c l e i n f o
Article history:
Received 25 April 2016
Received in revised form 23 June 2016
Accepted 29 June 2016
Keywords:
Cluster analysis
Indicator species analysis
Plant community
Species composition
Two way cluster analysis
Vegetation mapping
Thandiani sub Forests Division (TsFD)
a b s t r a c t
Questions: Does the plant species composition of Thandiani sub Forests Division (TsFD) correlate with
edaphic, topographic and climatic variables? Is it possible to identify different plant communities in
relation to environmental gradients with special emphasis on indicator species? Can this approach to
vegetation classification support conservation planning?
Location: Thandiani sub Forests Division, Western Himalayas.
Methods: Quantitative and qualitative characteristics of species along with environmental variables were
measured using a randomly stratified design to identify the major plant communities and indicator
species of the Thandiani sub Forests Division. Species composition was recorded in 10 × 2.5 × 2 and
0.5 × 0.5 m square plots for trees, shrubs and herbs, respectively. GPS, edaphic and topographic data were
also recorded for each sample plot. A total of 1500 quadrats were established in 50 sampling stations along
eight altitudinal transects encompassing eastern, western, northern and southern aspects (slopes). The
altitudinal range of the study area was 1290 m to 2626 m above sea level using. The relationships between
species composition and environmental variables were analyzed using Two Way Cluster Analysis (TWCA)
and Indicator Species Analysis (ISA) via PCORD version 5.
Results: A total of 252 plant species belonging to 97 families were identified. TWCA and ISA recognized
five plant communities. ISA additionally revealed that mountain slope aspect, soil pH and soil electrical
conductivity were the strongest environmental factors (p ≤ 0.05) determining plant community compo-
sition and indicator species in each habitat. The results also show the strength of the environment-species
relationship using Monte Carlo procedures.
Conclusions: An analysis of vegetation along an environmental gradient in the Thandiani sub Forests
Division using the Braun-Blanquet approach confirmed by robust tools of multivariate statistics identified
indicators of each sort of microclimatic zones/vegetation communities which could further be used in
conservation planning and management not only in the area studied but in the adjacent regions exhibit
similar sort of environmental conditions.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
Across a range of different scales, vegetation structure is
controlled by environmental gradients (Leonard-Barton, 1988).
Discovering and understanding the association between the biotic
and environmental components of an ecosystem and particularly
∗ Corresponding author.
E-mail addresses: shuja60@gmail.com, smkhan@qau.edu.pk,
smulkkhan@gmail.com (S.M. Khan).
the variation in species diversity and abundance along envi-
ronmental gradients, are critical branches of ecological research
(Daubenmire, 1968; Grytnes and Vetaas, 2002; Tavili and Jafari,
2009). For example, the effect of soil pH on the species composi-
tion and richness of plant communities is a well-known ecological
phenomenon (Ellenberg 1988 Moldan et al., 2012; Haberl et al.,
2012; Ullah et al., 2015), while in mountainous regions, aspect and
altitude show the greatest effects in limiting plant species and com-
munity types (Chawla et al., 2008; Khan and Ahmad, 2015). In terms
of identifying the effects of environmental gradients on vegetation,
http://dx.doi.org/10.1016/j.ecolind.2016.06.059
1470-160X/© 2016 Elsevier Ltd. All rights reserved.

W. Khan et al. / Ecological Indicators 71 (2016) 336–351 337
the use of computer-based statistical and multivariate analytical
programs can help ecologists to discover structure in vegetation
data sets and enable them to analyze the effects of environmen-
tal gradients on whole groups of species in a more efficient way
(Massberg et al., 2002; Hair et al., 2006). Statistical programs reduce
the complexity of data by classifying vegetation data and relat-
ing it to environmental components (Dufrêne and Legendre, 1997;
McCune and Mefford, 1999 Khan et al., 2011a,b Haq et al., 2015;
Chahouki et al., 2010). Classification also overcomes problems of
comprehension by summarizing field data in a low-dimensional
space by bringing species with similar requirements together in
various groups (Khan et al., 2013a,b). Such approaches have, how-
ever, rarely been used in vegetation studies in Pakistan (Malik and
Malik 2004; Malik and Husain, 2006; Saima et al., 2009; Wazir
et al., 2008; Malik and Husain, 2008 Khan et al., 2011a,b). Eco-
logical groups can be defined on the basis of indicator values for
different environmental gradients like light, moisture, soil reac-
tion and nitrogen content (Anderson et al., 1992). In addition, the
occurrence of certain associated vascular plant species may indi-
cate vegetation history, illustrated, for example, by those species
termed “ancient woodland indicator plants” that are recognized as
the species elements denoting continuity of woodland cover in the
United Kingdom (Glaves et al., 2009). Species can be grouped on the
basis of their indicator values and the nature of the assemblage;
such assemblages are usually a mixture of eurytopic (wide eco-
logical tolerance) and stenotopic (restricted ecological tolerance)
species (Kremen et al., 1993; Shah et al., 2015). In support of this
approach, a large data set on the distribution of species in open
habitats in Belgium was used as a case study to illustrate the utility
of a new method of identifying species assemblages and indica-
tor species (Dufrêne and Legendre, 1997), which may be useful for
planning of regional conservation priorities.
The aim of this study is to achieve an empirical model of vege-
tation using plant species combinations to characterize vegetation
types in the study area (Weber et al., 2000). Most of the West-
ern Himalayan Forests, such as those in the Thandiani sub Forests
Division (TsFD) area, have not been investigated using recently
developed analytical methods for vegetation characterization. In
part this is because these forests are located in remote areas with
poor access, uneven terrain and adverse geopolitics. But, in addi-
tion, previous accounts of montane vegetation in this region have
tended to be descriptive with a lack of quantitative approaches,
including computer-based vegetation data analysis. This study was
designed, therefore, to quantify the abundance of plant species,
analyze and define the communities and place them in an ecological
and vegetation framework in order to better understand indicator
groups for different microclimatic conditions within this moun-
tainous region. The specific research objectives were to explore the
influence of aspect, elevation and soil chemistry on the vegetation
assemblages of TsFD and to identify indicator species for each habi-
tat using multivariate statistical analyses. The study contributes
to wider efforts to systematically describe the plant communi-
ties of the mountainous regions of north-western Pakistan using
a phytosociological approach supported by robust statistical anal-
yses (Khan et al., 2011a,b) which will form the basis for strategic
conservation planning.
2. Study area
The “Thandiani sub Forests Division” (TsFD) encompasses the
Galis Forest Division of Abbottabad, the east Siran Forests Division,
the north Muzaffarabad & Garhi Habibullah in the south Abbottabad
sub forests division and the east Berangali forests range, between
3329◦ to 3421◦ North latitude and 7255◦ to 7329◦ East longitude
(Fig. 1).
The TsFD covers an area of 24987 ha in which 2484 ha are clas-
sified as Reserve Forests and 947 ha as Guzara Forests (Khan et al.,
2012a,b). The whole area is protected under the Guzara Forests
Division of the Khyber Pakhtunkhwa government in order to pre-
serve the valuable flora and fauna of the area. The highest point is
Thandiani top (Sikher) having an elevation of 2626 m. The dominant
vegetation cover is pine forest which may be divided into three ele-
vation ranges namely upper range (2200–2600 m), medium range
(1700 m–2200 m) and lower range (1200 m–1700 m). This study
was designed to record species composition pattern, quantify the
abundance of plant species across this elevation range and to estab-
lish the plant communities based on robust statistical approaches
in order to understand the environmental factors responsible for
determining the distribution of both species and communities with
special focus on indicator species. The research hypothesis was that
altitude, aspect, soil electrical conductivity and soil pH all have a
significant impact on species and community diversity of vascular
plants in the TsFD of the western Himalayas, Pakistan.
3. Materials and methods
In order to test the hypothesis, a phytosociological approach
(Rieley and Page, 1990; Kent and Coker, 1994 Khan et al., 2011a,b)
was used to record quantitative and qualitative attributes of vas-
cular plants in quadrats along edaphic, topographic and climatic
gradients during the summer months of 2012 and 2013. The study
area was divided into eight altitudinal transects covering a range of
1290–2626 m, along each of transects, sampling commenced from
the lowest elevation (forest bottom) and continued to the mountain
summit. Data collection stations were established at 100 m inter-
vals (total of 50 stations) along each transect with the help of a
GPS. At each station, 30 quadrats were enumerated: 5 for record-
ing trees, 10 for shrubs and 15 for herbs, each having an area of
10 × 2.5 × 2 and 0.5 × 0.5 m square, respectively. The quadrats were
positioned randomly at each station (Cox 1985; Malik 1990 Khan
et al., 2013a,b). Species composition and abundance in each quadrat
were recorded onto Excel data sheets. Absolute and relative density,
cover and frequency of each vascular plant species at each station
were subsequently calculated through the formulae designed by
Curtis and McIntosh (1950) using Microsoft Excel on an Asus palm-
top computer. The plant specimens were mostly identified with
the help of the Flora of Pakistan (Nasir and Ali, 1970 Nasir and
Ali, 1970–1989; Khan et al., 2014; Ali and Qaiser, 1993–2009) and
preserved in the Herbarium of Hazara University Pakistan (HUP).
Altitude was measured by GPS and slope aspect i.e., East (E), West
(W), South (S) and North (N), was determined with the help of a
digital compass. The soil was collected from each site up to a depth
of 15 cm and thoroughly mixed to make a composite sample. The
soil samples were kept in polythene bags and labeled appropriately
prior to analysis for a range of physical and chemical characteristics.
Particle-size analysis was determined following the destruction or
dispersion of soil aggregates into discrete units by mechanical or
chemical means, and then the separation of the soil particles by
sieving or sedimentation methods (Gee et al., 1986). Chemical dis-
persion was accomplished by first removing cementing substances,
such as organic matter and iron oxides, and then replacing calcium
and magnesium ions (which tend to bind soil particles together
into aggregates) with sodium ions (which surround each soil parti-
cle with a film of hydrated ions). The calcium and magnesium ions
were removed from solution by complexion with oxalate or hexa-
metaphosphate (Calgon) anions (Baver et al., 1972; Sheldrick and
Wang 1993; Monteith et al., 2014). Soil texture was determined by
the hydrometer method (Sarir et al., 2006; Bergeron et al., 2013;
the texture class was determined with the help of a textural trian-
gle (Adamu and Aliyu, 2012). For determination of pH, soil samples

338 W. Khan et al. / Ecological Indicators 71 (2016) 336–351
Fig. 1. GIS generated map showing location of the study area with reference to the Western Himalayas of Pakistan.
were mixed with an equal volume of deionized water, allowed to
equilibrate for at least an hour, and then the electrode of the pH
meter was immersed into the soil suspension and a reading was
directly recorded (Jackson, 1963). Electrical conductivity (EC) of
a soil extract was used to estimate the level of soluble salts. The
standard method is to saturate the soil sample with water, vacuum
filter to separate water from soil, and then measure EC of the satu-
rated paste extract (Hussain et al., 1999a,b; Jackson, 1963; Wilson
and Bayley, 2012). The soluble P was extracted from N mineral-
ization samples with hydrochloric-ammonium fluoride solution,
and determined calorimetrically (Kitayama and Aiba, 2002). The
organic matter was determined using the Walkley and Black’s titra-
tion method (Jackson, 1963; Hussain et al., 1999a,b).
Organic Matter% =
S − T
S
× 6.7
where S = Blank reading, T = Volume used of FeSO4.
4. Data analysis
Vegetation and environmental data sets were processed in MS
Excel in accordance with the PCORD V.5 requirements. The data
collected from the 50 sampling stations (1500 quadrats) revealed
the presence of 252 plant species. These species data along with
information on the six environmental variables (namely, altitude,
aspect, soil organic matter, soil pH, soil electrical conductivity, soil
phosphorous content and soil texture) were analyzed using PC-
ORD version 5 (McCune and Mefford, 1999). The Cluster Analysis
(CA) and the Two Way Cluster Analyses (TWCA) identified signifi-
cant habitat and plant community types using Sorensen measures,
based on presence/absence data (Greig-Smith, 1983) and were car-
ried out to identify pattern similarity in the species and station data.
Indicator Species Analysis (ISA) was subsequently used to link the
floristic composition and abundance data with the environmen-
tal variables. This combined information provided knowledge of
the concentration of species abundance in a particular group and
the faithfulness (fidelity) of occurrence of a species in that group.
Indicator values for each species in each group were obtained and
tested for statistical significance using the Monte Carlo test. Indi-
cator Species Analysis evaluated each species for the strength of its
response to the environmental variables, from the environmental
matrix (50 stations × 7 environmental gradients). A threshold level
of indicator value of 30% with 95% significance (p value ≤ 0.05) was
chosen as the cut off for identifying indicator species (Dufrene and
Legendre, 1997; Ter Braak and Prentice 1988) and the identified
indicator species were used for naming the communities.
5. Results
In total, 252 plant species belonging to 97 families were iden-
tified, comprising 51 trees, 48 shrubs and 153 herbs. Cluster and
Two Way Cluster Analyses broadly divided the plant species into 5
habitat types/communities which could be clearly seen in two main
branches of the dendrogram; (i) a lower altitude (1290 m–1900 m)
cluster including 3 communities/habitat types dominated by sub-
tropical vegetation and (ii) a higher altitude (1900 m–2626 m)
cluster including 2 communities/habitat types dominated by moist
temperate elements (Figs. 3 and 4 ).
Indicator Species Analysis (ISA) identified indicator species for
each habitat type under the influence of variables responsible
for those communities. The ISA results show that aspect, soil
pH and soil electrical conductivity have the strongest influence
on species occurrence. The results also show the strength of the
environment-species relationship using Monte Carlo procedures.
The five plant communities/habitat types established in TsFD are
described below, along with respective environmental variables.
5.1. Melia azedarach, Punica granatum and Euphorbia
helioscopia community
This community occurred at 12 stations (360 quadrats/releves)
at the lowest elevations (1299–1591 m asl). The tree, shrub and
herb layers were characterized by Melia azedarach, Punica granatum
and Euphorbia helioscopia respectively, which are the top diagnostic
(indicator) species (Table 1). Other indicator species of this commu-
nity are Ziziphus vulgaris Lam. Euclaptus globulus, Rosa moschata,
Zanthoxylum alatum, Cnicus argyracanthus, Medicago denticulata,
Poa annua, Themeda anathera, Rumex hastatus, Taraxacum officinale

Fig. 2. Cluster Dendrogram of 50 stations based on Sorensen measures showing 5 plant communities/habitat types (For more details see Table 8).
Table 1
The indicator species of the Melia azedarach, Punica granatum and Euphorbia helio-
scopia Community with their indicator values.
Top indicator of the community IV P* IVI TIVI
Melia azedarach L. 85.7 0.0406 36.53 61.11
Punica granatum L. 58.9 0.0008 80.1 69.5
Euphorbia helioscopia L. 40.3 0.0222 103.98 72.14
IV = Indicator Value, P = Probability, IVI = Importance Value Index in the community.
TIVI = Total Importance Value Index (Average Importance Value).
and Cynodon dactylon (Figs. 2–4 and Tables 7 and 8). The most
important environmental variables determining the gradient of this
community were low electrical conductivity (0.26–1.03dsm−1), low
soil organic matter content (0.5%–1.24%) and low soil pH (4.8–5.5),
coupled with associated co-variables of aspect (W-S), soil phospho-
rous content (5–8 ppm) and soil texture (silty loam) (Figs. 2–4 and
Tables 6–7).
Being located at lower elevations this community occurs in the
vicinity of human settlement and is therefore under pressure from
a range of anthropogenic activities, i.e., deforestation for fuel wood
and timber, expansion of agricultural land, grazing and multipur-
pose plant collection.
5.2. Ziziphus vulgaris, Zanthoxylum alatum and Rumex
nepalensis community
This community was found at the altitudinal range of
1600–1900 m asl and was represented by 16 different stations (480
quadrats). The tree, shrub and herb layers are characterized by the
indicator species Ziziphus vulgaris, Zanthoxylum alatum and Rumex
nepalensis (Table 2).
Other indicator species of this community are Abies pindrow,
Punica granatum, Rosa moschata, Rubus fruticosus, Achillea mille-
folium, Cnicus argyracanthus, Poa annua, Rumex hastatus, Nepeta
erecta, Taraxacum officinale, Medicago denticulata, Senecio chrysan-
Table 2
The indicator species of the Ziziphus vulgaris, Zanthoxylum alatum and Rumex
nepalensis Community with their indicator values.
Top Indicator of the community IV P* IVI TIVI
Ziziphus vulgaris Lam. 40.4 0.0126 43.4 41.9
Zanthoxylum alatum Roxb. 60.9 0.0006 63.26 62.08
Rumex nepalensis Spreng. 52.2 0.0188 97.52 74.86
IV = Indicator value, P = Probability, IVI = Importance value Index.
themoides, Cynodon dactylon, Chenopodium album and Capsella
bursa pastoris (Figs. 2–4 and Tables 7 and 8). North-West aspect was
one of the main environmental determinants of this community
indicating that this community receives comparatively less direct
sunlight. Other strong environmental variables were low soil pH
(4.9–6.6) and only trace amounts of organic matter (0.5%–1.24%)
coupled with low soil electrical conductivity (0.24–0.62dsm−1),
sandy loam and clay loam soil textures (Figs. 2–4 Tables 6–8).
5.3. Quercus incana, Cornus macrophylla and Viola biflora
plant community
This community occurs at mid-altitude elevations
(1900–2150 m asl) and was present at 11 stations and 330
quadrats (Table 3). In addition to the three main indicator species,
the additional characteristic species of this community are Abies
pindrow, Viburnum grandiflorum, Chrysanthemums cinerariifolium,
Euphorbia wallichii, Plantago lanceolata, Actaea spicata, Nepeta
erecta, Rumex nepalensis, Viola biflora and Achillea millefolium
(Figs. 2–4 and Tables 7 and 8). This community shows its best
development on south-east facing slopes where it is exposed to
direct solar radiation. Other strong influencing factors were higher
soil phosphorous content (5–7 ppm), moderate soil organic matter
(0.6%–1.18%), a weakly acidic soil pH (4.0), low soil electrical con-

Fig. 3. GIS map showing the 3D-DEM View (SRTM) of project area—Thandiani sub forests division with sampling localities (GIS based, stations distribution), graph and
elevation profile for the stations of all altitudinal transacts.
Table 3
The indicator species of the Quercus incana, Cornus macrophylla and Viola biflora
Community With their indicator values.
Quercus incana Roxb. 42.7 0.018 36.78 39.74
Cornus macrophylla Wall. Ex Roxb. 48.6 0.021 41.38 44.99
Viola biflora L. 54.4 0.008 47.17 50.79
IV = Indicator value, P = Probability, IVI = Importance Value Index.
ductivity (0.2–0.62dsm−1) and a sandy loam soil texture (Figs. 2–4
and Tables 6–8).
5.4. Cedrus deodara, Viburnum grandiflorum and Achillea
millefolium community
This community can be found at relatively high elevations
(2150–2400 m asl) occurring at six stations (180 quadrats). This is
a tree-dominated community comprising of moist temperate veg-
etation including the principal indicator species Cedrus deodara,
Viburnum grandiflorum and Achillea millefolium from the tree, shrub
Table 4
The indicator species of the Cedrus deodara, Viburnum grandiflorum and Achillea
millefolium Community with their indicator values.
Cedrus deodara Rox ex Lamb. 34.5 0.0574 88.2 61.35
Viburnum grandiflorum Wallich. 49.9 0.0016 31.44 40.67
Achillea millefolium L. 47.2 0.019 47.43 47.315
IV = Indicator Value, P = Probability, IVI = Importance Value Index.
and herb layers, respectively (Table 4). Abies pindrow was the other
notable indicator tree found in this community. The most important
environmental variables responsible for the formation of this com-
munity are mildly acidic soil pH (6.3–6.8), high soil organic matter
(1.07%–1.25%), low soil electrical conductivity (0.26–0.73dsm−1),
moderate soil phosphorous contents (5–6 ppm) and a sandy loam
soil texture (Figs. 2–4 and Tables 6–8).
The main anthropogenic pressure observed on this community
was the collection of medicinal and fodder plants.

Fig. 4. Two Way Cluster Dendrogram generated through PC-ORD Version 5 based on Sorensen measures, showing distribution of 252 plant species in 50 stations and 5 plant
communities (associations).
Table 5
The indicator species of the Abies pindrow, Daphne mucronata and Potentilla fruticosa
Community with their indicator values.
Abies pindrow Royle. 40.5 0.007 179.18 109.84
Daphne mucronata Royle. 75 0.0002 31.43 53.215
Potentilla fruticosa L. 44.4 0.0102 89.27 66.835
IV = Indicator Value, P = Probability, IVI = Importance Value Index.
5.5. Abies pindrow, Daphne mucronata and Potentilla
fruticosa plant community
This was the highest elevation community described in TsFD at
altitudes of 2400–2626 m asl. It was described from five stations
(150 quadrats). Abies pindrow, Daphne mucronata and Potentilla
fruticosa are the characteristic indicator species of this commu-
nity (Table 5). Other diagnostic indicator species are Berberis
orthobotrys, Viburnum grandiflorum, Rumex nepalensis, Drypteris
spp., Euphorbia wallichii, Plantago major and Pteris vittata (Figs. 2–4
and Tables 7 and 8). Due to the high altitude, low temperatures
prevail throughout the growing season. The important environ-
mental variables were soil pH (6.6–7.2), soil phosphorous content
(6–8 ppm), soil electrical conductivity (0.2–0.39dsm−1) and soil
organic matter (0.55%–0.75%).
This high altitude plant community has low species richness ( ´˛
diversity) with fewer plant species in comparison with the other
four communities. The near neutral soil pH in the range 6.6–7.0 was
one of most important environmental variables for this community.
Other associated variables were slightly higher soil phosphorous
contents, lower soil organic matter, lower soil electrical conductiv-
ity and sand dominated soil (Figs. 2–4 and Tables 6–8 ).
6. Discussion
The multivariate analyses carried out as part of this study estab-
lished five distinct plant communities in the TsFD study area. Being
located in the Western Himalayan Province, the vegetation was
mainly Sino-Japanese in nature and the communities were clas-
sified on the basis of environmental factors/gradients i.e., soil pH,
soil organic matter, soil phosphorous contents, soil texture, aspect,
altitude and soil electrical conductivity. This allows our results to
be compared with the studies already undertaken in other adja-
cent locations in the Sino Japanese Region (Takhtadzhian, 1997; Ali
and Qaiser 1986; Champion et al., 1965 Khan et al., 2011a,b, 2014;
Mehmood et al., 2015; Shaheen et al., 2015). At lower elevation
ranges the vegetation was of a sub-tropical nature with indica-
tor species including Dodonea viscosa, Punica granatum, Berberis
lyceum and Pinus roxburghii. A similar community was described by
Siddiqui et al. (2009) during a phytosociological survey of the lesser
Himalayan and Hindu Kush ranges of Pakistan. At upper altitudi-
nal ranges, the vegetation contains characteristic species of moist
temperate types of forests, e.g., Pinus wallichiana, Abies pindrow,
Aesculus indica, Prunus padus, Indigofira heterantha, Viburnum gran-
diflorum, Paeonia emodi, Bistorta amplexicaule, Euphorbia wallichii
and Trifolium repens; which could be compared with the assem-
blage reported in the moist temperate Himalaya by Saima et al.,
2009 and Ahmed et al., (2006). Species diversity reached an opti-
mum at middle elevations (1700 m–2200 m), as compared to the
lower locations where there was greater impact of anthropogenic
activities, while at high elevations (2200 m–2626 m) diversity was
lowest mainly due to extreme conditions. Such kinds of species
distributional phenomena have also been observed in other moun-
tainous ecosystems (Anderson et al., 1992; Ahmad et al., 2015).
Moreover an increase in herbaceous vegetation is positively cor-
related to increase in elevation which seems to be a function of
eco-physiological processes associated with these higher eleva-

Table 6
Influence of various environmental variables on top indicator species of each community.
1st Community
SNO BN (IV) P* I.V.I-1 T.I.V.I
Aspect
1 Ziziphus vulgaris Lam. 40.4 0.0126 40.66 40.53
2 Cnicus argyracanthus (DC) Hk.f. 35.5 0.0488 71.49 53.50
3 Euphorbia helioscopia L. 40.3 0.0222 103.98 72.14
4 Poa annua L. 43.3 0.014 91.00 67.15
Soil Electrical Conductivity
1 Melia azedarach L. 85.7 0.0406 36.53 61.11
2 Themeda anathera (Ness) Hack. 85.7 0.0358 114.72 100.21
Soil organic matter Content
1 Punica granatum L. 38.7 0.039 80.11 59.40
2 Rumex nepalensis Spreng. 52.2 0.0188 125.71 88.96
Soil Phosphorous
2 Taraxacum officinale Weber. 39 0.047 127.33 83.16
Soil pH
1 Euclaptus globulus L. 51.3 0.006 44.41 47.85
3 Rosa moschata non J. Herrm. 53 0.001 51.86 52.43
4 Zanthoxylum alatum Roxb. 60.9 0.0006 33.29 47.09
5 Capsella bursa pastoris Moench. 54.7 0.017 48.63 51.66
6 Cynodon dactylon L. 60.8 0.0008 115.76 88.28
7 Euphorbia helioscopia L. 50.2 0.0316 103.98 77.09
8 Medicago denticulata Willd. 55.8 0.0006 92.86 74.33
9 Poa annua L. 54.1 0.0158 91.00 72.55
10 Rumex hastatus D.Don. 48.6 0.0348 73.75 61.17
2nd Community
Aspect
1 Ziziphus vulgaris Lam. 40.4 0.0126 43.4 41.9
2 Chenopodium album L. 37.9 0.0328 82.08 59.99
4 Poa annua L. 43.3 0.014 47.63 45.465
Soil Electrical Conductivity
1 Themeda anathera (Ness) Hack. 85.7 0.0358 36.5 61.1
Soil Organic matter contents
Soil Phosphorous
2 Taraxacum officinale Weber. 39 0.047 83.28 61.14
Soil pH
1 Abies pindrow Royle. 40.5 0.007 98.94 69.72
3 Rosa moschata non J. Herrm. 53 0.001 56.38 54.69
4 Rubus fruticosus Hk.f. 41.9 0.0364 42.54 42.22
5 Zanthoxylum alatum Roxb. 60.9 0.0006 63.26 62.08
6 Achillea millefolium L. 47.2 0.019 58.45 52.825
7 Capsella bursa pastoris Moench. 54.7 0.017 56.95 55.825
8 Cynodon dactylon L. 60.8 0.0008 55.92 58.36
9 Medicago denticulata Willd. 55.8 0.0006 83.36 69.58
10 Poa annua L. 54.1 0.0158 47.63 50.865
11 Rumex hastatus D.Don. 48.6 0.0348 81.3 64.95
Soil texture
2 Nepeta erecta Bh Bth. 45.4 0.0196 68.16 56.78
3 Senecio chrysenthemoides DC. 36.8 0.0428 50.25 43.525
3rd Community
Aspect
1 Quercus incana Roxb. 35 0.0306 36.79 35.89
Soil organic matter contents
Soil phosphorous
1 Quercus incana Roxb. 37.3 0.0392 36.79 37.04
Soil pH
2 Cornus macrophylla Wall. Ex Roxb. 48.6 0.021 41.38 44.99
3 Viburnum grandiflorum Wallich. 49.9 0.0016 52.64 51.27
5 Actaea spicata L. 42.9 0.0362 46.94 44.92
6 Chrysanthimum cenarifolium Trey 60.9 0.0044 35.00 47.95
7 Euphorbia wallichii Hk.f. 58.3 0.0006 89.31 73.80
8 Plantago lanceolata Linn. 44.2 0.0186 36.74 40.47
9 Viola biflora L. 42.9 0.0384 47.17 45.04

Table 6 (Continued)
Soil texture
1 Quercus incana Roxb. 42.7 0.018 36.79 39.74
4 Actaea spicata L. 54.4 0.0078 46.94 50.67
5 Nepeta erecta Bh Bth. 45.4 0.0196 38.40 41.90
6 Plantago lanceolata Linn. 54.4 0.0082 36.74 45.57
7 Viola biflora L. 54.4 0.008 47.17 50.79
4th Community
Soil pH
4 Cedrus deodara Rox ex Lamb. 34.5 0.0574 88.20 61.35
Soil texture
5th Community
Soil organic matter contents
Soil phosphorous
1 Drypteris spp. 43.5 0.0184 40.70 42.10
Soil pH
2 Acacia arabica (Lam.) Willd. 29 0.0352 89.07 59.03
3 Berberis orthobotyrus Bien. Ex Aitch. 69.5 0.0016 33.83 51.67
4 Daphne mucronata Royle. 75 0.0002 31.44 53.22
6 Drypteris spp. 69.7 0.0012 40.70 55.20
7 Euphorbia wallichii Hk.f. 58.3 0.0006 36.05 47.18
8 Plantago major L. 38.7 0.0308 42.10 40.40
9 Potentilla fruticosa L. 44.4 0.0102 89.28 66.84
10 Pteris vittata L. 72 0.0006 37.93 54.97
Soil texture
tions. The findings of this study clearly indicate that the lower
elevational ranges exhibit sub-tropical floristic elements which
gradually change on the one hand to moist temperate types in the
upper ranges, i.e. along the latitudinal gradient, and to subalpine
types near the peaks of the mountains in response to the altitudinal
gradient.
The methods applied in this study allow users to compare multi-
ple classification procedures of the same sites, for authentication of
the information resulting from the analysis. However, in mountain-
ous regions, which are difficult to access, vegetation surveys need
to be conducted rapidly and with limited resources, such as for veg-
etation mapping. In such situations, it may be desirable to survey
the largest possible number of localities, but simplify the fieldwork
protocol by focusing on a small subset of species that have high pre-
dictive value. The use of indicator species to monitor environmental
conditions or to determine habitat or community types is a firmly
established technique for both theoretical and applied purposes
in vegetation ecology in the recent past. Such indicators are used
as indicative of a specific microclimatic condition or environmental
change. The use of a suite of multispecies ecological or environmen-
tal indicators rather than single indicators has been recommended
to increase the reliability of bio-indication systems (Carignan and
Villard 2002; McGEOCH, 1998; Niemi and McDonald 2004; Butler
et al., 2012; Mouillot et al., 2013). In order to determine indicator
species, the characteristic to be predicted is represented in the form
of a classification of the sites, which is compared to the patterns of
distribution of the species found at the sites. For this purpose, Indi-
cator Species Analysis (ISA) takes into account the fact that species
have different niche breadths.
Another important application, of this paper is illustration of
vegetation classification schemes according to the modern rules.
Vegetation types are often defined using the complete compo-
sition of vascular plants (Cáceres et al., 2012). When complete
composition is available, there are several alternatives for assign-
ing vegetation plot records to predefined vegetation types (Van
Tongeren et al., 2008; Tongren and Hennekens 2008; Cáceres and
Legendre, 2009), which are preferable to the approach presented
here. When an indicator value index is used, the method provides
the set of site-groups that best matches the observed distribu-
tion pattern of the species. When applied to community types, it
allows one to distinguish those species that characterize individ-
ual types from those that characterize the relationships between
them. This distinction is useful to determine the number of types
that maximizes the number of indicator species. Consideration of
combinations of groups of sites provides an extra flexibility to qual-
itatively model the habitat preferences of the species of interest
(Acker, 1990). If at a given site, one finds a species combination
with high predictive value, the site can be assigned with confi-
dence to the indicated type. If none of the valid indicators is found,
then a full vegetation plot may need to be established. Users of
the method should bear in mind that when site groups have been
defined using species composition data, they are by definition non
independent from species. In these cases, the indicator value statis-
tic will be larger than the value expected under the null hypothesis
of independence, leading to a high rate of rejection in inferential
tests (De Cáceres et al., 2010). When confidence intervals are being
used to assess the uncertainty of the estimation, however, they
are still valid. A variety of environmental gradients determines the
boundaries of altitudinal zones found on mountains, ranging from
direct effects of temperature and precipitation to indirect charac-
teristics of the mountain itself, as well as biological interactions
of the species. Zonation produces distinct communities along an
elevation gradient (Haq et al., 2015; Khan et al., 2015). In addition
to environmental factors, other factors related to historical plant
geography may also be responsible for the determination of a plant
community (Poore, 1955).
The Western Himalayan TsFD in Pakistan is a highly diverse
region, particularly in terms of the wide range of natural for-

344W.Khanetal./EcologicalIndicators71(2016)336–351
Table 7
Results of Indicator Species Analysis (ISA) through PC-ORD, showing top Indicator plant species (with bold font) of each of the five plant communities (1–5) at a threshold level of Indicator 30% and Monte Carlo tests of significance
for observed maximum indicator value of species (P value ≤ 0.05).
S NO BOTANICAL NAME Melia, Punica and Euphorbia
Community
Ziziphus, Zanthoxylum and
Rumex Community
Quercus, Cornus and Viola
Community
Cedrus, Viburnum and
Achillea Community
Abies, Daphne and Potentilla
Community
Group was defined by
value of Electrical
Conductivity
value of Aspect
value of Texture Classes
value of Soil pH
value of Soil pH
Max val (IV) P* Max val (IV) P* Max val (IV) P* Max val (IV) P* Max val (IV) P*
1 5
Abies pindrow Royle. 0 64.6 0.1394 3 21.4 0.8276 3 38 0.0614 7 40.5 0.007 7 40.5 0.007
2 Acacia arabica (Lam.) Willd. 1 46.2 0.1212 2 12 0.3653 1 8.7 0.7578 4 29 0.0352 4 29 0.0352
3 Acacia nilotica (Linn.) Delile 1 38.7 0.2861 2 19.6 0.2094 4 17 0.3651 4 17.6 0.2747 4 17.6 0.2747
4 Acer caesium Wall. Ex Brandis 0 29.2 1 3 19.5 0.3041 2 23.6 0.2364 6 33.5 0.0696 6 33.5 0.0696
5 Aesculus indica (Comb) Hook. 0 18.7 1 4 9.8 0.7197 3 25.4 0.1576 7 44.3 0.0082 7 44.3 0.0082
6 Ailanthus altissima (Mill.) Swingle 1 75 0.1292 2 23 0.2805 1 14.3 0.8458 4 32.7 0.0912 4 32.7 0.0912
7 Broussonetia papyrifera Vent. 1 38.7 0.2907 1 8.7 0.7584 4 8.3 0.9144 5 17.5 0.2833 5 17.5 0.2833
8 Cedrela serrata Royle 0 14.6 1 4 7.5 0.8522 2 8 1 6 33.3 0.0728 6 33.3 0.0728
9 Cedrela toona Roxb. Ex Rottl. & Willd. 0 8.3 1 4 16.3 0.2222 4 8.5 0.8486 6 10.6 0.5415 6 10.6 0.5415
10 4
Cedrus deodara Rox ex Lamb. 0 72.9 0.0828 1 30.4 0.2208 3 33.4 0.1732 6 34.5 0.0574 6 34.5 0.0574
11 Celtus australis L. 0 22.9 1 3 9.3 0.8506 3 23.8 0.173 6 34.7 0.0954 6 34.7 0.0954
12 3
Cornus macrophylla Wall. Ex Roxb. 0 29.2 1 4 17.4 0.3559 6 48.6 0.021 6 48.6 0.021 6 48.6 0.021
13 Cotoneaster minuta Klotz. 0 27.1 1 4 11.9 0.8758 3 36.2 0.0592 6 20.1 0.3229 6 20.1 0.3229
14 Dalbergia sissoo Roxb. 0 6.2 1 1 6.6 0.8262 4 4.9 1 6 6.2 1 6 6.2 1
15 1
Diospyros kaki L. 1 87.3 0.0364 3 7.5 0.9474 2 21.5 0.2539 4 16.3 0.3527 4 16.3 0.3527
16 Diospyros lotus L. 0 29.2 1 2 13.5 0.6741 2 31 0.1158 4 23.9 0.3055 4 23.9 0.3055
17 Eucalyptus globulus L. 0 16.7 1 2 20.1 0.2034 1 28.6 0.1006 4 51.3 0.006 4 51.3 0.006
18 Ficus carica L. 1 64 0.3373 2 28.5 0.1582 3 22.8 0.6981 5 38.1 0.1156 5 38.1 0.1156
19 Ficus palmata Forssk. 1 51 0.2373 2 28.5 0.1482 5 4.9 1 3 21.9 0.3052 3 21.9 0.3052
20 Grewia optiva Drum. ex. Burret 0 14.6 1 2 6.7 0.957 1 10.7 0.6019 6 10.7 0.776 6 10.7 0.776
21 Ilex dipyrena Walld. 0 12.5 1 3 10.5 0.4767 3 13.6 0.2869 6 28.6 0.0556 6 28.6 0.0556
22 Jacaranda mimosifolia D. Don. 0 16.7 1 3 8.3 0.808 2 17.6 0.3257 5 7.9 0.9198 5 7.9 0.9198
23 Juglans regia L. 0 25 1 1 13.7 0.6839 1 20.5 0.3115 6 10.1 0.8922 6 10.1 0.8922
24 1
Melia azedarach L. 1 85.7 0.0406 1 9.2 0.796 1 8.6 0.8574 5 32.8 0.1062 5 32.8 0.1062
25 Morus alba L. 1 81.4 0.0686 3 14.9 0.4901 4 14.5 0.6681 4 25.1 0.2753 4 25.1 0.2753
26 Morus nigra L. 0 33.3 0.5541 2 23.8 0.225 2 28.5 0.1678 5 21.9 0.4245 5 21.9 0.4245
27 Olea ferruginea Royle. 0 18.7 1 2 21 0.1094 2 21 0.2803 5 21.7 0.15 5 21.7 0.15
28 Pinus roxburghii Sargent. 1 37.5 0.3279 2 18.6 0.2729 4 14.7 0.4597 5 17.9 0.211 5 17.9 0.211
29 Pinus wallichiana A.B.Jackson. 0 52.1 1 1 24.4 0.7209 2 28 0.6961 6 30.2 0.1942 6 30.2 0.1942
30 Pistacia integerrima J.L. 1 38.7 0.2917 2 40.4 0.0086 1 13.8 0.4455 4 17.7 0.2623 4 17.7 0.2623
31 Platanus orientalis L. 0 6.2 1 1 19.7 0.102 4 4.9 1 4 10.5 0.5263 4 10.5 0.5263
32 Populus ciliata Wall. Ex Royle 1 38.7 0.2971 3 10.6 0.6185 1 13.8 0.4461 7 13.9 0.6587 7 13.9 0.6587
33 Populus nigra L. 0 20.8 1 3 6.9 1 3 24 0.207 6 47.6 0.0056 6 47.6 0.0056
34 Prunus armeniaca L. 1 36.4 0.3627 3 10.2 0.7532 1 18.4 0.3643 5 21.8 0.2156 5 21.8 0.2156
35 Prunus domestica L. 1 33.3 0.4585 2 13.5 0.6791 2 16.9 0.5319 5 18.3 0.4079 5 18.3 0.4079
36 Prunus padus (Hk) f. 0 35.4 0.5445 4 21.3 0.3533 4 11.9 1 7 60 0.0008 7 60 0.0008
37 Prunus persica (Linn.) Batsch 0 10.4 1 2 8.3 0.6843 4 6.4 1 7 17.1 0.3649 7 17.1 0.3649
38 Pyrus pashia D.Don. 0 43.7 0.4931 2 20.9 0.3993 2 21.2 0.4907 5 21.9 0.5643 5 21.9 0.5643
39 Quercus dilatata Lindl. Ex Royle 0 10.4 1 1 15.3 0.1896 2 10.4 0.6963 6 10.2 0.5075 6 10.2 0.5075
40 3
Quercus incana Roxb. 0 31.2 1 4 35 0.0306 3 42.7 0.018 6 27.2 0.2402 6 27.2 0.2402
41 Robinia pseudoacacia L. 1 65.8 0.2861 1 26.3 0.3315 2 16.7 0.933 5 18.4 0.9662 5 18.4 0.9662
42 Salix alba L. 0 4.2 1 1 9.2 0.4423 1 3.4 1 4 12.8 0.3253 4 12.8 0.3253
43 Salix angustifolia Willd. 0 29.2 1 3 13.8 0.6169 2 27.7 0.1248 6 40.7 0.0266 6 40.7 0.0266
44 Salix denticulata N.J. Anderss. 1 38.7 0.2901 2 21.7 0.1264 3 8.3 0.8554 7 14 0.6089 7 14 0.6089
45 Sarcococca saligna (Don) Muell. 0 10.4 1 3 6.9 0.829 2 10 0.8286 6 15.1 0.4537 6 15.1 0.4537
46 Sorbaria tomentosa (Lindl.) 0 31.2 1 4 20.5 0.2977 3 37.6 0.056 6 32.5 0.0796 6 32.5 0.0796
47 Staphylea emodi Wall. Ex Brandis. 0 4.2 1 2 12.3 0.3013 2 16.6 0.2394 5 2.6 1 5 2.6 1
48 Taxus wallichiana (Zucc.) 0 2.1 1 1 12.5 0.2565 1 5.9 0.5203 7 33.3 0.0608 7 33.3 0.0608
49 Ulmus wallichiana Planch. 0 14.6 1 4 8.7 0.7199 3 11.7 0.4947 7 17.9 0.2627 7 17.9 0.2627

W.Khanetal./EcologicalIndicators71(2016)336–351345
Table 7 (Continued)
Community
Rumex Community
Community
Achillea Community
Community
value of Electrical
Conductivity
value of Aspect
value of Soil pH
value of Soil pH
50 Vincetoxicum arnottianum Wight. 0 2.1 1 3 4.5 1 1 5.9 0.5067 5 5 0.5763 5 5 0.5763
51 2
Ziziphus vulgaris Lam. 1 38.7 0.2951 2 40.4 0.0126 2 7.4 1 4 33.3 0.0962 4 33.3 0.0962
52 Abelia triflora R. Br. 0 22.9 1 4 25.5 0.1018 4 9.9 0.7802 6 27.3 0.2022 6 27.3 0.2022
53 Andrachne cordifolia (Don) Muell 0 45.8 0.5013 3 19.6 0.5621 3 30.1 0.189 6 37.6 0.098 6 37.6 0.098
54 Arundo donaxL. 0 16.7 1 3 13.8 0.3895 2 22.8 0.2224 4 17.7 0.2621 4 17.7 0.2621
55 Astragalus flaccidum (Royle) 0 29.2 1 4 10.5 0.9616 3 23 0.2623 6 23.1 0.3347 6 23.1 0.3347
56 Berberis lycium Royle. 1 63.2 0.3557 2 25.1 0.4101 2 25.9 0.3625 5 28.7 0.3465 5 28.7 0.3465
57 Berberis orthobotrys Bien. Ex Aitch. 0 25 1 1 16.2 0.3685 2 18.1 0.4527 7 69.5 0.0016 7 69.5 0.0016
58 Berberis pachyacantha Koehne, Deutsche Dender. 0 12.5 1 2 8.9 0.6181 3 61.9 0.0026 6 28.6 0.058 6 28.6 0.058
59 Berberis parkeriana C.K.Schn. 0 10.4 1 4 5.1 1 2 10.4 0.6967 6 23.8 0.0546 6 23.8 0.0546
60 Buddleja asiatica Lour. 0 10.4 1 4 9 0.5985 3 10.3 0.7341 7 21 0.239 7 21 0.239
61 Buddleja crispa Bth., 0 25 1 3 11.2 0.8686 3 22.2 0.243 6 20.8 0.2753 6 20.8 0.2753
62 Buxus papillosa C.K.Schn. 1 38.7 0.3043 3 18.3 0.2611 2 18.3 0.2869 6 14.6 0.4987 6 14.6 0.4987
63 Clematis amplexicaulis Edgew. 0 16.7 1 3 9.2 0.6717 2 34.3 0.033 5 10.2 0.8562 5 10.2 0.8562
64 Clematis montana Buch.- 0 20.8 1 3 15.9 0.4223 3 52.3 0.0072 6 30.2 0.1184 6 30.2 0.1184
65 Cuscuta reflexa Roxb Amar. 0 14.6 1 2 6.7 0.9576 2 24.2 0.18 4 19.2 0.1854 4 19.2 0.1854
66 5
Daphne mucronata Royle. 0 20.8 1 4 17.9 0.2631 1 24 0.1772 7 75 0.0002 7 75 0.0002
67 Debregeasia salicifolia (D. Don) Rendle, 0 8.3 1 4 7.9 0.7516 1 6.9 1 5 5.1 1 5 5.1 1
68 Desmodium gangeticum (Linn) DC. 0 16.7 1 4 6.9 0.8824 3 23.1 0.1912 6 11.2 0.8064 6 11.2 0.8064
69 Desmodium podocarpum DC. 1 35.3 0.4151 3 10.3 0.7688 2 30.6 0.0726 6 20.8 0.2753 6 20.8 0.2753
70 Dodonea viscosa Jack 1 36.4 0.3637 2 17.2 0.3503 4 10 0.7027 5 21.8 0.2128 5 21.8 0.2128
71 Euonymus hamiltonianus Wall. 0 8.3 1 3 9.2 0.5683 2 12.3 0.4141 6 19 0.2555 6 19 0.2555
72 Hedera nepalensis K. Koch., 0 22.9 1 3 19.4 0.1806 2 35.9 0.0594 4 28 0.1786 4 28 0.1786
73 Indigofera gerardiana Wall. 1 67.6 0.2498 2 29.1 0.1618 2 17.4 0.8532 5 28.8 0.23 5 28.8 0.23
74 Indigofera heterantha Wall. Ex Brandis. 0 35.4 0.5485 4 17.6 0.5315 3 17.6 0.5917 7 32 0.0844 7 32 0.0844
75 Isodon coetsa (Spr.) 0 10.4 1 1 16.2 0.1512 1 20 0.1654 4 7.6 1 4 7.6 1
76 Lonicera hispida Pall. Loony 1 35.3 0.3987 4 7.9 0.9562 3 7 1 5 13 0.6315 5 13 0.6315
77 Lonicerar bicolor KI. & Garcke., 0 33.3 0.5533 4 14.7 0.6105 2 24.8 0.229 4 22.1 0.3799 4 22.1 0.3799
78 Lonicerar quinquelocularis Hardw. 0 22.9 1 3 17.5 0.2609 4 7.1 0.9468 6 17.1 0.3257 6 17.1 0.3257
79 Parrotiopsis jacquemontiana (Dcne.) 0 4.2 1 4 4 1 4 8.3 0.6773 6 9.5 0.6573 6 9.5 0.6573
80 Paeonia emodi Wall. 0 12.5 1 3 4.7 1 2 9.4 0.8308 6 28.6 0.0584 6 28.6 0.0584
81 1
Punica granatum L. 0 38.7 0.039 1 20.8 0.4205 1 29.7 0.1894 4 58.9 0.0008 4 58.9 0.0008
82 Rhamnus purpurea Edgew. 0 12.5 1 4 8.1 0.7493 3 12.7 0.3991 6 28.6 0.0576 6 28.6 0.0576
83 Rhus punjabensis Stewart ex Brandis., 0 10.4 1 3 13.3 0.3341 3 26.1 0.1036 6 23.8 0.0516 6 23.8 0.0516
84 Rosa moschata non J. Herrm. 1 68.6 0.2296 2 28.9 0.183 4 17.7 0.8222 4 53 0.001 4 53 0.001
85 Rosa webbiana Wall. Ex. Royle., 0 25 1 3 12.9 0.7676 3 19 0.3845 6 25 0.2412 6 25 0.2412
86 Rubus ellipticus Smith in Rees., 0 29.2 1 4 13.9 0.6003 2 13.4 0.8316 6 18.2 0.4655 6 18.2 0.4655
87 Rubus fruticosus Hk.f. 1 30.8 1 2 22.9 0.2585 1 16.7 0.6253 4 41.9 0.0364 4 41.9 0.0364
88 Rubus macilentus Camb. In Jacq. 0 18.7 1 4 14.4 0.4645 3 55.3 0.005 7 15.5 0.3913 7 15.5 0.3913
89 Rubus ulmifolius Schott in Oken., 0 8.3 1 3 9.2 0.5689 2 11.7 0.5573 6 10.6 0.5459 6 10.6 0.5459
90 Sageretia brandrethiana Aitch., J.L.S. 0 12.5 1 3 10.5 0.4841 2 9.4 0.8312 5 7.7 0.8068 5 7.7 0.8068
91 Skimmia laureola D.C. 0 10.4 1 1 14.6 0.2252 2 10 0.8306 7 91.3 0.0004 7 91.3 0.0004
92 Solanum pseudocapsicum L. 0 12.5 1 2 7.3 0.8886 2 9.4 0.8406 4 38.6 0.0488 4 38.6 0.0488
93 Spiraea gracilis Maxim., 0 16.7 1 1 11.1 0.5403 4 11.2 0.5235 6 16.7 0.2883 6 16.7 0.2883
94 Syringa emodi Wall. Ex G. Don., 0 12.5 1 2 8.5 0.7175 4 9.8 0.7273 6 28.6 0.059 6 28.6 0.059
95 Viburnum cotinifolium D.Don. 0 22.9 1 1 7.1 1 3 20.1 0.3045 6 11.9 0.6743 6 11.9 0.6743
96 4
Viburnum grandiflorum Wallich. 0 50 0.4963 4 20.5 0.4685 3 44.1 0.0288 7 49.9 0.0016 7 49.9 0.0016
97 Vitex negundo Linn. 0 18.7 1 1 16.2 0.4049 2 21 0.2781 7 44.1 0.0242 7 44.1 0.0242
98 2
Zanthoxylum alatum Roxb. 1 28.6 1 4 60.9 0.0006 1 22.2 0.3861 4 60.9 0.0006 4 60.9 0.0006
99 Ziziphus jujuba Lam. 1 40 0.2561 2 24.7 0.055 1 9.1 0.7568 5 19.3 0.1232 5 19.3 0.1232

Table 7 (Continued)
Community
Rumex Community
Community
Achillea Community
Community
value of Electrical
Conductivity
value of Aspect
value of Soil pH
value of Soil pH
100 4
Acmountainea millefolium L. 0 47.9 0.4871 3 22.3 0.3395 3 44.9 0.0276 6 47.2 0.019 6 47.2 0.019
101 Achyranthus spp 0 20.8 1 1 7.7 0.911 2 15.6 0.5213 7 12.4 0.7686 7 12.4 0.7686
102 Aconitum violaceum Jacq. ex Stapf 1 31.6 0.5137 3 12.9 0.7417 2 25.5 0.1904 5 14.4 0.6675 5 14.4 0.6675
103 Actaea spicata L. 0 18.7 1 3 8.2 0.826 3 54.4 0.0078 6 42.9 0.0362 6 42.9 0.0362
104 Adiantum venustum Linn. Sraj, 0 14.6 1 1 12.5 0.3813 2 8 1 5 8.7 0.841 5 8.7 0.841
105 Aegopodium burttii E. 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1
106 Ainsliaea aptera DC., 0 16.7 1 4 7.9 0.8616 3 8.7 0.7536 6 29 0.1094 6 29 0.1094
107 Ajuga bracteosa L. 0 39.6 0.5121 2 20.3 0.4591 1 17.8 0.5785 4 31.6 0.109 4 31.6 0.109
108 Anemone falconeri T. T. in Hk., 1 34.3 0.4315 3 12 0.8062 2 34.5 0.071 5 20.8 0.2643 5 20.8 0.2643
109 Anemone tetrasepaqla Royle. 0 27.1 1 3 14.9 0.4967 3 19.1 0.3817 6 23 0.3027 6 23 0.3027
110 Anemone vitifolia Ham. DC. 0 18.7 1 4 12.4 0.5475 3 29.1 0.1042 6 18.7 0.2132 6 18.7 0.2132
111 Aquilegia missouriensis Royle. 0 10.4 1 3 6.9 0.8228 3 13.9 0.3295 6 15.1 0.4635 6 15.1 0.4635
112 Aquilegia pubiﬂora Wall ex Royle 0 20.8 1 4 14 0.5655 3 7.3 1 6 30.2 0.1156 6 30.2 0.1156
113 Argemone mexicana L. 0 10.4 1 3 22.7 0.1182 2 39.6 0.0116 4 7.7 0.8108 4 7.7 0.8108
114 Arisaema ﬂavum Forrsk. 0 18.7 1 1 9.9 0.6905 3 30.3 0.0714 7 15.5 0.3897 7 15.5 0.3897
115 Arisaema jacquemontii Blume. 0 16.7 1 1 8.4 0.7846 3 8.3 0.8468 6 16.7 0.2875 6 16.7 0.2875
116 Arisaema utile Hk.f., 0 4.2 1 3 9.1 0.6235 4 8.3 0.6669 6 9.5 0.6489 6 9.5 0.6489
117 Artemisia absinthium L. 1 80 0.0772 2 14.4 0.5495 4 13.4 0.8054 5 32.5 0.0922 5 32.5 0.0922
118 Aster molliusculus (DC.) 0 16.7 1 3 9.2 0.6837 3 8.5 0.803 6 29 0.1038 6 29 0.1038
119 Atropa acuminata Royle., 0 4.2 1 4 13.3 0.2693 4 8.3 0.6591 6 9.5 0.6601 6 9.5 0.6601
120 Bergenia ciliata (Haw) Sternb. 0 18.7 1 4 16.5 0.3785 4 7.4 1 6 20.3 0.1814 6 20.3 0.1814
121 Bistorta amplexicaule (D.Don) Greene. 0 25 1 1 17 0.2999 3 19.5 0.3529 7 35.1 0.0624 7 35.1 0.0624
122 Bupleurum candollei Wall. Ex DC., 1 38.7 0.3021 1 8.4 0.7776 3 8.3 0.8526 5 15.9 0.4691 5 15.9 0.4691
123 Bupleurum falcatum L. 0 14.6 1 3 9.4 0.6151 3 28.3 0.0924 6 33.3 0.0764 6 33.3 0.0764
124 Bupleurum jacundum Kurz., 0 18.7 1 1 9.9 0.6775 2 16.5 0.4129 6 14.8 0.5059 6 14.8 0.5059
125 Bupleurum lanceolatum Wall. Ex DC., 0 12.5 1 3 8.9 0.6757 3 9.9 0.6347 6 19.7 0.2068 6 19.7 0.2068
126 Calamintha vulgaris (L.) 0 10.4 1 3 13.3 0.3453 3 10.3 0.7329 6 8.4 0.7457 6 8.4 0.7457
127 Cannabis sativa L. 1 80 0.0742 2 26.8 0.1288 4 17.4 0.5403 5 28.8 0.1992 5 28.8 0.1992
128 Capsella bursa pastoris Moench. 1 30 1 1 23 0.2625 1 32.5 0.1162 4 54.7 0.017 4 54.7 0.017
129 Capsicum annuum L. 0 10.4 1 2 28.7 0.0528 2 51.4 0.0052 4 23.1 0.1236 4 23.1 0.1236
130 Caryopteris odorata (Ham) B. L 0 12.5 1 4 11.9 0.4107 2 9.4 0.8358 6 12.5 0.6353 6 12.5 0.6353
131 Chenopodium album L. 0 50 0.4993 1 37.9 0.0328 2 35.8 0.086 5 23.3 0.3339 5 23.3 0.3339
132 Chrysanthemum cinerariifolium Trey 0 29.2 1 1 20.6 0.2851 3 23.4 0.2216 7 60.9 0.0044 7 60.9 0.0044
133 Cichorium intybus Linn. 0 8.3 1 1 5.5 1 2 12.3 0.4313 6 10.6 0.5469 6 10.6 0.5469
134 Cirsium argyracanthum DC. 0 8.3 1 3 9.2 0.5719 3 11.7 0.4487 6 19 0.2639 6 19 0.2639
135 Cnicus argyracanthus (DC) Hk.f. 1 73.8 0.135 2 35.5 0.0488 4 17.3 0.6019 4 31.6 0.1054 4 31.6 0.1054
136 Colchicum luteum Baker 0 14.6 1 3 7.5 0.8028 3 8.8 0.85 5 11.7 0.7119 5 11.7 0.7119
137 Convolvulus prostratus Forssk 0 4.2 1 4 13.3 0.2767 1 3.4 1 5 2.6 1 5 2.6 1
138 Conyza canadensis (L.) 0 14.6 1 3 12.3 0.4241 2 24.9 0.1574 6 7.9 0.9102 6 7.9 0.9102
139 Coriandrum sativum Linn. 0 6.2 1 2 13.7 0.1622 4 4.9 1 6 6.2 1 6 6.2 1
140 Corydalis diphylla Wall., 0 4.2 1 4 4 1 4 8.3 0.6663 6 9.5 0.6507 6 9.5 0.6507
141 Corydalis stewartii Fedde, 0 16.7 1 3 18.3 0.2691 2 34.8 0.0284 6 29 0.1076 6 29 0.1076
142 Cynodon dactylon L. 1 73.8 0.1414 2 34 0.0756 2 22.8 0.3449 4 60.8 0.0008 4 60.8 0.0008
143 Cyperus rotundusL. 0 20.8 1 2 17.6 0.2937 4 9.2 0.7634 7 14.4 0.4945 7 14.4 0.4945
144 Datura stramonium L. 0 10.4 1 4 18.2 0.1282 4 6.4 1 6 15.1 0.4707 6 15.1 0.4707
145 Dicliptera roxburghiana Nees in Wall., 0 11.4 1 4 17.2 0.1182 5 9.2 0.8634 6 14.4 0.4945 6 14.4 0.4945
146 Dioscorea bulbifera Linn. 0 4.2 1 4 4 1 1 3.4 1 5 2.6 1 5 2.6 1
147 Dipsacus sativus (Linn.) Honck. 0 6.2 1 4 9.9 0.5557 2 14.1 0.3019 6 14.3 0.3277 6 14.3 0.3277
148 Dipsacus strictus D.Don., 1 44.4 0.1632 4 7.9 0.7455 4 16.7 0.1658 5 11.4 0.4325 5 11.4 0.4325
149 Dryopteris spp 0 25 1 1 16.6 0.3279 3 20.9 0.3021 7 69.7 0.0012 7 69.7 0.0012

W.Khanetal./EcologicalIndicators71(2016)336–351347
Table 7 (Continued)
Community
Rumex Community
Community
Achillea Community
Community
value of Electrical
Conductivity
value of Aspect
value of Soil pH
value of Soil pH
150 Duchesnea indica (Andr.) Focke. 1 66.7 0.2735 2 27.5 0.2392 2 33.6 0.1124 4 39.1 0.0944 4 39.1 0.0944
151 Echinops niveus Wall. Ex DC., 0 10.4 1 4 5.1 1 2 10.9 0.5645 6 23.8 0.0562 6 23.8 0.0562
152 Elaeagnus parvifolia Wall 0 6.2 1 3 5.2 1 3 17.8 0.1696 6 14.3 0.3287 6 14.3 0.3287
153 Ephedra gerardiana Wall. ex Stapf 0 8.3 1 4 16.3 0.2104 2 11.7 0.5537 6 19 0.2643 6 19 0.2643
154 Epilobium royleanumHausskn., 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1
155 Epipactis helleborine (L.) 0 4.2 1 4 13.3 0.2693 4 8.3 0.6591 6 9.5 0.6601 6 9.5 0.6601
156 ErIgeron roylei DC., 0 6.2 1 1 6.6 0.8248 4 12.5 0.5849 6 14.3 0.3333 6 14.3 0.3333
157 Eulophia hormusji Du. 1 42.9 0.198 3 6.9 0.8234 3 66.1 0.0012 6 8.4 0.7469 6 8.4 0.7469
158 1
Euphorbia helioscopia L., 2 40.3 0.0222 2 40.3 0.0222 4 15.2 0.7281 4 50.2 0.0316 4 50.2 0.0316
159 Euphorbia hirta L., 0 10.4 1 1 16.2 0.1448 2 10.4 0.7003 4 7.6 1 4 7.6 1
160 Euphorbia wallichii Hk.f., 0 37.5 0.5433 4 21.9 0.3387 3 31.3 0.1468 7 58.3 0.0006 7 58.3 0.0006
161 Foeniculum vulgare Mill. 0 8.3 1 1 5.5 1 4 8.5 0.8264 4 25.8 0.0612 4 25.8 0.0612
162 Fragaria nubicola L. 0 27.1 1 1 15 0.4825 3 21.1 0.3017 6 20.1 0.3107 6 20.1 0.3107
163 Galium aparine L. 1 31.6 0.5245 2 27.5 0.0882 2 15.3 0.6819 5 32.7 0.0804 5 32.7 0.0804
164 Galium asperifolium Wall., 0 12.5 1 1 27.5 0.0236 3 9.4 0.8522 6 8.9 0.7117 6 8.9 0.7117
165 Galium elegans Wall. In Roxb., 0 8.3 1 4 5.9 0.8762 1 6.9 1 6 10.6 0.5383 6 10.6 0.5383
166 Galium hirtiflorum DC., 0 2.1 1 3 4.5 1 3 25 0.0856 6 4.8 1 6 4.8 1
167 Geranium wallichianum D. Don ex Sweet, 0 16.7 1 3 22.8 0.077 2 17.9 0.3011 5 15.9 0.4655 5 15.9 0.4655
168 Girardinia palmata (Forssk.) 0 6.2 1 3 5.2 1 4 4.9 1 4 10.4 0.5833 4 10.4 0.5833
169 Gnaphalium affine D. Don., 0 4.2 1 4 4 1 4 8.3 0.6773 6 9.5 0.6573 6 9.5 0.6573
170 Heliotropium paniculatum (R.Br.) 0 4.2 1 4 4 1 4 8.3 0.6613 6 9.5 0.6503 6 9.5 0.6503
171 Heracleum candicans Wall. ex. DC., 0 6.2 1 3 13.6 0.2442 2 14.1 0.3179 6 14.3 0.3277 6 14.3 0.3277
172 Hyoscyamus niger L. 0 4.2 1 4 4 1 2 15.5 0.3165 5 10 0.4787 5 10 0.4787
173 Hypericum oblongifolium Choisy., 0 2.1 1 4 6.7 0.5607 4 4.2 1 6 4.8 1 6 4.8 1
174 Hypericum perforatum L. 0 2.1 1 1 12.5 0.2669 4 4.2 1 6 4.8 1 6 4.8 1
175 Impatiens balsamina L. 0 10.4 1 2 9.9 0.5267 3 13.9 0.3377 6 23.8 0.0538 6 23.8 0.0538
176 Impatiens bicolor Royle. 0 8.3 1 4 7.9 0.7518 2 11.7 0.5535 6 19 0.2563 6 19 0.2563
177 Impatiens edgworthii Hk.f., 0 4.2 1 1 9.2 0.4437 4 8.3 0.6679 6 9.5 0.6635 6 9.5 0.6635
178 Impatiens flemingii Hk.f., 0 12.5 1 4 11.9 0.4019 3 36.6 0.0266 6 28.6 0.0522 6 28.6 0.0522
179 Jasminum officinale Linn 0 8.3 1 1 17.3 0.1608 3 16.7 0.162 4 9 0.5709 4 9 0.5709
180 Gerbera gossypina (Royle.) 0 16.7 1 2 6.2 0.9486 4 11.3 0.5151 4 17.5 0.2795 4 17.5 0.2795
181 Lactuca brunoniana (Wall. ex DC.) 0 2.1 1 3 4.5 1 2 20 0.1836 6 4.8 1 6 4.8 1
182 Lavatera Cashmeriana Camb., 0 4.2 1 3 9.1 0.6281 3 13.9 0.3299 6 9.5 0.6633 6 9.5 0.6633
183 Lecanthus peduncularis (Royle.) 0 3.2 1 3 8.1 0.5281 3 21 0.1936 5 9.2 0.5709 5 9.2 0.5709
184 Lepidium sativum L. 0 16.7 1 2 6.2 0.9432 3 10.2 0.6793 6 7.2 1 6 7.2 1
185 Lyonia ovalifolia (Wall.) 0 4.2 1 1 8.2 0.7357 1 3.4 1 6 9.5 0.6603 6 9.5 0.6603
186 Malcolmia africana (L.) 0 6.2 1 4 9.9 0.5499 3 18.7 0.0976 7 25.9 0.0778 7 25.9 0.0778
187 Malva neglecta Wallr. 0 25 1 1 17 0.2963 2 14.4 0.6871 4 13.5 0.5689 4 13.5 0.5689
188 Malva sylvestris L. 0 12.5 1 3 10.5 0.4733 3 9.4 0.8428 6 12.5 0.6399 6 12.5 0.6399
189 Medicago denticulata Willd. 1 70.6 0.188 2 30.5 0.136 2 23.8 0.3863 4 55.8 0.0006 4 55.8 0.0006
190 Mentha longifolia (Linn.), Huds 1 42.9 0.1886 2 9.4 0.5883 4 6.4 1 4 23.1 0.116 4 23.1 0.116
191 Micromeria biflora Benth. 0 2.1 1 4 6.7 0.5475 4 4.2 1 5 5 0.5753 5 5 0.5753
192 Myosotis asiatica Schischk. 0 10.4 1 4 5.1 1 2 10.9 0.5533 6 15.1 0.4595 6 15.1 0.4595
193 Myrsine africana Linn. 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1
194 Nepeta erecta Bh Bth. 1 28.6 1 3 16.9 0.6419 3 45.4 0.0196 5 19.4 0.6119 5 19.4 0.6119
195 Nerium oleander Linn. 0 2.1 1 4 6.7 0.5607 4 4.2 1 6 4.8 1 6 4.8 1
196 Oenothera rosea Soland., 0 47.9 0.4927 1 23.2 0.2975 3 25.2 0.4055 6 18.6 0.7864 6 18.6 0.7864
197 Onychium contiguum Wall. ex Hope., 0 8.3 1 4 16.3 0.2204 4 8.5 0.8464 6 19 0.2549 6 19 0.2549
198 Orchid sp. 0 7.3 1 4 15.3 0.2104 3 4.5 1 6 4.8 1 6 4.8 1
199 Otostegia limbata (Benth.) Boiss. 0 4.2 1 3 9.1 0.6299 3 50 0.0052 6 9.5 0.6529 6 9.5 0.6529

Table 7 (Continued)
Community
Rumex Community
Community
Achillea Community
Community
value of Electrical
Conductivity
value of Aspect
value of Soil pH
value of Soil pH
200 Oxalis corniculata L. 1 27.9 1 2 21 0.4087 3 14.7 0.9402 5 34.6 0.0896 5 34.6 0.0896
201 Papaver somniferum.Linn 0 2.1 1 3 4.5 1 2 20 0.1836 6 4.8 1 6 4.8 1
202 Phalaris minor Retz., 0 6.2 1 3 5.2 1 2 14.1 0.3103 6 14.3 0.3355 6 14.3 0.3355
203 Phytolacca latbenia (Moq.) 0 6.2 1 1 7.2 0.6429 4 12.5 0.5735 6 14.3 0.3259 6 14.3 0.3259
204 Pimpinella acuminata (Edgew.) 0 2.1 1 1 6.2 0.5429 4 4.5 1 7 4.8 1 7 4.8 1
205 Plectranthus rugosus Wall. 0 2.1 1 4 6.7 0.5553 1 5.9 0.5141 6 4.8 1 6 4.8 1
206 Plantago lanceolata Linn. 0 18.7 1 3 7.5 0.9476 3 54.4 0.0082 7 44.2 0.0186 7 44.2 0.0186
207 Plantago major L. 0 25 1 4 25.5 0.1282 1 9.1 0.946 7 38.7 0.0308 7 38.7 0.0308
208 Poa annua L. 1 80 0.0778 2 43.3 0.014 2 16.2 0.6035 4 54.1 0.0158 4 54.1 0.0158
209 Podophyllum emodi Wall. Ex Royle. 0 6.2 1 1 7.2 0.6343 3 12.7 0.4313 6 14.3 0.3287 6 14.3 0.3287
210 Podophyllum hexandrum Royle. 0 18.7 1 1 7.9 0.9032 1 9.2 0.784 7 13.3 0.5573 7 13.3 0.5573
211 Polygonatum verticillatum All., 0 8.3 1 3 9.2 0.5653 3 15.3 0.3045 4 8.8 0.7936 4 8.8 0.7936
212 Polygonum amplexicaule D. Don 1 36.4 0.3615 1 29.9 0.0718 4 12.9 0.6283 7 74.3 0.0012 7 74.3 0.0012
213 5
Potentilla fruticosa L. 0 18.7 1 4 12.4 0.5305 3 29.7 0.0908 7 44.4 0.0102 7 44.4 0.0102
214 Potentilla nepalensis Hk. f. 0 16.7 1 1 8.7 0.7441 2 17.6 0.3191 7 14.1 0.5227 7 14.1 0.5227
215 Primula veris L. 0 4.2 1 4 4 1 4 8.3 0.6663 6 9.5 0.6507 6 9.5 0.6507
216 Prunella vulgaris L. 0 10.4 1 1 14.6 0.2318 1 20 0.1768 7 17.2 0.3399 7 17.2 0.3399
217 Pseudomertensia parviflora (Decne.) 0 4.2 1 3 9.1 0.6423 2 16.6 0.2476 6 9.5 0.6557 6 9.5 0.6557
218 Pteris vittata L. 0 22.9 1 4 14.5 0.6075 2 18.2 0.4131 7 72 0.0006 7 72 0.0006
219 Ranunculus laetus Wall. ex H. 1 35.3 0.3951 3 15.7 0.4507 2 19.4 0.3667 7 40 0.0514 7 40 0.0514
220 Ranunculus muricatus L. 0 22.9 1 2 29.4 0.0724 3 22.3 0.2484 4 28 0.188 4 28 0.188
221 Reinwardtia indica Dumort. 0 6.2 1 3 13.6 0.2547 3 18.7 0.087 6 6.2 1 6 6.2 1
222 Rochelia stylaris Bioss. 0 6.2 1 4 9.9 0.5593 1 8.7 0.7558 4 10.6 0.4023 4 10.6 0.4023
223 Rumex dentatus L. 1 42.9 0.1928 2 25.1 0.082 2 28.6 0.1042 7 20.9 0.2581 7 20.9 0.2581
224 Rumex hastatus D.Don., 1 30 1 2 22.5 0.3023 1 32.5 0.1152 4 48.6 0.0348 4 48.6 0.0348
225 2
Rumex nepalensis Spreng. 0 36.4 1 0 52.2 0.0188 1 23.4 0.5779 4 32 0.3243 4 32 0.3243
226 Salvia Moorcroftiana Wall.ex Benth 0 6.2 1 4 9.9 0.5509 3 18.7 0.0936 6 6.2 1 6 6.2 1
227 Sauromatum venosum (Ait.) Schott., 0 2.1 1 4 6.7 0.5607 4 4.2 1 6 4.8 1 6 4.8 1
228 Scrophularia robusta Penn. 0 2.1 1 4 6.7 0.5649 1 5.9 0.5239 6 4.8 1 6 4.8 1
229 Scutellaria linearis Bth., 0 2.1 1 4 6.7 0.5645 4 4.2 1 6 4.8 1 6 4.8 1
230 Senecio chrysanthemoides DC. 0 20.8 1 3 15.5 0.4733 2 36.8 0.0428 5 24.9 0.1696 5 24.9 0.1696
231 Sibbaldia cuneata Kunze., 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1
232 Silene vulgaris (Moench.) 0 10.4 1 3 10.8 0.4425 3 31.2 0.0462 6 15.1 0.4685 6 15.1 0.4685
233 Silybum marianum Gaertn., 0 7.3 1 1 11.8 0.5425 3 4.2 1 5 21.9 0.1491 5 21.9 0.1491
234 Solanum nigrumL. 0 10.4 1 2 50.6 0.003 4 6.4 1 4 41.7 0.0322 4 41.7 0.0322
235 Sonchus arvensis (DC.) 0 8.3 1 3 18.2 0.1228 3 42.9 0.0116 6 10.6 0.5551 6 10.6 0.5551
236 Strobilanthes alatus Nees non Blume 0 6.2 1 4 9.9 0.5527 2 12.6 0.4659 6 14.3 0.3253 6 14.3 0.3253
237 Swertia alata (D.Don) 0 6.2 1 1 6.6 0.8248 3 17.8 0.1764 7 25.8 0.1372 7 25.8 0.1372
238 Swertia angustifolia Ham. Ex. D.Don., 0 8.3 1 3 9.2 0.5645 3 11.7 0.4435 6 19 0.2591 6 19 0.2591
239 Swertia ciliata (G. Don) B. L. Burtt 0 12.5 1 2 8.9 0.6135 2 9 0.9432 5 7.7 0.817 5 7.7 0.817
240 Tagetes minuta L. 1 38.7 0.2985 2 21.7 0.1328 1 9.9 0.7149 4 17.5 0.2807 4 17.5 0.2807
241 Taraxacum officinale Weber. 1 64.9 0.2995 2 25.2 0.4297 1 22.3 0.7087 4 37.7 0.1164 4 37.7 0.1164
242 Thalictrum cultratum Bl 0 8.3 1 1 17.3 0.1592 2 11.1 0.6589 7 23.1 0.1944 7 23.1 0.1944
243 1
Themeda anathera (Ness) Hack. 1 85.7 0.0358 2 18.1 0.2639 2 20.4 0.3031 4 29.5 0.1566 4 29.5 0.1566
244 Trifolium repens L. 0 6.2 1 3 5.2 1 3 12.7 0.4303 6 14.3 0.3449 6 14.3 0.3449
245 Tussilago farfara L. 0 8.3 1 3 9.2 0.5575 3 15.9 0.2523 5 11.4 0.4227 5 11.4 0.4227
246 Urtica ardens Link, Hort., 0 6.2 1 3 5.2 1 4 12.5 0.5765 6 14.3 0.3241 6 14.3 0.3241
247 Valeriana jatamansi Jones. 0 12.5 1 3 17.6 0.2034 3 63 0.003 6 8.9 0.7185 6 8.9 0.7185
248 Valeriana officinalis (non L.) Hk. F. 0 35.4 0.5497 1 25 0.1576 1 23.8 0.2891 7 27.1 0.2851 7 27.1 0.2851
249 Verbescum thapsis L. 1 36.4 0.3611 2 32.2 0.0528 4 12.9 0.6231 4 35.6 0.0854 4 35.6 0.0854
250 Verbena bonariensis L. 0 10.4 1 3 6.9 0.821 3 38.9 0.0242 6 15.1 0.4663 6 15.1 0.4663
251 3
Viola biflora L. 0 18.7 1 3 11.2 0.5637 3 54.4 0.008 6 42.9 0.0384 6 42.9 0.0384
252 Viola canescens Wall ex Roxb. 0 20.8 1 4 12.7 0.5969 2 21.3 0.2272 6 16.8 0.2841 6 16.8 0.2841

Table 8
Soil (Edaphic) factor analyses of all the sampling sites (stations) of Thandiani Sub Forests Division, Abbottabad—quantification in each of the five different plant communities.
S.No Stations PH EC (dsm-1) % O.M % CaCO3 % Sand % Silt % Clay T.Classes P (ppm) K (ppm)
Melia-Punica-Euphorbia Community
1 Mandroch 5.2 0.63 0.55 11 25.8 52 22.2 1 8 155
2 Battanga 5.3 0.29 1.04 6.5 49.8 36 14.2 4 6 125
3 Neelor 5 0.31 1.24 9.7 37.8 46 16.2 4 6 145
4 Bari Bak 5.3 0.28 0.85 12 39.4 42 16.2 4 7 140
5 Mand Dar 5.2 1.02 1.06 6.3 47.8 36 16.2 4 5 130
6 Pkhr Bnd 5.4 0.52 0.57 8.6 26.3 49.5 24.1 4 5 110
7 Lowr Dna 5.4 0.26 1.32 8 51.8 36 12.2 4 6 130
8 Bandi TC 4.9 0.92 0.5 12.5 15.8 64 20.2 1 6 135
9 Qalndrbd 4.8 0.54 0.65 13.7 17.8 64 18.2 1 7 145
10 Riala 4.8 0.54 0.55 8.5 26.4 49.4 24.2 4 5 110
11 Malch Lw 5.5 1.03 1.08 8.7 45.8 30 24.2 4 5 125
12 Malch Up 5.5 0.41 1.1 7.5 35.2 34 30.2 2 6 135
Ziziphus-Zanthoxylum-Rumex Community
1 Danna 5.5 0.62 1.15 8.3 33.8 48 18.2 4 6 135
2 Uper Dna 5.7 0.35 0.72 1.3 29.2 60 10.2 1 6 120
3 Pejjo 5.5 0.48 1.05 8.2 27.8 52 20.2 1 5 115
4 Lowr Bal 5.9 0.4 1.07 7.7 45.8 28 26.2 2 7 145
5 Upr Balo 6.4 0.36 1.1 6.6 29.9 40 32.1 2 6 120
6 Mera Bun 4.9 0.28 0.75 13 39.8 44 16.2 4 7 140
7 Lonr Pat 5.2 0.34 0.65 8 35.8 52 12.2 1 8 150
8 Gali Ban 5.8 0.27 1.2 8 41.8 44 14.2 4 6 105
9 Riala Ca 4.9 0.61 0.5 12.7 21.8 54 24.2 1 6 105
10 Resrv FC 6.6 0.41 1.15 6.8 37.8 44 18.2 4 6 110
11 Upper GB 6.2 0.24 1.06 8.4 35.8 40 24.2 4 6 120
12 Chatrri 6.4 0.43 1.1 9.2 21.8 58 20.2 1 6 115
13 Terarri 5.1 0.37 0.7 9.5 40.4 45.4 14.2 1 7 135
14 Upr Rial 4.9 0.31 0.72 1.1 29.8 60 10.2 1 6 125
15 Terari C 5.4 0.6 0.56 11 33.8 46 20.2 4 7 130
16 Mathrika 5.9 0.45 1.24 6.7 29.8 56 14.2 1 6 135
Quercus-Cornus-Viola Community
1 Mthrka T 6.2 0.22 1.2 7.4 55.8 34 10.2 3 7 145
2 Jabbra 6.3 0.49 1.07 7.3 69.6 20.1 10.1 3 5 120
3 Darral 5.5 0.2 0.6 13 41.8 42 16.2 4 5 110
4 Makali 6.5 0.25 0.55 10.5 35.8 50 14.2 4 6 120
5 Ladrri 6.1 0.62 0.6 9.5 16.8 58 26.2 1 7 130
6 Upper KP 6.5 0.53 1.05 7.2 69.2 20.6 10.2 3 5 90
7 Kakl RFC 5.8 0.51 1.18 5.8 29.2 44.6 26.2 4 5 140
8 Parringa 6.6 0.44 0.8 7.8 21.8 56 22.2 1 6 120
9 Satu Top 6.8 0.45 0.55 12 31.8 58 10.2 1 5 115
10 Lower KP 6.4 0.33 1.08 6.5 29.8 38 32.2 2 6 115
11 Larri 6.5 0.55 1.15 8 35.8 50 14.2 4 5 110
Cedrus-Viburnum-Achillea Community
1 Pallu Zr 6.7 0.41 1.2 6.9 37.1 43 18.1 4 6 115
2 Lari Tra 6.3 0.26 1.25 7 57.2 26.2 16.2 3 5 110
3 Lari Top 6.8 0.73 1.07 7.4 45.8 42 12.2 4 6 125
4 Sawan Gl 6.7 0.38 1.2 6 49.2 28.6 22.2 2 6 115
5 Lower Th 6.6 0.39 1.1 9.2 53.2 32 14.8 4 5 110
6 Upper TC 6.7 0.57 1.15 9 45.2 44.2 10.2 4 5 105
Abies-Daphne-Potentilla Community
1 Mera RKC 6.6 0.22 0.7 10 29.8 44 26.2 4 8 140
2 Mera RKT 6.8 0.34 0.65 8 21.8 54 24.2 1 8 145
3 Lwr Nmal 7.1 0.2 0.6 8 35.8 44 20.2 4 6 120
4 Upr Nmal 7.2 0.36 0.55 6 33.8 60.6 15.6 1 6 125
5 Sikher 7.2 0.39 0.75 9 46.8 35.2 18.2 1 7 135
est types that occur there. These forests are, however, under
considerable conversion pressure as land use intensifies with
expanding human population and economic development. Con-
servation strategies based on the geographic patterns of botanical
species richness and diversity, including the identification of mean-
ingful floristic regions and priority areas for conservation, could
improve the effectiveness of forest policy and management. These
strategies should also include current threats of loss due to forest
conversion to address the more urgent challenges for sustain-
able development. Here, we produce distribution models for 252
plant species using multivariate analysis, collecting geo-referenced
herbarium specimens. Our findings provide clear priorities for the
development of a sustainable and feasible biodiversity conserva-
tion strategy for TsFD through indicator species approach.
7. Conclusions
Plant ecologists have commonly been conscious that vegetation
shows a discrepancy over a broad variety of particular factors and
areas. We have demonstrated that both species composition and
species pattern of vegetation in the TsFD depend more strongly on
soil pH, aspect and soil electrical conductivity than on any other soil
or climatic variables. This relationship even exists across a narrow
range of near-neutral pH values; slopes with north-west and south-
east aspects and low electrical conductivity. This study indicates
that environmental factors have a strong influence on vegetation
gradients and that the association of plant species changed in
response to edaphic, topographic and climatic gradients. There are
three major implications of the current study: (1) How to document
species composition, pattern and abundance at peak growing sea-

son and classify vegetation to potential plant communities using
Cluster Analyses (CA) through PCORD. (2) How to use sampling
methods for finding relationships between plant communities and
complex set of environmental variables using robust statistical
approaches via Two Way Cluster (TWCA) and Indicator Species
Analyses (ISA). (3) These techniques give a way to identify indicator
vegetation of specific habitats and hence directly or indirectly con-
tribute to biodiversity and habitat conservation and management
plans.
Acknowledgements
We are thankful to the curator,Herbarium of Hazara Univer-
sity Mansehra Pakistan, for providing help in identification of all
the plant specimens. We are also thankful to Directorate of Higher
Education Khyber Pakhtun Khwah, Pakistan for financial assistance
towards this project.
References
Acker, S.A., 1990. Vegetation as a component of a non-nested hierarchy: a
conceptual model. J. Veg. Sci. 1, 683–690.
Adamu, G.K., Aliyu, A.K., 2012. Determination of the influence of texture and
organic matter on soil water holding capacity in and around Tomas Irrigation
Scheme, Dambatta Local Government Kano State. Res. J. Environ. Earth Sci. 12,
1038–1044.
Ahmad, H., Turk, M.O., Ahmad, W., Khan, S.M., 2015. Status of natural resources in
the uplands of the Swat valley, Pakistan. In: Ozturk, M., Hakeem, K.R.,
Faridah-Hanum, I., Efe, R. (Eds.), “Climate Change Impacts on High-Altitude
Ecosystems”. Springer, p. 647 (ISBN 978-3-319-12858-0) http://www.springer.
com/us/book/9783319128580.
Ahmed, M., Husain, T., Sheikh, A.H., Hussain, S.S., Siddiqui, M.F., 2006.
Phytosociology and structure of Himalayan forests from different climatic
zones of Pakistan. Pak. J. Bot. 38, 361–383.
Ali, S.I., Qaiser, M., 1986. A phyto-geographical analysis of the phenerogames of
Pakistan and Kashmir. R. Soc. Ed. 89, 89–101.
Ali, S.I., Qaiser, M., 1993–2009. Flora of Pakistan, 194–215.
Anderson, D.W., Ellenberg, J.H., Leventhal, C.M., Reingold, S.C., Rodriguez, M.,
Silberberg, D.H., 1992. Revised estimate of the prevalence of multiple sclerosis
in the United States. Ann. Neurol. 31, 333–336.
Baver, L.D., Walter, H.G., Wilford, H., 1972. Soil Phys. 36, 631–643.
Bergeron, S.P., Bradley, R.L., Munson, A., Parsons, W., 2013. Physico-chemical and
functional characteristics of soil charcoal produced at five different
temperatures. Soil Biol. Biochem. 58, 140–146.
Butler, S.J., Freckleton, R.P., Renwick, A.R., Norris, K., 2012. An objective,
niche-based approach to indicator species selection. Ecol. Evol. 3, 317–326.
Cáceres, M.D., Legendre, P., 2009. Associations between species and groups of sites.
Indices and statistical inference. J. Ecol. 90, 3566–3574.
Cáceres, M., Legendre, P., Wiser, S.K., Brotons, L., 2012. Using species combinations
in indicator value analyses. Ecol. Evol. 3, 973–982.
Carignan, V., Villard, M.A., 2002. Selecting indicator species to monitor ecological
integrity: a review. Environ. Monit. Asses. 78, 45–61.
Chahouki, M.Z., Azarnivand, H., Jafari, M., Tavili, A., 2010. Multivariate statistical
methods as a tool for model-based prediction of vegetation types. Russ. J. Ecol.
41, 84–94.
Champion, H.G., Seth, K., Khattak, G.M., 1965. Forest Types of Pakistan. PFI,
Peshawar.
Chawla, A., Rajkumar, S., Singh, K.N., Lal, B., Singh, R.D., Thukral, A.K., 2008. Plant
species diversity along an altitudinal gradient of Bhabha Valley in western
Himalaya. J. Mt. Sci. 5, 157–177.
Cox, T., 1985. The nature and measurement of stress. Ergonomics 28, 1155–1163.
Curtis, J.T., McIntosh, R.P., 1950. The interrelations of certain analytic and synthetic
phytosociological characters. Ecology, 434–455.
Daubenmire, R. 1968, Plant communities. A textbook of plant synecology Plant
Commun.
De Cáceres, A.M., Legendre, P., Moretti, M., 2010. Improving indicator species
analysis by combining groups of sites. Oikos 119, 1674–1684.
Dufrêne, M., Legendre, P., 1997. Species assemblages and indicator species: the
need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366.
Ellenberg, H., 1988. Vegetation Ecology of Central Europe. Cambridge University
Press.
Gee, G.W., Bauder, J.W., Klute, A., 1986. Particle-size analysis. Methods of soil
analysis. Part 1. Phys. Mineral. Methods, 383–411.
Glaves, P., Handley, C., Rotherham, I.D., Birbeck, J., Wright, B., 2009. Field surveys
for ancient woodlands: Issues and Approaches.
Greig-Smith, P., 1983. Quantitative Plant Ecology, vol. 9. Univ. of California Press.
Grytnes, J.A., Vetaas, O.R., 2002. Species richness and altitude: a comparison
between null models and interpolated plant species richness along the
Himalayan altitudinal gradient, Nepal. Am. Nat. 159, 294–304.
Haberl, H., Steinberger, J.K., Plutzar, C., Erb, K.H., Gaube, V., Gingrich, S.,
Krausmann, F., 2012. Natural and socioeconomic determinants of the
embodied human appropriation of net primary production and its relation to
other resource use indicators. Ecol. Indic. 23, 222–231.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., 2006. Multivariate
Data Analysis, vol. 6. Pearson Prentice Hall, Upper Saddle River, NJ.
Haq, F., Ahmed, H., Iqbal, Z.A., 2015. Vegetation composition and ecological
gradients of subtropical-moist temperate ecotonal forests of Nandiar Khuwar
catchment, Pakistan. Bangladesh J. Bot. 44, 267–276.
Hussain, I., Olson, K.R., Ebelhar, S.A., 1999a. Long-term tillage effects on soil
chemical properties and organic matter fractions. Soil Sci. Soc. Am. J. 63,
1335–1341.
Hussain, I., Olson, K.R., Wander, M.M., Karlen, D.L., 1999b. Adaptation of soil quality
indices and application to three tillage systems in southern Illinois. Soil Tillage
Res. 50, 237–249.
Jackson, M.L., 1963. Interlayering of expansible layer silicates in soils by chemical
weathering. Clays Clay Miner. 11, 29–46.
Kent, M., Coker, P., 1994. Vegetation Description and Analysis–A Pratical Approach.
Khan, S.M., Ahmad, H., 2015. Species Diversity and use patterns of the alpine flora
with special reference to climate change in the Naran, Pakistan. In: the Ozturk,
M., Hakeem, K.R., Faridah-Hanum, I., Efe, R. (Eds.), “Climate Change Impacts on
High-Altitude Ecosystems”. Springer, p. 647 (ISBN 978-3-319-12858-0) http://
www.springer.com/us/book/9783319128580.
Khan, S.M., Harper, D.M., Page, S., Ahmad, H., 2011a. Species and community
diversity of vascular flora along environmental gradient in Naran Valley: a
multivariate approach through indicator species analysis. Pak. J. Bot. 43,
2337–2346.
Khan, W., Ahmad, H., Shah, G.M., 2011b. Phytosociology and Geographical
Distribution of Thandiani Forests. LAP LAMBERT Academic Publishing GmbH &
Co. KG (ISBN 978 3–8465-1016-2).
Khan, S.M., Page, S., Ahmad, H., Shaheen, H., Harper, D.M., 2012a. Vegetation
dynamics in the Western Himalayas, diversity indices and climate change. Sci.
Techol. Dev. 31, 232–243.
Khan, W., Ahmad, H., Haq, F., Islam, M., Bibi, F., 2012. Present status of Moist
Temperate Vegetation of Thandiani Forests, District Abbottabad, KPK, Pakistan.
I.J.B, ISSN, 2220 6655 (Print) 2222–5234, Vol. 2, No. 10 (2), 80–88.
Khan, S.M., Page, S., Ahmad, H., Zahidullah, H., Shaheen, M., Harper, D.M., 2013a.
Phyto-climatic gradient of vegetation and habitat specificity in the high
elevation western Himalayas. Pak. J. Bot. 45, 223–230.
Khan, W., Majid, A., Afzal, M., Islam, M., Bibi, F., 2013. Degree of Aggregation and
Index of Similarity of plant communities recorded at Thandiani Hills, District
Abbottabad KPK, Pakistan. E.J.A.S, 68–72.
Khan, S.M., Page, S., Ahmad, H., Harper, D.M., 2014. Ethno-ecological importance of
plant biodiversity in mountain ecosystems with special emphasis on indicator
species; a case study of the Naran Valley in the Northern Pakistan. J. Ecol. Indic.
37, 175–185.
Khan, W., Khan, S.M., Ahmad, H., 2015. Altitudinal variation in plant species
richness and diversity at thandiani sub forests division, Abbottabad, Pakistan. J.
Biodivers. Environ. Sci. 7, 46–53.
Kitayama, K., Aiba, S.I., 2002. Ecosystem structure and productivity of tropical rain
forests along altitudinal gradients with contrasting soil phosphorus pools on
Mount Kinabalu, Borneo. J. Ecol. 90, 37–51.
Kremen, C., Colwell, R.K., Erwin, T.L., Murphy, D.D., Noss, R.F., Sanjayan, M.A., 1993.
Terrestrial arthropod assemblages: their use in conservation planning.
Conserv. Biol., 796–808.
Leonard-Barton, D., 1988. Implementation as mutual adaptation of technology and
organization. Res. Policy 17, 251–267.
Malik, R.N., Husain, S.Z., 2006. Classification and ordination of vegetation
communities of the Lohibehr reserve forest and its surrounding areas,
Rawalpindi, Pakistan. Pak. J. Bot. 38, 543–558.
Malik, R.N., Husain, S.Z., 2008. Linking remote sensing and ecological vegetation
communities: a multivariate approach. Pak. J. Bot. 40, 337–349.
Malik, N.Z., Malik, Z.H., 2004. Present status of subtropical Chir Pine vegetation of
Kotli Hills, Azad Jammu and Kashmir. J. Res. Sci. 15, 85–90.
Malik, R.S., 1990. Prospects for Brassica carinata as an oilseed crop in India. Exp.
Agric. 26, 125–129.
Massberg, S., Brand, K., Grüner, S., Page, S., Müller, E., Müller, I., Bergmeier, W.,
Richter, T., Lorenz, M., Konrad, I., Nieswandt, B., 2002. A critical role of platelet
adhesion in the initiation of atherosclerotic lesion formation. J. Exp. Med. 196,
887–896.
McCune, B., Mefford, M.J., 1999. PC-ORD: multivariate analysis of ecological data;
Version 4 for Windows [User’s Guide]. MjM software design.
McGEOCH, M.A., 1998. The selection, testing and application of terrestrial insects
as bioindicators. Biol. Rev. Camb. Philos. Soc. 73, 181–201.
Mehmood, A., Khan, S.M., Shah, A.H., Ahmad, H., 2015. First Floristic Exploration of
District Torghar, Khyber Pakhtunkhwa, Pakistan. Pak. J. Bot. 47, 57–70.
Moldan, B., Janouˇsková, S., Hák, T., 2012. How to understand and measure
environmental sustainability: indicators and targets. Ecol. Indic. 17, 4–13.
Monteith, D.T., Evans, C.D., Henrys, P.A., Simpson, G.L., Malcolm, I.A., 2014. Trends
in the hydrochemistry of acid-sensitive surface waters in the UK 1988–2008.
Ecol. Indic. 37, 287–303.
Mouillot, D., Bellwood, D.R., Baraloto, C., Chave, J., Galzin, R., Harmelin-Vivien, M.,
Kulbicki, M., Lavergne, S., Lavorel, S., Mouquet, N., Paine, C.E.T., Renaud, J.,
Thuiller, W., 2013. Rare species support vulnerable functions in high-diversity
ecosyst.
Nasir, E., Ali, S.I., 1970–1979. Flora of West Pakistan. (1–131).

Niemi, G.J., McDonald, M.E., 2004. Application of ecological indicators. Ann. Rev.
Ecol. Evol. Syst., 89–111.
Poore, M.E.D., 1955. The use of phytosociological methods in ecological
investigations: I. The Braun-Blanquet system. J. Ecol., 226–244.
Rieley, J.O., Page, S.E., 1990. Ecology of plant communities. A phytosociological
account of the British vegetation. Longman Sci. Tech. 10, 0582446392.
Saima, S., Dasti, A.A., Hussain, F., Wazir, S.M., Malik, S.A., 2009. Floristic
compositions along an 18-km long transect in ayubia National Park district
Abbottabad, Pakistan. Pak. J. Bot. 41, 2115–2127.
Sarir, M.S., Durrani, M.I., Mian, I.A., 2006. Effect of the source and rate of humic acid
on phosphorus transformations. J. Agric. Bio. Sci. 1.
Shah, A.H., Khan, S.M., Shah, A.H., Mehmood, A., Ahmad, H., 2015. Cultural uses of
plants among Basikhel Tribe of District Tor Ghar, Khyber Pakhtunkhwa,
Pakistan. Pak. J. Bot. 47, 23–41.
Shaheen, H., Sarwar, R., Firdous, S.S., Dar, M.E.I., Ullah, Z., Khan, S.M., 2015.
Distribution and Structure of Conifers with special emphasis on Taxus Baccata
in Moist Temperate Forests of Kashmir Himalayas. Pak. J. Bot. 47, 71–76.
Sheldrick, B.H., Wang, C., 1993. Soil Sampling and Methods of Analysis. Particle
Size Distribution. Canadian Society of Soil Science Lewis Publishers, Boca
Raton, Florida, USA, pp. 499–511.
Siddiqui, M.F., Ahmed, M., Wahab, M., Khan, N., Khan, M.U., Nazim, K., Hussain, S.S.,
2009. Phytosociology of Pinus roxburghii Sargent (Chir Pine) in lesser
Himalayan and Hindu Kush range of Pakistan. Pak. J. Bot. 41, 2357–2369.
Takhtadzhian, A.L., 1997. Diversity and Classiﬁcation of Flowering Plants.
University Press, Columbia.
Tavili, A., Jafari, M., 2009. Interrelations between plants and environmental
variables. Int. J. Environ. Res. 3, 239–246.
Ter Braak, C.J., Prentice, I.C., 1988. A theory of gradient analysis. Adv. Ecol. Res. 18,
271–317 (textbook of plant synecology).
Ullah, Z., Ahmad, M., Sher, H., Shaheen, H., Khan, S.M., 2015. Phytogeographic
analysis and diversity of the grasses and sedges (Poales) of northern Pakistan.
Pak. J. Bot. 47, 93–104.
Van Tongeren, O., Gremmen, N., Hennekens, S., 2008. Assignment of relevés to
pre-deﬁned classes by supervised clustering of plant communities using a new
composite index. J. Veg. Sci. 19, 525–536.
Wazir, S.M., Dasti, A.A., Saima, S., Shah, J., Hussain, F., 2008. Multivariate analysis of
vegetation of Chapursan valley: an alpine meadow in Pakistan. Pak. J. Bot. 40,
615–626.
Weber, J.D., Jeffers, John R., Rehg, J.E., Randle, D.H., Guillermina, L., Martine, F.,
Roussel, C.J.S., Gerard, P.Z., 2000. p53-independent functions of the p19ARF
tumor suppressor. Gene Dev. 18, 2358–2365.
Wilson, M.J., Bayley, S.E., 2012. Use of single versus multiple biotic communities as
indicatorsof biological integrity in northern prairie wetlands. Ecol. Indic. 20,
187–195.

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